正在加载视频...

视频加载失败

返回首页

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...

clairejyz's profile picture

Claire Zau

6,914 subscribers

45,448 次观看 • 1 个月前 •via X (Twitter)

科学技术教育新闻政治
LIVE

Anya Rossi• Live Now

Private livecam show

17 条评论

Claire Zau 的头像
Claire Zau1 个月前

full episode and breakdown on youtube

cogsec 的头像
cogsec1 个月前

@guruchahal @basepowerco super cool

Claire Zau 的头像
Claire Zau1 个月前

@guruchahal @basepowerco thank you 🙏

Dara Miao 的头像
Dara Miao1 个月前

@guruchahal @basepowerco so helpful i love this claire

Claire Zau 的头像
Claire Zau1 个月前

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

Michelle Sun 的头像
Michelle Sun1 个月前

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

Claire Zau 的头像
Claire Zau1 个月前

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

Dakshay Mehta 的头像
Dakshay Mehta1 个月前

@guruchahal @basepowerco Guru is awesome

Claire Zau 的头像
Claire Zau1 个月前

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

Dakshay Mehta 的头像
Dakshay Mehta1 个月前

@guruchahal @basepowerco 💯💯

Max Song (🌎, 🚀 , 🌕 ) 的头像
Max Song (🌎, 🚀 , 🌕 )1 个月前

@guruchahal @basepowerco Awesome content!!

Vatsalpandya333 的头像
Vatsalpandya3331 个月前

@guruchahal @basepowerco 🐐

Nirvana Labs 的头像
Nirvana Labs1 个月前

@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!

Stanley Wei 的头像
Stanley Wei1 个月前

@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.

AI Mastery Guide 的头像
AI Mastery Guide1 个月前

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

Anjuli Pierce 的头像
Anjuli Pierce1 个月前

@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!

Randy Compute 的头像
Randy Compute1 个月前

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

相关视频

“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:
1:12

“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

38,584 次观看 • 26 天前

🇺🇸 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
1:45

🇺🇸 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 次观看 • 6 个月前

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
1:10

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

a16z

223,439 次观看 • 4 个月前

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
1:23:35

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

Patrick OShaughnessy

4,623,015 次观看 • 29 天前

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.
0:26

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

1,646,966 次观看 • 3 个月前

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?
6:50

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

84,029 次观看 • 9 个月前

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside
2:43

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside

X Freeze

140,377 次观看 • 4 个月前

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:
0:30

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

24,998 次观看 • 2 个月前

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. ⬇️
0:43

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 次观看 • 3 个月前

With Planet’s AI app, users can combine the power of AI with near-daily satellite data to explore what’s happening on Earth, uncover insights, and better understand change at scale. This is a glimpse into the future of Planetary Intelligence: transforming the world’s most comprehensive Earth data into actionable intelligence. Sign up today, in open beta now:
1:00

With Planet’s AI app, users can combine the power of AI with near-daily satellite data to explore what’s happening on Earth, uncover insights, and better understand change at scale. This is a glimpse into the future of Planetary Intelligence: transforming the world’s most comprehensive Earth data into actionable intelligence. Sign up today, in open beta now:

Planet

10,384 次观看 • 1 个月前

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.
3:06

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.

Kevin O'Leary aka Mr. Wonderful

19,405 次观看 • 4 个月前

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.
1:10

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 次观看 • 4 个月前

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

246,547 次观看 • 2 个月前

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.
1:11

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 次观看 • 10 个月前

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.
0:56

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,633 次观看 • 5 个月前

AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.
1:14

AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

Six Five Media

131,501 次观看 • 6 个月前

Elon Musk in new G20 interview on the AI power crisis: "There actually is a crisis of power. If you just follow the AI topic on the 𝕏 platform, you'll get a very good sense of where things are headed. That's how I get my news; There will be a significant power shortfall next year. The consensus estimate is there will be a 15GW power shortfall in 2027 for AI chips. This is why Google and Anthropic are leasing compute from SpaceX, because we've been able to turn on AI better than anyone else so far. This is by constructing our own power plants is how we've been able to do it." Recently, SpaceX powered down 11 turbines at their Southaven datacenter facility as they make way for their new 1.2 GW power plant coming online in the near future (under the Clean Air Act permit).
3:04

Elon Musk in new G20 interview on the AI power crisis: "There actually is a crisis of power. If you just follow the AI topic on the 𝕏 platform, you'll get a very good sense of where things are headed. That's how I get my news; There will be a significant power shortfall next year. The consensus estimate is there will be a 15GW power shortfall in 2027 for AI chips. This is why Google and Anthropic are leasing compute from SpaceX, because we've been able to turn on AI better than anyone else so far. This is by constructing our own power plants is how we've been able to do it." Recently, SpaceX powered down 11 turbines at their Southaven datacenter facility as they make way for their new 1.2 GW power plant coming online in the near future (under the Clean Air Act permit).

Sawyer Merritt

133,997 次观看 • 22 天前

AI workload depends on: power, infrastructure and connectivity. At #NEOM, we're bringing together the conditions that make the next generation of AI compute possible: scalable energy and water capacity, digital connectivity, developed industrial land and infrastructure. Building on this, HUMAIN and DataVoltKSA are advancing the construction of the first AI data center at Oxagon, further strengthening NEOM’s position as a hub for AI infrastructure. Together, we're creating an ecosystem for AI to scale, driving investment, innovation and strategic partnerships that support Saudi Arabia's economic diversification.
1:19

AI workload depends on: power, infrastructure and connectivity. At #NEOM, we're bringing together the conditions that make the next generation of AI compute possible: scalable energy and water capacity, digital connectivity, developed industrial land and infrastructure. Building on this, HUMAIN and DataVoltKSA are advancing the construction of the first AI data center at Oxagon, further strengthening NEOM’s position as a hub for AI infrastructure. Together, we're creating an ecosystem for AI to scale, driving investment, innovation and strategic partnerships that support Saudi Arabia's economic diversification.

NEOM

28,289 次观看 • 22 天前