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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...

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Ricardo

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98,070 görüntüleme • 8 ay önce •via X (Twitter)

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

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

Ricardo

208,631 görüntüleme • 4 ay önce

One guy just raised $475 million for a company that's 2 months old No product. No revenue. No prototype. But a $4.5 BILLION valuation. Unconventional AI closed a $475 million seed round last week. Founded in September 2025. That's 8 weeks from launch to half a billion dollars. But what did they actually build? Nothing (yet). Here's what happened: Naveen Rao left Databricks in September as Head of AI. Started Unconventional AI with zero public details. Two months later: $475M from Andreessen Horowitz, Lightspeed, Jeff Bezos. The pitch? "We're building computers as efficient as biology." That's it. No technical paper. No demo. No proof of concept. Just a guy who sold his last company for $1.3 billion saying "trust me, I'm building brain computers." VCs threw half a billion at him. Why this deal is insane: AI is hitting an energy wall. Training GPT-4 cost $100 million in compute. So everyone's betting on new hardware that uses less energy. Neuromorphic chips. Brain-inspired computing. Analog circuits. Except nobody's proven it works at scale. VCs aren't funding the technology. They're funding the NARRATIVE. "AI needs new hardware or the whole thing collapses." When a guy who sold two AI companies for $1.7 billion combined says "I'm building the chip that saves AI" - VCs panic-buy equity. The track record that bought half a billion: 2016: Sold Nervana Systems to Intel for $350M 2023: Sold MosaicML to Databricks for $1.3B 2024: Raises $475M for a company with zero product That's his playbook. Build credibility with two exits. Use that to raise stupid money for attempt #3. The market WANTS to believe the next AI breakthrough is around the corner. So they fund it preemptively. But what nobody's saying: This isn't a seed round. It's a pre-IPO. $4.5 billion valuation means Unconventional AI is worth more than 90% of public tech companies. With literally no product. Rao admitted: "We're not going to have a product in two years. This is largely a research effort." Translation: You just gave me half a billion to fuck around in a lab for 24+ months. VCs don't care because they're playing a different game. If it works, they 100x their money. If it fails, they write it off. Unconventional AI raised more in 2 months than 99.99% of startups raise ever. No customers. No revenue. No proof. And the entire Valley said "here's $475 million." This is what happens when an industry has infinite money and zero patience. They stop funding innovation and start funding PROMISES of innovation. You can't be wrong if the product doesn't exist yet. My takeaway from this: If you have two exits, you can raise stupid money for anything. The idea doesn't have to be proven. The tech doesn't have to exist. You just need VCs to believe YOU are the person who MIGHT solve the problem. Not "build something great and raise money." But "convince investors you COULD build something great, then raise before you've built anything." Unconventional AI is either the future of computing or the biggest vaporware play since Theranos. We'll find out in 2027. What do you think?
0:42

One guy just raised $475 million for a company that's 2 months old No product. No revenue. No prototype. But a $4.5 BILLION valuation. Unconventional AI closed a $475 million seed round last week. Founded in September 2025. That's 8 weeks from launch to half a billion dollars. But what did they actually build? Nothing (yet). Here's what happened: Naveen Rao left Databricks in September as Head of AI. Started Unconventional AI with zero public details. Two months later: $475M from Andreessen Horowitz, Lightspeed, Jeff Bezos. The pitch? "We're building computers as efficient as biology." That's it. No technical paper. No demo. No proof of concept. Just a guy who sold his last company for $1.3 billion saying "trust me, I'm building brain computers." VCs threw half a billion at him. Why this deal is insane: AI is hitting an energy wall. Training GPT-4 cost $100 million in compute. So everyone's betting on new hardware that uses less energy. Neuromorphic chips. Brain-inspired computing. Analog circuits. Except nobody's proven it works at scale. VCs aren't funding the technology. They're funding the NARRATIVE. "AI needs new hardware or the whole thing collapses." When a guy who sold two AI companies for $1.7 billion combined says "I'm building the chip that saves AI" - VCs panic-buy equity. The track record that bought half a billion: 2016: Sold Nervana Systems to Intel for $350M 2023: Sold MosaicML to Databricks for $1.3B 2024: Raises $475M for a company with zero product That's his playbook. Build credibility with two exits. Use that to raise stupid money for attempt #3. The market WANTS to believe the next AI breakthrough is around the corner. So they fund it preemptively. But what nobody's saying: This isn't a seed round. It's a pre-IPO. $4.5 billion valuation means Unconventional AI is worth more than 90% of public tech companies. With literally no product. Rao admitted: "We're not going to have a product in two years. This is largely a research effort." Translation: You just gave me half a billion to fuck around in a lab for 24+ months. VCs don't care because they're playing a different game. If it works, they 100x their money. If it fails, they write it off. Unconventional AI raised more in 2 months than 99.99% of startups raise ever. No customers. No revenue. No proof. And the entire Valley said "here's $475 million." This is what happens when an industry has infinite money and zero patience. They stop funding innovation and start funding PROMISES of innovation. You can't be wrong if the product doesn't exist yet. My takeaway from this: If you have two exits, you can raise stupid money for anything. The idea doesn't have to be proven. The tech doesn't have to exist. You just need VCs to believe YOU are the person who MIGHT solve the problem. Not "build something great and raise money." But "convince investors you COULD build something great, then raise before you've built anything." Unconventional AI is either the future of computing or the biggest vaporware play since Theranos. We'll find out in 2027. What do you think?

