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How could you possibly be bearish on compute right now? (Save this). Every 10 seconds in 2026, the world generates 31.7 billion tokens and by 2030, that number hits 1.27 trillion, every 10 seconds. That's a 40x increase and that's before the full agent economy comes online. The Qualcomm...

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16,246 görüntüleme • 1 ay önce •via X (Twitter)

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Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!
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Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!

Milk Road AI

14,578 görüntüleme • 1 ay önce

Mark my words, Nebius will be the first Trillion dollar Neo-cloud company and here is why (Save this). Roman Chernin, CEO of Nebius just said on 20VC that Nebius raised prices and demand didn't move. When a company can raise prices and still have more demand than supply, that's the opportunity. Chernin also explained why he is deliberately not charging the maximum. As AI shifts from training, a one time cost to inference, which is the ongoing cost of serving every user and every query, compute pricing becomes the cost structure of the entire AI economy. If Nebius prices customers out, those customers cannot grow, and Nebius cannot grow with them. That is the compounding flywheel built directly into the revenue model. The numbers are already confirming it. Q1 2026 revenue came in at $399 million, up 684% year over year. The AI cloud segment grew 840% and represented 98% of total revenue. Adjusted EBITDA flipped positive to $129.5 million. And Nebius signed a long-term agreement with Meta worth up to $27 billion over five years, a hyperscaler outsourcing its own AI compute stack to a neocloud, which tells you that even companies with $50 billion capex budgets cannot build fast enough. Goldman Sachs says the consensus is underestimating 2027 hyperscaler capex by $500 billion. Every dollar hyperscalers cannot provision themselves flows to neoclouds like Nebius. As that gap widens, Nebius captures the overflow with 3 gigawatts of contracted power already secured and a CEO who just told you raising prices did not dent demand. Our subscribers are already up massively on Nebius and come join Milk Road Pro for our full breakdown, how to size Nebius against the broader neocloud opportunity, and our full AI thesis. Link below!
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Mark my words, Nebius will be the first Trillion dollar Neo-cloud company and here is why (Save this). Roman Chernin, CEO of Nebius just said on 20VC that Nebius raised prices and demand didn't move. When a company can raise prices and still have more demand than supply, that's the opportunity. Chernin also explained why he is deliberately not charging the maximum. As AI shifts from training, a one time cost to inference, which is the ongoing cost of serving every user and every query, compute pricing becomes the cost structure of the entire AI economy. If Nebius prices customers out, those customers cannot grow, and Nebius cannot grow with them. That is the compounding flywheel built directly into the revenue model. The numbers are already confirming it. Q1 2026 revenue came in at $399 million, up 684% year over year. The AI cloud segment grew 840% and represented 98% of total revenue. Adjusted EBITDA flipped positive to $129.5 million. And Nebius signed a long-term agreement with Meta worth up to $27 billion over five years, a hyperscaler outsourcing its own AI compute stack to a neocloud, which tells you that even companies with $50 billion capex budgets cannot build fast enough. Goldman Sachs says the consensus is underestimating 2027 hyperscaler capex by $500 billion. Every dollar hyperscalers cannot provision themselves flows to neoclouds like Nebius. As that gap widens, Nebius captures the overflow with 3 gigawatts of contracted power already secured and a CEO who just told you raising prices did not dent demand. Our subscribers are already up massively on Nebius and come join Milk Road Pro for our full breakdown, how to size Nebius against the broader neocloud opportunity, and our full AI thesis. Link below!

