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THE UNCOMFORTABLE TRUTH about AI spending: US tech giants spent $380 billion on AI‑driven infrastructure capex in 2025. But most CFOs still can't point to measurable returns. The latest Duke CFO Survey tells the story nobody wants to hear: When asked about AI's impact over the past 12 months,...

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George Noble

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61,242 次观看 • 6 个月前 •via X (Twitter)

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Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat 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

120,284 次观看 • 6 个月前

I've been saying it for months: The AI hype will die. And I'm watching it happen in real time. Let me tell you something about market cycles after 45 years on Wall Strett: When everyone believes the same story, when valuations price in perfection, when "this time is different" becomes consensus... That's when the trade is OVER. The Mag 7 spent $380 billion on AI infrastructure in 2025. CFOs across America can't point to measurable returns. No productivity gains. No labor savings. No revenue acceleration. Just massive capex and promises of transformation "coming soon." I've seen this play out before. Dotcom 2000. Everyone knew the internet would change everything. They were RIGHT about the technology. They were WRONG about the timing and the valuations. The companies that survived took 15 years to make new highs. Here's what's happening now: The Mag 7 has already started underperforming. Since October 2025, the S&P 493 is outperforming the Magnificent 7. Small caps are waking up. The Russell 2000 is finally showing signs of life after years of underperformance. This is the rotation I predicted. And it's just getting started. Why small caps now? Because they're trading at decade-low valuations relative to Big Tech. The P/E spread is 10+ points. But earnings growth is only 5 points lower. You're getting 95% of the growth at 50% of the valuation. The Mag 7 is priced for AI transformation happening NOW. But Goldman says measurable GDP impact doesn't start until 2027. That's a 2-3 year gap between expectations and reality. Markets don't wait politely when they realize they're wrong. I ran the #1 mutual fund in the country at Fidelity and worked under Peter Lynch. And I'm telling you: This is what a market top in a narrow leadership group looks like. Not a crash. Not a crisis. Just a slow realization that the future takes longer to arrive than the stock price assumed. Meanwhile, small and mid-caps are trading like it's 2008. Except we're not in a financial crisis. We're in a market where everyone is crowded into seven stocks and ignoring 2,500 others. That doesn't last. The AI infrastructure build is real. But infrastructure builders get paid first. Productivity beneficiaries get paid later. Much later. And the market is finally starting to figure that out.
2:22

I've been saying it for months: The AI hype will die. And I'm watching it happen in real time. Let me tell you something about market cycles after 45 years on Wall Strett: When everyone believes the same story, when valuations price in perfection, when "this time is different" becomes consensus... That's when the trade is OVER. The Mag 7 spent $380 billion on AI infrastructure in 2025. CFOs across America can't point to measurable returns. No productivity gains. No labor savings. No revenue acceleration. Just massive capex and promises of transformation "coming soon." I've seen this play out before. Dotcom 2000. Everyone knew the internet would change everything. They were RIGHT about the technology. They were WRONG about the timing and the valuations. The companies that survived took 15 years to make new highs. Here's what's happening now: The Mag 7 has already started underperforming. Since October 2025, the S&P 493 is outperforming the Magnificent 7. Small caps are waking up. The Russell 2000 is finally showing signs of life after years of underperformance. This is the rotation I predicted. And it's just getting started. Why small caps now? Because they're trading at decade-low valuations relative to Big Tech. The P/E spread is 10+ points. But earnings growth is only 5 points lower. You're getting 95% of the growth at 50% of the valuation. The Mag 7 is priced for AI transformation happening NOW. But Goldman says measurable GDP impact doesn't start until 2027. That's a 2-3 year gap between expectations and reality. Markets don't wait politely when they realize they're wrong. I ran the #1 mutual fund in the country at Fidelity and worked under Peter Lynch. And I'm telling you: This is what a market top in a narrow leadership group looks like. Not a crash. Not a crisis. Just a slow realization that the future takes longer to arrive than the stock price assumed. Meanwhile, small and mid-caps are trading like it's 2008. Except we're not in a financial crisis. We're in a market where everyone is crowded into seven stocks and ignoring 2,500 others. That doesn't last. The AI infrastructure build is real. But infrastructure builders get paid first. Productivity beneficiaries get paid later. Much later. And the market is finally starting to figure that out.

