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Windsurf CEO explains why traditional startup moats are mostly silly. Varun said even with 50-100 of the best MIT engineers, that's still just "hundreds of engineering years" in your product. Someone else can build something similar in the same space. He used Nvidia as an example. People think CUDA... is their real moat, but that's not accurate. Large companies like Google, OpenAI, and Anthropic spend tens of billions on chips. If CUDA didn't exist, they'd find a way to write assembly code and make GPUs work. People use CUDA because Nvidia made it really capable at low floating-point math, great interconnect, and fast computers. Not because they're locked in. Every year Nvidia has pressure - if they don't make hardware, interconnect, and memory bandwidth much faster, their profit margins shrink and AMD will compete. For startups, the only moat is speed. Learning where the dead bodies are. Understanding what doesn't work and building compounding advantages through that knowledge. Speed lets you learn from the market faster, which means you're first to the next idea too.show more

Aish

4,055 subscribers

334,732 просмотров • 1 год назад •via X (Twitter)

Финансы Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Austin Scholar

Austin Scholar2 лет назад

AI apps are the future of education. But I’ve already been using them for 12 years. Want to know which are the best ones? Read this:

Фото профиля Jenny Xiao 🌌

Jenny Xiao 🌌1 год назад

@HarryStebbings Moats are mostly a simplistic way for non-technical investors to understand what they are investing in.

Фото профиля Joe Blau

Joe Blau1 год назад

Harry was struggling. You could tell by the angle of his neck.

Фото профиля Dan Gray

Dan Gray1 год назад

In a pure software context he’s broadly right. Speed, initially, and unit economics over time. Obviously this is less true for hardware, logistics, infrastructure etc.

Фото профиля Akshat Bahety

Akshat Bahety1 год назад

Execution is the only thing that matters There is no secret sauce

Фото профиля Culttture

Culttture1 год назад

It’s crazy how the windsurf CEO wants to be a VC more than a CEO and is propped up by Indian accounts on Twitter. He loves doing podcasts and talking about moats. He’s never created a moat. 😂 Is OpenAI going to audit windsurf’s users? See how many are real? @sama

Фото профиля Subba Reddy

Subba Reddy1 год назад

>People think CUDA is their real moat, but that's not accurate >If CUDA didn't exist,AI Cos find a way to write assembly make GPUs work >People use CUDA coz Nvidia made it really capable at low floating-point math, great interconnect,and fast computers. Not coz they're locked in

Фото профиля Sam Tayyari

Sam Tayyari1 год назад

People forget that moat doesn’t mean impenetrable. It just makes it harder to beat. This is an all stakes game where anything can happen at any moment. The teams job is just to shift the odds ever in their favour.

Фото профиля Brian Sowards (he/they) 🇺🇸🇮🇱🇮🇳

Brian Sowards (he/they) 🇺🇸🇮🇱🇮🇳1 год назад

finally someone talking sense

Фото профиля Andrew 💥♻️

Andrew 💥♻️1 год назад

That’s an excellent point about CUDA. That moat is measured in months.

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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

1,617,371 просмотров • 9 месяцев назад

Ben Thompson explains how LLMs greatly diminished Nvidia's CUDA moat even as they sent the stock to the moon "So the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon." "They have been on and off the most valuable company in the world." "It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the play with CUDA is to build a developer ecosystem on top of CUDA." "But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use, and it's a tremendous investment. Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they were wasting money on it." "And that's why Jensen Huang will get bristly, particularly when people question their rent-seeking or profit, whatever. It's like, no, they earned their spot fair and square." "Absolutely. It shouldn't be forgotten. They have earned every dollar they've gotten through 25 years of taking massive risks." "It bottomed out in October 2022. I wrote an article like three weeks before ChatGPT came out, tracing their bottoming-out history and their search for what was next." "'Nvidia in the Valley.' So, go back to this GTC. So I wrote an article at the time called 'Nvidia Waves and Moats'." "And what was interesting about that GTC was, number one, it was very boring. All the cool stuff kind of got scrubbed out." "Now, Jensen Huang has brought that stuff back, so the last few GTCs he's more talking about other things. Now it comes across as, oh, you're still looking for something beyond the LLM." "Because the problem with the LLM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. And so no one who's writing an AI application today is using CUDA." "Now, some people are, if you're training your own model and you're doing some low-level things or non-LLM things." "But the vast majority of the energy and all the money and the ecosystem is far removed from CUDA." "They have no idea and don't need to know or care what chips their application is running on. They're just on the OpenAI API, or the Anthropic API, or using Bedrock on Amazon, and it's sitting on Trainium, and they're using a Chinese open-source model. It's totally abstracted away, and this is why LLMs were bad for Nvidia." "Now, again, all the money they made along the way is worth it, but their moat has been tremendously diminished." "CUDA is still a moat if you need to do stuff that requires CUDA. But the vast majority of stuff, in energy, doesn't require CUDA, like in a post-LLM world."

