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🥐 fresh "U Got Options" with Ben Hunt Narratives dominate markets over fundamentals in the post-2008 era. Using AI can detect story shifts for better timing and risk control 🔑 Key Takeaways Narratives Drive Prices "Truthy" stories shape moves more than reality; fundamentals matter long-term only AI Maps Narratives...

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Ozzy

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19,362 görüntüleme • 8 ay önce •via X (Twitter)

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Markets don't move on data alone — they move on stories. The question is not just whether a narrative is right, but where it is in its lifecycle: just beginning to spread, already fully priced in, or somewhere in between. Most investors sense this intuitively, but lack a systematic way to track it. That timing is the core problem. A compelling thesis can generate alpha at the right stage and destroy it at the wrong one. The same trade that delivers triple-digit returns during its diffusion phase can become a crowded exit trap once consensus forms. Today we're launching Narrative Tracker on our full-stack research platform to tell the difference. Here's what the Play covers: Alpha Opportunities The Play identifies narratives that have reached institutional confirmation and are still generating excess returns relative to broader markets. Each opportunity is ranked by realized alpha since confirmation, with lifecycle staging that indicates how much room remains before the trade becomes consensus. Emerging Narratives Before a narrative reaches confirmation, the Play tracks it in its earliest stages — initial institutional attention and cross-channel diffusion. Each emerging narrative carries a probability score reflecting confidence that alpha will materialize, giving investors a watchlist of themes before they become crowded. Exit Watch Not every confirmed narrative stays productive. The Play monitors exit urgency across multiple risk dimensions — retail frenzy, trade crowding, price absorption, heat decay, and counter-narrative strength. When conditions deteriorate, the system flags exit priority levels so investors can reduce exposure before the reversal. News-to-Narrative Mapping Fresh catalysts are mapped to their parent narratives in real time, with signal strength ratings. Rather than scanning headlines individually, investors see which news events reinforce or challenge existing theses — and which tickers are directly impacted. Narrative Tracker is now live on FUNDA for institutional clients. For a limited time, all paid Substack subscribers can access the full set through June 5, 2026. Feedback is more than welcome.
0:21

Markets don't move on data alone — they move on stories. The question is not just whether a narrative is right, but where it is in its lifecycle: just beginning to spread, already fully priced in, or somewhere in between. Most investors sense this intuitively, but lack a systematic way to track it. That timing is the core problem. A compelling thesis can generate alpha at the right stage and destroy it at the wrong one. The same trade that delivers triple-digit returns during its diffusion phase can become a crowded exit trap once consensus forms. Today we're launching Narrative Tracker on our full-stack research platform to tell the difference. Here's what the Play covers: Alpha Opportunities The Play identifies narratives that have reached institutional confirmation and are still generating excess returns relative to broader markets. Each opportunity is ranked by realized alpha since confirmation, with lifecycle staging that indicates how much room remains before the trade becomes consensus. Emerging Narratives Before a narrative reaches confirmation, the Play tracks it in its earliest stages — initial institutional attention and cross-channel diffusion. Each emerging narrative carries a probability score reflecting confidence that alpha will materialize, giving investors a watchlist of themes before they become crowded. Exit Watch Not every confirmed narrative stays productive. The Play monitors exit urgency across multiple risk dimensions — retail frenzy, trade crowding, price absorption, heat decay, and counter-narrative strength. When conditions deteriorate, the system flags exit priority levels so investors can reduce exposure before the reversal. News-to-Narrative Mapping Fresh catalysts are mapped to their parent narratives in real time, with signal strength ratings. Rather than scanning headlines individually, investors see which news events reinforce or challenge existing theses — and which tickers are directly impacted. Narrative Tracker is now live on FUNDA for institutional clients. For a limited time, all paid Substack subscribers can access the full set through June 5, 2026. Feedback is more than welcome.

FUNDA

15,914 görüntüleme • 4 ay önce

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.
1:42:20

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.

Morgan Lai

302,663 görüntüleme • 5 ay önce

Inside Goldman Sachs' natural gas trading desk with John Knorring John Knorring — former Managing Director & Head of Natural Gas Trading at Goldman Sachs. Traded through Katrina, Amaranth's collapse & the financial crisis. Now building electricity hedging markets in the emerging markets. "We priced Lehman's entire options book — 500,000 options, 17 million vega — in 30 minutes during the collapse. At Goldman, you had to know your position at all times or you got fired." We cover: - Trading in the actual pits vs today's algo-dominated markets — why electronic trading pushed him out - How shale dropped US nat gas from $16 to $1.50 & why Henry Hub futures unlocked the entire buildout - Absorbing Lehman's book during the crisis & managing massive dislocated positions - The information edge at Goldman vs pure price-taking at DRW — what you lose without client flow - Why discretionary trading can't be fully automated (gold hitting $4,000 wasn't in any model's history) - Building Green Tiger Markets — bringing forward curves to Philippine electricity (zero public pricing until 2 years ago) ~$2B notional in hedging orders processed, liquidity still early but growing - Manila vs New York business culture — "nobody wants to be first, everyone wants to be second" Timestamps: 00:00 Intro 01:35 Leading natural gas trading at Goldman Sachs 04:06 Trading hurricanes, Amaranth, and 2008 crisis volatility 04:48 How pit trading and voice execution worked 06:21 How Goldman risk systems handled massive positions 06:55 How electronic trading transformed energy markets 08:31 Did algorithmic trading kill discretionary edge? 09:59 Why coding became essential for commodity traders 11:10 What pit-era trading psychology felt like 12:34 How Dodd-Frank changed bank trading desks 14:19 Why John left Goldman for DRW prop trading 16:16 What discretionary traders actually did at DRW 19:14 How losing client flow changed information edges 21:20 What data powered discretionary energy strategies 24:19 Can discretionary trading ever be automated? 28:45 How traders detect paradigm shifts in commodities 30:21 John’s framework: ideas, execution, money management 34:30 John’s current trades in silver and gold 34:49 Why he built Green Tiger Markets in PH 36:30 How electricity forward hedging works in emerging markets 41:52 Growth outlook for Philippines electricity hedging 45:25 Are PH market participants sophisticated enough to hedge? 48:20 Cultural realities trading and doing business in Manila 51:20 How GTM differs from ICE and CME structures 59:20 Origin story: Carlos, technology, and building GTM 1:00:34 From Goldman trader to emerging-market exchange builder
1:11:26

Inside Goldman Sachs' natural gas trading desk with John Knorring John Knorring — former Managing Director & Head of Natural Gas Trading at Goldman Sachs. Traded through Katrina, Amaranth's collapse & the financial crisis. Now building electricity hedging markets in the emerging markets. "We priced Lehman's entire options book — 500,000 options, 17 million vega — in 30 minutes during the collapse. At Goldman, you had to know your position at all times or you got fired." We cover: - Trading in the actual pits vs today's algo-dominated markets — why electronic trading pushed him out - How shale dropped US nat gas from $16 to $1.50 & why Henry Hub futures unlocked the entire buildout - Absorbing Lehman's book during the crisis & managing massive dislocated positions - The information edge at Goldman vs pure price-taking at DRW — what you lose without client flow - Why discretionary trading can't be fully automated (gold hitting $4,000 wasn't in any model's history) - Building Green Tiger Markets — bringing forward curves to Philippine electricity (zero public pricing until 2 years ago) ~$2B notional in hedging orders processed, liquidity still early but growing - Manila vs New York business culture — "nobody wants to be first, everyone wants to be second" Timestamps: 00:00 Intro 01:35 Leading natural gas trading at Goldman Sachs 04:06 Trading hurricanes, Amaranth, and 2008 crisis volatility 04:48 How pit trading and voice execution worked 06:21 How Goldman risk systems handled massive positions 06:55 How electronic trading transformed energy markets 08:31 Did algorithmic trading kill discretionary edge? 09:59 Why coding became essential for commodity traders 11:10 What pit-era trading psychology felt like 12:34 How Dodd-Frank changed bank trading desks 14:19 Why John left Goldman for DRW prop trading 16:16 What discretionary traders actually did at DRW 19:14 How losing client flow changed information edges 21:20 What data powered discretionary energy strategies 24:19 Can discretionary trading ever be automated? 28:45 How traders detect paradigm shifts in commodities 30:21 John’s framework: ideas, execution, money management 34:30 John’s current trades in silver and gold 34:49 Why he built Green Tiger Markets in PH 36:30 How electricity forward hedging works in emerging markets 41:52 Growth outlook for Philippines electricity hedging 45:25 Are PH market participants sophisticated enough to hedge? 48:20 Cultural realities trading and doing business in Manila 51:20 How GTM differs from ICE and CME structures 59:20 Origin story: Carlos, technology, and building GTM 1:00:34 From Goldman trader to emerging-market exchange builder

