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SITUATION EXPLAINED: Dwarkesh argues compute could get 10X more expensive. • Anthropic's revenue has been 10X-ing year over year while lab compute only 3X's • Three ways that gap can close: margins rise, compute gets more expensive, or labs shift compute to inference • All three are already happening,...

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10,617 次观看 • 1 个月前 •via X (Twitter)

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SpaceX CFO Bret Johnsen just announced another MASSIVE hosting deal, giving SpaceX even more confidence to hit their $100 BILLION annualized revenue run-rate target The new deal starts December 1 and is expected to generate roughly: $1.11 BILLION per month ~$13 BILLION annually “We have even more conviction now about that $100 billion” And SpaceX’s compute business is already getting absolutely enormous • Anthropic — ~$15B/year • Google — ~$11B/year • Reflection AI — ~$1.8B/year • New hosting deal — ~$13B/year that’s roughly $41 BILLION per year from just four compute/hosting deals $41 BILLION.....from compute alone And this is only one piece of the SpaceX The scale SpaceX is building toward is absolutely insane
0:31

SpaceX CFO Bret Johnsen just announced another MASSIVE hosting deal, giving SpaceX even more confidence to hit their $100 BILLION annualized revenue run-rate target The new deal starts December 1 and is expected to generate roughly: $1.11 BILLION per month ~$13 BILLION annually “We have even more conviction now about that $100 billion” And SpaceX’s compute business is already getting absolutely enormous • Anthropic — ~$15B/year • Google — ~$11B/year • Reflection AI — ~$1.8B/year • New hosting deal — ~$13B/year that’s roughly $41 BILLION per year from just four compute/hosting deals $41 BILLION.....from compute alone And this is only one piece of the SpaceX The scale SpaceX is building toward is absolutely insane

X Freeze

15,051 次观看 • 9 天前

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever
2:03

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

Dwarkesh Patel

203,605 次观看 • 9 个月前

Had a lot of fun chatting again with my twin brother Dylan Patel We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone). And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities. One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. 0:00:00 – Two labs will soon control most of the world’s compute 0:07:01 – $6 billion in fab capex enables $1t+ of end revenue 0:13:08 – Compute prices will rise if the labs outbid everyone 0:18:22 – Which layer will capture most of the surplus? 0:25:40 – Will datacenter regulation slow down AI? 0:29:43 – Labs are shifting compute from inference to R&D 0:33:27 – China gets less than 10% of new compute, but its labs need less 0:48:48 – Will AI cause a sovereign debt crisis? 1:07:52 – Will the world's future workforce belong to a few companies?
1:16:52

Had a lot of fun chatting again with my twin brother Dylan Patel We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone). And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities. One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. 0:00:00 – Two labs will soon control most of the world’s compute 0:07:01 – $6 billion in fab capex enables $1t+ of end revenue 0:13:08 – Compute prices will rise if the labs outbid everyone 0:18:22 – Which layer will capture most of the surplus? 0:25:40 – Will datacenter regulation slow down AI? 0:29:43 – Labs are shifting compute from inference to R&D 0:33:27 – China gets less than 10% of new compute, but its labs need less 0:48:48 – Will AI cause a sovereign debt crisis? 1:07:52 – Will the world's future workforce belong to a few companies?

Dwarkesh Patel

1,024,520 次观看 • 26 天前

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."
2:33

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Patrick OShaughnessy

295,307 次观看 • 5 个月前

Dylan argues that even if AI progress continues at apace and generates lots of revenue, the semiconductor industry will not be able to keep up with the labs’ current 3xing+ of compute (in watts) year over year. By the end of the decade, you end up bottlenecked on wafer fab equipment - things like ASML’s EUV machines. This may be true. I just find it crazy that we'd be in a situation where a few billion in fab capex could generate hundreds of billions of end token revenue (so the semi + AI industry has basically figured out how to turn $1 into $100) and yet this doesn’t allow Zeiss to dramatically scale up the manufacturing for the mirrors required for ASML machines.
2:31

Dylan argues that even if AI progress continues at apace and generates lots of revenue, the semiconductor industry will not be able to keep up with the labs’ current 3xing+ of compute (in watts) year over year. By the end of the decade, you end up bottlenecked on wafer fab equipment - things like ASML’s EUV machines. This may be true. I just find it crazy that we'd be in a situation where a few billion in fab capex could generate hundreds of billions of end token revenue (so the semi + AI industry has basically figured out how to turn $1 into $100) and yet this doesn’t allow Zeiss to dramatically scale up the manufacturing for the mirrors required for ASML machines.

