Loading video...

Video Failed to Load

Go Home

CleanSpark $CLSK Targets Gigawatts for AI Data Centers CEO Matt Schultz's strategy: secure gigawatts under contract first. "Priority one is power and land because that is as scarce as it comes." Meta and peers seek 500-1,000 megawatt sites—full gigawatt deployments. CleanSpark built 33 datacenters in 5 years. Reliability drives...

TreasuryEdge's profile picture

Treasury Edge

16,325 subscribers

34,861 views • 9 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

This is Amazon's new $11 billion AI data center campus in St. Joseph County, Indiana. It will draw 2.2 gigawatts of electricity - the output of a large power plant, enough to supply 1 million homes. For years the public was told energy had to be rationed: use less, dismantle reliable sources, accept scarcity - all to save the planet. That story has now been abandoned. AI requires massive continuous electricity to power the data centers, and AI is now seen as the superior control system. So energy is no longer a liability. It is a strategic necessity. Power will now come from wherever. Whatever works. Whatever scales. The climate narrative was tossed out the moment AI dominance became the new priority. Power was to be rationed. Now it is required. Power was the problem. Now it is the solution.
0:47

This is Amazon's new $11 billion AI data center campus in St. Joseph County, Indiana. It will draw 2.2 gigawatts of electricity - the output of a large power plant, enough to supply 1 million homes. For years the public was told energy had to be rationed: use less, dismantle reliable sources, accept scarcity - all to save the planet. That story has now been abandoned. AI requires massive continuous electricity to power the data centers, and AI is now seen as the superior control system. So energy is no longer a liability. It is a strategic necessity. Power will now come from wherever. Whatever works. Whatever scales. The climate narrative was tossed out the moment AI dominance became the new priority. Power was to be rationed. Now it is required. Power was the problem. Now it is the solution.

Electroverse

41,103 views • 5 months ago

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.
1:39

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.

George Noble

23,931 views • 6 months ago

Schatz: This bill will kill 300,000 jobs in wind and solar per year. We're going to lose out on $450 billion in capital as thousands of projects go under. And because of that, we're going to generate about 500 gigawatts less energy in the next decade. The 500 gigawatts less energy in the next decade is pretty much exactly the amount of energy that we're going to need to meet rising demand. We are going to have energy shortages as a result of this legislation. Energy demand is soaring for the first time in decades, largely, not exclusively but largely because of A.I. data centers. And our best chance of meeting it in the next few years is with wind and solar, not oil and gas, even nuclear and geothermal are going to take awhile.
2:44

Schatz: This bill will kill 300,000 jobs in wind and solar per year. We're going to lose out on $450 billion in capital as thousands of projects go under. And because of that, we're going to generate about 500 gigawatts less energy in the next decade. The 500 gigawatts less energy in the next decade is pretty much exactly the amount of energy that we're going to need to meet rising demand. We are going to have energy shortages as a result of this legislation. Energy demand is soaring for the first time in decades, largely, not exclusively but largely because of A.I. data centers. And our best chance of meeting it in the next few years is with wind and solar, not oil and gas, even nuclear and geothermal are going to take awhile.

Acyn

743,491 views • 1 year ago

This young woman from Georgia, explains to us what’s happening right now in her community. Georgia Power is expanding power lines to support a massive new data center, and they’re using eminent domain to take homes and land from families — including her childhood home behind her. She explains that homeowners have no choice as the utility seizes property by force. This isn’t isolated to Georgia. The explosive growth of data centers is happening across at least 38 states, driven by the massive AI boom as tech giants like Microsoft, Amazon, Google, and Meta build out enormous computing infrastructure. In several states — including Georgia — utilities are using eminent domain to expand high-voltage power lines needed to feed these power-hungry facilities. Have you seen or heard about eminent domain being used for data centers or power lines in your area?
0:32

This young woman from Georgia, explains to us what’s happening right now in her community. Georgia Power is expanding power lines to support a massive new data center, and they’re using eminent domain to take homes and land from families — including her childhood home behind her. She explains that homeowners have no choice as the utility seizes property by force. This isn’t isolated to Georgia. The explosive growth of data centers is happening across at least 38 states, driven by the massive AI boom as tech giants like Microsoft, Amazon, Google, and Meta build out enormous computing infrastructure. In several states — including Georgia — utilities are using eminent domain to expand high-voltage power lines needed to feed these power-hungry facilities. Have you seen or heard about eminent domain being used for data centers or power lines in your area?

