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Broadcom turns Google’s TPU IP into chips and networking, while Google earns from its IP without doing the hardware build itself. "Broadcom is making ~$37 B from 1GW of Anthropic's orders, whereas Google is capturing ~$13-14 B of that"

Rohan Paul

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72,987 次观看 • 4 个月前 •via X (Twitter)

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$AVGO helps hyperscalers build the custom chips and networking fabric needed to run massive AI systems. If $GOOGL, $META, OpenAI or Anthropic want to build their own inference chips then Broadcom is one of the first calls because it has the IP, packaging expertise and networking portfolio required to scale custom AI systems. Thats why Broadcom remains one of the strongest infrastructure positions in the AI economy.

$AVGO helps hyperscalers build the custom chips and networking fabric needed to run massive AI systems. If $GOOGL, $META, OpenAI or Anthropic want to build their own inference chips then Broadcom is one of the first calls because it has the IP, packaging expertise and networking portfolio required to scale custom AI systems. Thats why Broadcom remains one of the strongest infrastructure positions in the AI economy.

Shay Boloor

77,765 次观看 • 2 个月前

Microsoft has access to ALL of the hardware accelerator IP from OpenAI, which is why Satya is not worried about their in-house Maia accelerators not progressing as fast as Google TPUs

Microsoft has access to ALL of the hardware accelerator IP from OpenAI, which is why Satya is not worried about their in-house Maia accelerators not progressing as fast as Google TPUs

Lisan al Gaib

156,886 次观看 • 9 个月前

As we wrote in our TPUv7 article, TPUv7 will have competitive perf per TCO verus Blackwell in 2025 for large labs but TPUv8 will have worse perf per TCO verus Rubin in EOY 2026/2027. We added an subway surfer video as some people don't have the attention span to read an 10,000 word article. There will be 2 versions of TPU v8 but this will be different from the “P” (full) and “E” (lite) SKUs that featured in the then 4th and 5th generations. Rather, it is a dual track with one SKU co-designed with Broadcom (TPU 8AX ) and one SKU co-designed with MediaTek (TPU 8X). The strategy behind Google choosing to partner with MediaTek is to reduce the margin that they pay to their silicon design partners or specifically Broadcom. Broadcom charges Google for the whole System in Package which they stack a healthy margin on. This includes HBM. This is despite Google being largely responsible for both the front-end and back-end design of the compute elements of the chip, with Broadcom contributing various PHYs (most importantly Broadcom’s best in class SerDes) and controllers. Google wants to move to a model where their silicon partner only charges for the things they add value on and get closer to paying BOM cost for their silicon.

As we wrote in our TPUv7 article, TPUv7 will have competitive perf per TCO verus Blackwell in 2025 for large labs but TPUv8 will have worse perf per TCO verus Rubin in EOY 2026/2027. We added an subway surfer video as some people don't have the attention span to read an 10,000 word article. There will be 2 versions of TPU v8 but this will be different from the “P” (full) and “E” (lite) SKUs that featured in the then 4th and 5th generations. Rather, it is a dual track with one SKU co-designed with Broadcom (TPU 8AX ) and one SKU co-designed with MediaTek (TPU 8X). The strategy behind Google choosing to partner with MediaTek is to reduce the margin that they pay to their silicon design partners or specifically Broadcom. Broadcom charges Google for the whole System in Package which they stack a healthy margin on. This includes HBM. This is despite Google being largely responsible for both the front-end and back-end design of the compute elements of the chip, with Broadcom contributing various PHYs (most importantly Broadcom’s best in class SerDes) and controllers. Google wants to move to a model where their silicon partner only charges for the things they add value on and get closer to paying BOM cost for their silicon.

SemiAnalysis

133,077 次观看 • 8 个月前

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 次观看 • 1 个月前

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!

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!

Melvin

63,457 次观看 • 26 天前

Dylan Patel explains that Nvidia’s biggest threat is the rise of custom silicon from companies like Google, Amazon, and Meta. He notes that Google’s TPUs are already fully utilized, Amazon is making millions of Trainium chips, and Anthropic will likely help adoption. Google could compete directly with Nvidia by selling TPUs on the open market, not just renting them through Cloud. He argues that if AI remains concentrated, custom silicon will win, but if open-source models spread widely, Nvidia benefits through its software ecosystem.

