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Nvidia $NVDA at 15x forward is not expensive. Dan Loeb's math. Reflexive take after 40% YTD: take profits. Dan Loeb (Daniel S. Loeb ) ran the numbers instead. Nvidia at 15x 2027, 12x 2028. He calls it the most attractive sector right now — unless you're "really draconian" and... show more

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Dan on why semis, cap equipment, and hyperscalers are the most attractive sector in the market, and why Nvidia is still cheap: "The SOXX is up 40%. I don't think I've ever seen an event like that. You can still buy Nvidia –– maybe the multiple's slightly higher right now –– at 15 times '27, 12 times '28 for the most dominant, very fast-growing company at its size. I looked through our whole semis, cap equipment, and hyperscaler portfolio. My instinct was we've gotta take profits here. But I looked at the valuations and the growth rates. Unless you think the AI world is going to roll over in 2031 or 2032, it's the most attractive sector. It's where the bulk of our capital is invested."

Dan on why semis, cap equipment, and hyperscalers are the most attractive sector in the market, and why Nvidia is still cheap: "The SOXX is up 40%. I don't think I've ever seen an event like that. You can still buy Nvidia –– maybe the multiple's slightly higher right now –– at 15 times '27, 12 times '28 for the most dominant, very fast-growing company at its size. I looked through our whole semis, cap equipment, and hyperscaler portfolio. My instinct was we've gotta take profits here. But I looked at the valuations and the growth rates. Unless you think the AI world is going to roll over in 2031 or 2032, it's the most attractive sector. It's where the bulk of our capital is invested."

Patrick OShaughnessy

321,106 views • 3 months ago

Nvidia CEO just exposed why companies lose billions on AI while he profits Jensen Huang sat with Channel News Asia and went straight at it. companies cut tens of thousands of workers and blamed AI he says that is just an excuse here is what he actually said: > CEOs blaming AI for layoffs are just lazy > half are saving billions and hiding behind a buzzword > AI was not even useful 6 months ago > the real story is complex and nobody tells it and here is the wild part Nvidia just crossed $4 trillion in market cap off this same AI boom so the guy earning more on AI than anyone alive is telling every CEO to stop using it as a scapegoat Save this, it is the cleanest counter to AI doom right now

Nvidia CEO just exposed why companies lose billions on AI while he profits Jensen Huang sat with Channel News Asia and went straight at it. companies cut tens of thousands of workers and blamed AI he says that is just an excuse here is what he actually said: > CEOs blaming AI for layoffs are just lazy > half are saving billions and hiding behind a buzzword > AI was not even useful 6 months ago > the real story is complex and nobody tells it and here is the wild part Nvidia just crossed $4 trillion in market cap off this same AI boom so the guy earning more on AI than anyone alive is telling every CEO to stop using it as a scapegoat Save this, it is the cleanest counter to AI doom right now

winkle.

12,693 views • 3 months ago

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

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

Nonzee

83,631 views • 3 months ago

Indian IT is the only sector on earth being sold off for AI working, not for AI failing. The market treats Nvidia as proof AI works and Indian IT as proof it does not. It may be exactly backwards. - Nifty IT: down ~39% from its peak, now near 16x forward earnings. - TCS is cutting 12,000 jobs. Entry-level tech hiring fell 44% in a year. Nikhil Kamath laid out their bull math: disrupt 10% of the world's $50 trillion services economy, and that $5 trillion justifies the spend. The catch he left open: disrupting $5 trillion of services is not the same as capturing it. Indian IT is where that gap already shows. The disruption is landing as falling revenue, not a new profit pool. Better at delivery, worse at billing. The same labs said to be killing it, OpenAI and Anthropic, just partnered with TCS and Infosys to sell through them. One of these AI bets is mispriced. Maybe both. The canary is not in a Santa Clara data centre. It is on the NSE. | Credits: YouTube, WTF Podcast |

Indian IT is the only sector on earth being sold off for AI working, not for AI failing. The market treats Nvidia as proof AI works and Indian IT as proof it does not. It may be exactly backwards. - Nifty IT: down ~39% from its peak, now near 16x forward earnings. - TCS is cutting 12,000 jobs. Entry-level tech hiring fell 44% in a year. Nikhil Kamath laid out their bull math: disrupt 10% of the world's $50 trillion services economy, and that $5 trillion justifies the spend. The catch he left open: disrupting $5 trillion of services is not the same as capturing it. Indian IT is where that gap already shows. The disruption is landing as falling revenue, not a new profit pool. Better at delivery, worse at billing. The same labs said to be killing it, OpenAI and Anthropic, just partnered with TCS and Infosys to sell through them. One of these AI bets is mispriced. Maybe both. The canary is not in a Santa Clara data centre. It is on the NSE. | Credits: YouTube, WTF Podcast |

Vishnu Agarwal

97,220 views • 2 months ago

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.