Ethan Kho

70,455 görüntüleme • 7 ay önce

The rise of the disinformation-for-hire industry The emergence of a global, large-scale disinformation industry has privatised influence operations, granting states strategic reach with plausible deniability. A quiet revolution has taken place in the world of propaganda. Operations that used to be run by authoritarian governments and intelligence agencies are now outsourced to private firms that sell disinformation and deception as a service. From fake social-media armies to AI-driven smear campaigns, disinformation and Foreign Information Manipulation and Interference (FIMI) have become a global business, giving authoritarian regimes new ways to influence others – and to deny everything. From state propaganda to disinformation for hire For decades, information operations were tightly controlled by states. The Soviet Union perfected the craft of dezinformatsiya; later, Russia institutionalised it through modern digital operations such as the Internet Research Agency (IRA)(opens in a new tab). But over the past decade, this model has commercialised. Disinformation and deception have become a for-profit service offered by companies with intelligence, military, or marketing backgrounds. These firms, operating around the world, sell complete FIMI campaign packages that include fake social-media campaigns, hacking, data leaks, and ‘narrative management’ in order to spread false and manipulated content in democratic countries. Outsourcing as a shield This outsourcing provides both efficiency and deniability. Authoritarian states are now actively trying to externalise information operations to private intermediaries, while shielding themselves from diplomatic and legal consequences. Through this model, malign actors can also experiment with risky tactics such as AI-generated content, hacking, or deepfakes – operations that would be politically or diplomatically explosive if carried out directly by state institutions. In doing so, they can target foreign populations through tailored influence campaigns while maintaining plausible deniability by claiming no connection to the private entities running them. Outsourcing also enables information laundering — hiding the true origin of disinformation by passing it through private firms, fake accounts, and proxy media. As these actors repeat and amplify the message, it begins to look organic and locally produced. This lets malign actors spread targeted narratives while denying any involvement. All this is the informational equivalent of using mercenaries: the client enjoys the results without bearing the blame. Team Jorge and the commercialisation of deception The 2023 Forbidden Stories investigation(opens in a new tab) into an entity called ‘Team Jorge’ exposed the inner workings of this new influence-for-hire ecosystem. The firm claimed to have interfered in 33 presidential elections, winning 27 of them. Its clients included political parties, corporations, and, allegedly, state-linked actors. At the heart of Team Jorge’s system was Advanced Impact Media Solutions (AIMS), software capable of creating and coordinating thousands of fake social-media accounts, complete with synthetic photos, biographies, and backstories. These avatars could be mobilised to flood debates, spread narratives, or harass opponents. Russia continues to be a major player in this outsourced ecosystem. Privately owned companies such as the Social Design Agency (SDA)(opens in a new tab) and Structura(opens in a new tab) now run large-scale influence operations that mirror, and in many ways replace, the functions of the old St. Petersburg troll factories. These firms manage covert online assets, push state-aligned narratives, and provide the Kremlin with an additional layer of deniability. Undercover journalists recorded the firm demonstrating hacking techniques, media infiltration, and the planting of fabricated news stories. The scale of these operations and their accessibility to paying clients revealed how disinformation has become a global commodity. Hybrid operations: where online meets offline Modern influence campaigns no longer live solely online but operate in the hybrid space between digital and physical realities. The Internet Research Agency (IRA)(opens in a new tab) demonstrated this during the 2016 US election when Russian operatives posing as American activists organised real-world rallies, paid participants, and coordinated online amplification around them. What began as meme warfare ended as physical mobilisation.(opens in a new tab) Today’s hybrid operations blend hacking(opens in a new tab), covertly funded local influencers(opens in a new tab), and covert media fronts. Campaign operators build credible-seeming news sites(opens in a new tab) and influencer personas(opens in a new tab) to insert tailored narratives into the public sphere. Once in circulation, these narratives mix with authentic content and spread across both digital and traditional media, making manipulation difficult to detect. Automation and AI: the new force multiplier The original troll-farm model – hundreds of young workers posting manually in shifts – is being replaced by AI-driven automation(opens in a new tab). Systems like Team Jorge’s AIMS, or newer tools powered by large language models, can now manage thousands of fake accounts and generate multilingual content tailored to target audiences in real time(opens in a new tab). AI allows campaigns that once required hundreds of people to be run by a handful of operators or even a single individual. What once took a troll farm and a whole building in St. Petersburg now takes a laptop. Asymmetrical information warfare The emergence of these influence-for-hire firms has created a new strategic imbalance – asymmetrical information warfare. In this asymmetry, autocracies enjoy maximum reach with minimal risk. At home, they are protected by censorship, control, and deniability. Democracies, however, are more exposed. Bound by transparency and law, they face maximum vulnerability with limited defences. This imbalance is not just political, but structural. Authoritarian regimes can use disinformation and AI tools to shape global narratives, influence elections abroad, and undermine trust while trying to avoid direct accountability. Democracies, meanwhile, must play defence on open networks designed for free expression. The stakes for democracy and the road ahead These operations are already reshaping political realities. Influence-for-hire firms have targeted elections in Africa, Europe, and Latin America(opens in a new tab). Disinformation campaigns amplify polarisation, delegitimise media institutions, and exploit social divisions to weaken democratic cohesion. The marketisation of disinformation risks creating a global grey zone where truth is optional and accountability elusive. As AI tools become cheaper and more capable, these operations will likely only grow in scale and sophistication. Recognising this asymmetry and responding with resilience and regulation is the only way to prevent truth itself from becoming a commodity.
0:45

The rise of the disinformation-for-hire industry The emergence of a global, large-scale disinformation industry has privatised influence operations, granting states strategic reach with plausible deniability. A quiet revolution has taken place in the world of propaganda. Operations that used to be run by authoritarian governments and intelligence agencies are now outsourced to private firms that sell disinformation and deception as a service. From fake social-media armies to AI-driven smear campaigns, disinformation and Foreign Information Manipulation and Interference (FIMI) have become a global business, giving authoritarian regimes new ways to influence others – and to deny everything. From state propaganda to disinformation for hire For decades, information operations were tightly controlled by states. The Soviet Union perfected the craft of dezinformatsiya; later, Russia institutionalised it through modern digital operations such as the Internet Research Agency (IRA)(opens in a new tab). But over the past decade, this model has commercialised. Disinformation and deception have become a for-profit service offered by companies with intelligence, military, or marketing backgrounds. These firms, operating around the world, sell complete FIMI campaign packages that include fake social-media campaigns, hacking, data leaks, and ‘narrative management’ in order to spread false and manipulated content in democratic countries. Outsourcing as a shield This outsourcing provides both efficiency and deniability. Authoritarian states are now actively trying to externalise information operations to private intermediaries, while shielding themselves from diplomatic and legal consequences. Through this model, malign actors can also experiment with risky tactics such as AI-generated content, hacking, or deepfakes – operations that would be politically or diplomatically explosive if carried out directly by state institutions. In doing so, they can target foreign populations through tailored influence campaigns while maintaining plausible deniability by claiming no connection to the private entities running them. Outsourcing also enables information laundering — hiding the true origin of disinformation by passing it through private firms, fake accounts, and proxy media. As these actors repeat and amplify the message, it begins to look organic and locally produced. This lets malign actors spread targeted narratives while denying any involvement. All this is the informational equivalent of using mercenaries: the client enjoys the results without bearing the blame. Team Jorge and the commercialisation of deception The 2023 Forbidden Stories investigation(opens in a new tab) into an entity called ‘Team Jorge’ exposed the inner workings of this new influence-for-hire ecosystem. The firm claimed to have interfered in 33 presidential elections, winning 27 of them. Its clients included political parties, corporations, and, allegedly, state-linked actors. At the heart of Team Jorge’s system was Advanced Impact Media Solutions (AIMS), software capable of creating and coordinating thousands of fake social-media accounts, complete with synthetic photos, biographies, and backstories. These avatars could be mobilised to flood debates, spread narratives, or harass opponents. Russia continues to be a major player in this outsourced ecosystem. Privately owned companies such as the Social Design Agency (SDA)(opens in a new tab) and Structura(opens in a new tab) now run large-scale influence operations that mirror, and in many ways replace, the functions of the old St. Petersburg troll factories. These firms manage covert online assets, push state-aligned narratives, and provide the Kremlin with an additional layer of deniability. Undercover journalists recorded the firm demonstrating hacking techniques, media infiltration, and the planting of fabricated news stories. The scale of these operations and their accessibility to paying clients revealed how disinformation has become a global commodity. Hybrid operations: where online meets offline Modern influence campaigns no longer live solely online but operate in the hybrid space between digital and physical realities. The Internet Research Agency (IRA)(opens in a new tab) demonstrated this during the 2016 US election when Russian operatives posing as American activists organised real-world rallies, paid participants, and coordinated online amplification around them. What began as meme warfare ended as physical mobilisation.(opens in a new tab) Today’s hybrid operations blend hacking(opens in a new tab), covertly funded local influencers(opens in a new tab), and covert media fronts. Campaign operators build credible-seeming news sites(opens in a new tab) and influencer personas(opens in a new tab) to insert tailored narratives into the public sphere. Once in circulation, these narratives mix with authentic content and spread across both digital and traditional media, making manipulation difficult to detect. Automation and AI: the new force multiplier The original troll-farm model – hundreds of young workers posting manually in shifts – is being replaced by AI-driven automation(opens in a new tab). Systems like Team Jorge’s AIMS, or newer tools powered by large language models, can now manage thousands of fake accounts and generate multilingual content tailored to target audiences in real time(opens in a new tab). AI allows campaigns that once required hundreds of people to be run by a handful of operators or even a single individual. What once took a troll farm and a whole building in St. Petersburg now takes a laptop. Asymmetrical information warfare The emergence of these influence-for-hire firms has created a new strategic imbalance – asymmetrical information warfare. In this asymmetry, autocracies enjoy maximum reach with minimal risk. At home, they are protected by censorship, control, and deniability. Democracies, however, are more exposed. Bound by transparency and law, they face maximum vulnerability with limited defences. This imbalance is not just political, but structural. Authoritarian regimes can use disinformation and AI tools to shape global narratives, influence elections abroad, and undermine trust while trying to avoid direct accountability. Democracies, meanwhile, must play defence on open networks designed for free expression. The stakes for democracy and the road ahead These operations are already reshaping political realities. Influence-for-hire firms have targeted elections in Africa, Europe, and Latin America(opens in a new tab). Disinformation campaigns amplify polarisation, delegitimise media institutions, and exploit social divisions to weaken democratic cohesion. The marketisation of disinformation risks creating a global grey zone where truth is optional and accountability elusive. As AI tools become cheaper and more capable, these operations will likely only grow in scale and sophistication. Recognising this asymmetry and responding with resilience and regulation is the only way to prevent truth itself from becoming a commodity.

