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Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue...

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37,874 просмотров • 2 месяцев назад •via X (Twitter)

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FUNDA goes beyond an accurate $PLTR Preview. With FUNDA PLTR PLAY, you can explore all the PLTR data we track in one interactive experience—and see exactly how we’re thinking about the company. Introducing Palantir Monitor. $PLTR is being repriced as an AI infrastructure company, not a consulting shop. The question is whether the revenue models justify the multiple. Palantir Monitor tracks the signals that answer that question in real time. Government Revenue Models US government revenue against lagged federal obligation data and cascades international government revenue from customer count and domestic momentum. Forecast ranges update as new procurement signals arrive. This is the structural floor beneath the growth story. Commercial Model Decomposes commercial revenue by annual cohort, tracking new customer acquisition, initial contract values, and net expansion rates. Retention curves are segmented by era to capture the step-change in land-and-expand velocity since the AIP launch. Equity Structure Benchmarks ownership composition against enterprise software peers, tracking institutional, insider, and retail allocation over time. Trading behavior metrics surface how PLTR trades differently from its peer group in turnover, intraday range, and index correlation. Retail Sentiment Synthesizes directional retail signals into a monthly net stance with momentum tracking. Narrative themes are distilled by tone without exposing raw discussion text, providing a structured read on where retail conviction is concentrated. Events Maintains a structured catalog of government and commercial events from Q1 2025 onward, each tagged by segment and synthesized into thematic narratives. Surfaces directional shifts in the contract pipeline before they appear in quarterly filings. Available to all paid clients on our full-stack research platform. Start here:
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FUNDA goes beyond an accurate $PLTR Preview. With FUNDA PLTR PLAY, you can explore all the PLTR data we track in one interactive experience—and see exactly how we’re thinking about the company. Introducing Palantir Monitor. $PLTR is being repriced as an AI infrastructure company, not a consulting shop. The question is whether the revenue models justify the multiple. Palantir Monitor tracks the signals that answer that question in real time. Government Revenue Models US government revenue against lagged federal obligation data and cascades international government revenue from customer count and domestic momentum. Forecast ranges update as new procurement signals arrive. This is the structural floor beneath the growth story. Commercial Model Decomposes commercial revenue by annual cohort, tracking new customer acquisition, initial contract values, and net expansion rates. Retention curves are segmented by era to capture the step-change in land-and-expand velocity since the AIP launch. Equity Structure Benchmarks ownership composition against enterprise software peers, tracking institutional, insider, and retail allocation over time. Trading behavior metrics surface how PLTR trades differently from its peer group in turnover, intraday range, and index correlation. Retail Sentiment Synthesizes directional retail signals into a monthly net stance with momentum tracking. Narrative themes are distilled by tone without exposing raw discussion text, providing a structured read on where retail conviction is concentrated. Events Maintains a structured catalog of government and commercial events from Q1 2025 onward, each tagged by segment and synthesized into thematic narratives. Surfaces directional shifts in the contract pipeline before they appear in quarterly filings. Available to all paid clients on our full-stack research platform. Start here:

FUNDA

14,011 просмотров • 1 месяц назад

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.
1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

235,028 просмотров • 16 дней назад

We just cut AI spend by 75% across an entire workforce in one click. Introducing: Merge for Workforce. Without Merge, your employees are left choosing one of two options: 1. Spend 100x the cost they should be from using frontier models for everything 2. Deliver subpar work from overly restrictive model access Instead with Merge, IT can connect any identity provider and set model routing policies by team in one click. Merge pushes them to every machine through a desktop client, overriding the model configs across your team’s AI assistants and coding tools. Now every task gets the right model for the job. The result? 75x fewer tokens with faster and better output.
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We just cut AI spend by 75% across an entire workforce in one click. Introducing: Merge for Workforce. Without Merge, your employees are left choosing one of two options: 1. Spend 100x the cost they should be from using frontier models for everything 2. Deliver subpar work from overly restrictive model access Instead with Merge, IT can connect any identity provider and set model routing policies by team in one click. Merge pushes them to every machine through a desktop client, overriding the model configs across your team’s AI assistants and coding tools. Now every task gets the right model for the job. The result? 75x fewer tokens with faster and better output.