Milk Road AI

15,677 görüntüleme • 2 ay önce

Chamath Palihapitiya just dropped the number that explains the entire AI infrastructure trade (Save this). A gigawatt of compute now costs $100 billion and when he started his Arizona data center project it was $4 to $5 billion, it has gone up 20x in a single investment cycle. The implication is not just that AI infrastructure is expensive but rather that the capital barrier to owning meaningful compute has become so high that only a handful of entities in the world can actually build it and the companies who got there early are sitting on what may be the most durable pricing power in the history of the technology industry. This is the neocloud trade. The neocloud market, purpose-built GPU cloud providers like CoreWeave, Nebius, and Lambda Labs was worth $35 billion in 2026 and is projected to reach $236 billion by 2031, compounding at 46% annually. For context, that is faster growth than cloud computing itself posted in its first decade. The reason is very simple, hyperscalers like AWS, Azure, and Google are building for everything, storage, databases, enterprise software, networking and their GPU pricing reflects the overhead of that full-stack infrastructure. Neoclouds build for one thing only, AI compute. The result is a 60% to 85% cost advantage on the same Nvidia silicon, bare metal H100s at $0.78 to $2.79 per GPU-hour on a neocloud versus $3.43 to $5.07 per GPU-hour on a hyperscaler. That spread does not close as AI demand scales but rather it widens, because hyperscalers have to amortize legacy infrastructure and margin expectations that neoclouds do not carry. Gartner projects that by 2030, neoclouds will capture 20% of the $267 billion AI cloud market, and Vultr's own analysis says at least 80% of GPU market share by end of 2026 will be held by a small group of scaled neocloud providers. Now zoom into Nebius specifically, because it is the most interesting publicly traded proxy for this trade. Nebius is the infrastructure arm of the former Yandex Russia's equivalent of Google rebuilt from the ground up after Russia's invasion of Ukraine by Arkady Volozh and relisted on Nasdaq in October 2024. The team that built it already knew how to run internet-scale infrastructure at the lowest possible cost, which is exactly the operational DNA a neocloud requires. In Q1 2026, Nebius reported revenue of $399 million and already generating serious cash on a young business with revenue growing nearly eightfold year-over-year. Then in March 2026, Meta signed a five-year infrastructure agreement with Nebius worth up to $27 billion, $12 billion in committed dedicated GPU capacity deployments beginning early 2027, plus up to $15 billion more tied to Meta purchasing Nebius's unsold third-party capacity. The deal will be executed on one of the first large-scale deployments of Nvidia's Vera Rubin platform, the next-generation architecture after Blackwell making Nebius one of a tiny number of operators in the world with confirmed priority access to the most advanced AI hardware available. Following the contract, Nebius guided to $7 to $9 billion in annualized recurring revenue for 2026 representing 540% year-over-year growth. Chamath Palihapitiya point about the $100 billion capital moat is the bear case for new entrants and the bull case for incumbents. No one can afford to build the next CoreWeave or Nebius from scratch at current hardware and power costs. The companies that are already built, already contracted, and already deploying Nvidia's latest silicon have a moat that compounds with every GPU generation cycle because they get allocations first, they deploy fastest, and their customers re-sign rather than wait for a new operator that does not yet exist. Come join Milk Road Pro for our full breakdown, the complete neocloud competitive landscape, how to think about Nebius's valuation versus CoreWeave and AI entire thesis. Link below.
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Chamath Palihapitiya just dropped the number that explains the entire AI infrastructure trade (Save this). A gigawatt of compute now costs $100 billion and when he started his Arizona data center project it was $4 to $5 billion, it has gone up 20x in a single investment cycle. The implication is not just that AI infrastructure is expensive but rather that the capital barrier to owning meaningful compute has become so high that only a handful of entities in the world can actually build it and the companies who got there early are sitting on what may be the most durable pricing power in the history of the technology industry. This is the neocloud trade. The neocloud market, purpose-built GPU cloud providers like CoreWeave, Nebius, and Lambda Labs was worth $35 billion in 2026 and is projected to reach $236 billion by 2031, compounding at 46% annually. For context, that is faster growth than cloud computing itself posted in its first decade. The reason is very simple, hyperscalers like AWS, Azure, and Google are building for everything, storage, databases, enterprise software, networking and their GPU pricing reflects the overhead of that full-stack infrastructure. Neoclouds build for one thing only, AI compute. The result is a 60% to 85% cost advantage on the same Nvidia silicon, bare metal H100s at $0.78 to $2.79 per GPU-hour on a neocloud versus $3.43 to $5.07 per GPU-hour on a hyperscaler. That spread does not close as AI demand scales but rather it widens, because hyperscalers have to amortize legacy infrastructure and margin expectations that neoclouds do not carry. Gartner projects that by 2030, neoclouds will capture 20% of the $267 billion AI cloud market, and Vultr's own analysis says at least 80% of GPU market share by end of 2026 will be held by a small group of scaled neocloud providers. Now zoom into Nebius specifically, because it is the most interesting publicly traded proxy for this trade. Nebius is the infrastructure arm of the former Yandex Russia's equivalent of Google rebuilt from the ground up after Russia's invasion of Ukraine by Arkady Volozh and relisted on Nasdaq in October 2024. The team that built it already knew how to run internet-scale infrastructure at the lowest possible cost, which is exactly the operational DNA a neocloud requires. In Q1 2026, Nebius reported revenue of $399 million and already generating serious cash on a young business with revenue growing nearly eightfold year-over-year. Then in March 2026, Meta signed a five-year infrastructure agreement with Nebius worth up to $27 billion, $12 billion in committed dedicated GPU capacity deployments beginning early 2027, plus up to $15 billion more tied to Meta purchasing Nebius's unsold third-party capacity. The deal will be executed on one of the first large-scale deployments of Nvidia's Vera Rubin platform, the next-generation architecture after Blackwell making Nebius one of a tiny number of operators in the world with confirmed priority access to the most advanced AI hardware available. Following the contract, Nebius guided to $7 to $9 billion in annualized recurring revenue for 2026 representing 540% year-over-year growth. Chamath Palihapitiya point about the $100 billion capital moat is the bear case for new entrants and the bull case for incumbents. No one can afford to build the next CoreWeave or Nebius from scratch at current hardware and power costs. The companies that are already built, already contracted, and already deploying Nvidia's latest silicon have a moat that compounds with every GPU generation cycle because they get allocations first, they deploy fastest, and their customers re-sign rather than wait for a new operator that does not yet exist. Come join Milk Road Pro for our full breakdown, the complete neocloud competitive landscape, how to think about Nebius's valuation versus CoreWeave and AI entire thesis. Link below.

