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Nvidia just made its biggest acquisition ever... A small startup. On Christmas Eve. While everyone was distracted. The startup is Groq. They make AI chips that run faster than Nvidia's. In September, Groq raised $750 million at a $6.9 billion valuation. Investors included BlackRock, Samsung, Cisco, and Donald Trump...

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Ricardo

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224,264 次观看 • 6 个月前 •via X (Twitter)

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Asked @pieterbot on AOL why NVIDIA acquired Groq Inc And they apparently did not, they just did a licensing deal for $20B Possibly to avoid antitrust laws (so Nvidia doesn't get a monopoly) "In a Christmas Eve deal, Nvidia has entered into a nonexclusive agreement to license technology from artificial intelligence chip start-up Groq, according to a statement on Groq’s website. Groq founder Jonathan Ross and sunny madra, Groq’s president, will leave the company and join Nvidia, bringing other engineering talent with them, according to the statement. Groq will remain an independent company, but it isn’t clear if Nvidia is taking an equity stake."
2:12

Asked @pieterbot on AOL why NVIDIA acquired Groq Inc And they apparently did not, they just did a licensing deal for $20B Possibly to avoid antitrust laws (so Nvidia doesn't get a monopoly) "In a Christmas Eve deal, Nvidia has entered into a nonexclusive agreement to license technology from artificial intelligence chip start-up Groq, according to a statement on Groq’s website. Groq founder Jonathan Ross and sunny madra, Groq’s president, will leave the company and join Nvidia, bringing other engineering talent with them, according to the statement. Groq will remain an independent company, but it isn’t clear if Nvidia is taking an equity stake."

@levelsio

655,867 次观看 • 6 个月前

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

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

Ricardo

159,113 次观看 • 2 个月前

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?
1:03

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?

Ricardo

28,088 次观看 • 5 个月前

Nvidia just invested $2 billion in CoreWeave yesterday. CoreWeave's entire business is renting out data centers packed with Nvidia GPUs. So Nvidia literally gave money to a company that EXISTS to buy Nvidia chips. Then promised to buy $6.3 billion of CoreWeave's unused capacity BACK. This is the most sophisticated circular financing scheme in tech history. Let me repeat that: Nvidia FUNDS CoreWeave → CoreWeave BUYS Nvidia chips → Nvidia BUYS BACK CoreWeave's unused capacity The money goes in a perfect circle. Nvidia → CoreWeave → Nvidia → CoreWeave → Nvidia Every transaction gets booked as "revenue" on both sides. But the cash just keeps rotating. In Bloomberg's own words: "circular financing deals that have lifted valuations of AI companies and fueled concerns about a bubble." Jensen Huang even went on CNBC after the announcement and accidentally admitted the truth: "We've invested $2 billion into CoreWeave, but the amount of funding that needs to be raised yet to support that five gigawatts is really quite significant. We're investing a small percentage of the amount that ultimately has to go and be provided." Translation: CoreWeave is underwater. The $2 billion is a fraction of what they actually need. CoreWeave has signed over $40 billion in contracts. $22.4 billion with OpenAI. $14.2 billion with Meta. $6.3 billion guaranteed purchase from Nvidia. But they don't have the capacity built yet. They're signing contracts for infrastructure that doesn't exist, funded by debt, backed by Nvidia's promise to buy unused capacity. And this goes deeper... CoreWeave started as a Bitcoin mining company called Atlantic Crypto in 2017. After the 2018 crypto crash, they "pivoted" to AI. Crypto crashes. AI booms. Former miners become "AI infrastructure experts" overnight. Now they're signing $40 billion contracts. Nvidia's play is obvious once you see it: Amazon, Google, and Microsoft are building their OWN AI chips. Trainium. TPU. Maia. So Nvidia's building a "shadow cloud" of smaller providers who are 100% dependent on Nvidia chips. Fund them. Lock them into contracts. Guarantee their purchases. When hyperscalers threaten to leave, Nvidia says "we don't need you, we have CoreWeave." It's vertical integration disguised as investment. But yesterday, something broke. CoreWeave's stock jumped 6% on the news. Bitcoin miners who pivoted to AI infrastructure? Crushed. CleanSpark, IREN, TeraWulf all down 10-15%. Because Nvidia just picked its winner. CoreWeave gets priority GPU access. Everyone else is irrelevant. The smart money already left. SoftBank sold its entire $5.8 billion Nvidia stake in November. CEO said he was "crying" to sell. Michael Burry has $1 billion in puts betting Nvidia crashes. Peter Thiel exited his position. They saw the circular financing and got out. Nvidia funds CoreWeave, CoreWeave buys Nvidia chips, Nvidia buys CoreWeave's capacity... This works as long as: 1. Nvidia keeps funding 2. CoreWeave keeps buying 3. Customers keep renting But the second ONE of those breaks, the whole loop collapses. If CoreWeave can't raise more capital, they can't build capacity. If they can't build capacity, they can't fulfill $40 billion in contracts. If they can't fulfill contracts, Meta and OpenAI walk. If customers walk, Nvidia's $6.3 billion capacity guarantee becomes a LIABILITY. This isn't about whether AI is real. AI IS real. This is about whether the infrastructure buildout is real or just financial engineering. Nvidia manufactures demand by funding customers who buy Nvidia products. Those customers sign contracts they can't fulfill without more funding. Nvidia guarantees to buy back unused capacity. Money circulates. Everyone books revenue. But NOBODY asks where actual demand is. This is either the most innovative infrastructure partnership in history, or the most sophisticated ponzi scheme since 2008. The next six months will tell us which.
1:19