End3of6Days9 (Helen) 🇺🇸

20,079 views • 2 months ago

Elon Musk just confirmed the most INSANE IPO in history. SpaceX is going public in 2026. $1.5 TRILLION valuation. Raising $30+ billion. That's the biggest IPO ever made. Beating Saudi Aramco's $29 billion record from 2019. But here's what everyone's missing: This isn't about space tourism or Mars missions. Elon is literally about to win the entire AI race. And 99% of people have no idea how... Here's the problem killing every AI company right now: POWER. Oracle just reported earnings. They burned through $12 BILLION in one quarter building data centers. Their free cash flow? NEGATIVE $10 billion. Revenue missed estimates. Stock crashed 11%. Microsoft, Amazon, Google all scrambling to find enough electricity for AI training. The brutal math: The US generates 490 gigawatts of total power. AI is projected to need 123 gigawatts by 2035. That's a QUARTER of the entire electrical grid. Just for artificial intelligence. Goldman Sachs says AI energy demand could jump 165% by 2030. There is literally not enough power on Earth to run AI at the scale these companies are promising. Every data center needs massive cooling systems. Billions of gallons of water per year. Insane energy costs. And the infrastructure can't keep up. Elon's solution? Stop building on Earth entirely. SpaceX is building data centers in SPACE. Not a concept. Not 10 years out. Literally starting in 2026. They're upgrading Starlink V3 satellites to carry AI computing chips. Each satellite gets 24/7 solar power. No clouds. No night. No weather disruptions. No grid bottlenecks. And the insane part is that Starship can deliver 300 to 500 gigawatts of solar-powered AI satellites into orbit every single year. At 300 gigawatts per year, the AI computing power in space would exceed the entire U.S. economy's total electricity consumption within two years. Just from satellites. Processing in orbit. While Oracle is begging banks for loans to finish data centers and OpenAI is stuck in circular funding arrangements with Microsoft, Elon already owns everything: The rockets. The satellites. The launch infrastructure. The AI company (xAI). He doesn't need to ask utilities for permission. Doesn't need grid approvals from local governments. Doesn't need to build nuclear plants or wait for clean energy. He just launches. And everyone else is scrambling to catch up: Jeff Bezos sees it. Blue Origin announced they're building their own orbital data centers. Google just launched "Project Suncatcher" with plans to deploy AI satellites by 2027. Eric Schmidt, the former CEO of Google, literally BOUGHT an entire rocket company (Relativity Space) just to compete in this space. But they're all 3+ years behind Elon. SpaceX already has 6,000+ Starlink satellites in orbit. The infrastructure is built. The $30 billion from the IPO? Going straight into scaling orbital compute. SpaceX revenue is jumping from $15 billion in 2025 to $24 billion in 2026. Most of that from Starlink. Now add space-based AI infrastructure on top. Here's why this matters: Whoever controls orbital computing controls the AI revolution. And there's only ONE company on Earth with fully reusable rockets that can launch at the scale required. Jensen Huang, Nvidia's CEO, called space data centers "a dream." Translation: Nvidia is screwed if Elon actually pulls this off. Because if SpaceX succeeds, every AI company on the planet becomes Elon's customer. OpenAI needs compute? Running on SpaceX satellites. Google needs more capacity? Renting orbital infrastructure. Microsoft needs power? Paying SpaceX for launch and compute access. Elon won't just be in the AI race. He'll own the entire track everyone else is running on. The $1.5 trillion valuation sounds crazy until you realize what he's actually building. It's not a rocket company. It's the infrastructure layer for the next 50 years of computing. People calling it overvalued have no idea what's coming.
2:27

Elon Musk just confirmed the most INSANE IPO in history. SpaceX is going public in 2026. $1.5 TRILLION valuation. Raising $30+ billion. That's the biggest IPO ever made. Beating Saudi Aramco's $29 billion record from 2019. But here's what everyone's missing: This isn't about space tourism or Mars missions. Elon is literally about to win the entire AI race. And 99% of people have no idea how... Here's the problem killing every AI company right now: POWER. Oracle just reported earnings. They burned through $12 BILLION in one quarter building data centers. Their free cash flow? NEGATIVE $10 billion. Revenue missed estimates. Stock crashed 11%. Microsoft, Amazon, Google all scrambling to find enough electricity for AI training. The brutal math: The US generates 490 gigawatts of total power. AI is projected to need 123 gigawatts by 2035. That's a QUARTER of the entire electrical grid. Just for artificial intelligence. Goldman Sachs says AI energy demand could jump 165% by 2030. There is literally not enough power on Earth to run AI at the scale these companies are promising. Every data center needs massive cooling systems. Billions of gallons of water per year. Insane energy costs. And the infrastructure can't keep up. Elon's solution? Stop building on Earth entirely. SpaceX is building data centers in SPACE. Not a concept. Not 10 years out. Literally starting in 2026. They're upgrading Starlink V3 satellites to carry AI computing chips. Each satellite gets 24/7 solar power. No clouds. No night. No weather disruptions. No grid bottlenecks. And the insane part is that Starship can deliver 300 to 500 gigawatts of solar-powered AI satellites into orbit every single year. At 300 gigawatts per year, the AI computing power in space would exceed the entire U.S. economy's total electricity consumption within two years. Just from satellites. Processing in orbit. While Oracle is begging banks for loans to finish data centers and OpenAI is stuck in circular funding arrangements with Microsoft, Elon already owns everything: The rockets. The satellites. The launch infrastructure. The AI company (xAI). He doesn't need to ask utilities for permission. Doesn't need grid approvals from local governments. Doesn't need to build nuclear plants or wait for clean energy. He just launches. And everyone else is scrambling to catch up: Jeff Bezos sees it. Blue Origin announced they're building their own orbital data centers. Google just launched "Project Suncatcher" with plans to deploy AI satellites by 2027. Eric Schmidt, the former CEO of Google, literally BOUGHT an entire rocket company (Relativity Space) just to compete in this space. But they're all 3+ years behind Elon. SpaceX already has 6,000+ Starlink satellites in orbit. The infrastructure is built. The $30 billion from the IPO? Going straight into scaling orbital compute. SpaceX revenue is jumping from $15 billion in 2025 to $24 billion in 2026. Most of that from Starlink. Now add space-based AI infrastructure on top. Here's why this matters: Whoever controls orbital computing controls the AI revolution. And there's only ONE company on Earth with fully reusable rockets that can launch at the scale required. Jensen Huang, Nvidia's CEO, called space data centers "a dream." Translation: Nvidia is screwed if Elon actually pulls this off. Because if SpaceX succeeds, every AI company on the planet becomes Elon's customer. OpenAI needs compute? Running on SpaceX satellites. Google needs more capacity? Renting orbital infrastructure. Microsoft needs power? Paying SpaceX for launch and compute access. Elon won't just be in the AI race. He'll own the entire track everyone else is running on. The $1.5 trillion valuation sounds crazy until you realize what he's actually building. It's not a rocket company. It's the infrastructure layer for the next 50 years of computing. People calling it overvalued have no idea what's coming.

