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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...

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Melvin

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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!
1:00

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 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

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!
0:47

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 просмотров • 2 месяцев назад

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 просмотров • 3 месяцев назад

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.
1:14

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.

Milk Road AI

74,945 просмотров • 2 месяцев назад

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

21,316 просмотров • 3 месяцев назад

Elon Musk's biggest competitor is secretly paying him $1.25 BILLION per month. SpaceX just revealed its financials for the first time in 23 years of existence. And buried deep in the S-1 is a detail that changes how you should think about the entire AI race. Anthropic, the company building Claude, the company that positions itself as OpenAI's biggest threat, the company valued at over $100 billion, is paying SpaceX $1.25 billion EVERY SINGLE MONTH for compute capacity through May 2029. That is $15 billion a year flowing directly from Elon's top AI competitor into Elon's bank account. Think about what that means: Every time Anthropic trains a new model, improves Claude, or lands an enterprise customer, a massive chunk of that revenue goes straight to the guy who owns the competing AI product. Anthropic is literally funding the war against itself. And that's just the beginning of what this filing reveals... The entire SpaceX IPO is structured around a bet most people haven't figured out yet. In 2025, SpaceX spent $20 billion in capex. 60% of that, roughly $12 billion, went to AI infrastructure. Rockets and satellites got the leftovers. In Q1 2026 alone, $7.7 billion out of $10 billion in total capex went to AI. The "rocket company" is spending like an AI company. Meanwhile, xAI, the division that houses Grok, generated $3.2 billion in revenue for the full year of 2025. But its R&D costs TRIPLED to $5 billion. It's burning cash at a pace that would have destroyed it as a standalone company. Which is exactly why Elon merged it into SpaceX two months before filing the IPO. And Starlink is the engine that makes the whole thing work: $11.4 billion in revenue, $4.4 billion in operating profit, and 10.3 million subscribers across 164 countries. It's one of the most profitable subscription businesses on the planet right now. But the average revenue per user DROPPED from $99 per month in 2023 to $66 per month in March 2026. Subscribers quadrupled but each one is paying a third less. Starlink is growing by getting cheaper. SpaceX has lost $37 BILLION since it was founded. Net loss in 2025 was $4.9 billion. This is a company that has never turned an annual profit in 23 years of operation, and it is about to IPO at a $1.75 trillion valuation. And the total addressable market SpaceX claims in the filing is $28.5 trillion. That is a QUARTER of global GDP. So here is what investors are actually buying when this IPO prices: They are buying the most profitable satellite internet business in history, stapled to an AI lab that is burning cash, wrapped inside a Mars colonization pitch that requires building a permanent city on another planet, funded by monthly billion-dollar payments from a direct competitor who has no other option for compute at that scale. This is the kind of thing only Elon could pull off.
2:03