Ben Thompson explains how LLMs greatly diminished Nvidia's CUDA moat even as they sent the stock to the moon "So the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon." "They have been on and off the most valuable company in the world." "It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the play with CUDA is to build a developer ecosystem on top of CUDA." "But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use, and it's a tremendous investment. Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they were wasting money on it." "And that's why Jensen Huang will get bristly, particularly when people question their rent-seeking or profit, whatever. It's like, no, they earned their spot fair and square." "Absolutely. It shouldn't be forgotten. They have earned every dollar they've gotten through 25 years of taking massive risks." "It bottomed out in October 2022. I wrote an article like three weeks before ChatGPT came out, tracing their bottoming-out history and their search for what was next." "'Nvidia in the Valley.' So, go back to this GTC. So I wrote an article at the time called 'Nvidia Waves and Moats'." "And what was interesting about that GTC was, number one, it was very boring. All the cool stuff kind of got scrubbed out." "Now, Jensen Huang has brought that stuff back, so the last few GTCs he's more talking about other things. Now it comes across as, oh, you're still looking for something beyond the LLM." "Because the problem with the LLM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. And so no one who's writing an AI application today is using CUDA." "Now, some people are, if you're training your own model and you're doing some low-level things or non-LLM things." "But the vast majority of the energy and all the money and the ecosystem is far removed from CUDA." "They have no idea and don't need to know or care what chips their application is running on. They're just on the OpenAI API, or the Anthropic API, or using Bedrock on Amazon, and it's sitting on Trainium, and they're using a Chinese open-source model. It's totally abstracted away, and this is why LLMs were bad for Nvidia." "Now, again, all the money they made along the way is worth it, but their moat has been tremendously diminished." "CUDA is still a moat if you need to do stuff that requires CUDA. But the vast majority of stuff, in energy, doesn't require CUDA, like in a post-LLM world."

Fireside Alpha

12,980 просмотров • 1 месяц назад

Jensen Huang was asked what NVIDIA's single biggest moat is. The most valuable company in the world. He didn't point to the chips. He said a competitor could clone CUDA exactly and it wouldn't matter. "if somebody came up with a GUDA or TUDA it wouldn't make any difference at all." That is the reframe. And it changes what NVIDIA is actually defending. The conventional story is that NVIDIA wins on silicon. Faster GPU, better transistors, more FLOPS. Which means the day someone ships a faster chip, the game is over. Every competitor is racing on that assumption. Jensen is defending something else entirely. He said it wasn't three people who made CUDA win. It was 43,000 people and several million developers who bet their software on it. The moat isn't the hardware. It's the install base. Now here's where it gets interesting. Put yourself in a developer's seat. Target CUDA and you reach a few hundred million machines: every cloud, every computer maker, every industry, every country. And the platform gets roughly 10x better every six months, for free, while you wait. A rival could ship a chip that is genuinely faster and still lose, because no rational developer ports a mountain of software off a platform that already owns the install base and improves itself every two quarters. He is not selling the best chip. He is renting out the largest install base in computing. The open question is whether better silicon can ever beat that, or whether the only way past a moat like this is to make the whole category obsolete.

Jensen Huang was asked what NVIDIA's single biggest moat is. The most valuable company in the world. He didn't point to the chips. He said a competitor could clone CUDA exactly and it wouldn't matter. "if somebody came up with a GUDA or TUDA it wouldn't make any difference at all." That is the reframe. And it changes what NVIDIA is actually defending. The conventional story is that NVIDIA wins on silicon. Faster GPU, better transistors, more FLOPS. Which means the day someone ships a faster chip, the game is over. Every competitor is racing on that assumption. Jensen is defending something else entirely. He said it wasn't three people who made CUDA win. It was 43,000 people and several million developers who bet their software on it. The moat isn't the hardware. It's the install base. Now here's where it gets interesting. Put yourself in a developer's seat. Target CUDA and you reach a few hundred million machines: every cloud, every computer maker, every industry, every country. And the platform gets roughly 10x better every six months, for free, while you wait. A rival could ship a chip that is genuinely faster and still lose, because no rational developer ports a mountain of software off a platform that already owns the install base and improves itself every two quarters. He is not selling the best chip. He is renting out the largest install base in computing. The open question is whether better silicon can ever beat that, or whether the only way past a moat like this is to make the whole category obsolete.

Vikram M

367,768 просмотров • 2 месяцев назад

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."

Ben Thompson says Nvidia is already cutting prices. The cut just doesn't show up in their margins: "Everyone's coming for them and they're still charging however much money for a chip. But they're not maintaining their margins, because who's buying? This circular financing people talk about, Nvidia's providing a 25% backstop. If you actually ascribe a value to Nvidia taking equity in the Neoclouds, they guarantee they're going to buy all their compute through 2030. And why do they do that? So the entity in question can get a lower cost of capital, so they can buy more GPUs. They get a lower cost of capital because Nvidia assumed the risk. Taking risk has a price. There's also a world where a ton of compute comes to market, the hyperscalers don't need this compute, and Nvidia's paying for compute that no one wants. That is a diminution of Nvidia's profitability if you look at their business holistically. What that is, is a price cut. The price just didn't show up in the margins."