EUvsDisinfo

35,180 görüntüleme • 10 ay önce

💰 A TRADER just ROBBED POLYMARKET for $600,000! Started with ONLY $500 and EXPLOITED the BITCOIN PRICE DELAY - placing bets BEFORE the odds UPDATED! $500 → $600K in hours PURE GENIUS HACK! Who’s NEXT?! Trader Turns $500 into $600,000 on Polymarket in Hours – The Bitcoin Price Delay “Life Hack” That’s Going Viral A mysterious trader may have just pulled off what many in the crypto world are calling the ultimate prediction‑market coup — turning a modest $500 into roughly $600,000 in a single session by exploiting a subtle timing gap in Bitcoin price updates on Polymarket. Social posts that have erupted across X today describe how the trader repeatedly capitalised on a tiny latency in Polymarket’s short‑term Bitcoin markets, placing bets a split second before the platform adjusted prices to reflect real‑time market moves. How the “Glitch in the Matrix” Worked Polymarket’s popular ultra‑short “Bitcoin Up or Down” markets, which resolve in 5‑ or 15‑minute intervals, pull price data from major exchanges such as Binance. There is a slight delay — typically a few hundred milliseconds to a couple of seconds — between live exchange price movements and when those changes are reflected in Polymarket’s odds. According to observers, the trader (widely believed to be operating an automated bot) exploited this delay by watching the live price on external feeds, submitting bets on the direction of Bitcoin before Polymarket’s internal prices had fully adjusted, and repeating the process many times in rapid succession. One viral post captured the dynamic this way: “He was seeing the move a fraction of a second earlier, basically seeing the future. He entered positions before Polymarket odds could adjust. Over and over again. Not luck. Just speed.” Another read: “This is a man who found a glitch in the matrix and ran it until someone noticed.” Screenshots and wallet growth charts circulating online show an account balance rocketing from an initial $500 to mid‑six figures over a few hours, a dramatic ascent that has captured widespread attention. Not the First Time — But Still Wildly Profitable Polymarket has previously acknowledged the challenges of ultra‑short crypto markets and in early 2026 introduced dynamic fees intended to reduce latency arbitrage opportunities. The recent run suggests either the fix was incomplete or the trader has found a new way to capture fleeting price disparities faster than the protocol can adjust. Importantly, this was not a smart‑contract exploit or a rug pull. The activity described aligns with classic high‑frequency trading principles: exploit microsecond advantages in price information and execution. On Wall Street, quant funds have built entire businesses around similar edges. Here, that edge allegedly appeared accessible to an individual with sufficiently fast infrastructure. The Internet’s Reaction The story has ignited discussion across X, with both Russian and English accounts sharing wallet screenshots and short videos of the trades. Commentary ranges from admiration to criticism of the Polymarket mechanism. One widely shared post states: “A trader found a loophole and robbed the bookmaker Polymarket for $600,000. He exploited the delay in Bitcoin price updates… Started with just $500.” Replies to that thread span “genius” and “Polymarket is cooked,” reflecting a split between those whispering admiration for the execution and those questioning the sustainability of the platform’s design. Whether Polymarket will attempt to retroactively claw back profits, adjust rules or simply patch the vulnerability again remains unsettled. For the moment, the anonymous trader is alternately being hailed as a folk hero and criticised as a parasite of an imperfect system. Moral of the Story In prediction markets where time and information intersect, speed can be money. Today’s episode may turn into a cautionary tale about the refinement of oracle feeds and the race to eliminate latency advantages — or it may become part of crypto folklore, a dramatic demonstration of how imperfect markets can be bent by those who read the technical seams most closely.
0:23

💰 A TRADER just ROBBED POLYMARKET for $600,000! Started with ONLY $500 and EXPLOITED the BITCOIN PRICE DELAY - placing bets BEFORE the odds UPDATED! $500 → $600K in hours PURE GENIUS HACK! Who’s NEXT?! Trader Turns $500 into $600,000 on Polymarket in Hours – The Bitcoin Price Delay “Life Hack” That’s Going Viral A mysterious trader may have just pulled off what many in the crypto world are calling the ultimate prediction‑market coup — turning a modest $500 into roughly $600,000 in a single session by exploiting a subtle timing gap in Bitcoin price updates on Polymarket. Social posts that have erupted across X today describe how the trader repeatedly capitalised on a tiny latency in Polymarket’s short‑term Bitcoin markets, placing bets a split second before the platform adjusted prices to reflect real‑time market moves. How the “Glitch in the Matrix” Worked Polymarket’s popular ultra‑short “Bitcoin Up or Down” markets, which resolve in 5‑ or 15‑minute intervals, pull price data from major exchanges such as Binance. There is a slight delay — typically a few hundred milliseconds to a couple of seconds — between live exchange price movements and when those changes are reflected in Polymarket’s odds. According to observers, the trader (widely believed to be operating an automated bot) exploited this delay by watching the live price on external feeds, submitting bets on the direction of Bitcoin before Polymarket’s internal prices had fully adjusted, and repeating the process many times in rapid succession. One viral post captured the dynamic this way: “He was seeing the move a fraction of a second earlier, basically seeing the future. He entered positions before Polymarket odds could adjust. Over and over again. Not luck. Just speed.” Another read: “This is a man who found a glitch in the matrix and ran it until someone noticed.” Screenshots and wallet growth charts circulating online show an account balance rocketing from an initial $500 to mid‑six figures over a few hours, a dramatic ascent that has captured widespread attention. Not the First Time — But Still Wildly Profitable Polymarket has previously acknowledged the challenges of ultra‑short crypto markets and in early 2026 introduced dynamic fees intended to reduce latency arbitrage opportunities. The recent run suggests either the fix was incomplete or the trader has found a new way to capture fleeting price disparities faster than the protocol can adjust. Importantly, this was not a smart‑contract exploit or a rug pull. The activity described aligns with classic high‑frequency trading principles: exploit microsecond advantages in price information and execution. On Wall Street, quant funds have built entire businesses around similar edges. Here, that edge allegedly appeared accessible to an individual with sufficiently fast infrastructure. The Internet’s Reaction The story has ignited discussion across X, with both Russian and English accounts sharing wallet screenshots and short videos of the trades. Commentary ranges from admiration to criticism of the Polymarket mechanism. One widely shared post states: “A trader found a loophole and robbed the bookmaker Polymarket for $600,000. He exploited the delay in Bitcoin price updates… Started with just $500.” Replies to that thread span “genius” and “Polymarket is cooked,” reflecting a split between those whispering admiration for the execution and those questioning the sustainability of the platform’s design. Whether Polymarket will attempt to retroactively claw back profits, adjust rules or simply patch the vulnerability again remains unsettled. For the moment, the anonymous trader is alternately being hailed as a folk hero and criticised as a parasite of an imperfect system. Moral of the Story In prediction markets where time and information intersect, speed can be money. Today’s episode may turn into a cautionary tale about the refinement of oracle feeds and the race to eliminate latency advantages — or it may become part of crypto folklore, a dramatic demonstration of how imperfect markets can be bent by those who read the technical seams most closely.

Russian Market

36,649 görüntüleme • 6 ay önce

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

328,397 görüntüleme • 4 ay önce

$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

120,774 görüntüleme • 11 ay önce

It’s taken nearly a full year of my life - First $LMND, soon everything else. I’ve finally launched IronicApe - not another data provider but what will become (overtime) the world’s leading digital analyst, 100x cheaper and 100x faster. I built it around a simple belief: data is only useful when you understand the context, the source, the business and why the number matters. It brings together narrated earnings updates, company “stories”, operating performance, ownership, fundamentals, market sentiment, technical analysis, valuation, SEC filings, source checks, point-in-time backtesting, community research and Cortex - our map of the drivers and dependencies behind a business… a business is not GAAP accounting, everything connects to everything else and every reaction has an equal and opposite reaction. The walk-forward Machine learning model moves through history one reporting period at a time, using only information available at that point (blind) - It forecasts, compares the result with what actually happened and learns which signals deserve more weight - without quietly learning from the future. It tests possible causal relationships; it doesn’t pretend correlation is proof. I’ve tried my best to make everything understandable for retail investors while retaining enough technical depth, granular analysis and transparent evidence to be useful to leading analysts, all financial data is deterministically retrieved and source backed. Portfolio management (that’s actually useful), a company screener and a much more comprehensive Learning Academy are coming soon… IronicApe is meant for all investors no matter their age, background, education or experience - longer term it will help take a first time investor from understanding the terminology most with years of experience just know, what is revenue or gross margin all the way to complex MBA level knowledge and beyond. For now, please go forth and try it. Find every bug, missing feature, confusing explanation, bad assumption and change you think it needs. Honest feedback is very welcome! If you read this far, fair play! There’s also 10 hidden Paper Bag Investor hidden around the app. I had Ai place them, even I don’t know where they all are, I’ve only managed to find 5 so far! 😅 Together, we can overtime level the playing field with Wall street and democratise financial analysis. I turned down a 7 figure investment to build this for the retail community. It’s not perfect, nothing is - but my commitment to you all is to never stop pushing the boundary of what’s possible.. to iterative, innovate and strive to win together!! And while it’s not perfect, I can tell you this for free… it’s a fu*k load better than Book value per share!!! 🍋 It has real cost to maintain - I’m not yet eligible to monetise from X and I really DON’T want to charge for this. So if you find value in it, please like, share and follow. I also appreciate I post a lot of stuff that people may not want to follow to get access, so I’ll be adding a non follower paid subscription and free ad’s plan soon. For now, when you link your X account it checks follower status to unlock content, it uses the least access x provides to check follower status - I don’t even get your email. Apes Stronger Together!
12:59

It’s taken nearly a full year of my life - First $LMND, soon everything else. I’ve finally launched IronicApe - not another data provider but what will become (overtime) the world’s leading digital analyst, 100x cheaper and 100x faster. I built it around a simple belief: data is only useful when you understand the context, the source, the business and why the number matters. It brings together narrated earnings updates, company “stories”, operating performance, ownership, fundamentals, market sentiment, technical analysis, valuation, SEC filings, source checks, point-in-time backtesting, community research and Cortex - our map of the drivers and dependencies behind a business… a business is not GAAP accounting, everything connects to everything else and every reaction has an equal and opposite reaction. The walk-forward Machine learning model moves through history one reporting period at a time, using only information available at that point (blind) - It forecasts, compares the result with what actually happened and learns which signals deserve more weight - without quietly learning from the future. It tests possible causal relationships; it doesn’t pretend correlation is proof. I’ve tried my best to make everything understandable for retail investors while retaining enough technical depth, granular analysis and transparent evidence to be useful to leading analysts, all financial data is deterministically retrieved and source backed. Portfolio management (that’s actually useful), a company screener and a much more comprehensive Learning Academy are coming soon… IronicApe is meant for all investors no matter their age, background, education or experience - longer term it will help take a first time investor from understanding the terminology most with years of experience just know, what is revenue or gross margin all the way to complex MBA level knowledge and beyond. For now, please go forth and try it. Find every bug, missing feature, confusing explanation, bad assumption and change you think it needs. Honest feedback is very welcome! If you read this far, fair play! There’s also 10 hidden Paper Bag Investor hidden around the app. I had Ai place them, even I don’t know where they all are, I’ve only managed to find 5 so far! 😅 Together, we can overtime level the playing field with Wall street and democratise financial analysis. I turned down a 7 figure investment to build this for the retail community. It’s not perfect, nothing is - but my commitment to you all is to never stop pushing the boundary of what’s possible.. to iterative, innovate and strive to win together!! And while it’s not perfect, I can tell you this for free… it’s a fu*k load better than Book value per share!!! 🍋 It has real cost to maintain - I’m not yet eligible to monetise from X and I really DON’T want to charge for this. So if you find value in it, please like, share and follow. I also appreciate I post a lot of stuff that people may not want to follow to get access, so I’ll be adding a non follower paid subscription and free ad’s plan soon. For now, when you link your X account it checks follower status to unlock content, it uses the least access x provides to check follower status - I don’t even get your email. Apes Stronger Together!

