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Dylan Patel Dylan Patel of SemiAnalysis: Anthropic's margin on an Opus 4.8 API token is north of 80%. It is net-income profitable excluding stock comp in Q2 2026, potentially profitable including it by Q3. Here is why that matters. At 80%-plus, even doubling compute costs leaves Anthropic above 50%... show more

Podcast Alpha

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100,167 次观看 • 3 个月前 •via X (Twitter)

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Dylan Patel on Anthropic: "I think by Q3 they may even be profitable including SBC. Their margins on Opus 4.8 token is north of 80% for API price. They have a lot of deals where their total corporate gross margin gets clawed down a little bit because of how they do deals. They have the capability to pay, buying GPUs at above market rate. It is something other companies can't necessarily do. Every GPU I rent because I am out of compute capacity, I can immediately turn around and sell tokens on it or every TPU or Trainium at a positive margin. If I am running at 75% gross margins and I double the cost of compute, its fine I am still running at 50% gross margins. My NOI still goes up, so I will rent GPUs at almost whatever price I want to pay." Via Sequoia Capital

Dylan Patel on Anthropic: "I think by Q3 they may even be profitable including SBC. Their margins on Opus 4.8 token is north of 80% for API price. They have a lot of deals where their total corporate gross margin gets clawed down a little bit because of how they do deals. They have the capability to pay, buying GPUs at above market rate. It is something other companies can't necessarily do. Every GPU I rent because I am out of compute capacity, I can immediately turn around and sell tokens on it or every TPU or Trainium at a positive margin. If I am running at 75% gross margins and I double the cost of compute, its fine I am still running at 50% gross margins. My NOI still goes up, so I will rent GPUs at almost whatever price I want to pay." Via Sequoia Capital

P Equity Research 📰

73,740 次观看 • 3 个月前

If you hold $CRWV, Dylan Patel just named your real risk, and it is not performance. Dylan Patel of SemiAnalysis: CoreWeave benchmarks above Amazon and Google on GPUs, because hyperscaler multi-tenant security overhead hurts rented-rack performance. The weakness is the balance sheet. Neoclouds pre-sell compute six months before it is live, funded by debt backed by paper contracts. The first wobble already showed up: a Crusoe customer asked to pause construction. Watch the contract book, not the benchmarks. The neocloud thesis in full, from SemiAnalysis on Sequoia Capital: Source: Sequoia Capital -

If you hold $CRWV, Dylan Patel just named your real risk, and it is not performance. Dylan Patel of SemiAnalysis: CoreWeave benchmarks above Amazon and Google on GPUs, because hyperscaler multi-tenant security overhead hurts rented-rack performance. The weakness is the balance sheet. Neoclouds pre-sell compute six months before it is live, funded by debt backed by paper contracts. The first wobble already showed up: a Crusoe customer asked to pause construction. Watch the contract book, not the benchmarks. The neocloud thesis in full, from SemiAnalysis on Sequoia Capital: Source: Sequoia Capital -

Podcast Alpha

55,590 次观看 • 3 个月前

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."

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 Patel thinks Anthropic will get to 5–6 GW by year end. But at a far higher cost than it would have had to pay if it had gone crazy on compute commitments early like OpenAI did. Ant will get the compute either through cloud partners like Bedrock and Vertex who take ~50% gross margin on revenue, or shorter-term deals where rates have climbed well above what long-term contracts locked in a year or two ago cost. They’ll probably also have to destroy demand by pushing up model prices.

.Dylan Patel thinks Anthropic will get to 5–6 GW by year end. But at a far higher cost than it would have had to pay if it had gone crazy on compute commitments early like OpenAI did. Ant will get the compute either through cloud partners like Bedrock and Vertex who take ~50% gross margin on revenue, or shorter-term deals where rates have climbed well above what long-term contracts locked in a year or two ago cost. They’ll probably also have to destroy demand by pushing up model prices.

Dwarkesh Patel

55,447 次观看 • 6 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 -

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 • 1 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.

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 • 7 个月前

In AI, the bottleneck isn't talent or models anymore. It's compute. Jo Bhakdi points to a claim that Anthropic and OpenAI could slow down sharply if they can't secure enough compute next year. Elon's reply is that SpaceX becoming the most valuable AI company is "one of the possible outcomes" if it reaches 10 gigawatts. On the ground, a gigawatt of AI compute costs $40 to $50 billion today. So 10 GW would be $400 to $500 billion of data centers. Elon says orbital compute gets much cheaper by 2028. The bigger play is SpaceX selling that compute, on the ground and in orbit, instead of keeping it all. Demand isn't slowing either. Nvidia is reportedly planning a big testing framework for AI agents, and Anthropic's IPO filing spends a lot of pages on agent risk. Testing all of that takes even more compute. $SPCX $TSLA

