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$NVDA sits at its lowest forward P/E in a decade. Gavin Baker says that multiple is missing two moats. The Street prices it like AMD and custom silicon are closing the gap. Baker's case: Nvidia rolled out a financing structure that pairs equity stakes in AI buyers with a...

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Podcast Alpha

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34,734 просмотров • 18 дней назад •via X (Twitter)

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GAVIN BAKER: NVIDIA IS NO LONGER JUST A CHIP COMPANY. Gavin Baker recently laid out one of the sharpest observations about the current AI hardware cycle, and it explains why Nvidia's multiple looks strange relative to the strategic position the company actually occupies. The starting point is a shift in how semiconductor supply is being allocated. Historically, buyers with enormous volume like Apple could break long-term agreements with suppliers whenever they wanted. There was no meaningful consequence, because the supplier had nowhere else to go. That world is over. In the current cycle, there are at least four major memory buyers with genuine scale, plus a wave of AI startups adding to demand. The dynamic has flipped. If a hyperscaler breaks an LTA on price, the supplier can now retaliate by reallocating volume to a competitor. In a cyclical industry where oversupply is always followed by undersupply, breaking an LTA today can cost you your entire allocation the next time capacity gets tight. That leverage is one reason Nvidia's position is so unusual. Every constraint in the AI buildout tilts in its favor. Nothing on earth is more financeable than an Nvidia GPU. And on land and power, Nvidia is playing the matchmaking chess game between customers, energy suppliers, and infrastructure partners. The most interesting development Baker highlighted is Nvidia's new business model. He described it as a credit wrapper with a revenue share above a certain GPU price floor. Nvidia is financing customer deployments while retaining upside if GPU prices stay elevated. This converts Nvidia from a pure hardware vendor into something structurally closer to a cloud franchise via royalties on the compute being deployed on its chips. Nvidia could end up with an effectively massive cloud business very quickly, not through building data centers but through collecting royalties on the compute those data centers produce. The market is still pricing $NVDA as a semiconductor company. The company itself has already moved beyond that model. The gap between the two will eventually close. Invest Like the Best Patrick OShaughnessy Gavin Baker
1:37

GAVIN BAKER: NVIDIA IS NO LONGER JUST A CHIP COMPANY. Gavin Baker recently laid out one of the sharpest observations about the current AI hardware cycle, and it explains why Nvidia's multiple looks strange relative to the strategic position the company actually occupies. The starting point is a shift in how semiconductor supply is being allocated. Historically, buyers with enormous volume like Apple could break long-term agreements with suppliers whenever they wanted. There was no meaningful consequence, because the supplier had nowhere else to go. That world is over. In the current cycle, there are at least four major memory buyers with genuine scale, plus a wave of AI startups adding to demand. The dynamic has flipped. If a hyperscaler breaks an LTA on price, the supplier can now retaliate by reallocating volume to a competitor. In a cyclical industry where oversupply is always followed by undersupply, breaking an LTA today can cost you your entire allocation the next time capacity gets tight. That leverage is one reason Nvidia's position is so unusual. Every constraint in the AI buildout tilts in its favor. Nothing on earth is more financeable than an Nvidia GPU. And on land and power, Nvidia is playing the matchmaking chess game between customers, energy suppliers, and infrastructure partners. The most interesting development Baker highlighted is Nvidia's new business model. He described it as a credit wrapper with a revenue share above a certain GPU price floor. Nvidia is financing customer deployments while retaining upside if GPU prices stay elevated. This converts Nvidia from a pure hardware vendor into something structurally closer to a cloud franchise via royalties on the compute being deployed on its chips. Nvidia could end up with an effectively massive cloud business very quickly, not through building data centers but through collecting royalties on the compute those data centers produce. The market is still pricing $NVDA as a semiconductor company. The company itself has already moved beyond that model. The gap between the two will eventually close. Invest Like the Best Patrick OShaughnessy Gavin Baker

Lumida Wealth Management

194,482 просмотров • 20 дней назад

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.
1:42

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

Bull Theory

26,765 просмотров • 2 месяцев назад

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 просмотров • 3 месяцев назад

