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$ASML CEO warns that without major gains in power efficiency, training frontier AI models could eventually consume the world’s energy supply. If scaling laws hold and no breakthroughs emerge, models trained in 2027 may require $100B+ compute clusters just to run.

Shay Boloor

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295,799 views • 6 months ago •via X (Twitter)

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$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 views • 20 days ago

"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 on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

"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 on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

Select Committee on China

41,578 views • 4 months ago

Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

NVIDIA AI Infrastructure

13,329 views • 7 months ago

“We want to orchestrate the world’s compute, in the same way Bitcoin did, but to train models that can rival ChatGPT” crux on the This Week in Startups podcast, sharing the motivations behind IOTA ・ SN9: to train frontier models efficiently and at scale, in an age where CapEx for centralized training is reaching unsustainable highs.

“We want to orchestrate the world’s compute, in the same way Bitcoin did, but to train models that can rival ChatGPT” crux on the This Week in Startups podcast, sharing the motivations behind IOTA ・ SN9: to train frontier models efficiently and at scale, in an age where CapEx for centralized training is reaching unsustainable highs.

Macrocosmos

14,467 views • 4 months ago

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

ELON CLIPS

95,313 views • 9 months ago

So what Warren Pies is saying: Anthropic’s Mythos rollout triggered a sharp collapse in $NVDA GPU availability, with supply effectively tightening to near zero as demand for frontier models accelerated. Q1 2026 revenue from Anthropic reinforces that this isn’t theoretical demand, it’s real dollars chasing compute at scale across the AI ecosystem, from $GOOGL GCP to $AMZN AWS. The takeaway is simple: better models require more compute, and that feedback loop continues to drive the entire AI infrastructure trade.

So what Warren Pies is saying: Anthropic’s Mythos rollout triggered a sharp collapse in $NVDA GPU availability, with supply effectively tightening to near zero as demand for frontier models accelerated. Q1 2026 revenue from Anthropic reinforces that this isn’t theoretical demand, it’s real dollars chasing compute at scale across the AI ecosystem, from $GOOGL GCP to $AMZN AWS. The takeaway is simple: better models require more compute, and that feedback loop continues to drive the entire AI infrastructure trade.

Sam Badawi

21,184 views • 3 months ago

Is uranium the future of AI? As demand for cloud computing and AI models continues to explode, the real bottleneck is becoming power. That’s what led me to start looking at nuclear energy and uranium. Small modular reactors could become a major part of the solution, and if AI keeps demanding more electricity, the commodities that power that infrastructure could become just as important as the chips themselves.

Is uranium the future of AI? As demand for cloud computing and AI models continues to explode, the real bottleneck is becoming power. That’s what led me to start looking at nuclear energy and uranium. Small modular reactors could become a major part of the solution, and if AI keeps demanding more electricity, the commodities that power that infrastructure could become just as important as the chips themselves.

Kevin O'Leary aka Mr. Wonderful

34,635 views • 6 days ago

ELON: AI CLUSTERS WILL BECOME A NORMAL THING EVERY COUNTRY HAS Compute is becoming a new currency. While building a "frontier model" today is a high-stakes gauntlet that requires a level of technical skill only a few companies possess, he predicts that AI compute clusters are about to become as essential to a nation as a power grid or a military. By 2026, "AI Sovereignty" will be the only thing keeping nations from becoming digital colonies of Silicon Valley or Beijing: “Probably all countries will have their own AI clusters over time. It is currently very difficult to actually build an AI cluster and have it run... because if you are training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But over time, I think every country will have AI compute clusters. It is just going to be a normal thing that every country has.” Source: vitrupo Elon Musk

ELON: AI CLUSTERS WILL BECOME A NORMAL THING EVERY COUNTRY HAS Compute is becoming a new currency. While building a "frontier model" today is a high-stakes gauntlet that requires a level of technical skill only a few companies possess, he predicts that AI compute clusters are about to become as essential to a nation as a power grid or a military. By 2026, "AI Sovereignty" will be the only thing keeping nations from becoming digital colonies of Silicon Valley or Beijing: “Probably all countries will have their own AI clusters over time. It is currently very difficult to actually build an AI cluster and have it run... because if you are training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But over time, I think every country will have AI compute clusters. It is just going to be a normal thing that every country has.” Source: vitrupo Elon Musk

Mario Nawfal

20,502 views • 7 months ago

📁 Demis Hassabis, CEO of Google DeepMind, says Gemini 3 is progressing exactly as expected and remains the fastest advancing project in the industry. He adds that more gains in reasoning, memory and world models are needed to reach fully consistent general intelligence. He expects one or two major breakthroughs and another five to ten years of development.

