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Dylan Patel, founder of SemiAnalysis, on why GitHub keeps breaking: "The entire cloud market ran out of CPUs. Microsoft sold all their spare ones to Anthropic and OpenAI. They have none left." That GitHub instability you keep hitting isn't a bug. It's the AI labs eating the world's compute.... show more

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Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no. The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference. Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer." Every rollout now needs a CPU-backed environment to verify against. Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs. The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8. That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%. When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.

Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no. The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference. Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer." Every rollout now needs a CPU-backed environment to verify against. Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs. The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8. That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%. When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.

Aakash Gupta

290,767 views • 3 months ago

Dylan Patel says GPUs are no longer the biggest bottleneck. According to Dylan Patel, now CPUs are the constraint. In the early AI era, CPUs were the laggers. You used them for storage, checkpointing, pre-processing, etc. (pretty light workloads) The models weren't agentic and couldn't go step by step. Just string in and string out (simple inference) Then OpenAI launched O1 preview in September '24, and RL training loops have since tightened every month. - initially it was checking model output with regex - then running classifiers - followed by code unit tests + compilation - and finally agentic flows calling databases & scientific simulations The model outputs to an environment, gets verified, and trains on it. Coding agent revenue went from a couple billion to north of $10B in roughly 6 months. Something like Codex 5.4 can work agentically on its own for 6-7 hrs straight - doing all sorts of calls (databases, cron servers, scraping) That requires insane CPU capabilities. And over the last two quarters, the entire cloud market ran out of CPUs. - GitHub has been really unstable lately - Amazon's CPU server installations 3x'd year over year - Microsoft sold all of its spare CPUs to Anthropic & OpenAI Earlier, it was 100 megawatts of GPUs served by 1 megawatt of CPUs. Now that ratio is getting much closer for both RL training and agentic inference. There's simply no capacity anywhere, and it's causing massive instability.

Dylan Patel says GPUs are no longer the biggest bottleneck. According to Dylan Patel, now CPUs are the constraint. In the early AI era, CPUs were the laggers. You used them for storage, checkpointing, pre-processing, etc. (pretty light workloads) The models weren't agentic and couldn't go step by step. Just string in and string out (simple inference) Then OpenAI launched O1 preview in September '24, and RL training loops have since tightened every month. - initially it was checking model output with regex - then running classifiers - followed by code unit tests + compilation - and finally agentic flows calling databases & scientific simulations The model outputs to an environment, gets verified, and trains on it. Coding agent revenue went from a couple billion to north of $10B in roughly 6 months. Something like Codex 5.4 can work agentically on its own for 6-7 hrs straight - doing all sorts of calls (databases, cron servers, scraping) That requires insane CPU capabilities. And over the last two quarters, the entire cloud market ran out of CPUs. - GitHub has been really unstable lately - Amazon's CPU server installations 3x'd year over year - Microsoft sold all of its spare CPUs to Anthropic & OpenAI Earlier, it was 100 megawatts of GPUs served by 1 megawatt of CPUs. Now that ratio is getting much closer for both RL training and agentic inference. There's simply no capacity anywhere, and it's causing massive instability.

Ivan Burazin

303,474 views • 4 months ago

Dylan Patel, founder of SemiAnalysis: "The upper bound on how much compute can be produced by 2030 is around 200 gigawatts a year." The entire world has about 20 gigawatts of AI deployed right now. The ceiling is 10x what exists today, and it still isn't enough to feed what Sam, Elon, Dario and Demis are racing to build. Everyone argues about which model wins. The real limit is a number measured in gigawatts, and it's already maxed out years in advance.

Dylan Patel, founder of SemiAnalysis: "The upper bound on how much compute can be produced by 2030 is around 200 gigawatts a year." The entire world has about 20 gigawatts of AI deployed right now. The ceiling is 10x what exists today, and it still isn't enough to feed what Sam, Elon, Dario and Demis are racing to build. Everyone argues about which model wins. The real limit is a number measured in gigawatts, and it's already maxed out years in advance.

zostaff

163,567 views • 2 months ago

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 views • 1 month ago

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Stocks

41,351 views • 19 days ago

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Macro

55,169 views • 2 months ago

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

zostaff

12,502 views • 22 days ago

Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."

Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."

Molly O’Shea

15,390 views • 3 months ago

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

295,226 views • 3 months ago

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

Milk Road AI

13,241 views • 3 months ago

China will crash the US stock market this year. They're dumping SOTA open-source models on the US, nearly as good as the frontier labs, basically free. Nobody can refuse cutting a $100M AI bill down to $5M. Once that deal is on the table for everyone, Anthropic and OpenAI can't justify their prices, or their valuations. The bubble isn't AI. It's the price of AI. And it's about to pop.