Gavin Baker Gavin Baker: the Anthropic S1 is going to break a lot of people's brains. The entire AI bear case rests on one assumption, that tokens are sold below cost. What the filing settles, and who has to cover: Baker says that assumption is wrong. Open-source tokens are profitable. Anthropic is profitable and generating cash. OpenAI is close. Most tokens in the chain make money today. The S1 is the first audited document that settles it either way. Jim Chanos James Chanos has spent the year on the other side, calling the AI buildout a circular financing story. Plain version for a position holder: a lot of macro and value money is bearish on AI because it believes tokens are subsidized. If the filing shows real margins, that money covers into a re-rating and $NVDA, $TSM, $MU and the hyperscalers all move on it. If the filing shows subsidized tokens, every valuation downstream of it is sitting on sand. One document, one binary, a date on the calendar. David Sacks David Sacks and Jason Calacanis @jason worked him over on it for a full segment. Source: All-In Podcast -
2:40

Gavin Baker Gavin Baker: the Anthropic S1 is going to break a lot of people's brains. The entire AI bear case rests on one assumption, that tokens are sold below cost. What the filing settles, and who has to cover: Baker says that assumption is wrong. Open-source tokens are profitable. Anthropic is profitable and generating cash. OpenAI is close. Most tokens in the chain make money today. The S1 is the first audited document that settles it either way. Jim Chanos James Chanos has spent the year on the other side, calling the AI buildout a circular financing story. Plain version for a position holder: a lot of macro and value money is bearish on AI because it believes tokens are subsidized. If the filing shows real margins, that money covers into a re-rating and $NVDA, $TSM, $MU and the hyperscalers all move on it. If the filing shows subsidized tokens, every valuation downstream of it is sitting on sand. One document, one binary, a date on the calendar. David Sacks David Sacks and Jason Calacanis @jason worked him over on it for a full segment. Source: All-In Podcast -

Podcast Alpha

29,375 просмотров • 8 дней назад

CITADEL QUANTS HIDE THE GAME BEHIND A FORMULA WITH TEN TERMS, PREDICTION MARKETS RUN ON EXACTLY ONE The secret is one line. Edge = P(model) − P(market) That Z-field monster on screen is a stack of simple signals wearing a costume. A prediction market strips all of it down to one number, the crowd's guess at a probability. In plain words. If you can turn fresh news into a probability before the crowd reprices, the gap between your number and their price is the whole edge. That is why the game is not IQ, it is speed to a number. An AI that reads live news does in ninety seconds what the tab takes hours to price in. I broke down the four exact prompts that turn Grok into a probability desk, the sizing rules and the ten minute daily routine in the article. Still bets, not a salary, so size like it & the full playbook is in the article. Read it ↓
0:46

CITADEL QUANTS HIDE THE GAME BEHIND A FORMULA WITH TEN TERMS, PREDICTION MARKETS RUN ON EXACTLY ONE The secret is one line. Edge = P(model) − P(market) That Z-field monster on screen is a stack of simple signals wearing a costume. A prediction market strips all of it down to one number, the crowd's guess at a probability. In plain words. If you can turn fresh news into a probability before the crowd reprices, the gap between your number and their price is the whole edge. That is why the game is not IQ, it is speed to a number. An AI that reads live news does in ninety seconds what the tab takes hours to price in. I broke down the four exact prompts that turn Grok into a probability desk, the sizing rules and the ten minute daily routine in the article. Still bets, not a salary, so size like it & the full playbook is in the article. Read it ↓

slash1s

15,977 просмотров • 1 месяц назад

$GOOGL fell 7-10% on its first negative free cash flow quarter since IPO. Chamath Palihapitiya Chamath Palihapitiya says buy the machine. Google has compounded capital at roughly 32% ROIC for 25 years, and it just guided capex to $195-205B. The Street read a red flag. Chamath reads a compounder spending into its lead. The deeper point - frontier models now commoditize within weeks, so value drains from the model layer toward infrastructure. That fragmentation hurts Anthropic and OpenAI and helps Google, which monetizes at silicon and cloud no matter which model wins. Cloud grew 82% on a $100B run rate. Gavin Baker Gavin Baker has argued hyperscaler capex is rational investment, not waste - the same read. The call - the negative-FCF print is an entry into the cleanest way to own the AI buildout without betting on a single model. Why model-layer commoditization is a tailwind for $GOOGL: Source: All-In Podcast -
6:11

