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Google just admitted it can't build data centers fast enough, so it's planning on baking its AI model directly into the chip instead (Save this). Google's new Frozen v2 chip permanently embeds parts of Gemini's architecture into the silicon itself, cutting down on the calculations and data movement needed... to answer a query. Engineers estimate it could process 6 to 10 times more tokens per unit of power than Google's current TPUs. The real story is why Google is building this in the first place because Frozen v2 is meant to ease a severe internal compute crunch that's caused friction between teams at Google. It reportedly pushed Google Cloud to turn away outside business because it simply doesn't have enough spare capacity to go around. If Google, one of the largest chipmakers in the world, is short enough on compute to turn away paying Cloud customers, that's confirmation this shortage isn't a scaling problem unique to smaller players, it's systemic across the entire industry. This ties into a much bigger power struggle happening right now. More AI companies are trying to cut their reliance on Nvidia by building their own chips. OpenAI rolled out a custom chip called Jalapeno alongside Broadcom last month and Anthropic is now partnering with Samsung on something similar. The reasoning is pretty simple, Nvidia effectively acts as landlord for every hyperscaler out there, and the rent isn't cheap. Nvidia hardware can account for anywhere from 20% to 60% of total AI infrastructure spend, and once a company is tied into its ecosystem, every hardware refresh forces another costly one. That's exactly why Google built TPUs and Amazon built Trainium, both trying to protect their own margins for shareholders. Bullish on Marvell + Broadcom who makes these custom chips and follow me Melvin for more infrastructure plays and check out the link below for more!show more

Melvin

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63,767 görüntüleme • 3 ay önce •via X (Twitter)

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22 Yorum

Melvin profil fotoğrafı

Melvin3 ay önce

I’m an analyst at Milk Road, and my job is to find underrated gems before the market catches on. We called names like MU, CRDO, NBIS, and BE over the last 3 months. Join me and my team for just $1.

Magik Insight profil fotoğrafı

Magik Insight3 ay önce

Very natural progression because there is so much cheap fab in the world today.

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı

安叫兽|Bird🕊️ 🔶 BNB3 ay önce

连芯片都开始内置模型了，真快

Jatin Garg profil fotoğrafı

Jatin Garg3 ay önce

baking model weights into silicon means you're frozen at that checkpoint. what happens when gemini 4 ships six months later?

m0xt profil fotoğrafı

m0xt3 ay önce

Bullish compute.

Milk Road AI profil fotoğrafı

Milk Road AI3 ay önce

great take Melvin!

Melvin profil fotoğrafı

Melvin3 ay önce

thank you

Gregor profil fotoğrafı

Gregor3 ay önce

not sure 'permanently embeds' is the win it looks like. chip tapeout to production is 18+ months and gemini's jumped major versions in that window. do they spin new silicon every release or just let the baked parts go stale?

. profil fotoğrafı

.3 ay önce

Fake and stupid post

Saeed Anwar profil fotoğrafı

Saeed Anwar3 ay önce

Baking model architecture into the chip ends the GPU procurement race for that workload, but silicon iterates every 2 to 3 years while model architecture iterates every 6 months. How do you keep a frozen chip from being architecturally obsolete before it even ships?

Josh profil fotoğrafı

Josh3 ay önce

I think you are comparing two different things; Nvidia GPUs and Google's Frozen v2 chip are built for different purposes.

Saeed Anwar profil fotoğrafı

Saeed Anwar3 ay önce

Google baking the model into the chip is the endgame of vertical integration. When inference is in silicon, API pricing dies and hardware licensing is born. This changes who captures margin in the AI stack completely. What does this do to model switching costs?

Ryan 🇮🇱🇵🇸🇺🇦🇨🇳🇺🇸🇹🇼 profil fotoğrafı

Ryan 🇮🇱🇵🇸🇺🇦🇨🇳🇺🇸🇹🇼3 ay önce

Congratulations they made an asic. The issue is that Ai models are constantly evolving. They would be implemented a hard solution to their silicon. It may not even be efficient compared to Chinese llm architecture which use better memory efficiency.