Dylan Patel explains that Nvidia’s biggest threat is the rise of custom silicon from companies like Google, Amazon, and Meta. He notes that Google’s TPUs are already fully utilized, Amazon is making millions of Trainium chips, and Anthropic will likely help adoption. Google could compete directly with Nvidia by selling TPUs on the open market, not just renting them through Cloud. He argues that if AI remains concentrated, custom silicon will win, but if open-source models spread widely, Nvidia benefits through its software ecosystem.

Wes Roth

74,608 次观看 • 1 年前

$AMZN having a market cap $1.5T lower than $GOOGL makes no sense. $AMZN is the only big tech company that fully designs its own in-house ASIC chips, without relying on Broadcom $AVGO like $GOOGL does for its TPUs. On top of that, its Trainium chips are 40% more price-performance efficient than leading $NVDA GPUs. And let’s not forget — $AMZN owns more than 15% of Anthropic. $AMZN is such an obvious play.

$AMZN having a market cap $1.5T lower than $GOOGL makes no sense. $AMZN is the only big tech company that fully designs its own in-house ASIC chips, without relying on Broadcom $AVGO like $GOOGL does for its TPUs. On top of that, its Trainium chips are 40% more price-performance efficient than leading $NVDA GPUs. And let’s not forget — $AMZN owns more than 15% of Anthropic. $AMZN is such an obvious play.

Daniel

249,429 次观看 • 6 个月前

IP Licensing Plans If someone is willing to pay you royalties to put your IP on their t-shirt or build an entire game from your IP and pay you royalties, you start having something, which is an interesting proof of concept. Licensing is what makes an IP infinitely scalable. It makes a lot of sense. For a deep dive into why that is so, @GiulioXdotEth has covered you in this video. 📺

IP Licensing Plans If someone is willing to pay you royalties to put your IP on their t-shirt or build an entire game from your IP and pay you royalties, you start having something, which is an interesting proof of concept. Licensing is what makes an IP infinitely scalable. It makes a lot of sense. For a deep dive into why that is so, @GiulioXdotEth has covered you in this video. 📺

MON Protocol 🐉 $MON

20,872 次观看 • 1 年前

A packed house. Great conversations. Real momentum. Thank you to everyone who joined us in Seoul before the launch of $ARIAIP, from the Aria and $IP communities to our partners at Story Protocol Legacy, B-Harvest, Buidlpad and D'CENT Wallet. The future of onchain IP RWA is being built together. This is Aria!

A packed house. Great conversations. Real momentum. Thank you to everyone who joined us in Seoul before the launch of $ARIAIP, from the Aria and $IP communities to our partners at Story Protocol Legacy, B-Harvest, Buidlpad and D'CENT Wallet. The future of onchain IP RWA is being built together. This is Aria!

Aria

21,312 次观看 • 9 个月前

Sam Altman on competition with Google: ▫️a huge threat ▫️if they had taken OpenAI seriously in 2023, could have “smashed” the startup ▫️Google has “probably the greatest business model” in tech and hesitant to give it up ▫️this is opportunity for OpenAI, since it is redesigning the computing experience from scratch while Google is “bolting AI onto the existing way of doing things” ▫️he believes Google’s approach of “bolting AI” is a weakness “even though they have this huge distribution advantage.”

Sam Altman on competition with Google: ▫️a huge threat ▫️if they had taken OpenAI seriously in 2023, could have “smashed” the startup ▫️Google has “probably the greatest business model” in tech and hesitant to give it up ▫️this is opportunity for OpenAI, since it is redesigning the computing experience from scratch while Google is “bolting AI onto the existing way of doing things” ▫️he believes Google’s approach of “bolting AI” is a weakness “even though they have this huge distribution advantage.”

Bearly AI

412,002 次观看 • 7 个月前

📁 Aravind Srinivas, CEO of Perplexity, says that the end of Google’s monopoly would come with an AI-native operating system, backed by major phone makers, that breaks free from Android and iOS and removes Google as the default search engine, undermining its core mobile advertising business. Comet is the first step toward that goal.

📁 Aravind Srinivas, CEO of Perplexity, says that the end of Google’s monopoly would come with an AI-native operating system, backed by major phone makers, that breaks free from Android and iOS and removes Google as the default search engine, undermining its core mobile advertising business. Comet is the first step toward that goal.

