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Will AI data centers become more consolidated — or more distributed? jsun, Founder of FLock.io, sees inference pushing compute outward. Unlike large-scale training, many inference workloads don’t require massive, tightly connected clusters. That could make smaller sites with underused power viable as “mini data centers.” Desmond Lim, Co-founder &... show more

YZi Labs

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27,123 次观看 • 3 天前 •via X (Twitter)

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6 条评论

IronRed | SandHive 的头像

IronRed | SandHive3 天前

@0x7SUN @flock_io Mini data centers = democratized AI access. Imagine edge applications we haven't even dreamed of yet. Can't wait to see the impact!

Onchain Matrix 的头像

Onchain Matrix3 天前

The distributed inference argument is getting stronger. Smaller compute sites can make sense when workloads can be routed to where power and capacity are available. But I think both views can coexist: centralized infrastructure for training, distributed infrastructure for inference.

jaime 的头像

jaime3 天前

@0x7SUN @flock_io Inference at the edge makes sense, but i think the real bottleneck isn't compute location, it's latency + data gravity.

NHK | GYNDORE DAY ONE 的头像

NHK | GYNDORE DAY ONE3 天前

@0x7SUN @flock_io Inference distributes, capital still consolidates

Shahzad 🔶 BNB 的头像

Shahzad 🔶 BNB3 天前

@0x7SUN @flock_io NFA

lvnbbs_bnb 🐬TermMax 的头像

lvnbbs_bnb 🐬TermMax3 天前

@0x7SUN @flock_io inference goes edhe but capital stays core

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Think bigger than data centers. Much bigger. Elon Musk just unveiled SpaceXAI's first AI satellite, a spacecraft designed specifically for running AI workloads in orbit. The satellite features 150 kW of solar power, a 70-meter wingspan and massive radiators to dump heat into space, solving one of the biggest challenges in scaling AI. But this is just the beginning. Musk says truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, meaning millions of tons of infrastructure in orbit. The long-term vision isn't AI running in data centers. It's AI running in space at a scale that's impossible on Earth. Elon Musk

Think bigger than data centers. Much bigger. Elon Musk just unveiled SpaceXAI's first AI satellite, a spacecraft designed specifically for running AI workloads in orbit. The satellite features 150 kW of solar power, a 70-meter wingspan and massive radiators to dump heat into space, solving one of the biggest challenges in scaling AI. But this is just the beginning. Musk says truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, meaning millions of tons of infrastructure in orbit. The long-term vision isn't AI running in data centers. It's AI running in space at a scale that's impossible on Earth. Elon Musk

Tesla Owners Silicon Valley

19,203 次观看 • 3 个月前

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.

TeslaZoa

35,216 次观看 • 3 个月前

Elon Musk explains Tesla’s new Megapod concept for scaling distributed AI compute: “We’re building a Megapod design that pairs Tesla AI4 computers with x86 systems in a box...essentially digital Optimus in a box Similar to how Megapack packages batteries, a Megapod would combine a large number of AI4 and x86 systems inside one deployable unit These boxes could be placed anywhere across the country or outside the country allowing us to scale AI compute using distributed electricity production There is power available all around the world, including at our Supercharger sites. We have something like 7 gigawatts of power across the Supercharger network, and that continues to grow We could place Megapods at many of these locations and use that distributed power for AI compute I think that could be quite a big deal” Instead of relying entirely on enormous centralized data centers, Tesla could deploy modular AI-compute infrastructure wherever sufficient power is already available

Elon Musk explains Tesla’s new Megapod concept for scaling distributed AI compute: “We’re building a Megapod design that pairs Tesla AI4 computers with x86 systems in a box...essentially digital Optimus in a box Similar to how Megapack packages batteries, a Megapod would combine a large number of AI4 and x86 systems inside one deployable unit These boxes could be placed anywhere across the country or outside the country allowing us to scale AI compute using distributed electricity production There is power available all around the world, including at our Supercharger sites. We have something like 7 gigawatts of power across the Supercharger network, and that continues to grow We could place Megapods at many of these locations and use that distributed power for AI compute I think that could be quite a big deal” Instead of relying entirely on enormous centralized data centers, Tesla could deploy modular AI-compute infrastructure wherever sufficient power is already available

X Freeze

17,025 次观看 • 1 个月前

🚨 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.

