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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...

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George Noble

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120,233 просмотров • 5 месяцев назад •via X (Twitter)

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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.
2:08

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

226,631 просмотров • 25 дней назад

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

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

Crypto Rover

58,862 просмотров • 1 месяц назад

THE UNCOMFORTABLE TRUTH about AI spending: US tech giants spent $380 billion on AI‑driven infrastructure capex in 2025. But most CFOs still can't point to measurable returns. The latest Duke CFO Survey tells the story nobody wants to hear: When asked about AI's impact over the past 12 months, the vast majority of CFOs reported "no change" across the board. Labor productivity? No change. Decision-making speed? No change. Customer satisfaction? No change. Time spent on high-value tasks? No change. These aren't small companies experimenting with ChatGPT either. 78% of large companies invested in AI during 2025. Real money. Real infrastructure. Real deployments. And most are getting... nothing measurable. The spending is something we've never seen: Microsoft committed $80 billion for fiscal 2025 on AI infrastructure. Google, Amazon, Meta - all racing to match it. Together, the big four hyperscalers will be spending over $380 billion throughout 2025 - 2026. Data centers. GPUs. Power plants. Cooling systems. Real assets. Real cash. Real bets on AI transformation. Meanwhile, in the real economy: Goldman Sachs surveyed their investment bankers about how clients are actually using AI: - 37% of companies are deploying AI - 47% using it to boost productivity/revenue - Only 11% using it to cut headcount So deployment is happening. But Goldman's economists found AI has had "negligible" impact on aggregate labor metrics so far. Their forecast? Measurable GDP impact doesn't start until 2027. Here's where the AI gains ARE showing up: Since ChatGPT launched in November 2022: - Magnificent 7 earnings: Up 145% - Remaining S&P 493 earnings: Up 4% The seven companies building AI infrastructure saw earnings explode. Everyone else got a rounding error. But that performance is about infrastructure BUILD-OUT, not productivity PAYOFF. Nvidia makes chips. Microsoft sells cloud compute. Meta runs models. They're selling picks and shovels in a gold rush. The companies trying to mine gold? Still digging holes. The productivity paradox: US productivity grew 2.3% in 2024. Some quarters hit 2.8%. That's solid - well above the 1.2% average from the 2010s. But here's the problem: that improvement started BEFORE the AI spending boom. Goldman estimates tech contributes 0.3-0.4 percentage points of the productivity acceleration. Meaningful. But not revolutionary. And certainly not enough to justify $380 billion in annual capex. The implementation gap: AI doesn't just plug into existing workflows. It requires: - Complete data infrastructure overhauls - Retraining entire workforces - Redesigning core processes - Managing change across every department Most companies bought the tools, bolted them on, and wondered why nothing changed. Here's the big math problem: $10 billion revenue company spends $100 million on AI. To justify that at 20% hurdle rate: $20 million in annual benefits needed. In reality that means: - Cut 100+ positions, OR - Add $20M revenue at zero marginal cost, OR - Slash 2% from operating expenses Most can't hit those numbers in year one. Many won't hit them in year two. But the Magnificent 7 are priced for transformation happening now. Here's what's coming: Gartner projects total AI infrastructure spending go up to $1.37 trillion in 2026, with hyperscaler capex alone exceeding $600 billion. By 2027, we're looking at $1.75 trillion in AI infrastructure spending globally. The spending isn't slowing down. But if productivity doesn't materialize until 2027-2030 like Goldman projects, we're looking at years of multiple compression in Big Tech. Smart play for investors: Trimming Mag 7 exposure and rotating into small and mid-caps where valuations actually make sense. The S&P 493 trades near decade lows relative to Big Tech, but earnings growth is only 5 points lower. Companies spending trillions today are making 3-5 year bets priced for immediate returns. That's not a trade I'm taking.
1:20