Jason傑森 🇭🇰 | 🛠️ profil fotoğrafı

Jason傑森 🇭🇰 | 🛠️3 ay önce

这ai功耗都快怼进指甲盖了吧

p_tom40 profil fotoğrafı

p_tom403 ay önce

You can build for yourself, if you have money and scale. But the utility of this chips for customers other than Google is lower than NVIDIA, plus there is no roadmap

Bojan profil fotoğrafı

Bojan3 ay önce

Freezing the architecture into silicon trades flexibility for efficiency. ASICs amortize 3-5 years; architectures shift every ~12 months. Frozen v2 bets Gemini's shape stays put, contradicting the field racing to change it. 6-10x/watt only pays if you stop innovating.

Kyle Reidhead | Milk Road profil fotoğrafı

Kyle Reidhead | Milk Road3 ay önce

"Nvidia is their landlord and the rent is too damn high"

Tgal 🥛 profil fotoğrafı

Tgal 🥛3 ay önce

it's non stop!

Melvin profil fotoğrafı

Melvin3 ay önce

yes sir

Vu Anh profil fotoğrafı

Vu Anh3 ay önce

Aboslutely agree ,the compute crunch is real. Custom ASICs are the only path to sustainable AI unit economics.

Melvin profil fotoğrafı

Melvin3 ay önce

well said

Utkarsh Singh profil fotoğrafı

Utkarsh Singh3 ay önce

the goal isn't to speed up deployment, it's to lock everyone into their ecosystem.

Benzer Videolar

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.

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

1,617,710 görüntüleme • 10 ay önce

$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 $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:$

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 görüntüleme • 11 ay önce

Broadcom's CEO just exposed the real fight underneath Google's AI chip strategy. It is not Google versus Broadcom. It is Google and Broadcom trying to make Nvidia replaceable. Within two minutes at Bloomberg Tech, Hock Tan was asked whether Google bringing more chip design in house keeps him up at night. His exact words: "So we just compete against my own customer." Then he named the real enemy: "the real competitor facing all this is the GPU out of Nvidia." That is the part most people miss. Custom AI chips are not just cheaper GPUs. They are ownership claims. If Google owns the workload, the compiler stack, the cloud customer, and the TPU roadmap, Nvidia becomes a benchmark instead of the toll booth. But Broadcom is still in the room because independence is not binary. The hard part is not drawing a chip. The hard part is shipping generation after generation at scale, matching Nvidia's cadence, keeping networking tight, and making the whole system useful enough that developers do not care what silicon sits underneath. That is why Tan can say Google is trying to create customer owned tooling and still sound calm. Broadcom is not selling picks and shovels. It is selling the bridge out of Nvidia dependency. The numbers explain why this is suddenly a board level issue. Broadcom reported $22.2 billion of Q2 2026 revenue. Its AI semiconductor revenue hit $10.8 billion, up 143 percent year over year. For Q3, Broadcom guided AI semiconductor revenue to $16.0 billion, up more than 200 percent year over year. In the clip, Tan says Broadcom has exactly 6 custom AI accelerator customers. He says OpenAI has been engaged for over 2 years, its accelerator is already working in labs and data centers, and production is on track for late this year. That is the hidden mechanism: The AI labs are not becoming software companies with some chips attached. They are becoming capacity companies with model interfaces attached. Once your margin depends on tokens, latency, memory bandwidth, power contracts, packaging slots, networking gear, and a private accelerator schedule, the "model company" label starts to look like a costume. The precedent is Apple. Apple did not move into custom silicon because it wanted a cute chip branding story. It moved because the iPhone needed control over performance per watt, release cadence, and differentiation. A series chips in 2010. M1 in 2020. More than a decade of slowly pulling the bottleneck inside the company. But Apple still needed TSMC. That is the useful analogy for Google, OpenAI, and the other AI giants. They want Nvidia's margin pool. They want Nvidia's roadmap power. They want Nvidia's ability to decide who gets capacity first. But the first supplier they replace becomes the supplier they cannot live without. Broadcom is the customs officer at the border of private silicon. Second order consequence: AI company valuation will shift from model demos to infrastructure custody. Who owns the workload? Who controls the accelerator roadmap? Who has memory secured? Who can afford to keep a bad first generation alive long enough to get to the second and third? My bet: by the end of 2027, at least one major AI lab will be judged more by its custom chip execution than by its model benchmark lead. The model race is public. The margin race is being negotiated in silicon.