Ricardo

263,131 görüntüleme • 9 ay önce

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

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

Milk Road AI

21,141 görüntüleme • 3 ay önce

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

228,416 görüntüleme • 3 ay önce

OpenAI entered 2026 with the most insane revenue targets in corporate history. $30 billion in sales. Up from $13 billion in 2025. While LOSING $14 billion doing it. Let's understand this: OpenAI needed to convert from nonprofit to for-profit by December 31st, 2025 to unlock their $40 billion SoftBank funding. Miss that deadline? The round drops to $20 billion. And they made it. But here's the thing: The nonprofit STILL controls everything. They spent an entire year fighting to become for-profit, got sued by Elon Musk, pissed off California's attorney general, lost key employees over it. Then ended up basically right where they started. Except now the nonprofit has a $130 billion stake and Microsoft got $135 billion for 27% ownership. So OpenAI burned a year of political capital to give away $265 billion in equity while keeping the same power structure that almost destroyed them in 2023. The revenue math is absolutely deranged: To hit $30 billion in 2026, they need to more than double revenue in 12 months. No company in history has done this from a $13 billion base. Not even Nvidia. Not even ByteDance. OpenAI wants to go from $10B to $100B in 3 years. And the losses are worse: $14 billion in losses in 2026. Triple their 2025 burn. They've committed to: - $250 billion to Microsoft Azure - $38 billion to Amazon AWS - $1+ trillion in chip deals with Nvidia, AMD, and Broadcom They won't be profitable until 2029. Maybe. But here's the part that makes this whole thing insane... They're not just competing anymore. Anthropic: Fully for-profit. On track for $15 billion revenue in 2026. AI insiders surveyed in December said they'd invest in Anthropic over OpenAI. Meta's pouring billions into Llama. Chinese models eating market share. And OpenAI still has to answer to a nonprofit board that can shut down AGI research whenever they decide it's not "benefiting humanity." The same board that fired Sam Altman in November 2023. The investors know this. That's why the $40B was contingent on conversion. When OpenAI reversed course and kept nonprofit control, they had to give the nonprofit a $130B stake. Basically: "You can keep control, but you better make us whole." What happens if they miss targets? The Azure commitment becomes a liability. The AWS deal gets renegotiated. The nonprofit board starts asking why they're burning billions while people die of preventable diseases. Investors start wondering if that $300B valuation was justified. OpenAI is betting they can: 1. More than double revenue annually for 3 years straight 2. Burn $44 billion doing it 3. Keep a nonprofit board happy 4. Fend off Anthropic, Meta, and Chinese competitors 5. Avoid another Sam Altman situation 6. Actually build AGI 7. Convince everyone it was worth it Nobody in history has pulled this off. We're 1 day into 2026. By December 31st, we'll know if OpenAI is the most ambitious company ever built or the biggest AI bubble in history. What are you betting on?
1:28

