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What are the physics constraints holding back AI infrastructure? Meltem Demirors and Kelly Greer: "AI is basically just taking electrons from the ground and running them through GPUs, you get tokens out of your computer." "Power is the ultimate constraint, gigawatts of backlog to connect to the grid. How... do we consume less power but spit out more tokens?" "When a computer consumes power, it generates heat. We're literally melting racks, Sam Altman tweeted about his GPUs melting at OpenAI." "The third constraint is information. It hasn't changed since Claude Shannon's seminal paper in 1941, we're still using the physics he uncovered then." "The next frontier is optics, computing done through light. But what's beyond the speed of light?"show more

MTS

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“I think next decade is going to be all about energy. Even Sam Altman from OpenAI talked about the evolution of AI — algorithms run on chips, but ultimately the chips need electricity. The algorithms will get better and better, and chips will get cheaper and cheaper. But at the end of the day, you have to get the electrons, and those electrons have a fundamental price. Even the AI competition between companies and countries is ultimately going to come back to electricity production. So next decade is going to be all about power — AI, manufacturing, the economy — they all come back to energy.” Scott Nolan

“I think next decade is going to be all about energy. Even Sam Altman from OpenAI talked about the evolution of AI — algorithms run on chips, but ultimately the chips need electricity. The algorithms will get better and better, and chips will get cheaper and cheaper. But at the end of the day, you have to get the electrons, and those electrons have a fundamental price. Even the AI competition between companies and countries is ultimately going to come back to electricity production. So next decade is going to be all about power — AI, manufacturing, the economy — they all come back to energy.” Scott Nolan

Shawn Ryan

118,797 views • 1 year ago

Sam Altman shared a number that should change how you think about AI. Six and a half years ago, the heaviest AI user on the planet was an OpenAI employee. Using 100,000 tokens a month. That was considered insane at the time. The global average was basically zero. Today the global average is 100,000 tokens a month. What was insane is now normal. And the heaviest user at OpenAI right now? Hundreds of billions of tokens a month. Now here's where it gets wild. If the same pattern repeats, in another six and a half years the average person will use 500 billion tokens a month. That's a 5 million x increase from today. Think about what that means. → AI is not something you open once a day to ask a question anymore. It becomes always on. Running in the background across your work, education, personal life. → Agentic workflows, coding assistants, multi-step reasoning — that's what consumes billions of tokens. Not chatting. → OpenAI is already building for this. Hundreds of billions in infrastructure spend through 2030. Stargate. Partnerships with Microsoft, Oracle, Nvidia. And cost per token keeps falling roughly 10x every 12 months for the same capability level. So usage explodes because it keeps getting cheaper. The pattern is the same one we saw with computing, internet, and mobile. What the power user does today, the average person does in six years. Right now you are the power user. Act like it.

Sam Altman shared a number that should change how you think about AI. Six and a half years ago, the heaviest AI user on the planet was an OpenAI employee. Using 100,000 tokens a month. That was considered insane at the time. The global average was basically zero. Today the global average is 100,000 tokens a month. What was insane is now normal. And the heaviest user at OpenAI right now? Hundreds of billions of tokens a month. Now here's where it gets wild. If the same pattern repeats, in another six and a half years the average person will use 500 billion tokens a month. That's a 5 million x increase from today. Think about what that means. → AI is not something you open once a day to ask a question anymore. It becomes always on. Running in the background across your work, education, personal life. → Agentic workflows, coding assistants, multi-step reasoning — that's what consumes billions of tokens. Not chatting. → OpenAI is already building for this. Hundreds of billions in infrastructure spend through 2030. Stargate. Partnerships with Microsoft, Oracle, Nvidia. And cost per token keeps falling roughly 10x every 12 months for the same capability level. So usage explodes because it keeps getting cheaper. The pattern is the same one we saw with computing, internet, and mobile. What the power user does today, the average person does in six years. Right now you are the power user. Act like it.

