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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...

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Sami Kassab

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35,706 次观看 • 6 个月前 •via X (Twitter)

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Kevin O’Leary: “I think the new play is energy” How do you supply enough energy to advance the economy and all this AI demand? With the AI buildout requiring 100+ gigawatts of power and compute, it’s clear there will be massive winners across energy, data centers, and hardware
0:37

Kevin O’Leary: “I think the new play is energy” How do you supply enough energy to advance the economy and all this AI demand? With the AI buildout requiring 100+ gigawatts of power and compute, it’s clear there will be massive winners across energy, data centers, and hardware

Small Cap Snipa

57,806 次观看 • 5 个月前

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time
0:57

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time

Anatoli Kopadze

403,186 次观看 • 2 个月前

Distributed compute needs to be deployed with speed, scale, and sovereignty. Dan Wright joined Bloomberg Technology this week to talk about Galleon Forge One, our Series B, and the infrastructure buildout required to meet growing demand for inference, fine-tuning, and distributed model deployment at scale. The next wave of compute will not live only in centralized clouds. It will need to be deployed where land, energy, and data already exist. That is what Armada was built for. That is what Galleon delivers.
3:58

Distributed compute needs to be deployed with speed, scale, and sovereignty. Dan Wright joined Bloomberg Technology this week to talk about Galleon Forge One, our Series B, and the infrastructure buildout required to meet growing demand for inference, fine-tuning, and distributed model deployment at scale. The next wave of compute will not live only in centralized clouds. It will need to be deployed where land, energy, and data already exist. That is what Armada was built for. That is what Galleon delivers.

Armada

934,749 次观看 • 2 个月前

$INFERRA Just came across Inferra and this one actually feels really interesting. 9ufM9TJd1UEmi9awnGfxCkCHAgQ3JZ5Sw6YxeSeEASY They're building a decentralized marketplace where AI compute becomes a tradable commodity. Instead of letting GPU power sit idle, providers can list verified compute capacity while users can buy, sell, trade, or redeem it for actual compute access. What caught my attention is that they're approaching compute like a real market, with spot trading, futures, and eventually inference markets. As AI demand keeps exploding, compute is becoming one of the most valuable resources in the industry. Projects focused on making that resource more liquid and accessible make a lot of sense to me. High risk as marketcap is tiny. But definitely an interesting compute bet I aped small. DYOR. RR
0:40

$INFERRA Just came across Inferra and this one actually feels really interesting. 9ufM9TJd1UEmi9awnGfxCkCHAgQ3JZ5Sw6YxeSeEASY They're building a decentralized marketplace where AI compute becomes a tradable commodity. Instead of letting GPU power sit idle, providers can list verified compute capacity while users can buy, sell, trade, or redeem it for actual compute access. What caught my attention is that they're approaching compute like a real market, with spot trading, futures, and eventually inference markets. As AI demand keeps exploding, compute is becoming one of the most valuable resources in the industry. Projects focused on making that resource more liquid and accessible make a lot of sense to me. High risk as marketcap is tiny. But definitely an interesting compute bet I aped small. DYOR. RR

Crypto Ideology ✍🏻

13,657 次观看 • 1 个月前

NVIDIA is about to grow a new moat that has the potential to be the company’s most impenetrable moat yet: The Compute Futures Market One of the reasons why there’s so much demand for U.S. T-bills is because the market for them is incredibly deep, and large pools of capital can come in and out freely without much disturbance. That attribute is attractive, which leads to more demand for T-bills Liquidity begets liquidity Another example is what Bill Ackman is describing in the attached clip - the more valuable a company becomes, the easier it is for said company to raise capital to fund expansion, which in of itself is virtuous and valuable The same concept is going to apply to compute, and in some ways- it already is. NVIDIA GPUs are already the most financeable (in many cases the only financeable) form of compute for various parties to deploy in their data centers. But the introduction of a forward curve that facilitates financial expression like hedging takes that concept to a different level Companies who consume compute (structurally short compute) will want to be able to hedge their input costs, and companies who produce compute (structurally long compute) will want to be able to hedge their output price. The market will coalesce even more around the deepest / most liquid pools to facilitate this - which will be NVIDIA based futures (WTI Crude) This liquidity itself will become part of the value of purchasing NVIDIA equipment, it will be part of the justification to pay the “Nvidia tax”. And when the liquidity reaches a certain depth, it will be almost impossible to break - because to break it would involve coordinating between massive amounts of misaligned parties- impossible So NVIDIA, by no additional virtue or effort of their own will likely absorb another massive structural advantage that may be the most difficult advantage to overcome for any competitor out of anything that has been built so far. The rich really do get richer.
0:48