In AI, the bottleneck isn't talent or models anymore. It's compute. Jo Bhakdi points to a claim that Anthropic and OpenAI could slow down sharply if they can't secure enough compute next year. Elon's reply is that SpaceX becoming the most valuable AI company is "one of the possible outcomes" if it reaches 10 gigawatts. On the ground, a gigawatt of AI compute costs $40 to $50 billion today. So 10 GW would be $400 to $500 billion of data centers. Elon says orbital compute gets much cheaper by 2028. The bigger play is SpaceX selling that compute, on the ground and in orbit, instead of keeping it all. Demand isn't slowing either. Nvidia is reportedly planning a big testing framework for AI agents, and Anthropic's IPO filing spends a lot of pages on agent risk. Testing all of that takes even more compute. $SPCX $TSLA

Herbert Ong

11,462 次观看 • 10 天前

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, Anthropic went from 40% margins in 2025 to possibly 80%+ this year • But labs don't want the third one, heavy inference spend signals AI progress has stalled and you're now a cloud provider • Spot compute prices are up 40%+ since February, and labs pay well above spot for security and scale • Google is reportedly paying SpaceXAI $900 million a month for 110K GPUs, roughly 2X spot • Key claim: if a human-level software engineer ran on an H100, that H100 should rent for $250K a year, 15X today's price • The 3X annual compute growth is 1.4X Moore's Law, 1.2X new fabs, and 1.8X from AI taking wafer allocation from other devices • The fab piece is bottlenecked by EUV tool supply through 2030, and the wafer piece hits a wall by end of 2027 • If compute stays scarce, new labs need far more capital just to reach the same starting line • Compute gets cheap again only once robots can turn sand and copper into computers, which is gated on robotics, not RSI sof 𓋹: "This is what I'm most concerned about, a lack of innovation, not within companies, but a lack of new companies that can actually do something substantially new, because of the scarcity of compute." Theo Jaffee: "The biggest companies in the world by revenue are Amazon and Walmart, at 743 billion and 725 billion. If Anthropic makes 100 billion by the end of this year, that puts them at Target. To go from Target to bigger than Walmart in a year would be very impressive."

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, Anthropic went from 40% margins in 2025 to possibly 80%+ this year • But labs don't want the third one, heavy inference spend signals AI progress has stalled and you're now a cloud provider • Spot compute prices are up 40%+ since February, and labs pay well above spot for security and scale • Google is reportedly paying SpaceXAI $900 million a month for 110K GPUs, roughly 2X spot • Key claim: if a human-level software engineer ran on an H100, that H100 should rent for $250K a year, 15X today's price • The 3X annual compute growth is 1.4X Moore's Law, 1.2X new fabs, and 1.8X from AI taking wafer allocation from other devices • The fab piece is bottlenecked by EUV tool supply through 2030, and the wafer piece hits a wall by end of 2027 • If compute stays scarce, new labs need far more capital just to reach the same starting line • Compute gets cheap again only once robots can turn sand and copper into computers, which is gated on robotics, not RSI sof 𓋹: "This is what I'm most concerned about, a lack of innovation, not within companies, but a lack of new companies that can actually do something substantially new, because of the scarcity of compute." Theo Jaffee: "The biggest companies in the world by revenue are Amazon and Walmart, at 743 billion and 725 billion. If Anthropic makes 100 billion by the end of this year, that puts them at Target. To go from Target to bigger than Walmart in a year would be very impressive."

MTS

10,617 次观看 • 2 个月前

Your Claude subscription is massively subsidized and it won't last forever. A $20/mo Pro plan burns through ~$180/mo in API-equivalent tokens. Heavy Max users hit $5K/mo on a $200 plan. Actual compute cost is roughly 10% of API pricing. Venture capital covers the rest. Anthropic just 2x'd Claude Code limits for "Spring Break'" during off hours. Enjoy it while it's here. At some point the math has to math. Clip from the The Modern Market Show

Your Claude subscription is massively subsidized and it won't last forever. A $20/mo Pro plan burns through ~$180/mo in API-equivalent tokens. Heavy Max users hit $5K/mo on a $200 plan. Actual compute cost is roughly 10% of API pricing. Venture capital covers the rest. Anthropic just 2x'd Claude Code limits for "Spring Break'" during off hours. Enjoy it while it's here. At some point the math has to math. Clip from the The Modern Market Show