Vaibhav Sisinty

24,323 views • 1 month ago

Today on CNBC Power Lunch, TeraWulf CEO Paul Prager summed up the AI/HPC bottleneck perfectly. Brian Sullivan: “Is there any indication that companies needing compute power are dialing back? Considering Google is one of your clients.” Paul Prager: “Not to my knowledge… GPUs are critical, but the real constraint now is scalable power infrastructure.” Paul made clear today that WULF’s edge in AI/HPC starts with energy infrastructure and scalable power, built on 25 years of experience. Interview faded out a bit at the end, apologies. Also, while this was airing, $WULF hit a new 52-wk high of $26.85 🐺

Today on CNBC Power Lunch, TeraWulf CEO Paul Prager summed up the AI/HPC bottleneck perfectly. Brian Sullivan: “Is there any indication that companies needing compute power are dialing back? Considering Google is one of your clients.” Paul Prager: “Not to my knowledge… GPUs are critical, but the real constraint now is scalable power infrastructure.” Paul made clear today that WULF’s edge in AI/HPC starts with energy infrastructure and scalable power, built on 25 years of experience. Interview faded out a bit at the end, apologies. Also, while this was airing, $WULF hit a new 52-wk high of $26.85 🐺

Scott

11,683 views • 4 months ago

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

MTS

35,125 views • 3 months ago

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Fireside Alpha

162,832 views • 2 months ago

Microsoft's Fairwater Wisconsin went live today, ahead of schedule. It's the world's most powerful AI data center. Hundreds of thousands of NVIDIA GB200 GPUs, wired to behave as a single computer. The physical build is biblical. 315 acres. 1.2 million square feet across three buildings. 26.5 million pounds of steel. 46.6 miles of foundation piles. 120 miles of underground cable. The storage wing alone runs the length of five football fields. Each rack holds 72 GPUs sharing 14 terabytes of pooled memory, pushing 865,000 tokens per second. One rack generates more text per second than you've written in your entire life. Fairwater holds thousands of racks. The speed of light became a real bottleneck. Cable lengths between racks were introducing latency that slowed training. Microsoft's fix was a two-story layout so racks sit directly above and below each other. We are now architecting around special relativity to train AI. The power numbers are where it gets surreal. Phase 1 pulls 400 megawatts. Full build-out approaches 900 megawatts. Roughly a nuclear reactor, feeding one computer. The grid draw equals 300,000 homes. Fairwater Wisconsin is only the first. Atlanta is already online. Norway and the UK are next. Microsoft is stringing them together over 120,000 miles of dedicated fiber into one AI superfactory with 2+ gigawatts of total capacity. The buildings outlast the chips. GB200 gets swapped for GB300, then Rubin. The cells are modular. Microsoft's real bet is on the real estate, the power hookups, and the fiber. AI progress is now gated by concrete and megawatts.

Microsoft's Fairwater Wisconsin went live today, ahead of schedule. It's the world's most powerful AI data center. Hundreds of thousands of NVIDIA GB200 GPUs, wired to behave as a single computer. The physical build is biblical. 315 acres. 1.2 million square feet across three buildings. 26.5 million pounds of steel. 46.6 miles of foundation piles. 120 miles of underground cable. The storage wing alone runs the length of five football fields. Each rack holds 72 GPUs sharing 14 terabytes of pooled memory, pushing 865,000 tokens per second. One rack generates more text per second than you've written in your entire life. Fairwater holds thousands of racks. The speed of light became a real bottleneck. Cable lengths between racks were introducing latency that slowed training. Microsoft's fix was a two-story layout so racks sit directly above and below each other. We are now architecting around special relativity to train AI. The power numbers are where it gets surreal. Phase 1 pulls 400 megawatts. Full build-out approaches 900 megawatts. Roughly a nuclear reactor, feeding one computer. The grid draw equals 300,000 homes. Fairwater Wisconsin is only the first. Atlanta is already online. Norway and the UK are next. Microsoft is stringing them together over 120,000 miles of dedicated fiber into one AI superfactory with 2+ gigawatts of total capacity. The buildings outlast the chips. GB200 gets swapped for GB300, then Rubin. The cells are modular. Microsoft's real bet is on the real estate, the power hookups, and the fiber. AI progress is now gated by concrete and megawatts.

