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📸⚡️ Shareholder Endgame: Level Changes from Distribution have changed AMC's future forever>> -Dilution Metrics & PT's -Ape, C/RS, Potential OTC -Percentage Level Drops -Future Mark-up -AMC's debt & Company Future 🔗💸 -YieldBoss Instagram (yieldbossofficial) -Youtube (npantano_) -TikTok (npantano) $amc $gme

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Nicholas A. Pantano

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61,084 просмотров • 6 месяцев назад •via X (Twitter)

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📉 AMC MOASS - Mathematically Impossible -Full chart Review -Thesis: AMC Company Future ⚡️ Also posted on: -YieldBoss Instagram (yieldbossofficial) -Youtube (npantano_) -TikTok (npantano) $amc #moass

Nicholas A. Pantano

43,652 просмотров • 6 месяцев назад

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.
19:01

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.

David Shapiro (L/0)

69,180 просмотров • 9 месяцев назад

Elon Musk wants us building successful businesses here on 𝕏, NOT relying on X payouts. Elon is a trillionaire entrepreneur that got there by NETWORKING ideas and visions with others. He now owns a social media platform and wants us doing the exact same thing here. Elon wants you to build a following, find a business that offers a valuable service to the community and country, succeed beyond your WILDEST dreams with it and then NETWORK TO REPLICATE IT AND HELP OTHERS HERE DO THE SAME! That's the winning model that will not only benefit all of us, but be financially beneficial to Elon as well, and I'd like to explain why. Meta generates their primary ad revenue from big corporations owned by corrupt elites advertising their products to our asleep family members. Big Corporations HATE Elon so that is absolutely OUT of the question here on this platform, so they need an alternative model to pull in revenue to see an ROI ont he purchase of Twitter, and it's up to all of us to help generate that. Instagram is the complete OPPOSITE of Meta and Youtube. Instagram generates roughly 87 Billion dollars each year in ad revenue with a substantial amount of that coming from regular users of the platform who are also entrepreneurs working successful business between their niche content, and they are paying INTO ads to advertise that business between said niche content. There are 19 year olds on Instagram making 6 figures a MONTH drop shipping and they make courses teaching you step by step instructions how you can do it too. They pay anywhere between $15 - $20K a month INTO the ad program to advertise their businesses, between their regular content. There are MILLIONS of instances of this on Instagram and that is what's feeding a massive portion of the ad revenue program. The #1 reason people create an instagram account and actively try to grow it, is entrepreneurship. THAT'S what Elon wants us doing HERE! Now, the conservative space on Instagram is MASSIVE and 👉 LITERALLY 👈 all they do is lazily scroll 𝕏 all day to see the content YOU ALL are sending viral here, and they STEAL IT, easily moving it to their Instagram accounts to build their own following to then work a successful business. You need to understand that part very clearly. They're lazily stealing YOUR content from here, building successful businesses off it and posting videos of them traveling the world living lives of luxury. I've been tracking them doing this for SIX YEARS since 2020. If you can explain to me why they are more privileged and deserving than YOU to be living successfully off of YOUR work, I'd love to hear your reasoning on this. Now, i had a Zoom meeting with a conservative mom on Instagram that works the same business from home that I do, and she's helped over 60 people on Instagram grow their following from 0-200K to then work that business with her and she opened up her back office to me to show me everything and teach me how she does what she does and precisely how the Instagram algorithm works to grow. I'm not going into specifics, but it's absolutely clear based on my assessment that the development team behind Instagram makes it hard to grow there on purpose because they psychologically understands how to keep toxicity from taking over the platform by ensuring the extremely impatient "I want it NOW" people aren't able to grow large platforms to take over the entire platform. They understand these people are significantly more likely to spread toxicity across the platform, original content to drop, entrepreneurship to disappear and incoming ad revenue to dip much lower as a result. They understand those with military style determination and grit will stick it out to succeed, have much higher chance of being more intelligent to end up bringing original content to the platform, and have a much higher confidence rate in themselves to seek entrepreneurship, build community based income opportunities where anyone can come in and succeed to create very healthy communities, and then pay into the ad program to advertise it to succeed even more, turning the platform into a truly symbiotic relationship between the entrepreneurial users and the platform itself that allows them to grow and succeed in the first place. We're doing things all wrong here. 𝕏 isn't broken — our community is. These massive 1+ million follower accounts here that grew by putting MSM articles into Grok AI to have him rewrite them to avoid plagiarism charges and milk the 𝕏 revenue system and they will NEVER be able to figure this out because they're FAKING their intelligence by doing this and Elon does NOT want us idolizing accounts like that because they're giving us false illusions and impressions as to what success looks like. Elon wants us aiming much higher than 𝕏 revenue because 𝕏 revenue is NOT generational wealth to pass onto our children and we're supposed to be working together as a community to build things that are. Elon wants to be able to advertise this platform as having the MOST self-made millionaires than ANY other platform on the planet. As things currently sit, 𝕏 is single handedly leading any other platform for world news distribution, but we're trailing significantly in wealth generation. I believe the changes in monetization are to force people out of their comfort zones to seek alternative means of income generation to help correct that. We're not supposed to be competing with each other to gain 𝕏 revenue, we're supposed to be working together to build communities and revenue to compete with Instagram. The problem is, most of us came to this platform as slaves to a broken and corrupted system because we want change and have no language for these things. So, I'll be the first one to plant my flag. Do I know what I'm doing? Absolutely not. Do my children deserve having a mother that steps far outside her comfort zone to give it everything she's got to build something that gives them a better life? You bet your ass. Before my son was born with his tumor in his throat and the medical field bankrupted me for everything I was worth, I worked a VERY successful business from home that allowed me to homeschool my children. We've been pointing out all this corruption but what we now need are SOLUTIONS and community based systems to the corrupt system we're all stuck in, that anyone can implement and benefit from. I think it's time for us all to build that together and I'd like to start. We do have tools to fight back against the corrupt system but it's going to take all of us coming together to do it. The 2 BIGGEST ways we fund these corrupt elites every single month, is buying food and basic household goods from their corporations. All of our stores and corporations have become owned by a small group of Wall Street shareholder elites that lobby our politicians in Congress to look the other way while their corporations poison us and our children with their toxic products. We don't have to keep funding that system anymore as a parallel economy does in fact exist where profits are CUT OFF from reaching them and are instead redirected back to the American people. Wall Street elites like BlackRock-Vanguard and central bankers like JP Morgan & Chase does own 93% of the consumer goods sold in America. If the "conspiracies" are true, then BlackRock and Vanguard are owned by private Central Bankers that serve ENGLAND and they partnered with China to steal our manufacturing. Powerful banking elites purchase majority shares of the corporations for these Central Bankers, then moves the manufacturer to China. ALL of our consumer wealth is being siphoned out of America and going to England and China via this model and they lobbied BOTH parties in Congress through donations from their corporations to keep them silent about it. When we buy stuff in their stores, the profits are going to Wall Street and some of those profits go to product advertising on TV and that's what pays networks heads to pay the salaries of Fake News anchors millions of dollars a year to LIE to us. The video I provided in this post was Dylan Ratigan addressing the illusion of the left vs right paradigm IN 2011! He talks about all of our wealth being siphoned out of America. We were so close to figuring this out in 2011, then all of the DIVISIONS started to significantly ramp up against us to keep us fighting each other to keep this hidden so the theft of our consumer wealth out of the country could continue, to further hollow out America. Every time we buy Tide laundry detergent, we're funding the Military Industrial Complex and their forever wars because the same shareholders that owns tide — BlackRock-Vanguard — owns the weapons defense contracting companies that makes missiles and profits from death in forever wars. It's not just Tide, it's ALL basic household goods that we buy every single month as we send those profits through Wall Street when we support these corporations. Laundry detergent, dish soap, deodorant, shampoo conditioner, toothpaste, and EVERY other product that props up Johnson&Johnson and all these other cabal corporations. We can't fix America if we continue to support these cabal corporations that poison us for profit because they also own the pharmaceutical companies too and they've lobbied both sides of Congress to look the other eat while they do it. There's ONE family owned consumer goods manufacturer left here in America that can ship to MILLIONS of doorsteps and they make 470+ basic household goods that we still need for our families every single month from safe laundry detergent that won't give your children hives and rashes, to shampoo, deodorant, fluoride free toothpaste, dish soap, safe cleaning products that won't give you cancers or harm the lungs of your children and pets, to 470+ other basic needs as well as clean grass fed beef and steaks, and they've been right here in America for the last 41 years. They're a privately owned company and NOT publicly traded in Wall Street so profits are NOT going to Wall Street elites who then give them to Congress, the profits go back to us instead, which is the way things should be. Companies like this made no sense 20 years ago when we were all asleep and loved Walmart, but we're all waking up to what these corrupt elites have been doing and we need an alternative system that benefits families in America instead of powerful wall street pedophiles that own and control all aspects of our lives. It's not good enough to just shop American made anymore, we need to keep our consumer dollars OUT of the hands of Wall Street elites. They have NOT been responsible with our tax dollars, and they have NOT been responsible with the profits we send them by supporting their massive corporations, so it's time to cut them off and shift those profits back to the American people who need them instead. Walk around your home and look at all the household products you're buying every month that funds this corrupt system. We can squash their monopoly and reverse engineer ACTUAL capitalism and Robin Hood profits from the Wall Street elites by shifting MILLIONS of dollars away from them and back to people who need it, for everyday stuff we're already buying every single month from them for our families. Elon Musk wants us building community based wealth generating systems ON THIS PLATFORM to tear down the elites and rebuild the middle-class instead. We don't need more BREAKING posts. We don't need more corruption exposed, as we've seen enough to understand the entire system THAT WE FUND every month, is rotten from the top down. What we need is a community based system built for us that allows all of us to legally rob that corrupt system, and I think it's time we build it. Winter is coming. There's no better time to LEARN and build an online business and start building something that can be passed down to our children in the future, that's also beneficial to America, and I'd like to help build it. If you're interested in building it with me, you can message me here directly or you can email me! 🥰 BFertig95@Outlook.com
4:15

Elon Musk wants us building successful businesses here on 𝕏, NOT relying on X payouts. Elon is a trillionaire entrepreneur that got there by NETWORKING ideas and visions with others. He now owns a social media platform and wants us doing the exact same thing here. Elon wants you to build a following, find a business that offers a valuable service to the community and country, succeed beyond your WILDEST dreams with it and then NETWORK TO REPLICATE IT AND HELP OTHERS HERE DO THE SAME! That's the winning model that will not only benefit all of us, but be financially beneficial to Elon as well, and I'd like to explain why. Meta generates their primary ad revenue from big corporations owned by corrupt elites advertising their products to our asleep family members. Big Corporations HATE Elon so that is absolutely OUT of the question here on this platform, so they need an alternative model to pull in revenue to see an ROI ont he purchase of Twitter, and it's up to all of us to help generate that. Instagram is the complete OPPOSITE of Meta and Youtube. Instagram generates roughly 87 Billion dollars each year in ad revenue with a substantial amount of that coming from regular users of the platform who are also entrepreneurs working successful business between their niche content, and they are paying INTO ads to advertise that business between said niche content. There are 19 year olds on Instagram making 6 figures a MONTH drop shipping and they make courses teaching you step by step instructions how you can do it too. They pay anywhere between $15 - $20K a month INTO the ad program to advertise their businesses, between their regular content. There are MILLIONS of instances of this on Instagram and that is what's feeding a massive portion of the ad revenue program. The #1 reason people create an instagram account and actively try to grow it, is entrepreneurship. THAT'S what Elon wants us doing HERE! Now, the conservative space on Instagram is MASSIVE and 👉 LITERALLY 👈 all they do is lazily scroll 𝕏 all day to see the content YOU ALL are sending viral here, and they STEAL IT, easily moving it to their Instagram accounts to build their own following to then work a successful business. You need to understand that part very clearly. They're lazily stealing YOUR content from here, building successful businesses off it and posting videos of them traveling the world living lives of luxury. I've been tracking them doing this for SIX YEARS since 2020. If you can explain to me why they are more privileged and deserving than YOU to be living successfully off of YOUR work, I'd love to hear your reasoning on this. Now, i had a Zoom meeting with a conservative mom on Instagram that works the same business from home that I do, and she's helped over 60 people on Instagram grow their following from 0-200K to then work that business with her and she opened up her back office to me to show me everything and teach me how she does what she does and precisely how the Instagram algorithm works to grow. I'm not going into specifics, but it's absolutely clear based on my assessment that the development team behind Instagram makes it hard to grow there on purpose because they psychologically understands how to keep toxicity from taking over the platform by ensuring the extremely impatient "I want it NOW" people aren't able to grow large platforms to take over the entire platform. They understand these people are significantly more likely to spread toxicity across the platform, original content to drop, entrepreneurship to disappear and incoming ad revenue to dip much lower as a result. They understand those with military style determination and grit will stick it out to succeed, have much higher chance of being more intelligent to end up bringing original content to the platform, and have a much higher confidence rate in themselves to seek entrepreneurship, build community based income opportunities where anyone can come in and succeed to create very healthy communities, and then pay into the ad program to advertise it to succeed even more, turning the platform into a truly symbiotic relationship between the entrepreneurial users and the platform itself that allows them to grow and succeed in the first place. We're doing things all wrong here. 𝕏 isn't broken — our community is. These massive 1+ million follower accounts here that grew by putting MSM articles into Grok AI to have him rewrite them to avoid plagiarism charges and milk the 𝕏 revenue system and they will NEVER be able to figure this out because they're FAKING their intelligence by doing this and Elon does NOT want us idolizing accounts like that because they're giving us false illusions and impressions as to what success looks like. Elon wants us aiming much higher than 𝕏 revenue because 𝕏 revenue is NOT generational wealth to pass onto our children and we're supposed to be working together as a community to build things that are. Elon wants to be able to advertise this platform as having the MOST self-made millionaires than ANY other platform on the planet. As things currently sit, 𝕏 is single handedly leading any other platform for world news distribution, but we're trailing significantly in wealth generation. I believe the changes in monetization are to force people out of their comfort zones to seek alternative means of income generation to help correct that. We're not supposed to be competing with each other to gain 𝕏 revenue, we're supposed to be working together to build communities and revenue to compete with Instagram. The problem is, most of us came to this platform as slaves to a broken and corrupted system because we want change and have no language for these things. So, I'll be the first one to plant my flag. Do I know what I'm doing? Absolutely not. Do my children deserve having a mother that steps far outside her comfort zone to give it everything she's got to build something that gives them a better life? You bet your ass. Before my son was born with his tumor in his throat and the medical field bankrupted me for everything I was worth, I worked a VERY successful business from home that allowed me to homeschool my children. We've been pointing out all this corruption but what we now need are SOLUTIONS and community based systems to the corrupt system we're all stuck in, that anyone can implement and benefit from. I think it's time for us all to build that together and I'd like to start. We do have tools to fight back against the corrupt system but it's going to take all of us coming together to do it. The 2 BIGGEST ways we fund these corrupt elites every single month, is buying food and basic household goods from their corporations. All of our stores and corporations have become owned by a small group of Wall Street shareholder elites that lobby our politicians in Congress to look the other way while their corporations poison us and our children with their toxic products. We don't have to keep funding that system anymore as a parallel economy does in fact exist where profits are CUT OFF from reaching them and are instead redirected back to the American people. Wall Street elites like BlackRock-Vanguard and central bankers like JP Morgan & Chase does own 93% of the consumer goods sold in America. If the "conspiracies" are true, then BlackRock and Vanguard are owned by private Central Bankers that serve ENGLAND and they partnered with China to steal our manufacturing. Powerful banking elites purchase majority shares of the corporations for these Central Bankers, then moves the manufacturer to China. ALL of our consumer wealth is being siphoned out of America and going to England and China via this model and they lobbied BOTH parties in Congress through donations from their corporations to keep them silent about it. When we buy stuff in their stores, the profits are going to Wall Street and some of those profits go to product advertising on TV and that's what pays networks heads to pay the salaries of Fake News anchors millions of dollars a year to LIE to us. The video I provided in this post was Dylan Ratigan addressing the illusion of the left vs right paradigm IN 2011! He talks about all of our wealth being siphoned out of America. We were so close to figuring this out in 2011, then all of the DIVISIONS started to significantly ramp up against us to keep us fighting each other to keep this hidden so the theft of our consumer wealth out of the country could continue, to further hollow out America. Every time we buy Tide laundry detergent, we're funding the Military Industrial Complex and their forever wars because the same shareholders that owns tide — BlackRock-Vanguard — owns the weapons defense contracting companies that makes missiles and profits from death in forever wars. It's not just Tide, it's ALL basic household goods that we buy every single month as we send those profits through Wall Street when we support these corporations. Laundry detergent, dish soap, deodorant, shampoo conditioner, toothpaste, and EVERY other product that props up Johnson&Johnson and all these other cabal corporations. We can't fix America if we continue to support these cabal corporations that poison us for profit because they also own the pharmaceutical companies too and they've lobbied both sides of Congress to look the other eat while they do it. There's ONE family owned consumer goods manufacturer left here in America that can ship to MILLIONS of doorsteps and they make 470+ basic household goods that we still need for our families every single month from safe laundry detergent that won't give your children hives and rashes, to shampoo, deodorant, fluoride free toothpaste, dish soap, safe cleaning products that won't give you cancers or harm the lungs of your children and pets, to 470+ other basic needs as well as clean grass fed beef and steaks, and they've been right here in America for the last 41 years. They're a privately owned company and NOT publicly traded in Wall Street so profits are NOT going to Wall Street elites who then give them to Congress, the profits go back to us instead, which is the way things should be. Companies like this made no sense 20 years ago when we were all asleep and loved Walmart, but we're all waking up to what these corrupt elites have been doing and we need an alternative system that benefits families in America instead of powerful wall street pedophiles that own and control all aspects of our lives. It's not good enough to just shop American made anymore, we need to keep our consumer dollars OUT of the hands of Wall Street elites. They have NOT been responsible with our tax dollars, and they have NOT been responsible with the profits we send them by supporting their massive corporations, so it's time to cut them off and shift those profits back to the American people who need them instead. Walk around your home and look at all the household products you're buying every month that funds this corrupt system. We can squash their monopoly and reverse engineer ACTUAL capitalism and Robin Hood profits from the Wall Street elites by shifting MILLIONS of dollars away from them and back to people who need it, for everyday stuff we're already buying every single month from them for our families. Elon Musk wants us building community based wealth generating systems ON THIS PLATFORM to tear down the elites and rebuild the middle-class instead. We don't need more BREAKING posts. We don't need more corruption exposed, as we've seen enough to understand the entire system THAT WE FUND every month, is rotten from the top down. What we need is a community based system built for us that allows all of us to legally rob that corrupt system, and I think it's time we build it. Winter is coming. There's no better time to LEARN and build an online business and start building something that can be passed down to our children in the future, that's also beneficial to America, and I'd like to help build it. If you're interested in building it with me, you can message me here directly or you can email me! 🥰 [email protected]

Bridgett Fertig

31,340 просмотров • 17 дней назад

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.
1:26

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.

Mike

120,532 просмотров • 10 месяцев назад

What if the U.S. starts buying Treasury bonds with ripple:native or RLUSD and puts them on the XRP Ledger? South Korea’s YTN just asked a question that sounds wild at first: “Buying U.S. Treasury Bonds with Crypto?” But when I started connecting it with what Scott Bessent, Ripple, RLUSD and the XRP Ledger are already doing, this stopped looking like some random crypto theory. The pieces are already sitting right in front of us. The United States has now crossed roughly $40 trillion in federal debt. That means the government constantly needs buyers for enormous amounts of Treasury securities. Not once. Again and again. Old debt matures. New debt gets issued. Short-term bills need buyers. Interest keeps getting paid. The whole system depends on keeping demand for U.S. government debt strong. And this is exactly where stablecoins suddenly become much more important than most people realize. Scott Bessent has already talked about stablecoins creating more demand for U.S. Treasuries. The logic is actually simple. A regulated dollar stablecoin needs real assets behind it. Under the GENIUS Act framework, stablecoins are backed 1:1 by eligible high-quality reserves such as cash, short-term Treasuries, Treasury-backed repo and government money-market funds. So when stablecoins grow, their reserve pools grow too. And when those reserves include Treasury bills, stablecoin adoption can create another source of demand for U.S. government debt. That means crypto growth does not have to weaken the dollar. It can actually create another global buyer base for dollar assets. That completely changes how I look at RLUSD. RLUSD is not just another dollar token sitting beside USDC and other stablecoins. Ripple’s own RLUSD reserve structure already allows short-term U.S. Treasury bills with three months or less remaining maturity, overnight reverse repos backed by Treasuries, U.S. government money-market funds and bank deposits. Think about what that means. If RLUSD grows, the pool of assets backing RLUSD grows. If RLUSD becomes a major institutional stablecoin, Ripple’s ecosystem can become a major holder of the same short-term government assets the U.S. Treasury needs constant demand for. Imagine RLUSD at $10 billion. Then $25 billion. Then $50 billion. Then $100 billion. The bigger the supply becomes, the bigger the reserve base behind it becomes. And part of that reserve base can be short-term U.S. government debt. That already gives Ripple a direct connection to the exact stablecoin-Treasury thesis Scott Bessent has been talking about. But this is where it gets even more interesting. Ripple is not stopping at Treasuries backing RLUSD. Treasuries themselves are already being brought onto the XRP Ledger. Ondo Finance launched OUSG on XRPL. OUSG gives qualified institutional investors exposure to short-term U.S. government Treasuries. And what can institutions use to mint and redeem that Treasury exposure on XRPL? RLUSD. That means this architecture already exists: RLUSD ↓ tokenized U.S. Treasury exposure ↓ OUSG ↓ XRP Ledger This is the part that really gets me. We are not imagining some future where Ripple eventually connects stablecoins with U.S. Treasuries. That connection is already being built. You have Treasury assets sitting behind the digital dollar. Then you also have Treasury products represented directly on the blockchain. And both can interact through the same ecosystem. That gives Ripple two different positions inside the Treasury market. First: Treasuries can back RLUSD. Second: Treasuries can themselves be tokenized on XRPL. That means Ripple could potentially sit on both sides of a new digital Treasury market. Digital cash on one side. Digital U.S. government debt on the other. XRP Ledger between them. And ripple:native sitting underneath the network as the native asset and potential bridge between different pools of liquidity. That is a much bigger story than “Ripple has a stablecoin.” Ripple has also committed $10 million to OpenEden’s tokenized U.S. Treasury-bill product on XRPL. That tells me Ripple clearly understands where this is going. They are not waiting for tokenized Treasuries to become a trend. They have already put capital behind bringing those products directly onto XRP Ledger. Then you have Guggenheim Treasury Services. Ripple highlighted digital commercial paper administered by Guggenheim Treasury Services on XRPL. That instrument is secured by U.S. Treasuries and carries a Prime-1 Moody’s rating. Now step back and look at what is forming. RLUSD. Ondo OUSG. OpenEden Treasury bills. Guggenheim Treasury Services. Tokenized fixed income. Institutional custody. Ripple Prime. Ripple Payments. XRP Ledger. ripple:native. All of these pieces are starting to sit inside the same financial stack. That is why I think people are looking at the $40 trillion U.S. debt problem from the wrong angle when they only ask: “How will America ever pay this?” The more interesting question for me is: How will America keep finding buyers for trillions of dollars of government debt while modernizing the financial system at the same time? Stablecoins can help create buyers. Tokenization can help create distribution. Blockchain can help create 24/7 settlement. And Ripple is building in all three areas. Imagine how Treasury investing works for a normal global institution today. You may need banking relationships. Custody. Brokerage. Settlement infrastructure. Different accounts. Different systems. Different operating hours. Now imagine Treasury exposure existing directly on XRPL. The investor can hold RLUSD. Move into tokenized Treasury exposure. Redeem back into RLUSD. Move the dollar liquidity somewhere else. Do it around the clock. That is a completely different experience. Treasuries stop being something that only sits inside old databases. They become programmable financial assets. That matters because America does not just need Treasuries to exist. America needs Treasuries to remain attractive. Liquid. Easy to buy. Easy to hold. Easy to use. Easy to move. And eventually, easy to use as collateral. That is where tokenization becomes much bigger than simply putting a bond onchain. Imagine buying a tokenized Treasury and then using it as collateral. Borrowing against it. Moving it between institutions. Settling it against digital dollars. Redeploying that liquidity instantly. Now a Treasury is no longer just something you buy and wait for. It becomes a working financial asset. And the more useful Treasuries become, the more reasons global institutions have to hold them. This is why the XRP Ledger piece matters. XRPL can become infrastructure where those assets move. RLUSD can become the digital cash side. Then ripple:native can become the neutral liquidity layer between all the different assets and currencies touching that network. Because the future XRPL does not have to contain only RLUSD and Treasury products. Imagine it contains: RLUSD. Tokenized Treasuries. EUR stablecoins. MXN stablecoins. Tokenized deposits. Money-market funds. Commercial paper. Foreign government debt. Private credit. Different institutions will hold different assets. Different countries will use different currencies. That creates a liquidity problem. You cannot expect every possible asset pair to have a massive direct market. A Japanese institution may start with yen liquidity. A European institution may need euros. A Mexican institution may need pesos. A U.S. institution may need RLUSD. A Treasury fund may need to move into cash. This is where ripple:native becomes much more interesting. XRP can potentially sit in the middle as the bridge. Asset A → ripple:native → Asset B. So imagine a Japanese bank wants $1 billion worth of tokenized U.S. Treasury exposure. It starts with Japanese liquidity. The route could eventually become: JPY ↓ ripple:native ↓ RLUSD ↓ tokenized Treasury Then later that institution wants to exit. Tokenized Treasury ↓ RLUSD ↓ ripple:native ↓ JPY Now imagine the same thing happening from Europe. -South Korea. -Singapore. -Hong Kong. -UAE. -Mexico. -Brazil. The United States gets another global distribution channel for its debt. Ripple gets institutional activity. XRPL gets settlement volume. RLUSD gets dollar demand. And ripple:native can become part of the liquidity connecting all of those markets. That is where this gets much bigger than payments. Because once tokenized Treasuries become collateral, you are no longer only talking about buying and selling government debt. You are talking about credit. -Repo. -Margin. -Working capital. -Liquidity management. -Treasury management. -Institutional trading. Imagine a company holds $2 billion in tokenized Treasuries on XRPL. It suddenly needs $500 million of liquidity. Instead of selling everything and moving through multiple systems, it uses the Treasury position as collateral. Receives RLUSD. Then converts part of that liquidity into another currency through ripple:native. Now ripple:native is sitting in the middle of: -money -government debt -FX -credit -collateral That is a completely different role from people simply trading XRP on an exchange. And Ripple has been building the institutional infrastructure around that role. Ripple Prime gives Ripple a connection into professional capital markets. Ripple Custody gives institutions infrastructure for holding digital assets. Ripple Payments handles movement. RLUSD provides regulated dollar liquidity. XRPL handles tokenization and settlement. ripple:native sits natively underneath the ledger. When I put all of that beside what Scott Bessent is saying about stablecoins and Treasuries, I cannot ignore the alignment. The U.S. wants stronger global demand for dollars. Stablecoins can extend dollars onto digital rails. The U.S. wants buyers for Treasury bills. Stablecoin reserves can become buyers. The U.S. wants more efficient capital markets. Tokenized Treasuries can make those assets easier to move and use. Ripple already has a regulated stablecoin. RLUSD already has Treasury-eligible reserve assets. XRPL already has tokenized Treasury products. RLUSD already interacts with OUSG. Ripple has already backed OpenEden Treasury infrastructure. Guggenheim Treasury Services already has Treasury-secured digital commercial paper on XRPL. This is not one random announcement. It is a system starting to form. And there is another point I think is being missed. The bullish XRP thesis does not require the U.S. dollar to fail. I actually think the opposite scenario is much stronger. Imagine the dollar becomes even more dominant because regulated stablecoins make it easier for anyone in the world to hold and move digital dollars. Those stablecoins create more demand for U.S. Treasuries. Treasuries themselves become tokenized. Global investors buy them 24/7. And ripple:native becomes one of the liquidity assets connecting those digital dollars and Treasury products to currencies around the world. In that world: the dollar wins. Treasuries win. Ripple wins. XRPL wins. And ripple:native gets a much bigger liquidity role. That is why the GENIUS Act matters here too. The framework is pushing stablecoins toward regulated 1:1 reserve structures. Bessent has talked about stablecoins strengthening dollar dominance. Ripple already has RLUSD. RLUSD is issued through a New York-regulated structure. BNY is the primary custodian for RLUSD reserves. That is serious financial infrastructure. It means Ripple is not building some completely separate parallel monetary system. It is building directly around the same regulated dollar and Treasury framework Washington is encouraging. And that is what makes this thesis so powerful to me. The path does not need to be: America abandons the dollar. America adopts XRP. That sounds unrealistic and honestly misses the point. The much bigger setup is: America keeps the dollar. America keeps Treasuries. Stablecoins make the dollar more digital. Tokenization makes Treasuries more accessible. Ripple builds the infrastructure around both. And ripple:native connects them to the rest of the global financial system. That is a completely different level of adoption. Now take this to the highly bullish scenario. Imagine the global stablecoin market reaches $3 trillion. RLUSD becomes one of the major institutional stablecoins. Maybe it reaches $100 billion or more in circulation. That means an enormous reserve pool exists behind it. Part of that reserve base holds short-term Treasury securities, Treasury-backed repo and government money-market instruments. Ripple becomes a major private-sector participant in short-term U.S. government debt demand. At the same time, tokenized Treasury products on XRPL grow from where they are today into tens of billions. Then hundreds of billions. Global asset managers start holding Treasury exposure directly on XRPL. Banks use RLUSD to enter and exit those positions. Treasuries get used as collateral. Institutions borrow against them. Ripple Prime connects the professional market. Ripple Custody holds the assets. XRPL settles them. Then currencies from around the world need to enter and exit that system. That is where ripple:native can explode in importance. Market makers need XRP inventory. Liquidity providers need deeper XRP books. Banks need larger settlement capacity. More XRP sits inside institutional liquidity operations. The amount of financial value that needs to move through the system keeps increasing. And suddenly the market has to ask a very different question: Is the current dollar value of ripple:native large enough to provide liquidity for this kind of financial system? Imagine $100 billion of tokenized Treasuries. Then $500 billion. Then trillions of tokenized fixed income across XRPL and connected markets. Imagine RLUSD at $100 billion. Imagine global currencies continuously moving in and out. At that point, the amount of liquidity required looks nothing like today's crypto market. A higher ripple:native price means every unit can represent more dollar value. That gives liquidity providers more settlement capacity without needing absurd quantities of XRP for every transaction. That is why I see price and liquidity eventually becoming connected. The bigger the financial system that XRP is asked to connect, the deeper the dollar value of XRP liquidity needs to become. The full loop could look like this: U.S. debt keeps growing ↓ Treasury needs more buyers ↓ stablecoins expand ↓ stablecoin issuers buy more short-term Treasury assets ↓ RLUSD grows ↓ Treasury products become tokenized ↓ XRPL captures more of those assets ↓ global investors enter through RLUSD ↓ more global currencies connect ↓ ripple:native bridges fragmented liquidity ↓ market makers need more XRP inventory ↓ Ripple Prime expands institutional liquidity ↓ XRPL becomes deeper financial infrastructure ↓ ripple:native represents more value inside that system ↓ price reprices higher. That is the scenario I keep coming back to. Because the wild part is that the starting pieces already exist. RLUSD already has Treasury-eligible reserves. Scott Bessent already sees stablecoins as a potential source of Treasury demand. The GENIUS Act already created the regulatory direction. Ondo OUSG already exists on XRP Ledger. RLUSD already provides an entry and redemption path for that Treasury exposure. Ripple already committed $10 million to OpenEden Treasury products. Guggenheim Treasury Services already has Treasury-secured fixed income on XRPL. BNY already sits behind RLUSD reserve custody. Ripple already has Prime, Payments and Custody. So when YTN asks: “Buying U.S. Treasury Bonds with Crypto?” I do not read that as some distant fantasy anymore. I look at the infrastructure being built and think: What happens when the world's largest government debt market meets regulated stablecoins, tokenized securities and 24/7 blockchain settlement? And what happens if XRP Ledger becomes one of the rails carrying it? That is the part people should be thinking about. Because the real ripple:native thesis may not be about replacing the dollar at all. It may be about becoming the liquidity layer underneath a stronger, more digital dollar system. RLUSD can bring dollars onchain. Tokenized Treasuries can bring U.S. debt onchain. XRPL can become the marketplace and settlement layer. And ripple:native can connect that system to the rest of the world. If that scales into trillions, we are no longer talking about XRP as just another crypto asset. We are talking about ripple:native sitting inside the liquidity architecture connecting digital dollars, U.S. government debt, FX, collateral and global institutional capital. That is the scenario I am watching. You?
0:51