Patrick OShaughnessy

159,693 просмотров • 1 месяц назад

Rick Rubin: "Make what you love, not what you think people will like" "If you want to live in a creative way, which will benefit everything in your life, be a better person in your family, do a better job starting a new business, it's all the same. I don't really know anything about music. It's more a way of looking at the world and wanting it to be the best it could possibly be. And doing whatever it takes to be the best it could possibly be." Rubin shares how his career happened: "From the beginning, I never thought any of the things I'm doing were possible or realistic. I just did things out of the love of them, thinking I would have real jobs. That my passion would be my hobby, and I'd have a job to support my hobby. And it just magically turned out different than that without me knowing it was possible." On why some things connect and others don't: "The stars line up at certain times for certain things to happen. Sometimes you can make something great, and it doesn't connect for whatever reason. Sometimes you make two things you think are the two best things you've ever made. One of them connects with the world. One of them doesn't. And it might not have anything to do with what's in the art. It might be that it came out the same day as something else. Or there was a bigger story at the time. There's so much to it that we don't understand." He continues: "All we can do is make something good and put it out and hope for the best. That's all there is. We never know why things work. Even if you make a piece of art and it works, you may not know why." On talent versus work ethic: "There are a lot of talented people who never make it because they don't have the work ethic. It's not just talent, talent's a piece. And you could argue for some people, the work ethic trumps the talent." Rubin explains what real collaboration is: "Having worked with a lot of bands, I see there's often this friction where people are trying to get their idea in. That's not a collaboration. A real collaboration is when everyone who's there is working together towards whatever is the best thing for the whole. Whether it's your idea or someone else's idea, it doesn't matter. If you're invested in the collaboration, you want the best idea to win. You don't want your idea to win." On what makes art great: "What makes it great is the personal. With all of its imperfections. With all of its quirkiness. That's what makes it great. How you see the world that's different from how everyone else sees the world. That's why you're an artist. That's your purpose in sharing your work with the world." He warns against being derivative: "There are these derivative voices where they're finding what they think other people want to hear, and they start saying it because they've heard other people say similar things that are now successful. Even if they have some short-term success doing that, it's not revolutionary. It doesn't change the world. It doesn't last. The people who you first see and you might not like that you come to like because you don't understand them at first, those are the ones that change the world. Those are the ones you dedicate your fandom to for life." Rubin shares his philosophy on taste: "You can't second-guess your own taste for what someone else is going to like. We're not smart enough to know what someone else is going to like. To make something thinking, 'Well, I don't really like it, but I think this group of people will like it,' it's a bad way to play the game of music or art. You have to do what's personal to you. Take it as far as you can go. Really push the boundaries. And people will resonate with it if they're supposed to resonate with it." He describes creativity as catching waves: "We're really talking about magic. The universe conspiring on our behalf if we let it. Being in this flow of catching these waves that anyone can catch. If you're trying to catch it, you're open to it, you see it coming, you take off on every chance you get. And sometimes the ride happens. It's remarkable how it happens. It doesn't come from preconception. It's not an idea. It's through the doing." Rubin explains how ideas exist in the universe: "Have you ever had that experience where you have an idea for something, you don't do it, and then six months later you see someone else has done it? It's not because they took your idea. It's that it's time for that, and you can act on it or not. The best artists are the ones who have the best antenna for this material that's available. It's coming through. The best comedians see the best jokes. They see them coming. We all live in the same world; the way you see it, you have the best joke because you see it best." He closes with how to stay open: "If we listen to what's going on around us, you can overhear a conversation in a coffee shop, and it is the setup for an idea you're working on. You hear a phrase you don't commonly use. My experience is: when you are open and looking for these clues in the world, they're happening all the time. And they're happening often right when you need them."

