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Compute Wars: OpenAI vs Anthopic. Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity. This got Anthropic got close to OpenAI's total capacity, and probably much higher effective capacity available for new model...

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Peter Gostev

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157,413 просмотров • 3 месяцев назад •via X (Twitter)

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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

295,086 просмотров • 2 месяцев назад

.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.
2:16

.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 просмотров • 3 месяцев назад

Chamath Palihapitiya, one of the most connected investors in tech and his warning is the clearest framing of the AI compute crisis anyone has put into words. "It is a five alarm fire for them. They need to have land, power, shell." He's talking about Anthropic and OpenAI and the threat he's describing is called the Friendster effect. Friendster was the dominant social network before MySpace and Facebook and it didn't lose because it had a bad product but rather it lost because it couldn't keep the site up. Demand outpaced infrastructure, the experience degraded, and users left for one that actually worked. Chamath's argument is that OpenAI and Anthropic are approaching exactly that moment. The numbers are already showing it, Anthropic is growing so fast that it had to cut Claude's thinking depth during peak hours, cap agentic sessions, and test removing Claude Code from its $20 plan entirely. GitHub Copilot paused new signups, paying enterprise customers are hitting usage walls they've never seen before. Dario Amodei himself admitted there is "no hedge on earth" against the risk of over-purchasing compute meaning he's deliberately staying lean on capacity, even as the demand wall approaches. The core problem is structural, OpenAI and Anthropic grew up renting capacity from hyperscalers AWS, Azure, Google Cloud. That was fine when they were small but now they're so large that dependency is a strategic liability. Every token they sell runs on someone else's infrastructure and every capacity decision belongs to someone else. And when demand spikes faster than anyone planned, there's nothing they can do in real time except throttle. Building your own infrastructure takes 18 to 24 months minimum, you need to acquire land, secure power, construct shell and none of that happens fast. That's why Google's $40 billion commitment to Anthropic this week is about more than just money but rather about securing the land, power, and infrastructure that Anthropic needs to not become Friendster. Whoever controls the compute controls the frontier and right now, the AI labs with the best products are the most dependent on infrastructure they don't own.
0:31

Chamath Palihapitiya, one of the most connected investors in tech and his warning is the clearest framing of the AI compute crisis anyone has put into words. "It is a five alarm fire for them. They need to have land, power, shell." He's talking about Anthropic and OpenAI and the threat he's describing is called the Friendster effect. Friendster was the dominant social network before MySpace and Facebook and it didn't lose because it had a bad product but rather it lost because it couldn't keep the site up. Demand outpaced infrastructure, the experience degraded, and users left for one that actually worked. Chamath's argument is that OpenAI and Anthropic are approaching exactly that moment. The numbers are already showing it, Anthropic is growing so fast that it had to cut Claude's thinking depth during peak hours, cap agentic sessions, and test removing Claude Code from its $20 plan entirely. GitHub Copilot paused new signups, paying enterprise customers are hitting usage walls they've never seen before. Dario Amodei himself admitted there is "no hedge on earth" against the risk of over-purchasing compute meaning he's deliberately staying lean on capacity, even as the demand wall approaches. The core problem is structural, OpenAI and Anthropic grew up renting capacity from hyperscalers AWS, Azure, Google Cloud. That was fine when they were small but now they're so large that dependency is a strategic liability. Every token they sell runs on someone else's infrastructure and every capacity decision belongs to someone else. And when demand spikes faster than anyone planned, there's nothing they can do in real time except throttle. Building your own infrastructure takes 18 to 24 months minimum, you need to acquire land, secure power, construct shell and none of that happens fast. That's why Google's $40 billion commitment to Anthropic this week is about more than just money but rather about securing the land, power, and infrastructure that Anthropic needs to not become Friendster. Whoever controls the compute controls the frontier and right now, the AI labs with the best products are the most dependent on infrastructure they don't own.