Nvidia just invested $2 billion in CoreWeave yesterday. CoreWeave's entire business is renting out data centers packed with Nvidia GPUs. So Nvidia literally gave money to a company that EXISTS to buy Nvidia chips. Then promised to buy $6.3 billion of CoreWeave's unused capacity BACK. This is the most sophisticated circular financing scheme in tech history. Let me repeat that: Nvidia FUNDS CoreWeave → CoreWeave BUYS Nvidia chips → Nvidia BUYS BACK CoreWeave's unused capacity The money goes in a perfect circle. Nvidia → CoreWeave → Nvidia → CoreWeave → Nvidia Every transaction gets booked as "revenue" on both sides. But the cash just keeps rotating. In Bloomberg's own words: "circular financing deals that have lifted valuations of AI companies and fueled concerns about a bubble." Jensen Huang even went on CNBC after the announcement and accidentally admitted the truth: "We've invested $2 billion into CoreWeave, but the amount of funding that needs to be raised yet to support that five gigawatts is really quite significant. We're investing a small percentage of the amount that ultimately has to go and be provided." Translation: CoreWeave is underwater. The $2 billion is a fraction of what they actually need. CoreWeave has signed over $40 billion in contracts. $22.4 billion with OpenAI. $14.2 billion with Meta. $6.3 billion guaranteed purchase from Nvidia. But they don't have the capacity built yet. They're signing contracts for infrastructure that doesn't exist, funded by debt, backed by Nvidia's promise to buy unused capacity. And this goes deeper... CoreWeave started as a Bitcoin mining company called Atlantic Crypto in 2017. After the 2018 crypto crash, they "pivoted" to AI. Crypto crashes. AI booms. Former miners become "AI infrastructure experts" overnight. Now they're signing $40 billion contracts. Nvidia's play is obvious once you see it: Amazon, Google, and Microsoft are building their OWN AI chips. Trainium. TPU. Maia. So Nvidia's building a "shadow cloud" of smaller providers who are 100% dependent on Nvidia chips. Fund them. Lock them into contracts. Guarantee their purchases. When hyperscalers threaten to leave, Nvidia says "we don't need you, we have CoreWeave." It's vertical integration disguised as investment. But yesterday, something broke. CoreWeave's stock jumped 6% on the news. Bitcoin miners who pivoted to AI infrastructure? Crushed. CleanSpark, IREN, TeraWulf all down 10-15%. Because Nvidia just picked its winner. CoreWeave gets priority GPU access. Everyone else is irrelevant. The smart money already left. SoftBank sold its entire $5.8 billion Nvidia stake in November. CEO said he was "crying" to sell. Michael Burry has $1 billion in puts betting Nvidia crashes. Peter Thiel exited his position. They saw the circular financing and got out. Nvidia funds CoreWeave, CoreWeave buys Nvidia chips, Nvidia buys CoreWeave's capacity... This works as long as: 1. Nvidia keeps funding 2. CoreWeave keeps buying 3. Customers keep renting But the second ONE of those breaks, the whole loop collapses. If CoreWeave can't raise more capital, they can't build capacity. If they can't build capacity, they can't fulfill $40 billion in contracts. If they can't fulfill contracts, Meta and OpenAI walk. If customers walk, Nvidia's $6.3 billion capacity guarantee becomes a LIABILITY. This isn't about whether AI is real. AI IS real. This is about whether the infrastructure buildout is real or just financial engineering. Nvidia manufactures demand by funding customers who buy Nvidia products. Those customers sign contracts they can't fulfill without more funding. Nvidia guarantees to buy back unused capacity. Money circulates. Everyone books revenue. But NOBODY asks where actual demand is. This is either the most innovative infrastructure partnership in history, or the most sophisticated ponzi scheme since 2008. The next six months will tell us which.

Ricardo

71,585 次观看 • 5 个月前

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.
1:10

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.