Rick Rubin: "Make what you love, not what you think people will like" "If you want to live in a creative way, which will benefit everything in your life, be a better person in your family, do a better job starting a new business, it's all the same. I don't really know anything about music. It's more a way of looking at the world and wanting it to be the best it could possibly be. And doing whatever it takes to be the best it could possibly be." Rubin shares how his career happened: "From the beginning, I never thought any of the things I'm doing were possible or realistic. I just did things out of the love of them, thinking I would have real jobs. That my passion would be my hobby, and I'd have a job to support my hobby. And it just magically turned out different than that without me knowing it was possible." On why some things connect and others don't: "The stars line up at certain times for certain things to happen. Sometimes you can make something great, and it doesn't connect for whatever reason. Sometimes you make two things you think are the two best things you've ever made. One of them connects with the world. One of them doesn't. And it might not have anything to do with what's in the art. It might be that it came out the same day as something else. Or there was a bigger story at the time. There's so much to it that we don't understand." He continues: "All we can do is make something good and put it out and hope for the best. That's all there is. We never know why things work. Even if you make a piece of art and it works, you may not know why." On talent versus work ethic: "There are a lot of talented people who never make it because they don't have the work ethic. It's not just talent, talent's a piece. And you could argue for some people, the work ethic trumps the talent." Rubin explains what real collaboration is: "Having worked with a lot of bands, I see there's often this friction where people are trying to get their idea in. That's not a collaboration. A real collaboration is when everyone who's there is working together towards whatever is the best thing for the whole. Whether it's your idea or someone else's idea, it doesn't matter. If you're invested in the collaboration, you want the best idea to win. You don't want your idea to win." On what makes art great: "What makes it great is the personal. With all of its imperfections. With all of its quirkiness. That's what makes it great. How you see the world that's different from how everyone else sees the world. That's why you're an artist. That's your purpose in sharing your work with the world." He warns against being derivative: "There are these derivative voices where they're finding what they think other people want to hear, and they start saying it because they've heard other people say similar things that are now successful. Even if they have some short-term success doing that, it's not revolutionary. It doesn't change the world. It doesn't last. The people who you first see and you might not like that you come to like because you don't understand them at first, those are the ones that change the world. Those are the ones you dedicate your fandom to for life." Rubin shares his philosophy on taste: "You can't second-guess your own taste for what someone else is going to like. We're not smart enough to know what someone else is going to like. To make something thinking, 'Well, I don't really like it, but I think this group of people will like it,' it's a bad way to play the game of music or art. You have to do what's personal to you. Take it as far as you can go. Really push the boundaries. And people will resonate with it if they're supposed to resonate with it." He describes creativity as catching waves: "We're really talking about magic. The universe conspiring on our behalf if we let it. Being in this flow of catching these waves that anyone can catch. If you're trying to catch it, you're open to it, you see it coming, you take off on every chance you get. And sometimes the ride happens. It's remarkable how it happens. It doesn't come from preconception. It's not an idea. It's through the doing." Rubin explains how ideas exist in the universe: "Have you ever had that experience where you have an idea for something, you don't do it, and then six months later you see someone else has done it? It's not because they took your idea. It's that it's time for that, and you can act on it or not. The best artists are the ones who have the best antenna for this material that's available. It's coming through. The best comedians see the best jokes. They see them coming. We all live in the same world; the way you see it, you have the best joke because you see it best." He closes with how to stay open: "If we listen to what's going on around us, you can overhear a conversation in a coffee shop, and it is the setup for an idea you're working on. You hear a phrase you don't commonly use. My experience is: when you are open and looking for these clues in the world, they're happening all the time. And they're happening often right when you need them."

Jaynit

109,182 просмотров • 5 месяцев назад

The founders of Stripe and Pinterest on how to convince people to join your startup Stripe CEO Patrick Collison argues that part of the reason startups resonate so much is because the outcome is not guaranteed: "If it were guaranteed, it would be boring... Whether or not you're the best person in the world at what you do, you're probably not going to alter Google's trajectory. But if you really want to benchmark yourself and see how much of a contribution and impact you can make--which is a really compelling prospect for a lot of the best people--a startup is a much better place to test that." Pinterest founder Ben Silbermann emphasized this as well: "No smart person that you're hiring is under the illusion that you have a crystal ball into the future and that joining is a guaranteed thing. In fact, if you're telling them that and they select in, you shouldn't hire them because they didn't pass a basic intelligence test. I think it's important to tell them what's exciting and where you think the company can go. But also tell them where it will be hard and chart your best plan. And then tell them why their role can be instrumental--because it will be... What I would discourage doing is whitewashing all of that. If people are joining your company because they want all of the certainty and safety of working at Google but also the perks of working at a small startup with lots of responsibility and transparency, that's a really negative sign." Apparently in the early days of PayPal, Peter Thiel and Max Levchin would tell people after they interviewed all the reasons that the company would fail: "Visa and MasterCard want to kill us. We also might be doing something that's illegal. But if we succeed, we'll redefine payments." Don't whitewash the risks. Instead tell them how your startup will change the world if you succeed and how their role will be instrumental in affecting that change. Video source: Y Combinator (2014)

The founders of Stripe and Pinterest on how to convince people to join your startup Stripe CEO Patrick Collison argues that part of the reason startups resonate so much is because the outcome is not guaranteed: "If it were guaranteed, it would be boring... Whether or not you're the best person in the world at what you do, you're probably not going to alter Google's trajectory. But if you really want to benchmark yourself and see how much of a contribution and impact you can make--which is a really compelling prospect for a lot of the best people--a startup is a much better place to test that." Pinterest founder Ben Silbermann emphasized this as well: "No smart person that you're hiring is under the illusion that you have a crystal ball into the future and that joining is a guaranteed thing. In fact, if you're telling them that and they select in, you shouldn't hire them because they didn't pass a basic intelligence test. I think it's important to tell them what's exciting and where you think the company can go. But also tell them where it will be hard and chart your best plan. And then tell them why their role can be instrumental--because it will be... What I would discourage doing is whitewashing all of that. If people are joining your company because they want all of the certainty and safety of working at Google but also the perks of working at a small startup with lots of responsibility and transparency, that's a really negative sign." Apparently in the early days of PayPal, Peter Thiel and Max Levchin would tell people after they interviewed all the reasons that the company would fail: "Visa and MasterCard want to kill us. We also might be doing something that's illegal. But if we succeed, we'll redefine payments." Don't whitewash the risks. Instead tell them how your startup will change the world if you succeed and how their role will be instrumental in affecting that change. Video source: Y Combinator (2014)