Ricardo

2,907,313 views • 7 months ago

David Sacks: Everything Kathy Hochul Said About Datacenters Was a False Accusation David Sacks: “These data centers become the scapegoat for all of the angst that people have about AI. Everything she's saying there is a false accusation on datacenters. So let's just go one by one.” POWER: So she's saying that they eat up all of the power. Well, yeah, if you connect to the grid without producing more power and you force datacenters to compete with residential ratepayers, then yeah, you could drive up utility prices. However, if you do what Chamath said and let them build behind the meter, then they bring their own power. And that's what the president has advocated for since the beginning of his administration, is let the AI companies become power companies. So that is the way to solve the energy problem or the utility problem. LAND: Then, she's talking about eating up land. The reality is these datacenters are a model of land use efficiency. We have a ton of land in this country, obviously. NOISE POLLUTION: The supposed noise pollution, that's largely made up. That can be dealt with. You obviously don't want to put these things right next to a residential area, but create a little bit of distance, and it's fine. WATER CONSUMPTION: The whole water consumption thing is largely a hoax. Modern datacenters recirculate the water. AIR POLLUTION: One final thing just on the point that Hochul is making. She said it created a lot of pollution. Natural gas, which is how most of these data centers are powered, is one of the most clean burning sources of power that we have. “When you compare economic impact to all these different things, datacenters are honestly one of the best things we could be building as a nation.”
1:31

David Sacks: Everything Kathy Hochul Said About Datacenters Was a False Accusation David Sacks: “These data centers become the scapegoat for all of the angst that people have about AI. Everything she's saying there is a false accusation on datacenters. So let's just go one by one.” POWER: So she's saying that they eat up all of the power. Well, yeah, if you connect to the grid without producing more power and you force datacenters to compete with residential ratepayers, then yeah, you could drive up utility prices. However, if you do what Chamath said and let them build behind the meter, then they bring their own power. And that's what the president has advocated for since the beginning of his administration, is let the AI companies become power companies. So that is the way to solve the energy problem or the utility problem. LAND: Then, she's talking about eating up land. The reality is these datacenters are a model of land use efficiency. We have a ton of land in this country, obviously. NOISE POLLUTION: The supposed noise pollution, that's largely made up. That can be dealt with. You obviously don't want to put these things right next to a residential area, but create a little bit of distance, and it's fine. WATER CONSUMPTION: The whole water consumption thing is largely a hoax. Modern datacenters recirculate the water. AIR POLLUTION: One final thing just on the point that Hochul is making. She said it created a lot of pollution. Natural gas, which is how most of these data centers are powered, is one of the most clean burning sources of power that we have. “When you compare economic impact to all these different things, datacenters are honestly one of the best things we could be building as a nation.”