Jon Hernandez

81,985 次观看 • 11 个月前

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

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

1,616,682 次观看 • 8 个月前

JUST IN: Anthropic confirms China is stealing its AI models, citing "multiple rounds of evidence." Anthropic's Frontier Red Team lead says Chinese research groups are distilling from US models and calls it "theft of American IP." "If you're taking the best of American innovation and throwing it back to attack America, that's really concerning."

JUST IN: Anthropic confirms China is stealing its AI models, citing "multiple rounds of evidence." Anthropic's Frontier Red Team lead says Chinese research groups are distilling from US models and calls it "theft of American IP." "If you're taking the best of American innovation and throwing it back to attack America, that's really concerning."

Coin Bureau

113,538 次观看 • 22 天前

I'm excited to announce that Wizard is officially in beta here at Story. Our founding team comes from Apple, Disney, Google, and ILM — we're hiring! Come build the future of editing.

I'm excited to announce that Wizard is officially in beta here at Story. Our founding team comes from Apple, Disney, Google, and ILM — we're hiring! Come build the future of editing.

Jason Carman

34,386 次观看 • 4 个月前

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - 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 Groq Inc - 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

326,663 次观看 • 2 年前

Sergey Brin on the genius of Jeff Dean. He credits Jeff’s early obsession with neural networks, back when they were “telling cats from dogs”, as the spark for everything. TPU was Jeff’s idea. He calculated that if users spoke to Google for just three minutes a day, Google would have to double its CPU data centers. Instead of buying more CPUs, Jeff decided to build a new chip for AI. What’s fascinating is that when AI was not as big as today, when it can only “tell cats from dogs,” Larry and Sergey were like, “Cool, let’s make a custom chip for it.” That’s a huge show of confidence in deep tech and top-tier technical talent. It makes Google the only AI company that has the top models, top chips, and top data centers.

Sergey Brin on the genius of Jeff Dean. He credits Jeff’s early obsession with neural networks, back when they were “telling cats from dogs”, as the spark for everything. TPU was Jeff’s idea. He calculated that if users spoke to Google for just three minutes a day, Google would have to double its CPU data centers. Instead of buying more CPUs, Jeff decided to build a new chip for AI. What’s fascinating is that when AI was not as big as today, when it can only “tell cats from dogs,” Larry and Sergey were like, “Cool, let’s make a custom chip for it.” That’s a huge show of confidence in deep tech and top-tier technical talent. It makes Google the only AI company that has the top models, top chips, and top data centers.

Yuchen Jin

473,372 次观看 • 8 个月前

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

250,930 次观看 • 3 个月前

Google just quit the AI race on purpose, and it is about to make MORE money than everyone still running it. 4 of the most cited AI researchers alive walked out of Google in a single afternoon. Jeff Dean, the man who built the systems Google runs on, gone after 27 years. Sanjay Ghemawat, his longtime partner, gone. Oriol Vinyals, a Gemini co-lead, gone. Quoc Le, a Google Brain co-founder, gone. That same day, Demis Hassabis stepped back from running DeepMind. Hassabis co-founded the lab, won a Nobel Prize for AlphaFold, and had been the face of Google AI for a decade. The stock dropped 5% within hours. Analysts called it a brain drain. Headlines called it the day Google fell behind. But turns out that's completely wrong, because the numbers underneath tell a completely different story: Google is not trying to win the frontier model race anymore. It looked at where the money is and walked toward it. Gemini, Google's flagship model business, generated about $12 billion in annual revenue last quarter. That is the entire payoff from competing head to head with OpenAI and Anthropic. Now look at the other number. By the end of 2027, Google Cloud is projected to do over $73 billion selling AI infrastructure to other companies, plus another $120 billion selling its TPU chips. That is roughly $200 billion of external sales at high margins, against a $12 billion model business. Google understood that the frontier race is the expensive part while selling the shovels is the profitable part. And the customers buying those shovels include Google's own rivals. Over 20% of Google's TPU shipments for 2026 and 2027 are going to Anthropic, one of the two labs supposedly beating Gemini. Google now makes money every time Anthropic trains a model designed to crush Google's OWN product. Cede the frontier, own the layer underneath it, and collect a toll from everyone racing across the top. The researchers leaving is the symptom of a company that already decided models are not where it wins. Jeff Dean said it himself on the way out. He told the New York Times that leaving a public company gives him room to make decisions "not necessarily in the company's purist financial interests." Read that from Google's side: The people who wanted to chase the science left, because Google is now optimizing for the FINANCIAL interest. Gemini 3.5 Pro is running months behind, with staff blaming low morale. DeepMind's comms, legal, and marketing teams are being folded into Google proper. A former manager told the Guardian the era of DeepMind as an independent lab is over. None of that reads as failure once you see the strategy. Yet Wall Street is pricing this as Google losing. The parallel that should worry the frontier labs: If open weight models keep compressing the price of inference, being the best model stops being a business. It becomes like semiconductor fabrication, strategically vital and financially brutal, a race you win and still lose money running. Google is the first giant to admit that. The company that invented the transformer just handed the frontier to OpenAI and Anthropic, and positioned itself to get paid on every model both of them ship. Those labs will be burning billions to stay one benchmark ahead, and Google will be cashing in hundreds of billions from it. The model business is actually just a race where everyone loses. Apple understood that from the get-go and never joined the race, Google understood it now and left it to OpenAI and Anthropic. Who will go bankrupt first?