THE UNCOMFORTABLE TRUTH about AI spending: US tech giants spent $380 billion on AI‑driven infrastructure capex in 2025. But most CFOs still can't point to measurable returns. The latest Duke CFO Survey tells the story nobody wants to hear: When asked about AI's impact over the past 12 months, the vast majority of CFOs reported "no change" across the board. Labor productivity? No change. Decision-making speed? No change. Customer satisfaction? No change. Time spent on high-value tasks? No change. These aren't small companies experimenting with ChatGPT either. 78% of large companies invested in AI during 2025. Real money. Real infrastructure. Real deployments. And most are getting... nothing measurable. The spending is something we've never seen: Microsoft committed $80 billion for fiscal 2025 on AI infrastructure. Google, Amazon, Meta - all racing to match it. Together, the big four hyperscalers will be spending over $380 billion throughout 2025 - 2026. Data centers. GPUs. Power plants. Cooling systems. Real assets. Real cash. Real bets on AI transformation. Meanwhile, in the real economy: Goldman Sachs surveyed their investment bankers about how clients are actually using AI: - 37% of companies are deploying AI - 47% using it to boost productivity/revenue - Only 11% using it to cut headcount So deployment is happening. But Goldman's economists found AI has had "negligible" impact on aggregate labor metrics so far. Their forecast? Measurable GDP impact doesn't start until 2027. Here's where the AI gains ARE showing up: Since ChatGPT launched in November 2022: - Magnificent 7 earnings: Up 145% - Remaining S&P 493 earnings: Up 4% The seven companies building AI infrastructure saw earnings explode. Everyone else got a rounding error. But that performance is about infrastructure BUILD-OUT, not productivity PAYOFF. Nvidia makes chips. Microsoft sells cloud compute. Meta runs models. They're selling picks and shovels in a gold rush. The companies trying to mine gold? Still digging holes. The productivity paradox: US productivity grew 2.3% in 2024. Some quarters hit 2.8%. That's solid - well above the 1.2% average from the 2010s. But here's the problem: that improvement started BEFORE the AI spending boom. Goldman estimates tech contributes 0.3-0.4 percentage points of the productivity acceleration. Meaningful. But not revolutionary. And certainly not enough to justify $380 billion in annual capex. The implementation gap: AI doesn't just plug into existing workflows. It requires: - Complete data infrastructure overhauls - Retraining entire workforces - Redesigning core processes - Managing change across every department Most companies bought the tools, bolted them on, and wondered why nothing changed. Here's the big math problem: $10 billion revenue company spends $100 million on AI. To justify that at 20% hurdle rate: $20 million in annual benefits needed. In reality that means: - Cut 100+ positions, OR - Add $20M revenue at zero marginal cost, OR - Slash 2% from operating expenses Most can't hit those numbers in year one. Many won't hit them in year two. But the Magnificent 7 are priced for transformation happening now. Here's what's coming: Gartner projects total AI infrastructure spending go up to $1.37 trillion in 2026, with hyperscaler capex alone exceeding $600 billion. By 2027, we're looking at $1.75 trillion in AI infrastructure spending globally. The spending isn't slowing down. But if productivity doesn't materialize until 2027-2030 like Goldman projects, we're looking at years of multiple compression in Big Tech. Smart play for investors: Trimming Mag 7 exposure and rotating into small and mid-caps where valuations actually make sense. The S&P 493 trades near decade lows relative to Big Tech, but earnings growth is only 5 points lower. Companies spending trillions today are making 3-5 year bets priced for immediate returns. That's not a trade I'm taking.

George Noble

61,242 просмотров • 5 месяцев назад

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.
1:54

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.