Broadcom's CEO just exposed the real fight underneath Google's AI chip strategy. It is not Google versus Broadcom. It is Google and Broadcom trying to make Nvidia replaceable. Within two minutes at Bloomberg Tech, Hock Tan was asked whether Google bringing more chip design in house keeps him up at night. His exact words: "So we just compete against my own customer." Then he named the real enemy: "the real competitor facing all this is the GPU out of Nvidia." That is the part most people miss. Custom AI chips are not just cheaper GPUs. They are ownership claims. If Google owns the workload, the compiler stack, the cloud customer, and the TPU roadmap, Nvidia becomes a benchmark instead of the toll booth. But Broadcom is still in the room because independence is not binary. The hard part is not drawing a chip. The hard part is shipping generation after generation at scale, matching Nvidia's cadence, keeping networking tight, and making the whole system useful enough that developers do not care what silicon sits underneath. That is why Tan can say Google is trying to create customer owned tooling and still sound calm. Broadcom is not selling picks and shovels. It is selling the bridge out of Nvidia dependency. The numbers explain why this is suddenly a board level issue. Broadcom reported $22.2 billion of Q2 2026 revenue. Its AI semiconductor revenue hit $10.8 billion, up 143 percent year over year. For Q3, Broadcom guided AI semiconductor revenue to $16.0 billion, up more than 200 percent year over year. In the clip, Tan says Broadcom has exactly 6 custom AI accelerator customers. He says OpenAI has been engaged for over 2 years, its accelerator is already working in labs and data centers, and production is on track for late this year. That is the hidden mechanism: The AI labs are not becoming software companies with some chips attached. They are becoming capacity companies with model interfaces attached. Once your margin depends on tokens, latency, memory bandwidth, power contracts, packaging slots, networking gear, and a private accelerator schedule, the "model company" label starts to look like a costume. The precedent is Apple. Apple did not move into custom silicon because it wanted a cute chip branding story. It moved because the iPhone needed control over performance per watt, release cadence, and differentiation. A series chips in 2010. M1 in 2020. More than a decade of slowly pulling the bottleneck inside the company. But Apple still needed TSMC. That is the useful analogy for Google, OpenAI, and the other AI giants. They want Nvidia's margin pool. They want Nvidia's roadmap power. They want Nvidia's ability to decide who gets capacity first. But the first supplier they replace becomes the supplier they cannot live without. Broadcom is the customs officer at the border of private silicon. Second order consequence: AI company valuation will shift from model demos to infrastructure custody. Who owns the workload? Who controls the accelerator roadmap? Who has memory secured? Who can afford to keep a bad first generation alive long enough to get to the second and third? My bet: by the end of 2027, at least one major AI lab will be judged more by its custom chip execution than by its model benchmark lead. The model race is public. The margin race is being negotiated in silicon.

Andrej Drats

10,572 görüntüleme • 4 ay önce

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.

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

Bull Theory

26,765 görüntüleme • 4 ay önce

The entire AI boom has hit a physical wall that no chip can break: America does NOT have enough electricity to run what these companies are building. You don't form an emergency coalition around a problem you've solved. You form one around a problem that's beating you. This morning Nvidia, Google, and a startup called Emerald AI launched something called the AI Energy Management Alliance. 18 members, including the AI lab Anthropic, the utility National Grid, and the power producers AES and NRG. The stated mission: Data centers will become "good citizens" of the grid, gently easing their power use in the rare hours the system is stressed, protecting your electricity bill and keeping the lights on for everyone else. But read what Nvidia said underneath the press release: Power has become "the defining constraint" on the expansion of AI infrastructure in the United States. The most valuable company in history, the one selling every chip the boom runs on, just told you the ceiling on its own business is the number of watts the country can physically deliver. So what is this alliance actually for? It's a lobbying group. And its real ask is this: Right now a new data center can wait a decade or more to connect to the grid, because utilities have to build for the worst hour of the year. The alliance wants governors and regulators to let any data center that promises to throttle its power during peak hours skip that line and plug in faster and bigger. And the grid is already buckling, and you're already paying for it. On PJM, the largest grid in America, serving 67 million people across 13 states, the wholesale cost of power jumped 76% in a single year. The independent market monitor pinned it directly on data centers and said the damage "is not reversible." Capacity prices in that market went from about $29 per megawatt-day to $329, roughly 11 times higher, and data centers drove most of it. This summer PJM had to beg households to cut their usage during a heat wave to avoid rolling blackouts. That's the backdrop the word "affordability" is doing so much work against. Now look at what the alliance actually brings to fix it: Google runs a demand-response program of roughly 1 gigawatt. Emerald and Nvidia have run 6 demonstrations. The first genuinely flexible AI data center they can point to is a single 100 megawatt site in Virginia that isn't even switched on yet. That's the pilot they're holding up against a buildout measured in tens of gigawatts, ordered years in advance, and already raising your rates today. So strip the civics off it and here's what actually happened: The best-funded companies on Earth just formed a coalition whose entire premise is that they need permission to sometimes use LESS electricity. They are announcing that the grid ran out of room before the models ran out of ambition, and they would like to jump the queue while you cover the upgrades. This has nothing to do with strength. Every earlier limit on AI was just a number on a slide, whether it was compute or funding or chip supply, and every one of them got solved with more money. This one can't be solved that way. You cannot venture-fund a power plant into existence in a quarter, and you cannot print a transmission line. Electricity is the first ceiling the AI industry can't buy its way through, and today it admitted that while dressing it up as a favor to your utility bill.