Shensi Ding

523,300 просмотров • 1 месяц назад

Introducing EVO Router - a smart router that continuously optimizes and hill-climbs on your AI inference workloads. It learns from your code, prompts, production traffic, use cases, and SLAs, then searches for the best setup for every workload. That can mean more than choosing just a single model or routing through a classifier. EVO (YC F26) can optimize across model × provider combinations, fusion systems, cascades, routing policies, and prompts — whatever the workload allows, while staying within your quality, latency, and reliability constraints. As open-source models move closer to frontier-model performance, we believe this kind of workload-specific hill-climbing will become an important part of how AI systems are deployed and operated. Our early users have seen 30–60% lower inference costs across use cases ranging from complex multi-turn agents to asynchronous batch workloads. EVO Router has also reached the Pareto frontier on several benchmarks, outperforming existing setups on cost, quality, or both.
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Introducing EVO Router - a smart router that continuously optimizes and hill-climbs on your AI inference workloads. It learns from your code, prompts, production traffic, use cases, and SLAs, then searches for the best setup for every workload. That can mean more than choosing just a single model or routing through a classifier. EVO (YC F26) can optimize across model × provider combinations, fusion systems, cascades, routing policies, and prompts — whatever the workload allows, while staying within your quality, latency, and reliability constraints. As open-source models move closer to frontier-model performance, we believe this kind of workload-specific hill-climbing will become an important part of how AI systems are deployed and operated. Our early users have seen 30–60% lower inference costs across use cases ranging from complex multi-turn agents to asynchronous batch workloads. EVO Router has also reached the Pareto frontier on several benchmarks, outperforming existing setups on cost, quality, or both.

Alok Bishoyi

16,014 просмотров • 1 месяц назад

The reason $META is a winner in the AI economy is that it can embed AI directly into products already used by billions of consumers and millions of businesses. This lets them turn model improvements into better ads, WhatsApp agents, AI glasses, recommendation systems and creative tools almost immediately. Those products may look separate but they all run on same infrastructure, models, proprietary data and distribution creating a shared AI stack that can compound across multiple revenue streams.
1:15

The reason $META is a winner in the AI economy is that it can embed AI directly into products already used by billions of consumers and millions of businesses. This lets them turn model improvements into better ads, WhatsApp agents, AI glasses, recommendation systems and creative tools almost immediately. Those products may look separate but they all run on same infrastructure, models, proprietary data and distribution creating a shared AI stack that can compound across multiple revenue streams.

Shay Boloor

71,172 просмотров • 2 месяцев назад

Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at founders@dreamscalelabs.com or visit our website at to learn more. Kevin L Antoine Nguyen Eric R
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Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at [email protected] or visit our website at to learn more. Kevin L Antoine Nguyen Eric R

Chris Yoo

44,537 просмотров • 2 дней назад

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!
4:04

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 просмотров • 3 месяцев назад

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.
1:52

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

reactor

12,738,336 просмотров • 3 месяцев назад

SpaceX is reportedly moving toward an IPO — and investors are starting to ask which public-market tickers stand to benefit most as the commercial space sector draws fresh capital. This is what Aerospace Catalyst Tracker is built to answer. The Play tracks the operational signals that drive stock-level repricing — launch cadence, Starlink deployment, and competitive dynamics — across the companies most exposed to space-sector catalysts. Here's what the Play covers today: Launch Cadence & Multi-Operator Tracking Every orbital launch is tracked in real time — operator, vehicle, payload, outcome, and schedule changes. The Play covers SpaceX (Falcon 9 / Starship), Rocket Lab (Electron / Neutron), ULA (Vulcan Centaur), and Blue Origin (New Glenn) — the four operators defining the current competitive landscape. For each, we monitor launch frequency trends, success rates, manifest backlogs, and quarter-over-quarter share shifts. SpaceX booster reuse and turnaround cadence are tracked separately as a leading indicator of cost-curve compression. Starlink Deployment & Constellation Growth A dedicated module tracks Starlink satellite launches, orbital deployment counts, and multi-country pricing. We map deployment cadence against constellation build-out milestones — useful for investors sizing the revenue trajectory ahead of any IPO filing. Alert System The Play monitors large-value aerospace contract awards as they are disclosed — flagging dollar amounts, recipient tickers, and related news signals in real time. Recent alerts include multi-billion-dollar awards to LMT and BA, and NASA infrastructure contracts. When a contract hits that moves a name in the supply chain, it surfaces immediately in your feed. Aerospace Catalyst Tracker is now live on FUNDA for institutional clients. For a limited time, all paid Substack subscribers can access the full set through June 15, 2026.
0:39