Elon Musk's biggest competitor is secretly paying him $1.25 BILLION per month. SpaceX just revealed its financials for the first time in 23 years of existence. And buried deep in the S-1 is a detail that changes how you should think about the entire AI race. Anthropic, the company building Claude, the company that positions itself as OpenAI's biggest threat, the company valued at over $100 billion, is paying SpaceX $1.25 billion EVERY SINGLE MONTH for compute capacity through May 2029. That is $15 billion a year flowing directly from Elon's top AI competitor into Elon's bank account. Think about what that means: Every time Anthropic trains a new model, improves Claude, or lands an enterprise customer, a massive chunk of that revenue goes straight to the guy who owns the competing AI product. Anthropic is literally funding the war against itself. And that's just the beginning of what this filing reveals... The entire SpaceX IPO is structured around a bet most people haven't figured out yet. In 2025, SpaceX spent $20 billion in capex. 60% of that, roughly $12 billion, went to AI infrastructure. Rockets and satellites got the leftovers. In Q1 2026 alone, $7.7 billion out of $10 billion in total capex went to AI. The "rocket company" is spending like an AI company. Meanwhile, xAI, the division that houses Grok, generated $3.2 billion in revenue for the full year of 2025. But its R&D costs TRIPLED to $5 billion. It's burning cash at a pace that would have destroyed it as a standalone company. Which is exactly why Elon merged it into SpaceX two months before filing the IPO. And Starlink is the engine that makes the whole thing work: $11.4 billion in revenue, $4.4 billion in operating profit, and 10.3 million subscribers across 164 countries. It's one of the most profitable subscription businesses on the planet right now. But the average revenue per user DROPPED from $99 per month in 2023 to $66 per month in March 2026. Subscribers quadrupled but each one is paying a third less. Starlink is growing by getting cheaper. SpaceX has lost $37 BILLION since it was founded. Net loss in 2025 was $4.9 billion. This is a company that has never turned an annual profit in 23 years of operation, and it is about to IPO at a $1.75 trillion valuation. And the total addressable market SpaceX claims in the filing is $28.5 trillion. That is a QUARTER of global GDP. So here is what investors are actually buying when this IPO prices: They are buying the most profitable satellite internet business in history, stapled to an AI lab that is burning cash, wrapped inside a Mars colonization pitch that requires building a permanent city on another planet, funded by monthly billion-dollar payments from a direct competitor who has no other option for compute at that scale. This is the kind of thing only Elon could pull off.

Ricardo

208,495 просмотров • 3 месяцев назад

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.
1:01

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

159,345 просмотров • 3 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

Microsoft is deceiving you by inflating its AI empire with money it handed its OWN customer first. They sold Wall Street a $37 billion AI business, then went silent the moment its own filing showed where that money came from. The line sits in the annual report for fiscal 2026: Microsoft recorded $24.1 billion of revenue from commercial arrangements with OpenAI, including revenue sharing payments. If you run that figure against Microsoft's own AI disclosures you'll find that OpenAI made up more than half, and likely around 70%, of everything the company counts as AI sales. ONE customer. A Microsoft spokesperson confirmed the figure covers all sales and revenue share from OpenAI. The 70% comes by assuming Microsoft's AI run rate kept growing at the 123% pace the company itself reported in March, which is the company's own optimistic math turned around on it. Now follow where that money starts: Microsoft has put around $12 billion into OpenAI since 2019. OpenAI spends its cash on computing power, and Microsoft is the cloud provider selling it. So the money leaves as an investment and comes back as an Azure bill. Microsoft then books that bill as AI revenue and shows it to investors as proof the AI business is "working." Microsoft invests in OpenAI -> OpenAI buys Microsoft compute -> Microsoft records the payment as AI revenue -> the AI growth story goes to Wall Street And a chunk of it never actually arrived. The same filing shows $6 billion of accounts receivable from OpenAI as of June 30. That is $6 billion of AI revenue Microsoft booked and had not been paid when the year closed. Now here's where it gets really concerning for anyone holding the stock... Microsoft has told the public how big its total AI business is exactly twice. Once for the quarter ending December 2024, when it said the unit was on pace for more than $13 billion a year. And once for the quarter ending March 2026, when Satya Nadella put it on pace for $37 billion. That $37 billion number went everywhere. It was the headline proof that Microsoft had won the AI race. Then fourth quarter earnings arrived, and Microsoft did NOT update it. The company that had been announcing the figure as its own scoreboard stopped announcing the figure. In the same stretch, the filing landed showing where most of it came from. So what is actually left underneath? The full year AI business ran near $34 billion. Take OpenAI out and roughly $10 billion remains. Microsoft has spent about $261 billion on capital expenditure since the start of 2022. That is the scale of the bet against what the rest of the AI business currently brings in. And the one customer holding it up is walking further away every quarter. In October, Microsoft's stake in OpenAI dropped to 27% from 32.5%. In April the partnership was rewritten so OpenAI can sell its products across any cloud it likes, which is how Amazon got a seat at the table. The exclusivity that made this arrangement valuable is gone. The compute bill and the unpaid $6 billion are still on Microsoft's books. Nadella spent two years telling the market Microsoft built the largest AI business in software. The filing shows one client bought most of it, on credit, using money Microsoft partly supplied. So watch the next earnings call: If Microsoft puts a fresh total AI number back on the board, the business found customers beyond OpenAI. If you hear a lot about AI momentum and never hear what it adds up to, you already know why the number went missing. But nonetheless, how is something like this even legal?
0:56