Legendary

341,245 次观看 • 6 个月前

Gavin Baker: “I think Anthropic is worth $3 trillion today.” Gavin laid out the case on the most recent All-In Podcast: – They will end 2026 with over $100B in revenue – They will likely end 2028 with $200-$300B in revenue – At that scale, Anthropic will be very profitable because compute will be inference-dominated – Anthropic reportedly has 85% gross margins on inference – The market has already absorbed Anthropic’s valuation, it will just shift from private to public @jason: “I'm sorry, did you say Anthropic is worth $3 trillion?” Gavin: “Yeah, I think that is roughly where it would probably trade as a public company.” Travis Kalanick: “Wow. Oh man. Holy sh*t.” Gavin: “They're going to end this year with well over $100 billion (in revenue). So what's the '28 number? Is it $200 billion? Is it $300 billion?” “It's probably not going to trade at 10 times that number, and it will be very profitable at that scale because it'll be inference-dominated and people are reporting they have 85% gross margins on inference.” “But in terms of the market absorbing this, like the market's already absorbed it. It's just shifting from private to public.” “And so in the scale of global capital markets, these seem like really big numbers, but you're just moving from the private markets to the public markets, which are even bigger.”

Gavin Baker: “I think Anthropic is worth $3 trillion today.” Gavin laid out the case on the most recent All-In Podcast: – They will end 2026 with over $100B in revenue – They will likely end 2028 with $200-$300B in revenue – At that scale, Anthropic will be very profitable because compute will be inference-dominated – Anthropic reportedly has 85% gross margins on inference – The market has already absorbed Anthropic’s valuation, it will just shift from private to public @jason: “I'm sorry, did you say Anthropic is worth $3 trillion?” Gavin: “Yeah, I think that is roughly where it would probably trade as a public company.” Travis Kalanick: “Wow. Oh man. Holy sh*t.” Gavin: “They're going to end this year with well over $100 billion (in revenue). So what's the '28 number? Is it $200 billion? Is it $300 billion?” “It's probably not going to trade at 10 times that number, and it will be very profitable at that scale because it'll be inference-dominated and people are reporting they have 85% gross margins on inference.” “But in terms of the market absorbing this, like the market's already absorbed it. It's just shifting from private to public.” “And so in the scale of global capital markets, these seem like really big numbers, but you're just moving from the private markets to the public markets, which are even bigger.”

The All-In Podcast

85,013 次观看 • 3 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."

.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 • 4 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

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 • 2 个月前

I asked Claude to find the most obvious edge on Polymarket. It came back with three bets on the End Of The World and a potential profit of +$18,400. I gave it just one prompt: "find where the crowd is lying to itself the most." Then it went silent for 40 minutes. It came back with a NASA link tracking 34,000 asteroids with meter-level precision. And NASA publishes it for free. The market "5kt meteor strike in 2026?" is priced at 38%. NASA says 0.3%. The market "10kt+ strike?" Market: 20%. NASA: 0.05%. The gap is measured using KL divergence. Anything above 0.05 is an edge. We got 4.76. That is 95x above the threshold. The agent went NO on all three. Resolve is in December 2026. +$18,400 if the world does not end because of a meteor strike. And if it does, we probably will not need the money anyway. But the more interesting question is this: If a 95x gap is visible to anyone with an API, why is the market still pricing it at 38%?

I asked Claude to find the most obvious edge on Polymarket. It came back with three bets on the End Of The World and a potential profit of +$18,400. I gave it just one prompt: "find where the crowd is lying to itself the most." Then it went silent for 40 minutes. It came back with a NASA link tracking 34,000 asteroids with meter-level precision. And NASA publishes it for free. The market "5kt meteor strike in 2026?" is priced at 38%. NASA says 0.3%. The market "10kt+ strike?" Market: 20%. NASA: 0.05%. The gap is measured using KL divergence. Anything above 0.05 is an edge. We got 4.76. That is 95x above the threshold. The agent went NO on all three. Resolve is in December 2026. +$18,400 if the world does not end because of a meteor strike. And if it does, we probably will not need the money anyway. But the more interesting question is this: If a 95x gap is visible to anyone with an API, why is the market still pricing it at 38%?

may.crypto {🦅}

97,354 次观看 • 7 个月前

"You cannot take a gold coin to 7-Eleven and convert it into a sandwich." Arthur Hayes breaks down the core thesis behind Flop Labs: why AI agents will reject legacy money and what currency they will actually accept. Here is the exact economic framework: 1. The definition of compute you pay LLM providers for "tokens", but a token has no standardized definition. It is an arbitrary marketing unit. The only universal definition of compute is physical: Floating Point Operations per unit of time (FLOPs). Your GPU consumes energy to deliver raw FLOPs. 2. The missing spot market you can buy an H100 or rent a Blackwell GPU. But you cannot trade the raw commodity itself. There is no unified spot market where compute is priced and tracked in a native currency. 3. The calorie theory of money why do you trade your time for fiat currency? Because you know that when you get hungry, you can convert that paper into calories with zero slippage. You do not accept gold for your labor because the merchant at 7-Eleven will not accept a gold coin for lunch. 4. The agentic calorie an AI agent has only one biological necessity: Compute. If an autonomous agent works 24/7, it will not accept a currency that requires bank accounts, KYC, and Stripe billing portals just to pay an AWS bill. An agent will only accept a currency that is directly convertible into floating-point operations. That currency is $FLOP: the agentic food.