Startup Archive

11,811 просмотров • 10 месяцев назад

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

TBPN

36,474 просмотров • 4 месяцев назад

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

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

Ricardo

28,088 просмотров • 8 месяцев назад

Nvidia just made its biggest acquisition ever... A small startup. On Christmas Eve. While everyone was distracted. The startup is Groq. They make AI chips that run faster than Nvidia's. In September, Groq raised $750 million at a $6.9 billion valuation. Investors included BlackRock, Samsung, Cisco, and Donald Trump Jr.'s fund 1789 Capital. Yesterday, Nvidia paid $20 billion for them. That's a 3x markup in 90 days. Trump Jr. just made a fortune overnight. But the money isn't even the craziest part... Nvidia dominates AI chips. They have 90%+ market share. All of big tech depends on them. So why would they pay 3X for a tiny competitor? Let me explain: Groq was building inference chips that threatened Nvidia's monopoly. Faster processing. Lower costs. Better architecture. If Groq succeeded, they'd crack open the market. So Nvidia did what monopolies always do: bought them before they became a threat. They're buying silence - not innovation. And this isn't a one-off. In September, Nvidia spent $900 million on Enfabrica, another AI chip startup. Same playbook. They committed $100 billion to OpenAI with a requirement that OpenAI deploy 10 gigawatts of Nvidia products. Now $20 billion for Groq. Nvidia isn't competing anymore. They're consolidating. They're using their cash pile to swallow every potential rival before they grow. The founder and CEO of Groq, Jonathan Ross, is joining Nvidia. So is the president and the entire leadership team. Groq's cloud business will keep running independently. But the tech, the IP, the competitive advantage? All Nvidia's now. This is the largest acquisition Nvidia has EVER made. Their previous record was $7 billion for Mellanox in 2019. They just tripled that. And nobody's questioning why. Everyone's celebrating "innovation" and "consolidation" and "strategic partnerships." But what actually happened is this: Nvidia looked at the one company that could challenge their monopoly and paid whatever it took to kill them. $20 billion is cheap insurance when you're worth trillions. The only question left is who's next.

Nvidia just made its biggest acquisition ever... A small startup. On Christmas Eve. While everyone was distracted. The startup is Groq. They make AI chips that run faster than Nvidia's. In September, Groq raised $750 million at a $6.9 billion valuation. Investors included BlackRock, Samsung, Cisco, and Donald Trump Jr.'s fund 1789 Capital. Yesterday, Nvidia paid $20 billion for them. That's a 3x markup in 90 days. Trump Jr. just made a fortune overnight. But the money isn't even the craziest part... Nvidia dominates AI chips. They have 90%+ market share. All of big tech depends on them. So why would they pay 3X for a tiny competitor? Let me explain: Groq was building inference chips that threatened Nvidia's monopoly. Faster processing. Lower costs. Better architecture. If Groq succeeded, they'd crack open the market. So Nvidia did what monopolies always do: bought them before they became a threat. They're buying silence - not innovation. And this isn't a one-off. In September, Nvidia spent $900 million on Enfabrica, another AI chip startup. Same playbook. They committed $100 billion to OpenAI with a requirement that OpenAI deploy 10 gigawatts of Nvidia products. Now $20 billion for Groq. Nvidia isn't competing anymore. They're consolidating. They're using their cash pile to swallow every potential rival before they grow. The founder and CEO of Groq, Jonathan Ross, is joining Nvidia. So is the president and the entire leadership team. Groq's cloud business will keep running independently. But the tech, the IP, the competitive advantage? All Nvidia's now. This is the largest acquisition Nvidia has EVER made. Their previous record was $7 billion for Mellanox in 2019. They just tripled that. And nobody's questioning why. Everyone's celebrating "innovation" and "consolidation" and "strategic partnerships." But what actually happened is this: Nvidia looked at the one company that could challenge their monopoly and paid whatever it took to kill them. $20 billion is cheap insurance when you're worth trillions. The only question left is who's next.

Ricardo

224,331 просмотров • 9 месяцев назад

Q: What's the secret to building a great product that grows really fast? Paul Graham explains that the secret to growing really fast is to: "start with a small, intense fire." He uses Apple as an example. They started by selling just 500 Apple I computers. Today Apple is the largest company in the world. It's impossible to make something that a large number of people really want when you're just starting out. So you have to find people who want what you're building A LOT. And that's necessarily going to be a small number at first. But that's ok because that's how almost every giant company gets started. PG continues: "You have to know who those first users are and how you're going to get them. Then you're going to sit down and just have a party with those first few users and focus entirely on them and making them super super happy." Another example he draws upon is a startup in a Y Combinator batch making a new mobile email client. Their beta group had one user: Sam Altman. This startup's goal was to just make Sam happy. Sam uses email a lot on the go, knows all of the other email client options, and is super demanding. So they know that if they build a product that makes Sam happy, odds are it will make lots of other people happy too. "One of the things we tell startups in these extreme cases where they can make just one user happy is to act like a consultant. Act like Sam has hired you to make an email app just for him. All you have to do is make Sam happy--it can say 'Sam Altman' at the top of the screen. That's ok! Just so long as Sam would feel bummed if you stopped working on it. That's the test." Ultimately the secret to building a great product that grows really fast is to build something a small group of people love so much that they'd be really disappointed if you stopped working on it. There's lots of important steps to get right after this, but this is the foundation for growth. It's somewhat counterintuitive, but most of the world's largest companies (e.g. Apple, Facebook, etc.) started by building a product that made a small group of people really happy.