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

Ricardo

162,447 views • 3 months ago

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?

Ricardo

61,747 views • 4 days ago

Dan on why hyperscalers will be a worse business model over time even as growth accelerates: "AWS, Azure, GCP are going to accelerate for a while just because their customer base -- Anthropic, OpenAI -- are growing at an enormous pace. The problem is you went from a dynamic where their customer base was like every corporation in the world and therefore they had fragmentation and it was a really good business. Going forward it's highly unlikely that LLMs are not concentrated in the hands of 4 or 5 companies. Those companies right now, are cashflow negative, and therefore they're looking for compute anywhere they can get it. In the next 5 to 10 years, they'll be generating enormous amounts of free cash flow. When that happens, they're likely to insource the compute. Right now, they look at the hyperscalers as more of a financing mechanism. But I don't think they are better than the LLMs at building data centers. Then you have this dynamic of neoclouds. I don't think they're going away like people thought, because they're better at running GPU clusters than the traditional hyperscalers, and there's a lot of interest from NVIDIA to make sure that their customer base is diversified."

Dan on why hyperscalers will be a worse business model over time even as growth accelerates: "AWS, Azure, GCP are going to accelerate for a while just because their customer base -- Anthropic, OpenAI -- are growing at an enormous pace. The problem is you went from a dynamic where their customer base was like every corporation in the world and therefore they had fragmentation and it was a really good business. Going forward it's highly unlikely that LLMs are not concentrated in the hands of 4 or 5 companies. Those companies right now, are cashflow negative, and therefore they're looking for compute anywhere they can get it. In the next 5 to 10 years, they'll be generating enormous amounts of free cash flow. When that happens, they're likely to insource the compute. Right now, they look at the hyperscalers as more of a financing mechanism. But I don't think they are better than the LLMs at building data centers. Then you have this dynamic of neoclouds. I don't think they're going away like people thought, because they're better at running GPU clusters than the traditional hyperscalers, and there's a lot of interest from NVIDIA to make sure that their customer base is diversified."

Patrick OShaughnessy

341,768 views • 6 months ago

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.

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 views • 3 months ago

$NVDA This is what you are buying when you buy a share of Nvidia... I heard this today and could not stop thinking about it. Jensen describes the sophistication that is required in designing chips, creating them, delivering them to customers, installing them in datacenters, and more. He argues that there has not been a company since $IBM that was faced with this much technology and investment in order to push the entire world forward with demand that is spanning throughout the globe in EVERY single industry. The stock trades at 27 times forward earnings. Costco and Walmart, amazing companies, are both more expensive than the name that is fueling the AI revolution and brings home 55 cents on every dollar of revenue as net income. This has been my main Mag 7 pick for the year in terms of establishing exposure to big tech and it's just getting harder to imagine a world in which AI matters but $NVDA is not more valuable than it is today.

$NVDA This is what you are buying when you buy a share of Nvidia... I heard this today and could not stop thinking about it. Jensen describes the sophistication that is required in designing chips, creating them, delivering them to customers, installing them in datacenters, and more. He argues that there has not been a company since $IBM that was faced with this much technology and investment in order to push the entire world forward with demand that is spanning throughout the globe in EVERY single industry. The stock trades at 27 times forward earnings. Costco and Walmart, amazing companies, are both more expensive than the name that is fueling the AI revolution and brings home 55 cents on every dollar of revenue as net income. This has been my main Mag 7 pick for the year in terms of establishing exposure to big tech and it's just getting harder to imagine a world in which AI matters but $NVDA is not more valuable than it is today.