Ricardo

1,813,258 просмотров • 5 месяцев назад

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

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

Milk Road AI

21,141 просмотров • 15 дней назад

Big Tech is spending $700 BILLION on AI this year. But their cash flow is collapsing. Amazon is going into debt. Google's free cash flow is dropping 90%. And they're literally paying influencers $600,000 each to convince you AI is worth using. If this technology was as revolutionary as they claim, why are they spending half a million dollars per creator to sell it? Here's what's actually happening behind the scenes: This week, all four tech giants reported earnings at once and every single one dropped a spending number that made Wall Street lose its mind. Amazon: $200 billion in capex. The largest corporate capital expenditure in HISTORY. Stock dropped 9%. Google: $185 billion. Wall Street expected $120 billion. Stock dropped 5%. Meta: $135 billion. Double what they spent last year. Microsoft: down 17% this year, worst performer in the group. Combined 2026 AI infrastructure spend: almost $700 billion. But here's where it gets ugly. Amazon's free cash flow collapsed 71%. Morgan Stanley projects they'll burn through $17 billion in NEGATIVE free cash flow this year. Bank of America says the deficit could hit $28 billion. Amazon quietly filed with the SEC on Friday saying they might need to raise debt to keep building. Google's free cash flow is projected to crater 90%, from $73 billion down to $8.2 billion. They already did a $25 billion bond sale in November and their long-term debt QUADRUPLED last year. These companies are spending everything they have, then borrowing more, then spending that too. Now here's the part that got me thinking: CNBC just reported that Google, Microsoft, OpenAI, Anthropic, and Meta are paying influencers between $400,000 and $600,000 EACH to promote AI products on Instagram and YouTube. AI platforms spent over $1 BILLION on digital ads in 2025, a 126% jump year-over-year. Google and Microsoft's AI ad spending jumped 495% in January 2026 alone. Anthropic is running Super Bowl ads. OpenAI is flying creators to private events and covering all expenses. When was the last time a truly revolutionary technology needed a $1 billion ad campaign and $600K influencer deals to get adoption? Did the iPhone need influencer campaigns? Did Google Search need Super Bowl ads in 1998? Did email need a billion dollar marketing push? No. People just used them because the value was obvious. You know what DOES need massive paid promotions? Pharmaceutical drugs. Crypto exchanges. Online gambling apps. MLM companies. Products where adoption is driven by hype, not utility. And now, apparently, AI. So the pitch from Big Tech is: "This technology will eliminate your job. Also please use it. Here's $600K if you tell your followers it's cool." They need HUMANS to sell a product they designed to REPLACE humans. They need creators to promote a technology that will eventually make creators obsolete. They need influencers to build trust in a system that will eliminate the need for influencer marketing entirely. The question everyone should be asking: If $700 billion per year in spending can't produce a product that sells itself, when exactly does this start making money? Because right now the math is messed up. $700 billion in spending, cash flow crashing, stocks tanking, SEC filings about raising more capital, and the best growth strategy they've got is paying tiktokers to demo features. Either AI is about to deliver the greatest economic transformation in human history, or we're watching the most expensive corporate Hail Mary ever thrown. And the fact that they need to pay half a million dollars per influencer to convince you it's the first one isn't a good sign.
0:49