📁 Demis Hassabis, CEO of Google DeepMind, says Gemini 3 is progressing exactly as expected and remains the fastest advancing project in the industry. He adds that more gains in reasoning, memory and world models are needed to reach fully consistent general intelligence. He expects one or two major breakthroughs and another five to ten years of development.

Jon Hernandez

30,907 views • 9 months ago

BREAKING: Zach Dell (Zach Dell) CEO of Base Power says " Chinese models are going to overtake domestic models in usage." "I spend a lot of my time thinking about energy, the country, & our ability to consume, generate, store, sell electricity. And I spend a lot of time thinking about China & their ability to produce & consume electricity. One thing that has been interesting to me is Chinese tokens & the rate at which they're being generated & the cost. My hot take is that Chinese models are going to overtake domestic models in terms of usage. If you look at platforms like OpenRouter & other online platforms where you can track model usage, I think the Chinese are gonna continue investing incredibly heavily in energy production. They're going to capture the Watt flop or the Watt token spread by turning a lot of that energy into compute. And that's gonna drive massive amount of usage in Chinese models, which just means we have to go faster. The US energy infrastructure industry has to go faster. We have to build power faster. We have to develop compute infrastructure faster. We must accelerate." "We're here at Hill & Valley, which has me feeling awfully patriotic." Base Power at Hill & Valley Forum 2026 (The Hill & Valley Forum)

BREAKING: Zach Dell (Zach Dell) CEO of Base Power says " Chinese models are going to overtake domestic models in usage." "I spend a lot of my time thinking about energy, the country, & our ability to consume, generate, store, sell electricity. And I spend a lot of time thinking about China & their ability to produce & consume electricity. One thing that has been interesting to me is Chinese tokens & the rate at which they're being generated & the cost. My hot take is that Chinese models are going to overtake domestic models in terms of usage. If you look at platforms like OpenRouter & other online platforms where you can track model usage, I think the Chinese are gonna continue investing incredibly heavily in energy production. They're going to capture the Watt flop or the Watt token spread by turning a lot of that energy into compute. And that's gonna drive massive amount of usage in Chinese models, which just means we have to go faster. The US energy infrastructure industry has to go faster. We have to build power faster. We have to develop compute infrastructure faster. We must accelerate." "We're here at Hill & Valley, which has me feeling awfully patriotic." Base Power at Hill & Valley Forum 2026 (The Hill & Valley Forum)

Molly O’Shea

41,529 views • 5 months ago

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

Nina Schick

17,743 views • 7 months ago

China’s arrival as a serious AI competitor threatens to upend an investment boom so far dominated by a small group of mostly US and allied companies. Chinese labs are already producing frontier-level models despite restricted access to advanced chips, while a determined push to build a domestic semiconductor supply chain could eventually challenge the incumbents in AI hardware. In this interview, Will Denyer considers the winners and losers from the disruption, arguing that while Chinese competition threatens model and hardware makers, cheaper models and computing power should ultimately benefit cloud providers and AI users.

China’s arrival as a serious AI competitor threatens to upend an investment boom so far dominated by a small group of mostly US and allied companies. Chinese labs are already producing frontier-level models despite restricted access to advanced chips, while a determined push to build a domestic semiconductor supply chain could eventually challenge the incumbents in AI hardware. In this interview, Will Denyer considers the winners and losers from the disruption, arguing that while Chinese competition threatens model and hardware makers, cheaper models and computing power should ultimately benefit cloud providers and AI users.