amit

277,134 views • 1 year ago

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

194,375 views • 3 months ago

🚨 THE AI TRADE JUST BROKE. Nasdaq 100 is down 10% from its record. Chips just closed lower four sessions in a row. The moment it turned was July 16. TSMC posted the best quarter in its history. Profit up 77%. Revenue up 33.7%. Then it raised 2026 capex from $56B to $64B and added $100B in Arizona. The stock sold off anyway. Record earnings, and the market sold it. That is the regime change. Good numbers stopped mattering the moment capex started eating the cash flow. Look at what actually changed. The entire AI bull case rested on one assumption: inference gets cheaper. Spend now, scale later, margins explode when compute collapses in price. Here is what happened instead. Memory was 8% of hyperscaler capex in 2023. It is 30% in 2026. Analysts model 48% by 2027. DRAM prices more than doubled this year. LPDDR5 is up over 3x since early 2025. HBM stays short through 2027. Costs are not collapsing. They are compounding. And the market finally noticed the tell. TSMC beat on profit and revenue, then guided capex higher, and the stock sold off. Good earnings are now bearish, because every dollar of capex needs a dollar of return that nobody can show yet. Meanwhile the money still moves in a circle. Anthropic at $965B. OpenAI at $852B. Both funded by the same players buying the same chips. SpaceX down 32% in six weeks, and it is the largest listing in history. This is the same structure as 2000, with better branding. But 2000 did not go straight down either. Nasdaq rallied 40% twice before the real collapse. Both rallies destroyed the shorts who were right too early. That is the phase we are entering now, not the crash. One more squeeze into early 2027. Then the actual dump. I called the $15,768 bottom and the $126,162 top by waiting for exactly this pattern. I am not shorting into the bounce. I am waiting for it to exhaust. Follow and turn notifications on. I post the moment it does.

🚨 THE AI TRADE JUST BROKE. Nasdaq 100 is down 10% from its record. Chips just closed lower four sessions in a row. The moment it turned was July 16. TSMC posted the best quarter in its history. Profit up 77%. Revenue up 33.7%. Then it raised 2026 capex from $56B to $64B and added $100B in Arizona. The stock sold off anyway. Record earnings, and the market sold it. That is the regime change. Good numbers stopped mattering the moment capex started eating the cash flow. Look at what actually changed. The entire AI bull case rested on one assumption: inference gets cheaper. Spend now, scale later, margins explode when compute collapses in price. Here is what happened instead. Memory was 8% of hyperscaler capex in 2023. It is 30% in 2026. Analysts model 48% by 2027. DRAM prices more than doubled this year. LPDDR5 is up over 3x since early 2025. HBM stays short through 2027. Costs are not collapsing. They are compounding. And the market finally noticed the tell. TSMC beat on profit and revenue, then guided capex higher, and the stock sold off. Good earnings are now bearish, because every dollar of capex needs a dollar of return that nobody can show yet. Meanwhile the money still moves in a circle. Anthropic at $965B. OpenAI at $852B. Both funded by the same players buying the same chips. SpaceX down 32% in six weeks, and it is the largest listing in history. This is the same structure as 2000, with better branding. But 2000 did not go straight down either. Nasdaq rallied 40% twice before the real collapse. Both rallies destroyed the shorts who were right too early. That is the phase we are entering now, not the crash. One more squeeze into early 2027. Then the actual dump. I called the $15,768 bottom and the $126,162 top by waiting for exactly this pattern. I am not shorting into the bounce. I am waiting for it to exhaust. Follow and turn notifications on. I post the moment it does.

Nonzee

17,990 views • 1 month ago

SoftBank just sold its entire $5.83 billion Nvidia stake and if anything this move is actually a bull case for AI. Son needs $30 billion in cash this quarter to fund OpenAI, Ampere Computing, and Stargate infrastructure, plus a bunch of other AI bets. Nvidia was generating paper gains but zero liquidity. When you need that much cash deployed immediately, you liquidate positions that can move the needle. Son's not saying semiconductors are broken. He's saying the bigger returns are in the layer above the chips, the AI models, applications, and infrastructure that actually use Nvidia's GPUs. OpenAI, not Nvidia, is where he thinks the profit pool sits. That's a strategic bet on where value concentrates, not a bearish call on chip makers. This is actually a pattern. Son bought Nvidia in 2017 and sold in January 2019 (right before generative AI took off). People always say look at the gains he missed. But they ignore that he deployed that capital into PayPay, Coupang, and other companies. We see the same pattern here. He's trying to make the best use of his capital to make more bets and the portfolio returns validate that strategy. Son is also hedging his AI bet. Instead of staying concentrated in semiconductors, he's diversifying across the entire software and infrastructure layer. That's defensive positioning. It signals he's starting to think AI valuations might be a bit stretched, so he's spreading risk across more beneficiaries instead of leaning on one horse. Son's also optimizing for portfolio returns, not for holding the single best stock. He liquidates Nvidia to deploy capital into higher conviction bets where he thinks the actual value creation happens.