Big Tech is spending $700 BILLION on AI this year. But their cash flow is collapsing. Amazon is going into debt. Google's free cash flow is dropping 90%. And they're literally paying influencers $600,000 each to convince you AI is worth using. If this technology was as revolutionary as they claim, why are they spending half a million dollars per creator to sell it? Here's what's actually happening behind the scenes: This week, all four tech giants reported earnings at once and every single one dropped a spending number that made Wall Street lose its mind. Amazon: $200 billion in capex. The largest corporate capital expenditure in HISTORY. Stock dropped 9%. Google: $185 billion. Wall Street expected $120 billion. Stock dropped 5%. Meta: $135 billion. Double what they spent last year. Microsoft: down 17% this year, worst performer in the group. Combined 2026 AI infrastructure spend: almost $700 billion. But here's where it gets ugly. Amazon's free cash flow collapsed 71%. Morgan Stanley projects they'll burn through $17 billion in NEGATIVE free cash flow this year. Bank of America says the deficit could hit $28 billion. Amazon quietly filed with the SEC on Friday saying they might need to raise debt to keep building. Google's free cash flow is projected to crater 90%, from $73 billion down to $8.2 billion. They already did a $25 billion bond sale in November and their long-term debt QUADRUPLED last year. These companies are spending everything they have, then borrowing more, then spending that too. Now here's the part that got me thinking: CNBC just reported that Google, Microsoft, OpenAI, Anthropic, and Meta are paying influencers between $400,000 and $600,000 EACH to promote AI products on Instagram and YouTube. AI platforms spent over $1 BILLION on digital ads in 2025, a 126% jump year-over-year. Google and Microsoft's AI ad spending jumped 495% in January 2026 alone. Anthropic is running Super Bowl ads. OpenAI is flying creators to private events and covering all expenses. When was the last time a truly revolutionary technology needed a $1 billion ad campaign and $600K influencer deals to get adoption? Did the iPhone need influencer campaigns? Did Google Search need Super Bowl ads in 1998? Did email need a billion dollar marketing push? No. People just used them because the value was obvious. You know what DOES need massive paid promotions? Pharmaceutical drugs. Crypto exchanges. Online gambling apps. MLM companies. Products where adoption is driven by hype, not utility. And now, apparently, AI. So the pitch from Big Tech is: "This technology will eliminate your job. Also please use it. Here's $600K if you tell your followers it's cool." They need HUMANS to sell a product they designed to REPLACE humans. They need creators to promote a technology that will eventually make creators obsolete. They need influencers to build trust in a system that will eliminate the need for influencer marketing entirely. The question everyone should be asking: If $700 billion per year in spending can't produce a product that sells itself, when exactly does this start making money? Because right now the math is messed up. $700 billion in spending, cash flow crashing, stocks tanking, SEC filings about raising more capital, and the best growth strategy they've got is paying tiktokers to demo features. Either AI is about to deliver the greatest economic transformation in human history, or we're watching the most expensive corporate Hail Mary ever thrown. And the fact that they need to pay half a million dollars per influencer to convince you it's the first one isn't a good sign.

Ricardo

724,929 просмотров • 4 месяцев назад

🚨 WARNING: AI BUBBLE IS ABOUT TO BURST Microsoft gave OpenAI $13 BILLION. OpenAI sent it back through Azure. Microsoft booked it as revenue. Amazon and Google are running similar loops with Anthropic while marking up huge paper gains from AI valuations. OpenAI is spending over $60 BILLION a year on compute while generating only $25 BILLION in revenue. Ray Dalio has warned that late stage bubbles are driven by fake flows, not real economics. That is exactly what this is starting to look like.
0:39

🚨 WARNING: AI BUBBLE IS ABOUT TO BURST Microsoft gave OpenAI $13 BILLION. OpenAI sent it back through Azure. Microsoft booked it as revenue. Amazon and Google are running similar loops with Anthropic while marking up huge paper gains from AI valuations. OpenAI is spending over $60 BILLION a year on compute while generating only $25 BILLION in revenue. Ray Dalio has warned that late stage bubbles are driven by fake flows, not real economics. That is exactly what this is starting to look like.

Rekt Fencer

261,402 просмотров • 27 дней назад

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,952,459 просмотров • 1 месяц назад

CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."
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CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."