Ricardo

1,615,983 次观看 • 6 个月前

PROOF THAT AI IS A PONZI SCHEME (and why it's the reason for Bitcoin's crash): Nvidia just posted the most insane earnings in tech history. $31.9 billion in profit. $57 billion in revenue. 65% profit jump year-over-year. Stock rallied immediately. Then 18 hours later, it dropped 5%. And when people looked closer at the numbers, they found something absolutely wild... The Unpaid Bills Nobody Talked About: Nvidia's accounts receivable jumped to $33.4 billion. That's up 89% in one year. Translation: $33 billion worth of "sales" that haven't been paid yet. The average wait time for payment went from 46 days to 53 days. That extra week of waiting? $10.4 billion that may never turn into actual cash. They're booking revenue. But customers aren't paying. The Inventory That Shouldn't Exist: Unsold chip inventory surged 32% in three months to $19.8 billion. Meanwhile, Nvidia's CEO keeps saying demand is "insane" and they can't make chips fast enough. If demand is so crazy, why is inventory piling up? Either customers aren't buying with cash, or the demand story is bullshit. The Profit vs Cash Problem: Nvidia reported $19.3 billion in profit. But only generated $14.5 billion in actual cash flow. That's a $4.8 billion gap. Their profit-to-cash conversion is 75%. TSMC and AMD? Over 95%. When profit doesn't turn into cash, something's wrong. Here's Where It Gets Insane: The money is going in circles. And the same dollars are being counted as revenue multiple times. Follow this: - Nvidia gave xAI $2 billion - xAI borrowed $12.5 billion to buy Nvidia chips - Microsoft invested $13 billion in OpenAI - OpenAI committed $50 billion to Microsoft's cloud - Microsoft ordered $100 billion in Nvidia chips for that cloud - Oracle gave OpenAI $300 billion in cloud credits - OpenAI used those credits to order Nvidia chips for Oracle data centers The money goes in a circle. Nvidia → xAI → Nvidia Microsoft → OpenAI → Microsoft → Nvidia Oracle → OpenAI → Oracle → Nvidia Everyone books revenue. Nobody's actually paying cash. It's financial engineering disguised as growth. The Smart Money Already Left: Peter Thiel sold his Nvidia stake. SoftBank dumped massive positions. Michael Burry (the guy who called 2008) bought $1.1 billion in put options betting Nvidia crashes. They saw the numbers before everyone else did. And they got out. The Bitcoin Collapse: Bitcoin crashed almost 30% from $126,000 to $89,567. Why does this matter? AI startups use Bitcoin as collateral for loans. If Nvidia's crisis deepens, those startups get margin called. They're forced to sell Bitcoin to cover. Which crashes Bitcoin further. Which triggers more margin calls. Analysts think it could hit $52,000 if this unravels. The MIT Reality Check: OpenAI is valued at $157 billion. MIT released a study saying 95% of AI projects will never be profitable. Not "might struggle." NEVER be profitable. The entire sector is built on inflated expectations. What Happens Next: February 2026: Nvidia's Q4 report shows how many bills are 60+ days overdue. March 2026: Credit agencies start downgrading Nvidia and related companies. April 2026: First earnings restatements hit. The whole thing unwinds. Some experts are calling this a Ponzi scheme. No formal fraud investigation yet. But the structure is there: - Use new investor money to pay old investors - Inflate revenue with circular deals - Book sales before receiving cash - Keep the music playing until someone asks for their money Nvidia executives deny everything. Say it's real growth. Real demand. Real transformation. But the numbers don't lie. $10.4 billion in delayed payments. $19.8 billion in unsold inventory. $4.8 billion profit-to-cash gap. Circular funding loops inflating revenue. This is either the biggest tech transformation in history, or the biggest financial engineering scam since 2008. The next three months will tell us which one it is. What are you betting on?
3:21

PROOF THAT AI IS A PONZI SCHEME (and why it's the reason for Bitcoin's crash): Nvidia just posted the most insane earnings in tech history. $31.9 billion in profit. $57 billion in revenue. 65% profit jump year-over-year. Stock rallied immediately. Then 18 hours later, it dropped 5%. And when people looked closer at the numbers, they found something absolutely wild... The Unpaid Bills Nobody Talked About: Nvidia's accounts receivable jumped to $33.4 billion. That's up 89% in one year. Translation: $33 billion worth of "sales" that haven't been paid yet. The average wait time for payment went from 46 days to 53 days. That extra week of waiting? $10.4 billion that may never turn into actual cash. They're booking revenue. But customers aren't paying. The Inventory That Shouldn't Exist: Unsold chip inventory surged 32% in three months to $19.8 billion. Meanwhile, Nvidia's CEO keeps saying demand is "insane" and they can't make chips fast enough. If demand is so crazy, why is inventory piling up? Either customers aren't buying with cash, or the demand story is bullshit. The Profit vs Cash Problem: Nvidia reported $19.3 billion in profit. But only generated $14.5 billion in actual cash flow. That's a $4.8 billion gap. Their profit-to-cash conversion is 75%. TSMC and AMD? Over 95%. When profit doesn't turn into cash, something's wrong. Here's Where It Gets Insane: The money is going in circles. And the same dollars are being counted as revenue multiple times. Follow this: - Nvidia gave xAI $2 billion - xAI borrowed $12.5 billion to buy Nvidia chips - Microsoft invested $13 billion in OpenAI - OpenAI committed $50 billion to Microsoft's cloud - Microsoft ordered $100 billion in Nvidia chips for that cloud - Oracle gave OpenAI $300 billion in cloud credits - OpenAI used those credits to order Nvidia chips for Oracle data centers The money goes in a circle. Nvidia → xAI → Nvidia Microsoft → OpenAI → Microsoft → Nvidia Oracle → OpenAI → Oracle → Nvidia Everyone books revenue. Nobody's actually paying cash. It's financial engineering disguised as growth. The Smart Money Already Left: Peter Thiel sold his Nvidia stake. SoftBank dumped massive positions. Michael Burry (the guy who called 2008) bought $1.1 billion in put options betting Nvidia crashes. They saw the numbers before everyone else did. And they got out. The Bitcoin Collapse: Bitcoin crashed almost 30% from $126,000 to $89,567. Why does this matter? AI startups use Bitcoin as collateral for loans. If Nvidia's crisis deepens, those startups get margin called. They're forced to sell Bitcoin to cover. Which crashes Bitcoin further. Which triggers more margin calls. Analysts think it could hit $52,000 if this unravels. The MIT Reality Check: OpenAI is valued at $157 billion. MIT released a study saying 95% of AI projects will never be profitable. Not "might struggle." NEVER be profitable. The entire sector is built on inflated expectations. What Happens Next: February 2026: Nvidia's Q4 report shows how many bills are 60+ days overdue. March 2026: Credit agencies start downgrading Nvidia and related companies. April 2026: First earnings restatements hit. The whole thing unwinds. Some experts are calling this a Ponzi scheme. No formal fraud investigation yet. But the structure is there: - Use new investor money to pay old investors - Inflate revenue with circular deals - Book sales before receiving cash - Keep the music playing until someone asks for their money Nvidia executives deny everything. Say it's real growth. Real demand. Real transformation. But the numbers don't lie. $10.4 billion in delayed payments. $19.8 billion in unsold inventory. $4.8 billion profit-to-cash gap. Circular funding loops inflating revenue. This is either the biggest tech transformation in history, or the biggest financial engineering scam since 2008. The next three months will tell us which one it is. What are you betting on?