The entire AI boom has hit a physical wall that no chip can break: America does NOT have enough electricity to run what these companies are building. You don't form an emergency coalition around a problem you've solved. You form one around a problem that's beating you. This morning Nvidia, Google, and a startup called Emerald AI launched something called the AI Energy Management Alliance. 18 members, including the AI lab Anthropic, the utility National Grid, and the power producers AES and NRG. The stated mission: Data centers will become "good citizens" of the grid, gently easing their power use in the rare hours the system is stressed, protecting your electricity bill and keeping the lights on for everyone else. But read what Nvidia said underneath the press release: Power has become "the defining constraint" on the expansion of AI infrastructure in the United States. The most valuable company in history, the one selling every chip the boom runs on, just told you the ceiling on its own business is the number of watts the country can physically deliver. So what is this alliance actually for? It's a lobbying group. And its real ask is this: Right now a new data center can wait a decade or more to connect to the grid, because utilities have to build for the worst hour of the year. The alliance wants governors and regulators to let any data center that promises to throttle its power during peak hours skip that line and plug in faster and bigger. And the grid is already buckling, and you're already paying for it. On PJM, the largest grid in America, serving 67 million people across 13 states, the wholesale cost of power jumped 76% in a single year. The independent market monitor pinned it directly on data centers and said the damage "is not reversible." Capacity prices in that market went from about $29 per megawatt-day to $329, roughly 11 times higher, and data centers drove most of it. This summer PJM had to beg households to cut their usage during a heat wave to avoid rolling blackouts. That's the backdrop the word "affordability" is doing so much work against. Now look at what the alliance actually brings to fix it: Google runs a demand-response program of roughly 1 gigawatt. Emerald and Nvidia have run 6 demonstrations. The first genuinely flexible AI data center they can point to is a single 100 megawatt site in Virginia that isn't even switched on yet. That's the pilot they're holding up against a buildout measured in tens of gigawatts, ordered years in advance, and already raising your rates today. So strip the civics off it and here's what actually happened: The best-funded companies on Earth just formed a coalition whose entire premise is that they need permission to sometimes use LESS electricity. They are announcing that the grid ran out of room before the models ran out of ambition, and they would like to jump the queue while you cover the upgrades. This has nothing to do with strength. Every earlier limit on AI was just a number on a slide, whether it was compute or funding or chip supply, and every one of them got solved with more money. This one can't be solved that way. You cannot venture-fund a power plant into existence in a quarter, and you cannot print a transmission line. Electricity is the first ceiling the AI industry can't buy its way through, and today it admitted that while dressing it up as a favor to your utility bill.