Milk Road AI

260,300 просмотров • 2 месяцев назад

Here’s my BBC interview: I went over how the OpenAI deal validates $AMZN AWS GenAI capabilities & shared my takeaway from the $PLTR thesis post-earnings. Every story you see about $GOOGL, $MSFT, $META, $ORCL or OpenAI building new compute factories connects directly to why Palantir can report a Rule of 40 score of 114. The trillions in AI infrastructure CapEx are creating raw capacity but capacity alone doesn’t create productivity and Palantir sits exactly at that conversion point. So yet it trades rich but it’s the only pure play with leverage to every dollar the hyperscalers spend. You’re not paying for today’s revenue but you’re paying for the right to own the switchboard that will orchestrate trillions in future AI infrastructure.
3:13

Here’s my BBC interview: I went over how the OpenAI deal validates $AMZN AWS GenAI capabilities & shared my takeaway from the $PLTR thesis post-earnings. Every story you see about $GOOGL, $MSFT, $META, $ORCL or OpenAI building new compute factories connects directly to why Palantir can report a Rule of 40 score of 114. The trillions in AI infrastructure CapEx are creating raw capacity but capacity alone doesn’t create productivity and Palantir sits exactly at that conversion point. So yet it trades rich but it’s the only pure play with leverage to every dollar the hyperscalers spend. You’re not paying for today’s revenue but you’re paying for the right to own the switchboard that will orchestrate trillions in future AI infrastructure.

Shay Boloor

62,985 просмотров • 8 месяцев назад

🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."
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🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."

Chris

242,578 просмотров • 3 месяцев назад

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

322,651 просмотров • 4 месяцев назад

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0:48

🚨 JUST NOW: JENSEN HINTED ANTHROPIC’S NEXT DEAL AND ITS LIKELY $NBIS On the $NVDA earnings call, Jensen said they’ve secured compute for Anthropic across Azure, AWS and $CRWV then added there’s a “whole list of others” they are bringing online. $NBIS is an NVIDIA Cloud Partner with early access to Vera Rubin, already deploying NVL72 across the US and Europe from H2 2026. The partnerships are in place. The infrastructure is being built. Expect a deal soon. If Jensen is flagging more capacity providers coming online for Anthropic, Nebius has everything in place to be that call.

Jordan

156,482 просмотров • 1 месяц назад

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.
0:29

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Shay Boloor

122,716 просмотров • 1 месяц назад

“Elon and Dario should do a deal tomorrow.” - Chamath explains how power constraints are giving Elon massive leverage to make AI deals “ To the extent that OpenAI missed, I think what that is, is an insight to not enough compute capacity today, and that problem is only getting worse. If you look at the actual amount of gigawatts that are under construction, we have a huge mismatch now. People have announced all these projects, but less than half of it is actually being built. Less than half. Most of it is stuck in red tape. There's no credible strategy to turn any of this stuff on. Who will this hurt? It will hurt Anthropic and OpenAI the most. Who will this benefit? It will benefit the hyperscalers, specifically Oracle, Amazon, Meta, Microsoft, and Google. And now what you're going to see is a negotiation and a trade back and forth. How much equity do I have to give up? How much control do I have to give up to get access to the compute? How badly will I miss my growth forecasts if I don't? That's a huge lane for Grok and SpaceX to run through because they have a ton of excess capacity. And so I think the Cursor deal was the appetizer. If I were Elon now, I'd be running all over this market because if the models catch up in quality, I think he could also do something really crazy with Anthropic. He and Dario should do a deal tomorrow.”
1:20

“Elon and Dario should do a deal tomorrow.” - Chamath explains how power constraints are giving Elon massive leverage to make AI deals “ To the extent that OpenAI missed, I think what that is, is an insight to not enough compute capacity today, and that problem is only getting worse. If you look at the actual amount of gigawatts that are under construction, we have a huge mismatch now. People have announced all these projects, but less than half of it is actually being built. Less than half. Most of it is stuck in red tape. There's no credible strategy to turn any of this stuff on. Who will this hurt? It will hurt Anthropic and OpenAI the most. Who will this benefit? It will benefit the hyperscalers, specifically Oracle, Amazon, Meta, Microsoft, and Google. And now what you're going to see is a negotiation and a trade back and forth. How much equity do I have to give up? How much control do I have to give up to get access to the compute? How badly will I miss my growth forecasts if I don't? That's a huge lane for Grok and SpaceX to run through because they have a ton of excess capacity. And so I think the Cursor deal was the appetizer. If I were Elon now, I'd be running all over this market because if the models catch up in quality, I think he could also do something really crazy with Anthropic. He and Dario should do a deal tomorrow.”