SoftBank just sold its entire $5.83 billion Nvidia stake and if anything this move is actually a bull case for AI. Son needs $30 billion in cash this quarter to fund OpenAI, Ampere Computing, and Stargate infrastructure, plus a bunch of other AI bets. Nvidia was generating paper gains but zero liquidity. When you need that much cash deployed immediately, you liquidate positions that can move the needle. Son's not saying semiconductors are broken. He's saying the bigger returns are in the layer above the chips, the AI models, applications, and infrastructure that actually use Nvidia's GPUs. OpenAI, not Nvidia, is where he thinks the profit pool sits. That's a strategic bet on where value concentrates, not a bearish call on chip makers. This is actually a pattern. Son bought Nvidia in 2017 and sold in January 2019 (right before generative AI took off). People always say look at the gains he missed. But they ignore that he deployed that capital into PayPay, Coupang, and other companies. We see the same pattern here. He's trying to make the best use of his capital to make more bets and the portfolio returns validate that strategy. Son is also hedging his AI bet. Instead of staying concentrated in semiconductors, he's diversifying across the entire software and infrastructure layer. That's defensive positioning. It signals he's starting to think AI valuations might be a bit stretched, so he's spreading risk across more beneficiaries instead of leaning on one horse. Son's also optimizing for portfolio returns, not for holding the single best stock. He liquidates Nvidia to deploy capital into higher conviction bets where he thinks the actual value creation happens.

StockMarket.News

317,998 views • 9 months ago

Dave Ramsey says all debt is stupid. Credit cards, student loans, car payments, borrowing against your house. All of it. He says your income is your number one wealth-building tool, and the second you hand it to someone else, you give up your economic future. He is half right. On credit cards, I agree completely. You are paying 28 to 30% on that. But notice what he never mentions. Cost of capital. That is the whole game, and he skips it. High-priced student debt, fine. But my own loans were at 3%, and they were the only way I got into college. I paid them back over time. That was a good investment, not a stupid one. Where he is dead wrong is real estate. Debt on real estate lets you use other people's money to buy an asset that pays for itself. That is what he misses. His whole philosophy depends on you earning more income. But with wages growing 3% while inflation runs 3%, you never get ahead. You run in place like a rat in a wheel, the exact thing he is warning you about. The only way out is to own hard assets that produce cash flow, and you buy those with debt. Here is the difference between us. He thinks all debt is bad. I think debt is a tool. Good debt and bad debt, high cost and low cost, and that difference is everything. He once said he would not take a billion dollars at zero interest. A billion dollars, costing him nothing. Put it in Treasuries and that is 30 to 40 million a year for doing nothing. He said he would pass. That is lunacy. When I borrow on real estate, someone else covers it. Always. The office building you work in and the Starbucks you walk into all carry debt, and the tenants pay it back. I own a single-family house, my tenant pays off the loan. I do not pay it. I do not need more income. I just need to keep a good tenant in that house. And yes, you get vacancies and turnover and the occasional problem tenant, but that is what management is for. He never had to learn that, because he does not use debt. And here is the part almost nobody gets. It is your money anyway. The cash sitting in your retirement account or your bank is yours. You are just borrowing it back at a lower rate and finding a tenant to cover it. That is why I disagree with him on debt. Used right, it is not the enemy. It is the entire engine.