Ricardo

212,902 次观看 • 7 个月前

Cannot believe this is available for free on the internet Brad Gerstner sits down with Sunny Madra at Stanford University to discuss the economics of AI chips, GPUs, NVIDIA and tokens Sunny is by far one of the most impressive serial entrepreneurs of our time > Co-founded Xtreme Labs, sold to Pivotal > Founded Autonomic, which sold to Ford > Founded Definitive Intelligence, which sold to Groq > Groq ultimately sold to NVIDIA, in their largest ever M&A deal at $20 billion Shoutout to Apoorv Agrawal for hosting them in his MS&E 435 class and publishing these talks for us to listen!
56:00

Cannot believe this is available for free on the internet Brad Gerstner sits down with Sunny Madra at Stanford University to discuss the economics of AI chips, GPUs, NVIDIA and tokens Sunny is by far one of the most impressive serial entrepreneurs of our time > Co-founded Xtreme Labs, sold to Pivotal > Founded Autonomic, which sold to Ford > Founded Definitive Intelligence, which sold to Groq > Groq ultimately sold to NVIDIA, in their largest ever M&A deal at $20 billion Shoutout to Apoorv Agrawal for hosting them in his MS&E 435 class and publishing these talks for us to listen!

Boring_Business

160,869 次观看 • 2 个月前

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

26,668 次观看 • 28 天前

Nvidia’s AI Monopoly: $100 Billion in Profit & Growing 🎙️⚡️ Everyone's hyped on OpenAI, ChatGPT, Grok, XAI, etc ... but the company powering them is making all the guap. Nvidia has printed over $100B in AI profits in the last 6 quarters alone and is showing now signs of stopping. Top-line growth was 69% last quarter. Despite this rapid growth and dominance in AI, Nvidia trades for 43X trailing earnings, and maybe around 30X forward earnings (depending on growth). This is nowhere near bubble territory. With Owner/Operator/Founder Jensen Huang continuing to innovate at a breakneck pace, why is the market skeptical this growth will continue? Isn't the AI revolution just getting started? Maybe it's because Nvidia relies on TSMC to make their chips and Grok/ChatGPT to build the UI for consumers ... let me know your thoughts on Nvidia stock in the comments below $NVDA
15:53

Nvidia’s AI Monopoly: $100 Billion in Profit & Growing 🎙️⚡️ Everyone's hyped on OpenAI, ChatGPT, Grok, XAI, etc ... but the company powering them is making all the guap. Nvidia has printed over $100B in AI profits in the last 6 quarters alone and is showing now signs of stopping. Top-line growth was 69% last quarter. Despite this rapid growth and dominance in AI, Nvidia trades for 43X trailing earnings, and maybe around 30X forward earnings (depending on growth). This is nowhere near bubble territory. With Owner/Operator/Founder Jensen Huang continuing to innovate at a breakneck pace, why is the market skeptical this growth will continue? Isn't the AI revolution just getting started? Maybe it's because Nvidia relies on TSMC to make their chips and Grok/ChatGPT to build the UI for consumers ... let me know your thoughts on Nvidia stock in the comments below $NVDA