WOLF

45,256 просмотров • 2 месяцев назад

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
0:36

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

163,400 просмотров • 6 месяцев назад

Meta literally spent $72 BILLION building AI infrastructure that generates ZERO revenue. Then a Chinese company launches an AI agent in March, hits $125 million in revenue by December, and Zuck writes a $2 billion check in 10 days. But this isn't a strategic acquisition... It's panic. Here's what happened: Meta has been burning cash on AI for years. Building data centers. Hiring researchers. Training models. Claiming they're building "superintelligence." The problem: None of it makes money. Meta AI is free. Their models are open source. Their chatbots generate zero revenue. Meanwhile, investors are getting twitchy about the $72 billion infrastructure spending spree with no clear path to profitability. Enter Manus. A startup that launched 8 months ago. Founded in Beijing. Chinese founders. Moved to Singapore in June. March 2025: Manus launches with a viral demo video showing an AI agent that screens job candidates, plans vacations, analyzes stock portfolios. April 2025: Benchmark leads a $75M funding round at $500M valuation. US Senator John Cornyn immediately drags them for investing in a Chinese AI company, asking "who thought it was a good idea for American investors to subsidize our biggest adversary in AI?" December 2025: Manus announces $100M in annual recurring revenue. The fastest startup in HISTORY to hit that milestone. Revenue run rate: $125M. That's when Meta started negotiating. The deal closed in 10 days for ~$2 billion. Meta paid 4x the valuation from 8 months ago for a company that's ACTUALLY making money from AI. Here's why this matters: Manus hit $125M revenue in 8 months. Meta spent $72B on AI and has generated exactly $0 in AI-specific revenue. Zuck couldn't build profitability, so he bought it. But there's a problem. Manus has Chinese founders. Started in Beijing. Backed by Tencent and HongShan Capital (formerly Sequoia China). In the current geopolitical climate, that's radioactive. So Meta immediately issued a statement: "There will be no continuing Chinese ownership interests in Manus following the transaction, and Manus will discontinue its services and operations in China." Translation: We're buying your revenue and your team, cutting all Chinese ties, and pretending this was always an American company. This is geopolitical cleanup. The numbers tell the real story: Manus processed 147 trillion tokens in 8 months. Created 80 million virtual computers. Hit $100M ARR faster than any startup in history. Meta spent years and $72 billion trying to build this and failed. So they panic-bought the Chinese company that figured it out in 8 months. Meanwhile, this is Meta's third major AI acquisition THIS YEAR: June: Bought 49% of Scale AI for $14 billion to get CEO Alexandr Wang. Earlier this month: Acquired AI-wearables startup Limitless. Now: Manus for $2B. Meta's AI strategy is literally pay-to-win. Because after burning $72 billion, they still can't answer the one question investors keep asking: "When does AI make money?" Manus answered that question in 8 months. Meta couldn't answer it in 3 years. The craziest part: Manus charges $39-$199/month for subscriptions. That's it. No fancy enterprise deals. No complex pricing. Just a simple SaaS model that actually works. And it took a Chinese startup to figure out what Silicon Valley couldn't: people will pay for AI that actually does work instead of just answering questions. So Zuck wrote a $2 billion check, promised to cut all Chinese ownership, and is now claiming credit for "accelerating AI innovation." But everyone watching knows the truth... Meta spent $72B building infrastructure for a business they couldn't figure out how to monetize. Then they bought the company that cracked the code in 8 months. The AI race isn't about who builds the best models. It's about who builds a business that actually makes money. And right now, Meta just admitted they can't do it alone.
1:26