Ironic Ape

29,916 görüntüleme • 1 ay önce

15 YEARS OF TRADING ADVICE I started trading in 2011, and my first lesson was brutal but simple: trust the process and focus on getting better every day. My start was awful. Real-time trading was fast, chaotic, and unforgiving. I hesitated on entries, chased moves, and held losers far past my stop. I made the same mistakes over and over. Before long, I was at the back of my training class at Trillium Trading. Progress didn’t come from trading more — it came when I stepped back and built a framework for improvement: deliberate practice, structured review, and systemizing my work. I wasn’t making money yet, but my win rate and consistency were improving. That mattered. In 2012, I learned something that changed everything: most profits come from very few opportunities. I coined the “Broken Slot Machine” concept and drastically narrowed my focus to in-play stocks — names with exceptional volume, volatility, or fresh news. Cutting out noise was a turning point. I still trade this way today. 2013 was my breakthrough year. I stopped chasing complexity and built a small playbook of “easy money” trades — simple, repeatable setups with defined risk. A few mean-reversion plays. Small, consistent wins. Fewer unforced errors. That’s when my PNL finally stabilized and my confidence started compounding. In 2014, I nearly blew up my career. A fat-tail after-hours loss wiped out over $100K in minutes. That drawdown forced me to build strict drawdown protocols — daily, weekly, and monthly loss limits with zero exceptions. Those rules didn’t just save my career — they’ve saved countless others since. By 2015, I shifted my mindset away from obsessing over PNL and toward improving expected value every day — what I now call the Bobblehead Concept. Red days became feedback. Growth became the metric. That mindset carried me through the emotional swings of trading. In 2016, I learned how much environment matters. Trading isn’t purely an individual sport. Being surrounded by serious traders — sharing ideas, accountability, and lessons — accelerated growth faster than anything else. In 2017, I adopted the Daily Report Card habit. Daily reflection. One improvement at a time. Treating trading like a professional athlete reviewing game film. This single habit was foundational to everything that followed. In 2018, I stopped trying to be early. I learned to wait for the right side of the V — letting moves exhaust before entering. Risk dropped. Win rate rose. Size increased. Being right mattered more than being first. From 2019 onward, it was about 1% improvements, adaptability, and pressing size only when edge was extreme. 2020 rewarded preparation. After almost 10 years of trading, COVID hit and it was a day trader's dream market. Most traders scale linearly, but the real outlier performance comes from identifying when you have an A+ setup and absolutely maximizing it. Doing so allowed me to have my first year making over $10m in PNL. By 2021, my technical skills were dialed in. What mattered most was psychology and performance optimization. I realized that how I managed my mental game determined how consistently I could execute at the highest level. Once you have edge, your mental state is what determines how consistently you stick to your system and capture the opportunities in front of you. Strong mental game allows you to avoid FOMO, ragetrading, and other bad habits that leech our PNL. 2022 the markets slowed down and punished many that didn't adapt. I realized that survival in trading isn’t about always being aggressive—it’s about trading based on what the market requires of you. The best traders don’t try to force the market to fit them, they fit their trading to the market and evolve faster than everyone else. 2023 I started allocating more energy toward swing trading—holding positions for days rather than seconds or minutes. This, coupled with selling options premium, gave me the space to step back, zoom out, and build a more sustainable lifestyle. I was no longer glued to the screen 10 hours a day. It reminded me that trading is a means to an end. 2024, I pushed myself to go beyond stocks. I dove deeper into options, futures, and overnight trading. Each new product came with its own quirks, nuances, and edge opportunities. Learning these instruments wasn’t just about more tools—it was about building a truly diversified and adaptable trading approach that maximizes your expected value based on the situation. At the elite level, the more markets and products you open yourself up to, the more opportunities you have to profit and the more mediums you have to express your trade in the most effective way possible. Now at the end of 2025, after 15 years, the biggest lesson is this: There’s no finish line in this game. The best traders stay curious. They stay hungry. And no matter how good you get, if you’re going to stay relevant in markets, you need to always be learning. I'm excited to see what 2026 will bring and I wish you all nothing but health, happiness, and the success you all are looking for.
14:26

15 YEARS OF TRADING ADVICE I started trading in 2011, and my first lesson was brutal but simple: trust the process and focus on getting better every day. My start was awful. Real-time trading was fast, chaotic, and unforgiving. I hesitated on entries, chased moves, and held losers far past my stop. I made the same mistakes over and over. Before long, I was at the back of my training class at Trillium Trading. Progress didn’t come from trading more — it came when I stepped back and built a framework for improvement: deliberate practice, structured review, and systemizing my work. I wasn’t making money yet, but my win rate and consistency were improving. That mattered. In 2012, I learned something that changed everything: most profits come from very few opportunities. I coined the “Broken Slot Machine” concept and drastically narrowed my focus to in-play stocks — names with exceptional volume, volatility, or fresh news. Cutting out noise was a turning point. I still trade this way today. 2013 was my breakthrough year. I stopped chasing complexity and built a small playbook of “easy money” trades — simple, repeatable setups with defined risk. A few mean-reversion plays. Small, consistent wins. Fewer unforced errors. That’s when my PNL finally stabilized and my confidence started compounding. In 2014, I nearly blew up my career. A fat-tail after-hours loss wiped out over $100K in minutes. That drawdown forced me to build strict drawdown protocols — daily, weekly, and monthly loss limits with zero exceptions. Those rules didn’t just save my career — they’ve saved countless others since. By 2015, I shifted my mindset away from obsessing over PNL and toward improving expected value every day — what I now call the Bobblehead Concept. Red days became feedback. Growth became the metric. That mindset carried me through the emotional swings of trading. In 2016, I learned how much environment matters. Trading isn’t purely an individual sport. Being surrounded by serious traders — sharing ideas, accountability, and lessons — accelerated growth faster than anything else. In 2017, I adopted the Daily Report Card habit. Daily reflection. One improvement at a time. Treating trading like a professional athlete reviewing game film. This single habit was foundational to everything that followed. In 2018, I stopped trying to be early. I learned to wait for the right side of the V — letting moves exhaust before entering. Risk dropped. Win rate rose. Size increased. Being right mattered more than being first. From 2019 onward, it was about 1% improvements, adaptability, and pressing size only when edge was extreme. 2020 rewarded preparation. After almost 10 years of trading, COVID hit and it was a day trader's dream market. Most traders scale linearly, but the real outlier performance comes from identifying when you have an A+ setup and absolutely maximizing it. Doing so allowed me to have my first year making over $10m in PNL. By 2021, my technical skills were dialed in. What mattered most was psychology and performance optimization. I realized that how I managed my mental game determined how consistently I could execute at the highest level. Once you have edge, your mental state is what determines how consistently you stick to your system and capture the opportunities in front of you. Strong mental game allows you to avoid FOMO, ragetrading, and other bad habits that leech our PNL. 2022 the markets slowed down and punished many that didn't adapt. I realized that survival in trading isn’t about always being aggressive—it’s about trading based on what the market requires of you. The best traders don’t try to force the market to fit them, they fit their trading to the market and evolve faster than everyone else. 2023 I started allocating more energy toward swing trading—holding positions for days rather than seconds or minutes. This, coupled with selling options premium, gave me the space to step back, zoom out, and build a more sustainable lifestyle. I was no longer glued to the screen 10 hours a day. It reminded me that trading is a means to an end. 2024, I pushed myself to go beyond stocks. I dove deeper into options, futures, and overnight trading. Each new product came with its own quirks, nuances, and edge opportunities. Learning these instruments wasn’t just about more tools—it was about building a truly diversified and adaptable trading approach that maximizes your expected value based on the situation. At the elite level, the more markets and products you open yourself up to, the more opportunities you have to profit and the more mediums you have to express your trade in the most effective way possible. Now at the end of 2025, after 15 years, the biggest lesson is this: There’s no finish line in this game. The best traders stay curious. They stay hungry. And no matter how good you get, if you’re going to stay relevant in markets, you need to always be learning. I'm excited to see what 2026 will bring and I wish you all nothing but health, happiness, and the success you all are looking for.

Lance Breitstein 🇺🇸🌎

45,319 görüntüleme • 9 ay önce

Been discussion on Blessing Adeoye Jr. of Kinda Funny taking issue with #Xbox going to Jez Corden of Windows Central, for new CEO Asha Sharma's interview. Don't think it's "jealousy" contrary to Xbox fandom musings. He listed numerous far more reputable journalists/outlets for the task, with no mention of Kinda Funny. Instead, think it's a matter of credibility and authenticity. Jez's often referred to as an Xbox and Microsoft "shill" with good reason (reasons later). Even by important figures such as Obsidian's co-founder. Xbox going to Jez signals they wanted controlled PR and favoured narratives, not opportunities for hard hitting insight or truth. Inadvertently very revealing. Xbox's moves this last week let us know how Xbox is still operating. +First Asha immediately begins aggressive toxic parasocial antics and "I'm one of you!" PR on social media that Xbox execs are famed for, to evangelise the fandom and have them run positive PR. +She creates a new Gamertag just a month ago with very irregular play, later admits it was a shared account played by others. +Her X responses seem robotic and inorganic. +Xbox have Tom Warren (another Xbox leaning journalist) of The Verge run a hit piece attempting to scapegoat Sarah Bond, which doesn't stand up to scrutiny. +In Satya Nadella's very long statement on the shake ups, Sarah Bond isn't mentioned once. +Asha's first interview is with Jez. +Messaging is a whole load of nothing. Teases things fans want to hear without actually committing to changes. What this all indirectly tells us is it's the same slippery and not so authentic Xbox, and we shouldn't really put much stock into anything they or their execs are currently saying. Only folk who are going to be won over by this controlled PR are the same naive echo chamber Xbox hopefuls who've fallen for or parrot every Xbox narrative/PR, despite countless flip flops, pivots and failings. So why's Jez Corden discredited or seen as an Xbox "shill" by so many? Myriad reasons. This is the same guy who said: +Development on Everwild was going well, gameplay loop was nailed and we'd see it soon, before it was cancelled shortly after. +He wasn't worried about The Initiative or Perfect Dark based on research he'd done, despite layoffs/Crystal Dynamics stuff. Was canned and studio shut shortly after. +Xbox wasn't porting games to PS5, rumour was fanboy hopium, before Xbox announced PS5 ports a few months later. +Implied all Laura Fryer worked on was Xbox One after her critical vid on Xbox, when she'd worked on OG Xbox and 360. +Physical games retail was dead in the UK after unfavourable Xbox sales, when data showed ~40% of sales of the Top 20 games were physical. +Said WuKong never mentioned Series S as a reason for Xbox version delay, implied it was a deal, despite WuKong director pinning blame on Series S. +Xbox might drop prices on games, only to suggest we knew price increases were coming when Xbox increased prices on GP. +No aspect of Halo rumours were true, when Halo moved to UE5 per rumours. +Rumours of Phil's retirement were made up, just months before he retired. +He'd never covered Avowed when called out, when he'd done an article hyping it. +Mischaracterised Tim Stuart's quote on bringing games to more platforms with clarification question to Phil on Game Pass instead of "first party experiences". +Sony's shown "no interest in mobile" when they acquired multiple mobile game studios, been open on mobile expansion and bringing more IP to mobile, set up a mobile gaming division in 2022 and have one of the worlds highest earning mobile games in Fate/Grand Order. +Promoted ABK acquisition and how good it'd be for devs/gamers, when Xbox has suffered 4k+ layoffs since (FAR higher than industry avg in % of total workforce), most in ABK, and GP has seen a 100% price increase in last 2+ years alone. I could go on and on. Point is, so much of the time Jez's running PR, defence and favoured narratives for Xbox, often with misinformation
1:30