Gavekal

10,590 views • 12 days ago

AI at scale is hitting a hard limit with energy. As AI infrastructure expands, power demand, not just compute architecture, is emerging as the gating constraint and pushing frontier builders toward vertically integrated deep tech bets spanning quantum discovery and fusion energy. Lenovo Data Center's Flynn Maloy and Jason Gibson, CEO and Co-founder of Quantum Formatics, discuss their collaboration to meet this challenge. They're betting on a dual-front strategy: leveraging quantum-driven simulation to accelerate material discovery while simultaneously pursuing fusion as the long-term energy unlock. Next-generation infrastructure requires breakthroughs not just in chips, but in the physical systems that power them.

AI at scale is hitting a hard limit with energy. As AI infrastructure expands, power demand, not just compute architecture, is emerging as the gating constraint and pushing frontier builders toward vertically integrated deep tech bets spanning quantum discovery and fusion energy. Lenovo Data Center's Flynn Maloy and Jason Gibson, CEO and Co-founder of Quantum Formatics, discuss their collaboration to meet this challenge. They're betting on a dual-front strategy: leveraging quantum-driven simulation to accelerate material discovery while simultaneously pursuing fusion as the long-term energy unlock. Next-generation infrastructure requires breakthroughs not just in chips, but in the physical systems that power them.

Six Five Media

224,069 views • 4 months ago

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 views • 4 months ago

Geoffrey Hinton says the real breakthrough in AI wasn’t just new algorithms, but massive scaling of data and computation. Ilya Sutskever predicted early that simply making models bigger would make them better, and he turned out to be right. While new ideas like transformers helped, the billion-fold increase in compute power and larger datasets drove most of the progress.

Geoffrey Hinton says the real breakthrough in AI wasn’t just new algorithms, but massive scaling of data and computation. Ilya Sutskever predicted early that simply making models bigger would make them better, and he turned out to be right. While new ideas like transformers helped, the billion-fold increase in compute power and larger datasets drove most of the progress.

Wes Roth

14,922 views • 1 year ago

Elon Musk predicts AI compute in space could become cheaper than terrestrial AI far sooner than most people expect....possibly within just 2–3 years Because solar panels in orbit can generate roughly 5x more usable energy than on Earth because there’s: • No atmosphere blocking sunlight • No day-night cycle • No seasonal variation • Continuous exposure to the Sun • Far higher solar energy efficiency than on Earth Space-based solar can eventually become cheaper than terrestrial solar because you don’t need massive protective structures, heavy glass, or weather-resistant infrastructure And once launch costs fall low enough with fully reusable rockets like Starship, deploying AI clusters in orbit could become economically compelling The future of AI will scale with solar-powered compute infrastructure operating directly in space

Elon Musk predicts AI compute in space could become cheaper than terrestrial AI far sooner than most people expect....possibly within just 2–3 years Because solar panels in orbit can generate roughly 5x more usable energy than on Earth because there’s: • No atmosphere blocking sunlight • No day-night cycle • No seasonal variation • Continuous exposure to the Sun • Far higher solar energy efficiency than on Earth Space-based solar can eventually become cheaper than terrestrial solar because you don’t need massive protective structures, heavy glass, or weather-resistant infrastructure And once launch costs fall low enough with fully reusable rockets like Starship, deploying AI clusters in orbit could become economically compelling The future of AI will scale with solar-powered compute infrastructure operating directly in space

X Freeze

20,162 views • 3 months ago

📁 Demis Hassabis, CEO of DeepMind, says that current AI systems are not self-aware or conscious in any real sense. He explains that self-awareness is not an explicit goal, but it could emerge implicitly as models develop a deeper understanding of self and others. He warns that if a machine did become self-aware, we might not even be able to recognize it.

📁 Demis Hassabis, CEO of DeepMind, says that current AI systems are not self-aware or conscious in any real sense. He explains that self-awareness is not an explicit goal, but it could emerge implicitly as models develop a deeper understanding of self and others. He warns that if a machine did become self-aware, we might not even be able to recognize it.

Jon Hernandez

78,350 views • 8 months ago