Meta literally spent $72 BILLION building AI infrastructure that generates ZERO revenue. Then a Chinese company launches an AI agent in March, hits $125 million in revenue by December, and Zuck writes a $2 billion check in 10 days. But this isn't a strategic acquisition... It's panic. Here's what happened: Meta has been burning cash on AI for years. Building data centers. Hiring researchers. Training models. Claiming they're building "superintelligence." The problem: None of it makes money. Meta AI is free. Their models are open source. Their chatbots generate zero revenue. Meanwhile, investors are getting twitchy about the $72 billion infrastructure spending spree with no clear path to profitability. Enter Manus. A startup that launched 8 months ago. Founded in Beijing. Chinese founders. Moved to Singapore in June. March 2025: Manus launches with a viral demo video showing an AI agent that screens job candidates, plans vacations, analyzes stock portfolios. April 2025: Benchmark leads a $75M funding round at $500M valuation. US Senator John Cornyn immediately drags them for investing in a Chinese AI company, asking "who thought it was a good idea for American investors to subsidize our biggest adversary in AI?" December 2025: Manus announces $100M in annual recurring revenue. The fastest startup in HISTORY to hit that milestone. Revenue run rate: $125M. That's when Meta started negotiating. The deal closed in 10 days for ~$2 billion. Meta paid 4x the valuation from 8 months ago for a company that's ACTUALLY making money from AI. Here's why this matters: Manus hit $125M revenue in 8 months. Meta spent $72B on AI and has generated exactly $0 in AI-specific revenue. Zuck couldn't build profitability, so he bought it. But there's a problem. Manus has Chinese founders. Started in Beijing. Backed by Tencent and HongShan Capital (formerly Sequoia China). In the current geopolitical climate, that's radioactive. So Meta immediately issued a statement: "There will be no continuing Chinese ownership interests in Manus following the transaction, and Manus will discontinue its services and operations in China." Translation: We're buying your revenue and your team, cutting all Chinese ties, and pretending this was always an American company. This is geopolitical cleanup. The numbers tell the real story: Manus processed 147 trillion tokens in 8 months. Created 80 million virtual computers. Hit $100M ARR faster than any startup in history. Meta spent years and $72 billion trying to build this and failed. So they panic-bought the Chinese company that figured it out in 8 months. Meanwhile, this is Meta's third major AI acquisition THIS YEAR: June: Bought 49% of Scale AI for $14 billion to get CEO Alexandr Wang. Earlier this month: Acquired AI-wearables startup Limitless. Now: Manus for $2B. Meta's AI strategy is literally pay-to-win. Because after burning $72 billion, they still can't answer the one question investors keep asking: "When does AI make money?" Manus answered that question in 8 months. Meta couldn't answer it in 3 years. The craziest part: Manus charges $39-$199/month for subscriptions. That's it. No fancy enterprise deals. No complex pricing. Just a simple SaaS model that actually works. And it took a Chinese startup to figure out what Silicon Valley couldn't: people will pay for AI that actually does work instead of just answering questions. So Zuck wrote a $2 billion check, promised to cut all Chinese ownership, and is now claiming credit for "accelerating AI innovation." But everyone watching knows the truth... Meta spent $72B building infrastructure for a business they couldn't figure out how to monetize. Then they bought the company that cracked the code in 8 months. The AI race isn't about who builds the best models. It's about who builds a business that actually makes money. And right now, Meta just admitted they can't do it alone.

Ricardo

98,070 просмотров • 6 месяцев назад

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

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

Milk Road AI

216,498 просмотров • 1 месяц назад

🚨 BREAKING: TECH COMPANIES ARE STRAIGHT FALSIFICATING EARNINGS Microsoft just gave OpenAI $13 BILLION OpenAI invested it back to Azure Microsoft booked it as revenue OpenAI booked it as a revenue too Amazon and Google are running similar loops with Anthropic while marking up huge paper gains from AI valuations Nvidia and Oracle don't even hide this scheme anymore OpenAI is spending over $60 BILLION a year on compute while generating only $25 BILLION in revenue. Ray Dalio, one of the most profitable investors in the world, has warned that late stage bubbles are driven by fake flows, not real economics He saw it before in 2000, 2008 Now he warns the same ponzi is happening at bigger scale That is exactly the definition of top of the market Keep in mind: I’ve called every major market top and bottom for over 10 YEARS I was one of the only people who called the top in October, and I’ll do it again That’s literally my job If you still haven’t followed me, you’ll regret
0:29

