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SITUATION EXPLAINED: DeepSeek is betting its next model on Huawei chips. The last attempt failed. • Liang Wenfeng told investors Huawei should start delivering training chips as early as Q4, and that the effort has to work • An earlier training run on Huawei's Ascend 910C failed, and DeepSeek...

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17,425 просмотров • 11 дней назад •via X (Twitter)

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Комментарии: 5

Фото профиля Michel Ardan ⚜️
Michel Ardan ⚜️11 дней назад

Liang Wenfeng's publicly stated objective is AGI.

Фото профиля jaime
jaime11 дней назад

The bet is whether Huawei can deliver silicon that trains at scale.

Фото профиля Ibesh
Ibesh10 дней назад

That is the real race: not “can Huawei train a model,” but whether it can keep closing the gap while Nvidia keeps moving the target. A static benchmark win would age fast.

Фото профиля why
why11 дней назад

This feels less like a chip benchmark and more like a full-stack bet. Compiler tooling, distributed training, and reliability will decide whether the next run works.

Фото профиля 𝕱𝖚𝖑𝖑 𝕶𝖊𝖑𝖑𝖞
𝕱𝖚𝖑𝖑 𝕶𝖊𝖑𝖑𝖞11 дней назад

DeepSeek's next model could double as a stress test for China's entire domestic AI hardware stack

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watched through the entire interview these guys aren't in the same league. jensen plays to win. dwarkesh has been one shotted by mythos marketing. in a way its jensen arguing against dario by proxy. think there's a fundamental underestimation that most ppl in the west have about huawei. they are a freak monster powerhouse and jensen is 100% correct in wanting to push nvidia in china versus letting huawei eat the domestic market. the worst outcome for the US would be for deepseek to be trained 100% on huawei chips versus a china that is dependent on the nvidia stack.
3:58

watched through the entire interview these guys aren't in the same league. jensen plays to win. dwarkesh has been one shotted by mythos marketing. in a way its jensen arguing against dario by proxy. think there's a fundamental underestimation that most ppl in the west have about huawei. they are a freak monster powerhouse and jensen is 100% correct in wanting to push nvidia in china versus letting huawei eat the domestic market. the worst outcome for the US would be for deepseek to be trained 100% on huawei chips versus a china that is dependent on the nvidia stack.

factorydoge

546,457 просмотров • 5 месяцев назад

🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.
0:22

🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.

CryptoGoos

43,232 просмотров • 4 месяцев назад

🚨 THIS IS EXTREMELY BAD FOR $NVDA. China is preparing a $295 billion plan to build AI data centers across the whole country. The catch for America: at least 80% of the chips must be homegrown. That means Huawei in. Nvidia out. Jensen Huang confirmed this last month. Nvidia has "largely conceded" China to Huawei, he told CNBC. China once accounted for at least 20% of Nvidia's entire data center revenue. That market is now all but GONE. America spent years locking China out. China just stopped knocking.
0:20

🚨 THIS IS EXTREMELY BAD FOR $NVDA. China is preparing a $295 billion plan to build AI data centers across the whole country. The catch for America: at least 80% of the chips must be homegrown. That means Huawei in. Nvidia out. Jensen Huang confirmed this last month. Nvidia has "largely conceded" China to Huawei, he told CNBC. China once accounted for at least 20% of Nvidia's entire data center revenue. That market is now all but GONE. America spent years locking China out. China just stopped knocking.