OpenAI entered 2026 with the most insane revenue targets in corporate history. $30 billion in sales. Up from $13 billion in 2025. While LOSING $14 billion doing it. Let's understand this: OpenAI needed to convert from nonprofit to for-profit by December 31st, 2025 to unlock their $40 billion SoftBank funding. Miss that deadline? The round drops to $20 billion. And they made it. But here's the thing: The nonprofit STILL controls everything. They spent an entire year fighting to become for-profit, got sued by Elon Musk, pissed off California's attorney general, lost key employees over it. Then ended up basically right where they started. Except now the nonprofit has a $130 billion stake and Microsoft got $135 billion for 27% ownership. So OpenAI burned a year of political capital to give away $265 billion in equity while keeping the same power structure that almost destroyed them in 2023. The revenue math is absolutely deranged: To hit $30 billion in 2026, they need to more than double revenue in 12 months. No company in history has done this from a $13 billion base. Not even Nvidia. Not even ByteDance. OpenAI wants to go from $10B to $100B in 3 years. And the losses are worse: $14 billion in losses in 2026. Triple their 2025 burn. They've committed to: - $250 billion to Microsoft Azure - $38 billion to Amazon AWS - $1+ trillion in chip deals with Nvidia, AMD, and Broadcom They won't be profitable until 2029. Maybe. But here's the part that makes this whole thing insane... They're not just competing anymore. Anthropic: Fully for-profit. On track for $15 billion revenue in 2026. AI insiders surveyed in December said they'd invest in Anthropic over OpenAI. Meta's pouring billions into Llama. Chinese models eating market share. And OpenAI still has to answer to a nonprofit board that can shut down AGI research whenever they decide it's not "benefiting humanity." The same board that fired Sam Altman in November 2023. The investors know this. That's why the $40B was contingent on conversion. When OpenAI reversed course and kept nonprofit control, they had to give the nonprofit a $130B stake. Basically: "You can keep control, but you better make us whole." What happens if they miss targets? The Azure commitment becomes a liability. The AWS deal gets renegotiated. The nonprofit board starts asking why they're burning billions while people die of preventable diseases. Investors start wondering if that $300B valuation was justified. OpenAI is betting they can: 1. More than double revenue annually for 3 years straight 2. Burn $44 billion doing it 3. Keep a nonprofit board happy 4. Fend off Anthropic, Meta, and Chinese competitors 5. Avoid another Sam Altman situation 6. Actually build AGI 7. Convince everyone it was worth it Nobody in history has pulled this off. We're 1 day into 2026. By December 31st, we'll know if OpenAI is the most ambitious company ever built or the biggest AI bubble in history. What are you betting on?

Ricardo

97,607 görüntüleme • 8 ay önce

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.
0:32

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

George Noble

120,301 görüntüleme • 7 ay önce

AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.
2:17

AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.