What if the U.S. starts buying Treasury bonds with ripple:native or RLUSD and puts them on the XRP Ledger? South Korea’s YTN just asked a question that sounds wild at first: “Buying U.S. Treasury Bonds with Crypto?” But when I started connecting it with what Scott Bessent, Ripple, RLUSD and the XRP Ledger are already doing, this stopped looking like some random crypto theory. The pieces are already sitting right in front of us. The United States has now crossed roughly $40 trillion in federal debt. That means the government constantly needs buyers for enormous amounts of Treasury securities. Not once. Again and again. Old debt matures. New debt gets issued. Short-term bills need buyers. Interest keeps getting paid. The whole system depends on keeping demand for U.S. government debt strong. And this is exactly where stablecoins suddenly become much more important than most people realize. Scott Bessent has already talked about stablecoins creating more demand for U.S. Treasuries. The logic is actually simple. A regulated dollar stablecoin needs real assets behind it. Under the GENIUS Act framework, stablecoins are backed 1:1 by eligible high-quality reserves such as cash, short-term Treasuries, Treasury-backed repo and government money-market funds. So when stablecoins grow, their reserve pools grow too. And when those reserves include Treasury bills, stablecoin adoption can create another source of demand for U.S. government debt. That means crypto growth does not have to weaken the dollar. It can actually create another global buyer base for dollar assets. That completely changes how I look at RLUSD. RLUSD is not just another dollar token sitting beside USDC and other stablecoins. Ripple’s own RLUSD reserve structure already allows short-term U.S. Treasury bills with three months or less remaining maturity, overnight reverse repos backed by Treasuries, U.S. government money-market funds and bank deposits. Think about what that means. If RLUSD grows, the pool of assets backing RLUSD grows. If RLUSD becomes a major institutional stablecoin, Ripple’s ecosystem can become a major holder of the same short-term government assets the U.S. Treasury needs constant demand for. Imagine RLUSD at $10 billion. Then $25 billion. Then $50 billion. Then $100 billion. The bigger the supply becomes, the bigger the reserve base behind it becomes. And part of that reserve base can be short-term U.S. government debt. That already gives Ripple a direct connection to the exact stablecoin-Treasury thesis Scott Bessent has been talking about. But this is where it gets even more interesting. Ripple is not stopping at Treasuries backing RLUSD. Treasuries themselves are already being brought onto the XRP Ledger. Ondo Finance launched OUSG on XRPL. OUSG gives qualified institutional investors exposure to short-term U.S. government Treasuries. And what can institutions use to mint and redeem that Treasury exposure on XRPL? RLUSD. That means this architecture already exists: RLUSD ↓ tokenized U.S. Treasury exposure ↓ OUSG ↓ XRP Ledger This is the part that really gets me. We are not imagining some future where Ripple eventually connects stablecoins with U.S. Treasuries. That connection is already being built. You have Treasury assets sitting behind the digital dollar. Then you also have Treasury products represented directly on the blockchain. And both can interact through the same ecosystem. That gives Ripple two different positions inside the Treasury market. First: Treasuries can back RLUSD. Second: Treasuries can themselves be tokenized on XRPL. That means Ripple could potentially sit on both sides of a new digital Treasury market. Digital cash on one side. Digital U.S. government debt on the other. XRP Ledger between them. And ripple:native sitting underneath the network as the native asset and potential bridge between different pools of liquidity. That is a much bigger story than “Ripple has a stablecoin.” Ripple has also committed $10 million to OpenEden’s tokenized U.S. Treasury-bill product on XRPL. That tells me Ripple clearly understands where this is going. They are not waiting for tokenized Treasuries to become a trend. They have already put capital behind bringing those products directly onto XRP Ledger. Then you have Guggenheim Treasury Services. Ripple highlighted digital commercial paper administered by Guggenheim Treasury Services on XRPL. That instrument is secured by U.S. Treasuries and carries a Prime-1 Moody’s rating. Now step back and look at what is forming. RLUSD. Ondo OUSG. OpenEden Treasury bills. Guggenheim Treasury Services. Tokenized fixed income. Institutional custody. Ripple Prime. Ripple Payments. XRP Ledger. ripple:native. All of these pieces are starting to sit inside the same financial stack. That is why I think people are looking at the $40 trillion U.S. debt problem from the wrong angle when they only ask: “How will America ever pay this?” The more interesting question for me is: How will America keep finding buyers for trillions of dollars of government debt while modernizing the financial system at the same time? Stablecoins can help create buyers. Tokenization can help create distribution. Blockchain can help create 24/7 settlement. And Ripple is building in all three areas. Imagine how Treasury investing works for a normal global institution today. You may need banking relationships. Custody. Brokerage. Settlement infrastructure. Different accounts. Different systems. Different operating hours. Now imagine Treasury exposure existing directly on XRPL. The investor can hold RLUSD. Move into tokenized Treasury exposure. Redeem back into RLUSD. Move the dollar liquidity somewhere else. Do it around the clock. That is a completely different experience. Treasuries stop being something that only sits inside old databases. They become programmable financial assets. That matters because America does not just need Treasuries to exist. America needs Treasuries to remain attractive. Liquid. Easy to buy. Easy to hold. Easy to use. Easy to move. And eventually, easy to use as collateral. That is where tokenization becomes much bigger than simply putting a bond onchain. Imagine buying a tokenized Treasury and then using it as collateral. Borrowing against it. Moving it between institutions. Settling it against digital dollars. Redeploying that liquidity instantly. Now a Treasury is no longer just something you buy and wait for. It becomes a working financial asset. And the more useful Treasuries become, the more reasons global institutions have to hold them. This is why the XRP Ledger piece matters. XRPL can become infrastructure where those assets move. RLUSD can become the digital cash side. Then ripple:native can become the neutral liquidity layer between all the different assets and currencies touching that network. Because the future XRPL does not have to contain only RLUSD and Treasury products. Imagine it contains: RLUSD. Tokenized Treasuries. EUR stablecoins. MXN stablecoins. Tokenized deposits. Money-market funds. Commercial paper. Foreign government debt. Private credit. Different institutions will hold different assets. Different countries will use different currencies. That creates a liquidity problem. You cannot expect every possible asset pair to have a massive direct market. A Japanese institution may start with yen liquidity. A European institution may need euros. A Mexican institution may need pesos. A U.S. institution may need RLUSD. A Treasury fund may need to move into cash. This is where ripple:native becomes much more interesting. XRP can potentially sit in the middle as the bridge. Asset A → ripple:native → Asset B. So imagine a Japanese bank wants $1 billion worth of tokenized U.S. Treasury exposure. It starts with Japanese liquidity. The route could eventually become: JPY ↓ ripple:native ↓ RLUSD ↓ tokenized Treasury Then later that institution wants to exit. Tokenized Treasury ↓ RLUSD ↓ ripple:native ↓ JPY Now imagine the same thing happening from Europe. -South Korea. -Singapore. -Hong Kong. -UAE. -Mexico. -Brazil. The United States gets another global distribution channel for its debt. Ripple gets institutional activity. XRPL gets settlement volume. RLUSD gets dollar demand. And ripple:native can become part of the liquidity connecting all of those markets. That is where this gets much bigger than payments. Because once tokenized Treasuries become collateral, you are no longer only talking about buying and selling government debt. You are talking about credit. -Repo. -Margin. -Working capital. -Liquidity management. -Treasury management. -Institutional trading. Imagine a company holds $2 billion in tokenized Treasuries on XRPL. It suddenly needs $500 million of liquidity. Instead of selling everything and moving through multiple systems, it uses the Treasury position as collateral. Receives RLUSD. Then converts part of that liquidity into another currency through ripple:native. Now ripple:native is sitting in the middle of: -money -government debt -FX -credit -collateral That is a completely different role from people simply trading XRP on an exchange. And Ripple has been building the institutional infrastructure around that role. Ripple Prime gives Ripple a connection into professional capital markets. Ripple Custody gives institutions infrastructure for holding digital assets. Ripple Payments handles movement. RLUSD provides regulated dollar liquidity. XRPL handles tokenization and settlement. ripple:native sits natively underneath the ledger. When I put all of that beside what Scott Bessent is saying about stablecoins and Treasuries, I cannot ignore the alignment. The U.S. wants stronger global demand for dollars. Stablecoins can extend dollars onto digital rails. The U.S. wants buyers for Treasury bills. Stablecoin reserves can become buyers. The U.S. wants more efficient capital markets. Tokenized Treasuries can make those assets easier to move and use. Ripple already has a regulated stablecoin. RLUSD already has Treasury-eligible reserve assets. XRPL already has tokenized Treasury products. RLUSD already interacts with OUSG. Ripple has already backed OpenEden Treasury infrastructure. Guggenheim Treasury Services already has Treasury-secured digital commercial paper on XRPL. This is not one random announcement. It is a system starting to form. And there is another point I think is being missed. The bullish XRP thesis does not require the U.S. dollar to fail. I actually think the opposite scenario is much stronger. Imagine the dollar becomes even more dominant because regulated stablecoins make it easier for anyone in the world to hold and move digital dollars. Those stablecoins create more demand for U.S. Treasuries. Treasuries themselves become tokenized. Global investors buy them 24/7. And ripple:native becomes one of the liquidity assets connecting those digital dollars and Treasury products to currencies around the world. In that world: the dollar wins. Treasuries win. Ripple wins. XRPL wins. And ripple:native gets a much bigger liquidity role. That is why the GENIUS Act matters here too. The framework is pushing stablecoins toward regulated 1:1 reserve structures. Bessent has talked about stablecoins strengthening dollar dominance. Ripple already has RLUSD. RLUSD is issued through a New York-regulated structure. BNY is the primary custodian for RLUSD reserves. That is serious financial infrastructure. It means Ripple is not building some completely separate parallel monetary system. It is building directly around the same regulated dollar and Treasury framework Washington is encouraging. And that is what makes this thesis so powerful to me. The path does not need to be: America abandons the dollar. America adopts XRP. That sounds unrealistic and honestly misses the point. The much bigger setup is: America keeps the dollar. America keeps Treasuries. Stablecoins make the dollar more digital. Tokenization makes Treasuries more accessible. Ripple builds the infrastructure around both. And ripple:native connects them to the rest of the global financial system. That is a completely different level of adoption. Now take this to the highly bullish scenario. Imagine the global stablecoin market reaches $3 trillion. RLUSD becomes one of the major institutional stablecoins. Maybe it reaches $100 billion or more in circulation. That means an enormous reserve pool exists behind it. Part of that reserve base holds short-term Treasury securities, Treasury-backed repo and government money-market instruments. Ripple becomes a major private-sector participant in short-term U.S. government debt demand. At the same time, tokenized Treasury products on XRPL grow from where they are today into tens of billions. Then hundreds of billions. Global asset managers start holding Treasury exposure directly on XRPL. Banks use RLUSD to enter and exit those positions. Treasuries get used as collateral. Institutions borrow against them. Ripple Prime connects the professional market. Ripple Custody holds the assets. XRPL settles them. Then currencies from around the world need to enter and exit that system. That is where ripple:native can explode in importance. Market makers need XRP inventory. Liquidity providers need deeper XRP books. Banks need larger settlement capacity. More XRP sits inside institutional liquidity operations. The amount of financial value that needs to move through the system keeps increasing. And suddenly the market has to ask a very different question: Is the current dollar value of ripple:native large enough to provide liquidity for this kind of financial system? Imagine $100 billion of tokenized Treasuries. Then $500 billion. Then trillions of tokenized fixed income across XRPL and connected markets. Imagine RLUSD at $100 billion. Imagine global currencies continuously moving in and out. At that point, the amount of liquidity required looks nothing like today's crypto market. A higher ripple:native price means every unit can represent more dollar value. That gives liquidity providers more settlement capacity without needing absurd quantities of XRP for every transaction. That is why I see price and liquidity eventually becoming connected. The bigger the financial system that XRP is asked to connect, the deeper the dollar value of XRP liquidity needs to become. The full loop could look like this: U.S. debt keeps growing ↓ Treasury needs more buyers ↓ stablecoins expand ↓ stablecoin issuers buy more short-term Treasury assets ↓ RLUSD grows ↓ Treasury products become tokenized ↓ XRPL captures more of those assets ↓ global investors enter through RLUSD ↓ more global currencies connect ↓ ripple:native bridges fragmented liquidity ↓ market makers need more XRP inventory ↓ Ripple Prime expands institutional liquidity ↓ XRPL becomes deeper financial infrastructure ↓ ripple:native represents more value inside that system ↓ price reprices higher. That is the scenario I keep coming back to. Because the wild part is that the starting pieces already exist. RLUSD already has Treasury-eligible reserves. Scott Bessent already sees stablecoins as a potential source of Treasury demand. The GENIUS Act already created the regulatory direction. Ondo OUSG already exists on XRP Ledger. RLUSD already provides an entry and redemption path for that Treasury exposure. Ripple already committed $10 million to OpenEden Treasury products. Guggenheim Treasury Services already has Treasury-secured fixed income on XRPL. BNY already sits behind RLUSD reserve custody. Ripple already has Prime, Payments and Custody. So when YTN asks: “Buying U.S. Treasury Bonds with Crypto?” I do not read that as some distant fantasy anymore. I look at the infrastructure being built and think: What happens when the world's largest government debt market meets regulated stablecoins, tokenized securities and 24/7 blockchain settlement? And what happens if XRP Ledger becomes one of the rails carrying it? That is the part people should be thinking about. Because the real ripple:native thesis may not be about replacing the dollar at all. It may be about becoming the liquidity layer underneath a stronger, more digital dollar system. RLUSD can bring dollars onchain. Tokenized Treasuries can bring U.S. debt onchain. XRPL can become the marketplace and settlement layer. And ripple:native can connect that system to the rest of the world. If that scales into trillions, we are no longer talking about XRP as just another crypto asset. We are talking about ripple:native sitting inside the liquidity architecture connecting digital dollars, U.S. government debt, FX, collateral and global institutional capital. That is the scenario I am watching. You?

X Finance Bull

237,245 просмотров • 16 дней назад

Canada has fallen a long way in a very short time and the evidence is everywhere for anyone willing to look without the rose colored glasses the government keeps handing out. What used to be one of the safest wealthiest and freest countries on earth now feels like a place where basic order affordability and common sense have been deliberately sacrificed on the altar of ideology and political vanity. Start with the streets themselves. Violent crime has exploded upward fifty percent since the middle of the last decade. Homicides are up nearly forty percent sexual assaults have almost doubled and home invasions in major cities have tripled in some years. Auto theft has become so routine that insurance companies treat it like weather. None of this is random. A huge share of these crimes are committed by people who were already out on bail or probation when they struck again. In Saskatchewan almost half the people charged with murder in a recent year were on judicial release at the time. Across the country hundreds of murders have been committed by individuals the system had already caught and then let walk. The phrase catch and release is not hyperbole it is the lived reality of police officers victims and anyone paying attention. The bail disaster traces directly to Bill C75 passed in 2019 under the banner of progressive reform. That law turned the principle of restraint in custody into a default preference for release no matter how violent or repeat the offender. Judges were told to find alternatives to detention and the result was entirely predictable. Roughly seventy percent of accused now get bail even when they have long records and even when the new charges involve guns drugs or serious violence. When foreign nationals commit crimes that should trigger automatic deportation the same broken process applies. They get bail they disappear and the Canada Border Services Agency admits hundreds of convicted foreign criminals are currently unaccounted for inside the country. Some have convictions for sexual assault or trafficking yet they walk free while their removal orders gather dust in a backlog that never shrinks. Cost of living has become a daily punishment. Groceries for a family of four now run close to fourteen hundred dollars a month and that number keeps climbing even as headline inflation supposedly cools. Rent in every major city has risen between twenty and fifty percent in five years while wages have barely moved. Household debt sits at the highest level in the G7 and mortgage renewals at five percent plus interest rates are about to crush another wave of families. Food bank use has doubled tent cities sprawl in parks that used to be safe for children and one in four Canadian kids now lives in a household that cannot reliably put food on the table. The carbon tax alone added hundreds of dollars a year to heating and fuel costs for the average home before it was finally paused in a panic last year. None of the promised rebates ever covered the real hit especially for rural families and low income workers. Free speech the thing Canada used to brag about has been systematically strangled. The government passed Bill C11 giving itself power to regulate what algorithms show Canadians online. It tried to pass Bill C63 which would have allowed house arrest for things people might say in the future. And through Bill C18 it created the absurd situation where news from legitimate Canadian outlets is still banned on Facebook and Instagram more than two years later. Ottawa knew Meta would block news rather than pay the forced link tax and they passed the law anyway. When the blackout hit they shrugged and blamed the company. The result is that millions of Canadians get their information from random memes influencers and conspiracy accounts while local journalism dies and emergency alerts during wildfires get buried under cat videos. The same government that claims to defend democracy has engineered a system where the largest public square in the country is deliberately stripped of verifiable news. Corruption and unaccountability are no longer exceptions they are the operating model. Politicians face ethics investigations and simply prorogue Parliament to kill them. Contracts worth hundreds of millions go to connected insiders with no competitive bid. A single two person consulting firm with no employees managed to bill the government more than a hundred million dollars for work it subcontracted out at massive markups. Green slush funds handed out four hundred million dollars with almost two hundred conflicts of interest. Banks launder money for foreign cartels and pay fines in other countries while no executive here sees a courtroom. The ethics commissioner issues reports that go nowhere because the office has no real power. Scandals that would end careers in any serious democracy are treated as minor public relations problems to be managed until the news cycle moves on. Healthcare waits have become a national humiliation. The median wait time from referral to treatment is now over thirty weeks longer than anywhere else in the developed world. Two and a half million people have no family doctor. People die on wait lists for heart surgery and cancer care while the system pushes medical assistance in dying as a cost saving measure. Mental health supports are a cruel joke with waits measured in years not months. Immigration policy turned a manageable program into a population tsunami that outran every piece of infrastructure the country had. Temporary residents alone now exceed three million people many working low wage jobs that used to go to young Canadians. Housing construction never kept pace rents soared and the feel of entire neighborhoods changed overnight. The government finally admitted the scale was unsustainable and began slashing numbers but the damage is done and the reversal is slow. Taken together these failures paint a picture of a country that has chosen ideology over competence control over freedom and short term political wins over long term national health. The people in charge spent a decade telling Canadians everything was fine while debt exploded crime surged speech narrowed and the basic social contract frayed. Trust in institutions sits at historic lows and anger is no longer fringe it is mainstream. Canada is not broken beyond repair but the longer the current path continues the harder the eventual correction will be. The evidence is overwhelming the country most of us grew up in has been radically and deliberately transformed and not for the better.
3:20

Canada has fallen a long way in a very short time and the evidence is everywhere for anyone willing to look without the rose colored glasses the government keeps handing out. What used to be one of the safest wealthiest and freest countries on earth now feels like a place where basic order affordability and common sense have been deliberately sacrificed on the altar of ideology and political vanity. Start with the streets themselves. Violent crime has exploded upward fifty percent since the middle of the last decade. Homicides are up nearly forty percent sexual assaults have almost doubled and home invasions in major cities have tripled in some years. Auto theft has become so routine that insurance companies treat it like weather. None of this is random. A huge share of these crimes are committed by people who were already out on bail or probation when they struck again. In Saskatchewan almost half the people charged with murder in a recent year were on judicial release at the time. Across the country hundreds of murders have been committed by individuals the system had already caught and then let walk. The phrase catch and release is not hyperbole it is the lived reality of police officers victims and anyone paying attention. The bail disaster traces directly to Bill C75 passed in 2019 under the banner of progressive reform. That law turned the principle of restraint in custody into a default preference for release no matter how violent or repeat the offender. Judges were told to find alternatives to detention and the result was entirely predictable. Roughly seventy percent of accused now get bail even when they have long records and even when the new charges involve guns drugs or serious violence. When foreign nationals commit crimes that should trigger automatic deportation the same broken process applies. They get bail they disappear and the Canada Border Services Agency admits hundreds of convicted foreign criminals are currently unaccounted for inside the country. Some have convictions for sexual assault or trafficking yet they walk free while their removal orders gather dust in a backlog that never shrinks. Cost of living has become a daily punishment. Groceries for a family of four now run close to fourteen hundred dollars a month and that number keeps climbing even as headline inflation supposedly cools. Rent in every major city has risen between twenty and fifty percent in five years while wages have barely moved. Household debt sits at the highest level in the G7 and mortgage renewals at five percent plus interest rates are about to crush another wave of families. Food bank use has doubled tent cities sprawl in parks that used to be safe for children and one in four Canadian kids now lives in a household that cannot reliably put food on the table. The carbon tax alone added hundreds of dollars a year to heating and fuel costs for the average home before it was finally paused in a panic last year. None of the promised rebates ever covered the real hit especially for rural families and low income workers. Free speech the thing Canada used to brag about has been systematically strangled. The government passed Bill C11 giving itself power to regulate what algorithms show Canadians online. It tried to pass Bill C63 which would have allowed house arrest for things people might say in the future. And through Bill C18 it created the absurd situation where news from legitimate Canadian outlets is still banned on Facebook and Instagram more than two years later. Ottawa knew Meta would block news rather than pay the forced link tax and they passed the law anyway. When the blackout hit they shrugged and blamed the company. The result is that millions of Canadians get their information from random memes influencers and conspiracy accounts while local journalism dies and emergency alerts during wildfires get buried under cat videos. The same government that claims to defend democracy has engineered a system where the largest public square in the country is deliberately stripped of verifiable news. Corruption and unaccountability are no longer exceptions they are the operating model. Politicians face ethics investigations and simply prorogue Parliament to kill them. Contracts worth hundreds of millions go to connected insiders with no competitive bid. A single two person consulting firm with no employees managed to bill the government more than a hundred million dollars for work it subcontracted out at massive markups. Green slush funds handed out four hundred million dollars with almost two hundred conflicts of interest. Banks launder money for foreign cartels and pay fines in other countries while no executive here sees a courtroom. The ethics commissioner issues reports that go nowhere because the office has no real power. Scandals that would end careers in any serious democracy are treated as minor public relations problems to be managed until the news cycle moves on. Healthcare waits have become a national humiliation. The median wait time from referral to treatment is now over thirty weeks longer than anywhere else in the developed world. Two and a half million people have no family doctor. People die on wait lists for heart surgery and cancer care while the system pushes medical assistance in dying as a cost saving measure. Mental health supports are a cruel joke with waits measured in years not months. Immigration policy turned a manageable program into a population tsunami that outran every piece of infrastructure the country had. Temporary residents alone now exceed three million people many working low wage jobs that used to go to young Canadians. Housing construction never kept pace rents soared and the feel of entire neighborhoods changed overnight. The government finally admitted the scale was unsustainable and began slashing numbers but the damage is done and the reversal is slow. Taken together these failures paint a picture of a country that has chosen ideology over competence control over freedom and short term political wins over long term national health. The people in charge spent a decade telling Canadians everything was fine while debt exploded crime surged speech narrowed and the basic social contract frayed. Trust in institutions sits at historic lows and anger is no longer fringe it is mainstream. Canada is not broken beyond repair but the longer the current path continues the harder the eventual correction will be. The evidence is overwhelming the country most of us grew up in has been radically and deliberately transformed and not for the better.

Vote Canada

25,186 просмотров • 9 месяцев назад

Here is how I am using AI right now at work, at home, and for my finances... Every founder, executive or investor I talk with these days wants to know how others are using AI in their daily lives. I figured it would be helpful to pull back the curtain on what I am using and how I have implemented the various products. There are three areas where I have adopted AI in a material way: professionally, financially, and personally. Professional use of AI On the professional side, I am currently using Grok Grok Bot extensively. I started with a Chief of Staff bot that I put in charge of the entire operation, followed by a number of more specialized bots for various bodies of work (talent recruiter, product designer, podcast researcher, book launch manager, email organizer, etc). Once I had the initial team of bots set up, I spent about an hour “onboarding” the Chief of Staff to my professional life. I treated this exactly how I would onboard a human Chief of Staff. I explained each business I am involved with, including their products, business model, personnel, metrics, and goals. I explicitly called out what the business is doing well and where we need to improve. I also gave the Chief of Staff access to relevant systems (email, calendar, Slack, analytics dashboards, etc). Once I had given as much context as I thought necessary, I asked the Chief of Staff to create an overview document to send me so I could double-check the accuracy and thoroughness of the bots understanding. I also asked the CoS bot to interview me for any other information that would be relevant to ensuring the bot could help me. This entire process was fairly quick and painless, but I believe it was the single most important thing I did to get value from Grok Bot. The more context that the AI system has, the more helpful it can be. That context can come from static, institutional knowledge or it can come from dynamic daily updates like email and Slack messages. After getting the bots set up and giving them context, I have done two other things that I think are worth sharing. The first is that my team of bots holds a daily standup meeting where they all come together and share what they did yesterday, what they are going to do today, and what they need my help or approval on (aka what they are blocked on). These “exec meeting” or daily standup allows for the bots to collaborate in a more seamless way, while also creating a very simple process for the Chief of Staff bot to put together a daily brief for me on what happened yesterday, what is going to happen today, and where I am needed to unblock productivity. The second thing I have done is treat the AI system as the brain of the company. Most people try to use AI as an augmentation to themselves, which can be helpful to a degree. I have flipped the relationship though. I look at my job as persistently giving the AI bots as much context as possible, so I can leverage their superhuman intelligence to make decisions and achieve our goals. For example, the recruiter bot recently surfaced a number of very high-quality candidates for an open role we have. After meeting with each candidate, I wrote a quick message to the recruiter bot to tell it what I liked about the person, what I thought were potential issues, improvements for future searches, and what the next steps were with each individual. All of that information and context is getting stored in the bot’s memory, which will compound over time and help us improve as an organization. Quick pro tip: If you are worried about putting all of the context into a single system’s memory, but unsure if that is the system you will use forever, you can have Grok Bot or another system dump their memory and context into a Notion document as well. This way you have a duplicate copy of the memory so it can be referenced by any AI system you use in the future. My takeaway from using Grok Bot to manage our companies is that we are having to hire less people, we are seeing a direct impact on revenue growth, and it appears to drive higher quality in our decision-making process. That is a win-win-win. I highly recommend going through these steps to setup your system correctly and it will pay off big time later on. Financial use of AI On the financial side, it was nearly impossible to find a good AI product to use for personal finance. Everything seemed to be a Chat-GPT wrapper that technically worked from an engineering standpoint, but didn’t solve any of the user problems I was facing. A big issue is that most of the fintech products are focused on budgeting and saving, rather than investing and growing your portfolio. This is why I eventually spent the time and money to build CFO Silvia. I went through a similar process of getting Silvia set up with the necessary context. I attached my bank accounts, brokerage accounts, crypto accounts, and credit cards, along with uploading real estate, cars, collectibles, and private investments. Silvia allows me to dynamically track the value of these assets (and my overall net worth) in real-time. But the real unlock for me has been talking to Silvia about two specific topics: tax and estate planning. As most of you know, I am not a frequent trader, so although you could use Silvia for stock analysis or trading activities, that is not my approach to investing. Instead, I have had great success in using Silvia to find creative and valuable tax mitigation strategies that are personalized to my situation, including ideas that had not previously been surfaced by my accountants, lawyers, or tax experts. Additionally, I have used Silvia for estate planning purposes. I am married and have four children, so there is a decent amount of complexity and opportunities to pursue. Having a dedicated resource with superhuman intelligence and the full context of my personal financial situation has been incredibly powerful. One funny thing I have noticed is that I am willing to tell Silvia certain things that I would hesitate to tell other humans (financial goals, areas of concern, etc) and I ask numerous “dumb” questions that I would probably shy away from asking a human. Regardless of why I feel more comfortable talking to the AI product, it has unlocked a few different ideas and strategies that I was previously unaware of, so that has been an added bonus to using the product. If you aren’t using AI to help manage your finances, I think it is a no brainer to start using the technology. I am biased towards Silvia since we built it, but you can give it a try for free here: Personal use of AI On the personal side, I use almost all of the traditional AI products (Chat-GPT, Claude, Grok, Gemini, Perplexity, etc). Those are well understood at this point, but one product that I started using recently that I am impressed with is Instinct AI. They have built a personal assistant AI bot that you communicate with through iMessage or SMS. The experience has been delightful, but I am most excited about the bot’s ability to anticipate the second or third-step in a process before I have to tell it anything. For example, Instinct got access to my calendar and immediately started identifying scheduling conflicts and asked me if I would like the bot to reach out to one of the parties to reschedule. I never told it to look for conflicts, nor did I tell it I wanted help rescheduling things. It’s “instincts” knew what the basic task would be and began executing. Another example is that Instinct was told my wife is Polina, so whenever it deems something important to the household or family, Instinct will add Polina to the calendar invite, communicate the information to her, or ask me if Polina should be aware of the information. This is very helpful for someone like me who has too many things floating around in my brain and should always do a better job of keeping Polina informed about various things. Lastly, Instinct is very helpful in scanning my personal email and understanding what is most important. It ignores things that are trivial, but somehow can parse out the high priority items, summarize them for me in a text message, draft a response to the email, and then ask me for permission to respond. As I said, it is the most impressive personal assistant AI product I have used so far. So those are the three big areas that I use AI today and the specific products I have incorporated into my life. Before I let you go, I figured I could share some best practices I have learned as well. I also make sure to tell AI bots they are not allowed to respond to any message or email without my explicit approval. This reduces the risk of having a bot go rogue with a message or commitment that I am not onboard with. I also ensure that each bot only has read access to our business systems like an analytics dashboard, etc. While I am a big proponent of using these products and believe they will fundamentally transform how we operate professionally, I am still not ready to let them loose without human oversight. I am sure that will change in the coming weeks and months, but I need more time to get comfortable with that level of delegation and trust. I hope this overview was helpful for each of you. It would be great if you could respond to this post with any products you are using or tips/tricks that you have learned to get more productivity and value in your life. I love writing these letters each day because I learn just as much from me as I learn from you all. Onwards!
12:57

Here is how I am using AI right now at work, at home, and for my finances... Every founder, executive or investor I talk with these days wants to know how others are using AI in their daily lives. I figured it would be helpful to pull back the curtain on what I am using and how I have implemented the various products. There are three areas where I have adopted AI in a material way: professionally, financially, and personally. Professional use of AI On the professional side, I am currently using Grok Grok Bot extensively. I started with a Chief of Staff bot that I put in charge of the entire operation, followed by a number of more specialized bots for various bodies of work (talent recruiter, product designer, podcast researcher, book launch manager, email organizer, etc). Once I had the initial team of bots set up, I spent about an hour “onboarding” the Chief of Staff to my professional life. I treated this exactly how I would onboard a human Chief of Staff. I explained each business I am involved with, including their products, business model, personnel, metrics, and goals. I explicitly called out what the business is doing well and where we need to improve. I also gave the Chief of Staff access to relevant systems (email, calendar, Slack, analytics dashboards, etc). Once I had given as much context as I thought necessary, I asked the Chief of Staff to create an overview document to send me so I could double-check the accuracy and thoroughness of the bots understanding. I also asked the CoS bot to interview me for any other information that would be relevant to ensuring the bot could help me. This entire process was fairly quick and painless, but I believe it was the single most important thing I did to get value from Grok Bot. The more context that the AI system has, the more helpful it can be. That context can come from static, institutional knowledge or it can come from dynamic daily updates like email and Slack messages. After getting the bots set up and giving them context, I have done two other things that I think are worth sharing. The first is that my team of bots holds a daily standup meeting where they all come together and share what they did yesterday, what they are going to do today, and what they need my help or approval on (aka what they are blocked on). These “exec meeting” or daily standup allows for the bots to collaborate in a more seamless way, while also creating a very simple process for the Chief of Staff bot to put together a daily brief for me on what happened yesterday, what is going to happen today, and where I am needed to unblock productivity. The second thing I have done is treat the AI system as the brain of the company. Most people try to use AI as an augmentation to themselves, which can be helpful to a degree. I have flipped the relationship though. I look at my job as persistently giving the AI bots as much context as possible, so I can leverage their superhuman intelligence to make decisions and achieve our goals. For example, the recruiter bot recently surfaced a number of very high-quality candidates for an open role we have. After meeting with each candidate, I wrote a quick message to the recruiter bot to tell it what I liked about the person, what I thought were potential issues, improvements for future searches, and what the next steps were with each individual. All of that information and context is getting stored in the bot’s memory, which will compound over time and help us improve as an organization. Quick pro tip: If you are worried about putting all of the context into a single system’s memory, but unsure if that is the system you will use forever, you can have Grok Bot or another system dump their memory and context into a Notion document as well. This way you have a duplicate copy of the memory so it can be referenced by any AI system you use in the future. My takeaway from using Grok Bot to manage our companies is that we are having to hire less people, we are seeing a direct impact on revenue growth, and it appears to drive higher quality in our decision-making process. That is a win-win-win. I highly recommend going through these steps to setup your system correctly and it will pay off big time later on. Financial use of AI On the financial side, it was nearly impossible to find a good AI product to use for personal finance. Everything seemed to be a Chat-GPT wrapper that technically worked from an engineering standpoint, but didn’t solve any of the user problems I was facing. A big issue is that most of the fintech products are focused on budgeting and saving, rather than investing and growing your portfolio. This is why I eventually spent the time and money to build CFO Silvia. I went through a similar process of getting Silvia set up with the necessary context. I attached my bank accounts, brokerage accounts, crypto accounts, and credit cards, along with uploading real estate, cars, collectibles, and private investments. Silvia allows me to dynamically track the value of these assets (and my overall net worth) in real-time. But the real unlock for me has been talking to Silvia about two specific topics: tax and estate planning. As most of you know, I am not a frequent trader, so although you could use Silvia for stock analysis or trading activities, that is not my approach to investing. Instead, I have had great success in using Silvia to find creative and valuable tax mitigation strategies that are personalized to my situation, including ideas that had not previously been surfaced by my accountants, lawyers, or tax experts. Additionally, I have used Silvia for estate planning purposes. I am married and have four children, so there is a decent amount of complexity and opportunities to pursue. Having a dedicated resource with superhuman intelligence and the full context of my personal financial situation has been incredibly powerful. One funny thing I have noticed is that I am willing to tell Silvia certain things that I would hesitate to tell other humans (financial goals, areas of concern, etc) and I ask numerous “dumb” questions that I would probably shy away from asking a human. Regardless of why I feel more comfortable talking to the AI product, it has unlocked a few different ideas and strategies that I was previously unaware of, so that has been an added bonus to using the product. If you aren’t using AI to help manage your finances, I think it is a no brainer to start using the technology. I am biased towards Silvia since we built it, but you can give it a try for free here: Personal use of AI On the personal side, I use almost all of the traditional AI products (Chat-GPT, Claude, Grok, Gemini, Perplexity, etc). Those are well understood at this point, but one product that I started using recently that I am impressed with is Instinct AI. They have built a personal assistant AI bot that you communicate with through iMessage or SMS. The experience has been delightful, but I am most excited about the bot’s ability to anticipate the second or third-step in a process before I have to tell it anything. For example, Instinct got access to my calendar and immediately started identifying scheduling conflicts and asked me if I would like the bot to reach out to one of the parties to reschedule. I never told it to look for conflicts, nor did I tell it I wanted help rescheduling things. It’s “instincts” knew what the basic task would be and began executing. Another example is that Instinct was told my wife is Polina, so whenever it deems something important to the household or family, Instinct will add Polina to the calendar invite, communicate the information to her, or ask me if Polina should be aware of the information. This is very helpful for someone like me who has too many things floating around in my brain and should always do a better job of keeping Polina informed about various things. Lastly, Instinct is very helpful in scanning my personal email and understanding what is most important. It ignores things that are trivial, but somehow can parse out the high priority items, summarize them for me in a text message, draft a response to the email, and then ask me for permission to respond. As I said, it is the most impressive personal assistant AI product I have used so far. So those are the three big areas that I use AI today and the specific products I have incorporated into my life. Before I let you go, I figured I could share some best practices I have learned as well. I also make sure to tell AI bots they are not allowed to respond to any message or email without my explicit approval. This reduces the risk of having a bot go rogue with a message or commitment that I am not onboard with. I also ensure that each bot only has read access to our business systems like an analytics dashboard, etc. While I am a big proponent of using these products and believe they will fundamentally transform how we operate professionally, I am still not ready to let them loose without human oversight. I am sure that will change in the coming weeks and months, but I need more time to get comfortable with that level of delegation and trust. I hope this overview was helpful for each of you. It would be great if you could respond to this post with any products you are using or tips/tricks that you have learned to get more productivity and value in your life. I love writing these letters each day because I learn just as much from me as I learn from you all. Onwards!

