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🚨 THIS ONE STATEMENT WILL BREAK MARKETS September 12: Amodei called for slowing frontier AI development. Wall Street just heard the worst possible sentence. Every AI bet depends on one assumption: Massive spending today → massive profits tomorrow That timeline just collapsed. THE REVENUE PROBLEM Amodei proposed safety checkpoints...

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Nonzee

110,571 subscribers

13,316 просмотров • 10 дней назад •via X (Twitter)

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Комментарии: 10

Фото профиля MARMOT
MARMOT10 дней назад

🤔

Фото профиля Kabuki🔴
Kabuki🔴10 дней назад

👀

Фото профиля David Arnal
David Arnal10 дней назад

The key risk isn’t regulation alone—it’s utilization. If AI revenue grows slower than data-center capacity, depreciation and power costs turn aggressive capex into a margin problem well before debt does.

Фото профиля Macro Bombastic
Macro Bombastic10 дней назад

ai capex bros staring at their lease obligations like

Фото профиля Robin | Poker x AI
Robin | Poker x AI10 дней назад

The funding squeeze will force labs to trim capex, turning the AI hype cycle into a classic lagging revenue correction

Фото профиля Robbie
Robbie10 дней назад

The bubble gonna burst

Фото профиля Exa Audi
Exa Audi10 дней назад

I tested my assumption instead of repeating it. I keep thinking about what I've learned.

Фото профиля Jeroen May Cry
Jeroen May Cry10 дней назад

You enjoy bear posting don't you? AI will get cheaper we know that, but adoption will still 100x. It is nowhere near done. But these are interesting times.

Фото профиля Duy Nguyen
Duy Nguyen10 дней назад

Short guy cry soon

Фото профиля Value Signals
Value Signals9 дней назад

I could see this happen, but I doubt they'll actually slow down. If they do, they'll probably just release "safe" versions while going full speed behind the scenes

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🚨 SOMETHING VERY BAD IS HAPPENING The stock market keeps making new all-time highs. OpenAI and Anthropic are now worth $2.1T. That is 10% of the entire Nasdaq. Look at the math: – $450B burned per year – $50B in actual revenue The entire AI bull case depends on one assumption: Inference gets cheaper. That is how funds justify the math. Spend massively today, scale later, margins explode when inference costs collapse. But that assumption is breaking: - Memory is getting expensive. - Compute is not getting cheap fast enough. - Inference is not falling the way everyone modeled. And if inference does not get dramatically cheaper, the whole AI margin story starts to crack. The loop is obvious: – Big players fund each other – Partnerships look perfect on paper – Revenue moves around inside the same system Everyone calls it growth. I call it the final stage of mania. In 2000, companies added “.com” to the name and valuations exploded: – Small profits – Massive valuations – Perfect stories Then reality hit. Nasdaq collapsed 80%. Now companies add “AI” to the name and reprice instantly: – Small profits – Massive valuations – Perfect AI stories This is the dot-com bubble with better AI branding. And bubbles do not warn you before they break. They break when everyone thinks the story is untouchable. Turn notifications on. The next move won’t wait for you.
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🚨 SOMETHING VERY BAD IS HAPPENING The stock market keeps making new all-time highs. OpenAI and Anthropic are now worth $2.1T. That is 10% of the entire Nasdaq. Look at the math: – $450B burned per year – $50B in actual revenue The entire AI bull case depends on one assumption: Inference gets cheaper. That is how funds justify the math. Spend massively today, scale later, margins explode when inference costs collapse. But that assumption is breaking: - Memory is getting expensive. - Compute is not getting cheap fast enough. - Inference is not falling the way everyone modeled. And if inference does not get dramatically cheaper, the whole AI margin story starts to crack. The loop is obvious: – Big players fund each other – Partnerships look perfect on paper – Revenue moves around inside the same system Everyone calls it growth. I call it the final stage of mania. In 2000, companies added “.com” to the name and valuations exploded: – Small profits – Massive valuations – Perfect stories Then reality hit. Nasdaq collapsed 80%. Now companies add “AI” to the name and reprice instantly: – Small profits – Massive valuations – Perfect AI stories This is the dot-com bubble with better AI branding. And bubbles do not warn you before they break. They break when everyone thinks the story is untouchable. Turn notifications on. The next move won’t wait for you.