CryptoGoos

187,769 просмотров • 3 месяцев назад

Where are DeepSeek and other Chinese AI firms going to be getting their chips over the next 5 years? Jordan Schneider says that without Nvidia hardware, they're just not going to be able to keep up. "There's this ratio between the US semi ecosystem and the China semi ecosystem. The capacity is like 15:1. "And you can project that out pretty clearly, doing some math around how much capex is being spent, how much equipment is being acquired, yield rates, etc." "So where is [DeepSeek founder Liang Wenfeng] going to get the chips? He's probably going to get as much Nvidia as he can get." "But the larger takeaway with the Chinese and Western ecosystems is that the Western one is way better capitalized and has way more straightforward access to chips, which is basically an order of magnitude better over at least the next 5 years." "So it's going to be rough sledding for Chinese firms." "The Chinese AI IPOs are making like, $500M. Or like $1B. And it's cute. But it's not $3 trillion. Or even $10B. And the reason that's the case is that they're not on the frontier — so they can't charge a premium — and they're compute-constrained." "If all the firms have is access to Chinese chips, it's going to be really difficult to make a lot of money, and keep paying for more chips, in the way that the Western labs have been able to mobilize the entire global financial system."
2:28

Where are DeepSeek and other Chinese AI firms going to be getting their chips over the next 5 years? Jordan Schneider says that without Nvidia hardware, they're just not going to be able to keep up. "There's this ratio between the US semi ecosystem and the China semi ecosystem. The capacity is like 15:1. "And you can project that out pretty clearly, doing some math around how much capex is being spent, how much equipment is being acquired, yield rates, etc." "So where is [DeepSeek founder Liang Wenfeng] going to get the chips? He's probably going to get as much Nvidia as he can get." "But the larger takeaway with the Chinese and Western ecosystems is that the Western one is way better capitalized and has way more straightforward access to chips, which is basically an order of magnitude better over at least the next 5 years." "So it's going to be rough sledding for Chinese firms." "The Chinese AI IPOs are making like, $500M. Or like $1B. And it's cute. But it's not $3 trillion. Or even $10B. And the reason that's the case is that they're not on the frontier — so they can't charge a premium — and they're compute-constrained." "If all the firms have is access to Chinese chips, it's going to be really difficult to make a lot of money, and keep paying for more chips, in the way that the Western labs have been able to mobilize the entire global financial system."

TBPN

52,507 просмотров • 1 месяц назад

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

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

Ricardo

162,581 просмотров • 4 месяцев назад

David Friedberg: Nvidia's Greatest Threat is Huawei @jason: “Who do you think has the best chance of challenging Nvidia?” david friedberg: “The other black swan that I think is missing in the equation today, my early prediction for 2026, is Huawei.” “I think that there's lithography technology that exists in China that is not publicly discussed, that is gonna be deployed in Huawei and all these fabs that they're building in mainland China.” “And Huawei can create, at a very low cost, probably very high volume and probably in reasonably short order, chips that can start to rival, for certain market applications, chips that might be expensive and (have) long lead times.” Jason: “Give a timeline for that. Two years, three years out?” Friedberg: “I think they start to make announcements, and by the way, remember chip architecture, and even Jenssen's talked about this, is being redesigned with AI, so AI can design better chips.” Jason: “So announcements 2026, impact 2027?” Friedberg: “Probably fair.” Recorded in the brand new poker studio at The Venetian Las Vegas. Thanks The Venetian Resort Las Vegas!
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David Friedberg: Nvidia's Greatest Threat is Huawei @jason: “Who do you think has the best chance of challenging Nvidia?” david friedberg: “The other black swan that I think is missing in the equation today, my early prediction for 2026, is Huawei.” “I think that there's lithography technology that exists in China that is not publicly discussed, that is gonna be deployed in Huawei and all these fabs that they're building in mainland China.” “And Huawei can create, at a very low cost, probably very high volume and probably in reasonably short order, chips that can start to rival, for certain market applications, chips that might be expensive and (have) long lead times.” Jason: “Give a timeline for that. Two years, three years out?” Friedberg: “I think they start to make announcements, and by the way, remember chip architecture, and even Jenssen's talked about this, is being redesigned with AI, so AI can design better chips.” Jason: “So announcements 2026, impact 2027?” Friedberg: “Probably fair.” Recorded in the brand new poker studio at The Venetian Las Vegas. Thanks The Venetian Resort Las Vegas!