Been discussion on Blessing Adeoye Jr. of Kinda Funny taking issue with #Xbox going to Jez Corden of Windows Central, for new CEO Asha Sharma's interview. Don't think it's "jealousy" contrary to Xbox fandom musings. He listed numerous far more reputable journalists/outlets for the task, with no mention of Kinda Funny. Instead, think it's a matter of credibility and authenticity. Jez's often referred to as an Xbox and Microsoft "shill" with good reason (reasons later). Even by important figures such as Obsidian's co-founder. Xbox going to Jez signals they wanted controlled PR and favoured narratives, not opportunities for hard hitting insight or truth. Inadvertently very revealing. Xbox's moves this last week let us know how Xbox is still operating. +First Asha immediately begins aggressive toxic parasocial antics and "I'm one of you!" PR on social media that Xbox execs are famed for, to evangelise the fandom and have them run positive PR. +She creates a new Gamertag just a month ago with very irregular play, later admits it was a shared account played by others. +Her X responses seem robotic and inorganic. +Xbox have Tom Warren (another Xbox leaning journalist) of The Verge run a hit piece attempting to scapegoat Sarah Bond, which doesn't stand up to scrutiny. +In Satya Nadella's very long statement on the shake ups, Sarah Bond isn't mentioned once. +Asha's first interview is with Jez. +Messaging is a whole load of nothing. Teases things fans want to hear without actually committing to changes. What this all indirectly tells us is it's the same slippery and not so authentic Xbox, and we shouldn't really put much stock into anything they or their execs are currently saying. Only folk who are going to be won over by this controlled PR are the same naive echo chamber Xbox hopefuls who've fallen for or parrot every Xbox narrative/PR, despite countless flip flops, pivots and failings. So why's Jez Corden discredited or seen as an Xbox "shill" by so many? Myriad reasons. This is the same guy who said: +Development on Everwild was going well, gameplay loop was nailed and we'd see it soon, before it was cancelled shortly after. +He wasn't worried about The Initiative or Perfect Dark based on research he'd done, despite layoffs/Crystal Dynamics stuff. Was canned and studio shut shortly after. +Xbox wasn't porting games to PS5, rumour was fanboy hopium, before Xbox announced PS5 ports a few months later. +Implied all Laura Fryer worked on was Xbox One after her critical vid on Xbox, when she'd worked on OG Xbox and 360. +Physical games retail was dead in the UK after unfavourable Xbox sales, when data showed ~40% of sales of the Top 20 games were physical. +Said WuKong never mentioned Series S as a reason for Xbox version delay, implied it was a deal, despite WuKong director pinning blame on Series S. +Xbox might drop prices on games, only to suggest we knew price increases were coming when Xbox increased prices on GP. +No aspect of Halo rumours were true, when Halo moved to UE5 per rumours. +Rumours of Phil's retirement were made up, just months before he retired. +He'd never covered Avowed when called out, when he'd done an article hyping it. +Mischaracterised Tim Stuart's quote on bringing games to more platforms with clarification question to Phil on Game Pass instead of "first party experiences". +Sony's shown "no interest in mobile" when they acquired multiple mobile game studios, been open on mobile expansion and bringing more IP to mobile, set up a mobile gaming division in 2022 and have one of the worlds highest earning mobile games in Fate/Grand Order. +Promoted ABK acquisition and how good it'd be for devs/gamers, when Xbox has suffered 4k+ layoffs since (FAR higher than industry avg in % of total workforce), most in ABK, and GP has seen a 100% price increase in last 2+ years alone. I could go on and on. Point is, so much of the time Jez's running PR, defence and favoured narratives for Xbox, often with misinformation

NIB

19,584 görüntüleme • 7 ay önce

A surveillance regime is being assembled in front of our very eyes. Most of you do not understand the tyrannical nightmare directly ahead. People still talk like this is a future problem. It's not. The battle against dystopia is now. 1984 is here. It arrives as "efficiency," procurement, integrations, device adoption, and a slow widening of what the state can do to you without asking, without noticing you, without needing you to consent. The attached video shows a federal agent in tactical gear wearing Meta Ray-Ban smart glasses on his face while carrying out state work. A camera at eye level. A microphone. A network connection. Constant recording that becomes a file, then a feed, then a searchable object that can be attached to a case, cross-referenced, stored, shared, and re-used. Raw footage is noise until it can be fused, searched, and operationalized. That is where AI enters. ICE is paying Palantir to build "ImmigrationOS," described as a platform for near real-time visibility into enforcement workflows, including tracking people and helping decide who gets targeted. Immigration is a perfect testing ground because the public has been trained to tolerate exceptional measures when the target group is already politically disposable. The capability does not stay there. It never stays there. The nightmare is the stack itself. Capture at the edge, aggregation in the middle, AI scoring and targeting at the top. Doorbell cameras feed the state. License plate readers feed the state. Data brokers feed the state. Local sharing agreements feed the state. Wearables feed the state. AI compresses the labor cost of suspicion, so the state can watch more people, more often, with less human effort. That is the mechanical shift most people still do not understand. AI does not need to be sentient to be oppressive. It needs to be cheap enough to scale the state’s attention and fast enough to outrun your ability to contest what it thinks it knows. Let me paint you the picture: an agent walks up the block wearing smart glasses, and your face, your voice, your license plate, and the people you are standing with get captured immediately. That clip hits an internal system where AI transcribes, tags, and links it to whatever identifiers already exist, your name, your address, your contacts, your past crossings, your employer, your car, your social graph. The platform fuses that with location trails and camera networks, then assigns a "priority" score that quietly moves you up a queue nobody outside the system can see. A caseworker opens a dashboard and clicks through prebuilt options that generate a task list, knock, detain, transfer, pressure, repeat, with the paperwork already half-written by AI. This is tyranny by procedure. The real chokehold is anticipatory control. Once people know they can be indexed, scored, and surfaced for enforcement, they start trimming their speech, their associations, their routes, their friends. Dissent becomes a risk factor, organizing becomes exposure, and the state does not need to ban protest when it can make participation costly enough to thin the crowd. It inevitably expands to include anyone who interrupts the smooth operation of power, and this kind of surveillance gives power the ability to punish quietly, repeatedly, and selectively, with plausible deniability stapled to every click. This is the formula for tyranny. The state gains capability, then finds incentives to justify using it. Agencies protect budgets by "demonstrating output." Contractors protect revenue by "delivering results." Politicians protect narratives by demanding visible "enforcement." Restraint gets treated as "inefficiency." Efficiency gets treated as "virtue." Your rights get treated as "friction." Any system that allows institutions to assemble your biography without your consent, then act on it without due process, is an engine of domination. Some people tolerate it because they think they will never be the target. That belief is childish. Power does not remain polite. It expands to fill the permissions you give it. A surveillance regime always needs new enemies to justify itself, because a machine built for pursuit must pursue. The regime being assembled is not subtle, it is simply normalized. It runs on boredom, exhaustion, and the assumption that someone else will handle it. It's all being wired now, and once it is wired, it will not ask your permission to be used.
1:04

A surveillance regime is being assembled in front of our very eyes. Most of you do not understand the tyrannical nightmare directly ahead. People still talk like this is a future problem. It's not. The battle against dystopia is now. 1984 is here. It arrives as "efficiency," procurement, integrations, device adoption, and a slow widening of what the state can do to you without asking, without noticing you, without needing you to consent. The attached video shows a federal agent in tactical gear wearing Meta Ray-Ban smart glasses on his face while carrying out state work. A camera at eye level. A microphone. A network connection. Constant recording that becomes a file, then a feed, then a searchable object that can be attached to a case, cross-referenced, stored, shared, and re-used. Raw footage is noise until it can be fused, searched, and operationalized. That is where AI enters. ICE is paying Palantir to build "ImmigrationOS," described as a platform for near real-time visibility into enforcement workflows, including tracking people and helping decide who gets targeted. Immigration is a perfect testing ground because the public has been trained to tolerate exceptional measures when the target group is already politically disposable. The capability does not stay there. It never stays there. The nightmare is the stack itself. Capture at the edge, aggregation in the middle, AI scoring and targeting at the top. Doorbell cameras feed the state. License plate readers feed the state. Data brokers feed the state. Local sharing agreements feed the state. Wearables feed the state. AI compresses the labor cost of suspicion, so the state can watch more people, more often, with less human effort. That is the mechanical shift most people still do not understand. AI does not need to be sentient to be oppressive. It needs to be cheap enough to scale the state’s attention and fast enough to outrun your ability to contest what it thinks it knows. Let me paint you the picture: an agent walks up the block wearing smart glasses, and your face, your voice, your license plate, and the people you are standing with get captured immediately. That clip hits an internal system where AI transcribes, tags, and links it to whatever identifiers already exist, your name, your address, your contacts, your past crossings, your employer, your car, your social graph. The platform fuses that with location trails and camera networks, then assigns a "priority" score that quietly moves you up a queue nobody outside the system can see. A caseworker opens a dashboard and clicks through prebuilt options that generate a task list, knock, detain, transfer, pressure, repeat, with the paperwork already half-written by AI. This is tyranny by procedure. The real chokehold is anticipatory control. Once people know they can be indexed, scored, and surfaced for enforcement, they start trimming their speech, their associations, their routes, their friends. Dissent becomes a risk factor, organizing becomes exposure, and the state does not need to ban protest when it can make participation costly enough to thin the crowd. It inevitably expands to include anyone who interrupts the smooth operation of power, and this kind of surveillance gives power the ability to punish quietly, repeatedly, and selectively, with plausible deniability stapled to every click. This is the formula for tyranny. The state gains capability, then finds incentives to justify using it. Agencies protect budgets by "demonstrating output." Contractors protect revenue by "delivering results." Politicians protect narratives by demanding visible "enforcement." Restraint gets treated as "inefficiency." Efficiency gets treated as "virtue." Your rights get treated as "friction." Any system that allows institutions to assemble your biography without your consent, then act on it without due process, is an engine of domination. Some people tolerate it because they think they will never be the target. That belief is childish. Power does not remain polite. It expands to fill the permissions you give it. A surveillance regime always needs new enemies to justify itself, because a machine built for pursuit must pursue. The regime being assembled is not subtle, it is simply normalized. It runs on boredom, exhaustion, and the assumption that someone else will handle it. It's all being wired now, and once it is wired, it will not ask your permission to be used.