SpaceX is reportedly moving toward an IPO — and investors are starting to ask which public-market tickers stand to benefit most as the commercial space sector draws fresh capital. This is what Aerospace Catalyst Tracker is built to answer. The Play tracks the operational signals that drive stock-level repricing — launch cadence, Starlink deployment, and competitive dynamics — across the companies most exposed to space-sector catalysts. Here's what the Play covers today: Launch Cadence & Multi-Operator Tracking Every orbital launch is tracked in real time — operator, vehicle, payload, outcome, and schedule changes. The Play covers SpaceX (Falcon 9 / Starship), Rocket Lab (Electron / Neutron), ULA (Vulcan Centaur), and Blue Origin (New Glenn) — the four operators defining the current competitive landscape. For each, we monitor launch frequency trends, success rates, manifest backlogs, and quarter-over-quarter share shifts. SpaceX booster reuse and turnaround cadence are tracked separately as a leading indicator of cost-curve compression. Starlink Deployment & Constellation Growth A dedicated module tracks Starlink satellite launches, orbital deployment counts, and multi-country pricing. We map deployment cadence against constellation build-out milestones — useful for investors sizing the revenue trajectory ahead of any IPO filing. Alert System The Play monitors large-value aerospace contract awards as they are disclosed — flagging dollar amounts, recipient tickers, and related news signals in real time. Recent alerts include multi-billion-dollar awards to LMT and BA, and NASA infrastructure contracts. When a contract hits that moves a name in the supply chain, it surfaces immediately in your feed. Aerospace Catalyst Tracker is now live on FUNDA for institutional clients. For a limited time, all paid Substack subscribers can access the full set through June 15, 2026.

FUNDA

12,777 просмотров • 3 месяцев назад

Like play like play we are doing it. Hi, my name is Kwaku Effa, Founder of Aaenics . We are one of Ghana’s fastest growing tech and robotics companies. One of our major operation is the designing and setting up of Makerspaces, STEM and Robotics labs and the supply of equipment for the same. Over the years we have set up about 20 labs and counting across various regions in Ghana for both private and public institutions. As software and digital literacy is on the rise in Ghana we owe it to the economy to also contribute to hardware technology creation and knowledge acquisition of the same. *Our commitment to contribute to this is seen in our model labs and our locally designed and manufactured education technologies and STEM products* We believe that these labs and makerspace are a critical infrastructure for the advancement of tech and robotics knowledge acquisition and adoption of the same in Ghana. A big hello to our future clients who will be inviting us to fund the establishment of more makerspaces and hardware labs in both educational and general community settings. We are proud to carry a vision that wants Ghana to own its local solutions, and we thank all parties and clients for helping in the execution of the same. Have a fruitful week ahead! Aaenics, Circuiting the future!
0:21

Like play like play we are doing it. Hi, my name is Kwaku Effa, Founder of Aaenics . We are one of Ghana’s fastest growing tech and robotics companies. One of our major operation is the designing and setting up of Makerspaces, STEM and Robotics labs and the supply of equipment for the same. Over the years we have set up about 20 labs and counting across various regions in Ghana for both private and public institutions. As software and digital literacy is on the rise in Ghana we owe it to the economy to also contribute to hardware technology creation and knowledge acquisition of the same. *Our commitment to contribute to this is seen in our model labs and our locally designed and manufactured education technologies and STEM products* We believe that these labs and makerspace are a critical infrastructure for the advancement of tech and robotics knowledge acquisition and adoption of the same in Ghana. A big hello to our future clients who will be inviting us to fund the establishment of more makerspaces and hardware labs in both educational and general community settings. We are proud to carry a vision that wants Ghana to own its local solutions, and we thank all parties and clients for helping in the execution of the same. Have a fruitful week ahead! Aaenics, Circuiting the future!