NVIDIA is about to grow a new moat that has the potential to be the company’s most impenetrable moat yet: The Compute Futures Market One of the reasons why there’s so much demand for U.S. T-bills is because the market for them is incredibly deep, and large pools of capital can come in and out freely without much disturbance. That attribute is attractive, which leads to more demand for T-bills Liquidity begets liquidity Another example is what Bill Ackman is describing in the attached clip - the more valuable a company becomes, the easier it is for said company to raise capital to fund expansion, which in of itself is virtuous and valuable The same concept is going to apply to compute, and in some ways- it already is. NVIDIA GPUs are already the most financeable (in many cases the only financeable) form of compute for various parties to deploy in their data centers. But the introduction of a forward curve that facilitates financial expression like hedging takes that concept to a different level Companies who consume compute (structurally short compute) will want to be able to hedge their input costs, and companies who produce compute (structurally long compute) will want to be able to hedge their output price. The market will coalesce even more around the deepest / most liquid pools to facilitate this - which will be NVIDIA based futures (WTI Crude) This liquidity itself will become part of the value of purchasing NVIDIA equipment, it will be part of the justification to pay the “Nvidia tax”. And when the liquidity reaches a certain depth, it will be almost impossible to break - because to break it would involve coordinating between massive amounts of misaligned parties- impossible So NVIDIA, by no additional virtue or effort of their own will likely absorb another massive structural advantage that may be the most difficult advantage to overcome for any competitor out of anything that has been built so far. The rich really do get richer.

Nick Dorsey

33,944 次观看 • 16 天前

There is a reason why $TSLA is building a 750,000+ sq ft cortex 2 super computer at Giga Texas, it’s because of AUTONOMY. The Robotaxi and Optimus robot will both need neural networks to work and massive training data is needed to run those two businesses efficiently and effectively. Tesla will have superior compute power and data compared with its peers. These investments in compute power will pay DIVIDENDS in the near future. Many people still aren’t aware that Tesla is building Cortex. This is ALPHA.
0:48

There is a reason why $TSLA is building a 750,000+ sq ft cortex 2 super computer at Giga Texas, it’s because of AUTONOMY. The Robotaxi and Optimus robot will both need neural networks to work and massive training data is needed to run those two businesses efficiently and effectively. Tesla will have superior compute power and data compared with its peers. These investments in compute power will pay DIVIDENDS in the near future. Many people still aren’t aware that Tesla is building Cortex. This is ALPHA.

Cole Grinde

31,232 次观看 • 1 年前

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.
4:06

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

Milk Road AI

16,609 次观看 • 1 个月前

Nvidia continues to dominate the AI industry with record earnings, explosive data center growth, and massive demand for compute. But the bigger story is what’s happening globally behind the scenes. China is aggressively competing with the United States on AI infrastructure, power generation, and data center expansion and they don’t want to rely on American chips to do it. This is no longer just about stocks it’s a global race for AI dominance.
1:15

Nvidia continues to dominate the AI industry with record earnings, explosive data center growth, and massive demand for compute. But the bigger story is what’s happening globally behind the scenes. China is aggressively competing with the United States on AI infrastructure, power generation, and data center expansion and they don’t want to rely on American chips to do it. This is no longer just about stocks it’s a global race for AI dominance.

Kevin O'Leary aka Mr. Wonderful

12,462 次观看 • 2 个月前

What if we could connect all the dark compute across the globe to build the world's biggest AI data center? Most of the compute in the world is dark: phones, laptops, Tesla's, PS5's, TV's. These devices have powerful GPUs but are mostly sitting idle. Today, EXO Labs is announcing the first step in activating all of this dark compute for AI workloads: evML, a distributed computing protocol that enforces honest behavior through hardware security and spot-checks with just 5% performance overhead. Version 0.0.1 of evML is compatible with Apple, Samsung and LG devices. Our preliminary report on evML is now available (link below).
0:24

What if we could connect all the dark compute across the globe to build the world's biggest AI data center? Most of the compute in the world is dark: phones, laptops, Tesla's, PS5's, TV's. These devices have powerful GPUs but are mostly sitting idle. Today, EXO Labs is announcing the first step in activating all of this dark compute for AI workloads: evML, a distributed computing protocol that enforces honest behavior through hardware security and spot-checks with just 5% performance overhead. Version 0.0.1 of evML is compatible with Apple, Samsung and LG devices. Our preliminary report on evML is now available (link below).

EXO Labs

137,913 次观看 • 1 年前

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:
0:34

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

Uomi

18,816 次观看 • 1 个月前

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.
0:23

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.