Dwarkesh Patel

106,332 次观看 • 24 天前

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there
2:22:19

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

Dami-Defi

16,105 次观看 • 4 个月前

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."
1:29

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."

Patrick OShaughnessy

159,693 次观看 • 1 个月前

OpenAI and Anthropic are currently taking a third of the incremental world compute supply, and Dylan thinks that this will go up to half next year. At the current rate of physical compute scaling and algorithmic progress, we're a single-digit number of years away from having individual AI companies with a greater effective workforce than there are humans on the planet. So many forces are barrelling us towards centralization in AI - the economies of scale in training (where you get to amortize any learnings across billions of sessions), the fact that being slightly ahead allows you to better economize on ever-more-expensive compute. And this isn’t even accounting for the fact that eventually you’ll have models that are capable of learning from deployment and doing proper RSI One of the key questions of our age, in my view, is how we can have broad empowerment and control over AI despite the structural factors favoring centralization.
1:59

OpenAI and Anthropic are currently taking a third of the incremental world compute supply, and Dylan thinks that this will go up to half next year. At the current rate of physical compute scaling and algorithmic progress, we're a single-digit number of years away from having individual AI companies with a greater effective workforce than there are humans on the planet. So many forces are barrelling us towards centralization in AI - the economies of scale in training (where you get to amortize any learnings across billions of sessions), the fact that being slightly ahead allows you to better economize on ever-more-expensive compute. And this isn’t even accounting for the fact that eventually you’ll have models that are capable of learning from deployment and doing proper RSI One of the key questions of our age, in my view, is how we can have broad empowerment and control over AI despite the structural factors favoring centralization.

Dwarkesh Patel

117,453 次观看 • 25 天前

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.
3:31

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 次观看 • 2 个月前

Elon Musk announced a chip factory 10 times the size of Tesla's Gigafactory. The goal is to produce enough AI compute to equal twice the entire electricity consumption of the United States. He called it the Terafab. Here is the number that stopped me cold. The entire global AI chip industry right now is on track to hit around 100 gigawatts per year of compute. Every Nvidia GPU, every Google TPU, every chip from every company on earth combined. 100 gigawatts. Musk wants one factory to produce a terawatt per year. A terawatt is 1,000 gigawatts. Ten times the output of the entire global industry. From a single building. To put the scale in physical terms, the Terafab would need to be around 100 million square feet. You would need Starship point to point transport just to get from one end to the other. But the reason for the scale is not ambition for its own sake. To launch meaningful AI compute into space, you need a billion chips per year running at a kilowatt each. That is not a number the current industry can produce. The Terafab is the only way to get there. The timeline he put out: a gigawatt of space AI compute annualized by end of next year. Then 10x per year from there. 10 gigawatts by year two. 100 gigawatts by year three. A terawatt beyond that. Most people think orbital data centers are a decade away. Musk is building the factory to make them possible by next year.
3:26

Elon Musk announced a chip factory 10 times the size of Tesla's Gigafactory. The goal is to produce enough AI compute to equal twice the entire electricity consumption of the United States. He called it the Terafab. Here is the number that stopped me cold. The entire global AI chip industry right now is on track to hit around 100 gigawatts per year of compute. Every Nvidia GPU, every Google TPU, every chip from every company on earth combined. 100 gigawatts. Musk wants one factory to produce a terawatt per year. A terawatt is 1,000 gigawatts. Ten times the output of the entire global industry. From a single building. To put the scale in physical terms, the Terafab would need to be around 100 million square feet. You would need Starship point to point transport just to get from one end to the other. But the reason for the scale is not ambition for its own sake. To launch meaningful AI compute into space, you need a billion chips per year running at a kilowatt each. That is not a number the current industry can produce. The Terafab is the only way to get there. The timeline he put out: a gigawatt of space AI compute annualized by end of next year. Then 10x per year from there. 10 gigawatts by year two. 100 gigawatts by year three. A terawatt beyond that. Most people think orbital data centers are a decade away. Musk is building the factory to make them possible by next year.