🚨 BREAKING: TECH COMPANIES ARE STRAIGHT FALSIFICATING EARNINGS Microsoft just gave OpenAI $13 BILLION OpenAI invested it back to Azure Microsoft booked it as revenue OpenAI booked it as a revenue too Amazon and Google are running similar loops with Anthropic while marking up huge paper gains from AI valuations Nvidia and Oracle don't even hide this scheme anymore OpenAI is spending over $60 BILLION a year on compute while generating only $25 BILLION in revenue. Ray Dalio, one of the most profitable investors in the world, has warned that late stage bubbles are driven by fake flows, not real economics He saw it before in 2000, 2008 Now he warns the same ponzi is happening at bigger scale That is exactly the definition of top of the market Keep in mind: I’ve called every major market top and bottom for over 10 YEARS I was one of the only people who called the top in October, and I’ll do it again That’s literally my job If you still haven’t followed me, you’ll regret

Hopper

17,431 просмотров • 10 дней назад

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there
2:22:19

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

Dami-Defi

16,080 просмотров • 1 месяц назад

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

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

Milk Road AI

28,622 просмотров • 1 месяц назад

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

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

Milk Road AI

48,673 просмотров • 1 месяц назад

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.
0:20

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

138,185 просмотров • 16 дней назад

Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle
1:21

Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle

Boring_Business

144,062 просмотров • 1 месяц назад

Larry Ellison borrowed $125 billion to bet everything on a single customer that LOSES $5 billion a year. American banks are already refusing to lend him another dollar. And now that single customer has started to slowly walk away. This is one of the biggest gambles in tech history - and it’s NOT looking good: Oracle has $124.7 billion in debt on its books right now. That's more than the GDP of 100+ countries. Their free cash flow over the last 12 months? Negative $13.18 billion. They are spending more money than they make. And they're doing it on PURPOSE. Every other hyperscaler funds their AI buildout with cash. Google has cash. Amazon has cash. Microsoft has cash. Oracle has IOUs. They raised $58 billion in debt in just two months. $38 billion for Texas and Wisconsin data centers. $20 billion for New Mexico. And they need another $100 billion on top of that. Even US banks are starting to say no. TD Cowen reported that multiple banks have pulled back from Oracle lending. Borrowing costs have roughly DOUBLED since September. They're now paying interest rates typically reserved for companies rated below investment grade. Barclays downgraded their debt to underweight and warned Oracle could run out of cash by November 2026. So what does Larry Ellison do? He FIRES 30,000 people. Oracle is planning layoffs affecting up to 18% of its entire workforce. The goal is to free up $8 to $10 billion in cash flow just to keep the lights on while they build data centers for ONE customer: OpenAI. Oracle's $553 billion backlog sounds incredible until you realize a massive chunk of it flows through a single relationship. If OpenAI sneezes, Oracle catches pneumonia. And OpenAI is already sneezing... Sam Altman DROPPED plans to expand the Stargate site in Abilene, Texas. And the reason is insane: Nvidia's chips are improving so fast that by the time Oracle finishes building the data center, the processors inside it will already be outdated. Oracle is building with Blackwell chips. But Nvidia's new Vera Rubin platform delivers 5x the inference performance at 10x lower cost per token. So Oracle is borrowing billions to build facilities that will house yesterday's technology before they even open. The world of bits moves faster than the world of atoms. And Oracle is trapped in between. But here's where it gets wild: The earnings call revealed something most people missed... Oracle now REQUIRES certain customers to buy their own GPUs upfront and hand them over. They call it the "bring your own chips" model. Translation: Oracle can't afford the hardware anymore. So they're asking customers to fund the construction of Oracle's OWN data centers. The stock is still down 23% this year even after the 12% earnings pop. Moody's rates Oracle just two notches above junk status. Lower than Amazon, Alphabet, Meta, and Microsoft. And they have $248 billion in ADDITIONAL lease obligations that aren't even on the balance sheet yet. Larry Ellison is 81 years old and making the biggest bet in corporate history. He's trying to turn a legacy database company into a hyperscale AI cloud provider using other people's money. All while his only major customer is a startup that burns $5 billion a year and just had its expansion partner refuse to fund the next campus. The earnings beat was real. Revenue up 22%. Cloud infrastructure up 84%. But revenue growth funded by debt isn't growth. It's leverage. And leverage works both ways. If OpenAI stays loyal, if the Stargate buildout continues, if the debt markets keep lending, if Vera Rubin doesn't make their entire infrastructure obsolete overnight, then Larry Ellison pulled off the greatest corporate reinvention in history. But that's a lot of ifs for a company two notches above junk. Oracle is either the most undervalued AI play on the market or the most overleveraged house of cards since 2008. The next six months will tell us which one.
1:44