China will crash the US stock market this year. They're dumping SOTA open-source models on the US, nearly as good as the frontier labs, basically free. Nobody can refuse cutting a $100M AI bill down to $5M. Once that deal is on the table for everyone, Anthropic and OpenAI can't justify their prices, or their valuations. The bubble isn't AI. It's the price of AI. And it's about to pop.

Ben Cera

546,015 views • 1 month ago

Good Alexander explains why he thinks OpenAI is today's Amazon “Everyone was in Amazon because of AWS and they all marked the retail business at zero” “That business they thought was worth zero a decade ago is actually this money printing machine because it has an advertising engine. As of Q1 2026 it's a $69B a year run rate advertising business” “OpenAI has three times the number of users as Amazon” “Currently the market narrative and the market consensus is that Sam Altman's an idiot because he has 900 million DAUs and he's losing money on the DAUs” “It's literally the exact same thing that caused everyone to be sidelined in Amazon for like a solid 10 year run. They didn't understand that it's really hard to get 900M users and appealing to normies is not easy”

Good Alexander explains why he thinks OpenAI is today's Amazon “Everyone was in Amazon because of AWS and they all marked the retail business at zero” “That business they thought was worth zero a decade ago is actually this money printing machine because it has an advertising engine. As of Q1 2026 it's a $69B a year run rate advertising business” “OpenAI has three times the number of users as Amazon” “Currently the market narrative and the market consensus is that Sam Altman's an idiot because he has 900 million DAUs and he's losing money on the DAUs” “It's literally the exact same thing that caused everyone to be sidelined in Amazon for like a solid 10 year run. They didn't understand that it's really hard to get 900M users and appealing to normies is not easy”

Jack

24,053 views • 2 months ago

Dylan Patel: “CPUs are sold out.” $INTC earnings and conference call confirmed the looming CPU shortage. There are three main drivers: - Agentic systems, as CPUs do the planning and orchestration. - Increasing complexity of and demand for reinforcement learning environments that run on CPUs. - Increasing deployment of AI instilled apps. Models run on GPUs but apps themselves run on CPUs. Demand for all these will grow secularly as AI systems will only get more agentic, RL environments will only get more complex, and deployment will only accelerate. As a result, GPU-to-CPU ratio in AI workloads will move from 1:8 to 1:2 or maybe even 1:1 in the future. CPU supercycle is about to start. $AMD $INTC $ARM

Dylan Patel: “CPUs are sold out.” $INTC earnings and conference call confirmed the looming CPU shortage. There are three main drivers: - Agentic systems, as CPUs do the planning and orchestration. - Increasing complexity of and demand for reinforcement learning environments that run on CPUs. - Increasing deployment of AI instilled apps. Models run on GPUs but apps themselves run on CPUs. Demand for all these will grow secularly as AI systems will only get more agentic, RL environments will only get more complex, and deployment will only accelerate. As a result, GPU-to-CPU ratio in AI workloads will move from 1:8 to 1:2 or maybe even 1:1 in the future. CPU supercycle is about to start. $AMD $INTC $ARM

Oguz Erkan

93,419 views • 3 months ago

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 views • 2 months ago

$Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?$

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

92,971 views • 1 month ago

$AMD will become a multi-trillion dollar company. The AI boom is no longer just about GPUs. In early AI data centers, the CPU-to-GPU ratio was around 1:8. Now it’s moving closer to 1:1 because agentic AI requires dramatically more CPU compute. Lisa Su: “Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030.” Add to that the 6GW AI infrastructure deals with $META and OpenAI, plus deep collaboration on multiple future chip generations, and it becomes clear why $AMD is positioned to compound for years. I’m not chasing $AMD here after a +110% move in just over a month. But the long-term story remains intact.

$AMD will become a multi-trillion dollar company. The AI boom is no longer just about GPUs. In early AI data centers, the CPU-to-GPU ratio was around 1:8. Now it’s moving closer to 1:1 because agentic AI requires dramatically more CPU compute. Lisa Su: “Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030.” Add to that the 6GW AI infrastructure deals with $META and OpenAI, plus deep collaboration on multiple future chip generations, and it becomes clear why $AMD is positioned to compound for years. I’m not chasing $AMD here after a +110% move in just over a month. But the long-term story remains intact.