Gali

11,975 次观看 • 1 年前

Jensen Huang just made the most direct argument of his career about why banning Nvidia from China is not a national security strategy but rather a national security failure. Dwarkesh asks why Nvidia should be allowed to sell chips to China at all, if China would just use Huawei chips without them. Jensen's answer was that in the absence of a better choice, you take the only choice you have. As long as China has to settle for inferior chips, they are building their AI infrastructure on a foundation that is slower, harder to program, and years behind American technology. The moment the US decides to ban Nvidia from selling to China entirely, it removes that disadvantage. China is 40 percent of the global technology industry, Jensen said. Conceding that market, handing it entirely to Huawei is a disservice to American national security, American technology leadership, and American economic power. The data shows what has already happened since the export bans tightened. Nvidia's share of China's AI chip market collapsed from 95 percent to 55 percent in 2025 and at one point during the H20 ban, Jensen himself declared Nvidia had gone from 95 percent share to zero on advanced accelerators. The Trump administration's ban on H20 chips cost Nvidia an estimated 15 billion dollars in lost sales, plus a 4.5 billion dollar inventory write-down. Without the export controls, Nvidia was on track to generate roughly 23 billion dollars in H20 chip sales to China in 2025 alone. Meanwhile Huawei shipped 812,000 AI chips in 2025 and Beijing has now mandated that all state-funded data centers must switch to domestic chips. Jensen's deeper argument is about the global stack, not the quarterly revenue. When developers around the world build AI on CUDA, Nvidia's programming platform, they are building on American technology. When those AI models deploy into every country, the American stack goes with them. Cutting Nvidia out of China does not slow Chinese AI but rather accelerates the construction of a parallel Chinese tech stack that, once built at scale, competes with American technology everywhere else in the world.
1:42

Jensen Huang just made the most direct argument of his career about why banning Nvidia from China is not a national security strategy but rather a national security failure. Dwarkesh asks why Nvidia should be allowed to sell chips to China at all, if China would just use Huawei chips without them. Jensen's answer was that in the absence of a better choice, you take the only choice you have. As long as China has to settle for inferior chips, they are building their AI infrastructure on a foundation that is slower, harder to program, and years behind American technology. The moment the US decides to ban Nvidia from selling to China entirely, it removes that disadvantage. China is 40 percent of the global technology industry, Jensen said. Conceding that market, handing it entirely to Huawei is a disservice to American national security, American technology leadership, and American economic power. The data shows what has already happened since the export bans tightened. Nvidia's share of China's AI chip market collapsed from 95 percent to 55 percent in 2025 and at one point during the H20 ban, Jensen himself declared Nvidia had gone from 95 percent share to zero on advanced accelerators. The Trump administration's ban on H20 chips cost Nvidia an estimated 15 billion dollars in lost sales, plus a 4.5 billion dollar inventory write-down. Without the export controls, Nvidia was on track to generate roughly 23 billion dollars in H20 chip sales to China in 2025 alone. Meanwhile Huawei shipped 812,000 AI chips in 2025 and Beijing has now mandated that all state-funded data centers must switch to domestic chips. Jensen's deeper argument is about the global stack, not the quarterly revenue. When developers around the world build AI on CUDA, Nvidia's programming platform, they are building on American technology. When those AI models deploy into every country, the American stack goes with them. Cutting Nvidia out of China does not slow Chinese AI but rather accelerates the construction of a parallel Chinese tech stack that, once built at scale, competes with American technology everywhere else in the world.

Milk Road AI

21,133 次观看 • 2 个月前

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

285,101 次观看 • 1 个月前

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.
2:28

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 次观看 • 8 个月前

🚨 WARNING: NVIDIA x ELON MUSK DEAL IS BUILT ON FAKE NUMBERS!! Michael Burry published an analysis calling the structure “Fugazi”, meaning fake. If the structure is real, we could be heading for a COLLAPSE: He is alleging that BILLIONS of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing. Nvidia, the world's largest AI chip company sold $5.4 BILLION worth of its most advanced GPUs, the GB200, to a company called Valor. Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 BILLION of its own money directly into Valor on top of the sale. Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models. But here is what Burry is flagging. Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 BILLION in GPU assets do not show up on Nvidia's balance sheet as inventory. They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies. Nvidia gets to book the $5.4 BILLION as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle. Now here is where American retirees enter the picture. Valor needed $3.5 BILLION in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 TRILLION under management and $834 BILLION specifically in private credit. Apollo raised the $3.5 BILLION, packaged it into debt securities, and sold those securities to Athene. Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans. When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center. The numbers inside Athene are most alarming. Athene holds $74.2 BILLION in reserves. It has moved $217 BILLION in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight. Of the entire portfolio, 34.7%, equal to $103 BILLION, is classified as Level 3 assets. Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth. The leverage sitting on top of those unpriced assets is 16 times. Burry's says: Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing. Nvidia books the revenue. Apollo collects the fees. xAI gets the computing power. And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 BILLION in assets with no market price carry the risk without knowing it exists. I’ve been in finance for more than 15 years. When I EXIT the markets completely, I’ll say it here publicly, like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:33