Aakash Gupta

382,823 views • 5 months ago

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 views • 3 months ago

$Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:$

Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:

Computer

200,997 views • 10 months ago

Eric Schmidt just told Congress the number that kills the AI race on Earth: 92 gigawatts of new power, and we can’t deliver it. Former Google CEO laid out math everyone’s ignoring. Average nuclear plant: 1.5 gigawatts. AI demand: 92 gigawatts. That’s 60+ new nuclear facilities needed now, not decades from now. Schmidt: “We need 92 gigawatts more power.” Not happening. Infrastructure doesn’t exist. Approval takes years. Grid physically can’t absorb it. We’re out of electricity. Schmidt investing in Relativity Space isn’t billionaire space hobby. He spotted the bottleneck killing everything and he’s building the only exit that works. Can’t build power plants on Earth fast enough? Move compute off Earth. Schmidt: “You see the problem.” AI doesn’t hit an algorithm wall or chip shortage. It hits power ceiling. The grid can’t deliver 92 gigawatts at the speed AI development demands. Physically impossible to build that capacity terrestrially in relevant timeframes. Not a grid problem. A location problem. Next phase of compute can’t happen on the surface. Period. Heat, power draw, infrastructure limits, all of it forces migration to orbit. Only place with unlimited energy and zero conflicts is space. Schmidt: “We’re running out of electricity.” Direct assessment from someone watching what’s actually being deployed. The gap separating what AI needs and what Earth can provide is unbridgeable at required speeds. Not technical constraints. Physical reality. His aerospace play isn’t exploration. It’s escape route from a grid approaching collapse under computational demand it was never designed to handle. Scaling AI to the levels every major company is planning requires abandoning the planet. Not eventually. Now. Because the alternative is power walls that stop everything regardless of algorithmic genius or hardware breakthroughs. Doesn’t matter how perfect your models are or how many chips you fabricate if you can’t turn them on. And Earth can’t generate power fast enough for what the next five years require. Space isn’t the ambitious choice anymore. It’s the only choice avoiding hard physics limits on how fast you can deploy power generation on a regulated planetary surface. The AI race doesn’t end when someone builds superior intelligence. It ends when they can’t power it while competitors in orbit operate without energy ceilings. And that’s not distant future. That’s the constraint arriving right now that nobody building exclusively on Earth has an answer for.

Eric Schmidt just told Congress the number that kills the AI race on Earth: 92 gigawatts of new power, and we can’t deliver it. Former Google CEO laid out math everyone’s ignoring. Average nuclear plant: 1.5 gigawatts. AI demand: 92 gigawatts. That’s 60+ new nuclear facilities needed now, not decades from now. Schmidt: “We need 92 gigawatts more power.” Not happening. Infrastructure doesn’t exist. Approval takes years. Grid physically can’t absorb it. We’re out of electricity. Schmidt investing in Relativity Space isn’t billionaire space hobby. He spotted the bottleneck killing everything and he’s building the only exit that works. Can’t build power plants on Earth fast enough? Move compute off Earth. Schmidt: “You see the problem.” AI doesn’t hit an algorithm wall or chip shortage. It hits power ceiling. The grid can’t deliver 92 gigawatts at the speed AI development demands. Physically impossible to build that capacity terrestrially in relevant timeframes. Not a grid problem. A location problem. Next phase of compute can’t happen on the surface. Period. Heat, power draw, infrastructure limits, all of it forces migration to orbit. Only place with unlimited energy and zero conflicts is space. Schmidt: “We’re running out of electricity.” Direct assessment from someone watching what’s actually being deployed. The gap separating what AI needs and what Earth can provide is unbridgeable at required speeds. Not technical constraints. Physical reality. His aerospace play isn’t exploration. It’s escape route from a grid approaching collapse under computational demand it was never designed to handle. Scaling AI to the levels every major company is planning requires abandoning the planet. Not eventually. Now. Because the alternative is power walls that stop everything regardless of algorithmic genius or hardware breakthroughs. Doesn’t matter how perfect your models are or how many chips you fabricate if you can’t turn them on. And Earth can’t generate power fast enough for what the next five years require. Space isn’t the ambitious choice anymore. It’s the only choice avoiding hard physics limits on how fast you can deploy power generation on a regulated planetary surface. The AI race doesn’t end when someone builds superior intelligence. It ends when they can’t power it while competitors in orbit operate without energy ceilings. And that’s not distant future. That’s the constraint arriving right now that nobody building exclusively on Earth has an answer for.