Dylan Allman

327,629 görüntüleme • 8 ay önce

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky aren’t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality aren’t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. I’ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: “What very important truth do few people agree with you on?” 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: “The new workflow for me is I think with AI and work with my colleagues.” When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People won’t care how it was made. They’ll just care that it’s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.
1:29:31

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky aren’t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality aren’t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. I’ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: “What very important truth do few people agree with you on?” 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: “The new workflow for me is I think with AI and work with my colleagues.” When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People won’t care how it was made. They’ll just care that it’s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.

David Perell

257,480 görüntüleme • 1 yıl önce

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!
2:25

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 görüntüleme • 4 ay önce

OPENAI IS FALLING APART IN REAL TIME I've watched companies implode for decades. This one has all the warning signs. OpenAI declared "Code Red" in December. Altman sent an internal memo telling employees to drop everything because Google's Gemini 3 is eating their lunch. Salesforce CEO Marc Benioff publicly ditched ChatGPT for Gemini after using it for two hours. ChatGPT traffic fell in November. Second month-over-month decline of 2025. Meanwhile Gemini jumped to 650 million monthly active users. The company that was supposed to build AGI can't keep its chatbot competitive. But the real story is the money... OpenAI lost $12 BILLION in a single quarter according to Microsoft's own fiscal disclosures. Deutsche Bank estimates $143 billion in cumulative negative cash flow before the company turns profitable. Their analysts put it bluntly: "No startup in history has operated with losses on anything approaching this scale." They're burning $15 million per day on Sora alone. $5 billion annually to generate copyright-infringing memes. Even Sora's lead engineer admitted the "economics are currently completely unsustainable." Here's the big math problem nobody wants to discuss: It's going to cost 5x the energy and money to make these models 2x better. The low-hanging fruit is gone. Every incremental improvement now requires exponentially more compute, more data centers, more power. Reports suggest OpenAI's large training runs in 2025 failed to produce models better than prior versions. GPT-5 launched to widespread disappointment. Users called it "underwhelming" and "horrible." OpenAI had to restore GPT-4o within 24 hours because users preferred the old model. Altman had promised GPT-5 would make GPT-4 feel "mildly embarrassing." Instead, users complained it was worse at basic math and geography. They've released GPT-5.1, GPT-5.2 since. Same complaints each time: too corporate, too safe, robotic, boring. The talent exodus makes this even worse: CTO Mira Murati. Gone. Chief Research Officer Bob McGrew. Gone. Chief Scientist Ilya Sutskever. Gone. President Greg Brockman. Gone. Half the AI safety team departed. Multiple executives reportedly cited "psychological abuse" under Altman's leadership. And now Elon Musk is suing for up to $134 billion. A federal judge just ruled the case goes to jury trial in April. There's "plenty of evidence" that OpenAI's leaders promised to maintain the nonprofit structure that Musk funded. Musk provided $38 million in early funding based on those assurances. Now he wants his share of the $500 billion valuation. OpenAI called it "harassment." But the judge disagreed. Here's what I think happens next: The AI hype cycle is peaking. The diminishing returns are becoming impossible to hide. Competitors are catching up. The lawsuits are piling up. OpenAI needs to generate $200 billion in annual revenue by 2030 to justify their projections. That's 15x growth in five years while costs keep exploding. Even Sam Altman admitted investors are "overexcited" about AI. His exact words: "Someone is going to lose a phenomenal amount of money." If I were running an AI startup with good traction right now, I'd be looking for an exit. Sell into the hype before the music stops. My positioning: I'm not touching OpenAI-adjacent plays at these valuations. The risk profile is astronomical. If you're exposed to the Magnificent 7 through AI infrastructure bets, consider trimming. The gap between promised revolution and delivered reality has never been wider. The smart money is rotating into sectors where valuations actually reflect fundamentals. Small and mid-caps are trading near decade lows relative to Big Tech while earnings growth is only marginally lower. Markets can price risk. But they can't price chaos. And OpenAI is chaos dressed up in a $500 billion valuation.
2:19

OPENAI IS FALLING APART IN REAL TIME I've watched companies implode for decades. This one has all the warning signs. OpenAI declared "Code Red" in December. Altman sent an internal memo telling employees to drop everything because Google's Gemini 3 is eating their lunch. Salesforce CEO Marc Benioff publicly ditched ChatGPT for Gemini after using it for two hours. ChatGPT traffic fell in November. Second month-over-month decline of 2025. Meanwhile Gemini jumped to 650 million monthly active users. The company that was supposed to build AGI can't keep its chatbot competitive. But the real story is the money... OpenAI lost $12 BILLION in a single quarter according to Microsoft's own fiscal disclosures. Deutsche Bank estimates $143 billion in cumulative negative cash flow before the company turns profitable. Their analysts put it bluntly: "No startup in history has operated with losses on anything approaching this scale." They're burning $15 million per day on Sora alone. $5 billion annually to generate copyright-infringing memes. Even Sora's lead engineer admitted the "economics are currently completely unsustainable." Here's the big math problem nobody wants to discuss: It's going to cost 5x the energy and money to make these models 2x better. The low-hanging fruit is gone. Every incremental improvement now requires exponentially more compute, more data centers, more power. Reports suggest OpenAI's large training runs in 2025 failed to produce models better than prior versions. GPT-5 launched to widespread disappointment. Users called it "underwhelming" and "horrible." OpenAI had to restore GPT-4o within 24 hours because users preferred the old model. Altman had promised GPT-5 would make GPT-4 feel "mildly embarrassing." Instead, users complained it was worse at basic math and geography. They've released GPT-5.1, GPT-5.2 since. Same complaints each time: too corporate, too safe, robotic, boring. The talent exodus makes this even worse: CTO Mira Murati. Gone. Chief Research Officer Bob McGrew. Gone. Chief Scientist Ilya Sutskever. Gone. President Greg Brockman. Gone. Half the AI safety team departed. Multiple executives reportedly cited "psychological abuse" under Altman's leadership. And now Elon Musk is suing for up to $134 billion. A federal judge just ruled the case goes to jury trial in April. There's "plenty of evidence" that OpenAI's leaders promised to maintain the nonprofit structure that Musk funded. Musk provided $38 million in early funding based on those assurances. Now he wants his share of the $500 billion valuation. OpenAI called it "harassment." But the judge disagreed. Here's what I think happens next: The AI hype cycle is peaking. The diminishing returns are becoming impossible to hide. Competitors are catching up. The lawsuits are piling up. OpenAI needs to generate $200 billion in annual revenue by 2030 to justify their projections. That's 15x growth in five years while costs keep exploding. Even Sam Altman admitted investors are "overexcited" about AI. His exact words: "Someone is going to lose a phenomenal amount of money." If I were running an AI startup with good traction right now, I'd be looking for an exit. Sell into the hype before the music stops. My positioning: I'm not touching OpenAI-adjacent plays at these valuations. The risk profile is astronomical. If you're exposed to the Magnificent 7 through AI infrastructure bets, consider trimming. The gap between promised revolution and delivered reality has never been wider. The smart money is rotating into sectors where valuations actually reflect fundamentals. Small and mid-caps are trading near decade lows relative to Big Tech while earnings growth is only marginally lower. Markets can price risk. But they can't price chaos. And OpenAI is chaos dressed up in a $500 billion valuation.

George Noble

3,323,992 görüntüleme • 8 ay önce

I spoke with Pooja Arora about why people are drawn to pessimism despite historic progress, and how cognitive biases and media dynamics distort our perception of modern life. Pooja Arora (Pooja Arora) Now, the human mind seems to be attracted to pessimism and cynicism a lot nowadays. And even though in your books, Better Angels of Our Nature and Enlightenment Now, you show how human progress has evolved over centuries—we have moved from a tribal era to living in luxuries that monarchs of the Middle Ages couldn’t even imagine—when you try to explain this to somebody, it’s extremely difficult. The world is bad for different reasons for different groups of people. Why is that happening, and how do you convince somebody that it’s a good era to live in? I don’t want to be born in the 1930s Me: No, no—or before. I mean, as I like to say, would you prefer your surgery with or without anesthesia, for example? Would you like dentistry in the 21st century or the 19th century? So yes, one part of the explanation is there’s a widespread pattern in polling that people are much more optimistic about their own lives than about the country as a whole. Reliably, if you ask people about the quality of schools, they’ll say the quality of schools in the country is terrible. What about your kid’s school? Oh, it’s actually pretty good. If you ask them, is the country safe? They’ll say, no, there’s crime everywhere—muggings and knifings and shootings. You say, what about your neighborhood? Do you feel safe? They say, well, yeah, I feel pretty safe. So partly there’s a dissociation between people’s vision of the whole country and their own lives. That is driven in part by what cognitive psychologists call the availability bias—heuristic—namely, people judge probability and risk and danger by salient examples, by narratives, by images. And that’s what the news delivers. The news is selectively biased toward the negative—not necessarily because editors prefer negative stories, although they do—but, on top of that overt bias, the mere fact that they report newsworthy events means there’s a built-in bias toward the negative. And that’s because anything that happens suddenly is much more likely to be bad than good. A shooting, a terrorist attack, a natural disaster, a man-made disaster—those are news. Things that are improvements, such as the decline in extreme poverty—which has been one of the most important events in the history of humanity—that extreme poverty has gone from 90% of humanity to less than 9%—the decline in crime, the decline in war, the gradual rise of human rights—those tend not to be reported in the news because they are not discrete events that happened on a Thursday morning in October. They creep up a few percentage points a year, and so they’re never reported. In fact, sometimes the reporting can convey the exact opposite impression. Imagine that you’ve got a curve that goes up, with occasional setbacks, and then up with a setback, and then up with a setback—and the only thing that gets reported is the setback, because it’s news. This year, for the first time in 10 years, life expectancy got shorter instead of longer. Well, if every time that happens there’s a new story, but there isn’t a new story about the nine years out of 10 in which life expectancy goes up—because it isn’t news, it’s the same as last year—then people get a systematically wrong impression about global trends. Finally, I mentioned that there is, on top of that, a negativity bias among journalists—but there’s a negativity bias in everyone, in that overall bad emotions are felt more strongly than good emotions. There’s a greater number of negative emotions than positive emotions. We remember the things that went wrong recently better than the things that went right. So human psychology is already tilted toward the negative. The very possibility of progress is a very recent development in human history. For thousands of years, there was imperceptible progress. People didn’t invent things. Things didn’t change. But over very short periods of time, the idea of a country getting better or the world getting better within the span of a human lifetime is something that only began to happen pretty much after the Industrial Revolution, itself following the scientific revolution and the Enlightenment. So I don’t think our intuitions were prepared by evolution for the very concept of global long-term progress.
5:13