The All-In Podcast

389,819 просмотров • 2 месяцев назад

Why did Anthropic hold back Mythos? David Sacks: “ Marc Andreessen pointed out that one of the reasons why Anthropic might've wanted to hold back Mythos, is they simply didn't have the compute to serve it. The model was huge and very expensive to serve, something like 10 or 20x the token cost of, say, Opus. They knew Opus 4.7 was coming out, right? So they hold it back knowing that they don't have the compute to serve it anyway, and they save their compute for the next iteration of Opus. And then by holding it back, they create this impression of scarcity and altruism, and it turns into this gigantic marketing event for their product, because everyone in the government's like, 'Oh wow, they're holding it back because it's so amazing.' Now look, I think it may have been genuinely altruistic as well in the sense that Mythos does reveal coding vulnerabilities that people didn't know about before. And it does make sense to give time to companies with large code bases to patch these dormant bugs and vulnerabilities. But, it's looking more and more like Anthropic could not have offered that model commercially anyway, because it was just too big and expensive, and they needed to create space for Opus 4.7. So it's an interesting theory on what actually happened there.”
1:03

Why did Anthropic hold back Mythos? David Sacks: “ Marc Andreessen pointed out that one of the reasons why Anthropic might've wanted to hold back Mythos, is they simply didn't have the compute to serve it. The model was huge and very expensive to serve, something like 10 or 20x the token cost of, say, Opus. They knew Opus 4.7 was coming out, right? So they hold it back knowing that they don't have the compute to serve it anyway, and they save their compute for the next iteration of Opus. And then by holding it back, they create this impression of scarcity and altruism, and it turns into this gigantic marketing event for their product, because everyone in the government's like, 'Oh wow, they're holding it back because it's so amazing.' Now look, I think it may have been genuinely altruistic as well in the sense that Mythos does reveal coding vulnerabilities that people didn't know about before. And it does make sense to give time to companies with large code bases to patch these dormant bugs and vulnerabilities. But, it's looking more and more like Anthropic could not have offered that model commercially anyway, because it was just too big and expensive, and they needed to create space for Opus 4.7. So it's an interesting theory on what actually happened there.”

The All-In Podcast

170,208 просмотров • 2 месяцев назад

SpaceX may be headed for a $2T public debut just as Anthropic takes the ARR crown from OpenAI. Add in lunar missions, orbital data centers, and AI that’s getting cheaper and more dangerous at the same time, and this week feels like a preview of the 2030s. -- NASA is back in deep space: Artemis II put humans around the Moon for the first time in 54 years. -- The one-person unicorn era has begun: one founder hit $41M in year-one revenue and a $1.8B valuation. -- Anthropic just threw a punch at OpenAI: ~$30B ARR vs. OpenAI’s ~$24–25B, while Mythos is rumored to be 400x better than humans on key AI research benchmarks -- OpenAI reportedly shut down Sora after it was losing $1M per day in compute
0:45

SpaceX may be headed for a $2T public debut just as Anthropic takes the ARR crown from OpenAI. Add in lunar missions, orbital data centers, and AI that’s getting cheaper and more dangerous at the same time, and this week feels like a preview of the 2030s. -- NASA is back in deep space: Artemis II put humans around the Moon for the first time in 54 years. -- The one-person unicorn era has begun: one founder hit $41M in year-one revenue and a $1.8B valuation. -- Anthropic just threw a punch at OpenAI: ~$30B ARR vs. OpenAI’s ~$24–25B, while Mythos is rumored to be 400x better than humans on key AI research benchmarks -- OpenAI reportedly shut down Sora after it was losing $1M per day in compute

Peter H. Diamandis, MD

29,454 просмотров • 3 месяцев назад

Dan on why hyperscalers will be a worse business model over time even as growth accelerates: "AWS, Azure, GCP are going to accelerate for a while just because their customer base -- Anthropic, OpenAI -- are growing at an enormous pace. The problem is you went from a dynamic where their customer base was like every corporation in the world and therefore they had fragmentation and it was a really good business. Going forward it's highly unlikely that LLMs are not concentrated in the hands of 4 or 5 companies. Those companies right now, are cashflow negative, and therefore they're looking for compute anywhere they can get it. In the next 5 to 10 years, they'll be generating enormous amounts of free cash flow. When that happens, they're likely to insource the compute. Right now, they look at the hyperscalers as more of a financing mechanism. But I don't think they are better than the LLMs at building data centers. Then you have this dynamic of neoclouds. I don't think they're going away like people thought, because they're better at running GPU clusters than the traditional hyperscalers, and there's a lot of interest from NVIDIA to make sure that their customer base is diversified."
2:25