George Noble

109,015 次观看 • 5 个月前

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 次观看 • 2 个月前

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

228,090 次观看 • 1 个月前

Schlumberger OUTPERFORMED Microsoft by 100% Read that again. In the last 3 months, a boring oilfield services company doubled the returns of one of the most valuable companies on earth. Why is no one talking about this? Energy is 3% of the S&P 500. Tech is 35%. That's not a weighting... That's a setup. I watched Japan go from 66% of the non-US index to an afterthought. I watched tech go from nothing to everything twice. And I'm telling you right now: This rotation is just getting started... If you're running $250 billion at Fidelity, you can't overweight energy 10x. The math doesn't work. A 3% sector means you maybe go to 5% if you're feeling aggressive. But tech at 35%? You can buy 40 different names and still be underweight. So institutional money piles into the same mega-caps. Microsoft. Apple. Nvidia. The same seven stocks that carried the market for years. Meanwhile energy gets ignored. Too small to matter. Too volatile to own. Too unfashionable to pitch. That's EXACTLY when you want to be there. The reflationary impulse is real. Interest rates are stimulative. Resources, metals, energy - everything geared into that impulse is working. And tech? The AI spending story is FALLING APART. $380 billion in capex last year. CFOs can't point to measurable returns. The Duke survey shows "no change" across productivity metrics at most companies deploying AI. You're paying 30x earnings for companies spending hundreds of billions on infrastructure that doesn't show up in the numbers yet. Or you can buy energy at single-digit multiples with real cash flows and a structural tailwind. The offshore drillers are where I see the most upside. Valaris got taken out at 84 after I pitched it at 54 in December. Tidewater is next. But if you want to keep it simple: long XLE, short XLK. One trade. Captures the entire rotation. This is the year active management finally matters. The dispersion is here. The setup is perfect. 5 years of underperformance trained everyone to just buy the index and forget about it. That's about to change.
2:58

Schlumberger OUTPERFORMED Microsoft by 100% Read that again. In the last 3 months, a boring oilfield services company doubled the returns of one of the most valuable companies on earth. Why is no one talking about this? Energy is 3% of the S&P 500. Tech is 35%. That's not a weighting... That's a setup. I watched Japan go from 66% of the non-US index to an afterthought. I watched tech go from nothing to everything twice. And I'm telling you right now: This rotation is just getting started... If you're running $250 billion at Fidelity, you can't overweight energy 10x. The math doesn't work. A 3% sector means you maybe go to 5% if you're feeling aggressive. But tech at 35%? You can buy 40 different names and still be underweight. So institutional money piles into the same mega-caps. Microsoft. Apple. Nvidia. The same seven stocks that carried the market for years. Meanwhile energy gets ignored. Too small to matter. Too volatile to own. Too unfashionable to pitch. That's EXACTLY when you want to be there. The reflationary impulse is real. Interest rates are stimulative. Resources, metals, energy - everything geared into that impulse is working. And tech? The AI spending story is FALLING APART. $380 billion in capex last year. CFOs can't point to measurable returns. The Duke survey shows "no change" across productivity metrics at most companies deploying AI. You're paying 30x earnings for companies spending hundreds of billions on infrastructure that doesn't show up in the numbers yet. Or you can buy energy at single-digit multiples with real cash flows and a structural tailwind. The offshore drillers are where I see the most upside. Valaris got taken out at 84 after I pitched it at 54 in December. Tidewater is next. But if you want to keep it simple: long XLE, short XLK. One trade. Captures the entire rotation. This is the year active management finally matters. The dispersion is here. The setup is perfect. 5 years of underperformance trained everyone to just buy the index and forget about it. That's about to change.