Nonzee

160,906 просмотров • 5 месяцев назад

SOMETHING VERY BAD IS HAPPENING The stock market keeps trying to push higher. OpenAI and Anthropic are now worth $2.1T. SpaceX is worth $2.05T. That is 10% of the entire Nasdaq. Look at the math: – $450B spent – $52B in actual revenue The entire AI bull case depends on one assumption: Inference gets cheaper. That is how funds justify the math. Spend massively today, scale later, margins explode when inference costs collapse. But that assumption is breaking: - Memory is getting expensive. - Compute is not getting cheap fast enough. - Inference is not falling the way everyone modeled. And if inference does not get dramatically cheaper, the whole AI margin story starts to crack. The loop is obvious: – Big players fund each other – Partnerships look perfect on paper – Revenue moves around inside the same system Everyone calls it growth. I call it the final stage of mania. In 2000, companies added “.com” to the name and valuations exploded: – Small profits – Massive valuations – Perfect stories Then reality hit. Nasdaq collapsed 80%. Now companies add “AI” to the name and reprice instantly: – Small profits – Massive valuations – Perfect AI stories This is the dot-com bubble with better AI branding. And bubbles do not warn you before they break. They break when everyone thinks the story is untouchable. If you've been following me, you already caught my $16K $BTC bottom call and $126K top. The next call is already playing out and it's bigger than both. Follow and turn notifications on.
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SOMETHING VERY BAD IS HAPPENING The stock market keeps trying to push higher. OpenAI and Anthropic are now worth $2.1T. SpaceX is worth $2.05T. That is 10% of the entire Nasdaq. Look at the math: – $450B spent – $52B in actual revenue The entire AI bull case depends on one assumption: Inference gets cheaper. That is how funds justify the math. Spend massively today, scale later, margins explode when inference costs collapse. But that assumption is breaking: - Memory is getting expensive. - Compute is not getting cheap fast enough. - Inference is not falling the way everyone modeled. And if inference does not get dramatically cheaper, the whole AI margin story starts to crack. The loop is obvious: – Big players fund each other – Partnerships look perfect on paper – Revenue moves around inside the same system Everyone calls it growth. I call it the final stage of mania. In 2000, companies added “.com” to the name and valuations exploded: – Small profits – Massive valuations – Perfect stories Then reality hit. Nasdaq collapsed 80%. Now companies add “AI” to the name and reprice instantly: – Small profits – Massive valuations – Perfect AI stories This is the dot-com bubble with better AI branding. And bubbles do not warn you before they break. They break when everyone thinks the story is untouchable. If you've been following me, you already caught my $16K $BTC bottom call and $126K top. The next call is already playing out and it's bigger than both. Follow and turn notifications on.

Nonzee

40,102 просмотров • 2 месяцев назад

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

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

Milk Road AI

21,141 просмотров • 3 месяцев назад

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 просмотров • 7 месяцев назад