Microsoft is deceiving you by inflating its AI empire with money it handed its OWN customer first. They sold Wall Street a $37 billion AI business, then went silent the moment its own filing showed where that money came from. The line sits in the annual report for fiscal 2026: Microsoft recorded $24.1 billion of revenue from commercial arrangements with OpenAI, including revenue sharing payments. If you run that figure against Microsoft's own AI disclosures you'll find that OpenAI made up more than half, and likely around 70%, of everything the company counts as AI sales. ONE customer. A Microsoft spokesperson confirmed the figure covers all sales and revenue share from OpenAI. The 70% comes by assuming Microsoft's AI run rate kept growing at the 123% pace the company itself reported in March, which is the company's own optimistic math turned around on it. Now follow where that money starts: Microsoft has put around $12 billion into OpenAI since 2019. OpenAI spends its cash on computing power, and Microsoft is the cloud provider selling it. So the money leaves as an investment and comes back as an Azure bill. Microsoft then books that bill as AI revenue and shows it to investors as proof the AI business is "working." Microsoft invests in OpenAI -> OpenAI buys Microsoft compute -> Microsoft records the payment as AI revenue -> the AI growth story goes to Wall Street And a chunk of it never actually arrived. The same filing shows $6 billion of accounts receivable from OpenAI as of June 30. That is $6 billion of AI revenue Microsoft booked and had not been paid when the year closed. Now here's where it gets really concerning for anyone holding the stock... Microsoft has told the public how big its total AI business is exactly twice. Once for the quarter ending December 2024, when it said the unit was on pace for more than $13 billion a year. And once for the quarter ending March 2026, when Satya Nadella put it on pace for $37 billion. That $37 billion number went everywhere. It was the headline proof that Microsoft had won the AI race. Then fourth quarter earnings arrived, and Microsoft did NOT update it. The company that had been announcing the figure as its own scoreboard stopped announcing the figure. In the same stretch, the filing landed showing where most of it came from. So what is actually left underneath? The full year AI business ran near $34 billion. Take OpenAI out and roughly $10 billion remains. Microsoft has spent about $261 billion on capital expenditure since the start of 2022. That is the scale of the bet against what the rest of the AI business currently brings in. And the one customer holding it up is walking further away every quarter. In October, Microsoft's stake in OpenAI dropped to 27% from 32.5%. In April the partnership was rewritten so OpenAI can sell its products across any cloud it likes, which is how Amazon got a seat at the table. The exclusivity that made this arrangement valuable is gone. The compute bill and the unpaid $6 billion are still on Microsoft's books. Nadella spent two years telling the market Microsoft built the largest AI business in software. The filing shows one client bought most of it, on credit, using money Microsoft partly supplied. So watch the next earnings call: If Microsoft puts a fresh total AI number back on the board, the business found customers beyond OpenAI. If you hear a lot about AI momentum and never hear what it adds up to, you already know why the number went missing. But nonetheless, how is something like this even legal?

Ricardo

24,085 просмотров • 6 дней назад

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.
1:06

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.

Milk Road AI

115,726 просмотров • 3 месяцев назад

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 просмотров • 3 месяцев назад

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.
1:25

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

Milk Road Macro

55,169 просмотров • 2 месяцев назад

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.
0:22

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.