Ricardo

26,524 görüntüleme • 1 ay önce

Google is making $62 billion a quarter destroying the websites it NEEDS to survive. This is literally a death spiral that ends with Google killing itself. Let me explain what's going on... Google added AI summaries to the top of every search result in 2024. When you Google something now, the answer sits right there on Google's page. You never have to click anywhere. Google took the information from someone else's website, summarized it, and kept you inside Google's ecosystem. The result: 60% of all Google searches now end without a single click to any website. Small publishers lost 60% of their traffic in one year. Medium publishers lost 47%. Even the biggest names in media, the New York Times, the Washington Post, Business Insider, all saw traffic fall between 22% and 55%. The Axios CEO called it "a referral extinction event for the ad-supported web." Google's response to all of this was to tell publishers they can "opt out" of having their content summarized. But opting out also REMOVES your description from normal search results. So the choice Google gives you is let us steal your content for free, or become invisible on the internet. That's extortion. The Washington Post laid off another round of journalists this year because of it. Stereogum, one of the most respected music publications on the internet, had to BEG readers for donations. Business Insider cut 21% of its staff. Dozens of smaller publishers have shut down entirely. The people who actually CREATE the information Google summarizes are going bankrupt while Google posts record revenue. But here's where this gets interesting and where everyone stops thinking: Google's AI summaries are only as good as the content they summarize. If the publishers who write the original articles, run the original investigations, and create the original data go out of business, there is nothing left for Google to summarize. The AI starts recycling old information, the answers get stale, the quality drops, and users start noticing that Google's summaries are increasingly wrong, outdated, or useless. Google is essentially strip-mining the internet for short-term revenue. They are extracting all the value from content creators without paying for it, driving those creators out of business, and then wondering why the quality of their own product is declining. This is exactly what Napster did to the music industry in the early 2000s: Made content free, creators went broke, and quality collapsed. It took a decade to rebuild. Google is doing the same thing to the entire internet at 100x the scale. Rolling Stone, Variety, Deadline, The Hollywood Reporter, and Billboard are now suing Google for antitrust violations. Chegg, the education platform, lost 49% of its traffic and is suing too. The UK's competition authority just ordered Google to let publishers opt out without being punished. The DOJ already ruled Google is an illegal monopoly. And Google's defense in court is genuinely unbelievable. They argue that publishers CHOOSE to let Google index their content and can leave anytime they want. That's like saying you choose to pay protection money to the mob because technically you could close your business and move to another city. Google controls 90% of search. Leaving Google means leaving the internet. Meanwhile Google is investing billions in custom AI chips to make these summaries cheaper at scale. Every quarter the problem gets worse. The internet as we've known it for 25 years ran on a simple deal: Publishers make content. Google sends traffic. Advertisers pay for the traffic. Everyone wins. But Google just BROKE that deal and kept all the money.

Google is making $62 billion a quarter destroying the websites it NEEDS to survive. This is literally a death spiral that ends with Google killing itself. Let me explain what's going on... Google added AI summaries to the top of every search result in 2024. When you Google something now, the answer sits right there on Google's page. You never have to click anywhere. Google took the information from someone else's website, summarized it, and kept you inside Google's ecosystem. The result: 60% of all Google searches now end without a single click to any website. Small publishers lost 60% of their traffic in one year. Medium publishers lost 47%. Even the biggest names in media, the New York Times, the Washington Post, Business Insider, all saw traffic fall between 22% and 55%. The Axios CEO called it "a referral extinction event for the ad-supported web." Google's response to all of this was to tell publishers they can "opt out" of having their content summarized. But opting out also REMOVES your description from normal search results. So the choice Google gives you is let us steal your content for free, or become invisible on the internet. That's extortion. The Washington Post laid off another round of journalists this year because of it. Stereogum, one of the most respected music publications on the internet, had to BEG readers for donations. Business Insider cut 21% of its staff. Dozens of smaller publishers have shut down entirely. The people who actually CREATE the information Google summarizes are going bankrupt while Google posts record revenue. But here's where this gets interesting and where everyone stops thinking: Google's AI summaries are only as good as the content they summarize. If the publishers who write the original articles, run the original investigations, and create the original data go out of business, there is nothing left for Google to summarize. The AI starts recycling old information, the answers get stale, the quality drops, and users start noticing that Google's summaries are increasingly wrong, outdated, or useless. Google is essentially strip-mining the internet for short-term revenue. They are extracting all the value from content creators without paying for it, driving those creators out of business, and then wondering why the quality of their own product is declining. This is exactly what Napster did to the music industry in the early 2000s: Made content free, creators went broke, and quality collapsed. It took a decade to rebuild. Google is doing the same thing to the entire internet at 100x the scale. Rolling Stone, Variety, Deadline, The Hollywood Reporter, and Billboard are now suing Google for antitrust violations. Chegg, the education platform, lost 49% of its traffic and is suing too. The UK's competition authority just ordered Google to let publishers opt out without being punished. The DOJ already ruled Google is an illegal monopoly. And Google's defense in court is genuinely unbelievable. They argue that publishers CHOOSE to let Google index their content and can leave anytime they want. That's like saying you choose to pay protection money to the mob because technically you could close your business and move to another city. Google controls 90% of search. Leaving Google means leaving the internet. Meanwhile Google is investing billions in custom AI chips to make these summaries cheaper at scale. Every quarter the problem gets worse. The internet as we've known it for 25 years ran on a simple deal: Publishers make content. Google sends traffic. Advertisers pay for the traffic. Everyone wins. But Google just BROKE that deal and kept all the money.