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?
0:26

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

Ricardo

167,768 просмотров • 5 месяцев назад

This is the moment Chinese AI beat American AI. One of the largest public crypto companies in the world just DUMPED OpenAI and Anthropic. Coinbase switched to open-weight Chinese models from Zhipu and DeepSeek, and shaved nearly 50% off the company's internal AI spending. The numbers are absolutely ridiculous: Running the same enterprise workload through Anthropic's Claude costs $4,811. Running it through Zhipu's GLM 5.2 costs $544. That's a 9x price difference for equivalent output. OpenAI's GPT-5.5 sits in the middle at $3,357. DeepSeek's V4 lands at $1,071. Moonshot's Kimi at $948. On the actual benchmarks: Zhipu's GLM 5.2 scored 62.1 on SWE-bench Pro, the gold standard for coding. OpenAI's GPT-5.5 scored 58.6. One AI researcher called GLM 5.2 "at least as good as Opus 4.8 and GPT 5.5." Another called it "the first open model that can really compete with closed-source systems." The Chinese models are not just cheaper but they are now also beating American models on the benchmarks American companies pay $4,811 per workload for. Coinbase did the math first and reacted - more companies will certainly follow. Now watch what happens to the IPO timeline: Anthropic confidentially filed for an IPO targeting October at a $965 billion valuation. OpenAI followed days later with its own confidential filing. Both companies built their financial models on the assumption that they could keep charging enterprise prices that are 9 to 33x what Chinese competitors charge for the same task. Brian Armstrong publicly proved customers WILL leave. 45% of companies are now spending over $100,000 per month on AI, up from 20% last year. Every one of those customers is one quarterly budget review away from dumping American AI. OpenAI has reportedly already started preparing major token price cuts. Anthropic is expected to follow. And here's the thing... The export controls were supposed to CRUSH Chinese AI. The US government banned American AI chips, restricted model weights, blacklisted Alibaba and Baidu as Chinese military companies, and just banned Anthropic's flagship model from every foreign national on the planet. The entire premise of the American AI valuation bubble is that Washington can keep China two generations behind. But Chinese labs responded by building cheaper, more efficient models on inferior hardware and pricing them at one ninth the cost of the American alternative. And now American companies are voting with their checkbooks. The dominant American labs are valued at nearly $2 trillion combined on the assumption that their pricing power is durable. Coinbase proved it is not, and every customer doing a year-end budget review will be looking at the same math. For investors, the question here is what happens to the Anthropic IPO at $965 billion when the company is being forced to cut prices to defend share against open-weight Chinese models that score higher on the benchmarks. For everyone else, the bigger question is what happens when Washington spent four years and billions of dollars trying to contain Chinese AI, and the only thing that actually shifted in the end was American customers.
1:11

This is the moment Chinese AI beat American AI. One of the largest public crypto companies in the world just DUMPED OpenAI and Anthropic. Coinbase switched to open-weight Chinese models from Zhipu and DeepSeek, and shaved nearly 50% off the company's internal AI spending. The numbers are absolutely ridiculous: Running the same enterprise workload through Anthropic's Claude costs $4,811. Running it through Zhipu's GLM 5.2 costs $544. That's a 9x price difference for equivalent output. OpenAI's GPT-5.5 sits in the middle at $3,357. DeepSeek's V4 lands at $1,071. Moonshot's Kimi at $948. On the actual benchmarks: Zhipu's GLM 5.2 scored 62.1 on SWE-bench Pro, the gold standard for coding. OpenAI's GPT-5.5 scored 58.6. One AI researcher called GLM 5.2 "at least as good as Opus 4.8 and GPT 5.5." Another called it "the first open model that can really compete with closed-source systems." The Chinese models are not just cheaper but they are now also beating American models on the benchmarks American companies pay $4,811 per workload for. Coinbase did the math first and reacted - more companies will certainly follow. Now watch what happens to the IPO timeline: Anthropic confidentially filed for an IPO targeting October at a $965 billion valuation. OpenAI followed days later with its own confidential filing. Both companies built their financial models on the assumption that they could keep charging enterprise prices that are 9 to 33x what Chinese competitors charge for the same task. Brian Armstrong publicly proved customers WILL leave. 45% of companies are now spending over $100,000 per month on AI, up from 20% last year. Every one of those customers is one quarterly budget review away from dumping American AI. OpenAI has reportedly already started preparing major token price cuts. Anthropic is expected to follow. And here's the thing... The export controls were supposed to CRUSH Chinese AI. The US government banned American AI chips, restricted model weights, blacklisted Alibaba and Baidu as Chinese military companies, and just banned Anthropic's flagship model from every foreign national on the planet. The entire premise of the American AI valuation bubble is that Washington can keep China two generations behind. But Chinese labs responded by building cheaper, more efficient models on inferior hardware and pricing them at one ninth the cost of the American alternative. And now American companies are voting with their checkbooks. The dominant American labs are valued at nearly $2 trillion combined on the assumption that their pricing power is durable. Coinbase proved it is not, and every customer doing a year-end budget review will be looking at the same math. For investors, the question here is what happens to the Anthropic IPO at $965 billion when the company is being forced to cut prices to defend share against open-weight Chinese models that score higher on the benchmarks. For everyone else, the bigger question is what happens when Washington spent four years and billions of dollars trying to contain Chinese AI, and the only thing that actually shifted in the end was American customers.

