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The US government is spending $7.6 trillion on AI infrastructure by 2031. Microsoft has $80 BILLION in unfulfilled Azure orders... NOT because of demand, but because they simply CANNOT get enough GPUs, power, or land. GPUs have a 36-52 week lead time. Blackwell is sold out through mid-2027. TSMC's... 3nm wafers have demand exceeding capacity by 3x. The hyperscalers can't build fast enough. Enter the NEOCLOUDS. Pure-play AI cloud providers that do ONE thing, and one thing ONLY: rent GPUs. The neocloud market went from $0 to $25 billion revenue in just 3 years. It's projected to hit $400 billion by 2031. The biggest question is this: WILL THOSE PREDICTIONS ACTUALLY COME TRUE? If so, this industry is set to explode. If not, it may crumble. Three companies are leading the charge: $NBIS $IREN $CRWV Along with a slew of small and microcaps: $BRUN $WYFI $SHAZ and more... This is the biggest infrastructure buildout since the internet itself. The last time we saw spending at this scale, it created Amazon, Google, and Meta. This is the infrastructure layer of the AI revolution. Don't sleep on it. Timestamps: 0:00 Intro 0:40 The Black Swan 1:20 What is a Neocloud 3:40 Iren Overview 7:15 Nebius Overview 10:22 Corweave Overview 11:26 The Small Fish 12:47 The Black Swan That Wasn't 16:58 Final Thoughtsshow more

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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!

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

Milk Road AI

14,578 görüntüleme • 2 ay önce

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

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

Milk Road AI

139,047 görüntüleme • 2 ay önce

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

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

Milk Road AI

46,800 görüntüleme • 4 ay önce

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

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

Milk Road AI

92,855 görüntüleme • 2 ay önce

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.

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 görüntüleme • 2 ay önce

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Ricardo

228,416 görüntüleme • 2 ay önce

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

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

Milk Road AI

28,622 görüntüleme • 3 ay önce

What if the AI boom is not just a technology race, but a capital machine hiding in plain sight? The deeper I look at this ecosystem, the less it feels like a messy market and the more it looks like a closed financial loop. That is what makes this so striking. → Big Tech funds AI labs and infrastructure → AI labs and cloud players buy chips, GPUs, and networking → Model companies license capabilities back to the same giants funding the buildout What looks complicated is, in many ways, brutally simple. A money machine. And right now, that machine is being priced as if demand, revenue, and adoption will keep compounding with very little friction. That is the part I find most fascinating. Because the numbers are not just big. They are staggering. → Microsoft has invested more than $13B into OpenAI since 2019 → Oracle signed a $300B data centre capacity deal tied to OpenAI through 2029 → Meta is racing from roughly 150,000 NVIDIA GPUs in 2023 to around 1.3 million by the end of 2025 → Broadcom’s AI chip revenue is projected to jump from $3.8B in 2023 to $40B by 2026 What really stands out to me is how concentrated this loop has become. NVIDIA gets paid by nearly everyone. Infrastructure providers benefit early. AI companies are still betting on future monetization. Maybe it works. But that is the real question. Are we looking at durable economics, or one of the most elegantly circular bets the tech world has ever built? Do you think this AI capital loop is sustainable, or are we watching a beautifully engineered cycle that still has to prove itself? #AI #ArtificialIntelligence #OpenAI #NVIDIA #Microsoft #Infrastructure #DataCenters #Investing #BusinessStrategy #Innovation

What if the AI boom is not just a technology race, but a capital machine hiding in plain sight? The deeper I look at this ecosystem, the less it feels like a messy market and the more it looks like a closed financial loop. That is what makes this so striking. → Big Tech funds AI labs and infrastructure → AI labs and cloud players buy chips, GPUs, and networking → Model companies license capabilities back to the same giants funding the buildout What looks complicated is, in many ways, brutally simple. A money machine. And right now, that machine is being priced as if demand, revenue, and adoption will keep compounding with very little friction. That is the part I find most fascinating. Because the numbers are not just big. They are staggering. → Microsoft has invested more than $13B into OpenAI since 2019 → Oracle signed a $300B data centre capacity deal tied to OpenAI through 2029 → Meta is racing from roughly 150,000 NVIDIA GPUs in 2023 to around 1.3 million by the end of 2025 → Broadcom’s AI chip revenue is projected to jump from $3.8B in 2023 to $40B by 2026 What really stands out to me is how concentrated this loop has become. NVIDIA gets paid by nearly everyone. Infrastructure providers benefit early. AI companies are still betting on future monetization. Maybe it works. But that is the real question. Are we looking at durable economics, or one of the most elegantly circular bets the tech world has ever built? Do you think this AI capital loop is sustainable, or are we watching a beautifully engineered cycle that still has to prove itself? #AI #ArtificialIntelligence #OpenAI #NVIDIA #Microsoft #Infrastructure #DataCenters #Investing #BusinessStrategy #Innovation