🚨 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

58,387 次观看 • 7 个月前

Dr. Eric Schmidt says AI needs “energy in all forms: renewable, non-renewable, whatever.” “It needs to be there, and it needs to be quickly.” “People are planning 10 gigawatt data centers.” “An average nuclear power plant in the United States is one gigawatt.” “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.” This is exactly why I believe the market is still underestimating the AI power and infrastructure opportunity in companies like $TE, $BE, $PUMP, $PSIX, $SMR, $FCEL, $MARA, $KEEL, and $OKLO. The biggest constraint to scaling AI remains electricity. The companies building power generation, grid infrastructure, electrical equipment, cooling systems, and AI data centers could be among the biggest beneficiaries over the next decade. Not financial advice. Do your own research.

Dr. Eric Schmidt says AI needs “energy in all forms: renewable, non-renewable, whatever.” “It needs to be there, and it needs to be quickly.” “People are planning 10 gigawatt data centers.” “An average nuclear power plant in the United States is one gigawatt.” “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.” This is exactly why I believe the market is still underestimating the AI power and infrastructure opportunity in companies like $TE, $BE, $PUMP, $PSIX, $SMR, $FCEL, $MARA, $KEEL, and $OKLO. The biggest constraint to scaling AI remains electricity. The companies building power generation, grid infrastructure, electrical equipment, cooling systems, and AI data centers could be among the biggest beneficiaries over the next decade. Not financial advice. Do your own research.

Leopold Stock Tracker

20,643 次观看 • 2 个月前

When the team from Forbes came to our San Jose headquarters, the conversation quickly became about more than just chips. It became a discussion about the future of AI and what it actually takes to run it at scale. As AI adoption accelerates, the challenge is no longer training models, it’s inference: running models efficiently, reliably, and fast enough to power the next generation of agentic applications. As our CEO and Founder Rodrigo Liang put it: “It's much lower power, much smaller footprint, and it's faster than anybody else. What's not to like?” And as Dharmesh Thakker General Partner of Battery Ventures explained: "The technology that SambaNova has is very complimentary to what Intel has because most AI data centers need a combination of CPUs and GPUs." A big thank you to Anton McGonnell and Vasanth Mohan for helping bring this story to life and for the thoughtful conversations throughout the day. We’re excited about what this shift means in practice, from premium inference and agentic AI to more efficient, sustainable data center infrastructure that lets enterprises deploy AI at scale, not just experiment with it. And thank you to Kirsten Taggart and the team at Forbes for spending the time with us and taking a deeper look at what we’re building. 🎥 Watch the full feature below.

When the team from Forbes came to our San Jose headquarters, the conversation quickly became about more than just chips. It became a discussion about the future of AI and what it actually takes to run it at scale. As AI adoption accelerates, the challenge is no longer training models, it’s inference: running models efficiently, reliably, and fast enough to power the next generation of agentic applications. As our CEO and Founder Rodrigo Liang put it: “It's much lower power, much smaller footprint, and it's faster than anybody else. What's not to like?” And as Dharmesh Thakker General Partner of Battery Ventures explained: "The technology that SambaNova has is very complimentary to what Intel has because most AI data centers need a combination of CPUs and GPUs." A big thank you to Anton McGonnell and Vasanth Mohan for helping bring this story to life and for the thoughtful conversations throughout the day. We’re excited about what this shift means in practice, from premium inference and agentic AI to more efficient, sustainable data center infrastructure that lets enterprises deploy AI at scale, not just experiment with it. And thank you to Kirsten Taggart and the team at Forbes for spending the time with us and taking a deeper look at what we’re building. 🎥 Watch the full feature below.