Ricardo

252,999 просмотров • 2 месяцев назад

🚨 TOMORROW COULD BE THE DAY THE AI BUBBLE FINALLY BREAKS Three of the biggest names in AI are suddenly talking about SLOWING DOWN China isn’t And Wall Street is sitting on one of the most crowded AI trades in history This is the setup almost nobody is prepared for For years, the entire AI trade has depended on one assumption: AI gets better FAST Companies spend hundreds of billions on chips, data centers and infrastructure Capabilities explode Profits eventually justify the spending But what happens when the people building the technology themselves start saying the frontier needs to be paced? That changes the equation completely Because the spending doesn’t magically disappear The infrastructure bills keep coming The expectations stay enormous But the timeline for monetizing it could get pushed further out Meanwhile, China keeps competing And we already know how violently markets can react when cheaper Chinese AI challenges the economics behind U.S. spending Now add regulators moving closer Add safety concerns from inside the industry Add hundreds of billions in AI capex that still needs to produce a return You suddenly have THREE risks hitting the same crowded trade: AI development slows Regulatory pressure rises Spending stays massive That combination is what I’m watching when the U.S. market opens tomorrow If Wall Street starts questioning the economics behind the AI boom, this won’t be about one company It could hit the entire trade at once And with the biggest tech names carrying so much weight in the market, the damage could spread FAST I publicly called the 2022 stock market crash I called Bitcoin’s $126K top in 2025 Now I’m warning about what I think could become the next major market move Tomorrow matters Follow and turn notifications on If the market starts breaking, I’ll post my next levels here first
0:28

🚨 TOMORROW COULD BE THE DAY THE AI BUBBLE FINALLY BREAKS Three of the biggest names in AI are suddenly talking about SLOWING DOWN China isn’t And Wall Street is sitting on one of the most crowded AI trades in history This is the setup almost nobody is prepared for For years, the entire AI trade has depended on one assumption: AI gets better FAST Companies spend hundreds of billions on chips, data centers and infrastructure Capabilities explode Profits eventually justify the spending But what happens when the people building the technology themselves start saying the frontier needs to be paced? That changes the equation completely Because the spending doesn’t magically disappear The infrastructure bills keep coming The expectations stay enormous But the timeline for monetizing it could get pushed further out Meanwhile, China keeps competing And we already know how violently markets can react when cheaper Chinese AI challenges the economics behind U.S. spending Now add regulators moving closer Add safety concerns from inside the industry Add hundreds of billions in AI capex that still needs to produce a return You suddenly have THREE risks hitting the same crowded trade: AI development slows Regulatory pressure rises Spending stays massive That combination is what I’m watching when the U.S. market opens tomorrow If Wall Street starts questioning the economics behind the AI boom, this won’t be about one company It could hit the entire trade at once And with the biggest tech names carrying so much weight in the market, the damage could spread FAST I publicly called the 2022 stock market crash I called Bitcoin’s $126K top in 2025 Now I’m warning about what I think could become the next major market move Tomorrow matters Follow and turn notifications on If the market starts breaking, I’ll post my next levels here first