Pascal Bornet

13,686 görüntüleme • 4 ay önce

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

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

Milk Road AI

61,932 görüntüleme • 3 ay önce

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

The Assembly

7,296,046 görüntüleme • 3 ay önce

Tom Lee says semis and AI infrastructure stocks are going to rip higher later this year. “Semis and infrastructure companies are at the center of one of the most important strategic initiatives for the US, which is AI and AI infrastructure, and that runway has years ahead. The last couple of weeks have been a rough ride. I think those names are going to bounce later this year. I don’t think the trade is over. It’s just taking a beating.” We don’t know who will ultimately build the best AI model. But we do know which companies are building the infrastructure that will make the AI revolution possible. That’s why I’m buying $CRWV, $ORCL, and $IREN.

Tom Lee says semis and AI infrastructure stocks are going to rip higher later this year. “Semis and infrastructure companies are at the center of one of the most important strategic initiatives for the US, which is AI and AI infrastructure, and that runway has years ahead. The last couple of weeks have been a rough ride. I think those names are going to bounce later this year. I don’t think the trade is over. It’s just taking a beating.” We don’t know who will ultimately build the best AI model. But we do know which companies are building the infrastructure that will make the AI revolution possible. That’s why I’m buying $CRWV, $ORCL, and $IREN.

Daniel

110,219 görüntüleme • 1 ay önce

SUNDAR PICHAI: AI IS REWRITING EARTH’S INFRASTRUCTURE IN TWO YEARS Sundar laid it out with quiet certainty: the AI boom isn’t coming, it’s already rewriting the laws of scale: "You know, maybe four years ago Google was spending less than $30 billion per year. This year, that number is gonna be over $90 billion. And if you collectively add what all the companies are doing, we have well over a trillion dollars of investment going in, in building the infrastructure for this moment. And one way I think about it is in the next couple of years, we'll end up building what we probably built in the past 10 to 20 years. So hopefully just in a couple of years. So that gives you the scale at which this is ramping up." It’s the kind of scale that makes empires blink. Alphabet’s AI capex shot from $24 billion in 2021 to over $90 billion in 2025, powered by Gemini, Vertex AI, and an arms race for compute that’s now burning through a trillion dollars across Google, Microsoft, Amazon, and Meta. Source: thewizeai / Sundar Pichai

SUNDAR PICHAI: AI IS REWRITING EARTH’S INFRASTRUCTURE IN TWO YEARS Sundar laid it out with quiet certainty: the AI boom isn’t coming, it’s already rewriting the laws of scale: "You know, maybe four years ago Google was spending less than $30 billion per year. This year, that number is gonna be over $90 billion. And if you collectively add what all the companies are doing, we have well over a trillion dollars of investment going in, in building the infrastructure for this moment. And one way I think about it is in the next couple of years, we'll end up building what we probably built in the past 10 to 20 years. So hopefully just in a couple of years. So that gives you the scale at which this is ramping up." It’s the kind of scale that makes empires blink. Alphabet’s AI capex shot from $24 billion in 2021 to over $90 billion in 2025, powered by Gemini, Vertex AI, and an arms race for compute that’s now burning through a trillion dollars across Google, Microsoft, Amazon, and Meta. Source: thewizeai / Sundar Pichai