Milk Road AI

139,047 görüntüleme • 2 ay önce

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!
1:04

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!

Milk Road AI

28,622 görüntüleme • 2 ay önce

Nebius is going to be a Trillion-dollar company! Twelve months ago, Nebius was trading near $18 per share with roughly $55 million in quarterly revenue. Today the stock trades above $225, quarterly revenue just came in at $399 million, up 684% year over year and the company has a contracted revenue backlog that would make most Fortune 500 companies envious. But the current market cap, sitting around $56 billion, prices in almost none of what is actually coming. The first reason Nebius reaches a trillion is the Meta deal alone. In March, Nebius signed a five year agreement with Meta worth up to $27 billion, one of the largest infrastructure contracts Meta has ever signed with any company under which Nebius will provide $12 billion in dedicated AI capacity across multiple locations, with Meta also having committed to purchase up to an additional $15 billion in third-party capacity over the same period. That contract barely starts until 2027, which means the revenue impact is not yet reflected in any trailing metric. The second reason is Microsoft, which is currently receiving its first deployment phases from Nebius and is expected to contribute at full annual run rate starting in 2027. Between Meta and Microsoft alone, Nebius has signed agreements worth more than $46 billion in total contracted value before a single additional customer is counted. The third reason is the ARR trajectory, which is the fastest revenue ramp of any infrastructure company in the public markets. Nebius ended 2025 at $1.25 billion in ARR and is guiding to $7–9 billion ARR by year-end 2026. Wall Street analysts project revenue growing 523% in 2026 and another 206% in 2027. One of the company's own institutional shareholders has already suggested the year-end ARR could come in more than twice the guided range if the Meta and Microsoft ramps hit their timelines. The fourth reason is Nvidia's direct involvement. Nvidia made a $2 billion strategic equity investment in Nebius and has given Nebius early access to the Vera Rubin platform, its next generation GPU architecture as part of the delivery commitments to Meta. The fifth reason is the capacity buildout, which is being funded by the revenue itself. Nebius invested $2.5 billion in capex in Q1 alone, CEO Arkady Volozh has guided for $16–20 billion in total investment for 2026, and contracted capacity is now on track to exceed 4 GW by year end with new owned sites in Pennsylvania at 1.2 GW and Finland at 310 MW now under development. The more capacity they build, the more they can sell and demand continues to outpace supply at every stage of the buildout. When you run the math on a business with $7–9 billion in ARR exiting 2026, a $27 billion Meta contract that begins in earnest in 2027, a Microsoft relationship at full run rate, 206% analyst projected growth in 2027, and a structural relationship with Nvidia that gives it hardware access no competitor can match, a trillion-dollar valuation within three to four years is not a moonshot. It is the base case if the compounding holds, and every data point so far suggests it is. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro at the link in bio/below!
1:04