$GOOGL fell 7-10% on its first negative free cash flow quarter since IPO. Chamath Palihapitiya Chamath Palihapitiya says buy the machine. Google has compounded capital at roughly 32% ROIC for 25 years, and it just guided capex to $195-205B. The Street read a red flag. Chamath reads a compounder spending into its lead. The deeper point - frontier models now commoditize within weeks, so value drains from the model layer toward infrastructure. That fragmentation hurts Anthropic and OpenAI and helps Google, which monetizes at silicon and cloud no matter which model wins. Cloud grew 82% on a $100B run rate. Gavin Baker Gavin Baker has argued hyperscaler capex is rational investment, not waste - the same read. The call - the negative-FCF print is an entry into the cleanest way to own the AI buildout without betting on a single model. Why model-layer commoditization is a tailwind for $GOOGL: Source: All-In Podcast -

Podcast Alpha

42,627 просмотров • 1 месяц назад

$SNDK is capping its own margins, and the market is reading it as the top. It is the most bullish thing in the print. (Save this) Gross margin ran from 51% to 84.6% over 3 quarters, and now Q1 guidance sits between 83-85%. On the surface, that looks like the classic warning sign. Every memory cycle ended with strong pricing pulling in CapEx until oversupply crushed margins. My view is that this plateau has a completely different cause. Gavin Baker explains it in the video below. Memory companies are trading short-term upside for long-term agreements, where customers prepay and both a floor and a ceiling are set on prices. Basically, SanDisk gave up the top of the price spike in exchange for guaranteed demand years out. Less/no cyclicality! My view is that a business with contracted revenue, a protected floor and volumes climbing deserves to trade like infrastructure, not like a commodity producer. = higher multiples for memory!
1:37

$SNDK is capping its own margins, and the market is reading it as the top. It is the most bullish thing in the print. (Save this) Gross margin ran from 51% to 84.6% over 3 quarters, and now Q1 guidance sits between 83-85%. On the surface, that looks like the classic warning sign. Every memory cycle ended with strong pricing pulling in CapEx until oversupply crushed margins. My view is that this plateau has a completely different cause. Gavin Baker explains it in the video below. Memory companies are trading short-term upside for long-term agreements, where customers prepay and both a floor and a ceiling are set on prices. Basically, SanDisk gave up the top of the price spike in exchange for guaranteed demand years out. Less/no cyclicality! My view is that a business with contracted revenue, a protected floor and volumes climbing deserves to trade like infrastructure, not like a commodity producer. = higher multiples for memory!

Vincent

56,810 просмотров • 18 дней назад

Why is OpenAI cutting codex pricing right now? Gavin Baker Gavin Baker of Atreides Management thinks every major player knows the answer - and is acting on it privately. Baker told John Coogan on TBPN: OpenAI's codex price cuts are not a margin decision. They're a strategic move to stay in the recursive self-improvement loop - the cycle where AI improves its own code. Companies that lose coding token share now may find themselves outside that loop when it matters most. Baker's read on $META: Zuckerberg has no articulated product anchor for "personal superintelligence." But the mechanism that forced his hand in 2022 - the stock - is likely to activate again. What the end game looks like and who survives it: Source: TBPN -
0:43

Why is OpenAI cutting codex pricing right now? Gavin Baker Gavin Baker of Atreides Management thinks every major player knows the answer - and is acting on it privately. Baker told John Coogan on TBPN: OpenAI's codex price cuts are not a margin decision. They're a strategic move to stay in the recursive self-improvement loop - the cycle where AI improves its own code. Companies that lose coding token share now may find themselves outside that loop when it matters most. Baker's read on $META: Zuckerberg has no articulated product anchor for "personal superintelligence." But the mechanism that forced his hand in 2022 - the stock - is likely to activate again. What the end game looks like and who survives it: Source: TBPN -