Dustin

160,358 views • 7 months ago

Daniel Roberts of $IREN just laid out the brutal reality of the AI infrastructure buildout. He noted we are still in the dial up era of AI. The ultimate choke point is power. It requires steel, concrete, and years of physical construction. Starting a gigawatt AI factory today means you will not see the first compute online until the 2030s. It takes 18 to 24 months just to get a utility company to confirm if a piece of land even has power access. The market is pricing in rapid software scaling, but the physical bottlenecks are severe and unavoidable. The capital moving into power generation and data center infrastructure is a multi year trend. It’s not a sprint it’s a marathon.

Daniel Roberts of $IREN just laid out the brutal reality of the AI infrastructure buildout. He noted we are still in the dial up era of AI. The ultimate choke point is power. It requires steel, concrete, and years of physical construction. Starting a gigawatt AI factory today means you will not see the first compute online until the 2030s. It takes 18 to 24 months just to get a utility company to confirm if a piece of land even has power access. The market is pricing in rapid software scaling, but the physical bottlenecks are severe and unavoidable. The capital moving into power generation and data center infrastructure is a multi year trend. It’s not a sprint it’s a marathon.

Patient Investor

176,637 views • 4 months ago

Sam Altman: "Very soon an AI will just be running for you in the background all the time" That entire model is on its way out. But the vision isn't the tell. This is: He says everyone's going to want "so much more of this AI infrastructure than we think." That's not a roadmap. That's a man admitting he can't build chips and power fast enough. The always-on assistant is the headline. The infrastructure bottleneck underneath it is the trade almost nobody is pricing in. That gap is the whole edge.

Sam Altman: "Very soon an AI will just be running for you in the background all the time" That entire model is on its way out. But the vision isn't the tell. This is: He says everyone's going to want "so much more of this AI infrastructure than we think." That's not a roadmap. That's a man admitting he can't build chips and power fast enough. The always-on assistant is the headline. The infrastructure bottleneck underneath it is the trade almost nobody is pricing in. That gap is the whole edge.

plutos

25,689 views • 3 months ago

Zach Dell reveals his board member Antonio Gracias asked only one question for two years, the business's constraint "And I've also learned a lot of this from Antonio Gracias, who's on my board and has worked fairly closely with Elon for decades." "The first year or two of working with Antonio, basically every conversation I had with him, and still to this day this is the main focus of all of our conversations, every conversation was the same." "Hey, Antonio, how's it going? Good. How are you? What's up?" "You know, Zach, what's your constraint? Like, what's the constraint of the business?" "And I would just answer the question, and that's all we would talk about. That's it. What's the constraint? What's the constraint? What's the constraint?" "Like, if we agree on the mission, the vision, and the strategy, okay, we're good there. What's constraining us to get to that? Let's just focus all of our time and energy on that."