Nebius is going to be a Trillion-dollar company! Twelve months ago, Nebius was trading near $18 per share with roughly $55 million in quarterly revenue. Today the stock trades above $225, quarterly revenue just came in at $399 million, up 684% year over year and the company has a contracted revenue backlog that would make most Fortune 500 companies envious. But the current market cap, sitting around $56 billion, prices in almost none of what is actually coming. The first reason Nebius reaches a trillion is the Meta deal alone. In March, Nebius signed a five year agreement with Meta worth up to $27 billion, one of the largest infrastructure contracts Meta has ever signed with any company under which Nebius will provide $12 billion in dedicated AI capacity across multiple locations, with Meta also having committed to purchase up to an additional $15 billion in third-party capacity over the same period. That contract barely starts until 2027, which means the revenue impact is not yet reflected in any trailing metric. The second reason is Microsoft, which is currently receiving its first deployment phases from Nebius and is expected to contribute at full annual run rate starting in 2027. Between Meta and Microsoft alone, Nebius has signed agreements worth more than $46 billion in total contracted value before a single additional customer is counted. The third reason is the ARR trajectory, which is the fastest revenue ramp of any infrastructure company in the public markets. Nebius ended 2025 at $1.25 billion in ARR and is guiding to $7–9 billion ARR by year-end 2026. Wall Street analysts project revenue growing 523% in 2026 and another 206% in 2027. One of the company's own institutional shareholders has already suggested the year-end ARR could come in more than twice the guided range if the Meta and Microsoft ramps hit their timelines. The fourth reason is Nvidia's direct involvement. Nvidia made a $2 billion strategic equity investment in Nebius and has given Nebius early access to the Vera Rubin platform, its next generation GPU architecture as part of the delivery commitments to Meta. The fifth reason is the capacity buildout, which is being funded by the revenue itself. Nebius invested $2.5 billion in capex in Q1 alone, CEO Arkady Volozh has guided for $16–20 billion in total investment for 2026, and contracted capacity is now on track to exceed 4 GW by year end with new owned sites in Pennsylvania at 1.2 GW and Finland at 310 MW now under development. The more capacity they build, the more they can sell and demand continues to outpace supply at every stage of the buildout. When you run the math on a business with $7–9 billion in ARR exiting 2026, a $27 billion Meta contract that begins in earnest in 2027, a Microsoft relationship at full run rate, 206% analyst projected growth in 2027, and a structural relationship with Nvidia that gives it hardware access no competitor can match, a trillion-dollar valuation within three to four years is not a moonshot. It is the base case if the compounding holds, and every data point so far suggests it is. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro at the link in bio/below!

Milk Road AI

48,673 görüntüleme • 3 ay önce

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.
0:20

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.

Milk Road AI

61,932 görüntüleme • 2 ay önce

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!
1:28

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!