George Noble

61,372 次观看 • 5 个月前

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 次观看 • 1 个月前

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

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

Ricardo

159,345 次观看 • 2 个月前

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

2,965,365 次观看 • 2 个月前

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

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

Ricardo

1,814,717 次观看 • 6 个月前

When does the AI spending actually end? It's the question Wall Street doesn't want to answer. The Big Four hyperscalers are pouring $600+ billion into AI infrastructure this year alone. That's triple what they spent two years ago. Amazon just guided $200 billion in 2026 capex. The company is expected to go negative on free cash flow this year - somewhere between $17 billion and $28 billion in the red, depending on which bank you ask. Alphabet's free cash flow is projected to fall 90%. From $73 billion to $8 billion. These are the most profitable companies in history. And they're borrowing money to fund a buildout with no clear end date. The depreciation problem is what nobody wants to discuss: Nvidia chips run on a 2-3 year product cycle. Each new generation delivers 2-3x better performance. So the H100s shipping today will be economically obsolete by 2027. BUT the hyperscalers are depreciating these assets over 5-6 years. Meta extended its useful life estimates to five-and-a-half years. That single change cut $2.9 billion from their 2025 depreciation expense. Microsoft, Alphabet, Oracle - all made similar moves. Run the numbers and depreciation is understated by roughly $176 billion between 2026 and 2028. That means Oracle's earnings could be inflated by 27% and Meta's by 21%. This isn't fraud. GAAP allows it. But it's aggressive accounting that makes current earnings look far better than the underlying economics. The debt picture makes it even WORSE. The top five hyperscalers raised $108 billion in debt last year - more than 3x the prior nine-year average. JP Morgan projects $1.5 trillion in tech debt issuance ahead. They're even securitizing data center debt into asset-backed securities. $13.3 billion this year alone. Those structures have a history. This looks eerily similar to the data connectivity buildout circa 2000. In that cycle, telecoms built massive infrastructure on borrowed money chasing demand that never materialized. By 2002, less than 5% of capacity was in use. The pattern is familiar: Capex explodes. Returns don't materialize. Accounting flatters earnings. Debt bridges the gap. Then the music stops. I'm not making predictions about timing. But when free cash flow turns negative, when hyperscalers hold more debt than cash for the first time, when accounting changes are inflating earnings by double digits... The math changes. We've seen this play out before multiple times. AND IT DOESN'T END WELL
1:12

When does the AI spending actually end? It's the question Wall Street doesn't want to answer. The Big Four hyperscalers are pouring $600+ billion into AI infrastructure this year alone. That's triple what they spent two years ago. Amazon just guided $200 billion in 2026 capex. The company is expected to go negative on free cash flow this year - somewhere between $17 billion and $28 billion in the red, depending on which bank you ask. Alphabet's free cash flow is projected to fall 90%. From $73 billion to $8 billion. These are the most profitable companies in history. And they're borrowing money to fund a buildout with no clear end date. The depreciation problem is what nobody wants to discuss: Nvidia chips run on a 2-3 year product cycle. Each new generation delivers 2-3x better performance. So the H100s shipping today will be economically obsolete by 2027. BUT the hyperscalers are depreciating these assets over 5-6 years. Meta extended its useful life estimates to five-and-a-half years. That single change cut $2.9 billion from their 2025 depreciation expense. Microsoft, Alphabet, Oracle - all made similar moves. Run the numbers and depreciation is understated by roughly $176 billion between 2026 and 2028. That means Oracle's earnings could be inflated by 27% and Meta's by 21%. This isn't fraud. GAAP allows it. But it's aggressive accounting that makes current earnings look far better than the underlying economics. The debt picture makes it even WORSE. The top five hyperscalers raised $108 billion in debt last year - more than 3x the prior nine-year average. JP Morgan projects $1.5 trillion in tech debt issuance ahead. They're even securitizing data center debt into asset-backed securities. $13.3 billion this year alone. Those structures have a history. This looks eerily similar to the data connectivity buildout circa 2000. In that cycle, telecoms built massive infrastructure on borrowed money chasing demand that never materialized. By 2002, less than 5% of capacity was in use. The pattern is familiar: Capex explodes. Returns don't materialize. Accounting flatters earnings. Debt bridges the gap. Then the music stops. I'm not making predictions about timing. But when free cash flow turns negative, when hyperscalers hold more debt than cash for the first time, when accounting changes are inflating earnings by double digits... The math changes. We've seen this play out before multiple times. AND IT DOESN'T END WELL