Mario Nawfal

163,412 görüntüleme • 8 ay önce

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

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

Milk Road AI

15,677 görüntüleme • 2 ay önce

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

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

Milk Road AI

77,554 görüntüleme • 2 ay önce

Nvidia $NVDA CEO, Jensen Huang, on if the semiconductor industry is due for a bust: "No, not for a while. This time is different because this is not demand driven, it is not seasonal...meaning seasonal demand driven. This is industrially driven, meaning the fundamental technology of computers is changing. We need a whole new layer of infrastructure in the world. We have energy, internet, roads, railroads, we now need the intelligence layer built on top of all of that and this layer needs chips. I believe we need our semiconductor industry to be somewhere between 5-10x larger than it is over the course of the next 10 years. It is way too small today, which is the reason why memory is in such shortage, storage is in such shortage, optical interconnects are short, packaging is short, and TSMC foundry chips are short. Everything is in such short supply because it is not a cyclical nature. It is fundamental infrastructure related. The whole industry is short." According to the WSTS, the semiconductor industry is projected to be $1.5 trillion in 2026. 5-10x larger puts it at $7.5 trillion to $15 trillion. Via Axios

Nvidia $NVDA CEO, Jensen Huang, on if the semiconductor industry is due for a bust: "No, not for a while. This time is different because this is not demand driven, it is not seasonal...meaning seasonal demand driven. This is industrially driven, meaning the fundamental technology of computers is changing. We need a whole new layer of infrastructure in the world. We have energy, internet, roads, railroads, we now need the intelligence layer built on top of all of that and this layer needs chips. I believe we need our semiconductor industry to be somewhere between 5-10x larger than it is over the course of the next 10 years. It is way too small today, which is the reason why memory is in such shortage, storage is in such shortage, optical interconnects are short, packaging is short, and TSMC foundry chips are short. Everything is in such short supply because it is not a cyclical nature. It is fundamental infrastructure related. The whole industry is short." According to the WSTS, the semiconductor industry is projected to be $1.5 trillion in 2026. 5-10x larger puts it at $7.5 trillion to $15 trillion. Via Axios

P Equity Research 📰

81,088 görüntüleme • 1 ay önce

Nvidia $NVDA CEO, Jensen Huang, on the AI bubble danger: "The bubble will come someday, just not today. This is in the very beginning of the buildout. It is very unlikely in the next 5 years, and we will see with the 5-10 years. It depends on how fast we can build. The rate at which we can build the infrastructure is limited by physical things. Today, the industry could build faster and run faster but we don't have enough chips, memory, land, power, construction workers to build data centers...we are constrained in every single way and direction. That constraint is what holds the system back so it gives us plenty of time to go build out these infrastructures." Via Axios

Nvidia $NVDA CEO, Jensen Huang, on the AI bubble danger: "The bubble will come someday, just not today. This is in the very beginning of the buildout. It is very unlikely in the next 5 years, and we will see with the 5-10 years. It depends on how fast we can build. The rate at which we can build the infrastructure is limited by physical things. Today, the industry could build faster and run faster but we don't have enough chips, memory, land, power, construction workers to build data centers...we are constrained in every single way and direction. That constraint is what holds the system back so it gives us plenty of time to go build out these infrastructures." Via Axios

P Equity Research 📰

36,655 görüntüleme • 1 ay önce

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

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

Dustin

28,395 görüntüleme • 6 ay önce

The neocloud category may be the most misunderstood corner of the AI trade because the market still treats these names as one uniform GPU-hours bet when they are actually very different business models: 1. $NBIS (Cloud Utility for the Agentic AI Age) $NVDA just chose Nebius as an architecture partner for the agentic AI era by co-designing AI factories with them, and the Rubin GPU access that comes with this partnership means Nebius gets the next-generation inference stack before almost anyone else in the market. At a $28B market cap, a 5GW power target and Nvidia’s engineering team embedded in the stack.. this is my favorite name in the neocloud category. 2. $IREN (Energy-to-Compute Engine of the AI Era) The dilution fear is real but the market is misreading it. IREN is not diluting to survive but diluting to scale into a $3.7B ARR target and the $9.3B in funding already secured through customer prepayments and GPU financing means the $6B ATM is optionality capital. The real bottleneck in AI infrastructure right now is power and IREN controls ~4.5GW of secured capacity while needing only ~500MW to support its ARR target by year-end. That 10x ratio of power capacity to near-term need is something no competitor can replicate quickly. 3. $CIFR (Landlord of the AI Utility Era) Cipher is not a pure neocloud but is a hyperscale infrastructure landlord signing decade-long leases to $AMZN AWS and $GOOGL while they fill the shells with compute. The AWS lease alone is expected to generate ~$700M in average annualized NOI for the next decade at nearly 100% NOI margins. Power-rich land is the scarcest resource in AI infrastructure and Cipher controls it with 600MW fully contracted, both facilities fully funded through non-recourse fixed-rate project debt and a 3.4GW development pipeline. 4. $CRWV (The Fragile Giant) CoreWeave’s demand backlog and revenue growth are very real but none of that matters if the capital markets close for even one quarter. Interest expense hit $388M in Q4 and management guided Q1 2026 interest expense to ~$550M which implies an annualized run rate above $2B before a single new data center comes online. The bull case requires capital markets to stay open, rates to cooperate, hyperscalers to honor take-or-pay contracts in full and construction to stay on time. That is a lot of dependencies in a macro environment where oil is approaching $100 and private credit is already showing signs of stress.