The All-In Podcast

373,654 просмотров • 10 месяцев назад

🚨 CHINA'S AI PLAYBOOK ISN'T TO BEAT OPENAI AND ANTHROPIC. IT'S TO MAKE THEM IRRELEVANT BY MAKING INTELLIGENCE FREE. Chinese labs are giving away frontier-level models for free. Zhipu's GLM-5 already beats Claude Opus 4.5 and Gemini 3.0 Pro on key benchmarks. DeepSeek, Qwen, and Kimi round out 4 of the top 5 open-weight models globally, all released under open licenses anyone can use commercially. The real weapon isn't the models, it's the cost to run them. Chinese provinces like Gansu, Guizhou, and Inner Mongolia are cutting data center power bills by up to 50%. DeepSeek trained its R1 model for around $6 million, compared to roughly $100 million for GPT-4. The remaining blocker is chips. US export controls still cut China off from Nvidia's best hardware. But Zhipu already trained a full frontier model entirely on Huawei's Ascend chips, no Nvidia involved. Huawei's next-gen Atlas 950 cluster, launching late 2026, claims 6.7 times the compute of Nvidia's planned NVL144 systems. If Huawei closes the chip gap while China keeps giving away free, near frontier models running on subsidized power. The entire business model OpenAI and Anthropic are built on, charging hundreds of billions in aggregate revenue for access to intelligence, stops making sense. Why pay premium prices for marginal capability gains when a free alternative running on cheap compute gets you 90% of the way there.
0:28

🚨 CHINA'S AI PLAYBOOK ISN'T TO BEAT OPENAI AND ANTHROPIC. IT'S TO MAKE THEM IRRELEVANT BY MAKING INTELLIGENCE FREE. Chinese labs are giving away frontier-level models for free. Zhipu's GLM-5 already beats Claude Opus 4.5 and Gemini 3.0 Pro on key benchmarks. DeepSeek, Qwen, and Kimi round out 4 of the top 5 open-weight models globally, all released under open licenses anyone can use commercially. The real weapon isn't the models, it's the cost to run them. Chinese provinces like Gansu, Guizhou, and Inner Mongolia are cutting data center power bills by up to 50%. DeepSeek trained its R1 model for around $6 million, compared to roughly $100 million for GPT-4. The remaining blocker is chips. US export controls still cut China off from Nvidia's best hardware. But Zhipu already trained a full frontier model entirely on Huawei's Ascend chips, no Nvidia involved. Huawei's next-gen Atlas 950 cluster, launching late 2026, claims 6.7 times the compute of Nvidia's planned NVL144 systems. If Huawei closes the chip gap while China keeps giving away free, near frontier models running on subsidized power. The entire business model OpenAI and Anthropic are built on, charging hundreds of billions in aggregate revenue for access to intelligence, stops making sense. Why pay premium prices for marginal capability gains when a free alternative running on cheap compute gets you 90% of the way there.

The Macro Paper

71,921 просмотров • 3 месяцев назад

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 просмотров • 8 месяцев назад

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?
1:35

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

Ricardo

64,411 просмотров • 1 месяц назад

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 просмотров • 8 месяцев назад