Phantom_Defi

54,362 просмотров • 10 дней назад

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

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

Ricardo

2,976,578 просмотров • 4 месяцев назад

🚨 WARNING: SOMETHING VERY WIERD IS HAPPENING. The S&P 500 keeps printing new highs. Everyone is celebrating. Nobody is looking at what's actually holding it up. Semiconductor stocks are now worth $13.4 trillion. 19.7% of the entire index. Five years ago that number was closer to 5%. It quadrupled in a single cycle on a single bet. AI. And here's the number that should concern everyone. AI chips generate 50% of all semiconductor revenue. They represent less than 0.2% of total chip shipments. Half the revenue, a fraction of the volume, trillions in market cap sitting on top of a sliver of actual production. Three companies are carrying all of it. Nvidia. Broadcom. TSMC. The same names in every major institutional portfolio simultaneously. Everyone owns them, nobody can afford to be the first one out. And the way the money moves inside this system should sound familiar. Big players fund AI startups. AI startups spend that money on Big Tech infrastructure. Big Tech reports record AI revenue. Valuations justify the next round, the same dollar completes the loop and gets counted as growth every time. We watched this exact dynamic play out once before. 2000. A handful of tech companies carried the entire market. Valuations made no sense, narratives did the work that fundamentals couldn't. Then one company missed earnings, then another, then the S&P lost 50% and the Nasdaq lost 78%. The current setup is more concentrated, the valuations are more extreme. And one cut in AI spending is all it takes to start the unwind. I called the $16K Bitcoin bottom. I called the $126K top. Every major turn for 15 years public, before the move. The next call is already forming. Follow now and turn on notifications, you'll understand why that matters sooner than you think.
0:34

🚨 WARNING: SOMETHING VERY WIERD IS HAPPENING. The S&P 500 keeps printing new highs. Everyone is celebrating. Nobody is looking at what's actually holding it up. Semiconductor stocks are now worth $13.4 trillion. 19.7% of the entire index. Five years ago that number was closer to 5%. It quadrupled in a single cycle on a single bet. AI. And here's the number that should concern everyone. AI chips generate 50% of all semiconductor revenue. They represent less than 0.2% of total chip shipments. Half the revenue, a fraction of the volume, trillions in market cap sitting on top of a sliver of actual production. Three companies are carrying all of it. Nvidia. Broadcom. TSMC. The same names in every major institutional portfolio simultaneously. Everyone owns them, nobody can afford to be the first one out. And the way the money moves inside this system should sound familiar. Big players fund AI startups. AI startups spend that money on Big Tech infrastructure. Big Tech reports record AI revenue. Valuations justify the next round, the same dollar completes the loop and gets counted as growth every time. We watched this exact dynamic play out once before. 2000. A handful of tech companies carried the entire market. Valuations made no sense, narratives did the work that fundamentals couldn't. Then one company missed earnings, then another, then the S&P lost 50% and the Nasdaq lost 78%. The current setup is more concentrated, the valuations are more extreme. And one cut in AI spending is all it takes to start the unwind. I called the $16K Bitcoin bottom. I called the $126K top. Every major turn for 15 years public, before the move. The next call is already forming. Follow now and turn on notifications, you'll understand why that matters sooner than you think.

Hanzo ㊗️

18,356 просмотров • 2 месяцев назад

Mark my words, Nebius will be the first Trillion dollar Neo-cloud company and here is why (Save this). Roman Chernin, CEO of Nebius just said on 20VC that Nebius raised prices and demand didn't move. When a company can raise prices and still have more demand than supply, that's the opportunity. Chernin also explained why he is deliberately not charging the maximum. As AI shifts from training, a one time cost to inference, which is the ongoing cost of serving every user and every query, compute pricing becomes the cost structure of the entire AI economy. If Nebius prices customers out, those customers cannot grow, and Nebius cannot grow with them. That is the compounding flywheel built directly into the revenue model. The numbers are already confirming it. Q1 2026 revenue came in at $399 million, up 684% year over year. The AI cloud segment grew 840% and represented 98% of total revenue. Adjusted EBITDA flipped positive to $129.5 million. And Nebius signed a long-term agreement with Meta worth up to $27 billion over five years, a hyperscaler outsourcing its own AI compute stack to a neocloud, which tells you that even companies with $50 billion capex budgets cannot build fast enough. Goldman Sachs says the consensus is underestimating 2027 hyperscaler capex by $500 billion. Every dollar hyperscalers cannot provision themselves flows to neoclouds like Nebius. As that gap widens, Nebius captures the overflow with 3 gigawatts of contracted power already secured and a CEO who just told you raising prices did not dent demand. Our subscribers are already up massively on Nebius and come join Milk Road Pro for our full breakdown, how to size Nebius against the broader neocloud opportunity, and our full AI thesis. Link below!
0:47