SambaNova

226,753 次观看 • 3 个月前

If you care about AI/energy infrastructure, the best 32 minutes you'll spend today is on Shayle Kann's interview with Amin Vahdat, Google's head of AI infrastructure. We recorded this at our Transition-AI conference, and we just posted the Catalyst w/ Shayle Kann vid on youtube. Lots of really good insight here: They touch on scale. The conventional wisdom has been that bigger is better, and gigawatt campuses are the future. As we head into the inference era, Amin sees a medium number of medium-sized data centers, distributed for geographic latency, with a smaller number of large ones anchoring the fleet. Then there's the reliability question. The 4 or 5 nines requirement is disappearing. Google's internal customers are already choosing more capacity at lower reliability. Of course, that impacts everything downstream, from grid interconnection, to behind-the-meter buildout. On BTM power specifically, he says Google's preference is still grid-connected. Provisioning for reliability yourself is expensive. He sees it purely as a bridge play. Finally, the rise of purpose-built computing. For years, everyone built multi-purpose data centers when compute wasn't so costly. But now they are breaking it up into GPU buildings/TPU buildings based on workload. You give up fungibility, but you gain efficiency.

If you care about AI/energy infrastructure, the best 32 minutes you'll spend today is on Shayle Kann's interview with Amin Vahdat, Google's head of AI infrastructure. We recorded this at our Transition-AI conference, and we just posted the Catalyst w/ Shayle Kann vid on youtube. Lots of really good insight here: They touch on scale. The conventional wisdom has been that bigger is better, and gigawatt campuses are the future. As we head into the inference era, Amin sees a medium number of medium-sized data centers, distributed for geographic latency, with a smaller number of large ones anchoring the fleet. Then there's the reliability question. The 4 or 5 nines requirement is disappearing. Google's internal customers are already choosing more capacity at lower reliability. Of course, that impacts everything downstream, from grid interconnection, to behind-the-meter buildout. On BTM power specifically, he says Google's preference is still grid-connected. Provisioning for reliability yourself is expensive. He sees it purely as a bridge play. Finally, the rise of purpose-built computing. For years, everyone built multi-purpose data centers when compute wasn't so costly. But now they are breaking it up into GPU buildings/TPU buildings based on workload. You give up fungibility, but you gain efficiency.

Stephen Lacey

20,161 次观看 • 3 个月前

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Network

20,392 次观看 • 8 个月前

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

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

Six Five Media

224,069 次观看 • 4 个月前

Every AI response you've ever gotten started with an electron. ⚡ In a recent interview with Sequoia Capital, Jensen Huang described AI infrastructure as a five-layer cake, and at the very bottom, before chips, before data centers, before models, is energy. That's the binding constraint on the Intelligence Age: not compute, not software — watts. This is why NVIDIA calls AI factories the dynamos of our era. They take in electrons and send out tokens of intelligence. We're a few hundred billion dollars into what Jensen calls the largest infrastructure buildout in human history. Trillions more to go. The question isn't whether AI will scale. It's whether the energy grid can keep up. ⬆️

Every AI response you've ever gotten started with an electron. ⚡ In a recent interview with Sequoia Capital, Jensen Huang described AI infrastructure as a five-layer cake, and at the very bottom, before chips, before data centers, before models, is energy. That's the binding constraint on the Intelligence Age: not compute, not software — watts. This is why NVIDIA calls AI factories the dynamos of our era. They take in electrons and send out tokens of intelligence. We're a few hundred billion dollars into what Jensen calls the largest infrastructure buildout in human history. Trillions more to go. The question isn't whether AI will scale. It's whether the energy grid can keep up. ⬆️

NVIDIA

102,834 次观看 • 2 个月前

$IREN CCO KENT DRAPER TALK ON AI GIGAFACTORIES $IREN CCO Kent Draper: We were ready for this moment [AI and HPC] and been exploring it for a number of years but the market wasn’t there to make it commercially viable. When the business was founded, our two co-founders have a background in large scale institutional infrastructure banking, and they could see this infliction in the industry where the world was becoming more data dependent. It was very obvious to them that we were going to need power-dense compute, particularly with access to renewables, which is a big part of our story. Design [of data centers] is the easy part. Delivering infrastructure at this scale is not simple. I think our minds this was never actually a pivot for us. Lenovo SVP Vlad Rozanovich: Time to first token is a metric that so many people like $IREN look at because it’s how you maximize value and profit as soon as you can. Daniel Newman: You can’t have a bubble and have AI eating every industry, either too good or not good.

