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What actually goes into a data center? Guru Chahal🇺🇸 whiteboards the entire stack - across land, power, cooling, storage, networking, compute - and shares where we're seeing the most interesting innovation + startup opportunities → Base Power: virtual power plants from residential batteries → Periodic Labs: using AI to...

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Claire Zau

7,796 subscribers

53,476 просмотров • 3 месяцев назад •via X (Twitter)

Наука и технологииОбразованиеНовости и политика
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Комментарии: 17

Фото профиля Claire Zau
Claire Zau3 месяцев назад

full episode and breakdown on youtube

Фото профиля cogsec
cogsec3 месяцев назад

@guruchahal @basepowerco super cool

Фото профиля Claire Zau
Claire Zau3 месяцев назад

@guruchahal @basepowerco thank you 🙏

Фото профиля Dara Miao
Dara Miao3 месяцев назад

@guruchahal @basepowerco so helpful i love this claire

Фото профиля Claire Zau
Claire Zau3 месяцев назад

@guruchahal @basepowerco so glad to hear - excited that we get to yap about all of this 🤗

Фото профиля Michelle Sun
Michelle Sun3 месяцев назад

@guruchahal @basepowerco fantastic. more of these whiteboard educational pieces, please!

Фото профиля Claire Zau
Claire Zau3 месяцев назад

@guruchahal @basepowerco thank you so much!! so glad they’re interesting

Фото профиля Dakshay Mehta
Dakshay Mehta3 месяцев назад

@guruchahal @basepowerco Guru is awesome

Фото профиля Claire Zau
Claire Zau3 месяцев назад

@guruchahal @basepowerco can confirm!! so much to learn from him

Фото профиля Dakshay Mehta
Dakshay Mehta3 месяцев назад

@guruchahal @basepowerco 💯💯

Фото профиля Max Song (🌎, 🚀 , 🌕 )
Max Song (🌎, 🚀 , 🌕 )3 месяцев назад

@guruchahal @basepowerco Awesome content!!

Фото профиля Vatsalpandya333
Vatsalpandya3333 месяцев назад

@guruchahal @basepowerco 🐐

Фото профиля Nirvana Labs
Nirvana Labs3 месяцев назад

@guruchahal @basepowerco We are a high-performance storage cloud purpose built for the most demanding, real-time, stateful workloads. Faster and cheaper than hyperscalers by at least 20-30%. We have a bunch of clients in production including Altinity, Goldsky, Bitgo, Nansen etc. Check us out!

Фото профиля Stanley Wei
Stanley Wei3 месяцев назад

@guruchahal @basepowerco The data center stack is a good reminder that AI infrastructure is a systems business, not a GPU shopping list. Land, power, cooling, and networking each become product surfaces—and each can bottleneck the others.

Фото профиля AI Mastery Guide
AI Mastery Guide3 месяцев назад

@guruchahal @basepowerco Every layer getting rebuilt at once is wild to actually see mapped out.

Фото профиля Anjuli Pierce
Anjuli Pierce3 месяцев назад

@guruchahal @basepowerco We want to go micro and have a plan for it; can’t afford to implement personally however would love the opportunity for a live pitch!

Фото профиля Randy Compute
Randy Compute3 месяцев назад

@guruchahal @basepowerco the seventh layer is a spreadsheet insisting the other six will arrive on time

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Narrowing the “cone of uncertainty" Microsoft’s custom AI chips are a hedge against uncertain demand. When forecasts change every week and data centers take 2–3 years to build..... getting more AI out of the same power and budget gives Microsoft room to adjust. "In a world where power is constrained, land is constrained, and materials are constrained... everything you can do to achieve more with less ultimately helps in the build out phase that we're in right now" - MSFT cloud chief Scott Guthrie
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Narrowing the “cone of uncertainty" Microsoft’s custom AI chips are a hedge against uncertain demand. When forecasts change every week and data centers take 2–3 years to build..... getting more AI out of the same power and budget gives Microsoft room to adjust. "In a world where power is constrained, land is constrained, and materials are constrained... everything you can do to achieve more with less ultimately helps in the build out phase that we're in right now" - MSFT cloud chief Scott Guthrie

Deirdre Bosa

10,701 просмотров • 1 месяц назад

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:
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“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

TIME

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🚀 AI Deep Dive Episode 3: Recursive Self-Improvement AI can design better chips. Better chips can build better AI. 🚀 Rocket and Ricursive Intelligence co-founders Anna Goldie and Azalia Mirhoseini go deep on the hardware behind recursive self-improvement—and what happens when that loop starts accelerating. – Intro – What Is Recursive Self-Improvement? – Can AI Automate Chip Design? – Co-Designing AI and Hardware – Beyond Today’s Models and Chips – How Much More Efficient Could AI Get? – What Happens When the Loop Accelerates? – Can Alignment Keep Up? – Should We Slow AI Progress?
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🚀 AI Deep Dive Episode 3: Recursive Self-Improvement AI can design better chips. Better chips can build better AI. 🚀 Rocket and Ricursive Intelligence co-founders Anna Goldie and Azalia Mirhoseini go deep on the hardware behind recursive self-improvement—and what happens when that loop starts accelerating. – Intro – What Is Recursive Self-Improvement? – Can AI Automate Chip Design? – Co-Designing AI and Hardware – Beyond Today’s Models and Chips – How Much More Efficient Could AI Get? – What Happens When the Loop Accelerates? – Can Alignment Keep Up? – Should We Slow AI Progress?