Predictive History

19,850 görüntüleme • 4 ay önce

The man who INVENTED modern AI just made a billion dollar bet that ChatGPT, Claude, and every AI company on earth is building the wrong technology. Yann LeCun won the Turing Award in 2018 for creating the neural networks that made AI possible. He spent a decade running AI research at Meta. Oversaw the creation of Llama and PyTorch, the tools that half the AI industry runs on. Then he quit. And raised $1.03 billion in a seed round. The LARGEST seed round in European history. $3.5 billion valuation before generating a single dollar of revenue. Bezos wrote the check. So did Nvidia. Samsung. Toyota. Temasek. Eric Schmidt. Mark Cuban. Tim Berners-Lee (the guy who invented the internet). His new company is called AMI Labs. And it's built on one thesis: Every AI company spending billions on large language models is wasting their money. ChatGPT, Claude, Gemini, Grok. They all work the same way. They predict the next word in a sequence. See "the cat sat on the" and predict "mat." Scale that to trillions of words and you get something that sounds intelligent. But LeCun says it doesn't UNDERSTAND anything. It can't reason. It can't plan. It can't predict what happens when you push a glass off a table. A two year old can do that. GPT-5 cannot. That's why AI hallucinates. It doesn't have a model of how the world actually works. It just predicts words. His solution? Something called JEPA. Instead of predicting words, it learns how the PHYSICAL WORLD works. Abstract representations of reality. Not language but physics. Think about what that means. Current AI can write your emails. LeCun's AI could design a car, run a factory, operate a robot, or diagnose a patient without hallucinating and killing someone. The CEO of AMI said it perfectly: "Factories, hospitals, and robots need AI that grasps reality. Predicting tokens doesn't cut it." And here's what's really crazy to me... LeCun isn't some outsider throwing rocks. He literally built the foundations that ChatGPT runs on. He knows exactly how these systems work because he helped create them. And after watching the entire industry sprint in one direction for three years, he raised a billion dollars to run the OPPOSITE way. No product. No revenue. No timeline. Just pure research. He told investors it could take YEARS to produce anything commercial. But they funded it anyway in just four months. Meanwhile OpenAI just raised $120 billion and still can't stop their models from making things up. Anthropic is building AI so dangerous they're afraid to release it. Google is burning billions trying to catch up. And the guy who started it all says they're all solving the wrong problem. Two Turing Award winners raised $2 billion in three weeks betting AGAINST the entire LLM approach. LeCun at AMI. Fei-Fei Li at World Labs. The smartest people in AI are quietly building the exit from the technology everyone else is betting their future on. Either they're wrong and the trillion dollar LLM industry keeps printing. Or they're right and every AI company on earth just built on a foundation that's about to crack.
1:35

The man who INVENTED modern AI just made a billion dollar bet that ChatGPT, Claude, and every AI company on earth is building the wrong technology. Yann LeCun won the Turing Award in 2018 for creating the neural networks that made AI possible. He spent a decade running AI research at Meta. Oversaw the creation of Llama and PyTorch, the tools that half the AI industry runs on. Then he quit. And raised $1.03 billion in a seed round. The LARGEST seed round in European history. $3.5 billion valuation before generating a single dollar of revenue. Bezos wrote the check. So did Nvidia. Samsung. Toyota. Temasek. Eric Schmidt. Mark Cuban. Tim Berners-Lee (the guy who invented the internet). His new company is called AMI Labs. And it's built on one thesis: Every AI company spending billions on large language models is wasting their money. ChatGPT, Claude, Gemini, Grok. They all work the same way. They predict the next word in a sequence. See "the cat sat on the" and predict "mat." Scale that to trillions of words and you get something that sounds intelligent. But LeCun says it doesn't UNDERSTAND anything. It can't reason. It can't plan. It can't predict what happens when you push a glass off a table. A two year old can do that. GPT-5 cannot. That's why AI hallucinates. It doesn't have a model of how the world actually works. It just predicts words. His solution? Something called JEPA. Instead of predicting words, it learns how the PHYSICAL WORLD works. Abstract representations of reality. Not language but physics. Think about what that means. Current AI can write your emails. LeCun's AI could design a car, run a factory, operate a robot, or diagnose a patient without hallucinating and killing someone. The CEO of AMI said it perfectly: "Factories, hospitals, and robots need AI that grasps reality. Predicting tokens doesn't cut it." And here's what's really crazy to me... LeCun isn't some outsider throwing rocks. He literally built the foundations that ChatGPT runs on. He knows exactly how these systems work because he helped create them. And after watching the entire industry sprint in one direction for three years, he raised a billion dollars to run the OPPOSITE way. No product. No revenue. No timeline. Just pure research. He told investors it could take YEARS to produce anything commercial. But they funded it anyway in just four months. Meanwhile OpenAI just raised $120 billion and still can't stop their models from making things up. Anthropic is building AI so dangerous they're afraid to release it. Google is burning billions trying to catch up. And the guy who started it all says they're all solving the wrong problem. Two Turing Award winners raised $2 billion in three weeks betting AGAINST the entire LLM approach. LeCun at AMI. Fei-Fei Li at World Labs. The smartest people in AI are quietly building the exit from the technology everyone else is betting their future on. Either they're wrong and the trillion dollar LLM industry keeps printing. Or they're right and every AI company on earth just built on a foundation that's about to crack.