The All-In Podcast

110,663 views • 10 days ago

The biggest power grab since Standard Oil is happening today and almost nobody is paying attention. Tech companies are building their own power grid. They're about to produce more electricity than entire COUNTRIES. Right now at the White House, CEOs from Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI are signing a pledge that most people will scroll past. But it might be the most important business deal of the decade. They're committing to build, bring, or buy 100% of their own electricity for every new AI data center. Their own power plants. Their own transmission lines. Their own energy infrastructure. These are SOFTWARE companies agreeing to become power utilities. Here's why this matters for everyone reading this: By the end of this year, at least 5 US data centers will each consume over 1 gigawatt of continuous power. 1 gigawatt powers 850,000 homes. 5 of these facilities will use more electricity than some entire countries. The US grid physically cannot handle it. Capacity prices in the PJM grid, which covers 13 states, exploded from $28.92 per megawatt-day to $329.17 in just two years. That's literally a 1,000% increase. So what do you do when the grid can't support you? You stop using the grid. Amazon is buying nuclear reactors. Microsoft restarted Three Mile Island. Meta signed 20-year nuclear deals. Chevron is building a 2.5 gigawatt natural gas plant in West Texas specifically to power data centers. These companies aren't supplementing the grid. They're replacing it. For themselves. Think about what's actually happening here: 7 companies now control more computing power than most governments. And today they're signing paperwork to control their own energy supply too. Computing. Data. Energy. Infrastructure. That's not a "tech" company anymore. A Harvard energy law professor already called the pledge "meaningless" because utilities in PJM are spending tens of billions on power projects for data centers and those costs are STILL being spread across ratepayers anyway. The pledge has zero legal teeth. No enforcement mechanism. No compliance monitoring. No penalty for breaking it. It's a political move designed to get tech companies through the midterms without becoming the villain of every campaign ad about electricity bills. But the underlying shift is real and irreversible: Tech companies are becoming energy companies. Energy companies are becoming AI infrastructure. And the line between Big Tech and Big Energy is about to disappear completely. The big question here: When seven companies control both the world's intelligence AND the power that runs it, who exactly is governing who?
2:25

The biggest power grab since Standard Oil is happening today and almost nobody is paying attention. Tech companies are building their own power grid. They're about to produce more electricity than entire COUNTRIES. Right now at the White House, CEOs from Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI are signing a pledge that most people will scroll past. But it might be the most important business deal of the decade. They're committing to build, bring, or buy 100% of their own electricity for every new AI data center. Their own power plants. Their own transmission lines. Their own energy infrastructure. These are SOFTWARE companies agreeing to become power utilities. Here's why this matters for everyone reading this: By the end of this year, at least 5 US data centers will each consume over 1 gigawatt of continuous power. 1 gigawatt powers 850,000 homes. 5 of these facilities will use more electricity than some entire countries. The US grid physically cannot handle it. Capacity prices in the PJM grid, which covers 13 states, exploded from $28.92 per megawatt-day to $329.17 in just two years. That's literally a 1,000% increase. So what do you do when the grid can't support you? You stop using the grid. Amazon is buying nuclear reactors. Microsoft restarted Three Mile Island. Meta signed 20-year nuclear deals. Chevron is building a 2.5 gigawatt natural gas plant in West Texas specifically to power data centers. These companies aren't supplementing the grid. They're replacing it. For themselves. Think about what's actually happening here: 7 companies now control more computing power than most governments. And today they're signing paperwork to control their own energy supply too. Computing. Data. Energy. Infrastructure. That's not a "tech" company anymore. A Harvard energy law professor already called the pledge "meaningless" because utilities in PJM are spending tens of billions on power projects for data centers and those costs are STILL being spread across ratepayers anyway. The pledge has zero legal teeth. No enforcement mechanism. No compliance monitoring. No penalty for breaking it. It's a political move designed to get tech companies through the midterms without becoming the villain of every campaign ad about electricity bills. But the underlying shift is real and irreversible: Tech companies are becoming energy companies. Energy companies are becoming AI infrastructure. And the line between Big Tech and Big Energy is about to disappear completely. The big question here: When seven companies control both the world's intelligence AND the power that runs it, who exactly is governing who?

Ricardo

146,765 views • 4 months ago

Here’s Elon Musk explaining that SpaceX could launch up to one terawatt per year of AI compute capacity into Earth orbit — equivalent to the power output of roughly 1,000 nuclear power plants. “The next step beyond Earth data centers is our Earth orbital data centers. And we'll be launching with SpaceX orbital data centers at the 100-to-200-gigawatt-per-year level. Not cumulative, per year. And ultimately, we see a path to maybe launching as much as a terawatt per year of compute from Earth. But what if you want to go beyond a mere terawatt per year? In order to do that, you have to go to the Moon. So by having factories on the Moon, building AI satellites, and having a mass driver, which is the kind of thing you really only learn about in or read about in science fiction, but we're going to make it real. We're actually going to have a mass driver on the Moon. And if you do that, you can go several orders of magnitude greater. You can go to 1,000 gigawatts or more per year and ultimately get to maybe a millionth and then a thousandth and maybe even a few percent of the Sun's energy.” xAI All-Hands, February 10, 2026
1:04