Crypto Rover

58,862 просмотров • 2 месяцев назад

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.
1:25

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

Milk Road Stocks

41,351 просмотров • 24 дней назад

Google just reported $99 billion in profits it never actually received. Alphabet posted net income of $112.1 billion for a single quarter. Earnings per share came in at $9.11 against a Wall Street estimate of $2.87. That is one of the largest profit quarters any company has ever printed. Yet the stock fell about 7% the same day. When people read past the headline and opened the earnings release, they found the reason sitting in one footnote... $99 billion of that profit came from a line called other income. Alphabet describes it as "primarily the result of net unrealized gains on our equity securities." So Google did not sell anything. It marked up shares it already owned and ran the increase through its income statement. That single line added $77.1 billion to net income after tax. It accounted for $6.26 of the $9.11 in earnings per share. Strip it out and adjusted earnings per share were $2.85. Analysts wanted $2.89. The ACTUAL business missed. Now here is what makes this insane: Most of that $99 billion came from two holdings, SpaceX and Anthropic. SpaceX went public on June 12 at roughly $1.77 trillion, up from about $400 billion a year earlier. Alphabet's stake is worth $94.1 billion, and roughly $80 billion of it sits under sale restrictions. Anthropic went from a $350 billion valuation to $965 billion inside the same quarter. Alphabet's private company holdings were worth about $124.3 billion on June 30, and the vast majority of that is Anthropic. Google cannot sell either position right now. Now trace where that valuation came from: Google started putting money into Anthropic in 2023. A $300 million bet has grown into a $13.3 billion position with commitments of up to $30 billion more. Anthropic committed to buying at least five gigawatts of computing capacity from Google Cloud. Google Cloud revenue then grew 82% to about $24.8 billion, the strongest quarter that business has ever had. That growth is part of the story the market uses to price both companies. And when Anthropic's valuation jumped, Google booked the jump as its OWN profit. Google is the investor, the supplier, and the party deciding what the asset is worth. A tax and accounting consultant named Robert Willens flagged this back in April, pointing out that Alphabet is able to influence the value of one of its own assets. And Alphabet's free cash flow for the quarter was negative $5.9 billion. That is the first negative quarter since Google went public in August 2004. Capital spending hit $44.9 billion. Operating cash flow was $39.1 billion. Capex now eats about 37.5% of every dollar of revenue, the highest share in the company's public life. To fund it, Alphabet has taken on roughly $100 billion of debt this year and raised about $85 billion in a June share sale, its first in more than two decades. This is a company that spent years buying its own stock back. What happens next: Alphabet raised 2026 capital spending guidance to between $195 billion and $205 billion, the second raise in three months. The finance chief told analysts 2027 spending will rise significantly. The company also disclosed $811 billion in contracted future spending commitments as of June, up nearly $500 billion from March. Those commitments are signed contracts that get paid in cash. The profit is an estimate of what a private company might be worth on a given day. Estimates move in both directions. If Anthropic or SpaceX gets repriced downward, the same line that produced the biggest quarter in Google's history runs backwards, and this quarter produced no free cash flow to absorb it. Meta, Microsoft and Amazon are all carrying their own private AI stakes into their own earnings reports. Watch how much of their profit they actually collected in cash...
0:48