Milk Road AI

77,554 görüntüleme • 2 ay önce

Nebius will be the first neocloud to hit $1 trillion dollar company and here is exactly why (Save this). As dylan patel says Jensen Huang absolutely hates a world where the hyperscalers have all the power. A world where Microsoft, Amazon, and Google are the only ones building compute is a world where Nvidia is slowly being squeezed by a handful of customers all simultaneously developing custom chips to replace Nvidia GPUs entirely. Google's TPU, Amazon's Trainium and Microsoft's Maia all exist for one reason, to cut Nvidia out of the stack and Jensen knows it so he is playing a long game most investors haven't registered yet. By funding NeoClouds and NeoLabs at scale, Jensen is deliberately engineering a multipolar compute world where no single hyperscaler can dictate terms and where Nvidia hardware remains the default infrastructure layer regardless of which model or platform ultimately wins. Nvidia has deployed roughly $40 billion in AI ecosystem investments across OpenAI, Anthropic, CoreWeave, Nebius, xAI, and dozens of infrastructure companies, all running almost exclusively on Nvidia chips, cementing GPU dependency across the entire AI stack.sedaily Every neocloud that survives and scales becomes a permanent Nvidia GPU customer structurally opposed to the hyperscalers building custom silicon expanding Nvidia's market while simultaneously weakening its biggest competitive threat. Dylan Patel described the neocloud ecosystem as throwing bait into the water and letting the best fish survive, warning that many heavily-backed teams will fail, but the ones that emerge will pull hundreds of millions in ARR right out of the gate. Nebius is that fish because it's the only neocloud operating at hyperscaler scale while remaining fully purpose-engineered for AI workloads from silicon to software. The numbers confirm Nebius has already cleared the survival bar that will eliminate most of the 200+ neoclouds competing right now. Revenue hit $399 million in Q1 2026, up 684% year-over-year, backed by $46 billion in contracted backlog, 3.5 GW of contracted power across seven site and a target of $7–$9 billion in annualized revenue by year-end. When Google approached neoclouds about deploying TPUs, Nebius said no, its Chief Revenue Officer noting that demand is 99% for Nvidia GPUs and that TPU interest comes almost entirely from former Google employees rather than the actual market. That alignment with Nvidia's ecosystem, at this scale, with this backlog, and this level of strategic backing is why Nebius sits in a category of one among the neocloud field. Patel framed the broader play correctly, every neocloud that survives makes Google's TPU and Amazon's Trainium structurally weaker simply by existing and five years from now, the winners will have reshaped the entire compute landscape in Nvidia's favor. Nebius is already hundreds of millions in ARR ahead of the competition while most of the field is still treading water. Milk Road subscribers are already up massively on the Nebius trade, and we are tracking the neocloud buildout as Nvidia works to reshape the entire compute market. Come join Milk Road Pro for our full Nebius breakdown, the valuation framework, the revenue targets we are watching, and the AI infrastructure names we like next for just $1. Link below!
2:12

Nebius will be the first neocloud to hit $1 trillion dollar company and here is exactly why (Save this). As dylan patel says Jensen Huang absolutely hates a world where the hyperscalers have all the power. A world where Microsoft, Amazon, and Google are the only ones building compute is a world where Nvidia is slowly being squeezed by a handful of customers all simultaneously developing custom chips to replace Nvidia GPUs entirely. Google's TPU, Amazon's Trainium and Microsoft's Maia all exist for one reason, to cut Nvidia out of the stack and Jensen knows it so he is playing a long game most investors haven't registered yet. By funding NeoClouds and NeoLabs at scale, Jensen is deliberately engineering a multipolar compute world where no single hyperscaler can dictate terms and where Nvidia hardware remains the default infrastructure layer regardless of which model or platform ultimately wins. Nvidia has deployed roughly $40 billion in AI ecosystem investments across OpenAI, Anthropic, CoreWeave, Nebius, xAI, and dozens of infrastructure companies, all running almost exclusively on Nvidia chips, cementing GPU dependency across the entire AI stack.sedaily Every neocloud that survives and scales becomes a permanent Nvidia GPU customer structurally opposed to the hyperscalers building custom silicon expanding Nvidia's market while simultaneously weakening its biggest competitive threat. Dylan Patel described the neocloud ecosystem as throwing bait into the water and letting the best fish survive, warning that many heavily-backed teams will fail, but the ones that emerge will pull hundreds of millions in ARR right out of the gate. Nebius is that fish because it's the only neocloud operating at hyperscaler scale while remaining fully purpose-engineered for AI workloads from silicon to software. The numbers confirm Nebius has already cleared the survival bar that will eliminate most of the 200+ neoclouds competing right now. Revenue hit $399 million in Q1 2026, up 684% year-over-year, backed by $46 billion in contracted backlog, 3.5 GW of contracted power across seven site and a target of $7–$9 billion in annualized revenue by year-end. When Google approached neoclouds about deploying TPUs, Nebius said no, its Chief Revenue Officer noting that demand is 99% for Nvidia GPUs and that TPU interest comes almost entirely from former Google employees rather than the actual market. That alignment with Nvidia's ecosystem, at this scale, with this backlog, and this level of strategic backing is why Nebius sits in a category of one among the neocloud field. Patel framed the broader play correctly, every neocloud that survives makes Google's TPU and Amazon's Trainium structurally weaker simply by existing and five years from now, the winners will have reshaped the entire compute landscape in Nvidia's favor. Nebius is already hundreds of millions in ARR ahead of the competition while most of the field is still treading water. Milk Road subscribers are already up massively on the Nebius trade, and we are tracking the neocloud buildout as Nvidia works to reshape the entire compute market. Come join Milk Road Pro for our full Nebius breakdown, the valuation framework, the revenue targets we are watching, and the AI infrastructure names we like next for just $1. Link below!