Anthony Pompliano 🌪

83,069 просмотров • 14 дней назад

My name is David Baumblatt, I am an American Citizen born and raised in New York in a Patriotic Family of generations of military veterans going all the way back to the civil war. An Eagle Scout and Boys State who has always been grateful to be an American. I am a former FBI Agent and Military Veteran, West Point Graduate and Heavyweight Captain of the Boxing Team. I am the 12th person in the history of America to earn a dual commission via West Point and ROTC. I am fluent in German and Mandarin Chinese. My education includes: 1. Harvard University: Master Public Administration 2. IMD Business School: Master Business Administration 3. University San Francisco: Master Chinese Studies 4. University Oklahoma: Master Human Relations 5. U.S. Military Academy: Bachelor Science 6. Marion Military Institute: Associate Arts In 2010 I left America as I continue to firmly believe that America which was once a Great Country founded by Brave Christian European Men has turned into a Globalist Corporate Empire whose value system is monetary greed, I predicted back then, that America is headed for a violent revolution and collapse. Already disillusioned by both the democrats and republicans, I originally was a staunch Trump Supporter as I politically identify myself as a Nationalist, however I am dismayed with the Trump Administration continued deviation away from America First and their censorship and avoidance to my requests for help regarding Government Corruption Whistleblowing. Joining the FBI in 2004, I developed a growing discontent with FBI Management and voiced my concern with them not only spying on innocent American Citizens, but also myself when I was an Agent. In 2007 I voluntarily left the FBI, I was not terminated, nor was I forced/coerced to resign, I simply did not want to work for the government anymore. Upon my departure I sent a letter to Senator Chuck Grassley reporting on the FBI corruption, most significantly, the unconstitutional spying on innocent American Citizens via the FISA. In 2010 I moved to China where I worked for both Boeing and Amazon in Beijing. It was during this time, that I reported both Amazon and Boeing`s collusion with the Chinese Government at the expense of America through two different lawsuits against both corporations. In one surprising twist, the lawsuit against Boeing was filed in both the Chinese Courts and the American Courts. Whereas I received legal victory in the Chinese Courts, my case in the American Courts which was influenced by the collusion of a corrupt Federal Judge in Chicago and the Boeing Corporation, the Judge would not even allow me to present my case in court and thus handed a direct victory to the Boeing Corporation. In 2021, I made an attempt to leave Mainland China due to personal safety reasons, as the conditions in Mainland China were becoming less and less safe for Americans, especially one with my background. Working through Amazon to obtain a work visa to Singapore via the vendor PricewaterhouseCoopers (PwC). Despite PwC giving me the highest rating possible on my work visa application to Singapore, not only was my work visa rejected, but I am permanently barred to ever reapply for a work visa ever again to Singapore. I am the only person in the history of Amazon to have my work visa denied to Singapore. Despite never having any legal issues myself with the Government of Singapore and always being on good terms with their government, it was reported from Singapore Government sources that my work visa was denied due to me being on a Terrorist Watchlist from the American Government. Due to safety reasons of living in Mainland China and also Amazons refusal to relocate me to another country, I subsequently quit Amazon and relocated myself to Hong Kong. At this time, I was fully knowledgeable that a full FBI Investigation against me has been underway for over a decade, with numerous friends, colleagues, partners, associates, etc. apprehensively and secretly communicating with me and informing me that the FBI had made secretive attempts using monetary persuasion to recruit them to spy on me and solicit information from them about me. I have made numerous attempts to communicate with the FBI both in person and online demanding that they stop going behind my back damaging my reputation, career, and life, and face me, and level the allegations. I have personally met with FBI Agents at the U.S. Embassy in Beijing who deny any knowledge of any investigation against me. However after gathering further evidence for my case, I then years later, went to the U.S. Consulate in Hong Kong, demanding to speak with the FBI Agent assigned to the Consulate. However this time, the FBI Agent there refused to meet with me, instead sending State Department Special Agents in his place who specifically told me that the FBI Agent refuses to meet with me. The reason of course is that the FBI does not want to self incriminate themselves. In January 2022 I wrote every US politician numerous of times, along with the FBI and Department of Justice telling them of my situation and asking for someone to assist me, I was ignored by all. After more than thousands of emails to the U.S. Government, Politicians, FBI, I finally gave up. In September 2023, I wrote a book and went public on social media to tell my story and ask the American Patriots for their support. Since then, I have been heavily censored on social media. I have been suspended three times on X, this is my 4th account, I have been suspended on Facebook and Instagram, I am on my fourth account on TikTok, I am on my second account on YouTube, I have been suspended on LinkedIn. I have lost numerous work opportunities, as it was revealed that the FBI had previously spoke with my potential customer or my future potential hiring manager. If you are an American Patriot, I am simply asking you to support a military veteran by reposting my message to pressure our government to answer my allegations on government corruption. When Government Corruption is reported, it is a public good for all American Citizens to hold their government accountable for the Safety and Justice for all citizens. It is time for the American Government to respond to my questions, I am a military veteran who honorably served my country. It is time for answers: 1. Why is an American Military Veteran not welcome back in the USA? 2. Why was I detained, searched, interrogated, deceived, surveilled, humiliated, and assaulted by the U.S. Government? 3. Why am I on the FBI Terrorist Watchlist? 4. Why am I under investigation from the FBI? 5. Why did I get illegally terminated from the Boeing Corporation, when I was offered a promotion, and then filed an ethical whistleblower claim against Boeing? 6. How can an American Citizen get legal justice in the Chinese Courts, however not even have his case presented in the USA? 7. Why is the Amazon Beijing Office used as an Intelligence Hub by the Chinese Government? 8. Why did Amazon give both my predecessor and successor (both foreign nationals) the option to work anywhere in Asia, however Amazon required me to work in Beijing, China even though my job did not require me to be in China, nor did I want to be stationed in China? 9. Did the U.S. Government directly or indirectly communicate with the Singaporean Government about me? 10. Why did the U.S. Consulate Hong Kong FBI Agent refuse to meet with me? Thank you for your support. Lead to Victory. Faith-Family-Freedom You can follow me on Rumble: You can read about my story:
23:43

My name is David Baumblatt, I am an American Citizen born and raised in New York in a Patriotic Family of generations of military veterans going all the way back to the civil war. An Eagle Scout and Boys State who has always been grateful to be an American. I am a former FBI Agent and Military Veteran, West Point Graduate and Heavyweight Captain of the Boxing Team. I am the 12th person in the history of America to earn a dual commission via West Point and ROTC. I am fluent in German and Mandarin Chinese. My education includes: 1. Harvard University: Master Public Administration 2. IMD Business School: Master Business Administration 3. University San Francisco: Master Chinese Studies 4. University Oklahoma: Master Human Relations 5. U.S. Military Academy: Bachelor Science 6. Marion Military Institute: Associate Arts In 2010 I left America as I continue to firmly believe that America which was once a Great Country founded by Brave Christian European Men has turned into a Globalist Corporate Empire whose value system is monetary greed, I predicted back then, that America is headed for a violent revolution and collapse. Already disillusioned by both the democrats and republicans, I originally was a staunch Trump Supporter as I politically identify myself as a Nationalist, however I am dismayed with the Trump Administration continued deviation away from America First and their censorship and avoidance to my requests for help regarding Government Corruption Whistleblowing. Joining the FBI in 2004, I developed a growing discontent with FBI Management and voiced my concern with them not only spying on innocent American Citizens, but also myself when I was an Agent. In 2007 I voluntarily left the FBI, I was not terminated, nor was I forced/coerced to resign, I simply did not want to work for the government anymore. Upon my departure I sent a letter to Senator Chuck Grassley reporting on the FBI corruption, most significantly, the unconstitutional spying on innocent American Citizens via the FISA. In 2010 I moved to China where I worked for both Boeing and Amazon in Beijing. It was during this time, that I reported both Amazon and Boeing`s collusion with the Chinese Government at the expense of America through two different lawsuits against both corporations. In one surprising twist, the lawsuit against Boeing was filed in both the Chinese Courts and the American Courts. Whereas I received legal victory in the Chinese Courts, my case in the American Courts which was influenced by the collusion of a corrupt Federal Judge in Chicago and the Boeing Corporation, the Judge would not even allow me to present my case in court and thus handed a direct victory to the Boeing Corporation. In 2021, I made an attempt to leave Mainland China due to personal safety reasons, as the conditions in Mainland China were becoming less and less safe for Americans, especially one with my background. Working through Amazon to obtain a work visa to Singapore via the vendor PricewaterhouseCoopers (PwC). Despite PwC giving me the highest rating possible on my work visa application to Singapore, not only was my work visa rejected, but I am permanently barred to ever reapply for a work visa ever again to Singapore. I am the only person in the history of Amazon to have my work visa denied to Singapore. Despite never having any legal issues myself with the Government of Singapore and always being on good terms with their government, it was reported from Singapore Government sources that my work visa was denied due to me being on a Terrorist Watchlist from the American Government. Due to safety reasons of living in Mainland China and also Amazons refusal to relocate me to another country, I subsequently quit Amazon and relocated myself to Hong Kong. At this time, I was fully knowledgeable that a full FBI Investigation against me has been underway for over a decade, with numerous friends, colleagues, partners, associates, etc. apprehensively and secretly communicating with me and informing me that the FBI had made secretive attempts using monetary persuasion to recruit them to spy on me and solicit information from them about me. I have made numerous attempts to communicate with the FBI both in person and online demanding that they stop going behind my back damaging my reputation, career, and life, and face me, and level the allegations. I have personally met with FBI Agents at the U.S. Embassy in Beijing who deny any knowledge of any investigation against me. However after gathering further evidence for my case, I then years later, went to the U.S. Consulate in Hong Kong, demanding to speak with the FBI Agent assigned to the Consulate. However this time, the FBI Agent there refused to meet with me, instead sending State Department Special Agents in his place who specifically told me that the FBI Agent refuses to meet with me. The reason of course is that the FBI does not want to self incriminate themselves. In January 2022 I wrote every US politician numerous of times, along with the FBI and Department of Justice telling them of my situation and asking for someone to assist me, I was ignored by all. After more than thousands of emails to the U.S. Government, Politicians, FBI, I finally gave up. In September 2023, I wrote a book and went public on social media to tell my story and ask the American Patriots for their support. Since then, I have been heavily censored on social media. I have been suspended three times on X, this is my 4th account, I have been suspended on Facebook and Instagram, I am on my fourth account on TikTok, I am on my second account on YouTube, I have been suspended on LinkedIn. I have lost numerous work opportunities, as it was revealed that the FBI had previously spoke with my potential customer or my future potential hiring manager. If you are an American Patriot, I am simply asking you to support a military veteran by reposting my message to pressure our government to answer my allegations on government corruption. When Government Corruption is reported, it is a public good for all American Citizens to hold their government accountable for the Safety and Justice for all citizens. It is time for the American Government to respond to my questions, I am a military veteran who honorably served my country. It is time for answers: 1. Why is an American Military Veteran not welcome back in the USA? 2. Why was I detained, searched, interrogated, deceived, surveilled, humiliated, and assaulted by the U.S. Government? 3. Why am I on the FBI Terrorist Watchlist? 4. Why am I under investigation from the FBI? 5. Why did I get illegally terminated from the Boeing Corporation, when I was offered a promotion, and then filed an ethical whistleblower claim against Boeing? 6. How can an American Citizen get legal justice in the Chinese Courts, however not even have his case presented in the USA? 7. Why is the Amazon Beijing Office used as an Intelligence Hub by the Chinese Government? 8. Why did Amazon give both my predecessor and successor (both foreign nationals) the option to work anywhere in Asia, however Amazon required me to work in Beijing, China even though my job did not require me to be in China, nor did I want to be stationed in China? 9. Did the U.S. Government directly or indirectly communicate with the Singaporean Government about me? 10. Why did the U.S. Consulate Hong Kong FBI Agent refuse to meet with me? Thank you for your support. Lead to Victory. Faith-Family-Freedom You can follow me on Rumble: You can read about my story:

David Baumblatt 叶大卫

211,927 просмотров • 4 месяцев назад

Like seemingly everyone on this app I have plenty of opinions about Twitter > X and figure now is a good time to open up a bit about my experience at the company. I tweeted for years into the void for the love of it like many of you, but after selling my startup to Twitter in 2020 I finally got to see it from the inside. Up close it was both amazing and terrible, like so many other companies and things in life. As someone with a maniacal sense of urgency built into me, Twitter often felt siloed and bureaucratic. Dumb power plays, reorgs and team name changes for the sake of someone’s ego were distractions that occurred too regularly. You couldn’t just be a builder — you also needed to be a politician. I was shocked by how old and bespoke the infrastructure was, but there was little will to think beyond quarterly earnings calls because we were all beholden to the masters of mDAU and revenue growth as a public company. It often felt like things were held together with duct tape and glue, and that many people had just accepted that a small product change could take months or quarters to build. Management had become bloated to accommodate career growth and the company culture felt too soft and entitled for my own taste. Healthy debate and criticism was replaced by a default refrain of “no, that can’t be done” or “another team owns that so don’t touch it”. Teams could spend months building a feature and then some last-minute kerfuffle meant it’d get killed for being too risky. Just talking directly to customers could turn into a turf war and create deadlocks between functions. I recall one such episode where a teammate spent a month trying to get clearance to reach out to some creators. He went through 3 layers of management and 6 different functional teams. In the end 4 executives were involved in the approval. It was insanity, and unfortunately I saw several top performers get burnt out and demoralized after exhausting experiences like that. Most people were good at their jobs but it was nearly impossible to fire poor performers — instead they got shuffled around to other teams because few managers had the will or resources to figure out how to get them out. A high performance culture pulls everyone up, but the opposite weighs everyone down. Twitter often felt like a place that kept squandering its own potential, which was sad and frustrating to see. The person who was best at cutting through the BS and inspiring a vision during my tenure was Kayvon Beykpour, but he wasn’t fully empowered to run the company since he wasn’t the CEO. Despite those real issues, I was lucky enough to work with some of the most talented people in the business at Twitter in product, design, engineering, research, legal, BD, trust & safety, marketing, PR and more. Often it was a small cross-functional team of intrinsically motivated people who made the biggest impact by challenging some core assumption. Those teams were very fun to be on but they felt like the exception rather than the rule. The months of waiting for the deal to close in 2022 were particularly slow and painful; it felt like leadership hid behind lawyers and legal language as all answers about the company’s future notoriously included the phrase “fiduciary duty”. Colleagues openly talked about how Twitter was being sold because leadership didn’t have conviction in their own plan or ability to fix longstanding problems. Although I didn’t know much about Elon I was cautiously optimistic – I saw him as the guy who built incredible and enduring companies like Tesla and SpaceX, so perhaps his private ownership could shake things up and breathe new life into the company. My take on what’s happened since then is full of lived nuance. When people ask why I stayed it’s easy to answer: optimism, curiosity, personal growth and money. From the beginning I saw that some changes Elon was going to make were smart and others were stupid, but when I’m on a team I uphold the philosophy of “praise in public and criticize in private”. I was far from a silent wallflower. I shared my opinions openly and pushed back often, both before and after the acquisition. I made peace with the fact that I didn’t have psychological safety at Twitter 2.0 and that meant I could be fired at any moment, and for no reason at all. I watched it happen repeatedly and saw how negatively it impacted team morale. Although I couldn’t change the situation I did my best to shine a light on folks who were doing important work while being an emotionally supportive leader for those who were struggling to adapt to the more brutalist and hardcore culture. In person Elon is oddly charming and he’s genuinely funny. He also has personality quirks like telling the same stories and jokes over and over. The challenge is his personality and demeanor can turn on a dime going from excited to angry. Since it was hard to read what mood he might be in and what his reaction would be to any given thing, people quickly became afraid of being called into meetings or having to share negative news with him. At times it felt like the inner circle was too zealous and fanatical in their unwavering support of everything he said. When individuals encouraged me to be careful about what I said I politely thanked them and said I would not be taking their advice. I had no interest in adding to a culture of fear or walking on eggshells around Elon. Either he would respect me for being real or he could fire me. Either outcome was okay. I quickly learned that product and business decisions were nearly always the result of him following his gut instinct, and he didn’t seem compelled to seek out or rely on a lot of data or expertise to inform it. That was particularly frustrating for me since I believed I had useful institutional knowledge that could help him make better decisions. Instead he'd poll Twitter, ask a friend, or even ask his biographer for product advice. At times it seemed he trusted random feedback more than the people in the room who spent their lives dedicated to tackling the problem at hand. I never figured out why and remain puzzled by it. I don’t think things had to be as difficult or dramatic as they turned out to be but I can’t say I’d bet against Elon or count him out. He’s smart and has enough money to make a lot of mistakes and then course correct when things go awry. As the largest shareholder he can tank the value in the short-term, but eventually he’ll need things to turn around. His focus on speed is incredible and he’s obviously not afraid of blowing things up, but now the real measure will be how it get reconstructed and if enough people want the new everything app he is building. I learned a ton from watching Elon up close – the good, the bad and the ugly. His boldness, passion and storytelling is inspiring, but his lack of process and empathy is painful. Elon has an exceptional talent for tackling hard physics-based problems but products that facilitate human connection and communication require a different type of social-emotional intelligence. Social networks are hard to kill but they’re not immune from death spirals. Only time will tell what the outcome will be but I hope X finds its footing because competition is good for consumers. In the meantime, I have a lot of empathy for the employees who are working tirelessly behind the scenes, the advertisers who want a stable platform to sell their stuff on, and the customers who are experiencing chaotic updates. It’s been a madhouse. Twitter moved at the speed of molasses and suffered from bureaucracy but now X is run by a mercurial leader whose instinct is driven by the unique and undoubtedly weird experience of being the biggest voice on the platform. Many of you know me from the sleeping bag incident where I slept on a conference room floor, so I figure, let’s talk about that too. Going viral was an odd and interesting experience. I was attacked by people on the left and called a billionaire bootlicker, while simultaneously being attacked by people on the right for being a working mom who was demonized as an example of a woman choosing her career over her family. Thankfully I can laugh at myself and I don’t take armchair keyboard ideologues too seriously. Being the main character on the timeline, even for a few minutes, requires a thick skin and a strong sense of self. The real story is pretty simple. I was given a nearly impossible deadline for his first project and as the product lead I would never ask anyone to do anything I wasn’t willing to do myself. So I worked round the clock alongside an amazing team spanning many timezones, and we delivered it on schedule – truly against the odds. It was intense but also fun. Those first few months were wildly crazy but I wanted to be there and I have no regrets. Showing up and giving it your all should, in most cases, be celebrated. Obviously you can’t work at that pace forever but there are moments where bursts are mission critical. I’ve pulled many all-nighters in my career and also when I was a student for something that mattered to me. I don’t regret putting in long hours or being ambitious, and feel proud of how far I’ve come from where I started thanks in part to that type of work ethic. I think of life as a game, and being at Twitter after the acquisition was like playing life at Level 10 on Hard Mode. Since I like taking on difficult challenges I found it interesting and rewarding because I was growing and learning so rapidly. I realize our society today trends toward polarization but when it comes to this app, its owner, and its future, I am neither a fangirl nor a hater — I’m an optimistic pragmatist. This may really irritate the internet but you cannot pigeonhole me into some radical position of either loving or hating every change that’s occurred. I escaped my fundamentalist upbringing and am a free thinker these days. Everyone can be seen as both a hero or a villain, depending on who is telling what angle of the story. Elon doesn’t deserve to be venerated or vilified. He’s a complicated person with an unfathomable amount of financial and geopolitical power which is why humanity needs him to err on the side of goodness, rather than political divisiveness and pettiness. I disagree with many of his decisions and am surprised by his willingness to burn so much down, but with enough money and time, something new & innovative may emerge. I hope it does. Sometimes I get asked about how I felt when I got laid off, and the truth is it was the best gift I’ve ever received. Sure the headlines and punchlines wrote themselves but I was battle hardened by then. I knew that I’d worked in a way where I could walk out with my head held high. I have no bitterness about the Product Management team being dismantled, and it made sense for me to exit as nearly all of the remaining PMs were let go. Going on a sabbatical afterward has been exactly what I needed to decompress and I’m finally feeling rested and relaxed. I’m a creative and a builder, so sooner than later I’ll jump back into a high intensity company but I’m grateful for this season of thinking, reading, traveling and being with people I love. After having time to reflect I believe more than ever that the very best outcomes flow from great leadership that combines the head and the heart. I’d be remiss if I didn’t note that in all of this there is also a cautionary tale for anyone who succeeds at something — which is that the higher you climb, the smaller your world becomes. It’s a strange paradox but the richest and most powerful people are also some of the most isolated. I found myself frequently looking at Elon and seeing a person who seemed quite alone because his time and energy was so purely devoted to work, which is not the model of a life I want to live. Money and fame can create psychological prisons which may worsen mental health conditions. We’ve all seen high profile cases of celebrities who end up with some combination of depression, paranoia, delusions of grandeur, mania and/or erratic behavior. Living in an echo chamber is dangerous and being at the top makes a person even more susceptible to being surrounded by yes people when nearly everyone around you is on the payroll and somehow stands to benefit from being in your orbit. Figuring out how to keep “better angels” around in the form of family, friends, and teammates is critical to staying on the rails and enduring intense ups and downs. Everyone needs to hear hard truths sometimes and if you fire all the people who speak up then the reality distortion field may just turn into a vortex. I was drawn to Twitter because I’m obsessed with the problem of loneliness and connection between people. I find it fascinating & troubling that humans are getting lonelier as we simultaneously create a world that’s both safer and wealthier. I don’t believe that trade-off has to exist, which is why I keep returning to that theme in my personal and professional life. I realize this is too long of a tweet but Twitter was a weird and special place on the internet, and I’m grateful to have played a teeny tiny role in its story and evolution. I’m here for whatever comes next — on this app and in new places. Consumer social is very much alive and at a fascinating juncture, so I’ll be watching and participating and sharing hot takes because I don’t want to, and probably can’t, turn that part of me off. Perhaps X becomes a resounding success. Or it fails epically. Either way, I expect it will continue to be a very entertaining ride. 🫡
14:49

Like seemingly everyone on this app I have plenty of opinions about Twitter > X and figure now is a good time to open up a bit about my experience at the company. I tweeted for years into the void for the love of it like many of you, but after selling my startup to Twitter in 2020 I finally got to see it from the inside. Up close it was both amazing and terrible, like so many other companies and things in life. As someone with a maniacal sense of urgency built into me, Twitter often felt siloed and bureaucratic. Dumb power plays, reorgs and team name changes for the sake of someone’s ego were distractions that occurred too regularly. You couldn’t just be a builder — you also needed to be a politician. I was shocked by how old and bespoke the infrastructure was, but there was little will to think beyond quarterly earnings calls because we were all beholden to the masters of mDAU and revenue growth as a public company. It often felt like things were held together with duct tape and glue, and that many people had just accepted that a small product change could take months or quarters to build. Management had become bloated to accommodate career growth and the company culture felt too soft and entitled for my own taste. Healthy debate and criticism was replaced by a default refrain of “no, that can’t be done” or “another team owns that so don’t touch it”. Teams could spend months building a feature and then some last-minute kerfuffle meant it’d get killed for being too risky. Just talking directly to customers could turn into a turf war and create deadlocks between functions. I recall one such episode where a teammate spent a month trying to get clearance to reach out to some creators. He went through 3 layers of management and 6 different functional teams. In the end 4 executives were involved in the approval. It was insanity, and unfortunately I saw several top performers get burnt out and demoralized after exhausting experiences like that. Most people were good at their jobs but it was nearly impossible to fire poor performers — instead they got shuffled around to other teams because few managers had the will or resources to figure out how to get them out. A high performance culture pulls everyone up, but the opposite weighs everyone down. Twitter often felt like a place that kept squandering its own potential, which was sad and frustrating to see. The person who was best at cutting through the BS and inspiring a vision during my tenure was Kayvon Beykpour, but he wasn’t fully empowered to run the company since he wasn’t the CEO. Despite those real issues, I was lucky enough to work with some of the most talented people in the business at Twitter in product, design, engineering, research, legal, BD, trust & safety, marketing, PR and more. Often it was a small cross-functional team of intrinsically motivated people who made the biggest impact by challenging some core assumption. Those teams were very fun to be on but they felt like the exception rather than the rule. The months of waiting for the deal to close in 2022 were particularly slow and painful; it felt like leadership hid behind lawyers and legal language as all answers about the company’s future notoriously included the phrase “fiduciary duty”. Colleagues openly talked about how Twitter was being sold because leadership didn’t have conviction in their own plan or ability to fix longstanding problems. Although I didn’t know much about Elon I was cautiously optimistic – I saw him as the guy who built incredible and enduring companies like Tesla and SpaceX, so perhaps his private ownership could shake things up and breathe new life into the company. My take on what’s happened since then is full of lived nuance. When people ask why I stayed it’s easy to answer: optimism, curiosity, personal growth and money. From the beginning I saw that some changes Elon was going to make were smart and others were stupid, but when I’m on a team I uphold the philosophy of “praise in public and criticize in private”. I was far from a silent wallflower. I shared my opinions openly and pushed back often, both before and after the acquisition. I made peace with the fact that I didn’t have psychological safety at Twitter 2.0 and that meant I could be fired at any moment, and for no reason at all. I watched it happen repeatedly and saw how negatively it impacted team morale. Although I couldn’t change the situation I did my best to shine a light on folks who were doing important work while being an emotionally supportive leader for those who were struggling to adapt to the more brutalist and hardcore culture. In person Elon is oddly charming and he’s genuinely funny. He also has personality quirks like telling the same stories and jokes over and over. The challenge is his personality and demeanor can turn on a dime going from excited to angry. Since it was hard to read what mood he might be in and what his reaction would be to any given thing, people quickly became afraid of being called into meetings or having to share negative news with him. At times it felt like the inner circle was too zealous and fanatical in their unwavering support of everything he said. When individuals encouraged me to be careful about what I said I politely thanked them and said I would not be taking their advice. I had no interest in adding to a culture of fear or walking on eggshells around Elon. Either he would respect me for being real or he could fire me. Either outcome was okay. I quickly learned that product and business decisions were nearly always the result of him following his gut instinct, and he didn’t seem compelled to seek out or rely on a lot of data or expertise to inform it. That was particularly frustrating for me since I believed I had useful institutional knowledge that could help him make better decisions. Instead he'd poll Twitter, ask a friend, or even ask his biographer for product advice. At times it seemed he trusted random feedback more than the people in the room who spent their lives dedicated to tackling the problem at hand. I never figured out why and remain puzzled by it. I don’t think things had to be as difficult or dramatic as they turned out to be but I can’t say I’d bet against Elon or count him out. He’s smart and has enough money to make a lot of mistakes and then course correct when things go awry. As the largest shareholder he can tank the value in the short-term, but eventually he’ll need things to turn around. His focus on speed is incredible and he’s obviously not afraid of blowing things up, but now the real measure will be how it get reconstructed and if enough people want the new everything app he is building. I learned a ton from watching Elon up close – the good, the bad and the ugly. His boldness, passion and storytelling is inspiring, but his lack of process and empathy is painful. Elon has an exceptional talent for tackling hard physics-based problems but products that facilitate human connection and communication require a different type of social-emotional intelligence. Social networks are hard to kill but they’re not immune from death spirals. Only time will tell what the outcome will be but I hope X finds its footing because competition is good for consumers. In the meantime, I have a lot of empathy for the employees who are working tirelessly behind the scenes, the advertisers who want a stable platform to sell their stuff on, and the customers who are experiencing chaotic updates. It’s been a madhouse. Twitter moved at the speed of molasses and suffered from bureaucracy but now X is run by a mercurial leader whose instinct is driven by the unique and undoubtedly weird experience of being the biggest voice on the platform. Many of you know me from the sleeping bag incident where I slept on a conference room floor, so I figure, let’s talk about that too. Going viral was an odd and interesting experience. I was attacked by people on the left and called a billionaire bootlicker, while simultaneously being attacked by people on the right for being a working mom who was demonized as an example of a woman choosing her career over her family. Thankfully I can laugh at myself and I don’t take armchair keyboard ideologues too seriously. Being the main character on the timeline, even for a few minutes, requires a thick skin and a strong sense of self. The real story is pretty simple. I was given a nearly impossible deadline for his first project and as the product lead I would never ask anyone to do anything I wasn’t willing to do myself. So I worked round the clock alongside an amazing team spanning many timezones, and we delivered it on schedule – truly against the odds. It was intense but also fun. Those first few months were wildly crazy but I wanted to be there and I have no regrets. Showing up and giving it your all should, in most cases, be celebrated. Obviously you can’t work at that pace forever but there are moments where bursts are mission critical. I’ve pulled many all-nighters in my career and also when I was a student for something that mattered to me. I don’t regret putting in long hours or being ambitious, and feel proud of how far I’ve come from where I started thanks in part to that type of work ethic. I think of life as a game, and being at Twitter after the acquisition was like playing life at Level 10 on Hard Mode. Since I like taking on difficult challenges I found it interesting and rewarding because I was growing and learning so rapidly. I realize our society today trends toward polarization but when it comes to this app, its owner, and its future, I am neither a fangirl nor a hater — I’m an optimistic pragmatist. This may really irritate the internet but you cannot pigeonhole me into some radical position of either loving or hating every change that’s occurred. I escaped my fundamentalist upbringing and am a free thinker these days. Everyone can be seen as both a hero or a villain, depending on who is telling what angle of the story. Elon doesn’t deserve to be venerated or vilified. He’s a complicated person with an unfathomable amount of financial and geopolitical power which is why humanity needs him to err on the side of goodness, rather than political divisiveness and pettiness. I disagree with many of his decisions and am surprised by his willingness to burn so much down, but with enough money and time, something new & innovative may emerge. I hope it does. Sometimes I get asked about how I felt when I got laid off, and the truth is it was the best gift I’ve ever received. Sure the headlines and punchlines wrote themselves but I was battle hardened by then. I knew that I’d worked in a way where I could walk out with my head held high. I have no bitterness about the Product Management team being dismantled, and it made sense for me to exit as nearly all of the remaining PMs were let go. Going on a sabbatical afterward has been exactly what I needed to decompress and I’m finally feeling rested and relaxed. I’m a creative and a builder, so sooner than later I’ll jump back into a high intensity company but I’m grateful for this season of thinking, reading, traveling and being with people I love. After having time to reflect I believe more than ever that the very best outcomes flow from great leadership that combines the head and the heart. I’d be remiss if I didn’t note that in all of this there is also a cautionary tale for anyone who succeeds at something — which is that the higher you climb, the smaller your world becomes. It’s a strange paradox but the richest and most powerful people are also some of the most isolated. I found myself frequently looking at Elon and seeing a person who seemed quite alone because his time and energy was so purely devoted to work, which is not the model of a life I want to live. Money and fame can create psychological prisons which may worsen mental health conditions. We’ve all seen high profile cases of celebrities who end up with some combination of depression, paranoia, delusions of grandeur, mania and/or erratic behavior. Living in an echo chamber is dangerous and being at the top makes a person even more susceptible to being surrounded by yes people when nearly everyone around you is on the payroll and somehow stands to benefit from being in your orbit. Figuring out how to keep “better angels” around in the form of family, friends, and teammates is critical to staying on the rails and enduring intense ups and downs. Everyone needs to hear hard truths sometimes and if you fire all the people who speak up then the reality distortion field may just turn into a vortex. I was drawn to Twitter because I’m obsessed with the problem of loneliness and connection between people. I find it fascinating & troubling that humans are getting lonelier as we simultaneously create a world that’s both safer and wealthier. I don’t believe that trade-off has to exist, which is why I keep returning to that theme in my personal and professional life. I realize this is too long of a tweet but Twitter was a weird and special place on the internet, and I’m grateful to have played a teeny tiny role in its story and evolution. I’m here for whatever comes next — on this app and in new places. Consumer social is very much alive and at a fascinating juncture, so I’ll be watching and participating and sharing hot takes because I don’t want to, and probably can’t, turn that part of me off. Perhaps X becomes a resounding success. Or it fails epically. Either way, I expect it will continue to be a very entertaining ride. 🫡