$IREN CCO KENT DRAPER TALK ON AI GIGAFACTORIES $IREN CCO Kent Draper: We were ready for this moment [AI and HPC] and been exploring it for a number of years but the market wasn’t there to make it commercially viable. When the business was founded, our two co-founders have a background in large scale institutional infrastructure banking, and they could see this infliction in the industry where the world was becoming more data dependent. It was very obvious to them that we were going to need power-dense compute, particularly with access to renewables, which is a big part of our story. Design [of data centers] is the easy part. Delivering infrastructure at this scale is not simple. I think our minds this was never actually a pivot for us. Lenovo SVP Vlad Rozanovich: Time to first token is a metric that so many people like $IREN look at because it’s how you maximize value and profit as soon as you can. Daniel Newman: You can’t have a bubble and have AI eating every industry, either too good or not good.

The Tech Investor

25,559 次观看 • 5 个月前

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career

Molly O’Shea

494,613 次观看 • 2 个月前

#WATCH | Delhi: At the #IndiaAIImpactSummit2026, Chairman & Managing Director of Reliance Industries Limited, Mukesh Ambani, says, "Today, on behalf of the Reliance Group and Jio Intelligence, I want to make three announcements. Announcement one, Jio connected India to the internet era. Jio will now connect India to the intelligence era. We will deliver intelligence to every citizen, every sector of the economy, and every facet of social development and every service of government. Jio will do so with the same reliability, quality, scale, and extreme affordability that transformed connectivity. India cannot afford to rent intelligence. Therefore, we will reduce the cost of intelligence as dramatically as we did the cost of data... Announcement 2, Jio, together with Reliance, will invest 10 lakh crores over the next seven years, starting this year... Announcement 3, Jio Intelligence will build India's sovereign compute infrastructure through three bold initiatives. One, gigawatt-scale data centres. We already started construction on multi-gigawatt AI-ready data centres at Jamnagar. Over 120 megawatts will come online in the second half of 2026 this year and a clear path to gigawatt-scale compute for training and large scale inference. Two, our green energy advantage. We have an in-house energy advantage with up to 10 gigawatts of ready green power surplus anchored by solar in both Kutch and Andhra Pradesh. Three, a nationwide edge compute, an edge compute layer deeply integrated with Jio's network will make intelligence responsive, low latency, and affordable close to where Indians live, learn, and work..." (Source: India AI)

#WATCH | Delhi: At the #IndiaAIImpactSummit2026, Chairman & Managing Director of Reliance Industries Limited, Mukesh Ambani, says, "Today, on behalf of the Reliance Group and Jio Intelligence, I want to make three announcements. Announcement one, Jio connected India to the internet era. Jio will now connect India to the intelligence era. We will deliver intelligence to every citizen, every sector of the economy, and every facet of social development and every service of government. Jio will do so with the same reliability, quality, scale, and extreme affordability that transformed connectivity. India cannot afford to rent intelligence. Therefore, we will reduce the cost of intelligence as dramatically as we did the cost of data... Announcement 2, Jio, together with Reliance, will invest 10 lakh crores over the next seven years, starting this year... Announcement 3, Jio Intelligence will build India's sovereign compute infrastructure through three bold initiatives. One, gigawatt-scale data centres. We already started construction on multi-gigawatt AI-ready data centres at Jamnagar. Over 120 megawatts will come online in the second half of 2026 this year and a clear path to gigawatt-scale compute for training and large scale inference. Two, our green energy advantage. We have an in-house energy advantage with up to 10 gigawatts of ready green power surplus anchored by solar in both Kutch and Andhra Pradesh. Three, a nationwide edge compute, an edge compute layer deeply integrated with Jio's network will make intelligence responsive, low latency, and affordable close to where Indians live, learn, and work..." (Source: India AI)