Podcast Alpha

41,051 просмотров • 2 месяцев назад

Not NVIDIA. Not OpenAI. Eli Lilly. Jordi Visser (Jordi Visser) says a 150-year-old pharma company in Indianapolis has the best shot at becoming the world's largest company within five years. The case: ~1,000 NVIDIA Blackwell GPUs in a private data center, a co-innovation lab with Jensen Huang, an AlphaFold partnership via Isomorphic Labs, Toon Lab in Silicon Valley, and 150 years of proprietary metabolic disease data no general model can replicate. That last moat is the one that matters. You cannot train your way to that dataset. Eli Lilly has it. The investor gap: the stock is filed under healthcare. The thesis is AI application. That category mismatch is where the repricing happens. The full breakdown on why $LLY is the AI trade the market hasn't priced: Source: Anthony Pompliano Podcast -
11:40

Not NVIDIA. Not OpenAI. Eli Lilly. Jordi Visser (Jordi Visser) says a 150-year-old pharma company in Indianapolis has the best shot at becoming the world's largest company within five years. The case: ~1,000 NVIDIA Blackwell GPUs in a private data center, a co-innovation lab with Jensen Huang, an AlphaFold partnership via Isomorphic Labs, Toon Lab in Silicon Valley, and 150 years of proprietary metabolic disease data no general model can replicate. That last moat is the one that matters. You cannot train your way to that dataset. Eli Lilly has it. The investor gap: the stock is filed under healthcare. The thesis is AI application. That category mismatch is where the repricing happens. The full breakdown on why $LLY is the AI trade the market hasn't priced: Source: Anthony Pompliano Podcast -

Podcast Alpha

63,599 просмотров • 2 месяцев назад

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.
2:06

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

235,419 просмотров • 3 месяцев назад

BREAKING: Michael Burry just compared Nvidia to the company that lost 90% of its value in the dot-com crash and took 25 years to recover. "I stand by my analysis. I am not claiming Nvidia is Enron. It is clearly Cisco." Here's the most recent warning from the investor who called the 2008 crash: Michael Burry built his reputation on one trade. He saw the housing market collapse before anyone else and bet against it. "The Big Short" made him famous. Now he's looking at Nvidia. And he says it looks like Cisco in March 2000. That comparison is not a casual insult. Cisco was the most valuable company in the world at the peak of the dot-com bubble. Its valuation crossed $500 billion. Then the bubble burst. The stock fell roughly 90% from its 2000 peak. Its market cap collapsed to about $60 billion by 2002. And it took roughly 25 years for the stock to climb back to where it started. An entire generation of investors waited a quarter century just to break even. That is the company Burry is comparing Nvidia to. Now here is the number that triggered the warning. In Nvidia's fiscal 2026 results, the company disclosed its purchase obligations. These are the commitments Nvidia makes to its suppliers to lock in future manufacturing capacity. A year ago, that figure sat at $16.1 billion. This year it jumped to $95.2 billion. Total supply obligations now sit at roughly $117 billion. Nvidia is committing $117 billion to build capacity for demand that has not arrived yet. Burry's argument is simple. A company does not lock in $117 billion in supplier commitments unless it is betting the demand keeps climbing. If that demand slows even slightly, Nvidia is holding billions in obligations it cannot unwind. And that is exactly what happened to Cisco. Cisco overcommitted to supplier capacity expecting roughly 50% annual growth. Then tech spending slowed. The inventory piled up. The stock cratered. Burry is not calling Nvidia a fraud. He is not saying it is the next Enron. He is saying it could be the market's Cisco. The single stock that becomes the symbol of an AI spending unwind that drags everything down with it. And the dot-com comparison carries weight because of what happened to the broader market. When that bubble burst, the Nasdaq 100 fell 77%. The S&P 500 dropped 49%. It was not just one stock. It was the whole market. Now here is the other side of the argument. Nvidia's supporters say the Cisco comparison is too simple. Because Cisco was riding hype. Nvidia is riding actual revenue. Nvidia reported fiscal 2026 revenue of $215.9 billion, up 65% year over year. Data center revenue alone hit roughly $193.7 billion, up 68%. Record quarterly data center revenue of $62.3 billion in the fourth quarter, up 75%. These are not promises. These are realized sales, booked and collected. The bulls argue that pricing power and margins this strong do not exist inside a pure bubble. In their view, Burry is warning about a future slowdown that has not shown up in a single quarterly report. So the debate splits into two clean halves. The bears say the $117 billion in commitments makes Nvidia dangerously sensitive to any demand slowdown. The bulls say the revenue is real, the growth is accelerating, and the buildout is justified by the orders already on the books. Both sides are looking at the same company. Both sides are looking at the same numbers. They just disagree on what those numbers mean. And there is a second force pulling at this market that has nothing to do with Nvidia's earnings. A wave of mega-IPOs is reportedly coming. SpaceX. OpenAI. Anthropic. Some estimates suggest the market may need to absorb close to $200 billion in fresh equity supply. That creates a quieter question underneath the Burry debate. Even if AI demand stays strong, capital is finite. When the next wave of private giants goes public, money has to come from somewhere. And the easiest place to pull it from is the stock that already tripled. The real test is not whether Burry is right or wrong today. It is whether demand growth, margins, and contract utilization keep matching the $117 billion that Nvidia and its entire ecosystem are committing right now. If the demand keeps climbing, the commitments look like foresight. If it stalls, they look like Cisco. The man who saw the last crash before anyone else just put a name on the risk. A company that was once worth over $500 billion, then lost 90%, then made its investors wait 25 years to get back to even. The numbers say Nvidia is booking record revenue. The same numbers say Nvidia is committing $117 billion to a future nobody can see. One of those facts ages well. The other one is the entire question.
7:21