George Noble

37,067 次观看 • 5 个月前

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.
1:39

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.

George Noble

23,931 次观看 • 6 个月前

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

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

Ricardo

725,581 次观看 • 5 个月前

Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!
1:00

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

Milk Road AI

14,578 次观看 • 1 个月前

SUNDAR PICHAI: AI IS REWRITING EARTH’S INFRASTRUCTURE IN TWO YEARS Sundar laid it out with quiet certainty: the AI boom isn’t coming, it’s already rewriting the laws of scale: "You know, maybe four years ago Google was spending less than $30 billion per year. This year, that number is gonna be over $90 billion. And if you collectively add what all the companies are doing, we have well over a trillion dollars of investment going in, in building the infrastructure for this moment. And one way I think about it is in the next couple of years, we'll end up building what we probably built in the past 10 to 20 years. So hopefully just in a couple of years. So that gives you the scale at which this is ramping up." It’s the kind of scale that makes empires blink. Alphabet’s AI capex shot from $24 billion in 2021 to over $90 billion in 2025, powered by Gemini, Vertex AI, and an arms race for compute that’s now burning through a trillion dollars across Google, Microsoft, Amazon, and Meta. Source: thewizeai / Sundar Pichai
0:36

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

Mario Nawfal

163,412 次观看 • 8 个月前

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.
0:28

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.

Crypto Rover

86,404 次观看 • 1 个月前

Elon Musk just confirmed the most INSANE IPO in history. SpaceX is going public in 2026. $1.5 TRILLION valuation. Raising $30+ billion. That's the biggest IPO ever made. Beating Saudi Aramco's $29 billion record from 2019. But here's what everyone's missing: This isn't about space tourism or Mars missions. Elon is literally about to win the entire AI race. And 99% of people have no idea how... Here's the problem killing every AI company right now: POWER. Oracle just reported earnings. They burned through $12 BILLION in one quarter building data centers. Their free cash flow? NEGATIVE $10 billion. Revenue missed estimates. Stock crashed 11%. Microsoft, Amazon, Google all scrambling to find enough electricity for AI training. The brutal math: The US generates 490 gigawatts of total power. AI is projected to need 123 gigawatts by 2035. That's a QUARTER of the entire electrical grid. Just for artificial intelligence. Goldman Sachs says AI energy demand could jump 165% by 2030. There is literally not enough power on Earth to run AI at the scale these companies are promising. Every data center needs massive cooling systems. Billions of gallons of water per year. Insane energy costs. And the infrastructure can't keep up. Elon's solution? Stop building on Earth entirely. SpaceX is building data centers in SPACE. Not a concept. Not 10 years out. Literally starting in 2026. They're upgrading Starlink V3 satellites to carry AI computing chips. Each satellite gets 24/7 solar power. No clouds. No night. No weather disruptions. No grid bottlenecks. And the insane part is that Starship can deliver 300 to 500 gigawatts of solar-powered AI satellites into orbit every single year. At 300 gigawatts per year, the AI computing power in space would exceed the entire U.S. economy's total electricity consumption within two years. Just from satellites. Processing in orbit. While Oracle is begging banks for loans to finish data centers and OpenAI is stuck in circular funding arrangements with Microsoft, Elon already owns everything: The rockets. The satellites. The launch infrastructure. The AI company (xAI). He doesn't need to ask utilities for permission. Doesn't need grid approvals from local governments. Doesn't need to build nuclear plants or wait for clean energy. He just launches. And everyone else is scrambling to catch up: Jeff Bezos sees it. Blue Origin announced they're building their own orbital data centers. Google just launched "Project Suncatcher" with plans to deploy AI satellites by 2027. Eric Schmidt, the former CEO of Google, literally BOUGHT an entire rocket company (Relativity Space) just to compete in this space. But they're all 3+ years behind Elon. SpaceX already has 6,000+ Starlink satellites in orbit. The infrastructure is built. The $30 billion from the IPO? Going straight into scaling orbital compute. SpaceX revenue is jumping from $15 billion in 2025 to $24 billion in 2026. Most of that from Starlink. Now add space-based AI infrastructure on top. Here's why this matters: Whoever controls orbital computing controls the AI revolution. And there's only ONE company on Earth with fully reusable rockets that can launch at the scale required. Jensen Huang, Nvidia's CEO, called space data centers "a dream." Translation: Nvidia is screwed if Elon actually pulls this off. Because if SpaceX succeeds, every AI company on the planet becomes Elon's customer. OpenAI needs compute? Running on SpaceX satellites. Google needs more capacity? Renting orbital infrastructure. Microsoft needs power? Paying SpaceX for launch and compute access. Elon won't just be in the AI race. He'll own the entire track everyone else is running on. The $1.5 trillion valuation sounds crazy until you realize what he's actually building. It's not a rocket company. It's the infrastructure layer for the next 50 years of computing. People calling it overvalued have no idea what's coming.
2:27