Ricardo

609,003 görüntüleme • 5 ay önce

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

1,816,700 görüntüleme • 7 ay önce

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 görüntüleme • 4 ay önce

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

163,412 görüntüleme • 9 ay önce

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

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

Ricardo

24,389 görüntüleme • 1 ay önce

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

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

Ricardo

162,447 görüntüleme • 4 ay önce

🚨A 25 YEAR OLD BUILT THE FASTEST GROWING SOFTWARE COMPANY IN HISTORY.. WITH ZERO MARKETING SPEND.. AND SPACEX JUST OFFERED $60 BILLION TO BUY IT.. His name is Michael Truell.. He started coding at 11.. Interned at Google at 18.. Dropped out of MIT to start a company that built AI tools for mechanical engineering.. That company failed.. So he pivoted.. And built Cursor.. An AI-powered code editor that writes software for you.. Here's how fast it grew.. $100 million in annual revenue in 12 months.. Fastest in SaaS history.. Broke every record ever set by Slack, Zoom, and Wiz.. $500 million by month 21.. $1 billion by November 2025.. $2 billion by February 2026.. Projected to hit $6 billion by end of year.. Zero marketing spend.. Not a single dollar.. Pure word of mouth from developers who couldn't stop talking about it.. Over 1 billion lines of code accepted per day.. Used by 70% of Fortune 1000 companies.. Every single one of Nvidia's 40,000 engineers uses it.. Coinbase hit 100% adoption among their developers.. And he did this with a team of four MIT co-founders.. One of them was a three-time International Math Olympiad competitor from Pakistan.. Another was a college squash captain with zero startup experience who built the entire product strategy.. They spent zero on sales.. Zero on ads.. Zero on growth hacking.. The product sold itself.. But here's where the story takes a turn nobody expected.. Even at $50 billion valuation.. Even generating billions in revenue.. They hit a wall.. Not a market wall.. A physics wall.. They couldn't get enough GPUs to train their next AI model.. The physical chips didn't exist in sufficient quantities for them to buy.. Money couldn't solve the problem.. Enter Elon Musk.. On April 21.. SpaceX announced a deal to potentially acquire Cursor for $60 billion.. The largest acquisition option in tech history.. The structure is insane.. SpaceX gives Cursor immediate access to Colossus.. xAI's supercomputer equivalent to one million Nvidia H100 GPUs.. For nine months of joint development.. At the end.. SpaceX can buy the company for $60 billion.. If they don't buy it.. They owe Cursor a $10 billion breakup fee.. The largest breakup fee in corporate history.. Think about what that means for Cursor.. Either they get acquired for $60 billion.. Or they walk away with $10 billion in cash and nine months of free training on the most powerful supercomputer on earth.. There is no losing scenario.. And here's why Musk wants it.. SpaceX is preparing for an IPO at $1.75 trillion.. The biggest IPO ever.. But aerospace alone can't justify that number.. By merging xAI into SpaceX.. And now acquiring Cursor.. Musk transforms SpaceX from a rocket company into an AI empire that owns the compute, the models, and the developer tools.. Cursor is the missing piece.. The application layer that puts xAI's models into the daily workflow of every Fortune 500 engineering team.. Oh and one more thing.. In 2022.. FTX's trading firm Alameda Research made a seed investment in Cursor.. During the FTX bankruptcy.. Liquidators sold that stake for $200,000.. That stake is now worth approximately $3 billion.. Sam Bankman-Fried called it the worst liquidation decision in venture capital history.. From a prison cell.. A failed mechanical engineering startup.. Pivoted by four kids from MIT.. Zero marketing.. Zero sales team.. Built the fastest growing software company in history.. And now SpaceX is writing a $60 billion check for it.. This is the most insane founder story in Silicon Valley history.. And most people haven't even heard of Michael Truell.
27:39