Daniel

39,622 views • 3 months ago

Anthropic CEO, Dario Amodei: "Half of entry-level white-collar jobs could be gone in one to five years." 3 things he said during the interview. The third, OpenAI completely ignores. One: inside Anthropic, engineers say "I don't write code anymore. The model does." Two: the real trigger isn't jobs. It's AI that builds AI, and the loop may close this year. Three: how soon superhuman AI actually lands. The massive buffers they are raising because the compute explosion is moving faster than anticipated. Save it. Reread in 2028 and see who was right.

Anthropic CEO, Dario Amodei: "Half of entry-level white-collar jobs could be gone in one to five years." 3 things he said during the interview. The third, OpenAI completely ignores. One: inside Anthropic, engineers say "I don't write code anymore. The model does." Two: the real trigger isn't jobs. It's AI that builds AI, and the loop may close this year. Three: how soon superhuman AI actually lands. The massive buffers they are raising because the compute explosion is moving faster than anticipated. Save it. Reread in 2028 and see who was right.

Ridark

14,555 views • 1 month ago

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% gross margin. Every GPU it rents, at any above-market rate, is immediately accretive. It can outbid the whole market for scarce compute and still print money. Lower-margin labs cannot. The compute crunch is an Anthropic tailwind, via Sequoia Capital: Source: Sequoia Capital -

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% gross margin. Every GPU it rents, at any above-market rate, is immediately accretive. It can outbid the whole market for scarce compute and still print money. Lower-margin labs cannot. The compute crunch is an Anthropic tailwind, via Sequoia Capital: Source: Sequoia Capital -

Podcast Alpha

98,948 views • 1 month ago

What is going on in American schools?? Student says her school “spent $80,000 on yonder pouches — It traps your phone in it, so you can't take it out” They did this while RUNNING OUT OF FOOD TO FEED STUDENTS, “We ran out of food, like literally for a week straight. We had baked potatoes at the end of the school year with cheese, and that was it. That was their lunch. And now this year the only thing that they've been giving us is mozzarella sticks and that's it. Like you can't get anything else. Like we're running out of food.” “So they spent $80,000 on those. And then tell me what you just told me about the cafeteria,” they ran out of food

What is going on in American schools?? Student says her school “spent $80,000 on yonder pouches — It traps your phone in it, so you can't take it out” They did this while RUNNING OUT OF FOOD TO FEED STUDENTS, “We ran out of food, like literally for a week straight. We had baked potatoes at the end of the school year with cheese, and that was it. That was their lunch. And now this year the only thing that they've been giving us is mozzarella sticks and that's it. Like you can't get anything else. Like we're running out of food.” “So they spent $80,000 on those. And then tell me what you just told me about the cafeteria,” they ran out of food

Wall Street Apes

840,004 views • 1 year ago

Eric says the biggest mistake investors made in cloud was undersizing the market, and everyone is doing it again with AI: "In 2014, a really common narrative was AWS is going to eat everything. There's no enterprise opportunity left. Think about 2014 to now. You have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out-Amazoning Amazon on Amazon. Confluent and Elastic and Mongo, Databricks. Then you have the whole app layer. Datadog, $100 billion company. The view that AWS was gonna eat everything was massively wrong because Azure and GCP were irrelevant then. Fast-forward to 2026, you ended up with an oligopoly. Even outside of those big three, you have Cloudflare, which is another $100 billion company. The biggest mistake everybody made was they just undersized the market. The market was so big that one vendor could not scale and consume it all. That whole notion right now with what you and I are seeing in AI sure feels like it rhymes. The takeaway to me is "What if it all works?"

Eric says the biggest mistake investors made in cloud was undersizing the market, and everyone is doing it again with AI: "In 2014, a really common narrative was AWS is going to eat everything. There's no enterprise opportunity left. Think about 2014 to now. You have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out-Amazoning Amazon on Amazon. Confluent and Elastic and Mongo, Databricks. Then you have the whole app layer. Datadog, $100 billion company. The view that AWS was gonna eat everything was massively wrong because Azure and GCP were irrelevant then. Fast-forward to 2026, you ended up with an oligopoly. Even outside of those big three, you have Cloudflare, which is another $100 billion company. The biggest mistake everybody made was they just undersized the market. The market was so big that one vendor could not scale and consume it all. That whole notion right now with what you and I are seeing in AI sure feels like it rhymes. The takeaway to me is "What if it all works?"

Patrick OShaughnessy

90,352 views • 4 days ago