BREAKING: Michael Burry just compared Nvidia to the company that lost 90% of its value in the dot-com crash and took 25 years to recover. "I stand by my analysis. I am not claiming Nvidia is Enron. It is clearly Cisco." Here's the most recent warning from the investor who called the 2008 crash: Michael Burry built his reputation on one trade. He saw the housing market collapse before anyone else and bet against it. "The Big Short" made him famous. Now he's looking at Nvidia. And he says it looks like Cisco in March 2000. That comparison is not a casual insult. Cisco was the most valuable company in the world at the peak of the dot-com bubble. Its valuation crossed $500 billion. Then the bubble burst. The stock fell roughly 90% from its 2000 peak. Its market cap collapsed to about $60 billion by 2002. And it took roughly 25 years for the stock to climb back to where it started. An entire generation of investors waited a quarter century just to break even. That is the company Burry is comparing Nvidia to. Now here is the number that triggered the warning. In Nvidia's fiscal 2026 results, the company disclosed its purchase obligations. These are the commitments Nvidia makes to its suppliers to lock in future manufacturing capacity. A year ago, that figure sat at $16.1 billion. This year it jumped to $95.2 billion. Total supply obligations now sit at roughly $117 billion. Nvidia is committing $117 billion to build capacity for demand that has not arrived yet. Burry's argument is simple. A company does not lock in $117 billion in supplier commitments unless it is betting the demand keeps climbing. If that demand slows even slightly, Nvidia is holding billions in obligations it cannot unwind. And that is exactly what happened to Cisco. Cisco overcommitted to supplier capacity expecting roughly 50% annual growth. Then tech spending slowed. The inventory piled up. The stock cratered. Burry is not calling Nvidia a fraud. He is not saying it is the next Enron. He is saying it could be the market's Cisco. The single stock that becomes the symbol of an AI spending unwind that drags everything down with it. And the dot-com comparison carries weight because of what happened to the broader market. When that bubble burst, the Nasdaq 100 fell 77%. The S&P 500 dropped 49%. It was not just one stock. It was the whole market. Now here is the other side of the argument. Nvidia's supporters say the Cisco comparison is too simple. Because Cisco was riding hype. Nvidia is riding actual revenue. Nvidia reported fiscal 2026 revenue of $215.9 billion, up 65% year over year. Data center revenue alone hit roughly $193.7 billion, up 68%. Record quarterly data center revenue of $62.3 billion in the fourth quarter, up 75%. These are not promises. These are realized sales, booked and collected. The bulls argue that pricing power and margins this strong do not exist inside a pure bubble. In their view, Burry is warning about a future slowdown that has not shown up in a single quarterly report. So the debate splits into two clean halves. The bears say the $117 billion in commitments makes Nvidia dangerously sensitive to any demand slowdown. The bulls say the revenue is real, the growth is accelerating, and the buildout is justified by the orders already on the books. Both sides are looking at the same company. Both sides are looking at the same numbers. They just disagree on what those numbers mean. And there is a second force pulling at this market that has nothing to do with Nvidia's earnings. A wave of mega-IPOs is reportedly coming. SpaceX. OpenAI. Anthropic. Some estimates suggest the market may need to absorb close to $200 billion in fresh equity supply. That creates a quieter question underneath the Burry debate. Even if AI demand stays strong, capital is finite. When the next wave of private giants goes public, money has to come from somewhere. And the easiest place to pull it from is the stock that already tripled. The real test is not whether Burry is right or wrong today. It is whether demand growth, margins, and contract utilization keep matching the $117 billion that Nvidia and its entire ecosystem are committing right now. If the demand keeps climbing, the commitments look like foresight. If it stalls, they look like Cisco. The man who saw the last crash before anyone else just put a name on the risk. A company that was once worth over $500 billion, then lost 90%, then made its investors wait 25 years to get back to even. The numbers say Nvidia is booking record revenue. The same numbers say Nvidia is committing $117 billion to a future nobody can see. One of those facts ages well. The other one is the entire question.