Zach Dell reveals his board member Antonio Gracias asked only one question for two years, the business's constraint "And I've also learned a lot of this from Antonio Gracias, who's on my board and has worked fairly closely with Elon for decades." "The first year or two of working with Antonio, basically every conversation I had with him, and still to this day this is the main focus of all of our conversations, every conversation was the same." "Hey, Antonio, how's it going? Good. How are you? What's up?" "You know, Zach, what's your constraint? Like, what's the constraint of the business?" "And I would just answer the question, and that's all we would talk about. That's it. What's the constraint? What's the constraint? What's the constraint?" "Like, if we agree on the mission, the vision, and the strategy, okay, we're good there. What's constraining us to get to that? Let's just focus all of our time and energy on that."

Fireside Alpha

132,041 views • 22 days ago

Gov. Spanberger: "In his farewell address, George Washington warned us about the possibility of 'cunning, ambitious, and unprincipled men' rising to power. But he also encouraged us, all Americans, to unite in a common cause to move this nation forward. That is our charge once more, and that is what we are seeing across the country. It is deeply American and patriotic to do so, and it is how we ensure that the state of our union remains strong, not just this year, but for the next 250 years as well. Because we, the people, have the power to make change, the power to stand up for what is right, the power to demand more of our nation."

Gov. Spanberger: "In his farewell address, George Washington warned us about the possibility of 'cunning, ambitious, and unprincipled men' rising to power. But he also encouraged us, all Americans, to unite in a common cause to move this nation forward. That is our charge once more, and that is what we are seeing across the country. It is deeply American and patriotic to do so, and it is how we ensure that the state of our union remains strong, not just this year, but for the next 250 years as well. Because we, the people, have the power to make change, the power to stand up for what is right, the power to demand more of our nation."

The Bulwark

56,334 views • 7 months ago

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

Arjun Khemani

33,110 views • 1 year ago

Jimmy Carr channeling Peter Thiel with something worth sitting with: "Minus the screens from any room, we're still living in the 1970s. Nothing's happened in physics since 1972. String theory has not got us anywhere." But here's where it gets interesting: "Take the compute power of AI and point it at physics… everything else in science is stamp collecting. Physics is the real thing. That gave us every bit of technology we have. What happens when you point AI at that?" He lays out two possible roads: 50× productivity and genuine human flourishing, or a future that goes "another way." AI unlocks physics within a decade. What do you want to come first: limitless energy, gravity control, new materials, or something we don't even have a word for yet? Interested in AI? Follow AI Evolution and never fall behind. I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.

Jimmy Carr channeling Peter Thiel with something worth sitting with: "Minus the screens from any room, we're still living in the 1970s. Nothing's happened in physics since 1972. String theory has not got us anywhere." But here's where it gets interesting: "Take the compute power of AI and point it at physics… everything else in science is stamp collecting. Physics is the real thing. That gave us every bit of technology we have. What happens when you point AI at that?" He lays out two possible roads: 50× productivity and genuine human flourishing, or a future that goes "another way." AI unlocks physics within a decade. What do you want to come first: limitless energy, gravity control, new materials, or something we don't even have a word for yet? Interested in AI? Follow AI Evolution and never fall behind. I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.

AI Evolution

162,335 views • 1 month ago

Is fear of AI holding the United States back? Some of the concern is understandable, we saw the same anxiety around the internet in the 1990s, but I believe AI will advance science, education, and create jobs we cannot even imagine today. The bigger issue is infrastructure. Our grid is tapped out, China is building power at an enormous pace, and the United States cannot afford to fall behind. If we want to lead in AI and remain globally competitive, we have to build the energy capacity to support it.

Is fear of AI holding the United States back? Some of the concern is understandable, we saw the same anxiety around the internet in the 1990s, but I believe AI will advance science, education, and create jobs we cannot even imagine today. The bigger issue is infrastructure. Our grid is tapped out, China is building power at an enormous pace, and the United States cannot afford to fall behind. If we want to lead in AI and remain globally competitive, we have to build the energy capacity to support it.

Kevin O'Leary aka Mr. Wonderful

36,307 views • 1 month ago

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

246,547 views • 2 months ago