🚨 WARNING: NVIDIA x ELON MUSK DEAL IS BUILT ON FAKE NUMBERS!! Michael Burry published an analysis calling the structure “Fugazi”, meaning fake. If the structure is real, we could be heading for a COLLAPSE: He is alleging that BILLIONS of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing. Nvidia, the world's largest AI chip company sold $5.4 BILLION worth of its most advanced GPUs, the GB200, to a company called Valor. Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 BILLION of its own money directly into Valor on top of the sale. Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models. But here is what Burry is flagging. Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 BILLION in GPU assets do not show up on Nvidia's balance sheet as inventory. They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies. Nvidia gets to book the $5.4 BILLION as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle. Now here is where American retirees enter the picture. Valor needed $3.5 BILLION in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 TRILLION under management and $834 BILLION specifically in private credit. Apollo raised the $3.5 BILLION, packaged it into debt securities, and sold those securities to Athene. Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans. When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center. The numbers inside Athene are most alarming. Athene holds $74.2 BILLION in reserves. It has moved $217 BILLION in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight. Of the entire portfolio, 34.7%, equal to $103 BILLION, is classified as Level 3 assets. Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth. The leverage sitting on top of those unpriced assets is 16 times. Burry's says: Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing. Nvidia books the revenue. Apollo collects the fees. xAI gets the computing power. And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 BILLION in assets with no market price carry the risk without knowing it exists. I’ve been in finance for more than 15 years. When I EXIT the markets completely, I’ll say it here publicly, like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

WhaleTwits

48,705 次观看 • 1 个月前

Our last AI episode with Jonathan Ross @ Groq exploded and there was just one piece of feedback everyone had: “Get Andrew Feldman on next.” So we did. And it breaks more conventional rules on AI than ever before 📈 Scaling laws are total BS 📉 AI algorithms are so inefficient ⚔️ NVIDIA is not suited to a world of inference 😮 CUDA is not defensible for NVIDIA More high fives in this than the Wiz boardroom and my 8 key takeaways 👇
0:29

Our last AI episode with Jonathan Ross @ Groq exploded and there was just one piece of feedback everyone had: “Get Andrew Feldman on next.” So we did. And it breaks more conventional rules on AI than ever before 📈 Scaling laws are total BS 📉 AI algorithms are so inefficient ⚔️ NVIDIA is not suited to a world of inference 😮 CUDA is not defensible for NVIDIA More high fives in this than the Wiz boardroom and my 8 key takeaways 👇

Harry Stebbings

13,158 次观看 • 1 年前

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.
0:49

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 次观看 • 3 个月前

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.
1:36

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

Ricardo

640,331 次观看 • 4 个月前

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.
0:41

Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

The Assembly

7,257,664 次观看 • 1 个月前

Trump just pulled off one of the smartest dealmaking moves in tech history. And Nvidia + China are both getting played. Let me explain... December 2025: Trump announces Nvidia can sell H200 chips to China. BUT the US government takes 25% of every sale. Not a 25% tariff. 25% OF THE REVENUE goes to US Treasury. Jensen Huang celebrates. Stock rallies. January 14, 2026: Commerce Department publishes the REAL terms. Chips manufactured in Taiwan have to TRANSIT through the US for "third-party verification." When they enter US soil? 25% tariff gets applied. So Nvidia pays 25% tariff to import their own chips. THEN Trump takes 25% of the sale price when they ship to China. Nvidia's getting hit TWICE. The math is crazy: H200 costs $30k to make. Nvidia sells for $50k. 25% tariff entering US = $10k to government 25% of sale to China = $12.5k to government Total to US: $22.5k per chip Nvidia's margin: Drops from 70%+ to maybe 15% Chinese companies ordered 2 MILLION H200 chips for 2026. But Nvidia only has 700,000 in inventory. $22,500 × 700,000 chips = $15.75 BILLION to US Treasury. From ONE deal. With ONE company. Now here's where it gets insane... China isn't actually approving these imports. Chinese state media called H200 chips "unsafe." Cyberspace Administration is blocking purchases except for "exceptional circumstances." So Trump negotiated a deal where: - Nvidia thinks they're getting China market access - China's government is quietly blocking the imports anyway - US Treasury collects billions on the few that do get through - Trump gets to claim he's "tough on China" AND "supporting American business" This move kinda makes sense if you think about it. Supreme Court is about to rule Trump's IEEPA tariffs are illegal. That's the $130 billion refund bomb. Trump needs NEW revenue sources to replace tariff income when that ruling drops. So he invents a completely new structure: Not a tariff (court can't overturn). A "licensing fee" (executive authority). On chips that are "national security sensitive" (unchallengeable). This is the blueprint for Tariffs 2.0. When Supreme Court kills IEEPA authority Trump just pivots to "licensing fees" on every strategic export. Can't challenge it as a tariff because it's not a tariff. It's a "condition of export approval." The semiconductor industry just became Trump's test case. And Nvidia walked right into it because Jensen spent 6 months lobbying for "market access." Meanwhile China's playing the long game: They're not approving H200 imports. They're forcing their companies to use domestic chips (inferior but improving). Accelerating DeepSeek-style efficiency research. Building AI models that work on weaker hardware. In 2-3 years, when Chinese chips catch up, they won't need Nvidia at all. And Trump's "$15.75 billion windfall" becomes $0. But by then he'll have established the legal framework to extract licensing fees from every other strategic export. Everyone thinks Trump's negotiating strategy is "chaos." But look at what he actually accomplished: Created legal precedent for non-tariff trade fees that survive Supreme Court challenges. That's not chaos. That's calculated. What happens next: Supreme Court rules (next 2 weeks). Trump loses on IEEPA tariffs. $130B refund chaos begins. Trump immediately announces "licensing fee" structure for 10 other export categories. Uses Nvidia deal as proof it "works." Markets freak out as every exporter realizes their margins are about to get crushed. China continues blocking H200 imports while building domestic alternatives. Nvidia's stock craters when 2026 guidance shows China revenue never materialized. And Trump goes into 2027 with a completely new trade policy framework that's legally unchallengeable. Everybody's focused on the tariffs. Nobody's watching the licensing fees. This is actually genius.
0:22