The neocloud category may be the most misunderstood corner of the AI trade because the market still treats these names as one uniform GPU-hours bet when they are actually very different business models: 1. $NBIS (Cloud Utility for the Agentic AI Age) $NVDA just chose Nebius as an architecture partner for the agentic AI era by co-designing AI factories with them, and the Rubin GPU access that comes with this partnership means Nebius gets the next-generation inference stack before almost anyone else in the market. At a $28B market cap, a 5GW power target and Nvidia’s engineering team embedded in the stack.. this is my favorite name in the neocloud category. 2. $IREN (Energy-to-Compute Engine of the AI Era) The dilution fear is real but the market is misreading it. IREN is not diluting to survive but diluting to scale into a $3.7B ARR target and the $9.3B in funding already secured through customer prepayments and GPU financing means the $6B ATM is optionality capital. The real bottleneck in AI infrastructure right now is power and IREN controls ~4.5GW of secured capacity while needing only ~500MW to support its ARR target by year-end. That 10x ratio of power capacity to near-term need is something no competitor can replicate quickly. 3. $CIFR (Landlord of the AI Utility Era) Cipher is not a pure neocloud but is a hyperscale infrastructure landlord signing decade-long leases to $AMZN AWS and $GOOGL while they fill the shells with compute. The AWS lease alone is expected to generate ~$700M in average annualized NOI for the next decade at nearly 100% NOI margins. Power-rich land is the scarcest resource in AI infrastructure and Cipher controls it with 600MW fully contracted, both facilities fully funded through non-recourse fixed-rate project debt and a 3.4GW development pipeline. 4. $CRWV (The Fragile Giant) CoreWeave’s demand backlog and revenue growth are very real but none of that matters if the capital markets close for even one quarter. Interest expense hit $388M in Q4 and management guided Q1 2026 interest expense to ~$550M which implies an annualized run rate above $2B before a single new data center comes online. The bull case requires capital markets to stay open, rates to cooperate, hyperscalers to honor take-or-pay contracts in full and construction to stay on time. That is a lot of dependencies in a macro environment where oil is approaching $100 and private credit is already showing signs of stress.