Esther Crawford ✨

5,504,975 просмотров • 3 лет назад

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything Most smart people don’t fail because they’re dumb. They fail because they apply clean logic to a messy world — and the world punishes that mistake with a smile. The messy truth is that modern life is shaped less by individual intent and more by systems: incentives, competition, scaling effects, path dependence, and statistical weirdness. These systems produce outcomes that feel unfair or mysterious until you learn the underlying “laws” — a set of lenses that let you predict how things actually behave. This is not about becoming cynical. It’s about becoming accurate. Once you internalize these lenses, you start noticing that most disagreements aren’t about values. They’re about which hidden force you think dominates: Do incentives matter more than morals? Do networks scale value more than craftsmanship? Do rare events matter more than averages? Do systems evolve, or can they be designed? This article is a guided map through those forces — told as one story. 1) The seduction of “doing the obvious thing” Imagine you’re in charge of improving something important: a company, a city, a hospital, a school, a product, maybe even your own life. You do what responsible people do: you define a goal. You pick a metric. And you tell everyone: we’re going to win on this number. This is where the first trap snaps shut. Goodhart’s Law: the metric stops being real When a measure becomes a target, it stops being a good measure. Before it became a target, the metric was an instrument: a thermometer. After it becomes a target, it becomes a game. Hospitals improve “wait times” by changing intake rules. Companies improve “engagement” by nudging addiction. Schools improve test scores by teaching to the test. Police departments improve crime stats by changing what counts as a crime. Not because anyone is evil. Because the system rewards it. The principal–agent problem: the doers don’t pay This is the deeper engine under Goodhart. The person deciding is not the person suffering the consequences. Executives chase quarterly optics; employees deal with the chaos. Politicians chase election cycles; citizens live with the long-term effects. Managers chase easy metrics; customers absorb the frustration. Once you see principal–agent problems, you start seeing why seemingly intelligent organizations keep doing self-destructive things: the incentives are miswired. The Cobra Effect: perverse incentives grow cobras Sometimes this miswiring gets darkly funny. Reward outcomes and people will manufacture the appearance of outcomes. In the original parable, a colonial government offered a bounty for dead cobras — and people began breeding cobras. This isn’t historical trivia; it’s a universal pattern: Reward bug counts → people file junk bugs. Reward convictions → plea bargains + overcharging. Reward content volume → SEO sludge. Reward “delivery” → rushed work + tech debt. The world is full of cobra farms. 2) Why fixing things often makes them worse Okay, so: choose better metrics, align incentives, done. Not quite. Because even well-intentioned fixes trigger the next law: second-order effects. Chesterton’s Fence: don’t remove constraints you don’t understand You walk into an old system and see “stupid rules.” You want to clean house. You want to simplify. But: why is that rule there? Don’t remove a fence until you know why it was built. A lot of institutional weirdness is scar tissue from past disasters. The rule might be dumb — but if you don’t understand it, you don’t know what disaster you’re re-inviting. This is why naive reformers are dangerous: they confuse “not understanding a thing” with “the thing being pointless.” Gall’s Law: complex systems must grow from simple working ones Even if the fence is removable, you still hit the next problem: A complex system that works is always evolved from a simple system that worked. This demolishes a common fantasy: that you can design complexity from scratch. Most large redesigns fail for one reason: They try to create a finished organism instead of growing a living embryo. If the system matters, you don’t “implement” the final form. You build something simpler that works. Then you iterate. Gall’s law is harsh, but kind: it explains why so many ambitious “transformations” flame out. 3) Efficiency doesn’t save you (and sometimes consumes you) Now suppose you do manage to improve a system. You make it cheaper, faster, more efficient. Surely this reduces resource usage? Often, no. Jevons Paradox: efficiency increases total consumption When you make something more efficient, you often make people use more of it. Make lighting cheaper → people illuminate more spaces. Make driving more fuel-efficient → people drive farther. Make computing cheaper → people compute vastly more. Efficiency doesn’t always shrink the pie. It can expand it. This is one of the most important and least emotionally intuitive truths about progress: efficiency changes behavior. 4) Some things don’t get more efficient — and get expensive forever Now meet the mirror image of Jevons: not everything can get dramatically more productive. Some work is bottlenecked by time, humans, and attention. Baumol’s Cost Disease: sectors that don’t scale inflate A string quartet takes as long to play Beethoven as it did 200 years ago. A therapist can’t 10× their clients without breaking the thing. A teacher can’t “scale” classroom attention the way software scales distribution. Meanwhile, other industries do scale — manufacturing, computing, logistics. So as society grows richer, productivity sectors get cheaper and cheaper… and human-time sectors get relatively more expensive. That’s why: healthcare education legal services childcare eldercare …feel like they eat the world. Baumol isn’t “a problem to solve” so much as a physics constraint: certain value comes from human presence. And presence doesn’t compress easily. 5) The invisible accelerant: networks At this point you might feel like everything is doom and friction. It’s not. Some forces make systems wildly better as they grow. The biggest one is networks. Metcalfe’s Law: value scales with connections A phone is useless alone. A fax machine is useless alone. A social app is useless without other humans. As users increase, connections increase faster than users do. That creates accelerating value. Reed’s Law: groups scale even faster than connections But it’s not just one-to-one links. Once people can form groups — communities, coalitions, companies, subcultures — the number of potential groupings explodes. That’s Reed’s law: group-forming networks can scale with frightening speed. This is why networked platforms can go from “niche” to “dominant” almost overnight: the product isn’t just features — it’s the social graph. 6) Progress has a heartbeat: learning curves Not all progress comes from networks. Some comes from repetition. Wright’s Law: cost falls with cumulative production This is the law behind why solar, batteries, and manufacturing tech get cheaper and cheaper: Every doubling of cumulative production yields a predictable cost reduction. The implications are enormous: the future is shaped by what we manufacture at scale volume is not just output; it’s learning building the thing teaches you to build the thing better Strategy through Wright’s law becomes: maximize learning rate. Not “be brilliant,” but “iterate relentlessly.” 7) Cooperation is rare — and competition forces ugliness Now we move from economics into game theory and moral physics. Even with good metrics, good redesign, good scaling… Sometimes the system makes people do bad things. Prisoner’s Dilemma: defecting is rational If you and I cooperate, we both win. But if I suspect you might defect, I should defect first. So we both defect. We both lose. This structure appears everywhere: labor vs management nations vs nations companies vs companies roommates siblings Twitter discourse It’s tragedy-by-incentives. Moloch: the god of coordination failure “Moloch” is the poetic version of the same idea: systems where competition forces everyone into worse behavior, even if nobody wants it. No one wants the attention economy. But creators compete for attention. Platforms compete for engagement. So everyone converges on outrage and addiction. Moloch doesn’t need villains. It only needs incentives. 8) The biggest mistake smart people make: believing in averages Now we arrive at the statistical heart of why forecasts fail. Most planning assumes the world behaves like a bell curve: most outcomes are near the average, extremes are rare. In many domains, that’s false. Fat tails: extremes happen way more than you think In fat-tailed worlds, the “average” is a comforting lie. Outliers dominate: venture returns blockbuster movies bestselling authors company outcomes war and peace pandemics market crashes In a fat-tailed world: one event can erase ten years of progress or create it overnight Black swans: surprise + impact + fake hindsight A black swan isn’t just an outlier. It’s an outlier we didn’t know how to model. The signature of black swans is: huge impact surprise beforehand “it was obvious” afterward We are story machines. We can rationalize anything after it happens. Survivorship bias: you’re studying the winners This is why business advice is mostly nonsense. We read biographies of billionaires and imitate their habits — forgetting the cemetery of equally hardworking, equally smart people who lost. Survivorship bias turns randomness into “wisdom.” A good thinker always asks: what am I not seeing because it died? 9) The final set of tools: tradeoffs, simplicity, and time After you’ve internalized incentives, scaling, networks, and tail risk, you earn the right to something important: Less ideology. More judgment. That’s what these last lenses provide. Pareto efficiency: every improvement has a cost At some point, you stop making “free” gains and enter a world of tradeoffs. If you want more of A, you give up B. This is what breaks utopian thinking: more safety can mean less liberty more speed can mean less quality more fairness can mean less efficiency more growth can mean more inequality Smart people aren’t the ones who avoid tradeoffs. They’re the ones who name the tradeoff out loud. Occam’s Razor: don’t add gears without proof Now that you’re thinking in systems, you could easily overcomplicate. Occam is your brake pedal: prefer the simplest explanation that predicts. It’s not “simplicity is truth.” It’s: don’t hallucinate complexity. Lindy: time is the best filter we have In fragile worlds, “new” is often a synonym for “untested.” The Lindy effect says: the longer something has survived, the longer it’s likely to survive. Ideas, books, institutions, even practices: time is a stress test. Lindy isn’t anti-innovation. It’s pro-robustness. Comparative advantage: specialization beats self-reliance Finally, comparative advantage gives you the social version of Occam. Even if you’re worse at everything than someone else… trade can still make both better off, because efficiency comes from relative differences. That lens dissolves a lot of macho self-sufficiency myths. So what does this worldview do? It does three things. First: it replaces naive optimism with durable optimism Not “everything will work out.” But: we can build systems that don’t collapse under their own incentives. Second: it changes what you fear Not competitors. Not critics. Not even failure. You start fearing: bad metrics misaligned incentives brittle complexity tail risks coordination failure Which are the real predators. Third: it gives you a usable strategy A decision-making style that looks like this: Start simple (Gall) Measure carefully (Goodhart) Align incentives (principal–agent) Expect adaptation (cobra effect) Respect old constraints (Chesterton) Model scaling honestly (Metcalfe/Reed/Wright) Don’t assume efficiency saves you (Jevons/Baumol) Prepare for tails (fat tails / black swans) Don’t trust winner stories (survivorship bias) Name tradeoffs and keep models simple (Pareto + Occam + Lindy) That list is more than theory. It’s a survival kit for reality. Closing: the meta-law If I had to compress this entire worldview into one sentence, it would be: Outcomes come from incentives and scaling under uncertainty—not from intentions and plans. Most people live inside stories. This toolkit makes you live inside systems. And once you do, you become harder to fool — including by yourself.
1:38

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything Most smart people don’t fail because they’re dumb. They fail because they apply clean logic to a messy world — and the world punishes that mistake with a smile. The messy truth is that modern life is shaped less by individual intent and more by systems: incentives, competition, scaling effects, path dependence, and statistical weirdness. These systems produce outcomes that feel unfair or mysterious until you learn the underlying “laws” — a set of lenses that let you predict how things actually behave. This is not about becoming cynical. It’s about becoming accurate. Once you internalize these lenses, you start noticing that most disagreements aren’t about values. They’re about which hidden force you think dominates: Do incentives matter more than morals? Do networks scale value more than craftsmanship? Do rare events matter more than averages? Do systems evolve, or can they be designed? This article is a guided map through those forces — told as one story. 1) The seduction of “doing the obvious thing” Imagine you’re in charge of improving something important: a company, a city, a hospital, a school, a product, maybe even your own life. You do what responsible people do: you define a goal. You pick a metric. And you tell everyone: we’re going to win on this number. This is where the first trap snaps shut. Goodhart’s Law: the metric stops being real When a measure becomes a target, it stops being a good measure. Before it became a target, the metric was an instrument: a thermometer. After it becomes a target, it becomes a game. Hospitals improve “wait times” by changing intake rules. Companies improve “engagement” by nudging addiction. Schools improve test scores by teaching to the test. Police departments improve crime stats by changing what counts as a crime. Not because anyone is evil. Because the system rewards it. The principal–agent problem: the doers don’t pay This is the deeper engine under Goodhart. The person deciding is not the person suffering the consequences. Executives chase quarterly optics; employees deal with the chaos. Politicians chase election cycles; citizens live with the long-term effects. Managers chase easy metrics; customers absorb the frustration. Once you see principal–agent problems, you start seeing why seemingly intelligent organizations keep doing self-destructive things: the incentives are miswired. The Cobra Effect: perverse incentives grow cobras Sometimes this miswiring gets darkly funny. Reward outcomes and people will manufacture the appearance of outcomes. In the original parable, a colonial government offered a bounty for dead cobras — and people began breeding cobras. This isn’t historical trivia; it’s a universal pattern: Reward bug counts → people file junk bugs. Reward convictions → plea bargains + overcharging. Reward content volume → SEO sludge. Reward “delivery” → rushed work + tech debt. The world is full of cobra farms. 2) Why fixing things often makes them worse Okay, so: choose better metrics, align incentives, done. Not quite. Because even well-intentioned fixes trigger the next law: second-order effects. Chesterton’s Fence: don’t remove constraints you don’t understand You walk into an old system and see “stupid rules.” You want to clean house. You want to simplify. But: why is that rule there? Don’t remove a fence until you know why it was built. A lot of institutional weirdness is scar tissue from past disasters. The rule might be dumb — but if you don’t understand it, you don’t know what disaster you’re re-inviting. This is why naive reformers are dangerous: they confuse “not understanding a thing” with “the thing being pointless.” Gall’s Law: complex systems must grow from simple working ones Even if the fence is removable, you still hit the next problem: A complex system that works is always evolved from a simple system that worked. This demolishes a common fantasy: that you can design complexity from scratch. Most large redesigns fail for one reason: They try to create a finished organism instead of growing a living embryo. If the system matters, you don’t “implement” the final form. You build something simpler that works. Then you iterate. Gall’s law is harsh, but kind: it explains why so many ambitious “transformations” flame out. 3) Efficiency doesn’t save you (and sometimes consumes you) Now suppose you do manage to improve a system. You make it cheaper, faster, more efficient. Surely this reduces resource usage? Often, no. Jevons Paradox: efficiency increases total consumption When you make something more efficient, you often make people use more of it. Make lighting cheaper → people illuminate more spaces. Make driving more fuel-efficient → people drive farther. Make computing cheaper → people compute vastly more. Efficiency doesn’t always shrink the pie. It can expand it. This is one of the most important and least emotionally intuitive truths about progress: efficiency changes behavior. 4) Some things don’t get more efficient — and get expensive forever Now meet the mirror image of Jevons: not everything can get dramatically more productive. Some work is bottlenecked by time, humans, and attention. Baumol’s Cost Disease: sectors that don’t scale inflate A string quartet takes as long to play Beethoven as it did 200 years ago. A therapist can’t 10× their clients without breaking the thing. A teacher can’t “scale” classroom attention the way software scales distribution. Meanwhile, other industries do scale — manufacturing, computing, logistics. So as society grows richer, productivity sectors get cheaper and cheaper… and human-time sectors get relatively more expensive. That’s why: healthcare education legal services childcare eldercare …feel like they eat the world. Baumol isn’t “a problem to solve” so much as a physics constraint: certain value comes from human presence. And presence doesn’t compress easily. 5) The invisible accelerant: networks At this point you might feel like everything is doom and friction. It’s not. Some forces make systems wildly better as they grow. The biggest one is networks. Metcalfe’s Law: value scales with connections A phone is useless alone. A fax machine is useless alone. A social app is useless without other humans. As users increase, connections increase faster than users do. That creates accelerating value. Reed’s Law: groups scale even faster than connections But it’s not just one-to-one links. Once people can form groups — communities, coalitions, companies, subcultures — the number of potential groupings explodes. That’s Reed’s law: group-forming networks can scale with frightening speed. This is why networked platforms can go from “niche” to “dominant” almost overnight: the product isn’t just features — it’s the social graph. 6) Progress has a heartbeat: learning curves Not all progress comes from networks. Some comes from repetition. Wright’s Law: cost falls with cumulative production This is the law behind why solar, batteries, and manufacturing tech get cheaper and cheaper: Every doubling of cumulative production yields a predictable cost reduction. The implications are enormous: the future is shaped by what we manufacture at scale volume is not just output; it’s learning building the thing teaches you to build the thing better Strategy through Wright’s law becomes: maximize learning rate. Not “be brilliant,” but “iterate relentlessly.” 7) Cooperation is rare — and competition forces ugliness Now we move from economics into game theory and moral physics. Even with good metrics, good redesign, good scaling… Sometimes the system makes people do bad things. Prisoner’s Dilemma: defecting is rational If you and I cooperate, we both win. But if I suspect you might defect, I should defect first. So we both defect. We both lose. This structure appears everywhere: labor vs management nations vs nations companies vs companies roommates siblings Twitter discourse It’s tragedy-by-incentives. Moloch: the god of coordination failure “Moloch” is the poetic version of the same idea: systems where competition forces everyone into worse behavior, even if nobody wants it. No one wants the attention economy. But creators compete for attention. Platforms compete for engagement. So everyone converges on outrage and addiction. Moloch doesn’t need villains. It only needs incentives. 8) The biggest mistake smart people make: believing in averages Now we arrive at the statistical heart of why forecasts fail. Most planning assumes the world behaves like a bell curve: most outcomes are near the average, extremes are rare. In many domains, that’s false. Fat tails: extremes happen way more than you think In fat-tailed worlds, the “average” is a comforting lie. Outliers dominate: venture returns blockbuster movies bestselling authors company outcomes war and peace pandemics market crashes In a fat-tailed world: one event can erase ten years of progress or create it overnight Black swans: surprise + impact + fake hindsight A black swan isn’t just an outlier. It’s an outlier we didn’t know how to model. The signature of black swans is: huge impact surprise beforehand “it was obvious” afterward We are story machines. We can rationalize anything after it happens. Survivorship bias: you’re studying the winners This is why business advice is mostly nonsense. We read biographies of billionaires and imitate their habits — forgetting the cemetery of equally hardworking, equally smart people who lost. Survivorship bias turns randomness into “wisdom.” A good thinker always asks: what am I not seeing because it died? 9) The final set of tools: tradeoffs, simplicity, and time After you’ve internalized incentives, scaling, networks, and tail risk, you earn the right to something important: Less ideology. More judgment. That’s what these last lenses provide. Pareto efficiency: every improvement has a cost At some point, you stop making “free” gains and enter a world of tradeoffs. If you want more of A, you give up B. This is what breaks utopian thinking: more safety can mean less liberty more speed can mean less quality more fairness can mean less efficiency more growth can mean more inequality Smart people aren’t the ones who avoid tradeoffs. They’re the ones who name the tradeoff out loud. Occam’s Razor: don’t add gears without proof Now that you’re thinking in systems, you could easily overcomplicate. Occam is your brake pedal: prefer the simplest explanation that predicts. It’s not “simplicity is truth.” It’s: don’t hallucinate complexity. Lindy: time is the best filter we have In fragile worlds, “new” is often a synonym for “untested.” The Lindy effect says: the longer something has survived, the longer it’s likely to survive. Ideas, books, institutions, even practices: time is a stress test. Lindy isn’t anti-innovation. It’s pro-robustness. Comparative advantage: specialization beats self-reliance Finally, comparative advantage gives you the social version of Occam. Even if you’re worse at everything than someone else… trade can still make both better off, because efficiency comes from relative differences. That lens dissolves a lot of macho self-sufficiency myths. So what does this worldview do? It does three things. First: it replaces naive optimism with durable optimism Not “everything will work out.” But: we can build systems that don’t collapse under their own incentives. Second: it changes what you fear Not competitors. Not critics. Not even failure. You start fearing: bad metrics misaligned incentives brittle complexity tail risks coordination failure Which are the real predators. Third: it gives you a usable strategy A decision-making style that looks like this: Start simple (Gall) Measure carefully (Goodhart) Align incentives (principal–agent) Expect adaptation (cobra effect) Respect old constraints (Chesterton) Model scaling honestly (Metcalfe/Reed/Wright) Don’t assume efficiency saves you (Jevons/Baumol) Prepare for tails (fat tails / black swans) Don’t trust winner stories (survivorship bias) Name tradeoffs and keep models simple (Pareto + Occam + Lindy) That list is more than theory. It’s a survival kit for reality. Closing: the meta-law If I had to compress this entire worldview into one sentence, it would be: Outcomes come from incentives and scaling under uncertainty—not from intentions and plans. Most people live inside stories. This toolkit makes you live inside systems. And once you do, you become harder to fool — including by yourself.

Carlos E. Perez

104,508 просмотров • 8 месяцев назад

Dwarkesh is WRONG about the "Output Gap" The narrative around Artificial Intelligence has shifted perceptibly in late 2025. After years of exponential hype, a sense of disillusionment has begun to settle over the industry. Commentators and analysts, most notably podcaster and writer Dwarkesh Patel, have recently highlighted what is being called the “Output Gap.” This is the uncomfortable discrepancy between our models’ skyrocketing performance on benchmarks and the relatively stagnant growth in macroeconomic productivity. We have reached “superhuman” capability on tests, yet global GDP hasn’t skyrocketed, and the promised revolution feels curiously delayed. This frustration stems from a fundamental misunderstanding of where we are in the technology cycle. The industry is currently fixated on “Day 0” capabilities—the raw intelligence of the models, the scaling laws, and the saturation of academic benchmarks. However, the bottleneck has shifted. We are no longer limited by the intelligence of the model, but by the inertia of the enterprise. The “Output Gap” is not a failure of technology; it is a lag in organizational digestion. We have invented a powerful jet engine, but we are essentially frustrated that it hasn’t revolutionized travel before we’ve even built the airframe to mount it on. The primary fallacy driving this disappointment is the expectation of the “drop-in remote worker.” Many observers equate Artificial General Intelligence (AGI) with a digital human that can be onboarded, culturally assimilated, and left to run autonomously with minimal supervision. Because current agents cannot seamlessly replace a human employee in this one-to-one fashion, the conclusion is often that the technology has stalled. This view misses the forest for the trees. Disruptive technologies rarely act as direct replacements; instead, they require a complete restructuring of how work is done. In reality, the barrier to adoption is what IT professionals call “Day 2 Operations.” Day 0 is the exciting launch; Day 2 is the boring, messy reality of governance, security, and maintenance. For an enterprise to deploy an autonomous agent, it isn’t enough for the model to be smart. The organization must solve for Role-Based Access Control (RBAC), SOC 2 compliance, liability frameworks, and data sovereignty. Right now, most organizations lack the infrastructure to handle “non-human” identities that have access to sensitive corporate data. Consider the security implications. A human employee has physical limitations and a single identity. An AI agent is an “always-on” entity that, if given improper permissions, could theoretically read every email in a company server to “optimize workflow.” Security teams (CISOs) are rightly terrified of this prospect. Until we develop granular access controls specifically designed for agents—effectively an “RBAC for AI”—the widespread deployment of autonomous agents will remain blocked by the “Departments of No”: Legal, HR, and Security. This operational friction explains why we are seeing a massive divergence between individual and enterprise adoption. Individually, adoption is rampant; we are in the “Shadow IT” era of AI, where employees secretly use tools like ChatGPT to boost their personal productivity. However, at the organizational level, adoption is glacial because the institution’s primary mandate is risk management, not speed. The C-suite is asking questions about ROI, liability, and data leakage that the current software ecosystem cannot yet answer satisfactorily. History offers a comforting precedent for this timeline. We are currently effectively in the “2002 era” of virtualization. In the early 2000s, virtualization technology (like VMware) was technically viable, but it took nearly a decade to become the default enterprise standard. It required years of maturing management software, security protocols, and cultural shifts before “The Cloud” became a reality. AI is undergoing the same digestion period. The technology is ready, but the enterprise “rails” required to run it are still being laid. This leads us to the “Mechanical Horse” fallacy. When the automobile was invented, people didn’t need a mechanical horse that walked on four legs; they needed a car. But the car required asphalt roads, not dirt paths. Similarly, we are currently trying to force AI into human-shaped workflows (”jobs”) rather than rebuilding the workflows to suit the AI. We are waiting for the mechanical horse, failing to realize that the true disruption comes from unbundling the work entirely. A “job” is essentially a bundle of tasks, context, and responsibilities aggregated for a single human. AI does not replace jobs; it unbundles tasks. The transition we are navigating involves breaking down these bundles and determining which specific value streams can be automated by agents. This requires task decomposition and new management frameworks—a “Council of AIs” approach—rather than a single omnipotent bot. This restructuring is an organizational physics problem, not a computer science problem. The disconnect is further exacerbated by the differing timelines of developers versus executives. Developers and researchers live in a world of root access and rapid iteration, often blinded to the molasses-like speed of corporate change management. A CFO, conversely, thinks in fiscal quarters and compliance audits. They require proven ROI and standardized best practices before signing off on widespread automation. Currently, there are no industry standards for deploying autonomous agents, which halts most conversations at the boardroom door. Therefore, the “stalling” progress is an illusion caused by looking at the wrong metrics. If you look at model cards and benchmarks, progress is linear and fast. If you look at widespread economic integration, the curve is flat. This S-curve of adoption is always significantly behind the S-curve of capability. We are in the flat part of the adoption curve, characterized by high hype, high friction, and frantic infrastructure building behind the scenes. The next three to five years will likely be dominated not by flashy new model capabilities, but by the “boring” work of integration. We will see the rise of startups and consultancies dedicated solely to “AI Governance,” “Agent Identity Management,” and “Context Orchestration.” These are the boring rails that will eventually allow the high-speed train of AI to actually run. The output gap is a temporary, necessary phase. It is the silence before the orchestra starts playing. The potential energy is building up in the form of capability, but it cannot convert into kinetic economic energy until the friction is reduced. The “stalled” revolution is simply a revolution that is currently under construction. We are not witnessing the ceiling of AI intelligence; we are witnessing the floor of AI adoption. The technology has done its part; now, the organizations must do theirs. The future isn’t late—it’s just waiting for legal to sign off.
26:05

Dwarkesh is WRONG about the "Output Gap" The narrative around Artificial Intelligence has shifted perceptibly in late 2025. After years of exponential hype, a sense of disillusionment has begun to settle over the industry. Commentators and analysts, most notably podcaster and writer Dwarkesh Patel, have recently highlighted what is being called the “Output Gap.” This is the uncomfortable discrepancy between our models’ skyrocketing performance on benchmarks and the relatively stagnant growth in macroeconomic productivity. We have reached “superhuman” capability on tests, yet global GDP hasn’t skyrocketed, and the promised revolution feels curiously delayed. This frustration stems from a fundamental misunderstanding of where we are in the technology cycle. The industry is currently fixated on “Day 0” capabilities—the raw intelligence of the models, the scaling laws, and the saturation of academic benchmarks. However, the bottleneck has shifted. We are no longer limited by the intelligence of the model, but by the inertia of the enterprise. The “Output Gap” is not a failure of technology; it is a lag in organizational digestion. We have invented a powerful jet engine, but we are essentially frustrated that it hasn’t revolutionized travel before we’ve even built the airframe to mount it on. The primary fallacy driving this disappointment is the expectation of the “drop-in remote worker.” Many observers equate Artificial General Intelligence (AGI) with a digital human that can be onboarded, culturally assimilated, and left to run autonomously with minimal supervision. Because current agents cannot seamlessly replace a human employee in this one-to-one fashion, the conclusion is often that the technology has stalled. This view misses the forest for the trees. Disruptive technologies rarely act as direct replacements; instead, they require a complete restructuring of how work is done. In reality, the barrier to adoption is what IT professionals call “Day 2 Operations.” Day 0 is the exciting launch; Day 2 is the boring, messy reality of governance, security, and maintenance. For an enterprise to deploy an autonomous agent, it isn’t enough for the model to be smart. The organization must solve for Role-Based Access Control (RBAC), SOC 2 compliance, liability frameworks, and data sovereignty. Right now, most organizations lack the infrastructure to handle “non-human” identities that have access to sensitive corporate data. Consider the security implications. A human employee has physical limitations and a single identity. An AI agent is an “always-on” entity that, if given improper permissions, could theoretically read every email in a company server to “optimize workflow.” Security teams (CISOs) are rightly terrified of this prospect. Until we develop granular access controls specifically designed for agents—effectively an “RBAC for AI”—the widespread deployment of autonomous agents will remain blocked by the “Departments of No”: Legal, HR, and Security. This operational friction explains why we are seeing a massive divergence between individual and enterprise adoption. Individually, adoption is rampant; we are in the “Shadow IT” era of AI, where employees secretly use tools like ChatGPT to boost their personal productivity. However, at the organizational level, adoption is glacial because the institution’s primary mandate is risk management, not speed. The C-suite is asking questions about ROI, liability, and data leakage that the current software ecosystem cannot yet answer satisfactorily. History offers a comforting precedent for this timeline. We are currently effectively in the “2002 era” of virtualization. In the early 2000s, virtualization technology (like VMware) was technically viable, but it took nearly a decade to become the default enterprise standard. It required years of maturing management software, security protocols, and cultural shifts before “The Cloud” became a reality. AI is undergoing the same digestion period. The technology is ready, but the enterprise “rails” required to run it are still being laid. This leads us to the “Mechanical Horse” fallacy. When the automobile was invented, people didn’t need a mechanical horse that walked on four legs; they needed a car. But the car required asphalt roads, not dirt paths. Similarly, we are currently trying to force AI into human-shaped workflows (”jobs”) rather than rebuilding the workflows to suit the AI. We are waiting for the mechanical horse, failing to realize that the true disruption comes from unbundling the work entirely. A “job” is essentially a bundle of tasks, context, and responsibilities aggregated for a single human. AI does not replace jobs; it unbundles tasks. The transition we are navigating involves breaking down these bundles and determining which specific value streams can be automated by agents. This requires task decomposition and new management frameworks—a “Council of AIs” approach—rather than a single omnipotent bot. This restructuring is an organizational physics problem, not a computer science problem. The disconnect is further exacerbated by the differing timelines of developers versus executives. Developers and researchers live in a world of root access and rapid iteration, often blinded to the molasses-like speed of corporate change management. A CFO, conversely, thinks in fiscal quarters and compliance audits. They require proven ROI and standardized best practices before signing off on widespread automation. Currently, there are no industry standards for deploying autonomous agents, which halts most conversations at the boardroom door. Therefore, the “stalling” progress is an illusion caused by looking at the wrong metrics. If you look at model cards and benchmarks, progress is linear and fast. If you look at widespread economic integration, the curve is flat. This S-curve of adoption is always significantly behind the S-curve of capability. We are in the flat part of the adoption curve, characterized by high hype, high friction, and frantic infrastructure building behind the scenes. The next three to five years will likely be dominated not by flashy new model capabilities, but by the “boring” work of integration. We will see the rise of startups and consultancies dedicated solely to “AI Governance,” “Agent Identity Management,” and “Context Orchestration.” These are the boring rails that will eventually allow the high-speed train of AI to actually run. The output gap is a temporary, necessary phase. It is the silence before the orchestra starts playing. The potential energy is building up in the form of capability, but it cannot convert into kinetic economic energy until the friction is reduced. The “stalled” revolution is simply a revolution that is currently under construction. We are not witnessing the ceiling of AI intelligence; we are witnessing the floor of AI adoption. The technology has done its part; now, the organizations must do theirs. The future isn’t late—it’s just waiting for legal to sign off.