Google just quit the AI race on purpose, and it is about to make MORE money than everyone still running it. 4 of the most cited AI researchers alive walked out of Google in a single afternoon. Jeff Dean, the man who built the systems Google runs on, gone after 27 years. Sanjay Ghemawat, his longtime partner, gone. Oriol Vinyals, a Gemini co-lead, gone. Quoc Le, a Google Brain co-founder, gone. That same day, Demis Hassabis stepped back from running DeepMind. Hassabis co-founded the lab, won a Nobel Prize for AlphaFold, and had been the face of Google AI for a decade. The stock dropped 5% within hours. Analysts called it a brain drain. Headlines called it the day Google fell behind. But turns out that's completely wrong, because the numbers underneath tell a completely different story: Google is not trying to win the frontier model race anymore. It looked at where the money is and walked toward it. Gemini, Google's flagship model business, generated about $12 billion in annual revenue last quarter. That is the entire payoff from competing head to head with OpenAI and Anthropic. Now look at the other number. By the end of 2027, Google Cloud is projected to do over $73 billion selling AI infrastructure to other companies, plus another $120 billion selling its TPU chips. That is roughly $200 billion of external sales at high margins, against a $12 billion model business. Google understood that the frontier race is the expensive part while selling the shovels is the profitable part. And the customers buying those shovels include Google's own rivals. Over 20% of Google's TPU shipments for 2026 and 2027 are going to Anthropic, one of the two labs supposedly beating Gemini. Google now makes money every time Anthropic trains a model designed to crush Google's OWN product. Cede the frontier, own the layer underneath it, and collect a toll from everyone racing across the top. The researchers leaving is the symptom of a company that already decided models are not where it wins. Jeff Dean said it himself on the way out. He told the New York Times that leaving a public company gives him room to make decisions "not necessarily in the company's purist financial interests." Read that from Google's side: The people who wanted to chase the science left, because Google is now optimizing for the FINANCIAL interest. Gemini 3.5 Pro is running months behind, with staff blaming low morale. DeepMind's comms, legal, and marketing teams are being folded into Google proper. A former manager told the Guardian the era of DeepMind as an independent lab is over. None of that reads as failure once you see the strategy. Yet Wall Street is pricing this as Google losing. The parallel that should worry the frontier labs: If open weight models keep compressing the price of inference, being the best model stops being a business. It becomes like semiconductor fabrication, strategically vital and financially brutal, a race you win and still lose money running. Google is the first giant to admit that. The company that invented the transformer just handed the frontier to OpenAI and Anthropic, and positioned itself to get paid on every model both of them ship. Those labs will be burning billions to stay one benchmark ahead, and Google will be cashing in hundreds of billions from it. The model business is actually just a race where everyone loses. Apple understood that from the get-go and never joined the race, Google understood it now and left it to OpenAI and Anthropic. Who will go bankrupt first?

Ricardo

237,844 次观看 • 3 天前

How is this not big news? BUSTED: The Kamala Harris campaign admits it conducted a massive online misinformation scheme with the help of Google. Google claims a glitch in its system allowed the Harris campaign to run counterfeit headlines that appeared to originate from the AP and Reuters.

How is this not big news? BUSTED: The Kamala Harris campaign admits it conducted a massive online misinformation scheme with the help of Google. Google claims a glitch in its system allowed the Harris campaign to run counterfeit headlines that appeared to originate from the AP and Reuters.

A Man Of Memes

1,006,760 次观看 • 1 年前