Trump just pulled off one of the smartest dealmaking moves in tech history. And Nvidia + China are both getting played. Let me explain... December 2025: Trump announces Nvidia can sell H200 chips to China. BUT the US government takes 25% of every sale. Not a 25% tariff. 25% OF THE REVENUE goes to US Treasury. Jensen Huang celebrates. Stock rallies. January 14, 2026: Commerce Department publishes the REAL terms. Chips manufactured in Taiwan have to TRANSIT through the US for "third-party verification." When they enter US soil? 25% tariff gets applied. So Nvidia pays 25% tariff to import their own chips. THEN Trump takes 25% of the sale price when they ship to China. Nvidia's getting hit TWICE. The math is crazy: H200 costs $30k to make. Nvidia sells for $50k. 25% tariff entering US = $10k to government 25% of sale to China = $12.5k to government Total to US: $22.5k per chip Nvidia's margin: Drops from 70%+ to maybe 15% Chinese companies ordered 2 MILLION H200 chips for 2026. But Nvidia only has 700,000 in inventory. $22,500 × 700,000 chips = $15.75 BILLION to US Treasury. From ONE deal. With ONE company. Now here's where it gets insane... China isn't actually approving these imports. Chinese state media called H200 chips "unsafe." Cyberspace Administration is blocking purchases except for "exceptional circumstances." So Trump negotiated a deal where: - Nvidia thinks they're getting China market access - China's government is quietly blocking the imports anyway - US Treasury collects billions on the few that do get through - Trump gets to claim he's "tough on China" AND "supporting American business" This move kinda makes sense if you think about it. Supreme Court is about to rule Trump's IEEPA tariffs are illegal. That's the $130 billion refund bomb. Trump needs NEW revenue sources to replace tariff income when that ruling drops. So he invents a completely new structure: Not a tariff (court can't overturn). A "licensing fee" (executive authority). On chips that are "national security sensitive" (unchallengeable). This is the blueprint for Tariffs 2.0. When Supreme Court kills IEEPA authority Trump just pivots to "licensing fees" on every strategic export. Can't challenge it as a tariff because it's not a tariff. It's a "condition of export approval." The semiconductor industry just became Trump's test case. And Nvidia walked right into it because Jensen spent 6 months lobbying for "market access." Meanwhile China's playing the long game: They're not approving H200 imports. They're forcing their companies to use domestic chips (inferior but improving). Accelerating DeepSeek-style efficiency research. Building AI models that work on weaker hardware. In 2-3 years, when Chinese chips catch up, they won't need Nvidia at all. And Trump's "$15.75 billion windfall" becomes $0. But by then he'll have established the legal framework to extract licensing fees from every other strategic export. Everyone thinks Trump's negotiating strategy is "chaos." But look at what he actually accomplished: Created legal precedent for non-tariff trade fees that survive Supreme Court challenges. That's not chaos. That's calculated. What happens next: Supreme Court rules (next 2 weeks). Trump loses on IEEPA tariffs. $130B refund chaos begins. Trump immediately announces "licensing fee" structure for 10 other export categories. Uses Nvidia deal as proof it "works." Markets freak out as every exporter realizes their margins are about to get crushed. China continues blocking H200 imports while building domestic alternatives. Nvidia's stock craters when 2026 guidance shows China revenue never materialized. And Trump goes into 2027 with a completely new trade policy framework that's legally unchallengeable. Everybody's focused on the tariffs. Nobody's watching the licensing fees. This is actually genius.

Ricardo

44,349 次观看 • 5 个月前

Nvidia will now pay you to put a mini AI data center on your house It looks like a normal AC unit in the yard. But inside sits 16 Nvidia Blackwell GPUs and Dell servers. A startup called Span builds them, backed by Nvidia. They bolt onto your home and you get paid for the power and Wi-Fi. Some estimates put that around $1,000 a month in your pocket. That is rent money just for hosting a box outside. Span says it deploys way faster and cheaper than a real data center. The AI boom is literally moving into the suburbs. Save this, the grid is getting rebuilt in real time.
1:09

Nvidia will now pay you to put a mini AI data center on your house It looks like a normal AC unit in the yard. But inside sits 16 Nvidia Blackwell GPUs and Dell servers. A startup called Span builds them, backed by Nvidia. They bolt onto your home and you get paid for the power and Wi-Fi. Some estimates put that around $1,000 a month in your pocket. That is rent money just for hosting a box outside. Span says it deploys way faster and cheaper than a real data center. The AI boom is literally moving into the suburbs. Save this, the grid is getting rebuilt in real time.

winkle.