Insider Trackers

285,273 просмотров • 3 месяцев назад

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:
5:16

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:

Podcast Alpha

15,261 просмотров • 2 месяцев назад

The market is too focused on what $NFLX isn't and not focused enough on what it still does exceptionally well. The flywheel is intact: better content drives engagement and retention which funds more content and gives Netflix the pricing power that flows straight to margins and cash flow. Meanwhile the stock trades near its lowest earnings multiple in years even as the business keeps executing.
0:49

The market is too focused on what $NFLX isn't and not focused enough on what it still does exceptionally well. The flywheel is intact: better content drives engagement and retention which funds more content and gives Netflix the pricing power that flows straight to margins and cash flow. Meanwhile the stock trades near its lowest earnings multiple in years even as the business keeps executing.

Shay Boloor

129,877 просмотров • 2 месяцев назад

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."
1:29

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."

Patrick OShaughnessy

155,027 просмотров • 6 дней назад

NEWS: SpaceX just made its AI space computer 67% more powerful, Elon Musk says The satellite is called AI1. It is basically a data center that floats in space and runs on sunlight instead of the power grid. Musk says its peak power is going up to ~250 kW, from 150 kW. Its average power is going up to 160 kW, from 120 kW. Why the boost. A single Nvidia Rubin NVL72 rack, 72 of Nvidia's newest AI chips, needs that much power to run. The bigger number means one AI1 can now handle a full rack. Musk's reasoning is simple. A GPU running all day uses about two-thirds of its peak power, even when it is very efficient. So the satellite has to be built for the peak. AI1 works with chips from any maker, not just Nvidia. SpaceX has filed to launch up to 1 million of these satellites. (Source: Elon Musk on X, July 16)
1:06

NEWS: SpaceX just made its AI space computer 67% more powerful, Elon Musk says The satellite is called AI1. It is basically a data center that floats in space and runs on sunlight instead of the power grid. Musk says its peak power is going up to ~250 kW, from 150 kW. Its average power is going up to 160 kW, from 120 kW. Why the boost. A single Nvidia Rubin NVL72 rack, 72 of Nvidia's newest AI chips, needs that much power to run. The bigger number means one AI1 can now handle a full rack. Musk's reasoning is simple. A GPU running all day uses about two-thirds of its peak power, even when it is very efficient. So the satellite has to be built for the peak. AI1 works with chips from any maker, not just Nvidia. SpaceX has filed to launch up to 1 million of these satellites. (Source: Elon Musk on X, July 16)