Dave Ramsey says all debt is stupid. Credit cards, student loans, car payments, borrowing against your house. All of it. He says your income is your number one wealth-building tool, and the second you hand it to someone else, you give up your economic future. He is half right. On credit cards, I agree completely. You are paying 28 to 30% on that. But notice what he never mentions. Cost of capital. That is the whole game, and he skips it. High-priced student debt, fine. But my own loans were at 3%, and they were the only way I got into college. I paid them back over time. That was a good investment, not a stupid one. Where he is dead wrong is real estate. Debt on real estate lets you use other people's money to buy an asset that pays for itself. That is what he misses. His whole philosophy depends on you earning more income. But with wages growing 3% while inflation runs 3%, you never get ahead. You run in place like a rat in a wheel, the exact thing he is warning you about. The only way out is to own hard assets that produce cash flow, and you buy those with debt. Here is the difference between us. He thinks all debt is bad. I think debt is a tool. Good debt and bad debt, high cost and low cost, and that difference is everything. He once said he would not take a billion dollars at zero interest. A billion dollars, costing him nothing. Put it in Treasuries and that is 30 to 40 million a year for doing nothing. He said he would pass. That is lunacy. When I borrow on real estate, someone else covers it. Always. The office building you work in and the Starbucks you walk into all carry debt, and the tenants pay it back. I own a single-family house, my tenant pays off the loan. I do not pay it. I do not need more income. I just need to keep a good tenant in that house. And yes, you get vacancies and turnover and the occasional problem tenant, but that is what management is for. He never had to learn that, because he does not use debt. And here is the part almost nobody gets. It is your money anyway. The cash sitting in your retirement account or your bank is yours. You are just borrowing it back at a lower rate and finding a tenant to cover it. That is why I disagree with him on debt. Used right, it is not the enemy. It is the entire engine.

Ken McElroy

38,406 views • 2 months ago

Nvidia's Jensen Huang on Fox Business: * Asked for a single word on the rest of the year, Huang says "demand accelerating" * Says the reason is AI does productive work and the tokens labs generate are "profitable tokens," so what's holding it back now is just compute. * Next year is already sold out and supply-constrained at 70% growth. * "Every single [chip] that we can make has already been sold." * Customers commit a year-plus ahead because "you're not buying a computer, you're building a factory," at $50-60 billion each. * With memory prices way up, Nvidia reset margin guidance this quarter to "rip the band-aid off." * Margins fall from 75% to 72 - 73% next year after absorbing memory cost increases and repricing, to clear what he called "a cloud on our stock." * Next year's Vera Rubin will ramp even faster than Blackwell did, which Huang calls "the fastest product launch we've ever had in history." * Bottlenecks "literally everywhere," the biggest buildout in history. * Constraints span TSMC CoWoS packaging, wafers, memory, networking, silicon photonics, power generation, and "high-quality land that's powered," plus a skilled-labor shortage in the US * Says scarcity is not a bad thing. This quarter's 100% growth and next year's 70% are both supply-constrained, which can be healthy because it "causes the best technologies to emerge and rise" and makes everybody sharper. * AWS alone is set to add 2 million GPUs across 2027 and 2028. Half of revenue comes from about six hyperscalers, much of it rented onward to "Nvidia developers all over the world" * Says on hyperscalers, the Nvidia platform is "the most rentable" and "captures the highest pricing" of anything hyperscalers own. * After Bill Gates suggested taxing tokens over AI job losses, Huang says: "I see things very differently than he does." * Argues AI is "a net job creator at a scale that we've never seen" because productive companies hire more people not lay people off Plus, more. See video link in reply. Bookmark & watch: _____ More on why Nvidia needed to "rip the band-aid" due to memory makers:

Nvidia's Jensen Huang on Fox Business: * Asked for a single word on the rest of the year, Huang says "demand accelerating" * Says the reason is AI does productive work and the tokens labs generate are "profitable tokens," so what's holding it back now is just compute. * Next year is already sold out and supply-constrained at 70% growth. * "Every single [chip] that we can make has already been sold." * Customers commit a year-plus ahead because "you're not buying a computer, you're building a factory," at $50-60 billion each. * With memory prices way up, Nvidia reset margin guidance this quarter to "rip the band-aid off." * Margins fall from 75% to 72 - 73% next year after absorbing memory cost increases and repricing, to clear what he called "a cloud on our stock." * Next year's Vera Rubin will ramp even faster than Blackwell did, which Huang calls "the fastest product launch we've ever had in history." * Bottlenecks "literally everywhere," the biggest buildout in history. * Constraints span TSMC CoWoS packaging, wafers, memory, networking, silicon photonics, power generation, and "high-quality land that's powered," plus a skilled-labor shortage in the US * Says scarcity is not a bad thing. This quarter's 100% growth and next year's 70% are both supply-constrained, which can be healthy because it "causes the best technologies to emerge and rise" and makes everybody sharper. * AWS alone is set to add 2 million GPUs across 2027 and 2028. Half of revenue comes from about six hyperscalers, much of it rented onward to "Nvidia developers all over the world" * Says on hyperscalers, the Nvidia platform is "the most rentable" and "captures the highest pricing" of anything hyperscalers own. * After Bill Gates suggested taxing tokens over AI job losses, Huang says: "I see things very differently than he does." * Argues AI is "a net job creator at a scale that we've never seen" because productive companies hire more people not lay people off Plus, more. See video link in reply. Bookmark & watch: _____ More on why Nvidia needed to "rip the band-aid" due to memory makers:

Fireside Alpha

13,018 views • 4 days ago

Andy Jassy explains why Amazon is willing to look inefficient for years: Cramer: "You spend $200 billion and you actually believe it is going to pay off." Jassy's answer is the part most operators miss. AI is not being treated like a normal product cycle. He compares it to AWS: After 3 years, AWS was at about $56M in revenue. After 3 years, Amazon's AI run-rate is over $15B. 260x larger than AWS at the same point. That is why Amazon is willing to absorb short-term capex pain. The public market sees a giant expense line. The operator sees infrastructure being built ahead of monetization. This is one of the hardest parts of company building: Knowing when the numbers look ugly because the bet is wrong... and when they look ugly because you are early to a compounding curve. After building $300M+ businesses, this is the pattern I respect most: The best operators are not reckless. They just understand the cash cycle better than the audience judging them. They know when a cost is actually an asset in disguise.

Andy Jassy explains why Amazon is willing to look inefficient for years: Cramer: "You spend $200 billion and you actually believe it is going to pay off." Jassy's answer is the part most operators miss. AI is not being treated like a normal product cycle. He compares it to AWS: After 3 years, AWS was at about $56M in revenue. After 3 years, Amazon's AI run-rate is over $15B. 260x larger than AWS at the same point. That is why Amazon is willing to absorb short-term capex pain. The public market sees a giant expense line. The operator sees infrastructure being built ahead of monetization. This is one of the hardest parts of company building: Knowing when the numbers look ugly because the bet is wrong... and when they look ugly because you are early to a compounding curve. After building $300M+ businesses, this is the pattern I respect most: The best operators are not reckless. They just understand the cash cycle better than the audience judging them. They know when a cost is actually an asset in disguise.

andrew engler

47,581 views • 3 months ago

Peter Thiel on $NVDA (about a year ago): It is probably quite tricky. If you had to concretize it, one thing that is very strange is if you just follow the money, at this point 80 to 85% of the money in AI is being made by one company, it is NVIDIA. It is all on this very weird hardware layer, which Silicon Valley does not even know very much about anymore. We do not really do hardware, we do not do silicon chips in Silicon Valley anymore. I get pitched on these companies once every three or four years, and it is always, I have no clue how to do this, it sounds like a pretty good idea, but man, I have no clue, and we never invest. There is this theory that the hardware piece makes the money initially, then gets more commodified over time, and it will shift to software. And the, I do not know, multi trillion dollar question is whether that is going to be true again this time, or whether NVIDIA will have this incredible monopoly. I suspect NVIDIA will. I think it will maintain its position for a while. I think the game theory on it is something like this. All the big tech companies are going to start trying to design their own AI chips so they do not have to pay the 10x markup to NVIDIA. How hard is it for them to do it? How long will it take? If they all do it, then the chips become a commodity and nobody makes money in chips. So do you go into hardware? You should do it if nobody else is doing it. If everybody does it, you should not do it. I am not sure how that nets out, but probably people stay stuck for a while and NVIDIA goes from strength to strength for a while.