Here’s Elon Musk explaining that SpaceX could launch up to one terawatt per year of AI compute capacity into Earth orbit — equivalent to the power output of roughly 1,000 nuclear power plants. “The next step beyond Earth data centers is our Earth orbital data centers. And we'll be launching with SpaceX orbital data centers at the 100-to-200-gigawatt-per-year level. Not cumulative, per year. And ultimately, we see a path to maybe launching as much as a terawatt per year of compute from Earth. But what if you want to go beyond a mere terawatt per year? In order to do that, you have to go to the Moon. So by having factories on the Moon, building AI satellites, and having a mass driver, which is the kind of thing you really only learn about in or read about in science fiction, but we're going to make it real. We're actually going to have a mass driver on the Moon. And if you do that, you can go several orders of magnitude greater. You can go to 1,000 gigawatts or more per year and ultimately get to maybe a millionth and then a thousandth and maybe even a few percent of the Sun's energy.” xAI All-Hands, February 10, 2026

ELON CLIPS

21,962 views • 4 months ago

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA
1:48

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Karl Mehta

14,976 views • 22 days ago

Today President Trump obtained a pledge from America’s leading tech companies that new data centers would not increase electricity prices for residential consumers. These companies (including Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI) signed the “Ratepayer Protection Pledge” under which they agree to cover the costs of all new power generation required for their data centers, ensuring such costs are not passed onto American households. This is a much better approach to affordability than Bernie Sanders’ total ban on new data centers, which would halt the construction boom currently driving wage growth and job growth for blue-collar workers. In fact, the Ratepayer Protection Pledge will lower electricity prices when AI companies pay for grid upgrades and sell their excess power back to the grid. Since the beginning of his second term, President Trump has championed the idea of letting our leading AI companies become power companies, and now this idea is becoming a reality thanks to his leadership and the commitments of these strong American companies. The right approach to data centers is not to stop progress altogether, but rather to protect residential rate payers from price increases, while making it easier to stand up new power generation.
1:20

Today President Trump obtained a pledge from America’s leading tech companies that new data centers would not increase electricity prices for residential consumers. These companies (including Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI) signed the “Ratepayer Protection Pledge” under which they agree to cover the costs of all new power generation required for their data centers, ensuring such costs are not passed onto American households. This is a much better approach to affordability than Bernie Sanders’ total ban on new data centers, which would halt the construction boom currently driving wage growth and job growth for blue-collar workers. In fact, the Ratepayer Protection Pledge will lower electricity prices when AI companies pay for grid upgrades and sell their excess power back to the grid. Since the beginning of his second term, President Trump has championed the idea of letting our leading AI companies become power companies, and now this idea is becoming a reality thanks to his leadership and the commitments of these strong American companies. The right approach to data centers is not to stop progress altogether, but rather to protect residential rate payers from price increases, while making it easier to stand up new power generation.

David Sacks

1,163,509 views • 4 months ago

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.
1:14

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.

Milk Road AI

74,611 views • 1 month 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.
0:22

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 • 5 months ago

Full Interview with Charles-Antoine Beyney "CAB" (CAB) - Founder & CEO of DataOne and Alasdair Mann As always - please share this interview, so we reach as many $NBIS (and AI infra investors) as possible! --- DataOne is building giga-scale AI data center infrastructure for the next decade of compute demand. Most importantly for $NBIS investors: DataOne is the company behind Nebius’ 300MW Vineland, New Jersey campus, one of the key capacity expansion projects that could determine how fast Nebius scales from here! Timestamps: 00:00 Meet the CEO of DataOne 00:29 50% of Datacenters Delayed 02:01 Paid Protesters 04:48 Real Water Consumption of His Data Centers 08:27 Condensation and Cooling 11:09 Pollutant Problem 12:14 Behind the Meter Power 15:50 US vs France Power Strategy 18:39 Speed to Power Crunch 19:56 Supply Chain Bottlenecks 23:46 Generative Video AI Economics 25:54 Building Faster Than Anyone 30:55 Budgeting With Rapidly Increasing Prices 31:32 Tariffs and Contingency Buffers 33:31 Supply Crunch Pricing Power 37:36 DataOne Infrastructure Strategy 38:44 Gas Turbines at Scale 40:40 Microturbines and Small Sites 42:30 Batteries and Renewables Mix 43:57 Nuclear and Grid Stability 44:51 Real World Lead Times Strategy 47:26 Cooling Knowhow and Market Hype 48:48 Building Under Extreme Pressure 50:36 AI as the New Enlightenment 54:23 Fusion and the Energy Endgame As always - please share
56:16

Full Interview with Charles-Antoine Beyney "CAB" (CAB) - Founder & CEO of DataOne and Alasdair Mann As always - please share this interview, so we reach as many $NBIS (and AI infra investors) as possible! --- DataOne is building giga-scale AI data center infrastructure for the next decade of compute demand. Most importantly for $NBIS investors: DataOne is the company behind Nebius’ 300MW Vineland, New Jersey campus, one of the key capacity expansion projects that could determine how fast Nebius scales from here! Timestamps: 00:00 Meet the CEO of DataOne 00:29 50% of Datacenters Delayed 02:01 Paid Protesters 04:48 Real Water Consumption of His Data Centers 08:27 Condensation and Cooling 11:09 Pollutant Problem 12:14 Behind the Meter Power 15:50 US vs France Power Strategy 18:39 Speed to Power Crunch 19:56 Supply Chain Bottlenecks 23:46 Generative Video AI Economics 25:54 Building Faster Than Anyone 30:55 Budgeting With Rapidly Increasing Prices 31:32 Tariffs and Contingency Buffers 33:31 Supply Crunch Pricing Power 37:36 DataOne Infrastructure Strategy 38:44 Gas Turbines at Scale 40:40 Microturbines and Small Sites 42:30 Batteries and Renewables Mix 43:57 Nuclear and Grid Stability 44:51 Real World Lead Times Strategy 47:26 Cooling Knowhow and Market Hype 48:48 Building Under Extreme Pressure 50:36 AI as the New Enlightenment 54:23 Fusion and the Energy Endgame As always - please share