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 Jr.'s fund 1789 Capital. Yesterday, Nvidia paid $20 billion for them. That's a 3x markup in 90 days. Trump Jr. just made a fortune overnight. But the money isn't even the craziest part... Nvidia dominates AI chips. They have 90%+ market share. All of big tech depends on them. So why would they pay 3X for a tiny competitor? Let me explain: Groq was building inference chips that threatened Nvidia's monopoly. Faster processing. Lower costs. Better architecture. If Groq succeeded, they'd crack open the market. So Nvidia did what monopolies always do: bought them before they became a threat. They're buying silence - not innovation. And this isn't a one-off. In September, Nvidia spent $900 million on Enfabrica, another AI chip startup. Same playbook. They committed $100 billion to OpenAI with a requirement that OpenAI deploy 10 gigawatts of Nvidia products. Now $20 billion for Groq. Nvidia isn't competing anymore. They're consolidating. They're using their cash pile to swallow every potential rival before they grow. The founder and CEO of Groq, Jonathan Ross, is joining Nvidia. So is the president and the entire leadership team. Groq's cloud business will keep running independently. But the tech, the IP, the competitive advantage? All Nvidia's now. This is the largest acquisition Nvidia has EVER made. Their previous record was $7 billion for Mellanox in 2019. They just tripled that. And nobody's questioning why. Everyone's celebrating "innovation" and "consolidation" and "strategic partnerships." But what actually happened is this: Nvidia looked at the one company that could challenge their monopoly and paid whatever it took to kill them. $20 billion is cheap insurance when you're worth trillions. The only question left is who's next.
0:35

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 Jr.'s fund 1789 Capital. Yesterday, Nvidia paid $20 billion for them. That's a 3x markup in 90 days. Trump Jr. just made a fortune overnight. But the money isn't even the craziest part... Nvidia dominates AI chips. They have 90%+ market share. All of big tech depends on them. So why would they pay 3X for a tiny competitor? Let me explain: Groq was building inference chips that threatened Nvidia's monopoly. Faster processing. Lower costs. Better architecture. If Groq succeeded, they'd crack open the market. So Nvidia did what monopolies always do: bought them before they became a threat. They're buying silence - not innovation. And this isn't a one-off. In September, Nvidia spent $900 million on Enfabrica, another AI chip startup. Same playbook. They committed $100 billion to OpenAI with a requirement that OpenAI deploy 10 gigawatts of Nvidia products. Now $20 billion for Groq. Nvidia isn't competing anymore. They're consolidating. They're using their cash pile to swallow every potential rival before they grow. The founder and CEO of Groq, Jonathan Ross, is joining Nvidia. So is the president and the entire leadership team. Groq's cloud business will keep running independently. But the tech, the IP, the competitive advantage? All Nvidia's now. This is the largest acquisition Nvidia has EVER made. Their previous record was $7 billion for Mellanox in 2019. They just tripled that. And nobody's questioning why. Everyone's celebrating "innovation" and "consolidation" and "strategic partnerships." But what actually happened is this: Nvidia looked at the one company that could challenge their monopoly and paid whatever it took to kill them. $20 billion is cheap insurance when you're worth trillions. The only question left is who's next.

Ricardo

224,340 просмотров • 9 месяцев назад

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.
1:06

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.

Ricardo

281,804 просмотров • 5 месяцев назад

Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:
1:36

Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:

Computer

200,997 просмотров • 10 месяцев назад

China is planning to spend around 2 trillion yuan ($295 billion) over the next five years to build data centers across the country At least 80% of all AI chips must come from domestic suppliers, primarily Huawei. Nvidia and AMD are locked out by design. Nvidia once held a 95% market share in China's AI market. Now? Zero. America did not lose China’s AI chip market because China “closed its doors,” but because America chose to weaponize chips, sanctions, export controls, entity lists, national security hysteria, and technological apartheid against its own largest customer. Washington took the world’s largest chip consumption market — a market that could have funded American semiconductor dominance for another generation — and taught it the most important lesson: Never let your future depend on a supplier that can be turned off by one paranoid empire. So China did what any serious civilization-state would do. It localized. It substituted. It built its own stack. It turned a customer relationship into a sovereignty project. And the same people who tried to choke Huawei are watching Huawei become the backbone of China’s AI infrastructure. This is a textbook own goal. America thought it was denying​ China the future. In reality, it compelled​ China to architect​ the future beyond​ America.
1:02