Ricardo

251,284 görüntüleme • 5 ay önce

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Ricardo

229,025 görüntüleme • 5 ay önce

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Macro

55,169 görüntüleme • 5 ay önce

$MRVL sits on both sides of the AI bottleneck at the same time (Save this). "Computing at this scale is fundamentally a connectivity challenge." That is Marvell CEO Matt Murphy explaining why the bottleneck has shifted from compute to connectivity. Marvell is a fabless chip designer meaning it designs chips and outsources the manufacturing. Its portfolio now spans five areas at once: 1. It holds a near monopoly on optical DSPs, the chips that act as the brains inside optical transceiver modules, through its 1.6T Nova platform. 2. It makes the coherent DSPs that carry data between data centers over long distances. 3. It supplies the physical layer chips that every chip to chip connection depends on. 4. Its ethernet cable business is on pace to cross $300 million this year and is guided above $600 million next year. 5. It is building custom AI chips for AWS, Microsoft and more than 50 pipeline customers targeting over $10 billion in custom chip revenue by fiscal 2029. The last point is the most important. Hyperscalers are increasingly designing their own chips to cut their dependence on Nvidia's expensive GPUs and Marvell is the second largest player building those chips behind Broadcom. In February, Marvell closed its acquisition of Celestial AI, a deal worth up to $5.5 billion, specifically for its photonic fabric technology. Nvidia backed this thesis directly. Jensen Huang put $2 billion into Marvell in March through NVLink Fusion, a program that lets custom chips connect into Nvidia's broader infrastructure. In June, at Computex, he called Marvell the next trillion dollar company. The stock popped 25% on that comment. $MRVL is now down about 37% from the all-time high it set on June 18. Our PRO team's Melvin has been buying the entire way down from $269 in late June through $186 last week. He does not see much room below $150 and expects earnings to reprice the story again. If you want to see what else he holds alongside $MRVL, check out his entire portfolio for $1. Link in first comment below.

$MRVL sits on both sides of the AI bottleneck at the same time (Save this). "Computing at this scale is fundamentally a connectivity challenge." That is Marvell CEO Matt Murphy explaining why the bottleneck has shifted from compute to connectivity. Marvell is a fabless chip designer meaning it designs chips and outsources the manufacturing. Its portfolio now spans five areas at once: 1. It holds a near monopoly on optical DSPs, the chips that act as the brains inside optical transceiver modules, through its 1.6T Nova platform. 2. It makes the coherent DSPs that carry data between data centers over long distances. 3. It supplies the physical layer chips that every chip to chip connection depends on. 4. Its ethernet cable business is on pace to cross $300 million this year and is guided above $600 million next year. 5. It is building custom AI chips for AWS, Microsoft and more than 50 pipeline customers targeting over $10 billion in custom chip revenue by fiscal 2029. The last point is the most important. Hyperscalers are increasingly designing their own chips to cut their dependence on Nvidia's expensive GPUs and Marvell is the second largest player building those chips behind Broadcom. In February, Marvell closed its acquisition of Celestial AI, a deal worth up to $5.5 billion, specifically for its photonic fabric technology. Nvidia backed this thesis directly. Jensen Huang put $2 billion into Marvell in March through NVLink Fusion, a program that lets custom chips connect into Nvidia's broader infrastructure. In June, at Computex, he called Marvell the next trillion dollar company. The stock popped 25% on that comment. $MRVL is now down about 37% from the all-time high it set on June 18. Our PRO team's Melvin has been buying the entire way down from $269 in late June through $186 last week. He does not see much room below $150 and expects earnings to reprice the story again. If you want to see what else he holds alongside $MRVL, check out his entire portfolio for $1. Link in first comment below.