Elon Musk just confirmed the most INSANE IPO in history. SpaceX is going public in 2026. $1.5 TRILLION valuation. Raising $30+ billion. That's the biggest IPO ever made. Beating Saudi Aramco's $29 billion record from 2019. But here's what everyone's missing: This isn't about space tourism or Mars missions. Elon is literally about to win the entire AI race. And 99% of people have no idea how... Here's the problem killing every AI company right now: POWER. Oracle just reported earnings. They burned through $12 BILLION in one quarter building data centers. Their free cash flow? NEGATIVE $10 billion. Revenue missed estimates. Stock crashed 11%. Microsoft, Amazon, Google all scrambling to find enough electricity for AI training. The brutal math: The US generates 490 gigawatts of total power. AI is projected to need 123 gigawatts by 2035. That's a QUARTER of the entire electrical grid. Just for artificial intelligence. Goldman Sachs says AI energy demand could jump 165% by 2030. There is literally not enough power on Earth to run AI at the scale these companies are promising. Every data center needs massive cooling systems. Billions of gallons of water per year. Insane energy costs. And the infrastructure can't keep up. Elon's solution? Stop building on Earth entirely. SpaceX is building data centers in SPACE. Not a concept. Not 10 years out. Literally starting in 2026. They're upgrading Starlink V3 satellites to carry AI computing chips. Each satellite gets 24/7 solar power. No clouds. No night. No weather disruptions. No grid bottlenecks. And the insane part is that Starship can deliver 300 to 500 gigawatts of solar-powered AI satellites into orbit every single year. At 300 gigawatts per year, the AI computing power in space would exceed the entire U.S. economy's total electricity consumption within two years. Just from satellites. Processing in orbit. While Oracle is begging banks for loans to finish data centers and OpenAI is stuck in circular funding arrangements with Microsoft, Elon already owns everything: The rockets. The satellites. The launch infrastructure. The AI company (xAI). He doesn't need to ask utilities for permission. Doesn't need grid approvals from local governments. Doesn't need to build nuclear plants or wait for clean energy. He just launches. And everyone else is scrambling to catch up: Jeff Bezos sees it. Blue Origin announced they're building their own orbital data centers. Google just launched "Project Suncatcher" with plans to deploy AI satellites by 2027. Eric Schmidt, the former CEO of Google, literally BOUGHT an entire rocket company (Relativity Space) just to compete in this space. But they're all 3+ years behind Elon. SpaceX already has 6,000+ Starlink satellites in orbit. The infrastructure is built. The $30 billion from the IPO? Going straight into scaling orbital compute. SpaceX revenue is jumping from $15 billion in 2025 to $24 billion in 2026. Most of that from Starlink. Now add space-based AI infrastructure on top. Here's why this matters: Whoever controls orbital computing controls the AI revolution. And there's only ONE company on Earth with fully reusable rockets that can launch at the scale required. Jensen Huang, Nvidia's CEO, called space data centers "a dream." Translation: Nvidia is screwed if Elon actually pulls this off. Because if SpaceX succeeds, every AI company on the planet becomes Elon's customer. OpenAI needs compute? Running on SpaceX satellites. Google needs more capacity? Renting orbital infrastructure. Microsoft needs power? Paying SpaceX for launch and compute access. Elon won't just be in the AI race. He'll own the entire track everyone else is running on. The $1.5 trillion valuation sounds crazy until you realize what he's actually building. It's not a rocket company. It's the infrastructure layer for the next 50 years of computing. People calling it overvalued have no idea what's coming.