Google just reported $99 billion in profits it never actually received. Alphabet posted net income of $112.1 billion for a single quarter. Earnings per share came in at $9.11 against a Wall Street estimate of $2.87. That is one of the largest profit quarters any company has ever printed. Yet the stock fell about 7% the same day. When people read past the headline and opened the earnings release, they found the reason sitting in one footnote... $99 billion of that profit came from a line called other income. Alphabet describes it as "primarily the result of net unrealized gains on our equity securities." So Google did not sell anything. It marked up shares it already owned and ran the increase through its income statement. That single line added $77.1 billion to net income after tax. It accounted for $6.26 of the $9.11 in earnings per share. Strip it out and adjusted earnings per share were $2.85. Analysts wanted $2.89. The ACTUAL business missed. Now here is what makes this insane: Most of that $99 billion came from two holdings, SpaceX and Anthropic. SpaceX went public on June 12 at roughly $1.77 trillion, up from about $400 billion a year earlier. Alphabet's stake is worth $94.1 billion, and roughly $80 billion of it sits under sale restrictions. Anthropic went from a $350 billion valuation to $965 billion inside the same quarter. Alphabet's private company holdings were worth about $124.3 billion on June 30, and the vast majority of that is Anthropic. Google cannot sell either position right now. Now trace where that valuation came from: Google started putting money into Anthropic in 2023. A $300 million bet has grown into a $13.3 billion position with commitments of up to $30 billion more. Anthropic committed to buying at least five gigawatts of computing capacity from Google Cloud. Google Cloud revenue then grew 82% to about $24.8 billion, the strongest quarter that business has ever had. That growth is part of the story the market uses to price both companies. And when Anthropic's valuation jumped, Google booked the jump as its OWN profit. Google is the investor, the supplier, and the party deciding what the asset is worth. A tax and accounting consultant named Robert Willens flagged this back in April, pointing out that Alphabet is able to influence the value of one of its own assets. And Alphabet's free cash flow for the quarter was negative $5.9 billion. That is the first negative quarter since Google went public in August 2004. Capital spending hit $44.9 billion. Operating cash flow was $39.1 billion. Capex now eats about 37.5% of every dollar of revenue, the highest share in the company's public life. To fund it, Alphabet has taken on roughly $100 billion of debt this year and raised about $85 billion in a June share sale, its first in more than two decades. This is a company that spent years buying its own stock back. What happens next: Alphabet raised 2026 capital spending guidance to between $195 billion and $205 billion, the second raise in three months. The finance chief told analysts 2027 spending will rise significantly. The company also disclosed $811 billion in contracted future spending commitments as of June, up nearly $500 billion from March. Those commitments are signed contracts that get paid in cash. The profit is an estimate of what a private company might be worth on a given day. Estimates move in both directions. If Anthropic or SpaceX gets repriced downward, the same line that produced the biggest quarter in Google's history runs backwards, and this quarter produced no free cash flow to absorb it. Meta, Microsoft and Amazon are all carrying their own private AI stakes into their own earnings reports. Watch how much of their profit they actually collected in cash...

Ricardo

364,107 просмотров • 25 дней назад

This isn’t talked about nearly as much as it should be. In a few years, $NBIS' 28% stake in ClickHouse could be worth close to its entire current market cap. Just listen to what a Senior Staff Software Engineer at $TSLA had to say: "Data in ClickHouse is better than data anywhere else. No other system lets you slice and dice your data, ask interesting questions, and get answers in an acceptable amount of time. There's nothing out there that competes with ClickHouse." That explains why it’s trusted by OpenAI, Anthropic, Microsoft, Meta, Cloudflare, ServiceNow, and dozens of other top-tier names. The most recent funding round valued ClickHouse at $6.35B. I wouldn’t be surprised if it IPOs in a few years at a much higher valuation. For $NBIS, this is a massive hidden lever, potentially unlocking billions in non-dilutive, essentially free capital to reinvest into its core business.
1:16

This isn’t talked about nearly as much as it should be. In a few years, $NBIS' 28% stake in ClickHouse could be worth close to its entire current market cap. Just listen to what a Senior Staff Software Engineer at $TSLA had to say: "Data in ClickHouse is better than data anywhere else. No other system lets you slice and dice your data, ask interesting questions, and get answers in an acceptable amount of time. There's nothing out there that competes with ClickHouse." That explains why it’s trusted by OpenAI, Anthropic, Microsoft, Meta, Cloudflare, ServiceNow, and dozens of other top-tier names. The most recent funding round valued ClickHouse at $6.35B. I wouldn’t be surprised if it IPOs in a few years at a much higher valuation. For $NBIS, this is a massive hidden lever, potentially unlocking billions in non-dilutive, essentially free capital to reinvest into its core business.

M. V. Cunha

52,612 просмотров • 1 год назад

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.
4:50

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Ihtesham Ali

98,071 просмотров • 2 месяцев назад