Milk Road Stocks

165,662 görüntüleme • 3 ay önce

🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

NVIDIA AI Infrastructure

268,567 görüntüleme • 10 ay önce

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Stocks

41,351 görüntüleme • 3 ay önce

Ben Thompson on why Google and Amazon are Nvidia's biggest competitors, and what they're doing about it: "Google and Amazon aren't selling their chips on differentiation. They're selling chips as commodities. Nvidia's the one selling the differentiation. So that's Nvidia's biggest problem, because what's the number one advantage that the hyperscalers have? Lower cost of capital. It's a capital fight. They have a lower cost of capital than the neoclouds do. Intel is a good example here. AMD cleaned them out in hyperscaler sales, because the hyperscalers put in the effort to get stuff working on AMD versus Intel. Because they're buying at such scale, the investment to do that is worth it. The part of Intel's business that never floundered was selling to government and selling to enterprises, because they don't have the resources of a hyperscaler. That's why Nvidia talks about selling to sovereign clouds, that's why they talk about selling to enterprises. They want to get into markets where they're not going to be balancing this chip versus that chip. The hyperscalers have always been the threat to Nvidia for that reason, because they're actually bigger."

Ben Thompson on why Google and Amazon are Nvidia's biggest competitors, and what they're doing about it: "Google and Amazon aren't selling their chips on differentiation. They're selling chips as commodities. Nvidia's the one selling the differentiation. So that's Nvidia's biggest problem, because what's the number one advantage that the hyperscalers have? Lower cost of capital. It's a capital fight. They have a lower cost of capital than the neoclouds do. Intel is a good example here. AMD cleaned them out in hyperscaler sales, because the hyperscalers put in the effort to get stuff working on AMD versus Intel. Because they're buying at such scale, the investment to do that is worth it. The part of Intel's business that never floundered was selling to government and selling to enterprises, because they don't have the resources of a hyperscaler. That's why Nvidia talks about selling to sovereign clouds, that's why they talk about selling to enterprises. They want to get into markets where they're not going to be balancing this chip versus that chip. The hyperscalers have always been the threat to Nvidia for that reason, because they're actually bigger."

Patrick OShaughnessy

38,415 görüntüleme • 2 ay önce

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

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

Ricardo

162,581 görüntüleme • 6 ay önce

Google's Ai Gemini was recently licensed by Apple to power Ai on their devices. Now Mark Zuckerberg seems to be upset at his Ai team and their lack of progress. They have delayed their "Avocado" until May after failing to perform against others. Google Gemini does not use Nvidia chips. Google designed their own Ai chips. Not all Ai hyperscalers will continue. Some will give up. Mark Zuckerberg's META might be the first to give up on their own Ai and just license Gemini, following the Apple model. This would save META a ton of money on infrastructure. It would be bad for Nvidia because they would lose a major Ai hyperscaler customer to Google's Ai chips. Follow: Wall Street Mav

Google's Ai Gemini was recently licensed by Apple to power Ai on their devices. Now Mark Zuckerberg seems to be upset at his Ai team and their lack of progress. They have delayed their "Avocado" until May after failing to perform against others. Google Gemini does not use Nvidia chips. Google designed their own Ai chips. Not all Ai hyperscalers will continue. Some will give up. Mark Zuckerberg's META might be the first to give up on their own Ai and just license Gemini, following the Apple model. This would save META a ton of money on infrastructure. It would be bad for Nvidia because they would lose a major Ai hyperscaler customer to Google's Ai chips. Follow: Wall Street Mav

Wall Street Mav

69,786 görüntüleme • 8 ay önce

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

Milk Road AI

34,720 görüntüleme • 4 ay önce

"The exponential growth curve for many years to come is on the inference side." — Mark Lohmeyer, VP/GM, AI and Computing Infrastructure, Google Cloud Training gets the attention. Inference is the actual bet. Patrick Moorhead and Mark Lohmeyer on why Google built a separate chip for it.

"The exponential growth curve for many years to come is on the inference side." — Mark Lohmeyer, VP/GM, AI and Computing Infrastructure, Google Cloud Training gets the attention. Inference is the actual bet. Patrick Moorhead and Mark Lohmeyer on why Google built a separate chip for it.

Six Five Media

58,024 görüntüleme • 6 ay önce

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of GroqInc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of GroqInc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

Chamath Palihapitiya

326,923 görüntüleme • 2 yıl önce

“It’s about $1 million a megawatt all-in, between infrastructure and compute, to build a Bitcoin mining site. The AI site, just the infrastructure without compute, is $10 to $15 million a megawatt.” $MARA CEO Fred Thiel points out the math of Bitcoin miners pivoting to AI: An AI data center costs 10-15x more per megawatt to build than a Bitcoin mine but it earns far more per unit of power. And because the tenants are companies like Google, banks will actually finance it. The pricier build is the easier one to fund. Don’t miss the full interview streaming now.