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

Milk Road AI

15,677 просмотров • 3 месяцев назад

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 просмотров • 3 месяцев назад

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Microsoft gave OpenAI $13B. OpenAI sent it back through Azure. Microsoft booked it as revenue. Google committed $40B to Anthropic. Anthropic agreed to send it back through Google Cloud and chips. They all call this loop “revenue.” Let's connect the dots: - Big Tech invests in the AI. - The AI buys Big Tech cloud. - Big Tech reports AI growth. - AI valuations explode. And Wall Street calls it demand. Ray Dalio warned that late-stage bubbles are driven by loops. That is what this looks like now. The technology is real. But the money is moving in circles. When holders finally ask how much of this “AI demand” is real, the bubble will burst. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:25

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Microsoft gave OpenAI $13B. OpenAI sent it back through Azure. Microsoft booked it as revenue. Google committed $40B to Anthropic. Anthropic agreed to send it back through Google Cloud and chips. They all call this loop “revenue.” Let's connect the dots: - Big Tech invests in the AI. - The AI buys Big Tech cloud. - Big Tech reports AI growth. - AI valuations explode. And Wall Street calls it demand. Ray Dalio warned that late-stage bubbles are driven by loops. That is what this looks like now. The technology is real. But the money is moving in circles. When holders finally ask how much of this “AI demand” is real, the bubble will burst. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Alex Mason 👁△

291,574 просмотров • 3 месяцев назад

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Microsoft gave OpenAI $13B. OpenAI sent it back through Azure. Microsoft booked it as revenue. Google committed $40B to Anthropic. Anthropic agreed to send it back through Google Cloud and chips. They all call this loop “revenue.” Let's connect the dots: - Big Tech invests in the AI. - The AI buys Big Tech cloud. - Big Tech reports AI growth. - AI valuations explode. And Wall Street calls it demand. Ray Dalio warned that late-stage bubbles are driven by loops. That is what this looks like now. The technology is real. But the money is moving in circles. When holders finally ask how much of this “AI demand” is real, the bubble will burst. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:34

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Microsoft gave OpenAI $13B. OpenAI sent it back through Azure. Microsoft booked it as revenue. Google committed $40B to Anthropic. Anthropic agreed to send it back through Google Cloud and chips. They all call this loop “revenue.” Let's connect the dots: - Big Tech invests in the AI. - The AI buys Big Tech cloud. - Big Tech reports AI growth. - AI valuations explode. And Wall Street calls it demand. Ray Dalio warned that late-stage bubbles are driven by loops. That is what this looks like now. The technology is real. But the money is moving in circles. When holders finally ask how much of this “AI demand” is real, the bubble will burst. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

WhaleTwits

52,667 просмотров • 3 месяцев назад

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

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

Crypto Rover

58,862 просмотров • 4 месяцев назад

Jensen Huang just replaced the most important metric in global economics. Not trade volume. Not oil output. Not manufacturing. Compute. Huang: “Compute equals GDP. I know that for certain.” He did not say probably. He said certain. If your nation does not produce compute, it does not produce intelligence. If it does not produce intelligence, it does not produce revenue. Two links in the chain. Miss one and the whole thing breaks. Huang: “Not one country in the future will say, ‘Guess what, we’re gonna opt out on intelligence.’” Because opting out of compute is not a strategic decision. It is an extinction schedule. Every country that does not build its own inference capacity becomes a tenant in someone else’s infrastructure. Not an ally. Not a partner. A dependent. And dependents do not negotiate terms. They accept them. But this is not just a story about nations. Huang: “The entire software industry will be token-driven.” Every product. Every platform. Every service you touch. The entire business model of software is about to be measured in tokens consumed. Not seats sold. Not licenses renewed. Tokens burned. Software used to be a thing you bought. Now it is a thing that thinks. And thinking costs compute. Every query. Every action. Every decision the machine makes on your behalf. The meter is always running. Huang: “The entire internet industry could take 100% of their CapEx and make it AI because it’s better.” Not ten percent. Not a pilot program. One hundred percent. The moment any internet service rebuilds itself on generative intelligence, it outperforms every version that came before it. Search. Ads. Recommendation. Infrastructure. All of it. Better on contact. CapEx follows. All of it. Trillions moving in one direction with no offramp. The companies still budgeting AI as a line item are telling you exactly how much they understand. AI is not the line item. AI is the budget. The global economy is being re-denominated in a currency most people have not even heard of yet. Tokens. Whoever controls the supply of that currency is not playing in the new economy. They are the house. And the house does not lose.
2:07