David Shapiro (L/0)

12,656 просмотров • 9 месяцев назад

What if I told you ripple:native just moved closer to a financial universe doing $17.5 TRILLION in FX and interest-rate derivatives every single day? I’m not talking about some random prediction. I’m talking about BIS Working Paper No. 1374. This is going to be a long read, because the headline barely scratches the surface. Four of the five authors work at the Bank for International Settlements, and instead of only mentioning XRP Ledger in theory, the researchers actually built, tested and published an open-source XRPL-based prototype. That distinction matters. This is a research implementation, not a production BIS deployment. But the technical choice itself is what caught me. The researchers needed a public blockchain that could help prove official economic and financial data had not been altered. They chose XRP Ledger. And they explained why: low fees, fast finality, developer resources and existing research around its consensus system. This wasn’t somebody adding an XRP logo to a presentation. They built the gateway. They created XRPL transactions. They used institutional anchoring wallets. They put cryptographic proofs inside transaction memos. They linked publisher identities to XRPL addresses. They retrieved those transactions again during verification. Then they measured how the system performed. Median publication latency came in around 3–5 seconds. Verification took around 1–2 seconds. That is where my brain immediately went beyond the headline. Because what exactly were they trying to verify? The kind of information the entire financial system runs on. -Inflation. -GDP. -Interest rates. -Banking statistics. -Debt information. -Financial-stability data. -Regulatory reporting. Imagine a central bank publishes an inflation number. Today that number gets copied everywhere. -Websites. -News terminals. -Databases. -Screenshots. -AI models. -Trading systems. Once it spreads across the internet, how does another machine independently prove that the number it received is exactly what the institution originally published? That is the problem BIS researchers were attacking. Their model creates a cryptographic fingerprint of the official dataset. Individual statistical series can receive fingerprints too. Those hashes are combined through a Merkle tree. A final Merkle root gets anchored to XRPL. The underlying economic data do not need to be dumped onto the blockchain. XRPL simply keeps the proof. Think of it like this: The official institution publishes the document. XRPL holds the tamper-proof receipt. Someone changes even one part of the underlying file? The cryptographic fingerprint changes. Now a bank, regulator, investor, trading engine or AI agent can check: Is this the original data? Has it been changed? Did it really come from the institution claiming to publish it? And that second part is where this paper gets even more serious. The BIS prototype combines the data proof with a W3C Verifiable Credential for the publisher. The publisher’s cryptographic identity is connected to an XRPL address. The paper even uses the format: did:xrpl: So you are not only verifying the information. You are verifying who published it. Now picture a financial world where machines can check both automatically. A central bank publishes CPI. A model receives it. Before touching money, the software checks XRPL. Correct file. Correct publisher. No alteration. Then it acts. That sounds simple until you realize what financial markets actually do with official data. -Rates move. -Currencies move. -Bond prices move. -Derivatives reprice. -Collateral requirements change. -Loans reset. -Inflation-linked instruments adjust. -Portfolio risk changes. And this is where BIS Working Paper 1374 stops being a boring statistics paper for me. Because the authors themselves discuss putting verified information beside digital financial assets. They specifically mention: -CBDCs -stablecoins -tokenized deposits -derivatives. That one section changes the entire way I look at this. The vision is not simply: “Put a hash on a blockchain.” It becomes: verified economic information + digital money + tokenized assets + automated execution. Now remember what Ripple has been building around XRPL. -Multi-Purpose Tokens. -Credentials. -Permissioned Domains. -Permissioned DEX infrastructure. -Confidential Transfers. -Stablecoins. -Institutional lending. -Tokenized collateral. -FX. -Onchain credit. And Ripple has repeatedly positioned XRP across payments, liquidity and credit. Now put those pieces beside what the BIS researchers are exploring. An official institution needs an identity. XRPL can represent identity and credentials. A regulated participant needs permission to enter a market. XRPL is building permissioned infrastructure. A bond needs trustworthy economic information. The BIS prototype shows one way that information can be authenticated through XRPL. A financial asset needs a digital representation. XRPL is being built for tokenization. A transaction needs money. Stablecoins and tokenized deposits can provide the cash side. Then all those different assets need liquidity. That is where ripple:native becomes much more interesting to me. But before getting there, look at the scale surrounding BIS itself. The BIS does not process the world’s $9.6 trillion of daily FX transactions. It measures that market through its Triennial Central Bank Survey. That distinction matters. According to the numbers in the context here: global OTC FX turnover = $9.6 TRILLION every day. Then add: OTC interest-rate derivatives turnover = $7.9 TRILLION every day. Together: $17.5 TRILLION per day. Just the FX number annualized across roughly 250 trading days comes to around: $2.4 QUADRILLION per year. That is the financial universe BIS research sits over. -Currencies. -Banks. -Central banks. -FX swaps. -Rates. -Derivatives. -Cross-border capital. -Collateral. -Dollar funding. And researchers inside that institution just chose XRP Ledger for an actual technical prototype. That is why I keep telling people not to reduce this to transaction fees. Yes, the worked example uses an XRPL Payment transaction. Yes, the reference cost is only: 10 drops = 0.00001 XRP. Yes, transaction fees on XRPL are destroyed. So if this kind of anchoring eventually ran on mainnet, publishing data itself would consume XRP. But that is not the part that gets me excited. The fee is intentionally tiny. The much bigger question is: What happens when verified information starts triggering financial activity on the same broader infrastructure? The paper itself talks about: inflation-linked products perpetual futures tokenized financial instruments derivative settlement interest payments automated compliance and even: automated monetary-policy applications. Now we are talking about information causing money to move. Imagine an inflation-linked bond. The government publishes inflation. That release gets cryptographically anchored. The bond checks the proof. The CPI number is verified. The contract adjusts what is owed. Digital cash settles the payment. No one has to manually copy a number from a website into another system. No one has to blindly trust a third-party data feed. The financial instrument can verify the economic input itself. That is the idea I keep coming back to: self-verifying finance. And the researchers even discuss using the XRPL EVM-compatible sidechain for more advanced applications where data verification and programmable financial execution exist in the same broader ecosystem. They mention: access controls, permissioning, automated compliance, multisignature requirements, oracle integration, programmable validation. Now connect that with Ripple’s institutional roadmap. Credentials can prove who a participant is. Permissioned Domains can define who belongs inside a regulated environment. Tokenized assets can represent financial instruments. RLUSD can represent digital dollar liquidity. Lending can make those assets productive. XRP can provide native network resources and, where economically useful, liquidity between fragmented assets. That is a very different picture of XRPL than the one people were arguing about years ago. It is not simply: “Can XRP send a payment quickly?” The question becomes: Can XRPL sit underneath parts of a machine-readable financial system? And Working Paper 1374 just gave that question much more weight for me. There is another section that barely gets discussed. The architecture is not limited to one data publisher. The researchers designed a multi-publisher system. Different institutions can create their own Merkle roots. Those roots can be combined into one larger super-root. One XRPL transaction can anchor that shared proof. Yet each publisher remains independently accountable for its own data. Now imagine the participants. Central Bank A. Central Bank B. Regulator C. Statistical Office D. International Organization E. One public verification system. Different publishers. Independent cryptographic accountability. That begins to resemble infrastructure for cross-border public-sector data exchange. And the paper’s own conclusion talks about trustworthy exchange among: national statistical offices central banks international organizations. Then look at who already uses the statistical standard the paper builds around. SDMX is sponsored by institutions including: BIS European Central Bank Eurostat International Monetary Fund OECD United Nations World Bank Group International Labour Organization. That does not mean those institutions are adopting XRPL. But it tells you something important about the design philosophy. The researchers did not create a blockchain system that requires the existing financial world to throw everything away. They designed it to sit underneath an existing institutional standard. That matters a lot. Because the easiest technology to adopt is often the technology that does not force everyone to rebuild from zero. Existing systems can continue publishing. XRPL can provide the cryptographic proof underneath. Then comes BIS Open Tech. The paper says the open-source reference implementation is being released as a prototype through BIS Open Tech and the SDMX community. That means other institutions can inspect it. Reuse it. Modify it. Build on it. This is how technical ideas can spread inside serious institutions. Not through hype. Through code. Documentation. Standards. Reuse. That is the kind of adoption path I pay attention to. Then there is the AI angle. This is where the whole thesis becomes almost unfairly interesting. The authors explicitly discuss AI agents. An AI system receives economic information. Instead of blindly trusting what it scraped from somewhere, it can ask: Is this data authentic? It checks the XRPL proof. Valid? Continue. Invalid? Do nothing. Now compare that with what Ripple launched in June 2026: the XRPL AI Starter Kit, designed around autonomous agents making payments with XRP and RLUSD. Two completely separate directions suddenly sit beside each other. BIS research: AI verifies information through XRPL. Ripple ecosystem: AI moves value through XRPL. Now imagine both ideas eventually meeting. An agent receives official inflation data. It verifies the release cryptographically. It recalculates risk. It reprices a bond. It adjusts collateral. It changes an FX position. It executes a payment. It settles in RLUSD. It routes through XRP where XRP is the best available liquidity path. That is machine-native finance. And now go back to the scale. The BIS 2025 Triennial Survey says: $9.6T/day FX. The dollar appears on one side of 89% of FX trades. The euro is involved in 28.9%. The Japanese yen in 16.8%. FX swaps alone are around $4T every day. Then another $7.9T/day exists in OTC interest-rate derivatives turnover. Think about what happens if only part of those markets becomes tokenized. Digital USD deposits. Digital EUR deposits. Tokenized JPY. RLUSD. CBDCs. Tokenized Treasuries. Interest-rate derivatives. FX derivatives. Collateral. Money-market instruments. The first problem is getting the assets onchain. The second is verifying the information those assets depend on. The third is moving liquidity between all the different forms of value. This BIS paper attacks the second problem using XRPL. Ripple has spent years attacking the first and third. That is why the combination gets my attention. And you do not need XRPL to capture the whole market for the numbers to become enormous. For scale only: 0.1% of $9.6T daily FX turnover = $9.6B per day. 1% = $96B per day. Again, that is not a forecast. It shows what even tiny percentages mean when the underlying market is measured in trillions every day. And that is only FX. It does not include the additional $7.9T/day of interest-rate derivatives turnover BIS measures. This is where the XRP liquidity thesis changes from a crypto argument into a market-structure argument. Suppose the future has hundreds of tokenized currencies and financial products. Every possible pair cannot maintain perfect direct liquidity. USD token / EUR token. EUR token / JPY token. JPY token / RLUSD. RLUSD / Treasury token. Treasury token / derivative. Derivative / deposit token. The combinations explode. A common intermediate asset becomes useful whenever routing through it provides a better market. That is where XRP’s role becomes interesting. Not replacing the dollar. Not replacing the euro. Not replacing CBDCs. Not replacing bank deposits. Connecting liquidity between them when that route makes economic sense. Now imagine the system is automated. No trader needs to shout: “Use XRP.” Software looks at: price, spread, depth, settlement, availability. If the XRP path wins, the software uses XRP. That is the outcome I care about. Machine-selected liquidity. And if those transactions grow large enough, the XRP market itself has to change. Institutional market makers need inventory. Liquidity providers need inventory. Prime brokers need financing capacity. Order books need deeper capital. Large transactions need to clear without huge price impact. That is where the price thesis becomes different from retail speculation. If XRP ever helps support institutional flows inside markets measured in trillions per day, the relevant question is not: “How many retail holders bought today?” It becomes: How much dollar liquidity does the XRP market need to represent? That is an entirely different valuation conversation. There is one more thing I think people are missing. BIS Working Paper 1374 does not only talk about SDMX statistics. The researchers say the same architecture can extend to: XBRL regulatory filings FINREP COREP and other forms of structured official information. Now imagine banks submitting regulatory reports that receive immutable XRPL proofs. The bank cannot quietly change an old filing later. The regulator can verify the exact version. Auditors can verify it. Another authority can verify it. AI software can consume it. One system can prove both: who submitted the data and whether it changed. That gives XRPL a potential role far beyond payments. It starts touching the information layer of finance. And this is why the line “BIS used XRP Ledger” actually undersells the paper. What happened is more specific. Researchers inside BIS took a real institutional problem. They selected XRPL. They built a working implementation. They measured performance. They published the code direction. Then they explored how authenticated data could coexist with: CBDCs, stablecoins, tokenized deposits, derivatives, AI agents, automated financial instruments. That is what I am bullish on. Not a logo. Not a rumor. Not a screenshot. Technical work. And when I look at the direction Ripple is independently pushing XRPL, the overlap is hard for me to ignore. Trusted identities. Verified information. Regulated participants. Tokenized assets. Digital money. Automated execution. Credit. Collateral. FX. Liquidity. AI. Put together, the long-term architecture can look like this: Official institutions publish information. XRPL anchors the proof. Banks and regulators verify it. AI consumes it. Tokenized instruments use it. Stablecoins and tokenized deposits provide cash. Institutional markets execute trades. XRP supplies native network resources and can supply cross-asset liquidity where the route makes sense. That is not simply a faster payment network. That starts looking like part of a digital financial operating system. And then remember where this conversation is happening. Inside the research world of the institution that measures: $9.6 trillion of FX turnover every day plus $7.9 trillion of interest-rate derivatives turnover every day. A combined: $17.5 TRILLION DAILY. No, that is not XRPL volume. No, BIS does not process those trades. The significance is that BIS researchers just tested XRP Ledger while working inside the institutional world surrounding markets of that size. That is the fact. And now I’m asking the question that matters to me as an ripple:native holder: What happens if XRPL earns even a small role inside the tokenized version of that financial system? Because 0.1% of a trillion-dollar market is not small. And this market is not one trillion. It is trillions every single day. That is why Working Paper 1374 changed the scale of the conversation for me. For years, people asked whether XRP could become part of the future financial system. Now researchers inside the BIS have taken XRP Ledger, built institutional infrastructure on it, and explicitly discussed a future combining trusted information with digital money and programmable financial assets. We are still at the prototype stage. But for me, the direction is the real story. The next financial system will need trusted data, tokenized assets, automated execution and deep liquidity. XRPL is now showing up in all four conversations. And XRP sits natively underneath the network where those pieces can eventually meet. $17.5T a day. Now look at your ripple:native bag again. Enough?
0:26

What if I told you ripple:native just moved closer to a financial universe doing $17.5 TRILLION in FX and interest-rate derivatives every single day? I’m not talking about some random prediction. I’m talking about BIS Working Paper No. 1374. This is going to be a long read, because the headline barely scratches the surface. Four of the five authors work at the Bank for International Settlements, and instead of only mentioning XRP Ledger in theory, the researchers actually built, tested and published an open-source XRPL-based prototype. That distinction matters. This is a research implementation, not a production BIS deployment. But the technical choice itself is what caught me. The researchers needed a public blockchain that could help prove official economic and financial data had not been altered. They chose XRP Ledger. And they explained why: low fees, fast finality, developer resources and existing research around its consensus system. This wasn’t somebody adding an XRP logo to a presentation. They built the gateway. They created XRPL transactions. They used institutional anchoring wallets. They put cryptographic proofs inside transaction memos. They linked publisher identities to XRPL addresses. They retrieved those transactions again during verification. Then they measured how the system performed. Median publication latency came in around 3–5 seconds. Verification took around 1–2 seconds. That is where my brain immediately went beyond the headline. Because what exactly were they trying to verify? The kind of information the entire financial system runs on. -Inflation. -GDP. -Interest rates. -Banking statistics. -Debt information. -Financial-stability data. -Regulatory reporting. Imagine a central bank publishes an inflation number. Today that number gets copied everywhere. -Websites. -News terminals. -Databases. -Screenshots. -AI models. -Trading systems. Once it spreads across the internet, how does another machine independently prove that the number it received is exactly what the institution originally published? That is the problem BIS researchers were attacking. Their model creates a cryptographic fingerprint of the official dataset. Individual statistical series can receive fingerprints too. Those hashes are combined through a Merkle tree. A final Merkle root gets anchored to XRPL. The underlying economic data do not need to be dumped onto the blockchain. XRPL simply keeps the proof. Think of it like this: The official institution publishes the document. XRPL holds the tamper-proof receipt. Someone changes even one part of the underlying file? The cryptographic fingerprint changes. Now a bank, regulator, investor, trading engine or AI agent can check: Is this the original data? Has it been changed? Did it really come from the institution claiming to publish it? And that second part is where this paper gets even more serious. The BIS prototype combines the data proof with a W3C Verifiable Credential for the publisher. The publisher’s cryptographic identity is connected to an XRPL address. The paper even uses the format: did:xrpl: So you are not only verifying the information. You are verifying who published it. Now picture a financial world where machines can check both automatically. A central bank publishes CPI. A model receives it. Before touching money, the software checks XRPL. Correct file. Correct publisher. No alteration. Then it acts. That sounds simple until you realize what financial markets actually do with official data. -Rates move. -Currencies move. -Bond prices move. -Derivatives reprice. -Collateral requirements change. -Loans reset. -Inflation-linked instruments adjust. -Portfolio risk changes. And this is where BIS Working Paper 1374 stops being a boring statistics paper for me. Because the authors themselves discuss putting verified information beside digital financial assets. They specifically mention: -CBDCs -stablecoins -tokenized deposits -derivatives. That one section changes the entire way I look at this. The vision is not simply: “Put a hash on a blockchain.” It becomes: verified economic information + digital money + tokenized assets + automated execution. Now remember what Ripple has been building around XRPL. -Multi-Purpose Tokens. -Credentials. -Permissioned Domains. -Permissioned DEX infrastructure. -Confidential Transfers. -Stablecoins. -Institutional lending. -Tokenized collateral. -FX. -Onchain credit. And Ripple has repeatedly positioned XRP across payments, liquidity and credit. Now put those pieces beside what the BIS researchers are exploring. An official institution needs an identity. XRPL can represent identity and credentials. A regulated participant needs permission to enter a market. XRPL is building permissioned infrastructure. A bond needs trustworthy economic information. The BIS prototype shows one way that information can be authenticated through XRPL. A financial asset needs a digital representation. XRPL is being built for tokenization. A transaction needs money. Stablecoins and tokenized deposits can provide the cash side. Then all those different assets need liquidity. That is where ripple:native becomes much more interesting to me. But before getting there, look at the scale surrounding BIS itself. The BIS does not process the world’s $9.6 trillion of daily FX transactions. It measures that market through its Triennial Central Bank Survey. That distinction matters. According to the numbers in the context here: global OTC FX turnover = $9.6 TRILLION every day. Then add: OTC interest-rate derivatives turnover = $7.9 TRILLION every day. Together: $17.5 TRILLION per day. Just the FX number annualized across roughly 250 trading days comes to around: $2.4 QUADRILLION per year. That is the financial universe BIS research sits over. -Currencies. -Banks. -Central banks. -FX swaps. -Rates. -Derivatives. -Cross-border capital. -Collateral. -Dollar funding. And researchers inside that institution just chose XRP Ledger for an actual technical prototype. That is why I keep telling people not to reduce this to transaction fees. Yes, the worked example uses an XRPL Payment transaction. Yes, the reference cost is only: 10 drops = 0.00001 XRP. Yes, transaction fees on XRPL are destroyed. So if this kind of anchoring eventually ran on mainnet, publishing data itself would consume XRP. But that is not the part that gets me excited. The fee is intentionally tiny. The much bigger question is: What happens when verified information starts triggering financial activity on the same broader infrastructure? The paper itself talks about: inflation-linked products perpetual futures tokenized financial instruments derivative settlement interest payments automated compliance and even: automated monetary-policy applications. Now we are talking about information causing money to move. Imagine an inflation-linked bond. The government publishes inflation. That release gets cryptographically anchored. The bond checks the proof. The CPI number is verified. The contract adjusts what is owed. Digital cash settles the payment. No one has to manually copy a number from a website into another system. No one has to blindly trust a third-party data feed. The financial instrument can verify the economic input itself. That is the idea I keep coming back to: self-verifying finance. And the researchers even discuss using the XRPL EVM-compatible sidechain for more advanced applications where data verification and programmable financial execution exist in the same broader ecosystem. They mention: access controls, permissioning, automated compliance, multisignature requirements, oracle integration, programmable validation. Now connect that with Ripple’s institutional roadmap. Credentials can prove who a participant is. Permissioned Domains can define who belongs inside a regulated environment. Tokenized assets can represent financial instruments. RLUSD can represent digital dollar liquidity. Lending can make those assets productive. XRP can provide native network resources and, where economically useful, liquidity between fragmented assets. That is a very different picture of XRPL than the one people were arguing about years ago. It is not simply: “Can XRP send a payment quickly?” The question becomes: Can XRPL sit underneath parts of a machine-readable financial system? And Working Paper 1374 just gave that question much more weight for me. There is another section that barely gets discussed. The architecture is not limited to one data publisher. The researchers designed a multi-publisher system. Different institutions can create their own Merkle roots. Those roots can be combined into one larger super-root. One XRPL transaction can anchor that shared proof. Yet each publisher remains independently accountable for its own data. Now imagine the participants. Central Bank A. Central Bank B. Regulator C. Statistical Office D. International Organization E. One public verification system. Different publishers. Independent cryptographic accountability. That begins to resemble infrastructure for cross-border public-sector data exchange. And the paper’s own conclusion talks about trustworthy exchange among: national statistical offices central banks international organizations. Then look at who already uses the statistical standard the paper builds around. SDMX is sponsored by institutions including: BIS European Central Bank Eurostat International Monetary Fund OECD United Nations World Bank Group International Labour Organization. That does not mean those institutions are adopting XRPL. But it tells you something important about the design philosophy. The researchers did not create a blockchain system that requires the existing financial world to throw everything away. They designed it to sit underneath an existing institutional standard. That matters a lot. Because the easiest technology to adopt is often the technology that does not force everyone to rebuild from zero. Existing systems can continue publishing. XRPL can provide the cryptographic proof underneath. Then comes BIS Open Tech. The paper says the open-source reference implementation is being released as a prototype through BIS Open Tech and the SDMX community. That means other institutions can inspect it. Reuse it. Modify it. Build on it. This is how technical ideas can spread inside serious institutions. Not through hype. Through code. Documentation. Standards. Reuse. That is the kind of adoption path I pay attention to. Then there is the AI angle. This is where the whole thesis becomes almost unfairly interesting. The authors explicitly discuss AI agents. An AI system receives economic information. Instead of blindly trusting what it scraped from somewhere, it can ask: Is this data authentic? It checks the XRPL proof. Valid? Continue. Invalid? Do nothing. Now compare that with what Ripple launched in June 2026: the XRPL AI Starter Kit, designed around autonomous agents making payments with XRP and RLUSD. Two completely separate directions suddenly sit beside each other. BIS research: AI verifies information through XRPL. Ripple ecosystem: AI moves value through XRPL. Now imagine both ideas eventually meeting. An agent receives official inflation data. It verifies the release cryptographically. It recalculates risk. It reprices a bond. It adjusts collateral. It changes an FX position. It executes a payment. It settles in RLUSD. It routes through XRP where XRP is the best available liquidity path. That is machine-native finance. And now go back to the scale. The BIS 2025 Triennial Survey says: $9.6T/day FX. The dollar appears on one side of 89% of FX trades. The euro is involved in 28.9%. The Japanese yen in 16.8%. FX swaps alone are around $4T every day. Then another $7.9T/day exists in OTC interest-rate derivatives turnover. Think about what happens if only part of those markets becomes tokenized. Digital USD deposits. Digital EUR deposits. Tokenized JPY. RLUSD. CBDCs. Tokenized Treasuries. Interest-rate derivatives. FX derivatives. Collateral. Money-market instruments. The first problem is getting the assets onchain. The second is verifying the information those assets depend on. The third is moving liquidity between all the different forms of value. This BIS paper attacks the second problem using XRPL. Ripple has spent years attacking the first and third. That is why the combination gets my attention. And you do not need XRPL to capture the whole market for the numbers to become enormous. For scale only: 0.1% of $9.6T daily FX turnover = $9.6B per day. 1% = $96B per day. Again, that is not a forecast. It shows what even tiny percentages mean when the underlying market is measured in trillions every day. And that is only FX. It does not include the additional $7.9T/day of interest-rate derivatives turnover BIS measures. This is where the XRP liquidity thesis changes from a crypto argument into a market-structure argument. Suppose the future has hundreds of tokenized currencies and financial products. Every possible pair cannot maintain perfect direct liquidity. USD token / EUR token. EUR token / JPY token. JPY token / RLUSD. RLUSD / Treasury token. Treasury token / derivative. Derivative / deposit token. The combinations explode. A common intermediate asset becomes useful whenever routing through it provides a better market. That is where XRP’s role becomes interesting. Not replacing the dollar. Not replacing the euro. Not replacing CBDCs. Not replacing bank deposits. Connecting liquidity between them when that route makes economic sense. Now imagine the system is automated. No trader needs to shout: “Use XRP.” Software looks at: price, spread, depth, settlement, availability. If the XRP path wins, the software uses XRP. That is the outcome I care about. Machine-selected liquidity. And if those transactions grow large enough, the XRP market itself has to change. Institutional market makers need inventory. Liquidity providers need inventory. Prime brokers need financing capacity. Order books need deeper capital. Large transactions need to clear without huge price impact. That is where the price thesis becomes different from retail speculation. If XRP ever helps support institutional flows inside markets measured in trillions per day, the relevant question is not: “How many retail holders bought today?” It becomes: How much dollar liquidity does the XRP market need to represent? That is an entirely different valuation conversation. There is one more thing I think people are missing. BIS Working Paper 1374 does not only talk about SDMX statistics. The researchers say the same architecture can extend to: XBRL regulatory filings FINREP COREP and other forms of structured official information. Now imagine banks submitting regulatory reports that receive immutable XRPL proofs. The bank cannot quietly change an old filing later. The regulator can verify the exact version. Auditors can verify it. Another authority can verify it. AI software can consume it. One system can prove both: who submitted the data and whether it changed. That gives XRPL a potential role far beyond payments. It starts touching the information layer of finance. And this is why the line “BIS used XRP Ledger” actually undersells the paper. What happened is more specific. Researchers inside BIS took a real institutional problem. They selected XRPL. They built a working implementation. They measured performance. They published the code direction. Then they explored how authenticated data could coexist with: CBDCs, stablecoins, tokenized deposits, derivatives, AI agents, automated financial instruments. That is what I am bullish on. Not a logo. Not a rumor. Not a screenshot. Technical work. And when I look at the direction Ripple is independently pushing XRPL, the overlap is hard for me to ignore. Trusted identities. Verified information. Regulated participants. Tokenized assets. Digital money. Automated execution. Credit. Collateral. FX. Liquidity. AI. Put together, the long-term architecture can look like this: Official institutions publish information. XRPL anchors the proof. Banks and regulators verify it. AI consumes it. Tokenized instruments use it. Stablecoins and tokenized deposits provide cash. Institutional markets execute trades. XRP supplies native network resources and can supply cross-asset liquidity where the route makes sense. That is not simply a faster payment network. That starts looking like part of a digital financial operating system. And then remember where this conversation is happening. Inside the research world of the institution that measures: $9.6 trillion of FX turnover every day plus $7.9 trillion of interest-rate derivatives turnover every day. A combined: $17.5 TRILLION DAILY. No, that is not XRPL volume. No, BIS does not process those trades. The significance is that BIS researchers just tested XRP Ledger while working inside the institutional world surrounding markets of that size. That is the fact. And now I’m asking the question that matters to me as an ripple:native holder: What happens if XRPL earns even a small role inside the tokenized version of that financial system? Because 0.1% of a trillion-dollar market is not small. And this market is not one trillion. It is trillions every single day. That is why Working Paper 1374 changed the scale of the conversation for me. For years, people asked whether XRP could become part of the future financial system. Now researchers inside the BIS have taken XRP Ledger, built institutional infrastructure on it, and explicitly discussed a future combining trusted information with digital money and programmable financial assets. We are still at the prototype stage. But for me, the direction is the real story. The next financial system will need trusted data, tokenized assets, automated execution and deep liquidity. XRPL is now showing up in all four conversations. And XRP sits natively underneath the network where those pieces can eventually meet. $17.5T a day. Now look at your ripple:native bag again. Enough?

X Finance Bull

68,367 просмотров • 12 дней назад

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.
13:04

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.

Arjun Mahadevan (Mr. LLC 🇺🇸)

37,852 просмотров • 5 месяцев назад

"How do you know you can trust what [a non-human intelligence is] saying?" ~Bigelow Bigelow/Knapp 2: If I Was President, I'd Tell the World We Have Non-Human Craft and Bodies "The President that took on the challenge of Disclosure and of confirmation...is gonna go down in history as having really accomplished something." ~Bigelow Do China and Russia also have non-human bodies and craft? "Oh yeah, freaking yes!" ~Bigelow ~ "You've opened up Pandora's box." ~Bigelow ~ 17:44 (I'm using the KLAS YT video for time stamps. As you'll see, I add a lot to these, so they take forever to put together.) George Knapp (GK): "Would you guess - based on what you know about the topic and how it's been handled over the decades - would you guess that [Trump] had been briefed, or that any other presidents have been briefed? You know that Bill Clinton was not, even though he expressed an interest in it. So at some point, the President has to be told, 'No, we can't tell ya.'" Robert Bigelow (RB): "I think Bush Sr. knew quite a bit because he was the head of the CIA." (Dr. Eric Davis says Bush. Sr. told him some interesting things, including that he (Bush) was partially briefed in 1976 about the alleged 1964 Holloman AFB landing and meeting, and more, when he first became the director of the CIA in 1976. Watch Davis explain it, here. ) ~ RB: "I think Nixon did." (We have the Jackie Gleason story, where Nixon allegedly snuck away from his Secret Service detail and took Gleason to see non-human bodies at Homestead Air Force Base. But that was a claim from his ex-wife, and Gleason, who died in 1987, never said anything about it, on the record. The controversial Larry Warren (Rendlesham) claims Gleason told him the same story, but that's it.) Bigelow: "I think Eisenhower did. So, it's kind of hit and miss over historic...over the period of history as to who has and who hasn't." 18:20 Knapp: "Can you tell us what would be in the briefing document that you left behind? Was it cases? Here's a wave of UFOs, here's a case, here was a crash. Anything like that?" Bigelow: "Yeah, I related conversations I have had with General De Brouwer, who was the chief of the Air Force for Belgium. And there was a very interesting flap that was about 18 months long (1989-1990) with triangular craft. And he was in command of all the Air Force, and he really chased the heck out of these. "And he told me, he said, Bob, 'I'm just burning fuel, and it makes no sense.' He said, 'We're gonna stop because we're not getting anywhere. All we're doing is getting closer, you know, photographs and things, but we're not getting anywhere. We're not getting any information that is really valuable for us to understand anything.'" (I wonder if those were OUR triangles? If so, it's still a solid case/flap of anomalous craft. Dick D'Amato was a longtime (18 years) senior staffer for Senate Democratic Leader Robert C. Byrd of West Virginia.) "Dick D'Amato has returned from Belgium where he met with Colonel De Brouwer, a 'very interesting fellow.' Dick’s conclusion, again, is that the triangular objects are very plain human craft. ~Jacques Vallée's Forbidden Science Volume 4 - November 1992 And then we have this... Jeremy Kenyon Lockyer Corbell: "We hear about all these people on military bases seeing triangles, and you're doing this study, AAWSAP, to try to figure out the physics of how to do that. Do you think it's already been achieved by the U.S. government, or it hasn't, and that's why AAWSAP had a lot of value." Lacatski: "[three-second pause] It hasn't been achieved to its full extent." Video... ~~~ RB: "And I gave [Trump] other examples of where large amounts of witnesses were involved, and so that he could read about these particular [incidents]. And, of course, the one with Fife Simington in '97 was so interesting. Because there you had a governor who was an actual witness, and he made a joke of it on and brought a guy in an alien suit on stage because he was scared to death." GK: "It's the Phoenix Lights case." (Great case for Bigelow to show Trump.) RB: "The Phoenix Lights, which weren't just lights, it was actually friggin' craft, and it came from northern Arizona. I think it started, actually, in southern Nevada, is where the craft began. In the Henderson area (not far from me. ~Joe), I think, somewhere in there. And, so you see this huge triangular craft, gigantic craft, going slowly, and [Symington] was scared to death. "But finally, fast forward 10 years later, and he confesses that he was an eyewitness, but he didn't know what to do. So he was there at a point in time when the dynamic of disclosure could have been forced, and actually, confirmation could have been from a governor of the state like that. He was a credible guy. "So we've had many [incidents] like that in the early 50s, flying over the White House and the Capitol building. We've had so many of those. So, in my report, I included a lot of very dramatic, beyond-a-reasonable-doubt kind of witnesses of exhibitions." GK: "Did you share with him anything about your knowledge of a classified program?" RB: "No." GK: "You got a Top-Secret security clearance, right?" RB: "Yeah" GK: "It was for the AAWSAP program for BAASS." RB: "Right." GK: "Operated under the DIA." RB: "Yeah." GK: "You learned a lot during that program. It's the largest accumulation of UFO info. of any government-funded UFO program ever." RB: "Yeah." GK: "You didn't tell him about it." RB: "It wasn't credible enough, I felt, for him." (Someone on here watched this interview and told me that Bigelow said the AAWSAP data wasn't credible. That is NOT what he said. Not credible enough for Trump, is what he said. And if you read "Skinwalkers at the Pentagon" or listen any of Lacatki's interviews where he talks about UFOs being under a paranormal umbrella, I think Bigelow made the right decision for an 11-minute briefing for someone (Trump) who seems to be struggling (at least in public comments) with the reality of the basics.) RB: "[The AAWSAP data is] just something pertaining to me. And whereas, these other examples are historic, and they're in a lot of literature, they're very famous. The amount of witnesses were huge. We operated under a private situation with the Skinwalker Ranch, and he wouldn't relate to...well, we all had hitchhikers, we all took things home with us. You know, we all saw a lot of stuff. Whether it was at the ranch or where we lived, it didn't make any difference where in the United States we lived. We saw a lot of things, but that wouldn't be relevant for him to be able to relate to that, right?" GK: "Well." RB: "So, why talk about it?" GK: "Yeah, I mean, you know, you're kind of jumping into the deep end of the pool when you get into hitchhikers and skinwalkers." RB: "Yeah." GK: "Maybe for your first big conversation on this topic, that might not be the place to go." RB: "No, you know. And I couldn't even...if he said, 'Oh, I need a beer (Knapp laughs),' I couldn't even give him something to drink. You know, maybe stronger than that. So, I didn't wanna do any, you know." 22:15 GK: "You used the term non-human intelligence in the sentence you gave him. Hey, try this as you're climbing aboard Air Force One. Say that to media, and then, you know, close the door." RB: "Right." GK: "You said non-human intelligence, not ET. Are they the same, and did you share anything with him about what you think it is?" RB: "Well, there have been reports of humans on board, right? But the non-human are more interesting. I mean, if a human's on board and he or she has free reign of the craft, that's pretty damn interesting, right? And you don't know where those people are when they're not on board." (I wonder what cases he's talking about? Travis Walton reported seeing humans with non-humans and John Keel wrote about humans being seen onboard craft.) ~ Bigelow: "But the other ones are...some are scary, you know? It's not surprising, that, in the Universe, everything is [not] gonna look like us. Every intelligent organism is [not] going to look like us. But you have to have... It's also a matter of respect for any really intelligent animal or creature, whatever it is, right? And the more advanced, the more respect because you don't know what they know, and you don't know the future. And they may know. They may know more than just the now." (In interviews (which I can't find right now), Bigelow has hinted at an unstoppable, not-so-rosy future for mankind. Have people in the Legacy programs told him that the NHI have warned us about our doomed future or some cataclysmic event? Or, has Bigelow encountered these non-humans (or humans) and had them make claims about our future?) ~ 23:34 GK: "If you were to have a second conversation with him, would you explain that ET is one idea, but it's not necessarily the only option for a non-human intelligence that's out there?" RB: "You have to tell me first how much time do I have with the second conversation?" GK: "[laughs] Let's make it an hour. You have an hour with the President. What would you tell them about who they are, who they might be?" RB: "Umm... So, I would try to get into the complexity of the relationship. That once you've had confirmation, you know, you've given that text of that little sentence I wrote and quoted there, and you start to go down the path of disclosure, where it's more than than just FLIR videos, and you're seeing other things that are much more crystal clear, you know, in video. "Or, legitimate people in places that you recognize these people, and they're around something, or with some thing, or somebody that's a holy cow, that's wow, you know. That's a very dramatic kind of disclosure about something landing on the lawn somewhere, you know." (I have no idea what he's talking about. Anybody else able to decipher that? Is he saying that there's evidence (that he's seen?) of people that we know (a President or head of state?) hanging with a non-human or next to a downed or landed craft?) RB: "So I think what I would have spent time doing is talking about, how do you establish: You've been in 80 years of denial as a nation, and the world. We have countries who are very upfront, and others that are also in denial. Now you wanna pivot 180 degrees. How do you start that relationship? How do you do that? "And it's not as though that we've ever been in control. We haven't been in control of anything, ever, for this 80 years of modern history. Nothing. We have no ability to control anything." GK: "That's a tough thing to tell the President, right? I would think that would be a tough thing for a President to accept." RB: "Well, you're giving me an hour to talk to him, you said." GK: "[laughs] Yeah, okay." RB: "So I'm using my hour." GK: "All right." 25:46 RB:" So, yeah! So, that's a really intriguing thing is: How do you do that, where do you start? And into a relationship? And I would say more like, well, I would wanna start with, the biggest question I would ask, I would say, what I wanna get from them is... My first question is: What are the chances we are gonna survive ourselves? That we are not going to annihilate ourselves. "Because they already are familiar with a whole lot of other species on a lot of other worlds or other planets. So, chances are they have a pretty good idea of percentage." (That assumes a lot, since we have no idea how long they've been around, what they've seen, or where they're from. Unless...Mr. Bigelow has seen evidence that relates to those questions or heard it from folks who work in the Legacy programs?) RB: "And it's not that we're a spiritual species. You talk to people that have near-death experiences; they're changed for the rest of their life. They've become a spiritual person of a caliber that they've never been. "Well, we're a long way, as an average, from that category, from that level. There are monsters among us, as human beings, and they're angelic people among us. So we are a danger to another species, we're potentially the Klingons. We have a technological maturity that is not just going on a line, but it's vertical and it's segmented because it's jumping. "And meanwhile, our spiritual maturity just bumps along the bottom, you know? So, we look at the 20th century; 60 million people were killed. Are we ever gonna get beyond that? So, the thing of it is, we know so little about how to have a relationship. I'm not in any position to really advise, except for the few ideas I have as to where to start, and the how is really super important. 27:49 "It's not gonna be like a 'Close Encounter of the Third, Kind,' which was a really cool movie. And it's not gonna be because of SETI. You know, it's gonna be by some other kinds of means that you're embarking on. And I'm interested in that kind of research." 28:06 GK: "We go back to your meeting with the President. You walk into the Oval Office, they clear off the Resolute Desk, you spread out your seven piles of stuff, and you start making a presentation about each one, in particular about ET, non-human intelligence. You can't say what he said to you. Can you say whether he asked questions? Was he curious?" RB: "He was distracted by other things. So, he also asked me... He did ask questions, but he asked questions about other stuff that wasn't necessarily just on what I was talking about." GK: "He's getting your input on other things going on in the world." RB: "Yeah, yeah." 28:49 GK: "If you had to make a guess, would you guess that you got through to him on this issue? I mean, he hasn't stood in the doorway of Air Force One and made those remarks yet, but he has taken some pretty dramatic steps on this topic since you met with him. Do you think you made a dent?" RB: "Don't know. I advised him on something that I can measure, and he hasn't taken my advice so far on what I suggested to him, on a totally different, unrelated subject. But so far, he has not taken my advice." GK: Well, somebody seems to have got through to him, because he's been taking steps that we're seeing real results. I mean, people are either..." RB: "Well, I'm talking about an unrelated subject altogether. So, yes, there there are a couple of different task forces that are involved. So, there is an initiation of an activity in this subject, [but] it remains to be seen, though, that he gives actual presidential confirmation. I don't, I haven't... Maybe you're more aware than I am. Has that been...has he done that?" GK: "Not, not really. Nothing like what I'd call confirmation. But that's really what you're talking about. I remember us having a conversation in 2008, right after you had signed the contract with DIA for BAASS to run [AAWSAP]. And you made the case then that what we need is not Disclosure; it's confirmation." RB: "Yeah." 30:25 GK: "For somebody like the President, I'm not sure if it could be anyone less than the President that steps forward and says this, and it carries the same kind of weight." RB: "Yeah. It's a big appetizer. You know, you're looking for the entree, and where's the dessert in this whole buffet? But the confirmation is a huge appetizer to start with, and that's why I started with that in my conversation with him. Is, pushing him to try and make a confirmation. And maybe he wants to get personally more comfortable?" (I have said that I want the entire enchilada NOW, but I'm also a realist, and would take a simple confirmation that somebody else is on this planet with us. That SHOULD wake up the mainstream media and masses, but no guarantees since Trump is so controversial and people might just ignore it, IF he ever did it. I've also said that I'd surround him with people like Schumer, Rubio, Rounds and Gillibrand in order to show that it's bipartisan. And a few firsthand whistleblowers who say they worked hands on, IN one of these alleged Legacy UFO/UAP programs.) 30:57 Bigelow: "And, you know, he will go down in history for a lot of things, for better or worse, that people are on both sides of the fence about him and so forth. But he's involved in so many different things and has been, that I don't know what his legacy is gonna be, I really don't. "He has an opportunity, in this serious subject, to build a legacy using this as part of the blocks, part of the brick and the structure...the content of a structure that is very unique. So, you can have all the political programs that you want, and so forth, and nobody is gonna really remember those as time goes by, as the decades fade away. "Nobody can remember Benghazi and Afghanistan and Iraq, different kinds of things, and that's just recent history. You know, the world goes so fast. But the President that took on the challenge of Disclosure and of confirmation, and actually built a satisfactory conclusion of a relationship between human beings and that subject is gonna go down in history as having really accomplished something. And if it's handled right, it can be done successfully." (To me, it's a no brainer. His presidency would go down as one that changed the world and our species. Or course, I'm sure he has people in his ear telling him that confirmation would cause societal disruption that we're not prepared to handle, and that THAT would be his legacy.) (32:21) GK: "A lot of Presidents have made comments, often after they're out of office, on this topic. 'Yeah, gosh, I'm real interested in that.' Or, 'Wouldn't that be something?' Or, 'I think aliens could exist.' Something like that. But nothing like what you're talking about, which amounts to confirmation, saying, 'It's real. They're here. We gotta figure it out,' and stopping it there, not disclosing..." RB: "Well, the dialogue, the ability to learn something from them is huge. What can we do for you? I mean, that's where...one of the first things I would ask is, not just, are we going to survive ourselves? Yeah, that's pretty damn important. That's like, probably number one, you know? "But close behind that is: Okay, we've been aware of you for a long time, and the government's never admitted it. But us as a public, we know it, and we're really curious. Is there anything that we can do for you? Not just what can you do... I loved what John Kennedy, what he said famously: Don't ask what the country can do for you, ask what you can do for your country. That huge. So that's giving them a kind of a respect that they deserve." (Do they deserve respect? I'm not so sure. It all depends on if they're upfront with us and tell us their true intentions. Of course, how do we know they're telling us the truth? Why have you (or some of you) been abducting us against our will, and injuring some of us? See Jim Semivan's abduction story with his wife. "I don't think [the phenomenon] cares whether it does harm. I think it may go out of its way not to do harm, but, if it does harm... "I had a hole in the back of my neck, and my wife...unexplained bleeding for 17 days." ~Former CIA Officer, Jim Semivan to Engaging The Phenomenon ~ 33:33 GK: "If you were President, would you announce that yes, we've got crash retrievals, yes, we have reverse engineering programs, and yes, we've got bodies?" RB: "Yeah, because the rest of the world already knows that. The people that count in Russia and China already know." GK: "Because they've got their own." RB: "Oh yeah, freaking yes! Yeah! They've had their missiles shut down, they've had their missiles activated. You know, depending on which country you're talking about." 33:56 GK: "Have you considered the political fallout for a President? Let's say it's Trump, because he might be the guy that would do it and just cast his fate to the winds. But, he makes an announcement: 'Yeah, they're here. Yeah, we've got programs. Yeah, we've got crashes, we've got bodies. That's as far as I'm able to go right now.' What happens to him, politically? Is he able to get anything else done?" RB: "That's more than I would have said, for him to say. What you just said." GK: "That goes too far." RB: "I wouldn't advise him to say, initially, what you just said." GK: "You just say, non-human intelligence, been here a long time, and leave it at that." RB: "Yeah, that one sentence is a starter because it's a huge canon. You've opened up Pandora's box, in terms of, 'Oh my gosh, what's gonna happen?' Okay. So now, you can categorize and segment into topics. And you need time to do that, and it needs to be digestible." ~ “I have met with people who I know are in the know. One of them told me the truth is indigestible.” ~Jim Semivan to James Iandoli ~ RB: "And it needs to be not packaged in a scary kind of way, and it can't be packaged in a way that you're divulging what you shouldn't be divulging. You know, other countries aren't, so there's reasons to have national secrets. There really are. I mean, that's kind of like common sense, right?" (Well, David Grusch claimed that some other countries aren't divulging for a specific reason." Grusch on Yes Theory: "So there are certainly friendly governments, both across the pond and say, local to where we...our landmass, that are for this. And a lot of them know that they got a raw deal with the U.S. because they were, basically, part of the secrecy, through kind of agreements like, bilateral, unilateral agreements. And they're like, you know, they kind of wanna be, 'Release me!' Like, you know, because they they do realize it was a bad deal. "With the ecosystem secrecy, some people, one of their arguments is like, 'Well how would they keep the secret?' I'm, like dude, I was cleared to some of the most nation's most sensitive programs, I used to handle the PDB (Presidential Daily Briefing). You know, I had full access to most DoD activities. And most of the stuff, BROAD programs that were enduring, have NEVER leaked. "So the U.S. and its allies are very good at keeping secrecies, to include programs that are, let's say, global in nature. And really, it's been leaking like a sieve in some weird way for many decades. Now it's been mixed in with some BS in ufology and stuff, but the general gist of it's actually been out there for a long time. It has been leaking in some sense, so." ~ 35:06 GK: "We don't want the Russians and Chinese and maybe other adversaries to know how far along we are, or are not, in configuring this out." RB: "Absolutely. Especially of that. I think that the relationship (with he phenomenon) thing is a mankind relationship. It's not just for America. When you are embarking as a President on trying to initiate a solemn relationship, you're not doing for U.S. of A. only. "And so, the...and then we probably can do a show on communication (with a non-human intelligence), but it's really complex because, who are you going to have initiate it? Are you going to have politicians initiate? Are you going to have military people? Because everybody has kind of their own mindset. Are you gonna have a bunch of lawyers initiate it? Are you gonna have scientists only, initiate it? (And what if we're dealing with more than one non-human intelligence? Who do we communicate with? And what if it's one intelligence pretended to be multiple? How would we even figure that out?) "[Hynek] cited the 'poltergeist' phenomena experienced by some...after a close encounter; the fact that some witnesses develop psychic abilities after an encounter. 'Do we have two aspects of one phenomenon or two different sets of phenomena?'" ~J. Allen Hynek ~ RB: "And how do you know to whom you're talking? You know, how do you know you can trust what they're saying?" (I'm so glad he said that. We don't, and we don't.) RB: "Because it depends on the form of communication, and what are you asking for in the way of proof or demonstrations to establish trust, both ways, in the communication process?" "Because, we haven't had that happen before. It is totally new ground, and frankly, we need The Others' help. We need the help of The Others to help, to guide us." (Firs off, it MAY have happened within these Legacy programs. And, if we don't know who we're talking to, or if we can trust them, why would we enlist the help of The Others? Doesn't seem to make sense to me.)
10:24