“It’s about $1 million a megawatt all-in, between infrastructure and compute, to build a Bitcoin mining site. The AI site, just the infrastructure without compute, is $10 to $15 million a megawatt.” $MARA CEO Fred Thiel points out the math of Bitcoin miners pivoting to AI: An AI data center costs 10-15x more per megawatt to build than a Bitcoin mine but it earns far more per unit of power. And because the tenants are companies like Google, banks will actually finance it. The pricier build is the easier one to fund. Don’t miss the full interview streaming now.

Natalie Brunell ⚡️

45,375 görüntüleme • 3 ay önce

Jensen Huang just looked at every tech giant building custom silicon. And laughed. Every major player is burning billions to escape the Nvidia tax. Google is building TPUs. Amazon is building Trainium. Meta is building MTIA. The logic makes sense on a spreadsheet. Design a chip perfectly tailored to your workload. Cut out the middleman. Own the stack. But the spreadsheet assumes the middleman is standing still. Huang: “Look at the number of ASICs that have been canceled… It’s not sensible, actually.” He is not questioning their engineering. He is questioning their math. Custom silicon takes years. Every design choice is a bet on a target that exists today. And Nvidia does not let today exist for long. Huang: “Because of our scale, our velocity, we’re the only company in the world that’s cranking it out every single year.” That is the real weapon. Not the chip. The clock. Nvidia stopped selling performance. They started selling time. By the time a custom ASIC tapes out, Nvidia has already shipped the next generation. The chip arrives obsolete. Not because it failed. Because it was built for a world that no longer exists. The graveyard of custom silicon is not filled with bad engineers. It is filled with slow ones. You cannot aim a three-year development cycle at a one-year moving target. Every company building custom silicon thinks they are building an escape route. They are building a time capsule.

Jensen Huang just looked at every tech giant building custom silicon. And laughed. Every major player is burning billions to escape the Nvidia tax. Google is building TPUs. Amazon is building Trainium. Meta is building MTIA. The logic makes sense on a spreadsheet. Design a chip perfectly tailored to your workload. Cut out the middleman. Own the stack. But the spreadsheet assumes the middleman is standing still. Huang: “Look at the number of ASICs that have been canceled… It’s not sensible, actually.” He is not questioning their engineering. He is questioning their math. Custom silicon takes years. Every design choice is a bet on a target that exists today. And Nvidia does not let today exist for long. Huang: “Because of our scale, our velocity, we’re the only company in the world that’s cranking it out every single year.” That is the real weapon. Not the chip. The clock. Nvidia stopped selling performance. They started selling time. By the time a custom ASIC tapes out, Nvidia has already shipped the next generation. The chip arrives obsolete. Not because it failed. Because it was built for a world that no longer exists. The graveyard of custom silicon is not filled with bad engineers. It is filled with slow ones. You cannot aim a three-year development cycle at a one-year moving target. Every company building custom silicon thinks they are building an escape route. They are building a time capsule.

Dustin

43,254 görüntüleme • 6 ay önce

Every AI model needs somewhere to run. Compute. Power. Data centres. That's the bottleneck now, not the software. Canada is putting billions on the table through 2030 to secure sovereign AI capacity. HIVE Digital Technologies is building that infrastructure now, ahead of the funding curve. Why investors are watching: - Sovereign AI compute infrastructure, live in Canada - Data centres, GPU capacity, and power, already in motion - Dual-engine business model generating revenue while expanding AI infrastructure - Sitting at the intersection of AI, energy, and infrastructure - Exposure to one of tech's fastest-growing long-term trends Railroads built the industrial economy. The internet built the information economy. AI infrastructure is building this one. Every company will use AI. Fewer will own what it runs on. Learn More:

Every AI model needs somewhere to run. Compute. Power. Data centres. That's the bottleneck now, not the software. Canada is putting billions on the table through 2030 to secure sovereign AI capacity. HIVE Digital Technologies is building that infrastructure now, ahead of the funding curve. Why investors are watching: - Sovereign AI compute infrastructure, live in Canada - Data centres, GPU capacity, and power, already in motion - Dual-engine business model generating revenue while expanding AI infrastructure - Sitting at the intersection of AI, energy, and infrastructure - Exposure to one of tech's fastest-growing long-term trends Railroads built the industrial economy. The internet built the information economy. AI infrastructure is building this one. Every company will use AI. Fewer will own what it runs on. Learn More:

BTV & CEO Clips

24,998 görüntüleme • 3 ay önce