Milk Road AI

92,855 görüntüleme • 1 ay önce

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!
0:20

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!

Milk Road AI

216,498 görüntüleme • 3 ay önce

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

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

Milk Road AI

184,643 görüntüleme • 3 ay önce

Jensen Huang just told you exactly which AI bottleneck never goes away and it points directly to one of the best trades in the market right now. He did not say chip bottlenecks are permanent. He said the opposite, more chip capacity is a two to three year problem, more CoWoS packaging capacity is a two to three year problem, and none of the manufacturing constraints currently limiting Nvidia's ability to ship are structural barriers that cannot be solved. What he said is permanent or at least, far harder to solve is energy. You cannot build AI factories, reindustrialize the United States or build robots and next-generation compute without energy and energy does not respond to large purchase orders the way foundry capacity does. It involves regulatory timelines, grid interconnection queues, permitting cycles, and national policy decisions that no single company can accelerate regardless of how much capital they deploy. The US is staring down a 19 gigawatt power gap by 2028, and PJM launched an emergency integration plan earlier this year just to handle current data center load, not future load, current load. Nearly half of the data centers planned for 2026 are already delayed or canceled not because of chip shortages but because of transformer shortages, switchgear backlogs and grid capacity constraints that have nothing to do with silicon. This is the exact environment that makes Nebius a structurally differentiated position. While every other AI cloud buildout is fighting the same power bottleneck Jensen described, Nebius has already secured over 2 gigawatts of contracted power capacity with a 1.2 gigawatt campus in Missouri, another 310 megawatts in Finland and a Pennsylvania site adding another 1.2 gigawatts to the pipeline. Power is the constraint Jensen says no one can shortcut and Nebius has already locked in more of it than almost any independent AI cloud operator on the planet. That power moat sits underneath a $27 billion contracted revenue deal with Meta, a Microsoft partnership ramping to full run rate in 2027, and a 684% year over year revenue growth number that just printed in Q1. The Nebius thesis was always about infrastructure scarcity in a world of accelerating demand, Jensen just confirmed on camera which scarcity actually matters long-term, and it is exactly the one Nebius spent the last two years solving before anyone else was paying attention. Milk Road Pro called Nebius early, has been sitting on a massive gain on the position, and continues to track the infrastructure plays that matter before they become obvious to the rest of the market, come join us at the link in bio/below!
0:32

Jensen Huang just told you exactly which AI bottleneck never goes away and it points directly to one of the best trades in the market right now. He did not say chip bottlenecks are permanent. He said the opposite, more chip capacity is a two to three year problem, more CoWoS packaging capacity is a two to three year problem, and none of the manufacturing constraints currently limiting Nvidia's ability to ship are structural barriers that cannot be solved. What he said is permanent or at least, far harder to solve is energy. You cannot build AI factories, reindustrialize the United States or build robots and next-generation compute without energy and energy does not respond to large purchase orders the way foundry capacity does. It involves regulatory timelines, grid interconnection queues, permitting cycles, and national policy decisions that no single company can accelerate regardless of how much capital they deploy. The US is staring down a 19 gigawatt power gap by 2028, and PJM launched an emergency integration plan earlier this year just to handle current data center load, not future load, current load. Nearly half of the data centers planned for 2026 are already delayed or canceled not because of chip shortages but because of transformer shortages, switchgear backlogs and grid capacity constraints that have nothing to do with silicon. This is the exact environment that makes Nebius a structurally differentiated position. While every other AI cloud buildout is fighting the same power bottleneck Jensen described, Nebius has already secured over 2 gigawatts of contracted power capacity with a 1.2 gigawatt campus in Missouri, another 310 megawatts in Finland and a Pennsylvania site adding another 1.2 gigawatts to the pipeline. Power is the constraint Jensen says no one can shortcut and Nebius has already locked in more of it than almost any independent AI cloud operator on the planet. That power moat sits underneath a $27 billion contracted revenue deal with Meta, a Microsoft partnership ramping to full run rate in 2027, and a 684% year over year revenue growth number that just printed in Q1. The Nebius thesis was always about infrastructure scarcity in a world of accelerating demand, Jensen just confirmed on camera which scarcity actually matters long-term, and it is exactly the one Nebius spent the last two years solving before anyone else was paying attention. Milk Road Pro called Nebius early, has been sitting on a massive gain on the position, and continues to track the infrastructure plays that matter before they become obvious to the rest of the market, come join us at the link in bio/below!