Daniel Koss

99,568 views • 2 months ago

CNBC: How GE Vernova builds the massive gas turbines powering the AI data center boom > GE Vernova’s $GEV order book is full until 2029, with orders going as far out as 2031. > Hyperscalers like Amazon, Google, Microsoft and Oracle are lining up to buy the company’s gas turbines to help power AI data centers. > Executives from nearly every major hyperscaler have walked the floor of the factory, according to a person familiar with the visits, who asked not to be named because the details are not public. > The turbines are massive, at 31 feet tall and weighing 280 tons. One turbine can power roughly half a million homes. > Microsoft just bought seven of them to power its data center in Texas. At 2.7 gigawatts, it’s enough electricity to power about 3 million homes. > "About 20% of our gas power order book is going to a data center, artificial intelligence-type of application." - Koziner, Chief Commercial and Operations Officer of GE Vernova. > One turbine can cost more than $250 million, according to industry estimates. The price has soared, up 300% in the last 3 years, according to analysts at Melius.
3:44

CNBC: How GE Vernova builds the massive gas turbines powering the AI data center boom > GE Vernova’s $GEV order book is full until 2029, with orders going as far out as 2031. > Hyperscalers like Amazon, Google, Microsoft and Oracle are lining up to buy the company’s gas turbines to help power AI data centers. > Executives from nearly every major hyperscaler have walked the floor of the factory, according to a person familiar with the visits, who asked not to be named because the details are not public. > The turbines are massive, at 31 feet tall and weighing 280 tons. One turbine can power roughly half a million homes. > Microsoft just bought seven of them to power its data center in Texas. At 2.7 gigawatts, it’s enough electricity to power about 3 million homes. > "About 20% of our gas power order book is going to a data center, artificial intelligence-type of application." - Koziner, Chief Commercial and Operations Officer of GE Vernova. > One turbine can cost more than $250 million, according to industry estimates. The price has soared, up 300% in the last 3 years, according to analysts at Melius.

P Equity Research 📰

52,846 views • 1 month ago

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.
0:45

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.

Black Panther Capital

57,828 views • 6 months ago

The man who turned 225 million dollars into 5.5 billion dollars explained on camera exactly why he made his biggest bet. This is Leopold Aschenbrenner, the same person whose Bloom Energy position is now worth close to 2 billion dollars after Oracle's 2.8 gigawatt fuel cell deal laying out the power math that drove every investment decision his fund has made. In 2022, the GPT-4 training cluster consumed roughly 10 megawatts of power and cost about 500 million dollars. AI compute has been scaling at roughly half an order of magnitude per year meaning the largest training cluster doubles in power requirement every 12 to 18 months without stopping. By 2024, the largest cluster was approximately 100 megawatts, the equivalent of 100,000 high-end GPUs and costs in the billions. By 2026, right now, the leading training cluster requires a full gigawatt of continuous power and that is the output of a large nuclear reactor. By 2028, the projection reaches 10 gigawatts, more electricity than most US states generate in total. By 2030, the trillion-dollar cluster, 100 gigawatts, over 20 percent of everything the United States currently produces in electricity, consumed by a single AI training installation. And that is just the training cluster. Inference, the continuous compute required to actually run AI products for hundreds of millions of users requires multiples of that on top. Meanwhile, total US electricity production has barely grown five percent over the last decade and the grid was not built for this. And the transformer shortage, the switchgear backorders, and the canceled data center projects that are making headlines right now are the first visible symptoms of a power system hitting a wall that Aschenbrenner saw coming years before the rest of the market. This is exactly why he built a 875 million dollar position in Bloom Energy, a company that generates electricity directly at the data center site using fuel cells, completely bypassing the grid bottleneck that is already stopping half of all planned US data centers from opening on schedule. The thesis was never complicated. The bottleneck in AI is not the models, not the chips, and not the software. The bottleneck is whether civilization can generate enough electricity to run the machines fast enough to matter.
1:30