Q: What's the secret to building a great product that grows really fast? Paul Graham explains that the secret to growing really fast is to: "start with a small, intense fire." He uses Apple as an example. They started by selling just 500 Apple I computers. Today Apple is the largest company in the world. It's impossible to make something that a large number of people really want when you're just starting out. So you have to find people who want what you're building A LOT. And that's necessarily going to be a small number at first. But that's ok because that's how almost every giant company gets started. PG continues: "You have to know who those first users are and how you're going to get them. Then you're going to sit down and just have a party with those first few users and focus entirely on them and making them super super happy." Another example he draws upon is a startup in a Y Combinator batch making a new mobile email client. Their beta group had one user: Sam Altman. This startup's goal was to just make Sam happy. Sam uses email a lot on the go, knows all of the other email client options, and is super demanding. So they know that if they build a product that makes Sam happy, odds are it will make lots of other people happy too. "One of the things we tell startups in these extreme cases where they can make just one user happy is to act like a consultant. Act like Sam has hired you to make an email app just for him. All you have to do is make Sam happy--it can say 'Sam Altman' at the top of the screen. That's ok! Just so long as Sam would feel bummed if you stopped working on it. That's the test." Ultimately the secret to building a great product that grows really fast is to build something a small group of people love so much that they'd be really disappointed if you stopped working on it. There's lots of important steps to get right after this, but this is the foundation for growth. It's somewhat counterintuitive, but most of the world's largest companies (e.g. Apple, Facebook, etc.) started by building a product that made a small group of people really happy.

Michael McGuiness

890,721 просмотров • 3 лет назад

"Every former Nvidia employee that goes to a large tech company like Google and MSFT says it's extremely difficult to adjust to the culture, Nvidia is faster but the work ethic is on a different level" 3dfx was the No 1 graphics company in the mid-to-late 90s, their main competitor Nvidia beat them and essentially destroyed them - acquired their assets and hired 100 of their engineers. The 3dfx engineers kept looking for Nvidia's secret or magic trick, but it turned out the only secret was relentless execution & working crazy hard. This culture obviously stems from the CEO, Jensen - who loves to work on Sunday nights & finds it relaxing. Jensen is constantly focused on cultivating talent - he rewards highly performing engineers by doubling or tripling their stock compensation. To avoid the internal politics that plague large organizations (where projects get stuck in quicksand needing multiple approvals), Jensen made the organization flat. Every time they need to make decisions, he brings together top 10-15 people, including lower-level employees, to hash out the main problems in an intellectually honest way and execute quickly - what they call "speed of light" at Nvidia.

"Every former Nvidia employee that goes to a large tech company like Google and MSFT says it's extremely difficult to adjust to the culture, Nvidia is faster but the work ethic is on a different level" 3dfx was the No 1 graphics company in the mid-to-late 90s, their main competitor Nvidia beat them and essentially destroyed them - acquired their assets and hired 100 of their engineers. The 3dfx engineers kept looking for Nvidia's secret or magic trick, but it turned out the only secret was relentless execution & working crazy hard. This culture obviously stems from the CEO, Jensen - who loves to work on Sunday nights & finds it relaxing. Jensen is constantly focused on cultivating talent - he rewards highly performing engineers by doubling or tripling their stock compensation. To avoid the internal politics that plague large organizations (where projects get stuck in quicksand needing multiple approvals), Jensen made the organization flat. Every time they need to make decisions, he brings together top 10-15 people, including lower-level employees, to hash out the main problems in an intellectually honest way and execute quickly - what they call "speed of light" at Nvidia.

Aish

692,758 просмотров • 1 год назад

Amplitude CEO Spenser Skates says the SaaSpocalypse is real, and your old moat is dead. The only moat now is speed of innovation: "You have to be pushing the bleeding edge of what capabilities are. If you create a company that can do that, that's what customers will buy." "What the SaaSpocalypse has gotten right is - if you look at the median SaaS company, their innovation has actually slowed to a standstill." "It's crazy how little they ship in terms of net new products." "Speed of using the bleeding edge of these model capabilities is all that matters. You're even seeing it in the foundational model companies. Opus was the hottest thing a week ago, then it Codex literally launched the same day, saying, 'We're even better, and we're faster and we're on Cerebras chips.' I think all that matters is the rate of improvement." "It's like sushi. Buyers are always going to want the best thing. So if you're keeping up with innovating the best thing, you will be able to charge a premium. It's fine that the 7/11 gas station now sells sushi. It's not going to put Jiro in Japan out of business." "The lesson is - there is no moat anymore from what we've built. There's only a moat if we're able to deliver the most bleeding edge capability."