🚨A 25 YEAR OLD BUILT THE FASTEST GROWING SOFTWARE COMPANY IN HISTORY.. WITH ZERO MARKETING SPEND.. AND SPACEX JUST OFFERED $60 BILLION TO BUY IT.. His name is Michael Truell.. He started coding at 11.. Interned at Google at 18.. Dropped out of MIT to start a company that built AI tools for mechanical engineering.. That company failed.. So he pivoted.. And built Cursor.. An AI-powered code editor that writes software for you.. Here's how fast it grew.. $100 million in annual revenue in 12 months.. Fastest in SaaS history.. Broke every record ever set by Slack, Zoom, and Wiz.. $500 million by month 21.. $1 billion by November 2025.. $2 billion by February 2026.. Projected to hit $6 billion by end of year.. Zero marketing spend.. Not a single dollar.. Pure word of mouth from developers who couldn't stop talking about it.. Over 1 billion lines of code accepted per day.. Used by 70% of Fortune 1000 companies.. Every single one of Nvidia's 40,000 engineers uses it.. Coinbase hit 100% adoption among their developers.. And he did this with a team of four MIT co-founders.. One of them was a three-time International Math Olympiad competitor from Pakistan.. Another was a college squash captain with zero startup experience who built the entire product strategy.. They spent zero on sales.. Zero on ads.. Zero on growth hacking.. The product sold itself.. But here's where the story takes a turn nobody expected.. Even at $50 billion valuation.. Even generating billions in revenue.. They hit a wall.. Not a market wall.. A physics wall.. They couldn't get enough GPUs to train their next AI model.. The physical chips didn't exist in sufficient quantities for them to buy.. Money couldn't solve the problem.. Enter Elon Musk.. On April 21.. SpaceX announced a deal to potentially acquire Cursor for $60 billion.. The largest acquisition option in tech history.. The structure is insane.. SpaceX gives Cursor immediate access to Colossus.. xAI's supercomputer equivalent to one million Nvidia H100 GPUs.. For nine months of joint development.. At the end.. SpaceX can buy the company for $60 billion.. If they don't buy it.. They owe Cursor a $10 billion breakup fee.. The largest breakup fee in corporate history.. Think about what that means for Cursor.. Either they get acquired for $60 billion.. Or they walk away with $10 billion in cash and nine months of free training on the most powerful supercomputer on earth.. There is no losing scenario.. And here's why Musk wants it.. SpaceX is preparing for an IPO at $1.75 trillion.. The biggest IPO ever.. But aerospace alone can't justify that number.. By merging xAI into SpaceX.. And now acquiring Cursor.. Musk transforms SpaceX from a rocket company into an AI empire that owns the compute, the models, and the developer tools.. Cursor is the missing piece.. The application layer that puts xAI's models into the daily workflow of every Fortune 500 engineering team.. Oh and one more thing.. In 2022.. FTX's trading firm Alameda Research made a seed investment in Cursor.. During the FTX bankruptcy.. Liquidators sold that stake for $200,000.. That stake is now worth approximately $3 billion.. Sam Bankman-Fried called it the worst liquidation decision in venture capital history.. From a prison cell.. A failed mechanical engineering startup.. Pivoted by four kids from MIT.. Zero marketing.. Zero sales team.. Built the fastest growing software company in history.. And now SpaceX is writing a $60 billion check for it.. This is the most insane founder story in Silicon Valley history.. And most people haven't even heard of Michael Truell.

Evan Luthra

990,301 görüntüleme • 5 ay önce

Chinese AI models are about seven months behind the best American ones, even though China has roughly 5% of the AI chips the US does. The seven-month figure comes from Epoch AI, which has tracked the gap since 2023. The 5% is a rough estimate from Anand Kannappan, who runs the evaluation company Patronus AI. If the US has 20 times the chips, you would expect its models to be more than seven months ahead. The lead stays that small because training a model has a finish line. You borrow enough chips, run the training, and you are done. Serving millions of users every day never finishes, and Chinese labs do not have the chips for that. Moonshot (a Chinese AI lab) launched its flagship model in July, suspended new subscriptions after three days because it ran out of computing power, and eight days later published the model for free so anyone could download and run it themselves. DeepSeek (a Chinese lab spun out of a Hangzhou hedge fund) ran its entire global service at a peak of 278 nodes, about 2,000 chips. MacroPolo's Global AI Talent Tracker (which surveys the major machine-learning conferences) finds that 38% of the world's top-tier AI researchers did their undergraduate degrees in China against 24% in America, and that 72% of the China-educated ones now work at American institutions. China trains more top AI researchers than any other country, most of them leave to work in America, and Chinese models still trail by only seven months. The chips China is short on are all printed by one kind of machine: an EUV lithography scanner. One Dutch company builds it, at about $380 million and 165 tons apiece. Under US pressure, the Dutch license to send one to China lapsed in mid-2019, and none has been issued since. ASML tracks all 314 operational machines on Earth for connectivity, so it knows where every one of them is.
2:21