The man who turned 225 million dollars into 5.5 billion dollars explained on camera exactly why he made his biggest bet. This is Leopold Aschenbrenner, the same person whose Bloom Energy position is now worth close to 2 billion dollars after Oracle's 2.8 gigawatt fuel cell deal laying out the power math that drove every investment decision his fund has made. In 2022, the GPT-4 training cluster consumed roughly 10 megawatts of power and cost about 500 million dollars. AI compute has been scaling at roughly half an order of magnitude per year meaning the largest training cluster doubles in power requirement every 12 to 18 months without stopping. By 2024, the largest cluster was approximately 100 megawatts, the equivalent of 100,000 high-end GPUs and costs in the billions. By 2026, right now, the leading training cluster requires a full gigawatt of continuous power and that is the output of a large nuclear reactor. By 2028, the projection reaches 10 gigawatts, more electricity than most US states generate in total. By 2030, the trillion-dollar cluster, 100 gigawatts, over 20 percent of everything the United States currently produces in electricity, consumed by a single AI training installation. And that is just the training cluster. Inference, the continuous compute required to actually run AI products for hundreds of millions of users requires multiples of that on top. Meanwhile, total US electricity production has barely grown five percent over the last decade and the grid was not built for this. And the transformer shortage, the switchgear backorders, and the canceled data center projects that are making headlines right now are the first visible symptoms of a power system hitting a wall that Aschenbrenner saw coming years before the rest of the market. This is exactly why he built a 875 million dollar position in Bloom Energy, a company that generates electricity directly at the data center site using fuel cells, completely bypassing the grid bottleneck that is already stopping half of all planned US data centers from opening on schedule. The thesis was never complicated. The bottleneck in AI is not the models, not the chips, and not the software. The bottleneck is whether civilization can generate enough electricity to run the machines fast enough to matter.

Milk Road AI

1,242,155 views • 3 months ago

Eric Schmidt, former CEO of Google: "The AI revolution is underhyped. None of us is prepared for the implications of this." He opens with a warning: "The arrival of this new intelligence will profoundly change our country and the world in ways we cannot fully understand." He explains what's happening right now in the industry: "We're very very quickly developing AI programmers. And these AI programmers will replace traditional software programmers. We're building in the next year AI mathematicians that are as good as the top level graduate students in math. This is happening very quickly." Schmidt argues most people fundamentally misunderstand what AI has become: "Today you think of AI as ChatGPT, but what it really is is a reasoning and planning system that we've never seen before." The implications, he warns, extend far beyond software. These new systems demand resources at an industrial scale we've never encountered. "They're going to need a lot more computation than we've ever had. They're going to need a lot more energy." To illustrate the scale of the energy crisis ahead, Schmidt offers a sobering comparison: "People are planning 10 gigawatt data centers. Now just to do the translation, an average nuclear power plant in the United States is 1 gigawatt. How many nuclear power plants can we make in one year where we're planning this 10 gigawatt data center? Gives you a sense of how big this crisis is." Eric Schmidt shares an estimate he finds most likely: "Data centers will require an additional 29 gigawatts of power by 2027 and 67 more gigawatts by 2030. These things are industrial at a scale I have never seen in my life." Schmidt says the industry needs high skills immigration, light touch regulation around cyber and bio threats, and most critically, energy in all forms. He's personally investing in fusion, but acknowledges it won't arrive in time. He closes with the stakes: "When you build these systems, you have intelligence in the computer and then eventually human level intelligence. Some people think it's within 3 to four years. Then after that, you have something called super intelligence, the intelligence that's higher than of humans. We believe as an industry that this could occur within a decade. It is crucial that America get there first."
2:35

Eric Schmidt, former CEO of Google: "The AI revolution is underhyped. None of us is prepared for the implications of this." He opens with a warning: "The arrival of this new intelligence will profoundly change our country and the world in ways we cannot fully understand." He explains what's happening right now in the industry: "We're very very quickly developing AI programmers. And these AI programmers will replace traditional software programmers. We're building in the next year AI mathematicians that are as good as the top level graduate students in math. This is happening very quickly." Schmidt argues most people fundamentally misunderstand what AI has become: "Today you think of AI as ChatGPT, but what it really is is a reasoning and planning system that we've never seen before." The implications, he warns, extend far beyond software. These new systems demand resources at an industrial scale we've never encountered. "They're going to need a lot more computation than we've ever had. They're going to need a lot more energy." To illustrate the scale of the energy crisis ahead, Schmidt offers a sobering comparison: "People are planning 10 gigawatt data centers. Now just to do the translation, an average nuclear power plant in the United States is 1 gigawatt. How many nuclear power plants can we make in one year where we're planning this 10 gigawatt data center? Gives you a sense of how big this crisis is." Eric Schmidt shares an estimate he finds most likely: "Data centers will require an additional 29 gigawatts of power by 2027 and 67 more gigawatts by 2030. These things are industrial at a scale I have never seen in my life." Schmidt says the industry needs high skills immigration, light touch regulation around cyber and bio threats, and most critically, energy in all forms. He's personally investing in fusion, but acknowledges it won't arrive in time. He closes with the stakes: "When you build these systems, you have intelligence in the computer and then eventually human level intelligence. Some people think it's within 3 to four years. Then after that, you have something called super intelligence, the intelligence that's higher than of humans. We believe as an industry that this could occur within a decade. It is crucial that America get there first."