8,373,137 次观看 • 1 个月前

In 45 years on Wall Street, I've never seen anything like this. Sam Altman just convinced 3 of the world's smartest investors to fund his losses. $110 billion. But ZERO profit in sight. The largest private funding round in history. Let me explain why this is borderline criminal & what you have to understand as an investor: Amazon. Nvidia. SoftBank. 3 of the world's most sophisticated investors just handed OpenAI $110 billion at an $840 billion valuation. That's more than double the $40 billion OpenAI raised last year. For context: all US venture capital combined invested $170 billion into American startups in all of 2023. Altman just raised 65% of that. Alone. In one round. And the company STILL isn't profitable. Let's look at the actual numbers: OpenAI burned $8 billion in 2025. They project burning $17 billion in 2026. $35 billion in 2027. $47 billion in 2028. Cumulative losses before any projected path to profitability: over $115 billion. Meanwhile, Amazon's $50 billion comes with strings attached. $35 billion is contingent on OpenAI either achieving AGI or completing its IPO by year end. Read that again. $35 billion is conditioned on ACHIEVING AGI. They're literally writing checks against a scientific breakthrough that may not happen on any predictable timeline. This is what peak cycle financing looks like. The circular logic every investor should understand: Amazon invests $50 billion in OpenAI. OpenAI commits to spending $100 billion on Amazon Web Services. Nvidia invests $30 billion. OpenAI commits to buying 3 gigawatts of Nvidia compute. These aren't arms-length investments. They're vendor financing dressed up as venture capital. Amazon and Nvidia are essentially paying OpenAI to buy their own products. The $840 billion valuation prices in a future that doesn't exist yet. At $13 billion in 2025 revenue, that's 65x revenue. Even in 2021 - the most speculative bubble in recent tech history - Snowflake peaked at 50-80x revenue. And Snowflake was actually profitable. J.P. Morgan calculates that the AI industry needs $650 billion in annual revenue just to generate a 10% return on total infrastructure buildout. The entire industry currently generates a fraction of that. I've seen cycles my entire 45-year career. The 1980s defense build-up. The dot-com bubble. The 2008 mortgage machine. The pattern is always the same: When the biggest players start financing each other's growth through circular investment structures, you're not witnessing a revolution... You're watching the LAST PHASE of a credit cycle. Amazon CEO Andy Jassy said OpenAI is going to be "one of the very big winners long term." Maybe. But $840 billion assumes they've already won. Stock prices follow earnings. Always have. Always will. And right now, OpenAI's earnings are deeply, structurally, massively negative. The IPO is coming. The hype will peak. And the question every serious investor needs to answer is simple: At what price does this actually make sense? Sam Altman doesn’t know either - he just keeps raising money faster than he can burn it. This can’t end well.
1:14

In 45 years on Wall Street, I've never seen anything like this. Sam Altman just convinced 3 of the world's smartest investors to fund his losses. $110 billion. But ZERO profit in sight. The largest private funding round in history. Let me explain why this is borderline criminal & what you have to understand as an investor: Amazon. Nvidia. SoftBank. 3 of the world's most sophisticated investors just handed OpenAI $110 billion at an $840 billion valuation. That's more than double the $40 billion OpenAI raised last year. For context: all US venture capital combined invested $170 billion into American startups in all of 2023. Altman just raised 65% of that. Alone. In one round. And the company STILL isn't profitable. Let's look at the actual numbers: OpenAI burned $8 billion in 2025. They project burning $17 billion in 2026. $35 billion in 2027. $47 billion in 2028. Cumulative losses before any projected path to profitability: over $115 billion. Meanwhile, Amazon's $50 billion comes with strings attached. $35 billion is contingent on OpenAI either achieving AGI or completing its IPO by year end. Read that again. $35 billion is conditioned on ACHIEVING AGI. They're literally writing checks against a scientific breakthrough that may not happen on any predictable timeline. This is what peak cycle financing looks like. The circular logic every investor should understand: Amazon invests $50 billion in OpenAI. OpenAI commits to spending $100 billion on Amazon Web Services. Nvidia invests $30 billion. OpenAI commits to buying 3 gigawatts of Nvidia compute. These aren't arms-length investments. They're vendor financing dressed up as venture capital. Amazon and Nvidia are essentially paying OpenAI to buy their own products. The $840 billion valuation prices in a future that doesn't exist yet. At $13 billion in 2025 revenue, that's 65x revenue. Even in 2021 - the most speculative bubble in recent tech history - Snowflake peaked at 50-80x revenue. And Snowflake was actually profitable. J.P. Morgan calculates that the AI industry needs $650 billion in annual revenue just to generate a 10% return on total infrastructure buildout. The entire industry currently generates a fraction of that. I've seen cycles my entire 45-year career. The 1980s defense build-up. The dot-com bubble. The 2008 mortgage machine. The pattern is always the same: When the biggest players start financing each other's growth through circular investment structures, you're not witnessing a revolution... You're watching the LAST PHASE of a credit cycle. Amazon CEO Andy Jassy said OpenAI is going to be "one of the very big winners long term." Maybe. But $840 billion assumes they've already won. Stock prices follow earnings. Always have. Always will. And right now, OpenAI's earnings are deeply, structurally, massively negative. The IPO is coming. The hype will peak. And the question every serious investor needs to answer is simple: At what price does this actually make sense? Sam Altman doesn’t know either - he just keeps raising money faster than he can burn it. This can’t end well.

George Noble

1,196,746 次观看 • 4 个月前