"You cannot take a gold coin to 7-Eleven and convert it into a sandwich." Arthur Hayes breaks down the core thesis behind Flop Labs: why AI agents will reject legacy money and what currency they will actually accept. Here is the exact economic framework: 1. The definition of compute you pay LLM providers for "tokens", but a token has no standardized definition. It is an arbitrary marketing unit. The only universal definition of compute is physical: Floating Point Operations per unit of time (FLOPs). Your GPU consumes energy to deliver raw FLOPs. 2. The missing spot market you can buy an H100 or rent a Blackwell GPU. But you cannot trade the raw commodity itself. There is no unified spot market where compute is priced and tracked in a native currency. 3. The calorie theory of money why do you trade your time for fiat currency? Because you know that when you get hungry, you can convert that paper into calories with zero slippage. You do not accept gold for your labor because the merchant at 7-Eleven will not accept a gold coin for lunch. 4. The agentic calorie an AI agent has only one biological necessity: Compute. If an autonomous agent works 24/7, it will not accept a currency that requires bank accounts, KYC, and Stripe billing portals just to pay an AWS bill. An agent will only accept a currency that is directly convertible into floating-point operations. That currency is $FLOP: the agentic food.

Tat Thang

31,510 次观看 • 23 天前

$NBIS may be the biggest beneficiary of $GOOGL capacity shortage because the company still needs third party compute even after doubling quarterly capex. That shortage is exactly what Nebius is built to solve by offering immediately available full stack AI infrastructure to customers that cannot wait years for new data centers. The demand is already visible with management saying at least four customers are competing for every GPU it brings online while deals with $MSFT an $META validate the platform.

$NBIS may be the biggest beneficiary of $GOOGL capacity shortage because the company still needs third party compute even after doubling quarterly capex. That shortage is exactly what Nebius is built to solve by offering immediately available full stack AI infrastructure to customers that cannot wait years for new data centers. The demand is already visible with management saying at least four customers are competing for every GPU it brings online while deals with $MSFT an $META validate the platform.

Shay Boloor

166,820 次观看 • 2 个月前

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.

Shay Boloor

589,512 次观看 • 5 个月前

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Fireside Alpha

284,032 次观看 • 2 个月前

CoreWeave and Crusoe were supposed to lose this market to the hyperscalers, and Dylan Patel Dylan Patel explains why they won it instead (Save this). The twist is that the hyperscaler edges became liabilities. Amazon's advantages, Nitro isolation, Graviton CPUs, custom storage, were all built for the CPU cloud and actively hurt AI performance. And the security and time-slicing expertise stopped mattering, because nobody rents a single GPU in a 72-GPU rack, they rent whole racks on long-term contracts. So the market opened three ways: ◽️First, the incumbent playbook of slicing and isolating fell away, and some of it turned into a net negative. ◽️Second, Microsoft's teams fell on their face when they had to double a year's forecast, and had to go rent Neocloud capacity themselves. ◽️Third, hyperlevered Neocloud teams got rich by delivering compute faster, undercutting on the $12 to 13 billion per gigawatt that GPUs rented for before the crunch while the SpaceX to Google deal ran near $25 billion, all off racks nobody splits into single GPUs. The Amazon cloud-crisis report and the GPU rental market say the same thing, an incumbency built for one workload is a liability for the next.

CoreWeave and Crusoe were supposed to lose this market to the hyperscalers, and Dylan Patel Dylan Patel explains why they won it instead (Save this). The twist is that the hyperscaler edges became liabilities. Amazon's advantages, Nitro isolation, Graviton CPUs, custom storage, were all built for the CPU cloud and actively hurt AI performance. And the security and time-slicing expertise stopped mattering, because nobody rents a single GPU in a 72-GPU rack, they rent whole racks on long-term contracts. So the market opened three ways: ◽️First, the incumbent playbook of slicing and isolating fell away, and some of it turned into a net negative. ◽️Second, Microsoft's teams fell on their face when they had to double a year's forecast, and had to go rent Neocloud capacity themselves. ◽️Third, hyperlevered Neocloud teams got rich by delivering compute faster, undercutting on the $12 to 13 billion per gigawatt that GPUs rented for before the crunch while the SpaceX to Google deal ran near $25 billion, all off racks nobody splits into single GPUs. The Amazon cloud-crisis report and the GPU rental market say the same thing, an incumbency built for one workload is a liability for the next.

Fireside Alpha

48,279 次观看 • 3 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.

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 • 2 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.

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