Ihtesham Ali

318,691 次观看 • 3 个月前

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.
0:53

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Dustin

28,395 次观看 • 7 个月前

There's an economics theorem called Alchian-Allen. And it has the very interesting implication that AI labs will be able to charge *higher* margins on their best models as compute gets scarcer. As compute gets more expensive, the cost of running any model goes up. So you might as well pay a bit more to make sure you're running the very best model. Which means the economics of being at the frontier improve, because if you’re not running the very best model, then you’re underutilizing this very precious compute. This pushes the AI model market towards winner-take-all; if you're the best, you can get away with charging an even higher margin. Dylan Patel tells me that we’re already seeing this today: all the revenue in the industry is on the best models. That’s the Alchian-Allen effect. If there’s a cost increase that’s roughly the same for all products, then the relative difference in price between higher and lower quality goods actually goes down. Consumers become relatively more willing to pay for the premium product. And it means that as the compute shortage hits, AI labs can capture more margin - not less, as you might expect - because consumers are choosing premium models more often.
1:21

There's an economics theorem called Alchian-Allen. And it has the very interesting implication that AI labs will be able to charge *higher* margins on their best models as compute gets scarcer. As compute gets more expensive, the cost of running any model goes up. So you might as well pay a bit more to make sure you're running the very best model. Which means the economics of being at the frontier improve, because if you’re not running the very best model, then you’re underutilizing this very precious compute. This pushes the AI model market towards winner-take-all; if you're the best, you can get away with charging an even higher margin. Dylan Patel tells me that we’re already seeing this today: all the revenue in the industry is on the best models. That’s the Alchian-Allen effect. If there’s a cost increase that’s roughly the same for all products, then the relative difference in price between higher and lower quality goods actually goes down. Consumers become relatively more willing to pay for the premium product. And it means that as the compute shortage hits, AI labs can capture more margin - not less, as you might expect - because consumers are choosing premium models more often.

Dwarkesh Patel

323,500 次观看 • 6 个月前

Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.
1:52

Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.

Clark Tang

176,622 次观看 • 11 个月前

David Sacks just said what every honest analyst in Silicon Valley is already thinking (Save this). Nobody has ever seen anything like this. Anthropic has grown at 10x per year for three straight years and going into 2026, the conventional wisdom was that the rate of growth had to slow at this level of scale but then the numbers came in. Q1 alone is $10B ARR to $30B, in April, $30B to $44B and that's $96 million in new ARR added every single day. Inference margins are now above 70%, up from 38% last year and the only thing holding them back was compute. That's solved now, the SpaceX deal and others Anthropic has been quietly signing unlocks the supply side. This is exactly why we are bullish on Nebius and AMD. When a single company is adding nearly $100M in ARR per day, the real trade isn't the frontier lab but rather the infrastructure underneath it. Nebius, one of the fastest-growing neoclouds on the planet posted 547% YoY revenue growth in Q4 2025, exited the year with $1.25B ARR, and is guiding for $7–9B ARR by year-end 2026. Their revenue backlog has reached $46B, with projections of $16B in revenue by 2028 and NVIDIA locked in a $2 billion stock buy agreement with them giving Nebius early access to cutting-edge chips while every other cloud scrambles for supply. AMD is the other side of the same coin. Data center revenue hit $5.78B in Q1, up 57% year-over-year with total company revenue at $10.25B, up 38%. Meta has committed to deploying up to 6 gigawatts of AMD Instinct GPUs. Data center GPU revenue is forecast to surge 114% year over year to $15B in 2026. MI400-series chips hit the market in H2 and analysts project segment operating margins climbing to 31% as the next generation ramps. The model is simple, Anthropic is printing revenue and that that revenue pays for compute. That compute flows through companies like Nebius and AMD. This is why Milk Road PRO remains bullish on them and our positions are up massively. Our analysts have broken down the full thesis, the allocations, and the price targets. Go PRO at Milk Road to see everything, link below!
1:03

David Sacks just said what every honest analyst in Silicon Valley is already thinking (Save this). Nobody has ever seen anything like this. Anthropic has grown at 10x per year for three straight years and going into 2026, the conventional wisdom was that the rate of growth had to slow at this level of scale but then the numbers came in. Q1 alone is $10B ARR to $30B, in April, $30B to $44B and that's $96 million in new ARR added every single day. Inference margins are now above 70%, up from 38% last year and the only thing holding them back was compute. That's solved now, the SpaceX deal and others Anthropic has been quietly signing unlocks the supply side. This is exactly why we are bullish on Nebius and AMD. When a single company is adding nearly $100M in ARR per day, the real trade isn't the frontier lab but rather the infrastructure underneath it. Nebius, one of the fastest-growing neoclouds on the planet posted 547% YoY revenue growth in Q4 2025, exited the year with $1.25B ARR, and is guiding for $7–9B ARR by year-end 2026. Their revenue backlog has reached $46B, with projections of $16B in revenue by 2028 and NVIDIA locked in a $2 billion stock buy agreement with them giving Nebius early access to cutting-edge chips while every other cloud scrambles for supply. AMD is the other side of the same coin. Data center revenue hit $5.78B in Q1, up 57% year-over-year with total company revenue at $10.25B, up 38%. Meta has committed to deploying up to 6 gigawatts of AMD Instinct GPUs. Data center GPU revenue is forecast to surge 114% year over year to $15B in 2026. MI400-series chips hit the market in H2 and analysts project segment operating margins climbing to 31% as the next generation ramps. The model is simple, Anthropic is printing revenue and that that revenue pays for compute. That compute flows through companies like Nebius and AMD. This is why Milk Road PRO remains bullish on them and our positions are up massively. Our analysts have broken down the full thesis, the allocations, and the price targets. Go PRO at Milk Road to see everything, link below!