"How do you know you can trust what [a non-human intelligence is] saying?" ~Bigelow Bigelow/Knapp 2: If I Was President, I'd Tell the World We Have Non-Human Craft and Bodies "The President that took on the challenge of Disclosure and of confirmation...is gonna go down in history as having really accomplished something." ~Bigelow Do China and Russia also have non-human bodies and craft? "Oh yeah, freaking yes!" ~Bigelow ~ "You've opened up Pandora's box." ~Bigelow ~ 17:44 (I'm using the KLAS YT video for time stamps. As you'll see, I add a lot to these, so they take forever to put together.) George Knapp (GK): "Would you guess - based on what you know about the topic and how it's been handled over the decades - would you guess that [Trump] had been briefed, or that any other presidents have been briefed? You know that Bill Clinton was not, even though he expressed an interest in it. So at some point, the President has to be told, 'No, we can't tell ya.'" Robert Bigelow (RB): "I think Bush Sr. knew quite a bit because he was the head of the CIA." (Dr. Eric Davis says Bush. Sr. told him some interesting things, including that he (Bush) was partially briefed in 1976 about the alleged 1964 Holloman AFB landing and meeting, and more, when he first became the director of the CIA in 1976. Watch Davis explain it, here. ) ~ RB: "I think Nixon did." (We have the Jackie Gleason story, where Nixon allegedly snuck away from his Secret Service detail and took Gleason to see non-human bodies at Homestead Air Force Base. But that was a claim from his ex-wife, and Gleason, who died in 1987, never said anything about it, on the record. The controversial Larry Warren (Rendlesham) claims Gleason told him the same story, but that's it.) Bigelow: "I think Eisenhower did. So, it's kind of hit and miss over historic...over the period of history as to who has and who hasn't." 18:20 Knapp: "Can you tell us what would be in the briefing document that you left behind? Was it cases? Here's a wave of UFOs, here's a case, here was a crash. Anything like that?" Bigelow: "Yeah, I related conversations I have had with General De Brouwer, who was the chief of the Air Force for Belgium. And there was a very interesting flap that was about 18 months long (1989-1990) with triangular craft. And he was in command of all the Air Force, and he really chased the heck out of these. "And he told me, he said, Bob, 'I'm just burning fuel, and it makes no sense.' He said, 'We're gonna stop because we're not getting anywhere. All we're doing is getting closer, you know, photographs and things, but we're not getting anywhere. We're not getting any information that is really valuable for us to understand anything.'" (I wonder if those were OUR triangles? If so, it's still a solid case/flap of anomalous craft. Dick D'Amato was a longtime (18 years) senior staffer for Senate Democratic Leader Robert C. Byrd of West Virginia.) "Dick D'Amato has returned from Belgium where he met with Colonel De Brouwer, a 'very interesting fellow.' Dick’s conclusion, again, is that the triangular objects are very plain human craft. ~Jacques Vallée's Forbidden Science Volume 4 - November 1992 And then we have this... Jeremy Kenyon Lockyer Corbell: "We hear about all these people on military bases seeing triangles, and you're doing this study, AAWSAP, to try to figure out the physics of how to do that. Do you think it's already been achieved by the U.S. government, or it hasn't, and that's why AAWSAP had a lot of value." Lacatski: "[three-second pause] It hasn't been achieved to its full extent." Video... ~~~ RB: "And I gave [Trump] other examples of where large amounts of witnesses were involved, and so that he could read about these particular [incidents]. And, of course, the one with Fife Simington in '97 was so interesting. Because there you had a governor who was an actual witness, and he made a joke of it on and brought a guy in an alien suit on stage because he was scared to death." GK: "It's the Phoenix Lights case." (Great case for Bigelow to show Trump.) RB: "The Phoenix Lights, which weren't just lights, it was actually friggin' craft, and it came from northern Arizona. I think it started, actually, in southern Nevada, is where the craft began. In the Henderson area (not far from me. ~Joe), I think, somewhere in there. And, so you see this huge triangular craft, gigantic craft, going slowly, and [Symington] was scared to death. "But finally, fast forward 10 years later, and he confesses that he was an eyewitness, but he didn't know what to do. So he was there at a point in time when the dynamic of disclosure could have been forced, and actually, confirmation could have been from a governor of the state like that. He was a credible guy. "So we've had many [incidents] like that in the early 50s, flying over the White House and the Capitol building. We've had so many of those. So, in my report, I included a lot of very dramatic, beyond-a-reasonable-doubt kind of witnesses of exhibitions." GK: "Did you share with him anything about your knowledge of a classified program?" RB: "No." GK: "You got a Top-Secret security clearance, right?" RB: "Yeah" GK: "It was for the AAWSAP program for BAASS." RB: "Right." GK: "Operated under the DIA." RB: "Yeah." GK: "You learned a lot during that program. It's the largest accumulation of UFO info. of any government-funded UFO program ever." RB: "Yeah." GK: "You didn't tell him about it." RB: "It wasn't credible enough, I felt, for him." (Someone on here watched this interview and told me that Bigelow said the AAWSAP data wasn't credible. That is NOT what he said. Not credible enough for Trump, is what he said. And if you read "Skinwalkers at the Pentagon" or listen any of Lacatki's interviews where he talks about UFOs being under a paranormal umbrella, I think Bigelow made the right decision for an 11-minute briefing for someone (Trump) who seems to be struggling (at least in public comments) with the reality of the basics.) RB: "[The AAWSAP data is] just something pertaining to me. And whereas, these other examples are historic, and they're in a lot of literature, they're very famous. The amount of witnesses were huge. We operated under a private situation with the Skinwalker Ranch, and he wouldn't relate to...well, we all had hitchhikers, we all took things home with us. You know, we all saw a lot of stuff. Whether it was at the ranch or where we lived, it didn't make any difference where in the United States we lived. We saw a lot of things, but that wouldn't be relevant for him to be able to relate to that, right?" GK: "Well." RB: "So, why talk about it?" GK: "Yeah, I mean, you know, you're kind of jumping into the deep end of the pool when you get into hitchhikers and skinwalkers." RB: "Yeah." GK: "Maybe for your first big conversation on this topic, that might not be the place to go." RB: "No, you know. And I couldn't even...if he said, 'Oh, I need a beer (Knapp laughs),' I couldn't even give him something to drink. You know, maybe stronger than that. So, I didn't wanna do any, you know." 22:15 GK: "You used the term non-human intelligence in the sentence you gave him. Hey, try this as you're climbing aboard Air Force One. Say that to media, and then, you know, close the door." RB: "Right." GK: "You said non-human intelligence, not ET. Are they the same, and did you share anything with him about what you think it is?" RB: "Well, there have been reports of humans on board, right? But the non-human are more interesting. I mean, if a human's on board and he or she has free reign of the craft, that's pretty damn interesting, right? And you don't know where those people are when they're not on board." (I wonder what cases he's talking about? Travis Walton reported seeing humans with non-humans and John Keel wrote about humans being seen onboard craft.) ~ Bigelow: "But the other ones are...some are scary, you know? It's not surprising, that, in the Universe, everything is [not] gonna look like us. Every intelligent organism is [not] going to look like us. But you have to have... It's also a matter of respect for any really intelligent animal or creature, whatever it is, right? And the more advanced, the more respect because you don't know what they know, and you don't know the future. And they may know. They may know more than just the now." (In interviews (which I can't find right now), Bigelow has hinted at an unstoppable, not-so-rosy future for mankind. Have people in the Legacy programs told him that the NHI have warned us about our doomed future or some cataclysmic event? Or, has Bigelow encountered these non-humans (or humans) and had them make claims about our future?) ~ 23:34 GK: "If you were to have a second conversation with him, would you explain that ET is one idea, but it's not necessarily the only option for a non-human intelligence that's out there?" RB: "You have to tell me first how much time do I have with the second conversation?" GK: "[laughs] Let's make it an hour. You have an hour with the President. What would you tell them about who they are, who they might be?" RB: "Umm... So, I would try to get into the complexity of the relationship. That once you've had confirmation, you know, you've given that text of that little sentence I wrote and quoted there, and you start to go down the path of disclosure, where it's more than than just FLIR videos, and you're seeing other things that are much more crystal clear, you know, in video. "Or, legitimate people in places that you recognize these people, and they're around something, or with some thing, or somebody that's a holy cow, that's wow, you know. That's a very dramatic kind of disclosure about something landing on the lawn somewhere, you know." (I have no idea what he's talking about. Anybody else able to decipher that? Is he saying that there's evidence (that he's seen?) of people that we know (a President or head of state?) hanging with a non-human or next to a downed or landed craft?) RB: "So I think what I would have spent time doing is talking about, how do you establish: You've been in 80 years of denial as a nation, and the world. We have countries who are very upfront, and others that are also in denial. Now you wanna pivot 180 degrees. How do you start that relationship? How do you do that? "And it's not as though that we've ever been in control. We haven't been in control of anything, ever, for this 80 years of modern history. Nothing. We have no ability to control anything." GK: "That's a tough thing to tell the President, right? I would think that would be a tough thing for a President to accept." RB: "Well, you're giving me an hour to talk to him, you said." GK: "[laughs] Yeah, okay." RB: "So I'm using my hour." GK: "All right." 25:46 RB:" So, yeah! So, that's a really intriguing thing is: How do you do that, where do you start? And into a relationship? And I would say more like, well, I would wanna start with, the biggest question I would ask, I would say, what I wanna get from them is... My first question is: What are the chances we are gonna survive ourselves? That we are not going to annihilate ourselves. "Because they already are familiar with a whole lot of other species on a lot of other worlds or other planets. So, chances are they have a pretty good idea of percentage." (That assumes a lot, since we have no idea how long they've been around, what they've seen, or where they're from. Unless...Mr. Bigelow has seen evidence that relates to those questions or heard it from folks who work in the Legacy programs?) RB: "And it's not that we're a spiritual species. You talk to people that have near-death experiences; they're changed for the rest of their life. They've become a spiritual person of a caliber that they've never been. "Well, we're a long way, as an average, from that category, from that level. There are monsters among us, as human beings, and they're angelic people among us. So we are a danger to another species, we're potentially the Klingons. We have a technological maturity that is not just going on a line, but it's vertical and it's segmented because it's jumping. "And meanwhile, our spiritual maturity just bumps along the bottom, you know? So, we look at the 20th century; 60 million people were killed. Are we ever gonna get beyond that? So, the thing of it is, we know so little about how to have a relationship. I'm not in any position to really advise, except for the few ideas I have as to where to start, and the how is really super important. 27:49 "It's not gonna be like a 'Close Encounter of the Third, Kind,' which was a really cool movie. And it's not gonna be because of SETI. You know, it's gonna be by some other kinds of means that you're embarking on. And I'm interested in that kind of research." 28:06 GK: "We go back to your meeting with the President. You walk into the Oval Office, they clear off the Resolute Desk, you spread out your seven piles of stuff, and you start making a presentation about each one, in particular about ET, non-human intelligence. You can't say what he said to you. Can you say whether he asked questions? Was he curious?" RB: "He was distracted by other things. So, he also asked me... He did ask questions, but he asked questions about other stuff that wasn't necessarily just on what I was talking about." GK: "He's getting your input on other things going on in the world." RB: "Yeah, yeah." 28:49 GK: "If you had to make a guess, would you guess that you got through to him on this issue? I mean, he hasn't stood in the doorway of Air Force One and made those remarks yet, but he has taken some pretty dramatic steps on this topic since you met with him. Do you think you made a dent?" RB: "Don't know. I advised him on something that I can measure, and he hasn't taken my advice so far on what I suggested to him, on a totally different, unrelated subject. But so far, he has not taken my advice." GK: Well, somebody seems to have got through to him, because he's been taking steps that we're seeing real results. I mean, people are either..." RB: "Well, I'm talking about an unrelated subject altogether. So, yes, there there are a couple of different task forces that are involved. So, there is an initiation of an activity in this subject, [but] it remains to be seen, though, that he gives actual presidential confirmation. I don't, I haven't... Maybe you're more aware than I am. Has that been...has he done that?" GK: "Not, not really. Nothing like what I'd call confirmation. But that's really what you're talking about. I remember us having a conversation in 2008, right after you had signed the contract with DIA for BAASS to run [AAWSAP]. And you made the case then that what we need is not Disclosure; it's confirmation." RB: "Yeah." 30:25 GK: "For somebody like the President, I'm not sure if it could be anyone less than the President that steps forward and says this, and it carries the same kind of weight." RB: "Yeah. It's a big appetizer. You know, you're looking for the entree, and where's the dessert in this whole buffet? But the confirmation is a huge appetizer to start with, and that's why I started with that in my conversation with him. Is, pushing him to try and make a confirmation. And maybe he wants to get personally more comfortable?" (I have said that I want the entire enchilada NOW, but I'm also a realist, and would take a simple confirmation that somebody else is on this planet with us. That SHOULD wake up the mainstream media and masses, but no guarantees since Trump is so controversial and people might just ignore it, IF he ever did it. I've also said that I'd surround him with people like Schumer, Rubio, Rounds and Gillibrand in order to show that it's bipartisan. And a few firsthand whistleblowers who say they worked hands on, IN one of these alleged Legacy UFO/UAP programs.) 30:57 Bigelow: "And, you know, he will go down in history for a lot of things, for better or worse, that people are on both sides of the fence about him and so forth. But he's involved in so many different things and has been, that I don't know what his legacy is gonna be, I really don't. "He has an opportunity, in this serious subject, to build a legacy using this as part of the blocks, part of the brick and the structure...the content of a structure that is very unique. So, you can have all the political programs that you want, and so forth, and nobody is gonna really remember those as time goes by, as the decades fade away. "Nobody can remember Benghazi and Afghanistan and Iraq, different kinds of things, and that's just recent history. You know, the world goes so fast. But the President that took on the challenge of Disclosure and of confirmation, and actually built a satisfactory conclusion of a relationship between human beings and that subject is gonna go down in history as having really accomplished something. And if it's handled right, it can be done successfully." (To me, it's a no brainer. His presidency would go down as one that changed the world and our species. Or course, I'm sure he has people in his ear telling him that confirmation would cause societal disruption that we're not prepared to handle, and that THAT would be his legacy.) (32:21) GK: "A lot of Presidents have made comments, often after they're out of office, on this topic. 'Yeah, gosh, I'm real interested in that.' Or, 'Wouldn't that be something?' Or, 'I think aliens could exist.' Something like that. But nothing like what you're talking about, which amounts to confirmation, saying, 'It's real. They're here. We gotta figure it out,' and stopping it there, not disclosing..." RB: "Well, the dialogue, the ability to learn something from them is huge. What can we do for you? I mean, that's where...one of the first things I would ask is, not just, are we going to survive ourselves? Yeah, that's pretty damn important. That's like, probably number one, you know? "But close behind that is: Okay, we've been aware of you for a long time, and the government's never admitted it. But us as a public, we know it, and we're really curious. Is there anything that we can do for you? Not just what can you do... I loved what John Kennedy, what he said famously: Don't ask what the country can do for you, ask what you can do for your country. That huge. So that's giving them a kind of a respect that they deserve." (Do they deserve respect? I'm not so sure. It all depends on if they're upfront with us and tell us their true intentions. Of course, how do we know they're telling us the truth? Why have you (or some of you) been abducting us against our will, and injuring some of us? See Jim Semivan's abduction story with his wife. "I don't think [the phenomenon] cares whether it does harm. I think it may go out of its way not to do harm, but, if it does harm... "I had a hole in the back of my neck, and my wife...unexplained bleeding for 17 days." ~Former CIA Officer, Jim Semivan to Engaging The Phenomenon ~ 33:33 GK: "If you were President, would you announce that yes, we've got crash retrievals, yes, we have reverse engineering programs, and yes, we've got bodies?" RB: "Yeah, because the rest of the world already knows that. The people that count in Russia and China already know." GK: "Because they've got their own." RB: "Oh yeah, freaking yes! Yeah! They've had their missiles shut down, they've had their missiles activated. You know, depending on which country you're talking about." 33:56 GK: "Have you considered the political fallout for a President? Let's say it's Trump, because he might be the guy that would do it and just cast his fate to the winds. But, he makes an announcement: 'Yeah, they're here. Yeah, we've got programs. Yeah, we've got crashes, we've got bodies. That's as far as I'm able to go right now.' What happens to him, politically? Is he able to get anything else done?" RB: "That's more than I would have said, for him to say. What you just said." GK: "That goes too far." RB: "I wouldn't advise him to say, initially, what you just said." GK: "You just say, non-human intelligence, been here a long time, and leave it at that." RB: "Yeah, that one sentence is a starter because it's a huge canon. You've opened up Pandora's box, in terms of, 'Oh my gosh, what's gonna happen?' Okay. So now, you can categorize and segment into topics. And you need time to do that, and it needs to be digestible." ~ “I have met with people who I know are in the know. One of them told me the truth is indigestible.” ~Jim Semivan to James Iandoli ~ RB: "And it needs to be not packaged in a scary kind of way, and it can't be packaged in a way that you're divulging what you shouldn't be divulging. You know, other countries aren't, so there's reasons to have national secrets. There really are. I mean, that's kind of like common sense, right?" (Well, David Grusch claimed that some other countries aren't divulging for a specific reason." Grusch on Yes Theory: "So there are certainly friendly governments, both across the pond and say, local to where we...our landmass, that are for this. And a lot of them know that they got a raw deal with the U.S. because they were, basically, part of the secrecy, through kind of agreements like, bilateral, unilateral agreements. And they're like, you know, they kind of wanna be, 'Release me!' Like, you know, because they they do realize it was a bad deal. "With the ecosystem secrecy, some people, one of their arguments is like, 'Well how would they keep the secret?' I'm, like dude, I was cleared to some of the most nation's most sensitive programs, I used to handle the PDB (Presidential Daily Briefing). You know, I had full access to most DoD activities. And most of the stuff, BROAD programs that were enduring, have NEVER leaked. "So the U.S. and its allies are very good at keeping secrecies, to include programs that are, let's say, global in nature. And really, it's been leaking like a sieve in some weird way for many decades. Now it's been mixed in with some BS in ufology and stuff, but the general gist of it's actually been out there for a long time. It has been leaking in some sense, so." ~ 35:06 GK: "We don't want the Russians and Chinese and maybe other adversaries to know how far along we are, or are not, in configuring this out." RB: "Absolutely. Especially of that. I think that the relationship (with he phenomenon) thing is a mankind relationship. It's not just for America. When you are embarking as a President on trying to initiate a solemn relationship, you're not doing for U.S. of A. only. "And so, the...and then we probably can do a show on communication (with a non-human intelligence), but it's really complex because, who are you going to have initiate it? Are you going to have politicians initiate? Are you going to have military people? Because everybody has kind of their own mindset. Are you gonna have a bunch of lawyers initiate it? Are you gonna have scientists only, initiate it? (And what if we're dealing with more than one non-human intelligence? Who do we communicate with? And what if it's one intelligence pretended to be multiple? How would we even figure that out?) "[Hynek] cited the 'poltergeist' phenomena experienced by some...after a close encounter; the fact that some witnesses develop psychic abilities after an encounter. 'Do we have two aspects of one phenomenon or two different sets of phenomena?'" ~J. Allen Hynek ~ RB: "And how do you know to whom you're talking? You know, how do you know you can trust what they're saying?" (I'm so glad he said that. We don't, and we don't.) RB: "Because it depends on the form of communication, and what are you asking for in the way of proof or demonstrations to establish trust, both ways, in the communication process?" "Because, we haven't had that happen before. It is totally new ground, and frankly, we need The Others' help. We need the help of The Others to help, to guide us." (Firs off, it MAY have happened within these Legacy programs. And, if we don't know who we're talking to, or if we can trust them, why would we enlist the help of The Others? Doesn't seem to make sense to me.)