Larry Ellison borrowed $125 billion to bet everything on a single customer that LOSES $5 billion a year. American banks are already refusing to lend him another dollar. And now that single customer has started to slowly walk away. This is one of the biggest gambles in tech history - and it’s NOT looking good: Oracle has $124.7 billion in debt on its books right now. That's more than the GDP of 100+ countries. Their free cash flow over the last 12 months? Negative $13.18 billion. They are spending more money than they make. And they're doing it on PURPOSE. Every other hyperscaler funds their AI buildout with cash. Google has cash. Amazon has cash. Microsoft has cash. Oracle has IOUs. They raised $58 billion in debt in just two months. $38 billion for Texas and Wisconsin data centers. $20 billion for New Mexico. And they need another $100 billion on top of that. Even US banks are starting to say no. TD Cowen reported that multiple banks have pulled back from Oracle lending. Borrowing costs have roughly DOUBLED since September. They're now paying interest rates typically reserved for companies rated below investment grade. Barclays downgraded their debt to underweight and warned Oracle could run out of cash by November 2026. So what does Larry Ellison do? He FIRES 30,000 people. Oracle is planning layoffs affecting up to 18% of its entire workforce. The goal is to free up $8 to $10 billion in cash flow just to keep the lights on while they build data centers for ONE customer: OpenAI. Oracle's $553 billion backlog sounds incredible until you realize a massive chunk of it flows through a single relationship. If OpenAI sneezes, Oracle catches pneumonia. And OpenAI is already sneezing... Sam Altman DROPPED plans to expand the Stargate site in Abilene, Texas. And the reason is insane: Nvidia's chips are improving so fast that by the time Oracle finishes building the data center, the processors inside it will already be outdated. Oracle is building with Blackwell chips. But Nvidia's new Vera Rubin platform delivers 5x the inference performance at 10x lower cost per token. So Oracle is borrowing billions to build facilities that will house yesterday's technology before they even open. The world of bits moves faster than the world of atoms. And Oracle is trapped in between. But here's where it gets wild: The earnings call revealed something most people missed... Oracle now REQUIRES certain customers to buy their own GPUs upfront and hand them over. They call it the "bring your own chips" model. Translation: Oracle can't afford the hardware anymore. So they're asking customers to fund the construction of Oracle's OWN data centers. The stock is still down 23% this year even after the 12% earnings pop. Moody's rates Oracle just two notches above junk status. Lower than Amazon, Alphabet, Meta, and Microsoft. And they have $248 billion in ADDITIONAL lease obligations that aren't even on the balance sheet yet. Larry Ellison is 81 years old and making the biggest bet in corporate history. He's trying to turn a legacy database company into a hyperscale AI cloud provider using other people's money. All while his only major customer is a startup that burns $5 billion a year and just had its expansion partner refuse to fund the next campus. The earnings beat was real. Revenue up 22%. Cloud infrastructure up 84%. But revenue growth funded by debt isn't growth. It's leverage. And leverage works both ways. If OpenAI stays loyal, if the Stargate buildout continues, if the debt markets keep lending, if Vera Rubin doesn't make their entire infrastructure obsolete overnight, then Larry Ellison pulled off the greatest corporate reinvention in history. But that's a lot of ifs for a company two notches above junk. Oracle is either the most undervalued AI play on the market or the most overleveraged house of cards since 2008. The next six months will tell us which one.

Ricardo

181,176 просмотров • 3 месяцев назад