Chinese AI models are about seven months behind the best American ones, even though China has roughly 5% of the AI chips the US does. The seven-month figure comes from Epoch AI, which has tracked the gap since 2023. The 5% is a rough estimate from Anand Kannappan, who runs the evaluation company Patronus AI. If the US has 20 times the chips, you would expect its models to be more than seven months ahead. The lead stays that small because training a model has a finish line. You borrow enough chips, run the training, and you are done. Serving millions of users every day never finishes, and Chinese labs do not have the chips for that. Moonshot (a Chinese AI lab) launched its flagship model in July, suspended new subscriptions after three days because it ran out of computing power, and eight days later published the model for free so anyone could download and run it themselves. DeepSeek (a Chinese lab spun out of a Hangzhou hedge fund) ran its entire global service at a peak of 278 nodes, about 2,000 chips. MacroPolo's Global AI Talent Tracker (which surveys the major machine-learning conferences) finds that 38% of the world's top-tier AI researchers did their undergraduate degrees in China against 24% in America, and that 72% of the China-educated ones now work at American institutions. China trains more top AI researchers than any other country, most of them leave to work in America, and Chinese models still trail by only seven months. The chips China is short on are all printed by one kind of machine: an EUV lithography scanner. One Dutch company builds it, at about $380 million and 165 tons apiece. Under US pressure, the Dutch license to send one to China lapsed in mid-2019, and none has been issued since. ASML tracks all 314 operational machines on Earth for connectivity, so it knows where every one of them is.

Sam Szuchan

12,999 görüntüleme • 20 gün önce

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?
1:21

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?

Ricardo

37,412 görüntüleme • 2 ay önce

Elon Musk just told lenders he's paying back $17.5 BILLION in debt across X and xAI. Including $3 billion in high-yield bonds being redeemed early at 117 cents on the dollar. NOBODY knows where the money is coming from. And nobody seems to care. Let me explain why you should: Morgan Stanley has been calling existing lenders and telling them everything gets repaid in full. The X debt from the Twitter buyout. The xAI bonds from June. All of it. The bonds were structured to stay outstanding for at least 2 years. They're being called back less than a year later at a 17% premium. Bondholders are thrilled. Of course they are. They're getting paid above par on junk paper. But here's the part that should make you uncomfortable: xAI lost $1.46 billion in a single quarter last year. Burned through $7.8 billion in cash in the first 9 months of 2025. Revenue for the September quarter was $107 million. That's a company hemorrhaging roughly $1 billion a month. On a standalone basis, xAI exited 2025 at about a $500 million annualized revenue run rate. Even with optimistic projections, they might hit $2 billion in 2026. So where does $17.5 billion come from? xAI raised $20 billion in a Series E round in January. That's the most likely answer. Take the money investors gave you to build AI infrastructure and use a huge chunk of it to retire debt. But that's NOT a sign of strength. That's financial engineering. You raise $20 billion from investors who think they're funding the next frontier of artificial intelligence, then you turn around and use most of it to clean up the balance sheet before an IPO. Because that's what this is really about. SpaceX is targeting a confidential SEC filing as early as this month. IPO could come in June. Valuation targets exceed $1.75 trillion. The combined SpaceX-xAI entity currently carries about $18 billion in obligations. You can't take a $1.25 trillion company public with $18 billion in legacy debt from a money-losing AI startup and a social media platform that was acquired with leveraged buyout financing. So you nuke the debt. Clean the balance sheet. Present a simpler story to IPO investors. Smart? Absolutely. But let's be honest about what it actually is. SpaceX proper generated about $15 billion in revenue and $8 billion in profit in 2025. xAI generated roughly $250 million in six months and lost $2.5 billion doing it. At a $1.5 trillion IPO valuation, you're looking at roughly 94x trailing sales and 500x trailing earnings for the combined business. Those are not rational multiples. Those are lottery ticket multiples with better branding. And the $17.5 billion debt payoff doesn't change the underlying economics. It only changes the optics. xAI is still burning close to $1 billion a month. Grok still has a fraction of ChatGPT's market share. The revenue doesn't come close to justifying the infrastructure spend. What this reminds me of is the classic pre-IPO playbook taken to an extreme: Use private capital to dress up the financials, time the listing for maximum enthusiasm, and let public market investors hold the bag if execution falls short. The companies that need to clean house before going public are rarely the ones that reward you for buying on day one. My positioning hasn't changed. The AI infrastructure spending boom is real. But the returns aren't materializing for the companies actually deploying the technology. That gap between spending and results is where fortunes get destroyed. Stay skeptical. Stay disciplined. And remember: If the source of $17.5 billion in repayment capital is a mystery, it's a WARNING.
0:48