Joe Murgia

53,256 просмотров • 1 месяц назад

BOOM!!! 💥💥💥 Dr. Aseem Malhotra's testimony was delivered in the Helsinski District Court on April 12, 2024, with the understanding that any deviation from the truth would constitute perjury. This clip was immediately banned by YouTube so please share widely. I've trimmed the clip, removing the interpreter's segment for a smoother listening experience. Here's the first hour of the testimony. ---------------------------------- My name is Doctor Aseem Malhotra. I am a consultant cardiologist. I've been a qualified doctor since 2001. I have held various roles both in academic health policy. In England, in the United Kingdom, and of the various roles, I won't bore you with all the details. I think three of the most relevant and prominent are the fact that I was an ambassador for the Academy of Medical Royal Colleges for six years, which represented every doctor in the UK. I served a full term of six years as a trustee of the King's fund. I was the youngest member to be appointed to this body which advises government on health policy. I was a founding member of Action on Sugar and a first science director. And through that role I'm considered the lead campaigner on bringing about a sugary drinks tax in the UK. And also, finally I served for five years as visiting professor of evidence based medicine at the Bahiana School of Medicine in Salvador, Brazil. In early 2020, at the beginning of the pandemic I was most vocal doctor on the mainstream, making the link very early on between COVID and those who are vulnerable to suffering serious complications from COVID In fact, in March 2020, I was asked to go on Sky News to explain my initial research findings of the link between especially obesity and COVID, but also to give people an opportunity and to suggest to the government this was a great time for them to implement public health policy to help people enhance or optimise their immune system, which could happen within just a few weeks of dietary changes and optimising vitamin D. This was later also backed up by medical journal publications a few months later. And I was first to mention on the back of an article I published in the Daily Telegraph newspaper, which became a front page commentary and was picked up by BBC News and Good Morning Britain, where I had said that it's likely our prime minister, Boris Johnson, was hospitalised because of his weight. As a result of that, the then secretary for health, Matt Hancock, and this was publicised in the news, had asked me to advise him on the link between COVID and obesity. ...before I explain my journey and in many ways U-turn on my understanding in terms of the benefits and harms of the COVID vaccine, my experience in this area over the last couple of years has made me realise more than ever that even for that the greatest barrier to the truth are not factual or intellectual barriers, but psychological. I think all of us as human beings are vulnerable to these psychological barriers and we should have compassion for ourselves. And I will just very briefly summarise those three psychological barriers before I get into my detailed account of what I was involved in in regards to the COVID vaccine. The first psychological barrier is one of fear. And many of us understandably, and I still remember from early on in the pandemic, we were all scared. We did not know what we were dealing with. The issue with fear is that when people and populations are in a state of fear, we are less likely to engage in critical thinking and we are more likely to be compliant. Although COVID was particularly devastating for vulnerable groups in the elderly and I even have managed and still manage people with long COVID, the fear was grossly exaggerated. And one of the examples of that is that when we had good information on the mortality rate of COVID in the United States, one survey in 2020 revealed that 50% of Americans believed that if they caught COVID, the risk of 19 hospitalisation was 50% one and two, when the actual figure, certainly an average for people in middle age, was less than 1%. The second barrier to the truth, which I think is very relevant to the situation we find ourselves in now, is one called willful blindness. This is when human beings, all of us, are vulnerable to this, turn a blind eye to the truth in order to feel safe, avoid conflict, reduce anxiety and to protect prestige and fragile egos. Some examples of this include, on a personal level, willful blindness can occur when a spouse turns a blind eye to the affair of their partner. On an institutional level, some great examples of willful blindness include Hollywood and Harvey Weinstein, the Catholic Church and child molestation. I believe the current situation we find ourselves in, with much of the mainstream narrative and the medical establishment and policy makers not acknowledging quite horrific, serious and common harms from this vaccine, is another example of willful blindness. And I also say this with full empathy, because I was one of those people that was for a very long time, willfully blind to the harms of the COVID vaccine. In January 2021, I was one of the first people to take two doses of the COVID mRNA vaccine because I volunteered in a vaccine centre. I still believe that traditional vaccines are some of the safest amongst all pharmacological interventions in medicine and I could not conceive of any possibility whatsoever of this vaccine causing harm. As a public figure and respected doctor in the UK, I have built relationships across the board with many other public figures, including celebrities and politicians, who often come to me for medical advice. One of those people was film director Gurinder Chadha, who you may be familiar with some of her work, including the movie "Bend It like Beckham", who had asked me whether or not she should take the vaccine and had sent me blogs which I dismissed and regarded as anti vax nonsense. I was then asked to go on good morning, Britain because Gurinder Chadha, the director herself tweeted that I had convinced her to take the vaccine. The main reason for this TV appearance was to help tackle vaccine hesitancy, which was very prominent amongst people from ethnic minority groups in the UK. I made the point on that programme that I understand where vaccine hesitancy was coming from because of the history that I have been involved with over many years in highlighting the shortcomings of pharmaceutical industry influence over medicine. And I even made the point, if I remember correctly, that they have been found guilty of fraud on many occasions, that the third most common cause of death, prepandemic after heart disease and cancer, is prescribed medications. I, however, reassured the public and said that despite these figures, of everything we do in medicine, traditional vaccinations are amongst the safest. I still believe this to be the case. A few months later, in April 2021, I met with a colleague and friend of mine who I regard as one of the brightest cardiologists in the United Kingdom. I was surprised when he told me that he had not taken the COVID vaccine. He explained to me that he had concerns because he had seen in the supplementary appendix of Pfizer's original trial that there were four cardiac arrests in the vaccine group and only one in the placebo. These numbers were small and did not reach statistical significance. So this could be random chance, or his concern was it could represent a signal of problems in the future. And if this was the case, we are going to have a huge problem. He said he'd rather wait and see what happens before taking the vaccine. On July 26, 2021, my father, aged 73, who was a very prominent, well known doctor in the UK, including being the honorary vice president of the British Medical Association and had received honours from the Queen of England with an OBE, suffered an unexpected sudden cardiac arrest. I was particularly devastated by this happening and I was also I find it difficult to understand why my father, who was a fit and well man, I knew his cardiac history and his cardiac status, would suffer a cardiac arrest. But also my initial investigation was to try and understand why there had been a 30 minutes ambulance delay arriving to his apartment. Two weeks later, the deputy chief nurse of NHS England, a government health body, called me up. She was very upset, she knew my father very well and she was crying and she told me, Aseem, there's something I need to tell you. She in effect told me that throughout the country, for the last two months prior to my father's cardiac arrest in most regions of the UK, ambulances were not getting to patients in time for heart attacks and cardiac arrests. And there had been a deliberate, and I will use these words because I mentioned it, I've mentioned it before, a cover up involving the government and the Department of Health to withhold this information from doctors and the public. I worked with an investigative journalist with the I newspaper in the UK to write an article and a news story that became BBC News headlines a few months later, exposing this. Just before I exposed this, I messaged a professor of cardiology who I trust in the UK. He has a leadership role to explain to him what had happened and what I was about to do. I have text message evidence of this. He told me not to do this because it would make me enemies. I explained to him that I had a duty to patients and the public. I'm highlighting this as one example and I'll give you more examples of a cultural problem within medicine. The next part of this story is the post mortem findings of my father. They did not make any sense to me. I am considered a leading expert, maybe in the world, on the development and progression of coronary artery disease. My father had two severe blockages in his coronary arteries. There was no actual evidence of heart attack and likely there was a rhythm disturbance because of reduced blood supply that led to his cardiac arrest. Then in, within the space of a few weeks, around October and November, 3, different sources of information was brought to my attention that made me realise that there was probably a significant problem with the COVID mRNA vaccine. The first in October 2021. I remember I was giving lectures in Stockholm. I was contacted by a journalist with a Times newspaper who reported to me and said, Dr Malhotra, we have reports of an unexplained 25% increase in heart attacks in hospitals in Scotland and asked me what I thought was going on. I explained to her that at that time, with the evidence I knew in my own experience, I said that two likely contributory factors were lockdown stress. We know that when populations undergo severe stress after war, for example, there is an increase in heart attacks and strokes that can last for many years. She asked me whether I thought that there was a contribution. I was surprised when she asked me whether I thought there may be a contribution of the COVID vaccine to these heart attacks. I said to her, a good scientist should never exclude any possibility. But I felt at the time it was unlikely to be related to the COVID vaccine. But we should watch this space and keep our eyes open. A few weeks later, a publication appeared in the Journal Circulation, which is considered the highest impact cardiology journal in the United States that revealed a potentially very strong link between the COVID mRNA vaccines and acceleration in heart attack risk. Very specifically, in several hundred people of middle age, there was a plausible mechanism, by use of inflammatory markers in the blood, that increased the baseline risk of those people having a heart attack in five years, from 11% to 25%, just within two months of having the COVID mRNA vaccines. Of course, this is one bit of data, but even if partially true, that is a huge increase in risk in a very short space of time. And for me now made me think and link back to why my father may have suffered a cardiac arrest six months after having two doses of the vaccine. I remember thinking and speaking to a colleague, that if this was true, then we were going to see an increase in cardiac arrests, heart attacks and excess deaths in heavily vaccinated countries for the next few years. Then within a few weeks, I was called up by a whistleblower at a very prestigious british institution. I will name that institution, which I have not done publicly before as a University of Oxford. This cardiologist explained to me that a group of researchers in his department had accidentally found, through the use of very specialised imaging of the heart, that there was a signal of increased inflammation of the heart arteries, which was there in the vaccinated, but not there in the unvaccinated. The lead researcher of that group had sat down, the juniors, and had said that we are not going to explore these findings any further because it may affect our funding from the pharmaceutical industry. At that point, with these three bits of information, I then felt it was my ethical duty to speak out. And I went on GBNews to talk about what I'd found what I'd heard and I'd asked for the Vaccine Committee of the UK on TV to investigate this, to see whether there was a real problem with the vaccine in relation to heart issues. Around the same time which I found very strange is that the Secretary of State for Health at that stage, who was not Matt Hancock, was Sajid Javid, had announced in parliament that we are going to introduce legislation to ensure that all healthcare workers are mandated to have the COVID vaccine. For me, this, by that stage had no ethical or scientific justification, because certainly after the summer of 2021, it had become very apparent that the COVID mRNA vaccine was not stopping infection and it certainly was not stopping transmission. It was understood that approximately 80,000 NHS workers had refused at this stage to have the COVID vaccine. And now they were threatened with losing their job if by April the following year they had not been fully vaccinated. Many of these people were very concerned and contacted me around that time, I was also conducting many interviews, both through the BBC and Sky News and GBNews in regards to what happened with my father's ambulance delay. And I used it as an opportunity on the mainstream media to call for Sajid Javid, the secretary for health, to U-turn on the introduction of a mandate for healthcare workers based upon the fact that I felt it was not scientific and it was unethical. I also received my own personal backlash from these comments where I was contacted by the Royal College of Physicians who I had an affiliation with, and they asked me to respond to anonymous complaints from doctors that I was spreading, in quotes, antivax disinformation. I felt with my own knowledge and experience of the healthcare system that this was a direct response probably fueled by a combination of willful blindness and institutional corruption. To elaborate a bit further, when I say institutional corruption, I mean that my view was that the complaints were likely being fueled by academics with financial ties to the pharmaceutical industry. I felt very concerned about the potential introduction of the vaccine, well, the vaccine mandate. And therefore I decided there were two things that I decided to do. The first was I made a phone call to the chairman of the British Medical Association in December 2021. I had a good relationship with him and he respected my opinion. And I spent 2 hours on the phone explaining to him everything that I knew up to that stage about my concerns of the COVID mRNA vaccine. He said to me, "Aseem, nobody appears to critically appraise the evidence on the COVID mRNA vaccine as well as you have from our conversation, he said, most of my colleagues are getting their information on the benefits and harms of the vaccine from the BBC". This was replicated by the former chair of the CDC in the United States, Rochelle Walensky, who in an interview later on had said that her initial optimism of the vaccine benefits came from CNN News report. I say this just to emphasise that we should all accept our vulnerabilities to where we receive health information. Even doctors, policymakers, judges and lawyers are all influenced on the public massively by mainstream media. The chairman of the BMA also agreed with me. There was no ethical or scientific justification for mandating the COVID vaccine. He said the BMA also did not support it. And he said because of my conversation with him, he would speak directly to the secretary for health, Sajid Javid. One month later, at the end of January 2022, the COVID vaccine mandate for healthcare workers was overturned. I at that stage, given the fact that there was some backlash happening towards me, I realised that because this is a very big issue and area, and not my initial area of expertise, I needed to carry out my own critical analysis of the COVID mRNA vaccines. I spent six to nine months critically appraising the data, including speaking to two Pfizer whistleblowers, three investigative medical journalists and eminent scientists from the University of Oxford, Stanford and Harvard. The most critical bit, the most critical research that was published on this issue, which I think the whole court should acknowledge in August 2022, was published in the journal Vaccine. That research was conducted by some of the world's top independent of drug industry influence academics. That research, we was able to reanalyze the original randomised control trials conducted by Pfizer and Moderna. They were able to do this because new information was made available on the FDA's website and Health Canada's website. The conclusions of that paper were really very disturbing. The original trials that led to the drug regulatory approval of these vaccines revealed that you were more likely to suffer serious harm from taking the vaccine, specifically hospitalisation, life changing event or disability, than you were to be hospitalised with COVID That rate of harm at two months was very high at 1 in 800. Just to give you some perspective, historically we have suspended other vaccines for much less. In 1976, the swine flu vaccine was pulled because it was found to cause a neurological syndrome called Guillain-Barre syndrome In one in 100,000 people. In 1999, the rotavirus vaccine was suspended because it was found to cause a form of bowel obstruction in children affecting 1 in 10,000. This was 1 in 800. In my view, it was very clear that given this information, published in the highest impact Vaccine journal in the world, peer reviewed, and has not had any significant rebuttals, that this vaccine now, in my view, should never have been approved for use in a single human being in the first place. In my view, this very important court case in some ways, actually is a distraction from the much bigger issue, which is there should be court cases around the world with a full inquiry into the pharmaceutical industry and an inquiry as to how we got this so very wrong. Of course, one could argue this is just one bit of research, but actually, unfortunately, there are different, many different strands of research that are showing a signal of considerable and common serious harm from these vaccines. From pharmacovigilance data that is reporting what we call yellow card reports from the public. We have plausible biological mechanism of harm. We have other research called observational data. We have autopsy data also confirming that certainly with the majority of people who died within a short space of time of having the vaccine in relation to the heart, was definitively caused by the vaccine. This is really a very, very, very horrific situation we find ourselves in. One would hope and expect that the regulators should be independently evaluating all medications. But of course, the evidence reveals this is far from true. There was an investigation by the BMJ, also published in the summer of 2022, which revealed that most of the major regulators across the world were taking most of their money from the drug industry. For example, the MHRA in the UK receives 86% of its funding from the drug industry, and the FDA in America receives 65% of its funding from the drug industry, A fact that most doctors do not know. And therefore, I would not expect members of the court to know this either, is that very, very rarely do drug industry sponsored research get independently evaluated. Clinical trial data can often involve thousands of pages of information on individual patients. The drug companies hold onto that raw data. They then give summary results to the regulator, who are then paying, who have an incentive to approve the drugs, and the drugs are then approved. I made these points in my peer reviewed article published in the Journal of Insulin Resistance in September 2022, where I concluded that we should pause and investigate the issue around the COVID mRNA vaccines. I have since then been campaigning and advocating for a return to ethical evidence based medical practise around the world. Some of the clear solutions moving forward would be changes in the law that are required so that patients, doctors, members of the public can have greater confidence in the information they receive to make decisions about their health. Two very clear, low hanging fruit solutions, which are both ethical, scientific and democratic, would be that the drug industry should be allowed to develop drugs, but they shouldn't be allowed to test them themselves. And they certainly shouldn't be allowed to design their own research to and hold onto the raw data. Their information needs to be independently evaluated. One other clear solution would also be that the medical regulators, again, should not be taking any money from the industry, as this is a gross conflict of interest. I also want to highlight for people to understand the bigger picture. Prior to the pandemic, I had realised that there was a big problem with the reliability of clinical research, where invariably the results of clinical trials on all drugs sponsored by the drug industry, grossly exaggerate their safety and benefits. I have taken this information to the European Parliament, where I spoke in 2019, and I spoke to very senior politicians in the UK government. But although they were sympathetic, they felt that the issue was much bigger than them as individuals, and therefore it also needed media attention to get public awareness on the importance of such an inquiry. Before we continue with further questions, as I've been speaking for quite a long time now I'll just finish with two references just for the court and the judges to understand just how bad this problem is. Prepandemic the man who I call the Stephen Hawking of medicine is Professor John Ioannidis from the University of Stanford. The reason I call him the Stephen Hawking of Medicine is he's the most cited medical researcher in the world and is a mathematical genius. In 2006, he published a paper which was entitled why most published research findings are false. In that paper, he makes a point that the greater the financial interests in a given field, the less likely the research findings are to be true. I say this in context of the Pfizer mRNA vaccine which has made the company $100 billion. The other point that he makes in a further paper in 2017 is, again, the reason the system continues as it is is most doctors are unaware of the information they receive when they make clinical decisions has been corrupted by commercial influence. The other credible name I will mention is the editor of the Lancet, Richard Horton, who I personally know. In 2015, he wrote an article in the Lancet in relation to a secret meeting that had taken place with himself and some of the world's top medical academics. In that, he wrote that possibly half of the medical published literature may simply be untrue. And he said that science has taken a turn towards darkness. But who's going to take the first step to clean up the system? I believe in this case and in this court today, this is going to be a very pivotal potential moment in history for that first step. ---------------------- Dr Aseem Malhotra H/T: Tiina Keskimäki 🇫🇮
29:17

BOOM!!! 💥💥💥 Dr. Aseem Malhotra's testimony was delivered in the Helsinski District Court on April 12, 2024, with the understanding that any deviation from the truth would constitute perjury. This clip was immediately banned by YouTube so please share widely. I've trimmed the clip, removing the interpreter's segment for a smoother listening experience. Here's the first hour of the testimony. ---------------------------------- My name is Doctor Aseem Malhotra. I am a consultant cardiologist. I've been a qualified doctor since 2001. I have held various roles both in academic health policy. In England, in the United Kingdom, and of the various roles, I won't bore you with all the details. I think three of the most relevant and prominent are the fact that I was an ambassador for the Academy of Medical Royal Colleges for six years, which represented every doctor in the UK. I served a full term of six years as a trustee of the King's fund. I was the youngest member to be appointed to this body which advises government on health policy. I was a founding member of Action on Sugar and a first science director. And through that role I'm considered the lead campaigner on bringing about a sugary drinks tax in the UK. And also, finally I served for five years as visiting professor of evidence based medicine at the Bahiana School of Medicine in Salvador, Brazil. In early 2020, at the beginning of the pandemic I was most vocal doctor on the mainstream, making the link very early on between COVID and those who are vulnerable to suffering serious complications from COVID In fact, in March 2020, I was asked to go on Sky News to explain my initial research findings of the link between especially obesity and COVID, but also to give people an opportunity and to suggest to the government this was a great time for them to implement public health policy to help people enhance or optimise their immune system, which could happen within just a few weeks of dietary changes and optimising vitamin D. This was later also backed up by medical journal publications a few months later. And I was first to mention on the back of an article I published in the Daily Telegraph newspaper, which became a front page commentary and was picked up by BBC News and Good Morning Britain, where I had said that it's likely our prime minister, Boris Johnson, was hospitalised because of his weight. As a result of that, the then secretary for health, Matt Hancock, and this was publicised in the news, had asked me to advise him on the link between COVID and obesity. ...before I explain my journey and in many ways U-turn on my understanding in terms of the benefits and harms of the COVID vaccine, my experience in this area over the last couple of years has made me realise more than ever that even for that the greatest barrier to the truth are not factual or intellectual barriers, but psychological. I think all of us as human beings are vulnerable to these psychological barriers and we should have compassion for ourselves. And I will just very briefly summarise those three psychological barriers before I get into my detailed account of what I was involved in in regards to the COVID vaccine. The first psychological barrier is one of fear. And many of us understandably, and I still remember from early on in the pandemic, we were all scared. We did not know what we were dealing with. The issue with fear is that when people and populations are in a state of fear, we are less likely to engage in critical thinking and we are more likely to be compliant. Although COVID was particularly devastating for vulnerable groups in the elderly and I even have managed and still manage people with long COVID, the fear was grossly exaggerated. And one of the examples of that is that when we had good information on the mortality rate of COVID in the United States, one survey in 2020 revealed that 50% of Americans believed that if they caught COVID, the risk of 19 hospitalisation was 50% one and two, when the actual figure, certainly an average for people in middle age, was less than 1%. The second barrier to the truth, which I think is very relevant to the situation we find ourselves in now, is one called willful blindness. This is when human beings, all of us, are vulnerable to this, turn a blind eye to the truth in order to feel safe, avoid conflict, reduce anxiety and to protect prestige and fragile egos. Some examples of this include, on a personal level, willful blindness can occur when a spouse turns a blind eye to the affair of their partner. On an institutional level, some great examples of willful blindness include Hollywood and Harvey Weinstein, the Catholic Church and child molestation. I believe the current situation we find ourselves in, with much of the mainstream narrative and the medical establishment and policy makers not acknowledging quite horrific, serious and common harms from this vaccine, is another example of willful blindness. And I also say this with full empathy, because I was one of those people that was for a very long time, willfully blind to the harms of the COVID vaccine. In January 2021, I was one of the first people to take two doses of the COVID mRNA vaccine because I volunteered in a vaccine centre. I still believe that traditional vaccines are some of the safest amongst all pharmacological interventions in medicine and I could not conceive of any possibility whatsoever of this vaccine causing harm. As a public figure and respected doctor in the UK, I have built relationships across the board with many other public figures, including celebrities and politicians, who often come to me for medical advice. One of those people was film director Gurinder Chadha, who you may be familiar with some of her work, including the movie "Bend It like Beckham", who had asked me whether or not she should take the vaccine and had sent me blogs which I dismissed and regarded as anti vax nonsense. I was then asked to go on good morning, Britain because Gurinder Chadha, the director herself tweeted that I had convinced her to take the vaccine. The main reason for this TV appearance was to help tackle vaccine hesitancy, which was very prominent amongst people from ethnic minority groups in the UK. I made the point on that programme that I understand where vaccine hesitancy was coming from because of the history that I have been involved with over many years in highlighting the shortcomings of pharmaceutical industry influence over medicine. And I even made the point, if I remember correctly, that they have been found guilty of fraud on many occasions, that the third most common cause of death, prepandemic after heart disease and cancer, is prescribed medications. I, however, reassured the public and said that despite these figures, of everything we do in medicine, traditional vaccinations are amongst the safest. I still believe this to be the case. A few months later, in April 2021, I met with a colleague and friend of mine who I regard as one of the brightest cardiologists in the United Kingdom. I was surprised when he told me that he had not taken the COVID vaccine. He explained to me that he had concerns because he had seen in the supplementary appendix of Pfizer's original trial that there were four cardiac arrests in the vaccine group and only one in the placebo. These numbers were small and did not reach statistical significance. So this could be random chance, or his concern was it could represent a signal of problems in the future. And if this was the case, we are going to have a huge problem. He said he'd rather wait and see what happens before taking the vaccine. On July 26, 2021, my father, aged 73, who was a very prominent, well known doctor in the UK, including being the honorary vice president of the British Medical Association and had received honours from the Queen of England with an OBE, suffered an unexpected sudden cardiac arrest. I was particularly devastated by this happening and I was also I find it difficult to understand why my father, who was a fit and well man, I knew his cardiac history and his cardiac status, would suffer a cardiac arrest. But also my initial investigation was to try and understand why there had been a 30 minutes ambulance delay arriving to his apartment. Two weeks later, the deputy chief nurse of NHS England, a government health body, called me up. She was very upset, she knew my father very well and she was crying and she told me, Aseem, there's something I need to tell you. She in effect told me that throughout the country, for the last two months prior to my father's cardiac arrest in most regions of the UK, ambulances were not getting to patients in time for heart attacks and cardiac arrests. And there had been a deliberate, and I will use these words because I mentioned it, I've mentioned it before, a cover up involving the government and the Department of Health to withhold this information from doctors and the public. I worked with an investigative journalist with the I newspaper in the UK to write an article and a news story that became BBC News headlines a few months later, exposing this. Just before I exposed this, I messaged a professor of cardiology who I trust in the UK. He has a leadership role to explain to him what had happened and what I was about to do. I have text message evidence of this. He told me not to do this because it would make me enemies. I explained to him that I had a duty to patients and the public. I'm highlighting this as one example and I'll give you more examples of a cultural problem within medicine. The next part of this story is the post mortem findings of my father. They did not make any sense to me. I am considered a leading expert, maybe in the world, on the development and progression of coronary artery disease. My father had two severe blockages in his coronary arteries. There was no actual evidence of heart attack and likely there was a rhythm disturbance because of reduced blood supply that led to his cardiac arrest. Then in, within the space of a few weeks, around October and November, 3, different sources of information was brought to my attention that made me realise that there was probably a significant problem with the COVID mRNA vaccine. The first in October 2021. I remember I was giving lectures in Stockholm. I was contacted by a journalist with a Times newspaper who reported to me and said, Dr Malhotra, we have reports of an unexplained 25% increase in heart attacks in hospitals in Scotland and asked me what I thought was going on. I explained to her that at that time, with the evidence I knew in my own experience, I said that two likely contributory factors were lockdown stress. We know that when populations undergo severe stress after war, for example, there is an increase in heart attacks and strokes that can last for many years. She asked me whether I thought that there was a contribution. I was surprised when she asked me whether I thought there may be a contribution of the COVID vaccine to these heart attacks. I said to her, a good scientist should never exclude any possibility. But I felt at the time it was unlikely to be related to the COVID vaccine. But we should watch this space and keep our eyes open. A few weeks later, a publication appeared in the Journal Circulation, which is considered the highest impact cardiology journal in the United States that revealed a potentially very strong link between the COVID mRNA vaccines and acceleration in heart attack risk. Very specifically, in several hundred people of middle age, there was a plausible mechanism, by use of inflammatory markers in the blood, that increased the baseline risk of those people having a heart attack in five years, from 11% to 25%, just within two months of having the COVID mRNA vaccines. Of course, this is one bit of data, but even if partially true, that is a huge increase in risk in a very short space of time. And for me now made me think and link back to why my father may have suffered a cardiac arrest six months after having two doses of the vaccine. I remember thinking and speaking to a colleague, that if this was true, then we were going to see an increase in cardiac arrests, heart attacks and excess deaths in heavily vaccinated countries for the next few years. Then within a few weeks, I was called up by a whistleblower at a very prestigious british institution. I will name that institution, which I have not done publicly before as a University of Oxford. This cardiologist explained to me that a group of researchers in his department had accidentally found, through the use of very specialised imaging of the heart, that there was a signal of increased inflammation of the heart arteries, which was there in the vaccinated, but not there in the unvaccinated. The lead researcher of that group had sat down, the juniors, and had said that we are not going to explore these findings any further because it may affect our funding from the pharmaceutical industry. At that point, with these three bits of information, I then felt it was my ethical duty to speak out. And I went on GBNews to talk about what I'd found what I'd heard and I'd asked for the Vaccine Committee of the UK on TV to investigate this, to see whether there was a real problem with the vaccine in relation to heart issues. Around the same time which I found very strange is that the Secretary of State for Health at that stage, who was not Matt Hancock, was Sajid Javid, had announced in parliament that we are going to introduce legislation to ensure that all healthcare workers are mandated to have the COVID vaccine. For me, this, by that stage had no ethical or scientific justification, because certainly after the summer of 2021, it had become very apparent that the COVID mRNA vaccine was not stopping infection and it certainly was not stopping transmission. It was understood that approximately 80,000 NHS workers had refused at this stage to have the COVID vaccine. And now they were threatened with losing their job if by April the following year they had not been fully vaccinated. Many of these people were very concerned and contacted me around that time, I was also conducting many interviews, both through the BBC and Sky News and GBNews in regards to what happened with my father's ambulance delay. And I used it as an opportunity on the mainstream media to call for Sajid Javid, the secretary for health, to U-turn on the introduction of a mandate for healthcare workers based upon the fact that I felt it was not scientific and it was unethical. I also received my own personal backlash from these comments where I was contacted by the Royal College of Physicians who I had an affiliation with, and they asked me to respond to anonymous complaints from doctors that I was spreading, in quotes, antivax disinformation. I felt with my own knowledge and experience of the healthcare system that this was a direct response probably fueled by a combination of willful blindness and institutional corruption. To elaborate a bit further, when I say institutional corruption, I mean that my view was that the complaints were likely being fueled by academics with financial ties to the pharmaceutical industry. I felt very concerned about the potential introduction of the vaccine, well, the vaccine mandate. And therefore I decided there were two things that I decided to do. The first was I made a phone call to the chairman of the British Medical Association in December 2021. I had a good relationship with him and he respected my opinion. And I spent 2 hours on the phone explaining to him everything that I knew up to that stage about my concerns of the COVID mRNA vaccine. He said to me, "Aseem, nobody appears to critically appraise the evidence on the COVID mRNA vaccine as well as you have from our conversation, he said, most of my colleagues are getting their information on the benefits and harms of the vaccine from the BBC". This was replicated by the former chair of the CDC in the United States, Rochelle Walensky, who in an interview later on had said that her initial optimism of the vaccine benefits came from CNN News report. I say this just to emphasise that we should all accept our vulnerabilities to where we receive health information. Even doctors, policymakers, judges and lawyers are all influenced on the public massively by mainstream media. The chairman of the BMA also agreed with me. There was no ethical or scientific justification for mandating the COVID vaccine. He said the BMA also did not support it. And he said because of my conversation with him, he would speak directly to the secretary for health, Sajid Javid. One month later, at the end of January 2022, the COVID vaccine mandate for healthcare workers was overturned. I at that stage, given the fact that there was some backlash happening towards me, I realised that because this is a very big issue and area, and not my initial area of expertise, I needed to carry out my own critical analysis of the COVID mRNA vaccines. I spent six to nine months critically appraising the data, including speaking to two Pfizer whistleblowers, three investigative medical journalists and eminent scientists from the University of Oxford, Stanford and Harvard. The most critical bit, the most critical research that was published on this issue, which I think the whole court should acknowledge in August 2022, was published in the journal Vaccine. That research was conducted by some of the world's top independent of drug industry influence academics. That research, we was able to reanalyze the original randomised control trials conducted by Pfizer and Moderna. They were able to do this because new information was made available on the FDA's website and Health Canada's website. The conclusions of that paper were really very disturbing. The original trials that led to the drug regulatory approval of these vaccines revealed that you were more likely to suffer serious harm from taking the vaccine, specifically hospitalisation, life changing event or disability, than you were to be hospitalised with COVID That rate of harm at two months was very high at 1 in 800. Just to give you some perspective, historically we have suspended other vaccines for much less. In 1976, the swine flu vaccine was pulled because it was found to cause a neurological syndrome called Guillain-Barre syndrome In one in 100,000 people. In 1999, the rotavirus vaccine was suspended because it was found to cause a form of bowel obstruction in children affecting 1 in 10,000. This was 1 in 800. In my view, it was very clear that given this information, published in the highest impact Vaccine journal in the world, peer reviewed, and has not had any significant rebuttals, that this vaccine now, in my view, should never have been approved for use in a single human being in the first place. In my view, this very important court case in some ways, actually is a distraction from the much bigger issue, which is there should be court cases around the world with a full inquiry into the pharmaceutical industry and an inquiry as to how we got this so very wrong. Of course, one could argue this is just one bit of research, but actually, unfortunately, there are different, many different strands of research that are showing a signal of considerable and common serious harm from these vaccines. From pharmacovigilance data that is reporting what we call yellow card reports from the public. We have plausible biological mechanism of harm. We have other research called observational data. We have autopsy data also confirming that certainly with the majority of people who died within a short space of time of having the vaccine in relation to the heart, was definitively caused by the vaccine. This is really a very, very, very horrific situation we find ourselves in. One would hope and expect that the regulators should be independently evaluating all medications. But of course, the evidence reveals this is far from true. There was an investigation by the BMJ, also published in the summer of 2022, which revealed that most of the major regulators across the world were taking most of their money from the drug industry. For example, the MHRA in the UK receives 86% of its funding from the drug industry, and the FDA in America receives 65% of its funding from the drug industry, A fact that most doctors do not know. And therefore, I would not expect members of the court to know this either, is that very, very rarely do drug industry sponsored research get independently evaluated. Clinical trial data can often involve thousands of pages of information on individual patients. The drug companies hold onto that raw data. They then give summary results to the regulator, who are then paying, who have an incentive to approve the drugs, and the drugs are then approved. I made these points in my peer reviewed article published in the Journal of Insulin Resistance in September 2022, where I concluded that we should pause and investigate the issue around the COVID mRNA vaccines. I have since then been campaigning and advocating for a return to ethical evidence based medical practise around the world. Some of the clear solutions moving forward would be changes in the law that are required so that patients, doctors, members of the public can have greater confidence in the information they receive to make decisions about their health. Two very clear, low hanging fruit solutions, which are both ethical, scientific and democratic, would be that the drug industry should be allowed to develop drugs, but they shouldn't be allowed to test them themselves. And they certainly shouldn't be allowed to design their own research to and hold onto the raw data. Their information needs to be independently evaluated. One other clear solution would also be that the medical regulators, again, should not be taking any money from the industry, as this is a gross conflict of interest. I also want to highlight for people to understand the bigger picture. Prior to the pandemic, I had realised that there was a big problem with the reliability of clinical research, where invariably the results of clinical trials on all drugs sponsored by the drug industry, grossly exaggerate their safety and benefits. I have taken this information to the European Parliament, where I spoke in 2019, and I spoke to very senior politicians in the UK government. But although they were sympathetic, they felt that the issue was much bigger than them as individuals, and therefore it also needed media attention to get public awareness on the importance of such an inquiry. Before we continue with further questions, as I've been speaking for quite a long time now I'll just finish with two references just for the court and the judges to understand just how bad this problem is. Prepandemic the man who I call the Stephen Hawking of medicine is Professor John Ioannidis from the University of Stanford. The reason I call him the Stephen Hawking of Medicine is he's the most cited medical researcher in the world and is a mathematical genius. In 2006, he published a paper which was entitled why most published research findings are false. In that paper, he makes a point that the greater the financial interests in a given field, the less likely the research findings are to be true. I say this in context of the Pfizer mRNA vaccine which has made the company $100 billion. The other point that he makes in a further paper in 2017 is, again, the reason the system continues as it is is most doctors are unaware of the information they receive when they make clinical decisions has been corrupted by commercial influence. The other credible name I will mention is the editor of the Lancet, Richard Horton, who I personally know. In 2015, he wrote an article in the Lancet in relation to a secret meeting that had taken place with himself and some of the world's top medical academics. In that, he wrote that possibly half of the medical published literature may simply be untrue. And he said that science has taken a turn towards darkness. But who's going to take the first step to clean up the system? I believe in this case and in this court today, this is going to be a very pivotal potential moment in history for that first step. ---------------------- Dr Aseem Malhotra H/T: Tiina Keskimäki 🇫🇮

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$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.
10:20