Shay Boloor

1,098,185 görüntüleme • 5 ay önce

SpaceX is about to shatter the largest IPO record in history. Not by a little. By more than double. The previous record was $29 billion. SpaceX is targeting $75 billion. Two months ago the number was $50 billion. Last week it was $70 billion. Now $75 billion. The filing has not even happened yet. Every time the market recalculates what SpaceX actually is, the answer gets bigger. Goldman Sachs. JPMorgan. Bank of America. Morgan Stanley. All lined up as underwriters. Target date: mid-June 2026. Target valuation: $1.75 trillion. That would make SpaceX larger than Meta. Larger than Tesla. Larger than every company on Earth except five. This is not some startup bleeding cash and calling it strategy. SpaceX made $8 billion in profit last year on $16 billion in revenue. They do not need the money. They are raising it because what comes next costs more than profit can fund at the speed they intend to move. Musk: “There just is no way to do a terawatt per year on Earth.” He ran the math on stage with Jensen Huang. Three hundred gigawatts of AI compute per year would consume two-thirds of all US electricity production. Not total energy. Just electricity. And three hundred gigawatts is not even the target. A terawatt is. More than three times that. Building enough power plants is not difficult. It is not expensive. It is physically impossible. Musk: “You have to do that in space.” Not should. Not could. Have to. Earth does not have the power. Cannot build it fast enough. Cannot cool the hardware. Not within a decade. Not at all. The bottleneck is not silicon. Not software. Not data. It is the planet itself. Musk: “You don’t actually need batteries because it’s always sunny in space. And the solar panels become cheaper because you don’t need glass or framing. And the cooling is just radiative.” No batteries. No night cycle. No weather. Just uninterrupted solar hitting bare panels in a vacuum. Heat dissipates on its own. Huang: “Each one of these GB300 racks is two tons. 1.95 of it is probably for cooling.” Ninety-seven percent of the weight of a supercomputer rack exists to keep it from overheating. Move it to space and that weight vanishes. The machine shrinks to something small enough to launch by the thousands. Running on free energy. Cooled by nothing. Musk: “I think even perhaps in the four or five year time frame, the lowest cost way to do AI compute will be with solar-powered AI satellites.” Not fifty years. Not twenty. Five. The cheapest AI compute on Earth will not be on Earth. It will be in orbit. And only one company can put it there at the cost and cadence required. That is what the market is pricing. Not a rocket company. The only organization on Earth capable of moving intelligence infrastructure off of it. Huang heard the pitch. The math. The timeline. Huang: “That’s the dream.” Musk: “Yes.” A trillion watts of compute. Powered by the Sun. Cooled by space. Launched by SpaceX. Every company building AI on the ground is building under the same ceiling. The atmosphere.

SpaceX is about to shatter the largest IPO record in history. Not by a little. By more than double. The previous record was $29 billion. SpaceX is targeting $75 billion. Two months ago the number was $50 billion. Last week it was $70 billion. Now $75 billion. The filing has not even happened yet. Every time the market recalculates what SpaceX actually is, the answer gets bigger. Goldman Sachs. JPMorgan. Bank of America. Morgan Stanley. All lined up as underwriters. Target date: mid-June 2026. Target valuation: $1.75 trillion. That would make SpaceX larger than Meta. Larger than Tesla. Larger than every company on Earth except five. This is not some startup bleeding cash and calling it strategy. SpaceX made $8 billion in profit last year on $16 billion in revenue. They do not need the money. They are raising it because what comes next costs more than profit can fund at the speed they intend to move. Musk: “There just is no way to do a terawatt per year on Earth.” He ran the math on stage with Jensen Huang. Three hundred gigawatts of AI compute per year would consume two-thirds of all US electricity production. Not total energy. Just electricity. And three hundred gigawatts is not even the target. A terawatt is. More than three times that. Building enough power plants is not difficult. It is not expensive. It is physically impossible. Musk: “You have to do that in space.” Not should. Not could. Have to. Earth does not have the power. Cannot build it fast enough. Cannot cool the hardware. Not within a decade. Not at all. The bottleneck is not silicon. Not software. Not data. It is the planet itself. Musk: “You don’t actually need batteries because it’s always sunny in space. And the solar panels become cheaper because you don’t need glass or framing. And the cooling is just radiative.” No batteries. No night cycle. No weather. Just uninterrupted solar hitting bare panels in a vacuum. Heat dissipates on its own. Huang: “Each one of these GB300 racks is two tons. 1.95 of it is probably for cooling.” Ninety-seven percent of the weight of a supercomputer rack exists to keep it from overheating. Move it to space and that weight vanishes. The machine shrinks to something small enough to launch by the thousands. Running on free energy. Cooled by nothing. Musk: “I think even perhaps in the four or five year time frame, the lowest cost way to do AI compute will be with solar-powered AI satellites.” Not fifty years. Not twenty. Five. The cheapest AI compute on Earth will not be on Earth. It will be in orbit. And only one company can put it there at the cost and cadence required. That is what the market is pricing. Not a rocket company. The only organization on Earth capable of moving intelligence infrastructure off of it. Huang heard the pitch. The math. The timeline. Huang: “That’s the dream.” Musk: “Yes.” A trillion watts of compute. Powered by the Sun. Cooled by space. Launched by SpaceX. Every company building AI on the ground is building under the same ceiling. The atmosphere.

Dustin

44,710 görüntüleme • 5 ay önce

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.

George Noble

120,284 görüntüleme • 7 ay önce