The Information

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$NVTS makes power semiconductors that help AI servers move electricity more efficiently down to the chips running the workloads If AI racks keep scaling to higher power levels then Navitas sits in picks-and-shovels layer of AI factory where power delivery becomes the bottleneck
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$NVTS makes power semiconductors that help AI servers move electricity more efficiently down to the chips running the workloads If AI racks keep scaling to higher power levels then Navitas sits in picks-and-shovels layer of AI factory where power delivery becomes the bottleneck

Shay Boloor

116,684 просмотров • 5 месяцев назад

🇺🇸 Elon confirms the “Terafab” model links xAI, SpaceX, and Tesla to scale chips, energy, and factories together, aiming for terawatt-level compute output. The idea is to control the full stack: chip design, manufacturing, power generation, and deployment, with most compute eventually supporting space-based systems. So it’s like a feedback loop, more factories build more chips, which train better AI, which then designs even better factories. Because why build a factory… when you can try to build the entire stack. Source: Elon Musk Tesla
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🇺🇸 Elon confirms the “Terafab” model links xAI, SpaceX, and Tesla to scale chips, energy, and factories together, aiming for terawatt-level compute output. The idea is to control the full stack: chip design, manufacturing, power generation, and deployment, with most compute eventually supporting space-based systems. So it’s like a feedback loop, more factories build more chips, which train better AI, which then designs even better factories. Because why build a factory… when you can try to build the entire stack. Source: Elon Musk Tesla

Mario Nawfal

152,538 просмотров • 7 месяцев назад

In the AI age, money will flow through compute. As AI scales, every request becomes a microtransaction of compute, and only infrastructure owners capture that flow. Aethir is building the decentralized compute layer positioned to power and benefit from the AI economy ahead⚡️
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In the AI age, money will flow through compute. As AI scales, every request becomes a microtransaction of compute, and only infrastructure owners capture that flow. Aethir is building the decentralized compute layer positioned to power and benefit from the AI economy ahead⚡️

Aethir

12,538 просмотров • 11 месяцев назад

ELON: "WE'LL HAVE MORE AI CHIPS THAN POWER TO RUN THEM" "The limiting factor for AI deployment is fundamentally electrical power. It's energy. We're seeing the rate of AI chip production increase exponentially, but the rate of electricity being brought online is 5% a year, max. It's clear that we're very soon, maybe even later this year, we'll be producing more chips than we can turn on." Source: Elon Musk
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ELON: "WE'LL HAVE MORE AI CHIPS THAN POWER TO RUN THEM" "The limiting factor for AI deployment is fundamentally electrical power. It's energy. We're seeing the rate of AI chip production increase exponentially, but the rate of electricity being brought online is 5% a year, max. It's clear that we're very soon, maybe even later this year, we'll be producing more chips than we can turn on." Source: Elon Musk

Mario Nawfal

40,874 просмотров • 9 месяцев назад

Marc Andreessen says AI is teaching sand to think and it could be the most important technology in the history of humanity: "Imagine a form of alchemy that turns sand into thought." "Chips are made out of sand. They're made out of silicon, so they're literally made out of sand." "We plug the chip into a data center, into power, we light it up, and we put AI on it, and all of a sudden it's thinking." "We've turned sand into thought. And so it's possibly the most revolutionary technology in the history of the species." "It's certainly on par with electricity and steam power. It's certainly more important than the internet." Marc Andreessen 🇺🇸 with Joe Rogan
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a16z

224,101 просмотров • 5 месяцев назад

Neil Movva (Neil Movva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power. We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are. What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow. Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible. We discuss: - Latency versus throughput - Why there are no bad chips, only bad pricing - The end of kernel engineering - Buying chips and power no one else wants - New chip architectures - Nvidia lore + his contrarian view of the company - Open source and the frontier labs I learned a ton. Enjoy! TIMESTAMPS 0:00 Intro 0:38 Building a “Token Factory” 4:21 The Future of Background Agents 13:09 Nvidia and the GPU Stack 23:27 Chips, Memory, and Transformers 36:14 The Future of AI Training Data 44:32 Chip Scarcity and Compute Arbitrage 52:44 Reinventing the AI Data Center 59:01 Power and the “Scavenger Strategy” 1:10:10 Open vs. Closed AI
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Trump: "We're letting people build their own power plants. A lot of them are being built with the AI and beyond the AI. Chips. We're letting them build their own power. Never been done before ... they'll sort of be their own power company. They couldn't believe it when I told them that."
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Trump: "We're letting people build their own power plants. A lot of them are being built with the AI and beyond the AI. Chips. We're letting them build their own power. Never been done before ... they'll sort of be their own power company. They couldn't believe it when I told them that."