Ricardo

2,907,604 次观看 • 7 个月前

Ken Griffin just asked the question everyone in AI is too scared to answer. Data center spending in the US this year alone is over $500 billion. Half a trillion dollars. To raise that kind of money, you have to make a promise. And the promise has to be big. "AI needs to be your savior almost. How else are you going to write $500 billion of checks in a single year?" He's not saying AI is fraud. He's saying the hype is structurally necessary. You can't fund a buildout at this scale without narrative that matches it. The real question is what AI actually delivers at the end. In some areas Griffin says it's going to be profound. Call centers. Software engineering productivity. Those are real, measurable, already happening. But in white collar work more broadly, he's more skeptical. A Harvard paper recently coined a term for it: AI Work Slop. Output that looks impressive on the surface. First few sentences read like genuine insight. Then you go deeper and it's all garbage. Griffin's colleague runs their commodities business. Got handed a report generated by an AI engine. First paragraph, genuinely good. The rest, useless. The model that can write a compelling opening can't yet think through the substance underneath it. This is the AI investing tension right now. The infrastructure spend is real. The hype is real. The productivity gains in specific verticals are real. But the blanket assumption that AI transforms every white collar job equally has not been proven yet.
1:20

Ken Griffin just asked the question everyone in AI is too scared to answer. Data center spending in the US this year alone is over $500 billion. Half a trillion dollars. To raise that kind of money, you have to make a promise. And the promise has to be big. "AI needs to be your savior almost. How else are you going to write $500 billion of checks in a single year?" He's not saying AI is fraud. He's saying the hype is structurally necessary. You can't fund a buildout at this scale without narrative that matches it. The real question is what AI actually delivers at the end. In some areas Griffin says it's going to be profound. Call centers. Software engineering productivity. Those are real, measurable, already happening. But in white collar work more broadly, he's more skeptical. A Harvard paper recently coined a term for it: AI Work Slop. Output that looks impressive on the surface. First few sentences read like genuine insight. Then you go deeper and it's all garbage. Griffin's colleague runs their commodities business. Got handed a report generated by an AI engine. First paragraph, genuinely good. The rest, useless. The model that can write a compelling opening can't yet think through the substance underneath it. This is the AI investing tension right now. The infrastructure spend is real. The hype is real. The productivity gains in specific verticals are real. But the blanket assumption that AI transforms every white collar job equally has not been proven yet.