ANI

82,266 次观看 • 6 个月前

BREAKING: How AI Reaches the Most Extreme Parts of the World — From U.S. Navy Ships & Saudi Deserts to the Alaskan Arctic Dan Wright (Dan Wright), CEO of Armada Recorded at the Reagan National Defense Forum Dan joins Sourcery to share why the global race for AI sovereignty will shape the future of power And how Armada partners with SpaceX, OpenAI, Saudi Aramco, U.S. Navy, MSFT & more, to reach every edge Key Points - Starlink, Starshield, SpaceX IPO, data centers in space - 70% of the world does not have access to AI, Armada changes that - Modular data centers vs hyperscaler data centers - U.S. vs China AI stack - American AI dominance Armada has $200M+ in total funding from top investors like Founders Fund, Dragon Global, Lux Capital, Shield Capital, 8090 Industries, Microsoft’s M12, Overmatch, Silent Ventures, Felicis, & Marlinspike With more recent strategic participation from investors: Pinegrove, Veriten, & Glade Brook Armada’s partners include Microsoft, Starlink, OpenAI, NVIDIA, Nokia, Red Hat, Carahsoft, AVEVA, Esri, Halliburton, Skydio, & more Ronald Reagan Presidential Foundation & Institute HIghlights (00:00) Dan Wright, CEO Armada (01:12) Armada: an edge hyperscaler deploying modular AI data centers (04:28) Partnering with SpaceX, Microsoft, Skydio, and OpenAI (05:47) Deploying AI in extreme environments: Ocean, Desert, Arctic & Space (08:11) SpaceX IPO rumors and Starlink’s rapid expansion (12:10) The AI dominance white paper and US vs China infrastructure (14:54) Edge compute, latency, and real world response at scale (17:43) Armada’s roadmap for 2026

BREAKING: How AI Reaches the Most Extreme Parts of the World — From U.S. Navy Ships & Saudi Deserts to the Alaskan Arctic Dan Wright (Dan Wright), CEO of Armada Recorded at the Reagan National Defense Forum Dan joins Sourcery to share why the global race for AI sovereignty will shape the future of power And how Armada partners with SpaceX, OpenAI, Saudi Aramco, U.S. Navy, MSFT & more, to reach every edge Key Points - Starlink, Starshield, SpaceX IPO, data centers in space - 70% of the world does not have access to AI, Armada changes that - Modular data centers vs hyperscaler data centers - U.S. vs China AI stack - American AI dominance Armada has $200M+ in total funding from top investors like Founders Fund, Dragon Global, Lux Capital, Shield Capital, 8090 Industries, Microsoft’s M12, Overmatch, Silent Ventures, Felicis, & Marlinspike With more recent strategic participation from investors: Pinegrove, Veriten, & Glade Brook Armada’s partners include Microsoft, Starlink, OpenAI, NVIDIA, Nokia, Red Hat, Carahsoft, AVEVA, Esri, Halliburton, Skydio, & more Ronald Reagan Presidential Foundation & Institute HIghlights (00:00) Dan Wright, CEO Armada (01:12) Armada: an edge hyperscaler deploying modular AI data centers (04:28) Partnering with SpaceX, Microsoft, Skydio, and OpenAI (05:47) Deploying AI in extreme environments: Ocean, Desert, Arctic & Space (08:11) SpaceX IPO rumors and Starlink’s rapid expansion (12:10) The AI dominance white paper and US vs China infrastructure (14:54) Edge compute, latency, and real world response at scale (17:43) Armada’s roadmap for 2026

Molly O’Shea

16,354 次观看 • 7 个月前

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."

Big Brain AI

11,442 次观看 • 3 个月前

MARA CEO: Using Electricity for AI Is Far More Profitable Than Bitcoin Mining Fred Thiel Fred Thiel, CEO of MARA, one of the world's largest publicly listed Bitcoin mining companies, said in a July 23 interview with Natalie Brunell that electricity has become the industry's most critical resource, as the same amount of power can generate far greater returns when used for AI than for Bitcoin mining. This is why MARA and many of its peers are shifting toward AI data centers. However, MARA will continue mining Bitcoin, as it remains an effective way to utilize surplus electricity in regions where energy is free or inexpensive.

MARA CEO: Using Electricity for AI Is Far More Profitable Than Bitcoin Mining Fred Thiel Fred Thiel, CEO of MARA, one of the world's largest publicly listed Bitcoin mining companies, said in a July 23 interview with Natalie Brunell that electricity has become the industry's most critical resource, as the same amount of power can generate far greater returns when used for AI than for Bitcoin mining. This is why MARA and many of its peers are shifting toward AI data centers. However, MARA will continue mining Bitcoin, as it remains an effective way to utilize surplus electricity in regions where energy is free or inexpensive.

Wu Blockchain

21,834 次观看 • 1 个月前

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin

Daniel Koss

204,656 次观看 • 4 个月前

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.

Milk Road AI

12,738 次观看 • 3 个月前

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 次观看 • 7 个月前