Muskonomy

56,507 просмотров • 1 месяц назад

Jensen Huang just revealed his $500 BILLION financing scheme with 6 of the biggest money managers on Earth. Here's the deal they signed: Nvidia and those 6 firms are mobilizing more than $500 billion of outside capital to finance AI infrastructure. The money goes to Nvidia's own customers so they can build data centers and buy Nvidia chips. Nvidia is not putting up a single dollar. The CHIPS themselves become the collateral. This is like "buying a GM car and getting the financing from GM." Jensen clarified that none of it is Nvidia's money. That part is technically true. But then Larry Fink explained what they are actually building... Fink runs BlackRock, the largest asset manager on Earth. Asked about financing data centers, he said this is the very beginning, the way it was when he started out in the mortgage backed securities market in the 1970s. Then he called it the "next frontier of financial engineering." He compared the AI buildout to the machine that CRASHED the world economy in 2008. Then KKR's global head of digital infrastructure laid out the mechanics: He said the revenue coming off those chips can be securitized, the risk divided up, and the slices sold to investors who want exposure anywhere in the stack. So the GPU is the house, the compute bill is the mortgage payment, and the slices get sold to whoever wants them. Blackstone's John Gray made the comparison himself. He said when you buy a house the bank underwrites you and also looks at the value of the home. When an airline buys a plane, they look at the credit of the airline and at the plane. The chips are the plane. But a plane holds its value because Boeing cannot make your plane obsolete on purpose. Nvidia can, and it does it roughly every year. KKR's own man spent part of that panel praising Vera Rubin, calling the jump in tokens per watt a step change. Every leap like that makes the previous generation worth less. And the price per token has already fallen 99%, so the collateral behind these loans depreciates on a schedule Nvidia controls, while the revenue those chips earn keeps getting cheaper. So what happens if the AI companies run out of cash? Jensen said somebody else can take the machines over and operate them. "There will always be a customer for that computing platform." He planned the repossession before the first loan even closed. Then who ends up holding this paper? Fink answered that one himself: He said they will be working with pension funds across the world. David Solomon pointed at the $9 trillion sitting in US money market funds. Fink pitched moving that cash into longer dated returns and said investors who are overweight equities will rotate in too. And $500 billion is only the opening bid. Fink said the US alone needs over 70 gigawatts of power for this, and every gigawatt costs $50 to $60 billion to build. That works out to more than $3.5 TRILLION for America by itself. Six days earlier, Apollo had called its $35 billion Broadcom financing the largest of its kind ever done. This deal is literally more than 14x bigger. Solomon was the only guy on that panel who mentioned the risk. He admitted the returns will NOT all be ample, that capital will get allocated to things that do not work, and that there will be winners and losers. Jensen's case is that the machines print money. He said AI tokens are incredibly profitable. Within months everyone will realize the AI labs are extremely profitable, and that when those labs go public it will be the biggest IPOs in history. None of that has happened yet. The financing is being built right now, ahead of the proof. So this is either the largest infrastructure buildout in history, or Wall Street just turned the AI bubble into bonds and handed them to the pension funds.
0:42

Jensen Huang just revealed his $500 BILLION financing scheme with 6 of the biggest money managers on Earth. Here's the deal they signed: Nvidia and those 6 firms are mobilizing more than $500 billion of outside capital to finance AI infrastructure. The money goes to Nvidia's own customers so they can build data centers and buy Nvidia chips. Nvidia is not putting up a single dollar. The CHIPS themselves become the collateral. This is like "buying a GM car and getting the financing from GM." Jensen clarified that none of it is Nvidia's money. That part is technically true. But then Larry Fink explained what they are actually building... Fink runs BlackRock, the largest asset manager on Earth. Asked about financing data centers, he said this is the very beginning, the way it was when he started out in the mortgage backed securities market in the 1970s. Then he called it the "next frontier of financial engineering." He compared the AI buildout to the machine that CRASHED the world economy in 2008. Then KKR's global head of digital infrastructure laid out the mechanics: He said the revenue coming off those chips can be securitized, the risk divided up, and the slices sold to investors who want exposure anywhere in the stack. So the GPU is the house, the compute bill is the mortgage payment, and the slices get sold to whoever wants them. Blackstone's John Gray made the comparison himself. He said when you buy a house the bank underwrites you and also looks at the value of the home. When an airline buys a plane, they look at the credit of the airline and at the plane. The chips are the plane. But a plane holds its value because Boeing cannot make your plane obsolete on purpose. Nvidia can, and it does it roughly every year. KKR's own man spent part of that panel praising Vera Rubin, calling the jump in tokens per watt a step change. Every leap like that makes the previous generation worth less. And the price per token has already fallen 99%, so the collateral behind these loans depreciates on a schedule Nvidia controls, while the revenue those chips earn keeps getting cheaper. So what happens if the AI companies run out of cash? Jensen said somebody else can take the machines over and operate them. "There will always be a customer for that computing platform." He planned the repossession before the first loan even closed. Then who ends up holding this paper? Fink answered that one himself: He said they will be working with pension funds across the world. David Solomon pointed at the $9 trillion sitting in US money market funds. Fink pitched moving that cash into longer dated returns and said investors who are overweight equities will rotate in too. And $500 billion is only the opening bid. Fink said the US alone needs over 70 gigawatts of power for this, and every gigawatt costs $50 to $60 billion to build. That works out to more than $3.5 TRILLION for America by itself. Six days earlier, Apollo had called its $35 billion Broadcom financing the largest of its kind ever done. This deal is literally more than 14x bigger. Solomon was the only guy on that panel who mentioned the risk. He admitted the returns will NOT all be ample, that capital will get allocated to things that do not work, and that there will be winners and losers. Jensen's case is that the machines print money. He said AI tokens are incredibly profitable. Within months everyone will realize the AI labs are extremely profitable, and that when those labs go public it will be the biggest IPOs in history. None of that has happened yet. The financing is being built right now, ahead of the proof. So this is either the largest infrastructure buildout in history, or Wall Street just turned the AI bubble into bonds and handed them to the pension funds.