Milk Road Macro

125,923 次观看 • 2 个月前

Mistral CEO Arthur Mensch walked into the French parliament this week and told lawmakers Europe has exactly two years to build independent AI infrastructure or hand over one trillion dollars in spending to American tech companies. The math he laid out should be front-page news. This is not a technology story. It is a macroeconomic one. Global wages are fifty trillion dollars. AI will cost around ten percent of that. Europe's share is nine trillion. Which means over one trillion dollars in AI spending over the next five years flowing somewhere. Europe already sends 250 billion dollars a year to the US in digital services. Every dollar that leaves funds American R&D. None of it comes back. Mario Draghi's competitiveness report last year made the same point from a different angle. Europe needs eight hundred billion euros in annual investment just to close its existing productivity gap with the US. AI dependency makes that gap structural. You cannot close a productivity gap when the tool driving productivity is owned by your competitor. Mensch compared it to gas. The same way Europe discovered too late what energy dependence costs in a crisis, it is standing at the same crossroads with AI right now. The compute is being allocated today. The chips are being spoken for today. Europe is watching it happen and calling it a technology debate. It is not. It is a sovereignty one. Watch the full podcast on YouTube at CNBC International
2:47

Mistral CEO Arthur Mensch walked into the French parliament this week and told lawmakers Europe has exactly two years to build independent AI infrastructure or hand over one trillion dollars in spending to American tech companies. The math he laid out should be front-page news. This is not a technology story. It is a macroeconomic one. Global wages are fifty trillion dollars. AI will cost around ten percent of that. Europe's share is nine trillion. Which means over one trillion dollars in AI spending over the next five years flowing somewhere. Europe already sends 250 billion dollars a year to the US in digital services. Every dollar that leaves funds American R&D. None of it comes back. Mario Draghi's competitiveness report last year made the same point from a different angle. Europe needs eight hundred billion euros in annual investment just to close its existing productivity gap with the US. AI dependency makes that gap structural. You cannot close a productivity gap when the tool driving productivity is owned by your competitor. Mensch compared it to gas. The same way Europe discovered too late what energy dependence costs in a crisis, it is standing at the same crossroads with AI right now. The compute is being allocated today. The chips are being spoken for today. Europe is watching it happen and calling it a technology debate. It is not. It is a sovereignty one. Watch the full podcast on YouTube at CNBC International

Ihtesham Ali

155,334 次观看 • 1 个月前

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

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

Milk Road AI

115,608 次观看 • 2 个月前

Goldman Sachs just published the list of jobs AI will eliminate first. 300 million jobs globally. 25% of all US work hours. And that's not in 10 years, it's starting NOW. Highest risk of displacement according to Goldman: 1. Computer programmers 2. Accountants 3. Auditors 4. Legal assistants 5. Administrative assistants 6. Customer service reps 7. Telemarketers 8. Proofreaders 9. Copy editors 10. Credit analysts. 46% of all office and administrative tasks can be automated. 44% of legal work. 37% of architecture and engineering. 36% of science. 35% of business and finance. These aren't warehouse jobs. These aren't factory floor positions. These are the careers parents told their kids to pursue. "Go to college. Get a degree. Get a desk job. You'll be safe." Goldman Sachs just told you that desk is getting emptied. And the data is already showing up in real time: Tech employment as a share of the US economy has dropped below its long-term trend for the first time since records began. Marketing consulting, graphic design, office administration, and call centers are all seeing employment growth fall below trend. Younger workers are getting hit first and hardest. Goldman's lead economist said it directly: "The big story in 2026 in labor will be AI." But here's what the report doesn't mention: Goldman Sachs is one of the biggest investors in the companies BUILDING the AI that eliminates these jobs. They underwrote OpenAI's funding rounds. They're advising on the $700 billion in AI infrastructure spending this year. They profit from every merger, every capex deal, every stock offering tied to AI. The same bank telling you 300 million jobs are at risk is making billions helping the companies that will take them. And the corporate playbook is already locked in: Meta is firing 16,000 people. 20% of its entire workforce. While doubling AI spending to $135 billion. Stock went up 3% on the announcement. Block fired 40% of its staff. Stock surged 24%. Atlassian cut 10%. Same pattern. Over 61,000 AI-linked layoffs since November. 764 people per day losing their jobs in tech alone. Every single time a company announces mass layoffs and says "AI," the stock price goes up. Wall Street has created a system where firing humans is the most profitable announcement a CEO can make. Goldman's report says the jobs most PROTECTED from AI are air traffic controllers, chief executives, radiologists, pharmacists, and members of the clergy. Notice who's safe? The people at the top and the people praying. Everyone in the middle is exposed. The entry-level white-collar worker who spent four years and $200,000 on a degree is now competing against software that works 24/7, never takes vacation, never asks for a raise, and improves every single week. Goldman even admits younger workers in their 20s and 30s entering knowledge and content creation sectors will be "most affected." The generation that was told AI would make their lives better is the one getting displaced by it first. And it gets even WORSE: Goldman says if this displacement happens faster than their 10 year base case, the economic impact "could be much larger." Basically: if companies move fast, which they already are, the fallout will be worse than their projections. They're already moving fast. $700 billion in AI infrastructure this year. Mass layoffs at every major tech company. Stock prices rewarding every single one. The report is 50 pages of data telling you exactly what's coming. Most people won't read past the headline. But you just did.
1:14