Amplitude CEO Spenser Skates says the SaaSpocalypse is real, and your old moat is dead. The only moat now is speed of innovation: "You have to be pushing the bleeding edge of what capabilities are. If you create a company that can do that, that's what customers will buy." "What the SaaSpocalypse has gotten right is - if you look at the median SaaS company, their innovation has actually slowed to a standstill." "It's crazy how little they ship in terms of net new products." "Speed of using the bleeding edge of these model capabilities is all that matters. You're even seeing it in the foundational model companies. Opus was the hottest thing a week ago, then it Codex literally launched the same day, saying, 'We're even better, and we're faster and we're on Cerebras chips.' I think all that matters is the rate of improvement." "It's like sushi. Buyers are always going to want the best thing. So if you're keeping up with innovating the best thing, you will be able to charge a premium. It's fine that the 7/11 gas station now sells sushi. It's not going to put Jiro in Japan out of business." "The lesson is - there is no moat anymore from what we've built. There's only a moat if we're able to deliver the most bleeding edge capability."

TBPN

95,450 просмотров • 7 месяцев назад

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓

bodila

312,443 просмотров • 2 месяцев назад

SemiAnalysis' Jordan Nanos says OpenAI beats Nvidia on both fast and cheap tokens at once, where every other challenger could only pick one side of the curve "I think the conclusion is that they're basically winning on both sides of the curve." "So both fast tokens and cheap tokens, which is just so interesting, because we've seen so many other companies make claims about how they're going to beat Nvidia and they just pick one of those." "Groq or Cerebras, or any of the other startups that are going to focus on SRAM, call it a d-Matrix that's coming up with stuff, or SambaNova, where we've even seen some results on the InferenceMAX benchmark or something similar to it." "They're saying they're going for fast tokens, just decode speed, low batch size, they don't worry about throughput." "And then you've got other guys that are worried about throughput, call it AMD just as a simple example, but even TPU or Trainium could be in this bucket of accelerators that are going for throughput." "And they go, 'Yeah, but we're not going to be able to compete with the other guys at high interactivity.'" "And OpenAI has a chip that can do both for them, really well." "I mean, at a minimum, this thing is doing really well right now and is going to serve real production tokens for them." _______ More takeaways from OpenAI's Jalapeno development:

SemiAnalysis' Jordan Nanos says OpenAI beats Nvidia on both fast and cheap tokens at once, where every other challenger could only pick one side of the curve "I think the conclusion is that they're basically winning on both sides of the curve." "So both fast tokens and cheap tokens, which is just so interesting, because we've seen so many other companies make claims about how they're going to beat Nvidia and they just pick one of those." "Groq or Cerebras, or any of the other startups that are going to focus on SRAM, call it a d-Matrix that's coming up with stuff, or SambaNova, where we've even seen some results on the InferenceMAX benchmark or something similar to it." "They're saying they're going for fast tokens, just decode speed, low batch size, they don't worry about throughput." "And then you've got other guys that are worried about throughput, call it AMD just as a simple example, but even TPU or Trainium could be in this bucket of accelerators that are going for throughput." "And they go, 'Yeah, but we're not going to be able to compete with the other guys at high interactivity.'" "And OpenAI has a chip that can do both for them, really well." "I mean, at a minimum, this thing is doing really well right now and is going to serve real production tokens for them." _______ More takeaways from OpenAI's Jalapeno development:

Fireside Alpha

759,506 просмотров • 29 дней назад

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

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

Ricardo

162,581 просмотров • 4 месяцев назад

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”

The All-In Podcast

227,777 просмотров • 2 месяцев назад

Chamath: US AI Startups Can Learn A Lot from DeepSeek "This is a case where necessity was the mother of invention." On E213, Chamath Palihapitiya explained how, even if the $6M number is inaccurate, DeepSeek still had some impressive breakthroughs: GRPO > PPO for reinforcement learning "These guys were like, 'Well, how am I going to do this reinforcement learning thing?' They invented a totally different algorithm." "It uses a lot less computer memory and it's highly performant." PTX > CUDA to build the model "And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language." "And these guys worked totally around CUDA and they did something called PTX, which goes right to the bare metal." What US AI startups can learn from this: "We, meaning the West, with all the money that we've had, didn't come up with these ideas." "And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist." "And so I just wonder how we make sure we learn this principle, meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200M, maybe that's not the right answer for a Series A or a Seed." "And maybe the right answer is $2M so that they do these DeepSeek-like innovations."