I spoke with Pooja Arora about why people are drawn to pessimism despite historic progress, and how cognitive biases and media dynamics distort our perception of modern life. Pooja Arora (Pooja Arora) Now, the human mind seems to be attracted to pessimism and cynicism a lot nowadays. And even though in your books, Better Angels of Our Nature and Enlightenment Now, you show how human progress has evolved over centuries—we have moved from a tribal era to living in luxuries that monarchs of the Middle Ages couldn’t even imagine—when you try to explain this to somebody, it’s extremely difficult. The world is bad for different reasons for different groups of people. Why is that happening, and how do you convince somebody that it’s a good era to live in? I don’t want to be born in the 1930s Me: No, no—or before. I mean, as I like to say, would you prefer your surgery with or without anesthesia, for example? Would you like dentistry in the 21st century or the 19th century? So yes, one part of the explanation is there’s a widespread pattern in polling that people are much more optimistic about their own lives than about the country as a whole. Reliably, if you ask people about the quality of schools, they’ll say the quality of schools in the country is terrible. What about your kid’s school? Oh, it’s actually pretty good. If you ask them, is the country safe? They’ll say, no, there’s crime everywhere—muggings and knifings and shootings. You say, what about your neighborhood? Do you feel safe? They say, well, yeah, I feel pretty safe. So partly there’s a dissociation between people’s vision of the whole country and their own lives. That is driven in part by what cognitive psychologists call the availability bias—heuristic—namely, people judge probability and risk and danger by salient examples, by narratives, by images. And that’s what the news delivers. The news is selectively biased toward the negative—not necessarily because editors prefer negative stories, although they do—but, on top of that overt bias, the mere fact that they report newsworthy events means there’s a built-in bias toward the negative. And that’s because anything that happens suddenly is much more likely to be bad than good. A shooting, a terrorist attack, a natural disaster, a man-made disaster—those are news. Things that are improvements, such as the decline in extreme poverty—which has been one of the most important events in the history of humanity—that extreme poverty has gone from 90% of humanity to less than 9%—the decline in crime, the decline in war, the gradual rise of human rights—those tend not to be reported in the news because they are not discrete events that happened on a Thursday morning in October. They creep up a few percentage points a year, and so they’re never reported. In fact, sometimes the reporting can convey the exact opposite impression. Imagine that you’ve got a curve that goes up, with occasional setbacks, and then up with a setback, and then up with a setback—and the only thing that gets reported is the setback, because it’s news. This year, for the first time in 10 years, life expectancy got shorter instead of longer. Well, if every time that happens there’s a new story, but there isn’t a new story about the nine years out of 10 in which life expectancy goes up—because it isn’t news, it’s the same as last year—then people get a systematically wrong impression about global trends. Finally, I mentioned that there is, on top of that, a negativity bias among journalists—but there’s a negativity bias in everyone, in that overall bad emotions are felt more strongly than good emotions. There’s a greater number of negative emotions than positive emotions. We remember the things that went wrong recently better than the things that went right. So human psychology is already tilted toward the negative. The very possibility of progress is a very recent development in human history. For thousands of years, there was imperceptible progress. People didn’t invent things. Things didn’t change. But over very short periods of time, the idea of a country getting better or the world getting better within the span of a human lifetime is something that only began to happen pretty much after the Industrial Revolution, itself following the scientific revolution and the Enlightenment. So I don’t think our intuitions were prepared by evolution for the very concept of global long-term progress.

Steven Pinker

75,178 görüntüleme • 7 ay önce

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution
1:10

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution

Dr. Filippo Cademartiri

11,849 görüntüleme • 1 yıl önce

When Elon Musk beams in virtually for a high-stakes fireside chat with JPMorgan Chase CEO Jamie Dimon, the conversation goes completely out of this world. The discussion was packed with massive milestones—from the bombshell that SpaceX is going public to plans for lunar AI data centers and the urgent need for the Terafab chip revolution. Here is the ultimate breakdown of their discussion: 💵 SpaceX has been self-funding and cash-flow positive for a decade Before the decision to go public, SpaceX didn't actually need to raise money to survive. The company has been cash-flow positive since around 2014–2015, meaning its private equity rounds were exclusively held to provide liquidity for employees and early investors. "We've been positive cash flow for quite a long time, I think, since around 2014-2015. And we've been self-funding. In fact, in our sort of private equity rounds, they actually have not been fundraising rounds. They've been liquidity rounds for investors and employees because we give everyone at the company stock." 🚀 The upcoming capital growth phase requires massive funding The primary trigger for going public now is an unprecedented capital expenditure phase. SpaceX is preparing to deploy an immense constellation of over 100,000 Next-Gen communication satellites and construct massive AI data centers in orbit. "we are embarking on a significant capital growth phase where we're going to put in over probably 100,000 satellites, probably over 100,000 satellites, just for communications... And then we're also doing the AI data centers in space, which is another massive capital endeavor." 📡 Starlink V3 introduces a massive bandwidth breakthrough The custom chips designed by SpaceX for the V3 satellites will completely alter global communications, offering 100 times the bandwidth of the current system and slashing latency in half by operating at a lower altitude. They are so large—the size of a small bus—that Starship is the only rocket on Earth capable of launching them, carrying 50 at a time. "The version three is, depending on how you count it, 10 to 20 times more capable than the version two satellite. And there were three chips that the SpaceX chip design team taped out that are specific to this... Which means it's 100 times more bandwidth than the SpaceX's Starlink system currently on the surface. And also half the latency because the altitude will be about half altitude." 🤖 AI and robots possess an insatiable appetite for data Musk points out that expanding infrastructure into space is vital because future AI and robotic systems will demand an astronomical amount of bandwidth compared to the relatively low data transmission rates of human beings. "And the future with AI and robots is actually going to require a lot more bandwidth than we currently use. Because you can imagine like what's the bandwidth of a human? Peak bandwidth of the human is a few hundred bits per second. But bandwidth of a computer can be a trillion bits a second. So the appetite for bandwidth of AI and robots is going to be enormous." ☀️ Space solves the looming terrestrial power plant crisis Building traditional power plants on Earth faces heavy community resistance. Moving data centers into space unlocks unlimited energy generation via solar power ("star power") without disrupting Earth's environment, tapping into an energy source that accounts for 99.8% of the solar system's mass. "It's increasingly difficult to build power plants on the ground. There are very few people who want a power plant in their backyard... But actually if we go to space, we can go far beyond the electricity generation of both. In fact, this is going to sound kind of crazy. But you could actually increase human energy by a factor of a million and still be using much less than a millionth of the sun's energy." 🌕 The Moon is a 1,000-Terawatt compute launchpad While Mars remains the long-term goal, the Moon is the immediate fast-track location for massive scaling. Because it lacks an atmosphere and has low gravity, SpaceX can use electromagnetic rail guns to shoot AI data centers into deep space from the lunar surface, scaling power to an incredible 1,000 terawatts per year. "I just think that we can build a self-sustaining city on the moon faster than we could do so on Mars. And there's also the potential... you can use an electromagnetic accelerator, a rail gun or mass driver. Basically, you don't need to use rockets to do AI data centers into deep space from the moon... We can do a thousand terawatts or more from the moon." 🪐 Mars is the ultimate "fixer-upper" planet Mars is being targeted as a full-scale terraforming project. Due to its atmosphere and gravity levels, warming up the planet could eventually unlock liquid oceans and allow humans to walk around without spacesuits. "And if you warm up Mars, you could one day make Mars like Earth. And with like liquid oceans and life. And where you could walk outside without a spacesuit type of thing. So Mars is, I call Mars a fixer upper of a planet. But it's got a lot of potential." 🚂 SpaceX is the modern-day Union Pacific Railroad Musk rejects the idea that SpaceX is moving into the hospitality or hotel business for space tourism. Instead, he views the company as a foundational infrastructure provider, comparable to the historic railroads that opened up the American West. "We're kind of like Union Pacific, you know. You know, when they built Union Pacific back in the day, people thought they were crazy. Because like, why are you trying to carry all this cargo and people to California? No one's there. But now California is the biggest state in the country." ♻️ Starship's core disruption is 100% reusability The true holy grail of Starship is full reusability, which drops orbit access costs down to the mere price of fuel. Because it utilizes ultra-cheap liquid oxygen and methane, shipping cargo to space will become more economical than flying cargo across Earth's oceans on an airplane. "The fundamental breakthrough of Starship is that it will be the first orbital rocket that is fully reusable... And the propellant we use for Starship is liquid oxygen and liquid methane, which is the cheapest propellant you could possibly get... which means that you should be able to actually send cargo to space for less than the cost of cargo on an airplane going on a trans-oceanic trip." 🔄 Starship V4 targets hourly launch cadences SpaceX's engineering pipeline is aiming for staggering operational frequencies and massive payloads. While Starship V3 targets 100 tons to orbit, the upcoming V4 variant is designed to carry over 200 tons and launch on an hourly schedule. "Because Starship V3 is aiming to do 100 tons to orbit with full reusability. And then Starship V4 we're aiming for over 200 tons per mission. And then being able to launch every hour." ☁️ Orbital data centers are entirely weather-proof Space-based AI data centers are highly practical because they are simpler to construct than communication satellites. Data is beamed via lasers between satellites, and then beamed to the ground using cloud-penetrating radio frequencies that completely bypass bad weather. "The AI data center would be much simpler by comparison. Because it's really just solar power plus radiator... The connection would happen no matter what the weather is. Because once you connect via the lasers to the Starlink communication constellation, the Starlink communication to the ground uses frequencies that are cloud penetrating." 🇺🇸 The U.S. faces a catastrophic "Zero Memory Fab" crisis A major vulnerability in domestic tech infrastructure is that the U.S. currently manufactures zero high-volume computer memory chips. Even with new facilities arriving online between 2028 and 2030, domestic supply will not match the exponential requirements of AI, which is why Musk is aggressively building the Terafab. "there's not a single high volume computer memory fab in America right now. Zero. There's one being built in Idaho by Micron. But that will not reach volume production until I believe 2028. And there's something being built in New York, but they are in, I think, 29 and 30. And this is a tiny fraction of the memory that's needed... That's why we need to do the Terafab." 🧠 SpaceX will offer proprietary AI chips and software While the orbital data center network will remain an open marketplace capable of running third-party hardware like NVIDIA GPUs, Google TPUs, or Amazon Trainium, SpaceX plans to deploy its own in-house AI chips and software stack in the near future. "So if NVIDIA GPUs can be put on it, Google TPUs can be put on it, Amazon Trainium or any other chips that you want to put on, can be put on. We'll also offer our chips in the future and I think we also want to offer our software, our AI software as well in the future." 🛡️ Starshield handles critical national intelligence Musk emphasizes his deeply pro-American stance, highlighting SpaceX's specialized Starshield division as a crucial backbone for the U.S. military and national intelligence agencies. "We have a division called Starshield which provides military communications. And you know, there's some other stuff that's kind of classified, I guess. We can't be talking about that. But we are helping the Department of War and intelligence part of the government. We're a vital element of that." 👥 Executive retention fuels the mission The core leadership bench at SpaceX is defined by extreme longevity, driven by a deep collective belief in turning science fiction into reality. Top executives like Gwynne Shotwell have remained with Musk for over two decades. "I guess Gwynne was, I think, around the seventh person to join the company. And that was 2002. It's just went to like 24 years. And generally the senior executives at the company, you have a very long tenure. I think Brent Johnson's been, you see, over 15 years... because people really believe in the mission, I think they want to stay and they want to keep building it." ❤️ Character overrides IQ in leadership Reflecting on how he has evolved over 20 years, Musk notes that he has become significantly more laid back. He has also learned that a candidate's moral character and heart are just as vital to a company's success as raw intellectual horsepower. "Well, I think I'm probably more chill than I used to be... And one of the things I've found over time... is that like in terms of like recruiting people to the company and having people work with the company, like their individual abilities and their intellectual capabilities matter a lot, but it also matters if they have a good heart. It's not just about whether somebody has a certain IQ or whatever, but just are they like a good person, that matters a lot."
27:20