Elon Musk just told lenders he's paying back $17.5 BILLION in debt across X and xAI. Including $3 billion in high-yield bonds being redeemed early at 117 cents on the dollar. NOBODY knows where the money is coming from. And nobody seems to care. Let me explain why you should: Morgan Stanley has been calling existing lenders and telling them everything gets repaid in full. The X debt from the Twitter buyout. The xAI bonds from June. All of it. The bonds were structured to stay outstanding for at least 2 years. They're being called back less than a year later at a 17% premium. Bondholders are thrilled. Of course they are. They're getting paid above par on junk paper. But here's the part that should make you uncomfortable: xAI lost $1.46 billion in a single quarter last year. Burned through $7.8 billion in cash in the first 9 months of 2025. Revenue for the September quarter was $107 million. That's a company hemorrhaging roughly $1 billion a month. On a standalone basis, xAI exited 2025 at about a $500 million annualized revenue run rate. Even with optimistic projections, they might hit $2 billion in 2026. So where does $17.5 billion come from? xAI raised $20 billion in a Series E round in January. That's the most likely answer. Take the money investors gave you to build AI infrastructure and use a huge chunk of it to retire debt. But that's NOT a sign of strength. That's financial engineering. You raise $20 billion from investors who think they're funding the next frontier of artificial intelligence, then you turn around and use most of it to clean up the balance sheet before an IPO. Because that's what this is really about. SpaceX is targeting a confidential SEC filing as early as this month. IPO could come in June. Valuation targets exceed $1.75 trillion. The combined SpaceX-xAI entity currently carries about $18 billion in obligations. You can't take a $1.25 trillion company public with $18 billion in legacy debt from a money-losing AI startup and a social media platform that was acquired with leveraged buyout financing. So you nuke the debt. Clean the balance sheet. Present a simpler story to IPO investors. Smart? Absolutely. But let's be honest about what it actually is. SpaceX proper generated about $15 billion in revenue and $8 billion in profit in 2025. xAI generated roughly $250 million in six months and lost $2.5 billion doing it. At a $1.5 trillion IPO valuation, you're looking at roughly 94x trailing sales and 500x trailing earnings for the combined business. Those are not rational multiples. Those are lottery ticket multiples with better branding. And the $17.5 billion debt payoff doesn't change the underlying economics. It only changes the optics. xAI is still burning close to $1 billion a month. Grok still has a fraction of ChatGPT's market share. The revenue doesn't come close to justifying the infrastructure spend. What this reminds me of is the classic pre-IPO playbook taken to an extreme: Use private capital to dress up the financials, time the listing for maximum enthusiasm, and let public market investors hold the bag if execution falls short. The companies that need to clean house before going public are rarely the ones that reward you for buying on day one. My positioning hasn't changed. The AI infrastructure spending boom is real. But the returns aren't materializing for the companies actually deploying the technology. That gap between spending and results is where fortunes get destroyed. Stay skeptical. Stay disciplined. And remember: If the source of $17.5 billion in repayment capital is a mystery, it's a WARNING.

George Noble

471,839 görüntüleme • 6 ay önce

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

2,976,578 görüntüleme • 4 ay önce

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

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

Milk Road AI

61,932 görüntüleme • 3 ay önce

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

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

Milk Road AI

28,622 görüntüleme • 4 ay önce