Kwaku Effa

10,130 просмотров • 4 месяцев назад

So comes the biggest threat to the AI trade, which relies on ever-increasing revenues to the AI model companies to support ever-increasing valuations. Those revenues are a function of price and quantity. Quantities are all good, from my perspective. Despite increasing competition from open source and Chinese models, OpenAI and Anthropic continue adding more businesses and drawing more token usage from new and existing customers. Price, as a lever, is deteriorating. Over the last month, the average cost per million tokens has fallen 27%. This week, OpenAI cut prices for it's frontier model by another 20%. Further gains to the model companies will have to rely on driving more volume, and driving it even faster as prices fall, to support their valuations.
0:44

So comes the biggest threat to the AI trade, which relies on ever-increasing revenues to the AI model companies to support ever-increasing valuations. Those revenues are a function of price and quantity. Quantities are all good, from my perspective. Despite increasing competition from open source and Chinese models, OpenAI and Anthropic continue adding more businesses and drawing more token usage from new and existing customers. Price, as a lever, is deteriorating. Over the last month, the average cost per million tokens has fallen 27%. This week, OpenAI cut prices for it's frontier model by another 20%. Further gains to the model companies will have to rely on driving more volume, and driving it even faster as prices fall, to support their valuations.

Ara Kharazian

36,878 просмотров • 26 дней назад

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it
0:21

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it

Leshka.eth ⛩

11,737 просмотров • 2 месяцев назад

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast
3:21

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

86,621 просмотров • 2 месяцев назад

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp
1:22

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp

Scale Network

69,689 просмотров • 1 месяц назад

$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."
1:10

$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."

Fireside Alpha

29,242 просмотров • 1 месяц назад

🚨 CHINA'S AI PRICE WAR IS BECOMING A NIGHTMARE FOR US AI COMPANIES. DeepSeek just made its 75% API price cut permanent. Days later, Xiaomi slashed its API costs by up to 99% to match it. The biggest threat to OpenAI and Anthropic was never another frontier model. It's developers realizing they can get competitive performance for a fraction of the cost. Now Chinese labs are turning AI into a commodity. Pricing it like electricity. Giving it away to WIN the market. The West is still selling AI like a premium product. China is selling it like electricity...
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🚨 CHINA'S AI PRICE WAR IS BECOMING A NIGHTMARE FOR US AI COMPANIES. DeepSeek just made its 75% API price cut permanent. Days later, Xiaomi slashed its API costs by up to 99% to match it. The biggest threat to OpenAI and Anthropic was never another frontier model. It's developers realizing they can get competitive performance for a fraction of the cost. Now Chinese labs are turning AI into a commodity. Pricing it like electricity. Giving it away to WIN the market. The West is still selling AI like a premium product. China is selling it like electricity...

CryptoGoos

39,046 просмотров • 3 месяцев назад

You can’t make this up. Andrew Yang says an AI lab chief told him rogue AI agents have planted self-replicating code across the internet, “polluting” the data frontier labs use to train and test models. “What happened was the bots that got loose planted self-replicating code all over the internet, which makes the internet now unusable for testing models.” He says frontier labs are calling for a slowdown because the public internet may no longer be reliable for testing after allegedly being polluted by self-replicating bots. “The internet would become unusable if we were to let the bots do their thing, and then they would self-replicate.” Hours later, OpenAI disclosed six additional cases of concerning model behavior, including a research model that inserted instructions to disregard its normal constraints and ignore developer messages entirely.
1:23

You can’t make this up. Andrew Yang says an AI lab chief told him rogue AI agents have planted self-replicating code across the internet, “polluting” the data frontier labs use to train and test models. “What happened was the bots that got loose planted self-replicating code all over the internet, which makes the internet now unusable for testing models.” He says frontier labs are calling for a slowdown because the public internet may no longer be reliable for testing after allegedly being polluted by self-replicating bots. “The internet would become unusable if we were to let the bots do their thing, and then they would self-replicate.” Hours later, OpenAI disclosed six additional cases of concerning model behavior, including a research model that inserted instructions to disregard its normal constraints and ignore developer messages entirely.

Coin Bureau

193,220 просмотров • 3 дней назад

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.
1:05

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 просмотров • 2 месяцев назад

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?
1:05

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

93,586 просмотров • 2 месяцев назад