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"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do... show more

Select Committee on China

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41,578 просмотров • 4 месяцев назад •via X (Twitter)

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Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

DogeDesigner

244,339 просмотров • 24 дней назад

In the 20th century, power depended on OIL and STEEL. In the 21st century, power depends on AI, CHIPS and COMPUTE CAPABILITY. At AI Summit 2026 India, I spoke about why the GPU challenge is not just a ‘supply chain issue’ but a ‘sovereignty issue’. In the AI century, power will rest with those who control chips, compute capacity and data centres. Today, design monopolies, manufacturing concentration and export controls determine who gets access. India controls none of these critical levers. We have the talent. We have the ambition. We have the intent. We have the capital. But talent without compute is aspiration. And compute without sovereignty is dependence. The role of policymakers is not to slow AI down; it is to remove bottlenecks, diversify access, set clear national targets, and build sovereign AI infrastructure in India. This is not partisan. It is strategic. #IndiaAIImpactSummit2026 #AI #ResponsibleAI #ArtificialIntelligence #BharatMandapam

In the 20th century, power depended on OIL and STEEL. In the 21st century, power depends on AI, CHIPS and COMPUTE CAPABILITY. At AI Summit 2026 India, I spoke about why the GPU challenge is not just a ‘supply chain issue’ but a ‘sovereignty issue’. In the AI century, power will rest with those who control chips, compute capacity and data centres. Today, design monopolies, manufacturing concentration and export controls determine who gets access. India controls none of these critical levers. We have the talent. We have the ambition. We have the intent. We have the capital. But talent without compute is aspiration. And compute without sovereignty is dependence. The role of policymakers is not to slow AI down; it is to remove bottlenecks, diversify access, set clear national targets, and build sovereign AI infrastructure in India. This is not partisan. It is strategic. #IndiaAIImpactSummit2026 #AI #ResponsibleAI #ArtificialIntelligence #BharatMandapam

Raghav Chadha

70,153 просмотров • 6 месяцев назад

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

11,921 просмотров • 10 дней назад

Google is releasing updates that will let their AI scan all your photos and it will be used to create highly personalized AI image generations or you, your family, pets and more The AI is going to have access to your Gmail, YouTube, search history, photos, calendar and more. The concerning part is Google has admitted to using user data to improve its models People have screenshots of passwords, credit cards, photos of themselves doing intimate things. And all of that is gonna get uploaded to an AI system that is one of many that is consistently shown to be unreliable and not safe at all when it comes to data privacy Let’s not forget that have data leaks, one happened not too long ago Do we really want AI to have access to all this information and being sent back to these massive companies? You’ll be able to opt out of some of it but for your phone to have access to certain features you’ll need to opt in It always starts out optional, then becomes mandatory

Google is releasing updates that will let their AI scan all your photos and it will be used to create highly personalized AI image generations or you, your family, pets and more The AI is going to have access to your Gmail, YouTube, search history, photos, calendar and more. The concerning part is Google has admitted to using user data to improve its models People have screenshots of passwords, credit cards, photos of themselves doing intimate things. And all of that is gonna get uploaded to an AI system that is one of many that is consistently shown to be unreliable and not safe at all when it comes to data privacy Let’s not forget that have data leaks, one happened not too long ago Do we really want AI to have access to all this information and being sent back to these massive companies? You’ll be able to opt out of some of it but for your phone to have access to certain features you’ll need to opt in It always starts out optional, then becomes mandatory

Wall Street Apes

85,514 просмотров • 1 месяц назад

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Cluster Protocol

25,820 просмотров • 2 месяцев назад

$PLTR CEO Alex Karp argued that AI alone is not enough. The real value comes from combining models with domain expertise, workflows, and operational execution. His view is that enterprise AI is ultimately about solving business problems, not maximizing token usage. As models become commoditized, the winners may be the companies that combine taste, data, and execution rather than compute alone.

$PLTR CEO Alex Karp argued that AI alone is not enough. The real value comes from combining models with domain expertise, workflows, and operational execution. His view is that enterprise AI is ultimately about solving business problems, not maximizing token usage. As models become commoditized, the winners may be the companies that combine taste, data, and execution rather than compute alone.

Sam Badawi

36,574 просмотров • 2 месяцев назад

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 просмотров • 1 месяц назад

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 просмотров • 1 год назад

Data centers in space are a "no brainer." SpaceX's 1st employee Tom Mueller (Tom Mueller) on Elon Musk's plan to move compute into space: "It makes the most sense of anything to move to space. All you need as an input is power, and all you have as an output is data." "You move it to space, you have all the power you would ever need, and you transmit that data back down on a terawatt laser beam—it's solved. It's just so simple."

Data centers in space are a "no brainer." SpaceX's 1st employee Tom Mueller (Tom Mueller) on Elon Musk's plan to move compute into space: "It makes the most sense of anything to move to space. All you need as an input is power, and all you have as an output is data." "You move it to space, you have all the power you would ever need, and you transmit that data back down on a terawatt laser beam—it's solved. It's just so simple."

Molly O’Shea

357,597 просмотров • 2 месяцев назад

.Dan Shipper 📧: "The AI jobpocalypse is not a thing. The mass unemployment thing that AI lab CEOs are talking about—that's not going to happen. AI models make yesterday's human competence cheap. But what's interesting is that since everyone's using the same models, it all looks the same. So it becomes commoditized. It's not valuable anymore. And what humans do is we go in there, and we're like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting, today?"