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

102,145 просмотров • 8 месяцев назад

$NVDA $MU $SNDK $LITE PAPER OVERVIEW AND CORE CLAIMS The paper “KV Cache Transform Coding for Compact Storage in LLM Inference” introduces kvtc, a transform-coding pipeline that compresses transformer key-value (KV) caches primarily for storage and transfer in LLM serving, rather than for accelerating the per-token attention kernel during active decoding. The method combines 3 stages: (1) feature decorrelation via a PCA basis computed from a calibration dataset and reused across requests; (2) adaptive, variable-precision quantization with bit allocation solved via dynamic programming (DP), including groupwise scaling/shift overhead; and (3) lossless entropy coding (DEFLATE via nvCOMP in the reference implementation) to exploit residual redundancy after quantization. The central empirical claim is that KV tensors contain large, exploitable redundancy across heads and layers, enabling approximately 20× compression versus a 16-bit baseline with negligible degradation across a broad set of accuracy and long-context benchmarks, with materially higher compression (≥40×) available at modest quality cost in some regimes. The system claim is that such compression materially improves the economics of multi-turn, prefix-reuse serving by extending effective KV cache capacity in GPU HBM and host tiers (DRAM/NVMe) and by reducing inter-node and GPU↔host bandwidth demands, thereby improving cache hit rates and reducing time-to-first-token (TTFT) relative to recomputation when caches would otherwise be evicted. KV CACHE AS THE DOMINANT STATE VARIABLE IN INFERENCE ECONOMICS KV cache growth is linear in context length and is multiplicative in layers and attention heads, making it an increasingly dominant constraint as (a) context lengths expand, (b) models add layers and maintain large hidden dimensions, and (c) production workloads shift toward iterative and tool-augmented interactions that repeatedly reuse long prefixes. The paper uses the canonical 16-bit KV cache size formula (4·l·h·d_head·t) bytes and reports 16-bit KV cache sizes per 1K tokens of context that are already operationally large: 128MiB for Llama 3.1 8B, 160MiB for Mistral NeMo 12B, and 320MiB for Llama 3.3 70B Instruct. In binary units, these figures imply per-token KV footprints of 128KiB/token (Llama 3.1 8B), 160KiB/token (Mistral NeMo 12B), and 320KiB/token (Llama 3.3 70B Instruct) at 16-bit. For a 10K-token prompt (10×1K in the paper’s binary convention), the 16-bit KV cache sizes scale to approximately 1.25GiB (Llama 3.1 8B), 1.56GiB (Mistral NeMo 12B), and 3.13GiB (Llama 3.3 70B Instruct). These magnitudes explain why stale caches create a throughput–latency dilemma: retaining them in HBM maximizes responsiveness on future turns but crowds out concurrent sessions; evicting them forces quadratic-cost prefill recomputation and increases TTFT; offloading them to host or storage introduces large transfer overhead and consumes DRAM/NVMe capacity. A key operational nuance emphasized is that modern serving stacks increasingly treat KV caches as a database, leveraging block paging and shared-prefix reuse. In the common disaggregated serving design (separate prefill and decode nodes), KV cache transfer becomes a dominant category of cross-node traffic. Under that design, any reduction in KV cache size directly increases effective fabric capacity and reduces tail latency attributable to congestion, while also enabling longer cache lifetimes in “hot” (HBM) and “warm” (CPU DRAM) tiers that raise cache hit rates and reduce recomputation frequency. The paper’s quantitative example illustrates the economic stakes: a 1,000-line code file tokenized at ~10 tokens/line yields ~10K tokens; for Llama 3.3 70B, an 8-bit KV cache for that context is ~1.6GiB. Reuse across subsequent turns or parallel chats around the same file is valuable, but HBM scarcity makes retaining many such caches infeasible without compression. TECHNICAL MECHANISM: WHY KV CACHES ARE COMPRESSIBLE AND HOW KVTC EXPLOITS IT The technical rationale begins with an empirical observation: keys (and, to a lesser extent, values) across different attention heads can be aligned into a shared latent space using orthogonal transformations (Procrustes alignment). This supports the hypothesis that head-specific projections introduce rotations of a common subspace rather than completely distinct information, implying that concatenating across heads and layers should reveal low-rank structure suitable for linear decorrelation and dimensionality reduction. The method operationalizes this using a PCA/SVD basis learned from calibration data rather than recomputing a decomposition per prompt. This design choice targets production viability: per-prompt SVD is computationally expensive and scales poorly with long prompts and frequent cache updates. kvtc is explicitly structured as an offline-calibrated, online-applied codec: Calibration (performed 1 time per model and compression setting for DP allocation) A calibration dataset is forwarded through the model to collect KV caches. Token positions are pooled, and a subset of positions is sampled. Keys and values are processed separately. Several implementation choices are highlighted as decisive for stability: Rotary positional embeddings are effectively removed prior to compression (“undo positional rotations”), because positional rotations degrade the apparent low-rank structure of keys. “Attention sink” tokens (the earliest tokens in the sequence) and a sliding window of most recent tokens are excluded from compression because they disproportionately affect attention patterns and are empirically more sensitive to reconstruction error. Cross-layer concatenation is used: keys (or values) from multiple layers and heads at the same token position are concatenated along the feature axis to form a higher-dimensional feature vector. PCA is computed over these concatenated vectors, improving robustness relative to per-layer or per-head PCA. The PCA basis is computed via SVD of centered calibration data, using randomized SVD for scalability with a target rank cutoff. The paper reports calibration regimes of 160K tokens for several models with a 10K PCA dimension cutoff (8K for Qwen variants with fewer KV heads), selected to fit within a single 80GB H100 memory envelope and complete within minutes. A critical economic detail is that the same PCA basis can be reused across multiple compression ratios; only the DP-derived precision assignment changes per compression target. Compression (applied between inference phases) Compression operates on stored KV cache tensors, not on weights, and does not modify attention computation. The KV cache is projected into the PCA basis, quantized, packed, and then entropy-coded. Compression is positioned as a background or between-phase operation (after decoding, or between prefill and decode), executed on GPU or CPU depending on where the cache currently resides. The design intent is that compression should not sit on the critical per-token decoding path; it is a storage and transport optimization. Decompression (performed prior to reuse) Decompression reverses the entropy coding and quantization and applies the inverse PCA projection. A practical latency optimization is proposed: inverse projection can be performed layer-by-layer using submatrices of the PCA basis, allowing generation to begin before the full cache is reconstructed, reducing TTFT. Quantization and bit allocation are the core differentiators versus simpler PCA truncation. PCA provides ordered components by variance; kvtc uses DP to allocate a global bit budget across PCA coordinates (and across groups of coordinates) to minimize reconstruction error in the decorrelated domain. Groups of subsequent PCA coordinates share 16-bit shift and scale factors (a microscaling-inspired design), and the DP algorithm jointly selects group size and precision type under a bit budget, including the overhead of per-group metadata. DP commonly assigns 0 bits to many trailing PCA components, which both increases compression and provides a mechanism to trim the PCA basis to the subset of components that actually carry payload, reducing compute and storage overhead of the projection matrices in deployment. Lossless entropy coding then exploits the structure induced by quantization. DEFLATE is used in the reference implementation, and the paper emphasizes that the incremental gain from the lossless stage is content-dependent but meaningful, with an average uplift of ~1.23× on top of quantization in the reported regime. An ablation in the appendices indicates that GPU-friendly variants (GDeflate) can achieve nearly identical compression ratios (≤0.1 difference in measured cases), implying that throughput-optimized lossless codecs can likely be substituted without sacrificing meaningful compression. EMPIRICAL RESULTS: ACCURACY, COMPRESSION, AND LATENCY General-purpose 8B–12B dense models The paper evaluates Llama 3.1 8B, MN-Minitron 8B, and Mistral NeMo 12B across math/knowledge (GSM8K, MMLU) and long-context tasks (Qasper, Lost in the Middle, RULER Variable Tracking) under a simulated multi-turn regime where compression/decompression is applied periodically, with a sliding window of recent tokens excluded. A consistent pattern appears: kvtc maintains near-vanilla performance through 16× compression settings, and remains competitive at 32×, with degradation becoming task- and model-dependent at 64×, particularly on long-context retrieval metrics when compression is pushed aggressively. Selected quantitative anchor points from the paper’s standard-error table (all values are reported with the paper’s evaluation setup and token-window exclusions): Llama 3.1 8B Vanilla: GSM8K 56.8, MMLU 60.5, Qasper 40.4, LITM 99.4, RULER-VT 99.8 kvtc16×: GSM8K 56.9, MMLU 60.1, Qasper 40.7, LITM 99.3, RULER-VT 99.1 kvtc32×: GSM8K 57.8, MMLU 60.6, Qasper 39.4, LITM 99.1, RULER-VT 98.9 kvtc64×: GSM8K 57.2, MMLU 60.7, Qasper 37.8, LITM 90.2, RULER-VT 95.9 These results indicate that, for this model, long-context sensitivity emerges at 64× with meaningful drops in LITM and RULER-VT, while math/knowledge scores remain stable, implying a differential sensitivity consistent with key-vector precision being more critical for retrieval-style behavior. Mistral NeMo 12B Vanilla: GSM8K 61.9, MMLU 64.5, Qasper 38.4, LITM 99.5, RULER-VT 99.8 kvtc16×: GSM8K 62.0, MMLU 64.4, Qasper 37.6, LITM 99.8, RULER-VT 99.5 kvtc32×: GSM8K 62.2, MMLU 63.8, Qasper 37.5, LITM 99.6, RULER-VT 98.7 kvtc64×: GSM8K 61.9, MMLU 61.4, Qasper 38.0, LITM 95.3, RULER-VT 98.0 Here, degradation at 64× is visible but materially smaller than the Llama 3.1 8B LITM drop, suggesting model-architecture or training-data differences can change the tolerance envelope for aggressive KV cache distortion. MN-Minitron 8B Vanilla: GSM8K 59.1, MMLU 64.3, Qasper 38.2, LITM 99.8, RULER-VT 99.4 kvtc16×: GSM8K 60.3, MMLU 64.1, Qasper 38.6, LITM 99.3, RULER-VT 98.8 kvtc32×: GSM8K 59.1, MMLU 63.7, Qasper 37.7, LITM 86.9, RULER-VT 96.0 kvtc64×: GSM8K 57.8, MMLU 62.1, Qasper 38.1, LITM 59.5, RULER-VT 93.4 This model shows markedly higher sensitivity on LITM at 32× and 64×, despite stable short-context metrics, reinforcing that “compression safety” is not monotonic in parameter count and that pruning/distillation choices can alter KV cache redundancy or robustness. Comparisons to baselines The paper compares kvtc to quantization baselines (KIVI, GEAR, FP8) and eviction baselines (H2O, TOVA), plus an SVD-based prefill-optimization method (xKV). Across the reported tasks: Low-bit quantization methods at modest compression (2-bit KV schemes) show earlier degradation in long-context behavior than kvtc at substantially higher compression settings. Eviction methods perform poorly as generic compressors for long-context tasks, consistent with their objective function (selective pruning) being misaligned with “lossless-ish storage for reuse.” xKV shows competitive results on some tasks but a consistent underperformance on Qasper relative to kvtc and vanilla in the provided tables, consistent with method-specific distortions introduced by its decomposition regime. Reasoning models and high-variance tasks For DeepSeek-R1-distilled Qwen 2.5 reasoning models, the paper evaluates AIME 2024/2025 and LiveCodeBench coding. Results are averaged over 8 runs with large variance, but a key inference is that kvtc at ~9×–21× compression achieves broadly similar AIME scores within variance bands, while coding performance remains stable at ~9× and degrades more visibly at ~18×–21× on the 7B model. An important nuance is that smaller reasoning models already have smaller KV footprints (reported ~29KiB/token for Qwen R1 1.5B versus 131KiB/token for Llama 3.1 8B), so the economic value of aggressive KV cache compression is proportionally higher for large models and long contexts than for small models with short contexts, unless the serving system’s bottleneck is dominated by cache transfer rather than HBM capacity. Multi-GPU inference and pipeline parallel For Llama 3.3 70B Instruct run pipeline-parallel across 4 GPUs (20 layers per GPU), the paper compresses KV cache chunks independently per GPU. On MATH-500, the reported accuracy declines from 75.6 (vanilla) to 74.4 at 10× and 72.6 at 20×, with standard errors near ~1.9. NIAH and LITM remain at 100.0 for all tested ratios in that table. The paper notes that joint compression across chunks could improve accuracy for some offload scenarios but is not required for feasibility, highlighting an engineering trade-off between deployment simplicity in distributed settings and optimal global compression. Latency and TTFT economics A critical system result is the measured compression/decompression latency on an H100 for a non-fused implementation. For Mistral NeMo 12B in bfloat16: BS=8, CTX=8K: compression 379ms, decompression 267ms; vanilla recompute TTFT 3098ms; kvtc decompression TTFT 380ms BS=2, CTX=16K: compression 194ms, decompression 143ms; vanilla recompute TTFT 1780ms; kvtc decompression TTFT 208ms These measurements imply that, when a cache would otherwise be recomputed, decompressing a stored compressed cache can reduce TTFT by ~8×–9× in these scenarios, even without kernel fusion. The decomposition of runtime shows PCA projection and entropy coding as the largest contributors, implying that GPU-optimized kernels and faster GPU-native lossless codecs could reduce overhead further. The fundamental economic conclusion is that, in multi-turn settings with long prefixes, compression-induced overhead is likely dominated by the avoided prefill compute and avoided transfer overhead for uncompressed caches. KEY DEPLOYMENT-SENSITIVE DESIGN CHOICES AND FAILURE MODES Several design choices appear to be “hard requirements” rather than optional optimizations: Sink tokens and sliding window exclusions The paper’s ablations show that compressing early “sink” tokens can catastrophically degrade accuracy at high compression ratios (example: Llama 3.1 8B at 64× collapses on multiple tasks when sink tokens are compressed). Similarly, compressing the most recent tokens hurts performance, motivating a sliding window (default 128 tokens) that remains uncompressed. This introduces a predictable engineering constraint: kvtc is not a uniform compression of the full cache; it is a policy-driven, token-position-dependent codec. Production integration therefore requires correct handling of token positions, attention sinks, and window management, and these policies must be aligned with attention-kernel behavior and model-specific sink dynamics. RoPE handling Removing positional rotations prior to compression is described as important for preserving low-rank structure. In deployment, this implies that the codec must be position-aware and must invert and reapply RoPE correctly. This is an additional source of complexity relative to pure per-token quantization and is sensitive to model variants and RoPE parameterizations. Calibration set representativeness The method’s quality hinges on the PCA basis generalizing from calibration data to production data. The paper demonstrates relative stability with 160K–200K calibration tokens and explores domain shifts (general web text vs math traces vs code). Results suggest that moderate domain mismatch is tolerated at 16×–64×, while extreme compression (e.g., 256× in ablations) becomes materially more sensitive to calibration choice. In production, this implies that operators targeting the “negligible degradation” regime should be able to calibrate with broadly representative corpora, while operators targeting ultra-high compression for specialized workloads should expect tighter coupling between calibration domain and achieved quality. PCA matrix storage overhead and operational footprint A non-trivial hidden cost is the need to store PCA projection matrices per model. The paper reports that, prior to DP trimming, PCA matrices stored at 16-bit can amount to a meaningful fraction of model parameter count (examples reported: ~2.4% for Llama 3.3 70B, ~8.7% for Llama 3.1 8B). This overhead is amortized across all cached sessions for a model but competes with HBM/DRAM budgets in multi-model serving. DP-driven trimming can reduce this overhead at higher compression ratios by removing zero-bit components, but the directionality is not guaranteed at low compression ratios if many components remain active. In distributed inference (pipeline parallel), per-chunk PCA can reduce matrix sizes, but may reduce cross-layer decorrelation benefits if fewer layers are concatenated. SYSTEM-LEVEL IMPLICATIONS FOR GENERATIVE AI INFRASTRUCTURE GPU AND HBM The principal infrastructure implication is that KV cache compression at storage time targets the dominant memory allocator stressor in stateful serving: the accumulation of idle or warm conversation state. For workloads with long reusable prefixes (code assistants, enterprise agents with large system prompts, repeated RAG scaffolds, document chat), the limiting resource frequently becomes HBM reserved for KV caches rather than compute. By compressing stale caches by ~20× (or more), the same HBM budget can retain a materially larger working set of cached prefixes, increasing cache hit rates and reducing recomputation. This effect is multiplicative with cache-aware routing and prefix sharing: more prefixes can remain resident (hot or warm) and can be routed to nodes that already hold them, improving both throughput and tail latency. However, kvtc as described does not reduce the active KV cache footprint during the actual attention computation for a currently decoding sequence, because the model operates on decompressed KV caches during decoding. Therefore, the method does not directly reduce HBM bandwidth consumed by attention kernels during steady-state decode, and does not directly address the “memory traffic per generated token” bottleneck that motivates online KV quantization and eviction strategies. The primary HBM benefit is increased effective capacity for caches between turns and reduced HBM pressure from storing many idle sessions, not reduced per-token decode bandwidth. Compression and decompression themselves consume GPU compute and memory bandwidth. The measured decompression TTFT of ~208ms–380ms in the provided benchmarks indicates that the overhead is real but can be materially smaller than recomputation of long prefixes. In an HBM-constrained serving environment, this overhead can be interpreted as a trade between (a) maintaining more caches warm and paying decompression on reuse versus (b) evicting caches and paying full prefill recomputation. The decision boundary will depend on distribution of inter-turn idle times, probability of reuse, and SLA sensitivity to TTFT. kvtc expands the feasible region where keeping caches is economically rational, especially for long prompts. CPU AND DRAM The method implies a stronger role for CPU DRAM as a warm KV cache tier. A ~20× compression ratio changes the practical scale of “warm state” that can be stored per server. Using the paper’s reported KV cache sizes, a 10K-token 16-bit KV cache for Llama 3.3 70B is ~3.13GiB; compressing by ~20× would reduce this to ~160MiB. At that size, storing hundreds to thousands of warm conversation states in DRAM becomes materially more feasible, increasing cache hit rates and reducing NVMe dependence. This can shift system design from “HBM-only hot caches with aggressive eviction” toward “HBM hot + DRAM warm with long retention,” which is structurally analogous to CPU page cache hierarchies in classical systems design. CPU compute implications depend on where compression is executed. The paper explicitly allows compression on CPU if the cache is already in storage, but the strongest bandwidth savings are achieved when compression happens before moving KV caches off the GPU. If an operator chooses GPU-side compression prior to PCIe/NVLink transfer, CPU compute overhead is modest (orchestrating and DP calibration offline). If an operator instead transfers uncompressed caches to CPU for compression, bandwidth savings are forfeited and CPU memory bandwidth becomes a bottleneck. Therefore, the most economically coherent deployment path is GPU-native compression/decompression with CPU DRAM used as the warm storage reservoir.
12:09

$NVDA $MU $SNDK $LITE PAPER OVERVIEW AND CORE CLAIMS The paper “KV Cache Transform Coding for Compact Storage in LLM Inference” introduces kvtc, a transform-coding pipeline that compresses transformer key-value (KV) caches primarily for storage and transfer in LLM serving, rather than for accelerating the per-token attention kernel during active decoding. The method combines 3 stages: (1) feature decorrelation via a PCA basis computed from a calibration dataset and reused across requests; (2) adaptive, variable-precision quantization with bit allocation solved via dynamic programming (DP), including groupwise scaling/shift overhead; and (3) lossless entropy coding (DEFLATE via nvCOMP in the reference implementation) to exploit residual redundancy after quantization. The central empirical claim is that KV tensors contain large, exploitable redundancy across heads and layers, enabling approximately 20× compression versus a 16-bit baseline with negligible degradation across a broad set of accuracy and long-context benchmarks, with materially higher compression (≥40×) available at modest quality cost in some regimes. The system claim is that such compression materially improves the economics of multi-turn, prefix-reuse serving by extending effective KV cache capacity in GPU HBM and host tiers (DRAM/NVMe) and by reducing inter-node and GPU↔host bandwidth demands, thereby improving cache hit rates and reducing time-to-first-token (TTFT) relative to recomputation when caches would otherwise be evicted. KV CACHE AS THE DOMINANT STATE VARIABLE IN INFERENCE ECONOMICS KV cache growth is linear in context length and is multiplicative in layers and attention heads, making it an increasingly dominant constraint as (a) context lengths expand, (b) models add layers and maintain large hidden dimensions, and (c) production workloads shift toward iterative and tool-augmented interactions that repeatedly reuse long prefixes. The paper uses the canonical 16-bit KV cache size formula (4·l·h·d_head·t) bytes and reports 16-bit KV cache sizes per 1K tokens of context that are already operationally large: 128MiB for Llama 3.1 8B, 160MiB for Mistral NeMo 12B, and 320MiB for Llama 3.3 70B Instruct. In binary units, these figures imply per-token KV footprints of 128KiB/token (Llama 3.1 8B), 160KiB/token (Mistral NeMo 12B), and 320KiB/token (Llama 3.3 70B Instruct) at 16-bit. For a 10K-token prompt (10×1K in the paper’s binary convention), the 16-bit KV cache sizes scale to approximately 1.25GiB (Llama 3.1 8B), 1.56GiB (Mistral NeMo 12B), and 3.13GiB (Llama 3.3 70B Instruct). These magnitudes explain why stale caches create a throughput–latency dilemma: retaining them in HBM maximizes responsiveness on future turns but crowds out concurrent sessions; evicting them forces quadratic-cost prefill recomputation and increases TTFT; offloading them to host or storage introduces large transfer overhead and consumes DRAM/NVMe capacity. A key operational nuance emphasized is that modern serving stacks increasingly treat KV caches as a database, leveraging block paging and shared-prefix reuse. In the common disaggregated serving design (separate prefill and decode nodes), KV cache transfer becomes a dominant category of cross-node traffic. Under that design, any reduction in KV cache size directly increases effective fabric capacity and reduces tail latency attributable to congestion, while also enabling longer cache lifetimes in “hot” (HBM) and “warm” (CPU DRAM) tiers that raise cache hit rates and reduce recomputation frequency. The paper’s quantitative example illustrates the economic stakes: a 1,000-line code file tokenized at ~10 tokens/line yields ~10K tokens; for Llama 3.3 70B, an 8-bit KV cache for that context is ~1.6GiB. Reuse across subsequent turns or parallel chats around the same file is valuable, but HBM scarcity makes retaining many such caches infeasible without compression. TECHNICAL MECHANISM: WHY KV CACHES ARE COMPRESSIBLE AND HOW KVTC EXPLOITS IT The technical rationale begins with an empirical observation: keys (and, to a lesser extent, values) across different attention heads can be aligned into a shared latent space using orthogonal transformations (Procrustes alignment). This supports the hypothesis that head-specific projections introduce rotations of a common subspace rather than completely distinct information, implying that concatenating across heads and layers should reveal low-rank structure suitable for linear decorrelation and dimensionality reduction. The method operationalizes this using a PCA/SVD basis learned from calibration data rather than recomputing a decomposition per prompt. This design choice targets production viability: per-prompt SVD is computationally expensive and scales poorly with long prompts and frequent cache updates. kvtc is explicitly structured as an offline-calibrated, online-applied codec: Calibration (performed 1 time per model and compression setting for DP allocation) A calibration dataset is forwarded through the model to collect KV caches. Token positions are pooled, and a subset of positions is sampled. Keys and values are processed separately. Several implementation choices are highlighted as decisive for stability: Rotary positional embeddings are effectively removed prior to compression (“undo positional rotations”), because positional rotations degrade the apparent low-rank structure of keys. “Attention sink” tokens (the earliest tokens in the sequence) and a sliding window of most recent tokens are excluded from compression because they disproportionately affect attention patterns and are empirically more sensitive to reconstruction error. Cross-layer concatenation is used: keys (or values) from multiple layers and heads at the same token position are concatenated along the feature axis to form a higher-dimensional feature vector. PCA is computed over these concatenated vectors, improving robustness relative to per-layer or per-head PCA. The PCA basis is computed via SVD of centered calibration data, using randomized SVD for scalability with a target rank cutoff. The paper reports calibration regimes of 160K tokens for several models with a 10K PCA dimension cutoff (8K for Qwen variants with fewer KV heads), selected to fit within a single 80GB H100 memory envelope and complete within minutes. A critical economic detail is that the same PCA basis can be reused across multiple compression ratios; only the DP-derived precision assignment changes per compression target. Compression (applied between inference phases) Compression operates on stored KV cache tensors, not on weights, and does not modify attention computation. The KV cache is projected into the PCA basis, quantized, packed, and then entropy-coded. Compression is positioned as a background or between-phase operation (after decoding, or between prefill and decode), executed on GPU or CPU depending on where the cache currently resides. The design intent is that compression should not sit on the critical per-token decoding path; it is a storage and transport optimization. Decompression (performed prior to reuse) Decompression reverses the entropy coding and quantization and applies the inverse PCA projection. A practical latency optimization is proposed: inverse projection can be performed layer-by-layer using submatrices of the PCA basis, allowing generation to begin before the full cache is reconstructed, reducing TTFT. Quantization and bit allocation are the core differentiators versus simpler PCA truncation. PCA provides ordered components by variance; kvtc uses DP to allocate a global bit budget across PCA coordinates (and across groups of coordinates) to minimize reconstruction error in the decorrelated domain. Groups of subsequent PCA coordinates share 16-bit shift and scale factors (a microscaling-inspired design), and the DP algorithm jointly selects group size and precision type under a bit budget, including the overhead of per-group metadata. DP commonly assigns 0 bits to many trailing PCA components, which both increases compression and provides a mechanism to trim the PCA basis to the subset of components that actually carry payload, reducing compute and storage overhead of the projection matrices in deployment. Lossless entropy coding then exploits the structure induced by quantization. DEFLATE is used in the reference implementation, and the paper emphasizes that the incremental gain from the lossless stage is content-dependent but meaningful, with an average uplift of ~1.23× on top of quantization in the reported regime. An ablation in the appendices indicates that GPU-friendly variants (GDeflate) can achieve nearly identical compression ratios (≤0.1 difference in measured cases), implying that throughput-optimized lossless codecs can likely be substituted without sacrificing meaningful compression. EMPIRICAL RESULTS: ACCURACY, COMPRESSION, AND LATENCY General-purpose 8B–12B dense models The paper evaluates Llama 3.1 8B, MN-Minitron 8B, and Mistral NeMo 12B across math/knowledge (GSM8K, MMLU) and long-context tasks (Qasper, Lost in the Middle, RULER Variable Tracking) under a simulated multi-turn regime where compression/decompression is applied periodically, with a sliding window of recent tokens excluded. A consistent pattern appears: kvtc maintains near-vanilla performance through 16× compression settings, and remains competitive at 32×, with degradation becoming task- and model-dependent at 64×, particularly on long-context retrieval metrics when compression is pushed aggressively. Selected quantitative anchor points from the paper’s standard-error table (all values are reported with the paper’s evaluation setup and token-window exclusions): Llama 3.1 8B Vanilla: GSM8K 56.8, MMLU 60.5, Qasper 40.4, LITM 99.4, RULER-VT 99.8 kvtc16×: GSM8K 56.9, MMLU 60.1, Qasper 40.7, LITM 99.3, RULER-VT 99.1 kvtc32×: GSM8K 57.8, MMLU 60.6, Qasper 39.4, LITM 99.1, RULER-VT 98.9 kvtc64×: GSM8K 57.2, MMLU 60.7, Qasper 37.8, LITM 90.2, RULER-VT 95.9 These results indicate that, for this model, long-context sensitivity emerges at 64× with meaningful drops in LITM and RULER-VT, while math/knowledge scores remain stable, implying a differential sensitivity consistent with key-vector precision being more critical for retrieval-style behavior. Mistral NeMo 12B Vanilla: GSM8K 61.9, MMLU 64.5, Qasper 38.4, LITM 99.5, RULER-VT 99.8 kvtc16×: GSM8K 62.0, MMLU 64.4, Qasper 37.6, LITM 99.8, RULER-VT 99.5 kvtc32×: GSM8K 62.2, MMLU 63.8, Qasper 37.5, LITM 99.6, RULER-VT 98.7 kvtc64×: GSM8K 61.9, MMLU 61.4, Qasper 38.0, LITM 95.3, RULER-VT 98.0 Here, degradation at 64× is visible but materially smaller than the Llama 3.1 8B LITM drop, suggesting model-architecture or training-data differences can change the tolerance envelope for aggressive KV cache distortion. MN-Minitron 8B Vanilla: GSM8K 59.1, MMLU 64.3, Qasper 38.2, LITM 99.8, RULER-VT 99.4 kvtc16×: GSM8K 60.3, MMLU 64.1, Qasper 38.6, LITM 99.3, RULER-VT 98.8 kvtc32×: GSM8K 59.1, MMLU 63.7, Qasper 37.7, LITM 86.9, RULER-VT 96.0 kvtc64×: GSM8K 57.8, MMLU 62.1, Qasper 38.1, LITM 59.5, RULER-VT 93.4 This model shows markedly higher sensitivity on LITM at 32× and 64×, despite stable short-context metrics, reinforcing that “compression safety” is not monotonic in parameter count and that pruning/distillation choices can alter KV cache redundancy or robustness. Comparisons to baselines The paper compares kvtc to quantization baselines (KIVI, GEAR, FP8) and eviction baselines (H2O, TOVA), plus an SVD-based prefill-optimization method (xKV). Across the reported tasks: Low-bit quantization methods at modest compression (2-bit KV schemes) show earlier degradation in long-context behavior than kvtc at substantially higher compression settings. Eviction methods perform poorly as generic compressors for long-context tasks, consistent with their objective function (selective pruning) being misaligned with “lossless-ish storage for reuse.” xKV shows competitive results on some tasks but a consistent underperformance on Qasper relative to kvtc and vanilla in the provided tables, consistent with method-specific distortions introduced by its decomposition regime. Reasoning models and high-variance tasks For DeepSeek-R1-distilled Qwen 2.5 reasoning models, the paper evaluates AIME 2024/2025 and LiveCodeBench coding. Results are averaged over 8 runs with large variance, but a key inference is that kvtc at ~9×–21× compression achieves broadly similar AIME scores within variance bands, while coding performance remains stable at ~9× and degrades more visibly at ~18×–21× on the 7B model. An important nuance is that smaller reasoning models already have smaller KV footprints (reported ~29KiB/token for Qwen R1 1.5B versus 131KiB/token for Llama 3.1 8B), so the economic value of aggressive KV cache compression is proportionally higher for large models and long contexts than for small models with short contexts, unless the serving system’s bottleneck is dominated by cache transfer rather than HBM capacity. Multi-GPU inference and pipeline parallel For Llama 3.3 70B Instruct run pipeline-parallel across 4 GPUs (20 layers per GPU), the paper compresses KV cache chunks independently per GPU. On MATH-500, the reported accuracy declines from 75.6 (vanilla) to 74.4 at 10× and 72.6 at 20×, with standard errors near ~1.9. NIAH and LITM remain at 100.0 for all tested ratios in that table. The paper notes that joint compression across chunks could improve accuracy for some offload scenarios but is not required for feasibility, highlighting an engineering trade-off between deployment simplicity in distributed settings and optimal global compression. Latency and TTFT economics A critical system result is the measured compression/decompression latency on an H100 for a non-fused implementation. For Mistral NeMo 12B in bfloat16: BS=8, CTX=8K: compression 379ms, decompression 267ms; vanilla recompute TTFT 3098ms; kvtc decompression TTFT 380ms BS=2, CTX=16K: compression 194ms, decompression 143ms; vanilla recompute TTFT 1780ms; kvtc decompression TTFT 208ms These measurements imply that, when a cache would otherwise be recomputed, decompressing a stored compressed cache can reduce TTFT by ~8×–9× in these scenarios, even without kernel fusion. The decomposition of runtime shows PCA projection and entropy coding as the largest contributors, implying that GPU-optimized kernels and faster GPU-native lossless codecs could reduce overhead further. The fundamental economic conclusion is that, in multi-turn settings with long prefixes, compression-induced overhead is likely dominated by the avoided prefill compute and avoided transfer overhead for uncompressed caches. KEY DEPLOYMENT-SENSITIVE DESIGN CHOICES AND FAILURE MODES Several design choices appear to be “hard requirements” rather than optional optimizations: Sink tokens and sliding window exclusions The paper’s ablations show that compressing early “sink” tokens can catastrophically degrade accuracy at high compression ratios (example: Llama 3.1 8B at 64× collapses on multiple tasks when sink tokens are compressed). Similarly, compressing the most recent tokens hurts performance, motivating a sliding window (default 128 tokens) that remains uncompressed. This introduces a predictable engineering constraint: kvtc is not a uniform compression of the full cache; it is a policy-driven, token-position-dependent codec. Production integration therefore requires correct handling of token positions, attention sinks, and window management, and these policies must be aligned with attention-kernel behavior and model-specific sink dynamics. RoPE handling Removing positional rotations prior to compression is described as important for preserving low-rank structure. In deployment, this implies that the codec must be position-aware and must invert and reapply RoPE correctly. This is an additional source of complexity relative to pure per-token quantization and is sensitive to model variants and RoPE parameterizations. Calibration set representativeness The method’s quality hinges on the PCA basis generalizing from calibration data to production data. The paper demonstrates relative stability with 160K–200K calibration tokens and explores domain shifts (general web text vs math traces vs code). Results suggest that moderate domain mismatch is tolerated at 16×–64×, while extreme compression (e.g., 256× in ablations) becomes materially more sensitive to calibration choice. In production, this implies that operators targeting the “negligible degradation” regime should be able to calibrate with broadly representative corpora, while operators targeting ultra-high compression for specialized workloads should expect tighter coupling between calibration domain and achieved quality. PCA matrix storage overhead and operational footprint A non-trivial hidden cost is the need to store PCA projection matrices per model. The paper reports that, prior to DP trimming, PCA matrices stored at 16-bit can amount to a meaningful fraction of model parameter count (examples reported: ~2.4% for Llama 3.3 70B, ~8.7% for Llama 3.1 8B). This overhead is amortized across all cached sessions for a model but competes with HBM/DRAM budgets in multi-model serving. DP-driven trimming can reduce this overhead at higher compression ratios by removing zero-bit components, but the directionality is not guaranteed at low compression ratios if many components remain active. In distributed inference (pipeline parallel), per-chunk PCA can reduce matrix sizes, but may reduce cross-layer decorrelation benefits if fewer layers are concatenated. SYSTEM-LEVEL IMPLICATIONS FOR GENERATIVE AI INFRASTRUCTURE GPU AND HBM The principal infrastructure implication is that KV cache compression at storage time targets the dominant memory allocator stressor in stateful serving: the accumulation of idle or warm conversation state. For workloads with long reusable prefixes (code assistants, enterprise agents with large system prompts, repeated RAG scaffolds, document chat), the limiting resource frequently becomes HBM reserved for KV caches rather than compute. By compressing stale caches by ~20× (or more), the same HBM budget can retain a materially larger working set of cached prefixes, increasing cache hit rates and reducing recomputation. This effect is multiplicative with cache-aware routing and prefix sharing: more prefixes can remain resident (hot or warm) and can be routed to nodes that already hold them, improving both throughput and tail latency. However, kvtc as described does not reduce the active KV cache footprint during the actual attention computation for a currently decoding sequence, because the model operates on decompressed KV caches during decoding. Therefore, the method does not directly reduce HBM bandwidth consumed by attention kernels during steady-state decode, and does not directly address the “memory traffic per generated token” bottleneck that motivates online KV quantization and eviction strategies. The primary HBM benefit is increased effective capacity for caches between turns and reduced HBM pressure from storing many idle sessions, not reduced per-token decode bandwidth. Compression and decompression themselves consume GPU compute and memory bandwidth. The measured decompression TTFT of ~208ms–380ms in the provided benchmarks indicates that the overhead is real but can be materially smaller than recomputation of long prefixes. In an HBM-constrained serving environment, this overhead can be interpreted as a trade between (a) maintaining more caches warm and paying decompression on reuse versus (b) evicting caches and paying full prefill recomputation. The decision boundary will depend on distribution of inter-turn idle times, probability of reuse, and SLA sensitivity to TTFT. kvtc expands the feasible region where keeping caches is economically rational, especially for long prompts. CPU AND DRAM The method implies a stronger role for CPU DRAM as a warm KV cache tier. A ~20× compression ratio changes the practical scale of “warm state” that can be stored per server. Using the paper’s reported KV cache sizes, a 10K-token 16-bit KV cache for Llama 3.3 70B is ~3.13GiB; compressing by ~20× would reduce this to ~160MiB. At that size, storing hundreds to thousands of warm conversation states in DRAM becomes materially more feasible, increasing cache hit rates and reducing NVMe dependence. This can shift system design from “HBM-only hot caches with aggressive eviction” toward “HBM hot + DRAM warm with long retention,” which is structurally analogous to CPU page cache hierarchies in classical systems design. CPU compute implications depend on where compression is executed. The paper explicitly allows compression on CPU if the cache is already in storage, but the strongest bandwidth savings are achieved when compression happens before moving KV caches off the GPU. If an operator chooses GPU-side compression prior to PCIe/NVLink transfer, CPU compute overhead is modest (orchestrating and DP calibration offline). If an operator instead transfers uncompressed caches to CPU for compression, bandwidth savings are forfeited and CPU memory bandwidth becomes a bottleneck. Therefore, the most economically coherent deployment path is GPU-native compression/decompression with CPU DRAM used as the warm storage reservoir.

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