Aaron Rupar

952,020 просмотров • 1 год назад

They are putting DATA CENTERS in the ocean now. Panthalassa, a startup from Portland, just raised $140 MILLION. what they do: build floating platforms that sit out at sea and run AI. no power grid needed. the ocean waves make all the electricity. the seawater keeps the chips cool. How it works: big floating balls bob up and down with the waves. that motion makes power. the power runs the AI chips inside. backed by PETER THIEL. company now worth almost $1 BILLION. land is running out of room and power for AI. so the next move is simple. go to the sea.
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They are putting DATA CENTERS in the ocean now. Panthalassa, a startup from Portland, just raised $140 MILLION. what they do: build floating platforms that sit out at sea and run AI. no power grid needed. the ocean waves make all the electricity. the seawater keeps the chips cool. How it works: big floating balls bob up and down with the waves. that motion makes power. the power runs the AI chips inside. backed by PETER THIEL. company now worth almost $1 BILLION. land is running out of room and power for AI. so the next move is simple. go to the sea.

shirish

1,647,854 просмотров • 4 месяцев назад

Jensen Huang is warning that China is winning the AI energy race. $NVDA $AMD $IREN $NBIS 🇨🇳 🇺🇸 🇪🇺 The US is generations ahead on chips, but that doesn't matter if there is no energy to power them. Europe is completely irrelevant in AI and doomed already. It takes 10 years to build a new data center in Germany, as there is no power, and they are turning off nuclear power plants. The US won't let China win, so we could be at the beginning stages of an energy boom. The question is which stocks will benefit from this boom the most?
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Jensen Huang is warning that China is winning the AI energy race. $NVDA $AMD $IREN $NBIS 🇨🇳 🇺🇸 🇪🇺 The US is generations ahead on chips, but that doesn't matter if there is no energy to power them. Europe is completely irrelevant in AI and doomed already. It takes 10 years to build a new data center in Germany, as there is no power, and they are turning off nuclear power plants. The US won't let China win, so we could be at the beginning stages of an energy boom. The question is which stocks will benefit from this boom the most?

Ray Myers

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Every AI model needs somewhere to run. Compute. Power. Data centres. That's the bottleneck now, not the software. Canada is putting billions on the table through 2030 to secure sovereign AI capacity. HIVE Digital Technologies is building that infrastructure now, ahead of the funding curve. Why investors are watching: - Sovereign AI compute infrastructure, live in Canada - Data centres, GPU capacity, and power, already in motion - Dual-engine business model generating revenue while expanding AI infrastructure - Sitting at the intersection of AI, energy, and infrastructure - Exposure to one of tech's fastest-growing long-term trends Railroads built the industrial economy. The internet built the information economy. AI infrastructure is building this one. Every company will use AI. Fewer will own what it runs on. Learn More:

BTV & CEO Clips

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China is our adversary. They are building more power than we are, and then using it to power data centers to advance both economic interests and of course military as well. We can’t let the Chinese beat us in AI. We’ve gotta build more data centers, we’ve gotta build more power. We’ve got a lot ahead of us here because they’re beating us on this. We cannot let this happen.
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China is our adversary. They are building more power than we are, and then using it to power data centers to advance both economic interests and of course military as well. We can’t let the Chinese beat us in AI. We’ve gotta build more data centers, we’ve gotta build more power. We’ve got a lot ahead of us here because they’re beating us on this. We cannot let this happen.

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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.
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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 просмотров • 5 месяцев назад

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️
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Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 просмотров • 5 месяцев назад

Bitzero is a power, permit, and land company. What they've done is search the globe looking for the lowest cost of power, sustainable electric power, some nuclear power, sub six cents a kilowatt hour. You can mine bitcoin on their land or you can do Ethereum, or you can build a data center. Everybody in AI has to have power, land, permits, and water. BitZero has them. It's an infrastructure play and I find a lot less risky than figuring out who's going to win in AI. Everybody needs what BitZero has, power.
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Bitzero is a power, permit, and land company. What they've done is search the globe looking for the lowest cost of power, sustainable electric power, some nuclear power, sub six cents a kilowatt hour. You can mine bitcoin on their land or you can do Ethereum, or you can build a data center. Everybody in AI has to have power, land, permits, and water. BitZero has them. It's an infrastructure play and I find a lot less risky than figuring out who's going to win in AI. Everybody needs what BitZero has, power.

Kevin O'Leary aka Mr. Wonderful

18,648 просмотров • 11 месяцев назад

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.”
4:51

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

251,096 просмотров • 3 месяцев назад