Jensen Huang just replaced the most important metric in global economics. Not trade volume. Not oil output. Not manufacturing. Compute. Huang: “Compute equals GDP. I know that for certain.” He did not say probably. He said certain. If your nation does not produce compute, it does not produce intelligence. If it does not produce intelligence, it does not produce revenue. Two links in the chain. Miss one and the whole thing breaks. Huang: “Not one country in the future will say, ‘Guess what, we’re gonna opt out on intelligence.’” Because opting out of compute is not a strategic decision. It is an extinction schedule. Every country that does not build its own inference capacity becomes a tenant in someone else’s infrastructure. Not an ally. Not a partner. A dependent. And dependents do not negotiate terms. They accept them. But this is not just a story about nations. Huang: “The entire software industry will be token-driven.” Every product. Every platform. Every service you touch. The entire business model of software is about to be measured in tokens consumed. Not seats sold. Not licenses renewed. Tokens burned. Software used to be a thing you bought. Now it is a thing that thinks. And thinking costs compute. Every query. Every action. Every decision the machine makes on your behalf. The meter is always running. Huang: “The entire internet industry could take 100% of their CapEx and make it AI because it’s better.” Not ten percent. Not a pilot program. One hundred percent. The moment any internet service rebuilds itself on generative intelligence, it outperforms every version that came before it. Search. Ads. Recommendation. Infrastructure. All of it. Better on contact. CapEx follows. All of it. Trillions moving in one direction with no offramp. The companies still budgeting AI as a line item are telling you exactly how much they understand. AI is not the line item. AI is the budget. The global economy is being re-denominated in a currency most people have not even heard of yet. Tokens. Whoever controls the supply of that currency is not playing in the new economy. They are the house. And the house does not lose.

Dustin

43,505 просмотров • 6 месяцев назад

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.
1:42

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.

Ricardo

1,391,865 просмотров • 6 месяцев назад

THIS BUBBLE IS WORSE THAN 2000 If you have money in the stock market, read this carefully. The market is climbing while liquidity gets pulled out underneath it. Now look at valuations. Shiller CAPE: 42.05. The only time it was higher was 1999, right before the dot-com crash. Buffett Indicator: 229.9%. In 2000, it was 146%. That means today’s market is 1.6x higher than the dot-com peak by that metric. Buffett is sitting on $325B in cash and selling stocks. He is not guessing. He is reading the same math. Now concentration. Top 10 stocks control 41% of the S&P 500. They generate only 32% of profits. In 2000, top concentration was 23%. This is not a diversified index anymore. It is a crowded bet on a handful of companies, and most of them are tied to the same AI story. Now add leverage. Margin debt hit $1.28T. That is 4.1% of GDP. In 2000, it was 2.7%. Investors are borrowing more to buy stocks than they did at the dot-com peak. And the reversal may have already started. Margin debt peaked in January 2026 and dropped 4.5% in two months. The S&P dropped 5.9% in the same window. Last time margin debt rolled before the market? 2000. 2007. Every time, the market followed. Now look at AI. In 2000, telecom companies spent billions building fiber for “the internet future.” Capex hit 4.5% of GDP. Today, hyperscalers are spending on data centers for “the AI future.” Tech capex is 4.4% of GDP. Almost the same number. Back then, Lucent and Nortel helped finance customers who bought their equipment. Today, Nvidia invests in companies that buy Nvidia chips. Same loop. Different label. In 2000, the bubble was internet infrastructure. In 2026, it is AI infrastructure. The companies are bigger now. The spending is bigger. The index concentration is worse. The leverage is higher. And the market is priced like the returns are already guaranteed. That is the danger. If one major earnings report shows AI spending is not paying off, the repricing starts. And with 41% of the index sitting in the same trade, there is nowhere clean to hide. That’s why I’m watching this situation very closely right now. When the next move becomes clear, I’ll post it here first. Follow and turn notifications on.
0:38