Big Brain AI

40,338 views • 2 months ago

ABC News investigated and found more than 3,000 data centers are already operating nationwide At least 1,000 more planned Residents near data centers are seeing much as 50%+ increases on their electric bills “ABC7 has also been covering public meetings over proposed data centers and there are questions about water use and the environment. The i-team in ABC News studied this private company's data center map and found that there are at least 4,302 data center projects across the U.S. Large and small. Of those, 3,038 are currently operational with another 1,203 either under construction or plan for construction. 61 have acquired land. In Illinois, there are 164 operating data centers with another 81 planned for construction. The largest state project plan is in Yorkville. It would be 2 gigawatts and according to the ABC7 data team would use the same energy that would power approximately 1.7 million homes more than every home in the city of Chicago.”
4:21

ABC News investigated and found more than 3,000 data centers are already operating nationwide At least 1,000 more planned Residents near data centers are seeing much as 50%+ increases on their electric bills “ABC7 has also been covering public meetings over proposed data centers and there are questions about water use and the environment. The i-team in ABC News studied this private company's data center map and found that there are at least 4,302 data center projects across the U.S. Large and small. Of those, 3,038 are currently operational with another 1,203 either under construction or plan for construction. 61 have acquired land. In Illinois, there are 164 operating data centers with another 81 planned for construction. The largest state project plan is in Yorkville. It would be 2 gigawatts and according to the ABC7 data team would use the same energy that would power approximately 1.7 million homes more than every home in the city of Chicago.”

Wall Street Apes

152,182 views • 5 months ago

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.
1:58

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.

Shay Boloor

108,342 views • 1 month ago

In the next 15 years, data centers are expected to add an additional $160 billion to grid costs in the US Estimate say electricity rates for average households will spike by as much as 70% Data centers are projected to triple their share of US electricity demand in the next few years The main driver is the explosive growth of data centers built by Big Tech companies like Amazon, Meta, Microsoft, Google, OpenAI and more to power artificial intelligence Places like Northern Virginia already has over 200 data centers with massive new ones planned. Utilities are striking secret proprietary deals with Big Tech companies. These are hidden behind NDAs that shift much of the infrastructure costs onto regular residential customers Just in the PJM energy market of 13 states covering 65 million people, data centers were responsible for 63% of last year’s record 800% spike in capacity prices (This is INSANE) Residential customers in places like Virginia and Louisiana are being forced to subsidize billions in new power plants and grid upgrades for data centers. An Examples of this is in Louisiana, Meta’s data center deal leaves the public potentially on the hook for half or more of a $3–4 billion power plant Again, without major policy changes, average household electricity bills could rise by up to 70% over the next 15 years due to data center demand. There is only one real way we can stop this, we must create a separate customer class for data centers Maryland and Oregon have already passed laws doing this Forces data centers to pay for the specific infrastructure they need instead of spreading the costs to everyone else. More states need to do the same Ban secret sweetheart deals Require full public disclosure of all contracts between utilities and Big Tech Prohibit deals where data centers pay below the actual cost of service Make data centers pay the full cost of new power plants and grid upgrades Change regulations so utilities cannot socialize the cost of data-center-driven infrastructure to residential and small business ratepayers This needs to be done immediately
13:33

In the next 15 years, data centers are expected to add an additional $160 billion to grid costs in the US Estimate say electricity rates for average households will spike by as much as 70% Data centers are projected to triple their share of US electricity demand in the next few years The main driver is the explosive growth of data centers built by Big Tech companies like Amazon, Meta, Microsoft, Google, OpenAI and more to power artificial intelligence Places like Northern Virginia already has over 200 data centers with massive new ones planned. Utilities are striking secret proprietary deals with Big Tech companies. These are hidden behind NDAs that shift much of the infrastructure costs onto regular residential customers Just in the PJM energy market of 13 states covering 65 million people, data centers were responsible for 63% of last year’s record 800% spike in capacity prices (This is INSANE) Residential customers in places like Virginia and Louisiana are being forced to subsidize billions in new power plants and grid upgrades for data centers. An Examples of this is in Louisiana, Meta’s data center deal leaves the public potentially on the hook for half or more of a $3–4 billion power plant Again, without major policy changes, average household electricity bills could rise by up to 70% over the next 15 years due to data center demand. There is only one real way we can stop this, we must create a separate customer class for data centers Maryland and Oregon have already passed laws doing this Forces data centers to pay for the specific infrastructure they need instead of spreading the costs to everyone else. More states need to do the same Ban secret sweetheart deals Require full public disclosure of all contracts between utilities and Big Tech Prohibit deals where data centers pay below the actual cost of service Make data centers pay the full cost of new power plants and grid upgrades Change regulations so utilities cannot socialize the cost of data-center-driven infrastructure to residential and small business ratepayers This needs to be done immediately

Wall Street Apes

57,720 views • 1 month ago