Dan on why hyperscalers will be a worse business model over time even as growth accelerates: "AWS, Azure, GCP are going to accelerate for a while just because their customer base -- Anthropic, OpenAI -- are growing at an enormous pace. The problem is you went from a dynamic where their customer base was like every corporation in the world and therefore they had fragmentation and it was a really good business. Going forward it's highly unlikely that LLMs are not concentrated in the hands of 4 or 5 companies. Those companies right now, are cashflow negative, and therefore they're looking for compute anywhere they can get it. In the next 5 to 10 years, they'll be generating enormous amounts of free cash flow. When that happens, they're likely to insource the compute. Right now, they look at the hyperscalers as more of a financing mechanism. But I don't think they are better than the LLMs at building data centers. Then you have this dynamic of neoclouds. I don't think they're going away like people thought, because they're better at running GPU clusters than the traditional hyperscalers, and there's a lot of interest from NVIDIA to make sure that their customer base is diversified."

Patrick OShaughnessy

341,476 просмотров • 4 месяцев назад

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

419,062 просмотров • 2 месяцев назад

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX
15:32

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX

Herbert Ong

33,728 просмотров • 1 месяц назад

📢 BREAKING: Nvidia, Microsoft, and Anthropic announce new strategic AI partnerships of roughly $45 Billion. They struck a 3-way deal where Anthropic commits $30B for Azure compute with capacity up to 1GW, and NVIDIA and Microsoft plan to invest up to $10B and $5B in Anthropic. Claude will scale on Azure using NVIDIA systems, which expands access for enterprises and makes Claude available across the major clouds. Anthropic and NVIDIA will co-design to tune both model stacks and future chips for performance, efficiency, and total cost of ownership, so the models run faster per dollar. The first wave targets NVIDIA Grace Blackwell and Vera Rubin systems, which pair high-bandwidth memory with dense interconnect to push training and inference throughput. Microsoft will expose Anthropic models in Azure AI Foundry, including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5, so teams can pick latency and cost profiles. Claude will also remain available across the Copilot family, which means coding, document automation, and workflow agents can call the same model line. The 1GW commitment signals many thousands of GPUs worth of capacity, which reduces queue times and stabilizes unit economics for long-running training runs. The joint optimization loop, models shaping chips and chips shaping models, typically yields double-digit efficiency gains over off-the-shelf deployments.
1:10

📢 BREAKING: Nvidia, Microsoft, and Anthropic announce new strategic AI partnerships of roughly $45 Billion. They struck a 3-way deal where Anthropic commits $30B for Azure compute with capacity up to 1GW, and NVIDIA and Microsoft plan to invest up to $10B and $5B in Anthropic. Claude will scale on Azure using NVIDIA systems, which expands access for enterprises and makes Claude available across the major clouds. Anthropic and NVIDIA will co-design to tune both model stacks and future chips for performance, efficiency, and total cost of ownership, so the models run faster per dollar. The first wave targets NVIDIA Grace Blackwell and Vera Rubin systems, which pair high-bandwidth memory with dense interconnect to push training and inference throughput. Microsoft will expose Anthropic models in Azure AI Foundry, including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5, so teams can pick latency and cost profiles. Claude will also remain available across the Copilot family, which means coding, document automation, and workflow agents can call the same model line. The 1GW commitment signals many thousands of GPUs worth of capacity, which reduces queue times and stabilizes unit economics for long-running training runs. The joint optimization loop, models shaping chips and chips shaping models, typically yields double-digit efficiency gains over off-the-shelf deployments.

Rohan Paul

39,781 просмотров • 7 месяцев назад

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

46,709 просмотров • 21 дней назад

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk
6:02

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

Milk Road AI

69,393 просмотров • 27 дней назад

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

16,536 просмотров • 16 дней назад

Dylan on why memory prices will double again: "Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM, but whatever. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. People are like, "oh, the memory story is overplayed." No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing."
2:17

Dylan on why memory prices will double again: "Memory can only grow capacity low double digit percentages a year. 20-30% a year, even less for NAND, a little bit higher for DRAM, but whatever. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until 2028, even if they wanted to build as fast as possible. So the result is memory prices have gone through the roof. They're going to double and triple again. DRAM, especially. People are like, "oh, the memory story is overplayed." No, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing."

Patrick OShaughnessy

189,478 просмотров • 2 месяцев назад

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 просмотров • 9 месяцев назад