Peter Thiel on $NVDA (about a year ago): It is probably quite tricky. If you had to concretize it, one thing that is very strange is if you just follow the money, at this point 80 to 85% of the money in AI is being made by one company, it is NVIDIA. It is all on this very weird hardware layer, which Silicon Valley does not even know very much about anymore. We do not really do hardware, we do not do silicon chips in Silicon Valley anymore. I get pitched on these companies once every three or four years, and it is always, I have no clue how to do this, it sounds like a pretty good idea, but man, I have no clue, and we never invest. There is this theory that the hardware piece makes the money initially, then gets more commodified over time, and it will shift to software. And the, I do not know, multi trillion dollar question is whether that is going to be true again this time, or whether NVIDIA will have this incredible monopoly. I suspect NVIDIA will. I think it will maintain its position for a while. I think the game theory on it is something like this. All the big tech companies are going to start trying to design their own AI chips so they do not have to pay the 10x markup to NVIDIA. How hard is it for them to do it? How long will it take? If they all do it, then the chips become a commodity and nobody makes money in chips. So do you go into hardware? You should do it if nobody else is doing it. If everybody does it, you should not do it. I am not sure how that nets out, but probably people stay stuck for a while and NVIDIA goes from strength to strength for a while.

Wall St Engine

824,912 views • 9 months ago

$IREN is by far my favorite buy among the neo cloud companies right now. While $NBIS is up more than 180% YTD, $IREN is sitting at just +55%. The market is still massively underestimating one thing: Power. $IREN 5GW of contracted power is a real moat, and in the AI era, that matters more than almost anything else. As Jensen $NVDA said: “None of the bottlenecks like memory or chip production last longer than a couple of years. What’s different is energy. You can’t create an industry without energy. You can’t build anything without energy, and those things take a long time.” This is the real AI bottleneck. Not chips. Not memory. Energy. $IREN already has it. Those 5GW will be fully utilized. Just like $AMD eventually massively outperformed $NVDA after lagging for a long time, I believe the same setup is happening here. Right now, $IREN is lagging $NBIS. That won’t last forever. Buy the laggard.

$IREN is by far my favorite buy among the neo cloud companies right now. While $NBIS is up more than 180% YTD, $IREN is sitting at just +55%. The market is still massively underestimating one thing: Power. $IREN 5GW of contracted power is a real moat, and in the AI era, that matters more than almost anything else. As Jensen $NVDA said: “None of the bottlenecks like memory or chip production last longer than a couple of years. What’s different is energy. You can’t create an industry without energy. You can’t build anything without energy, and those things take a long time.” This is the real AI bottleneck. Not chips. Not memory. Energy. $IREN already has it. Those 5GW will be fully utilized. Just like $AMD eventually massively outperformed $NVDA after lagging for a long time, I believe the same setup is happening here. Right now, $IREN is lagging $NBIS. That won’t last forever. Buy the laggard.

Daniel

123,728 views • 3 months ago

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Josh Kale

45,875 views • 5 months ago

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!

Jensen Huang just made a statement that every investor in AI infrastructure needs to hear (Save this). He said that the AI buildout is accelerating, the second half of this year is going to be much larger than the first half, and next year is going to be very, very large. Micron is the best positioned to win from this because every Nvidia GPU requires High Bandwidth Memory stacked directly on the chip to feed it data fast enough to keep up. There is no AI compute without memory, and right now there is simply not enough memory to go around. Micron's entire HBM supply for 2026 is already completely sold out under multi-year agreements before the year even started. Micron's own management has acknowledged they can only satisfy 50 to 65 percent of demand from some of their most important customers. That is not a problem that gets fixed quickly, because new fabs take years to build. Micron's Idaho expansion does not come online until mid-2026, a second Idaho facility is not expected until 2028, and a new New York fab is looking at 2030. The demand Jensen just described is arriving right now, and the supply to meet it is years away. The financial results already reflect this dynamic. Micron's Q2 fiscal 2026 revenue came in at $23.86 billion, nearly triple what it was a year earlier beating consensus by roughly $3.8 billion. The HBM market alone is expected to grow from $35 billion today to $100 billion by 2028, and Micron has been consistently ahead of that forecast. Jensen just told the world the second half of this year and all of next year are going to be larger than anything that came before. Micron is the company that supplies the memory those GPUs need to run, and it cannot build supply fast enough to keep up with demand. Come join Milk Road Pro for our full deep dive on Micron, the HBM supply thesis and our AI trade thesis! Link below!

Milk Road AI

77,554 views • 2 months ago

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

cvxv666

82,592 views • 24 days ago