.Dan Shipper 📧: "The AI jobpocalypse is not a thing. The mass unemployment thing that AI lab CEOs are talking about—that's not going to happen. AI models make yesterday's human competence cheap. But what's interesting is that since everyone's using the same models, it all looks the same. So it becomes commoditized. It's not valuable anymore. And what humans do is we go in there, and we're like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting, today?"

Lenny Rachitsky

276,114 просмотров • 2 месяцев назад

Why wouldn’t China want more data centers built in the United States? Simple, the nation that leads in AI will lead the future, economically, technologically, and militarily. We’re already seeing it happen in real time as AI drives unprecedented productivity and margin expansion across every sector of the economy. While America debates permits, regulations, and protests over data centers, China is rapidly building power infrastructure and scaling compute capacity as fast as possible. This race is no longer theoretical. Whoever has the most compute, the best models, and the strongest infrastructure will shape the next era of global power.

Why wouldn’t China want more data centers built in the United States? Simple, the nation that leads in AI will lead the future, economically, technologically, and militarily. We’re already seeing it happen in real time as AI drives unprecedented productivity and margin expansion across every sector of the economy. While America debates permits, regulations, and protests over data centers, China is rapidly building power infrastructure and scaling compute capacity as fast as possible. This race is no longer theoretical. Whoever has the most compute, the best models, and the strongest infrastructure will shape the next era of global power.

Kevin O'Leary aka Mr. Wonderful

35,533 просмотров • 3 месяцев назад

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

Latent.Space

529,014 просмотров • 6 месяцев назад

Ilya Sutskever just said that when it comes to AI models, we are back at the age of research & ending the age of scaling. What he is telling us is that more compute at this point won't help us get much better models; we need new breakthroughs. Not something that the semi companies like $NVDA, $AMD want to hear TBH.

Ilya Sutskever just said that when it comes to AI models, we are back at the age of research & ending the age of scaling. What he is telling us is that more compute at this point won't help us get much better models; we need new breakthroughs. Not something that the semi companies like $NVDA, $AMD want to hear TBH.

Rihard Jarc

361,477 просмотров • 8 месяцев назад

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 просмотров • 2 месяцев назад

Nvidia continues to dominate the AI industry with record earnings, explosive data center growth, and massive demand for compute. But the bigger story is what’s happening globally behind the scenes. China is aggressively competing with the United States on AI infrastructure, power generation, and data center expansion and they don’t want to rely on American chips to do it. This is no longer just about stocks it’s a global race for AI dominance.

Nvidia continues to dominate the AI industry with record earnings, explosive data center growth, and massive demand for compute. But the bigger story is what’s happening globally behind the scenes. China is aggressively competing with the United States on AI infrastructure, power generation, and data center expansion and they don’t want to rely on American chips to do it. This is no longer just about stocks it’s a global race for AI dominance.

Kevin O'Leary aka Mr. Wonderful

12,462 просмотров • 2 месяцев назад

Former EU foreign policy chief Josep Borrell: Russia will only be able to destroy Ukraine if Ukraine is unable to defend itself. If the US does not provide the means to do so, are we going to do it? Question mark. This is the existential question that Europe must answer for itself: Are we in a position to help Ukraine defend itself even if the United States does not? It depends on how willing we are. So far, we have helped Ukraine more than the United States, both in civilian and military terms. Can we do even more and replace the United States? This comes at a cost, and what is needed is not only the will and the ability to pay for it, but also to convert the money into weapons. Because people don't go to the front with a bag of cash; people go to the front with weapons. And between money and weapons there is something called industry, that is, production capacity.

Former EU foreign policy chief Josep Borrell: Russia will only be able to destroy Ukraine if Ukraine is unable to defend itself. If the US does not provide the means to do so, are we going to do it? Question mark. This is the existential question that Europe must answer for itself: Are we in a position to help Ukraine defend itself even if the United States does not? It depends on how willing we are. So far, we have helped Ukraine more than the United States, both in civilian and military terms. Can we do even more and replace the United States? This comes at a cost, and what is needed is not only the will and the ability to pay for it, but also to convert the money into weapons. Because people don't go to the front with a bag of cash; people go to the front with weapons. And between money and weapons there is something called industry, that is, production capacity.

Anton Gerashchenko

51,525 просмотров • 10 месяцев назад

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.

Milk Road AI

419,283 просмотров • 3 месяцев назад

Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms. Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat. Sergey thinks that framing is wrong. He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small. He says the same thing has happened in AI over the last decade. Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead. The market is pricing AI winners by who has the most chips. Sergey Brin just said that is the wrong scorecard. The ones who win this are not building the biggest data center. They are solving the harder math problem.

Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms. Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat. Sergey thinks that framing is wrong. He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small. He says the same thing has happened in AI over the last decade. Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead. The market is pricing AI winners by who has the most chips. Sergey Brin just said that is the wrong scorecard. The ones who win this are not building the biggest data center. They are solving the harder math problem.

Ihtesham Ali

130,249 просмотров • 2 месяцев назад

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

Milk Road AI

16,630 просмотров • 1 месяц назад