Goldman Sachs just published the list of jobs AI will eliminate first. 300 million jobs globally. 25% of all US work hours. And that's not in 10 years, it's starting NOW. Highest risk of displacement according to Goldman: 1. Computer programmers 2. Accountants 3. Auditors 4. Legal assistants 5. Administrative assistants 6. Customer service reps 7. Telemarketers 8. Proofreaders 9. Copy editors 10. Credit analysts. 46% of all office and administrative tasks can be automated. 44% of legal work. 37% of architecture and engineering. 36% of science. 35% of business and finance. These aren't warehouse jobs. These aren't factory floor positions. These are the careers parents told their kids to pursue. "Go to college. Get a degree. Get a desk job. You'll be safe." Goldman Sachs just told you that desk is getting emptied. And the data is already showing up in real time: Tech employment as a share of the US economy has dropped below its long-term trend for the first time since records began. Marketing consulting, graphic design, office administration, and call centers are all seeing employment growth fall below trend. Younger workers are getting hit first and hardest. Goldman's lead economist said it directly: "The big story in 2026 in labor will be AI." But here's what the report doesn't mention: Goldman Sachs is one of the biggest investors in the companies BUILDING the AI that eliminates these jobs. They underwrote OpenAI's funding rounds. They're advising on the $700 billion in AI infrastructure spending this year. They profit from every merger, every capex deal, every stock offering tied to AI. The same bank telling you 300 million jobs are at risk is making billions helping the companies that will take them. And the corporate playbook is already locked in: Meta is firing 16,000 people. 20% of its entire workforce. While doubling AI spending to $135 billion. Stock went up 3% on the announcement. Block fired 40% of its staff. Stock surged 24%. Atlassian cut 10%. Same pattern. Over 61,000 AI-linked layoffs since November. 764 people per day losing their jobs in tech alone. Every single time a company announces mass layoffs and says "AI," the stock price goes up. Wall Street has created a system where firing humans is the most profitable announcement a CEO can make. Goldman's report says the jobs most PROTECTED from AI are air traffic controllers, chief executives, radiologists, pharmacists, and members of the clergy. Notice who's safe? The people at the top and the people praying. Everyone in the middle is exposed. The entry-level white-collar worker who spent four years and $200,000 on a degree is now competing against software that works 24/7, never takes vacation, never asks for a raise, and improves every single week. Goldman even admits younger workers in their 20s and 30s entering knowledge and content creation sectors will be "most affected." The generation that was told AI would make their lives better is the one getting displaced by it first. And it gets even WORSE: Goldman says if this displacement happens faster than their 10 year base case, the economic impact "could be much larger." Basically: if companies move fast, which they already are, the fallout will be worse than their projections. They're already moving fast. $700 billion in AI infrastructure this year. Mass layoffs at every major tech company. Stock prices rewarding every single one. The report is 50 pages of data telling you exactly what's coming. Most people won't read past the headline. But you just did.

Ricardo

235,676 次观看 • 4 个月前