Milk Road AI

184,643 次观看 • 4 个月前

Gavin Baker says the SpaceX bull case is $400B a year if they build out 8 gigawatts of compute "FundaAI thinks that SpaceX is going to try and bring on eight gigawatts of compute. I will never bet against Elon, but I mean, that would be a truly incredible feat." "And rates have gone up since they signed those last contracts, not down. And they're monetizing at something like $50 billion a gig." "Consensus estimates for next year are $73 billion. So forget Starlink V3, forget Starlink Direct to Cell, Grok 4.5 and Cursor... the sum of that probably hits a 10 billion dollar mark." "There's a big New York hedge fund short case on [SPCX]. They think, 'oh, the spot price for compute's going to go down 90%, and you're going to bring on all this compute, it's not going to generate nearly as much revenue as you think.'" "I think one of Elon's phrases is, 'we specialize in making the impossible late.' I just think very little is built into that stock, for the amount of compute that they might be able to bring on."
3:01

Gavin Baker says the SpaceX bull case is $400B a year if they build out 8 gigawatts of compute "FundaAI thinks that SpaceX is going to try and bring on eight gigawatts of compute. I will never bet against Elon, but I mean, that would be a truly incredible feat." "And rates have gone up since they signed those last contracts, not down. And they're monetizing at something like $50 billion a gig." "Consensus estimates for next year are $73 billion. So forget Starlink V3, forget Starlink Direct to Cell, Grok 4.5 and Cursor... the sum of that probably hits a 10 billion dollar mark." "There's a big New York hedge fund short case on [SPCX]. They think, 'oh, the spot price for compute's going to go down 90%, and you're going to bring on all this compute, it's not going to generate nearly as much revenue as you think.'" "I think one of Elon's phrases is, 'we specialize in making the impossible late.' I just think very little is built into that stock, for the amount of compute that they might be able to bring on."

dnap

43,869 次观看 • 1 个月前

Baseten's Charlie O'Neill on why the gap to the frontier is just compute and data: "Despite OpenAI and Anthropic and the closed source labs having more compute and more data, the recipe is the same. We're all scaling the same recipe here, and we're at slightly different points of the scaling curve. It's not like OpenAI or Anthropic have any secret sauce that differentiates them scaling up their multi-trillion parameter models from the rest of the open source ecosystem." "It really is just a matter of taking the core underlying architecture and training recipe, and throwing more compute and data at it, which is really promising." "Of course, these closed source labs probably have a long tail of optimizations they've made, which we're gonna continue to try and figure out in the open source community. But the core mechanism is there." "K3 is a great example of, well, if you do scale this recipe up, we're gonna get a pretty good model that's very, very close to the frontier, if not frontier itself." Charlie O'Neill Baseten
1:51

Baseten's Charlie O'Neill on why the gap to the frontier is just compute and data: "Despite OpenAI and Anthropic and the closed source labs having more compute and more data, the recipe is the same. We're all scaling the same recipe here, and we're at slightly different points of the scaling curve. It's not like OpenAI or Anthropic have any secret sauce that differentiates them scaling up their multi-trillion parameter models from the rest of the open source ecosystem." "It really is just a matter of taking the core underlying architecture and training recipe, and throwing more compute and data at it, which is really promising." "Of course, these closed source labs probably have a long tail of optimizations they've made, which we're gonna continue to try and figure out in the open source community. But the core mechanism is there." "K3 is a great example of, well, if you do scale this recipe up, we're gonna get a pretty good model that's very, very close to the frontier, if not frontier itself." Charlie O'Neill Baseten

MTS

17,404 次观看 • 1 个月前

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -
1:16

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -

Podcast Alpha

26,746 次观看 • 15 天前

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

137,969 次观看 • 1 年前