Milk Road AI

21,140 görüntüleme • 3 ay önce

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.
3:19

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 görüntüleme • 3 ay önce

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

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

Milk Road AI

128,678 görüntüleme • 1 ay önce

Anthropic just had to throttle Claude’s thinking depth and cap usage for paying customers. Developers are switching to OpenAI Codex and the market is already sniffing out who wins from all of. Anthropic’s revenue tripled in one quarter to a $30B annual run rate, demand grew so fast that a single developer running an AI agent could drain a full day’s worth of compute in minutes. Anthropic had to reduce Claude’s default thinking mode, introduce peak-hour caps, and test pulling Claude Code off its $20 plan entirely and paying subscribers started hitting limits they’d never seen before. There are literally not enough GPUs to serve every request, even the hyperscalers can’t build fast enough, Microsoft, Google, Amazon, and Meta are committing a combined ~$700 billion in AI capex in 2026, approaching 100% of their combined operating free cash flow. And it’s still not sufficient to meet demand. That gap is where the neocloud trade lives. CoreWeave, Nebius ($NBIS), IREN, and CoreWeave ($CRWV) exist because hyperscalers physically can’t scale fast enough. These companies accumulated $131 billion in enterprise GPU commitments from zero in under three years. CoreWeave alone has a $66.8 billion revenue backlog, Nebius is running at 98% capacity utilization essentially sold out. Synergy Research forecasts the entire neocloud market hits $400 billion by 2031, growing at 58% per year. Google just committed $40 billion and 5 gigawatts to Anthropic. And every AI lab is in the same position: they have the demand, they have the revenue, they are actively looking for anyone who can hand them compute today. If you can bring capacity online fast, you are golden. The AI labs can’t say no, they have no other options.
4:44

Anthropic just had to throttle Claude’s thinking depth and cap usage for paying customers. Developers are switching to OpenAI Codex and the market is already sniffing out who wins from all of. Anthropic’s revenue tripled in one quarter to a $30B annual run rate, demand grew so fast that a single developer running an AI agent could drain a full day’s worth of compute in minutes. Anthropic had to reduce Claude’s default thinking mode, introduce peak-hour caps, and test pulling Claude Code off its $20 plan entirely and paying subscribers started hitting limits they’d never seen before. There are literally not enough GPUs to serve every request, even the hyperscalers can’t build fast enough, Microsoft, Google, Amazon, and Meta are committing a combined ~$700 billion in AI capex in 2026, approaching 100% of their combined operating free cash flow. And it’s still not sufficient to meet demand. That gap is where the neocloud trade lives. CoreWeave, Nebius ($NBIS), IREN, and CoreWeave ($CRWV) exist because hyperscalers physically can’t scale fast enough. These companies accumulated $131 billion in enterprise GPU commitments from zero in under three years. CoreWeave alone has a $66.8 billion revenue backlog, Nebius is running at 98% capacity utilization essentially sold out. Synergy Research forecasts the entire neocloud market hits $400 billion by 2031, growing at 58% per year. Google just committed $40 billion and 5 gigawatts to Anthropic. And every AI lab is in the same position: they have the demand, they have the revenue, they are actively looking for anyone who can hand them compute today. If you can bring capacity online fast, you are golden. The AI labs can’t say no, they have no other options.

Milk Road AI

46,800 görüntüleme • 3 ay önce

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.
0:44

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

Shay Boloor

589,512 görüntüleme • 3 ay önce

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire
2:24

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 görüntüleme • 2 ay önce

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.
0:54

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.

Milk Road AI

485,188 görüntüleme • 2 ay önce

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Long Nebius and make sure to follow me Melvin for more underlooked AI oppurtunities.
0:20

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Long Nebius and make sure to follow me Melvin for more underlooked AI oppurtunities.

Melvin

69,634 görüntüleme • 1 ay önce

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

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

Dustin

28,395 görüntüleme • 5 ay önce

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!
4:04

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 görüntüleme • 2 ay önce