When Elon Musk beams in virtually for a high-stakes fireside chat with JPMorgan Chase CEO Jamie Dimon, the conversation goes completely out of this world. The discussion was packed with massive milestones—from the bombshell that SpaceX is going public to plans for lunar AI data centers and the urgent need for the Terafab chip revolution. Here is the ultimate breakdown of their discussion: 💵 SpaceX has been self-funding and cash-flow positive for a decade Before the decision to go public, SpaceX didn't actually need to raise money to survive. The company has been cash-flow positive since around 2014–2015, meaning its private equity rounds were exclusively held to provide liquidity for employees and early investors. "We've been positive cash flow for quite a long time, I think, since around 2014-2015. And we've been self-funding. In fact, in our sort of private equity rounds, they actually have not been fundraising rounds. They've been liquidity rounds for investors and employees because we give everyone at the company stock." 🚀 The upcoming capital growth phase requires massive funding The primary trigger for going public now is an unprecedented capital expenditure phase. SpaceX is preparing to deploy an immense constellation of over 100,000 Next-Gen communication satellites and construct massive AI data centers in orbit. "we are embarking on a significant capital growth phase where we're going to put in over probably 100,000 satellites, probably over 100,000 satellites, just for communications... And then we're also doing the AI data centers in space, which is another massive capital endeavor." 📡 Starlink V3 introduces a massive bandwidth breakthrough The custom chips designed by SpaceX for the V3 satellites will completely alter global communications, offering 100 times the bandwidth of the current system and slashing latency in half by operating at a lower altitude. They are so large—the size of a small bus—that Starship is the only rocket on Earth capable of launching them, carrying 50 at a time. "The version three is, depending on how you count it, 10 to 20 times more capable than the version two satellite. And there were three chips that the SpaceX chip design team taped out that are specific to this... Which means it's 100 times more bandwidth than the SpaceX's Starlink system currently on the surface. And also half the latency because the altitude will be about half altitude." 🤖 AI and robots possess an insatiable appetite for data Musk points out that expanding infrastructure into space is vital because future AI and robotic systems will demand an astronomical amount of bandwidth compared to the relatively low data transmission rates of human beings. "And the future with AI and robots is actually going to require a lot more bandwidth than we currently use. Because you can imagine like what's the bandwidth of a human? Peak bandwidth of the human is a few hundred bits per second. But bandwidth of a computer can be a trillion bits a second. So the appetite for bandwidth of AI and robots is going to be enormous." ☀️ Space solves the looming terrestrial power plant crisis Building traditional power plants on Earth faces heavy community resistance. Moving data centers into space unlocks unlimited energy generation via solar power ("star power") without disrupting Earth's environment, tapping into an energy source that accounts for 99.8% of the solar system's mass. "It's increasingly difficult to build power plants on the ground. There are very few people who want a power plant in their backyard... But actually if we go to space, we can go far beyond the electricity generation of both. In fact, this is going to sound kind of crazy. But you could actually increase human energy by a factor of a million and still be using much less than a millionth of the sun's energy." 🌕 The Moon is a 1,000-Terawatt compute launchpad While Mars remains the long-term goal, the Moon is the immediate fast-track location for massive scaling. Because it lacks an atmosphere and has low gravity, SpaceX can use electromagnetic rail guns to shoot AI data centers into deep space from the lunar surface, scaling power to an incredible 1,000 terawatts per year. "I just think that we can build a self-sustaining city on the moon faster than we could do so on Mars. And there's also the potential... you can use an electromagnetic accelerator, a rail gun or mass driver. Basically, you don't need to use rockets to do AI data centers into deep space from the moon... We can do a thousand terawatts or more from the moon." 🪐 Mars is the ultimate "fixer-upper" planet Mars is being targeted as a full-scale terraforming project. Due to its atmosphere and gravity levels, warming up the planet could eventually unlock liquid oceans and allow humans to walk around without spacesuits. "And if you warm up Mars, you could one day make Mars like Earth. And with like liquid oceans and life. And where you could walk outside without a spacesuit type of thing. So Mars is, I call Mars a fixer upper of a planet. But it's got a lot of potential." 🚂 SpaceX is the modern-day Union Pacific Railroad Musk rejects the idea that SpaceX is moving into the hospitality or hotel business for space tourism. Instead, he views the company as a foundational infrastructure provider, comparable to the historic railroads that opened up the American West. "We're kind of like Union Pacific, you know. You know, when they built Union Pacific back in the day, people thought they were crazy. Because like, why are you trying to carry all this cargo and people to California? No one's there. But now California is the biggest state in the country." ♻️ Starship's core disruption is 100% reusability The true holy grail of Starship is full reusability, which drops orbit access costs down to the mere price of fuel. Because it utilizes ultra-cheap liquid oxygen and methane, shipping cargo to space will become more economical than flying cargo across Earth's oceans on an airplane. "The fundamental breakthrough of Starship is that it will be the first orbital rocket that is fully reusable... And the propellant we use for Starship is liquid oxygen and liquid methane, which is the cheapest propellant you could possibly get... which means that you should be able to actually send cargo to space for less than the cost of cargo on an airplane going on a trans-oceanic trip." 🔄 Starship V4 targets hourly launch cadences SpaceX's engineering pipeline is aiming for staggering operational frequencies and massive payloads. While Starship V3 targets 100 tons to orbit, the upcoming V4 variant is designed to carry over 200 tons and launch on an hourly schedule. "Because Starship V3 is aiming to do 100 tons to orbit with full reusability. And then Starship V4 we're aiming for over 200 tons per mission. And then being able to launch every hour." ☁️ Orbital data centers are entirely weather-proof Space-based AI data centers are highly practical because they are simpler to construct than communication satellites. Data is beamed via lasers between satellites, and then beamed to the ground using cloud-penetrating radio frequencies that completely bypass bad weather. "The AI data center would be much simpler by comparison. Because it's really just solar power plus radiator... The connection would happen no matter what the weather is. Because once you connect via the lasers to the Starlink communication constellation, the Starlink communication to the ground uses frequencies that are cloud penetrating." 🇺🇸 The U.S. faces a catastrophic "Zero Memory Fab" crisis A major vulnerability in domestic tech infrastructure is that the U.S. currently manufactures zero high-volume computer memory chips. Even with new facilities arriving online between 2028 and 2030, domestic supply will not match the exponential requirements of AI, which is why Musk is aggressively building the Terafab. "there's not a single high volume computer memory fab in America right now. Zero. There's one being built in Idaho by Micron. But that will not reach volume production until I believe 2028. And there's something being built in New York, but they are in, I think, 29 and 30. And this is a tiny fraction of the memory that's needed... That's why we need to do the Terafab." 🧠 SpaceX will offer proprietary AI chips and software While the orbital data center network will remain an open marketplace capable of running third-party hardware like NVIDIA GPUs, Google TPUs, or Amazon Trainium, SpaceX plans to deploy its own in-house AI chips and software stack in the near future. "So if NVIDIA GPUs can be put on it, Google TPUs can be put on it, Amazon Trainium or any other chips that you want to put on, can be put on. We'll also offer our chips in the future and I think we also want to offer our software, our AI software as well in the future." 🛡️ Starshield handles critical national intelligence Musk emphasizes his deeply pro-American stance, highlighting SpaceX's specialized Starshield division as a crucial backbone for the U.S. military and national intelligence agencies. "We have a division called Starshield which provides military communications. And you know, there's some other stuff that's kind of classified, I guess. We can't be talking about that. But we are helping the Department of War and intelligence part of the government. We're a vital element of that." 👥 Executive retention fuels the mission The core leadership bench at SpaceX is defined by extreme longevity, driven by a deep collective belief in turning science fiction into reality. Top executives like Gwynne Shotwell have remained with Musk for over two decades. "I guess Gwynne was, I think, around the seventh person to join the company. And that was 2002. It's just went to like 24 years. And generally the senior executives at the company, you have a very long tenure. I think Brent Johnson's been, you see, over 15 years... because people really believe in the mission, I think they want to stay and they want to keep building it." ❤️ Character overrides IQ in leadership Reflecting on how he has evolved over 20 years, Musk notes that he has become significantly more laid back. He has also learned that a candidate's moral character and heart are just as vital to a company's success as raw intellectual horsepower. "Well, I think I'm probably more chill than I used to be... And one of the things I've found over time... is that like in terms of like recruiting people to the company and having people work with the company, like their individual abilities and their intellectual capabilities matter a lot, but it also matters if they have a good heart. It's not just about whether somebody has a certain IQ or whatever, but just are they like a good person, that matters a lot."

Ming

60,910 görüntüleme • 4 ay önce

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for
2:40:29

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for

Moon Dev

11,196 görüntüleme • 7 ay önce