Ricardo

33,128 просмотров • 13 дней назад

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

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

Ricardo

37,412 просмотров • 1 месяц назад

Turkish Foreign Minister Hakan Fidan: "Israel is not a structure that produces power on its own. There is a mindset here that has managed to hack the American political system through Zionism. What the United States does in this region does not serve its own interests, but benefits Israel. So why does the United States do this? It is not a rational model of behavior. It happens when you influence (American) politics."
1:28

Turkish Foreign Minister Hakan Fidan: "Israel is not a structure that produces power on its own. There is a mindset here that has managed to hack the American political system through Zionism. What the United States does in this region does not serve its own interests, but benefits Israel. So why does the United States do this? It is not a rational model of behavior. It happens when you influence (American) politics."

Open Source Intel

67,752 просмотров • 4 месяцев назад

Turkish Foreign Minister Hakan Fidan: Israel is not a structure that produces power on its own. There is a mindset here that has managed to hack the American political system through Zionism. What the United States does in this region does not serve its own interests, but benefits Israel. So why does the United States do this? It is not a rational model of behavior. It happens when you influence (American) politics.
1:28

Turkish Foreign Minister Hakan Fidan: Israel is not a structure that produces power on its own. There is a mindset here that has managed to hack the American political system through Zionism. What the United States does in this region does not serve its own interests, but benefits Israel. So why does the United States do this? It is not a rational model of behavior. It happens when you influence (American) politics.

Clash Report

124,695 просмотров • 4 месяцев назад

CoreWeave and Nebius are not tech companies. They are equipment leasing businesses. Jim Chanos on iConnections: strip the AI narrative and you have companies that buy GPUs from $NVDA and rent them to hyperscalers. The economics confirm it - mid-to-high single-digit pre-tax returns on capital where the data is available. His rule: a middleman in a supply chain should never trade at a higher multiple than the company that controls its supply. Nvidia sets GPU prices. CoreWeave takes them. TSMC sets chip supply. Neo clouds depend on it. The market has this relationship inverted. That is the compression trade. Full supply chain breakdown and what it means for neo cloud valuations: Source: iConnections -
1:53

CoreWeave and Nebius are not tech companies. They are equipment leasing businesses. Jim Chanos on iConnections: strip the AI narrative and you have companies that buy GPUs from $NVDA and rent them to hyperscalers. The economics confirm it - mid-to-high single-digit pre-tax returns on capital where the data is available. His rule: a middleman in a supply chain should never trade at a higher multiple than the company that controls its supply. Nvidia sets GPU prices. CoreWeave takes them. TSMC sets chip supply. Neo clouds depend on it. The market has this relationship inverted. That is the compression trade. Full supply chain breakdown and what it means for neo cloud valuations: Source: iConnections -

Podcast Alpha

65,844 просмотров • 2 месяцев назад