THIS BUBBLE IS WORSE THAN 2000 If you have money in the stock market, read this carefully. The market is climbing while liquidity gets pulled out underneath it. Now look at valuations. Shiller CAPE: 42.05. The only time it was higher was 1999, right before the dot-com crash. Buffett Indicator: 229.9%. In 2000, it was 146%. That means today’s market is 1.6x higher than the dot-com peak by that metric. Buffett is sitting on $325B in cash and selling stocks. He is not guessing. He is reading the same math. Now concentration. Top 10 stocks control 41% of the S&P 500. They generate only 32% of profits. In 2000, top concentration was 23%. This is not a diversified index anymore. It is a crowded bet on a handful of companies, and most of them are tied to the same AI story. Now add leverage. Margin debt hit $1.28T. That is 4.1% of GDP. In 2000, it was 2.7%. Investors are borrowing more to buy stocks than they did at the dot-com peak. And the reversal may have already started. Margin debt peaked in January 2026 and dropped 4.5% in two months. The S&P dropped 5.9% in the same window. Last time margin debt rolled before the market? 2000. 2007. Every time, the market followed. Now look at AI. In 2000, telecom companies spent billions building fiber for “the internet future.” Capex hit 4.5% of GDP. Today, hyperscalers are spending on data centers for “the AI future.” Tech capex is 4.4% of GDP. Almost the same number. Back then, Lucent and Nortel helped finance customers who bought their equipment. Today, Nvidia invests in companies that buy Nvidia chips. Same loop. Different label. In 2000, the bubble was internet infrastructure. In 2026, it is AI infrastructure. The companies are bigger now. The spending is bigger. The index concentration is worse. The leverage is higher. And the market is priced like the returns are already guaranteed. That is the danger. If one major earnings report shows AI spending is not paying off, the repricing starts. And with 41% of the index sitting in the same trade, there is nowhere clean to hide. That’s why I’m watching this situation very closely right now. When the next move becomes clear, I’ll post it here first. Follow and turn notifications on.

Nonzee

83,631 просмотров • 4 месяцев назад

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”
1:42

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”

The All-In Podcast

152,633 просмотров • 3 месяцев назад

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?
1:05

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

93,654 просмотров • 2 месяцев назад

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Read this carefully before buying stocks like $SPCX. S&P 500 keeps hitting new all-time highs. The crowd thinks they are buying a resilient index. In reality, they are holding a ticking time bomb. And Big Tech is the one lighting the fuse. Apple, Microsoft, Google, Meta are no longer cash machines. Now they are just massive factories drowning in debt. By end of 2026, every cent of profit burns on chips and data centers. Even worse: their own billions aren't enough anymore. Big Tech borrowing exploded 357% in a single year. They are literally begging the market for cash. Why? This is pure FOMO. They're buried in debt for infrastructure that may never turn a profit. If AI doesn't fix productivity, this will be the most expensive bubble in history. And when this bubble bursts, it won't just hit tech. It will drag the entire S&P 500 into the abyss. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:22

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Read this carefully before buying stocks like $SPCX. S&P 500 keeps hitting new all-time highs. The crowd thinks they are buying a resilient index. In reality, they are holding a ticking time bomb. And Big Tech is the one lighting the fuse. Apple, Microsoft, Google, Meta are no longer cash machines. Now they are just massive factories drowning in debt. By end of 2026, every cent of profit burns on chips and data centers. Even worse: their own billions aren't enough anymore. Big Tech borrowing exploded 357% in a single year. They are literally begging the market for cash. Why? This is pure FOMO. They're buried in debt for infrastructure that may never turn a profit. If AI doesn't fix productivity, this will be the most expensive bubble in history. And when this bubble bursts, it won't just hit tech. It will drag the entire S&P 500 into the abyss. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

DANNY

42,675 просмотров • 3 месяцев назад