China is planning to spend around 2 trillion yuan ($295 billion) over the next five years to build data centers across the country At least 80% of all AI chips must come from domestic suppliers, primarily Huawei. Nvidia and AMD are locked out by design. Nvidia once held a 95% market share in China's AI market. Now? Zero. America did not lose China’s AI chip market because China “closed its doors,” but because America chose to weaponize chips, sanctions, export controls, entity lists, national security hysteria, and technological apartheid against its own largest customer. Washington took the world’s largest chip consumption market — a market that could have funded American semiconductor dominance for another generation — and taught it the most important lesson: Never let your future depend on a supplier that can be turned off by one paranoid empire. So China did what any serious civilization-state would do. It localized. It substituted. It built its own stack. It turned a customer relationship into a sovereignty project. And the same people who tried to choke Huawei are watching Huawei become the backbone of China’s AI infrastructure. This is a textbook own goal. America thought it was denying​ China the future. In reality, it compelled​ China to architect​ the future beyond​ America.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

36,839 просмотров • 3 месяцев назад

$NVDA CEO: “Demand for inference will go up by a billion times.” $AMD will be one of the biggest winners as it has the upper hand in inference thanks to larger memory bandwith and capacity of its chips. Stock is still at 22x 2027 earnings. How can’t you be bullish on $AMD?
0:20

$NVDA CEO: “Demand for inference will go up by a billion times.” $AMD will be one of the biggest winners as it has the upper hand in inference thanks to larger memory bandwith and capacity of its chips. Stock is still at 22x 2027 earnings. How can’t you be bullish on $AMD?

Oguz Erkan

288,805 просмотров • 5 месяцев назад

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

1,617,652 просмотров • 9 месяцев назад

The AI supply chain has the craziest value cascade of any industry in the world. thinks that over the next five years, the biggest bottleneck to deploying AI will be EUV machines. ASML sells EUV machines for $300-400 million. You need about three and a half machines, so $1.2 billion, to produce a gigawatt of compute each year. So $1.2 billion of ASL and ML machines are producing $35 billion worth of NVIDIA chips a year (and of course those machines can be used over many years). Then those Nvidia chips can generate $100s of billion of token revenue for AI labs. Or think about: over the last three years, TSMC spent $100 billion on CapEx. A small fraction of that went to building the fab capacity used for Nvidia's chips. Nvidia turned that fraction into $216 billion of revenue last fiscal year alone. Relative to the value of the thing they’re producing, TSMC’s CapEx looks crazy small. So why aren’t ASML/TSMC/etc juicing up margins to capture more of the value they’re generating? And why aren’t they scaling up CapEx so that they can take advantage of the crazy AI growth worlds? Dylan Patel says that it’s because these upstream players aren’t AGI-pilled enough. They’re still looking for stable, gentle long-term growth. The insane demand signal is taking a long time to percolate up the supply chain.
2:02

The AI supply chain has the craziest value cascade of any industry in the world. thinks that over the next five years, the biggest bottleneck to deploying AI will be EUV machines. ASML sells EUV machines for $300-400 million. You need about three and a half machines, so $1.2 billion, to produce a gigawatt of compute each year. So $1.2 billion of ASL and ML machines are producing $35 billion worth of NVIDIA chips a year (and of course those machines can be used over many years). Then those Nvidia chips can generate $100s of billion of token revenue for AI labs. Or think about: over the last three years, TSMC spent $100 billion on CapEx. A small fraction of that went to building the fab capacity used for Nvidia's chips. Nvidia turned that fraction into $216 billion of revenue last fiscal year alone. Relative to the value of the thing they’re producing, TSMC’s CapEx looks crazy small. So why aren’t ASML/TSMC/etc juicing up margins to capture more of the value they’re generating? And why aren’t they scaling up CapEx so that they can take advantage of the crazy AI growth worlds? Dylan Patel says that it’s because these upstream players aren’t AGI-pilled enough. They’re still looking for stable, gentle long-term growth. The insane demand signal is taking a long time to percolate up the supply chain.

Dwarkesh Patel

78,103 просмотров • 6 месяцев назад