Milk Road AI

419,283 次观看 • 2 个月前

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 次观看 • 1 个月前

David Ulevitch on how AI is transforming energy, defense, and logistics: "If you start at the most fundamental level, energy—we have an insatiable thirst for energy in this country, and AI compute is driving a huge amount of it." "AI compute will then power lots of things. It's not just going to power ChatGPT, but it's going to power autonomy systems, all kinds of other language models, and all other kinds of use cases." "If you look at defense, computer vision is transforming the battlefield." "Then you see AI transforming logistics... flight operations and planning using AI, so you can have the most efficient flight planning, save the most amount of fuel, and make sure that the least amount of flights get canceled." David Ulevitch 🇺🇸 on sourcery
3:30

David Ulevitch on how AI is transforming energy, defense, and logistics: "If you start at the most fundamental level, energy—we have an insatiable thirst for energy in this country, and AI compute is driving a huge amount of it." "AI compute will then power lots of things. It's not just going to power ChatGPT, but it's going to power autonomy systems, all kinds of other language models, and all other kinds of use cases." "If you look at defense, computer vision is transforming the battlefield." "Then you see AI transforming logistics... flight operations and planning using AI, so you can have the most efficient flight planning, save the most amount of fuel, and make sure that the least amount of flights get canceled." David Ulevitch 🇺🇸 on sourcery

a16z

17,567 次观看 • 5 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 • 7 个月前

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.
0:24

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

Shay Boloor

261,700 次观看 • 2 个月前

.Shaun Maguire says SpaceX's data center deal with Google may suggest there is something fundamentally different about the company's terrestrial compute. "I think a lot of people were surprised by the Anthropic and Google announcements. And I think a lot of people are getting the wrong takeaway there." "The way a lot of people are interpreting that is it's like, 'oh, we're just in this temporary compute shortage. And so anyone's willing to work with anyone, and they're willing to pay any price for compute.'" "I think that might be a reasonable way to look at the Anthropic announcement. I don't think that makes sense for looking at the Google announcement." "To me, I think an alternative hypothesis is that SpaceX's terrestrial compute is not actually a commodity. There's something that's better about it than other people's terrestrial compute."
2:22

.Shaun Maguire says SpaceX's data center deal with Google may suggest there is something fundamentally different about the company's terrestrial compute. "I think a lot of people were surprised by the Anthropic and Google announcements. And I think a lot of people are getting the wrong takeaway there." "The way a lot of people are interpreting that is it's like, 'oh, we're just in this temporary compute shortage. And so anyone's willing to work with anyone, and they're willing to pay any price for compute.'" "I think that might be a reasonable way to look at the Anthropic announcement. I don't think that makes sense for looking at the Google announcement." "To me, I think an alternative hypothesis is that SpaceX's terrestrial compute is not actually a commodity. There's something that's better about it than other people's terrestrial compute."

Jack Kuhr

44,998 次观看 • 1 个月前

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS
1:46

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

TBPN

36,474 次观看 • 2 个月前

Arthur Mensch confirmed on CNBC this week that OpenAI and Anthropic are calling Mistral asking for compute. The two companies racing to build the most powerful AI in the world are dependent on a European competitor they have been trying to outrun. Let that land for a second. The narrative you have been sold is that the US is running away with AI. That OpenAI and Anthropic have such a commanding lead that the rest of the world is playing catch-up. That narrative is missing something physical. The International Energy Agency projected this year that US data center electricity demand will double by 2030. The US grid is already at capacity. Europe has the power. Europe has the land. And right now the company fielding calls from the most funded AI labs on earth is a four-year-old French startup. Mensch said Mistral is prioritizing its own customers first. The surge in agentic usage is generating far more tokens than anyone planned for and existing capacity is spoken for. The US labs are in the queue. But they are in the queue. Most people following AI are watching benchmark scores and funding rounds. The actual constraint right now is electricity and physical space. And on that dimension, the scoreboard looks nothing like the one most people are reading. Watch the full podcast on YouTube at CNBC International
1:06

Arthur Mensch confirmed on CNBC this week that OpenAI and Anthropic are calling Mistral asking for compute. The two companies racing to build the most powerful AI in the world are dependent on a European competitor they have been trying to outrun. Let that land for a second. The narrative you have been sold is that the US is running away with AI. That OpenAI and Anthropic have such a commanding lead that the rest of the world is playing catch-up. That narrative is missing something physical. The International Energy Agency projected this year that US data center electricity demand will double by 2030. The US grid is already at capacity. Europe has the power. Europe has the land. And right now the company fielding calls from the most funded AI labs on earth is a four-year-old French startup. Mensch said Mistral is prioritizing its own customers first. The surge in agentic usage is generating far more tokens than anyone planned for and existing capacity is spoken for. The US labs are in the queue. But they are in the queue. Most people following AI are watching benchmark scores and funding rounds. The actual constraint right now is electricity and physical space. And on that dimension, the scoreboard looks nothing like the one most people are reading. Watch the full podcast on YouTube at CNBC International

Ihtesham Ali

47,092 次观看 • 1 个月前

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."
1:27

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."

The Iced Coffee Hour

276,694 次观看 • 2 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

242,548 次观看 • 8 天前