Chamath: US AI Startups Can Learn A Lot from DeepSeek "This is a case where necessity was the mother of invention." On E213, Chamath Palihapitiya explained how, even if the $6M number is inaccurate, DeepSeek still had some impressive breakthroughs: GRPO > PPO for reinforcement learning "These guys were like, 'Well, how am I going to do this reinforcement learning thing?' They invented a totally different algorithm." "It uses a lot less computer memory and it's highly performant." PTX > CUDA to build the model "And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language." "And these guys worked totally around CUDA and they did something called PTX, which goes right to the bare metal." What US AI startups can learn from this: "We, meaning the West, with all the money that we've had, didn't come up with these ideas." "And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist." "And so I just wonder how we make sure we learn this principle, meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200M, maybe that's not the right answer for a Series A or a Seed." "And maybe the right answer is $2M so that they do these DeepSeek-like innovations."

The All-In Podcast

108,815 просмотров • 1 год назад

Brian still spends over two hours a day on recruiting and personally hires the top 200 people at Airbnb. I loved this idea of being in the flow of talent to find the best people: "Don't do searches. Build pipelines. I try to map out all the best people in the Valley. So let's say I need to hire really good engineers. I don't do searches. I just informationally meet the best engineers in the world. Every meeting, the job is to get the next meeting, meet someone else. The mistake people make when they hire. They go, "I need to hire a blank." So they hire a search firm. They give you 50 profiles, and you pick the best one. That is the wrong way to do it. The best way to do it is pipeline recruiting. You're constantly recruiting, you're constantly meeting people. in advance of searches. And all of it is referral based. The two ways to find out if people are good – is to start with the results and work backwards to the people. Find an ad you like and figure out who made that ad. Start with the results. Work backwards to people. Don't start with the resume. The other thing to do is just keep asking people to build your Rolodex. The moment I find somebody that's really good, I ask them who all the best people they know are. And I build these little mafias and they tell you who the other good people are. I am the co-hiring manager for the top 200 people in the company. This is very radical. A lot of CEOs think it's their job to hire their executive team, and their executive team hires their team. I think that is fatal. You always want to be marrying up, hiring people of the future. It should be like we're reaching. If you can hire them without my help, we're not reaching far enough. You want to hire the very best person you can."

Brian still spends over two hours a day on recruiting and personally hires the top 200 people at Airbnb. I loved this idea of being in the flow of talent to find the best people: "Don't do searches. Build pipelines. I try to map out all the best people in the Valley. So let's say I need to hire really good engineers. I don't do searches. I just informationally meet the best engineers in the world. Every meeting, the job is to get the next meeting, meet someone else. The mistake people make when they hire. They go, "I need to hire a blank." So they hire a search firm. They give you 50 profiles, and you pick the best one. That is the wrong way to do it. The best way to do it is pipeline recruiting. You're constantly recruiting, you're constantly meeting people. in advance of searches. And all of it is referral based. The two ways to find out if people are good – is to start with the results and work backwards to the people. Find an ad you like and figure out who made that ad. Start with the results. Work backwards to people. Don't start with the resume. The other thing to do is just keep asking people to build your Rolodex. The moment I find somebody that's really good, I ask them who all the best people they know are. And I build these little mafias and they tell you who the other good people are. I am the co-hiring manager for the top 200 people in the company. This is very radical. A lot of CEOs think it's their job to hire their executive team, and their executive team hires their team. I think that is fatal. You always want to be marrying up, hiring people of the future. It should be like we're reaching. If you can hire them without my help, we're not reaching far enough. You want to hire the very best person you can."

Patrick OShaughnessy

317,217 просмотров • 4 месяцев назад

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”

David Senra

302,101 просмотров • 18 дней назад

🚨 Do you understand what you're looking at right now? $1,000,000,000,000. Count the zeros. Twelve. That's not a company's valuation. That's not a market cap... That's ONE MAN'S revenue prediction. For selling chips.. > Jensen Huang just said Nvidia will make $1 trillion from AI chips by 2027.. One trillion dollars. From one product. In one industry. That didn't exist 3 years ago.. > Every company fighting the AI war - OpenAI, Google, Meta, Amazon, xAI - is buying their weapons from the same arms dealer.. > Nvidia doesn't care who wins. They sell bullets to every side. > DeepSeek wiped $600B off Nvidia in January.. Jensen recovered in two weeks and just came back asking for a trillion.. This man doesn't flinch. The entire AI revolution has one chokepoint. One company. One man. One leather jacket. And he just named his price.

🚨 Do you understand what you're looking at right now? $1,000,000,000,000. Count the zeros. Twelve. That's not a company's valuation. That's not a market cap... That's ONE MAN'S revenue prediction. For selling chips.. > Jensen Huang just said Nvidia will make $1 trillion from AI chips by 2027.. One trillion dollars. From one product. In one industry. That didn't exist 3 years ago.. > Every company fighting the AI war - OpenAI, Google, Meta, Amazon, xAI - is buying their weapons from the same arms dealer.. > Nvidia doesn't care who wins. They sell bullets to every side. > DeepSeek wiped $600B off Nvidia in January.. Jensen recovered in two weeks and just came back asking for a trillion.. This man doesn't flinch. The entire AI revolution has one chokepoint. One company. One man. One leather jacket. And he just named his price.

Tuki

94,228 просмотров • 6 месяцев назад