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Jordi Visser explains why he thinks Google’s Gemini model has fallen behind. - Anthropic & OpenAI are fast, lean & focused - Google is bloated & distracted - Recursive learning extends advantages - Open source models put more pressure on $GOOG Jordi Visser

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Anthony Pompliano 🌪

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The US government is becoming more involved in controlling AI models, while open-source Chinese models are improving at a rapid pace. Jordi Visser explains why this should push more people to open-source AI in the coming months.
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The US government is becoming more involved in controlling AI models, while open-source Chinese models are improving at a rapid pace. Jordi Visser explains why this should push more people to open-source AI in the coming months.

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Jordy Visser Jordi Visser: "Google's not a player in this anymore." Visser is an AI-native power user who tracks his own model usage as a leading indicator. Two months ago Claude was 35% of his usage, ChatGPT 25%, Google 20%. Now ChatGPT and Claude are 90% combined and Gemini is near zero. The same week, two senior DeepMind scientists left for Anthropic. He would never short Google. But multiple compression is already underway, the same pattern that hit Meta and Microsoft. A business does not have to fail to destroy shareholder value. Why the AI race narrowed to two: Source: Anthony Pompliano Podcast -
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Jordy Visser Jordi Visser: "Google's not a player in this anymore." Visser is an AI-native power user who tracks his own model usage as a leading indicator. Two months ago Claude was 35% of his usage, ChatGPT 25%, Google 20%. Now ChatGPT and Claude are 90% combined and Gemini is near zero. The same week, two senior DeepMind scientists left for Anthropic. He would never short Google. But multiple compression is already underway, the same pattern that hit Meta and Microsoft. A business does not have to fail to destroy shareholder value. Why the AI race narrowed to two: Source: Anthony Pompliano Podcast -

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Matthew Berman

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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.
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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 Aufrufe • vor 11 Monaten

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.
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Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 Aufrufe • vor 17 Tagen

🚨🇺🇸 OPENAI DECLARES "CODE RED" AS GOOGLE AND ANTHROPIC EAT THEIR LUNCH Sam Altman has sent an internal memo telling OpenAI employees to drop everything and focus on fixing ChatGPT. Jason Calacanis from the All In Podcast: "A Code Red has been called by Sam Altman. He sent a memo on Monday, told employees to stop working on side quests, you know, like ads, et cetera, and focus on the core ChatGPT experience. Make it faster, make it better. And I think we all know why, because Gemini and Claude from Anthropic have been crushing it. ChatGPT 5, let's call it what it is, was a bit of a flop. It didn't perform to expectations. And Anthropic is beating OpenAI in enterprise revenue." For those of you unaware, Google released Gemini 3 last month, which surpassed ChatGPT in a range of benchmark tests and set a new record on "Humanity's Last Exam," a test designed to identify artificial superintelligence. Altman publicly called Gemini 3 a "great model." Privately, he told staff ChatGPT had fallen behind and they're "catching up fast." And then there's Grok. Elon Musk xAI started years behind everyone else and is already nipping at OpenAI's heels. OpenAI was supposed to be the unbeatable first mover. Now they're playing defense while the competition sprints ahead. The AI wars just got real. Source: The Information, Wall Street Journal, The All-In Podcast
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🚨🇺🇸 OPENAI DECLARES "CODE RED" AS GOOGLE AND ANTHROPIC EAT THEIR LUNCH Sam Altman has sent an internal memo telling OpenAI employees to drop everything and focus on fixing ChatGPT. Jason Calacanis from the All In Podcast: "A Code Red has been called by Sam Altman. He sent a memo on Monday, told employees to stop working on side quests, you know, like ads, et cetera, and focus on the core ChatGPT experience. Make it faster, make it better. And I think we all know why, because Gemini and Claude from Anthropic have been crushing it. ChatGPT 5, let's call it what it is, was a bit of a flop. It didn't perform to expectations. And Anthropic is beating OpenAI in enterprise revenue." For those of you unaware, Google released Gemini 3 last month, which surpassed ChatGPT in a range of benchmark tests and set a new record on "Humanity's Last Exam," a test designed to identify artificial superintelligence. Altman publicly called Gemini 3 a "great model." Privately, he told staff ChatGPT had fallen behind and they're "catching up fast." And then there's Grok. Elon Musk xAI started years behind everyone else and is already nipping at OpenAI's heels. OpenAI was supposed to be the unbeatable first mover. Now they're playing defense while the competition sprints ahead. The AI wars just got real. Source: The Information, Wall Street Journal, The All-In Podcast

Mario Nawfal

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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.”
0:54

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

227,201 Aufrufe • vor 1 Monat

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."
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Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Patrick OShaughnessy

295,226 Aufrufe • vor 3 Monaten

Yann LeCun just said something at Davos that nobody is talking about. The man who built Meta's AI for 12 years. The godfather of deep learning. Said this on stage, in front of the world's most powerful people: "The best open source AI models right now come from China. Everybody in the research community is using Chinese models." Here's what makes this more uncomfortable. He said it in the same breath as warning that the US is making a catastrophic mistake. OpenAI — increasingly closed. Anthropic — never open. Google — more closed every year. Meta — changing direction. Meanwhile China's labs are publishing. Open sourcing. Sharing. And the global research community is downloading their models. LeCun's warning was blunt: "We are slowing down progress in the West because of this." The irony is brutal. America built the open internet. Linux runs the world. The entire software stack humanity depends on is open source. And now the country that invented openness is closing its most important technology. While China does the opposite. The next AI revolution won't be won by the country with the most money. It will be won by the country whose models researchers actually use. Right now that answer is changing.
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Yann LeCun just said something at Davos that nobody is talking about. The man who built Meta's AI for 12 years. The godfather of deep learning. Said this on stage, in front of the world's most powerful people: "The best open source AI models right now come from China. Everybody in the research community is using Chinese models." Here's what makes this more uncomfortable. He said it in the same breath as warning that the US is making a catastrophic mistake. OpenAI — increasingly closed. Anthropic — never open. Google — more closed every year. Meta — changing direction. Meanwhile China's labs are publishing. Open sourcing. Sharing. And the global research community is downloading their models. LeCun's warning was blunt: "We are slowing down progress in the West because of this." The irony is brutal. America built the open internet. Linux runs the world. The entire software stack humanity depends on is open source. And now the country that invented openness is closing its most important technology. While China does the opposite. The next AI revolution won't be won by the country with the most money. It will be won by the country whose models researchers actually use. Right now that answer is changing.

Shruti

31,597 Aufrufe • vor 2 Monaten

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓
15:52

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Сarm1ne

124,996 Aufrufe • vor 9 Tagen

I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end
55:11

I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end

Anthony Pompliano 🌪

64,370 Aufrufe • vor 1 Monat

SemiAnalysis Dylan shares his thoughts on xAI Grok and research labs: > xAI has ALOT of compute concentrated > ALOT of great researchers > 200K GPUs already running > purchased a new factory in memphis > building out a new data center > now they are buying a power plant and shipping it from overseas because you can’t get a new one in time in the US > Grok models are good > elon is hyping up next model > maybe it’ll be great / good > will it be openai level? or slightly behind? > we’ll see > he uses grok for current events > grok knows exactly what’s happening way better than google search, gemini or openai > every research lab is generally doing the same thing > even SSI
2:04

SemiAnalysis Dylan shares his thoughts on xAI Grok and research labs: > xAI has ALOT of compute concentrated > ALOT of great researchers > 200K GPUs already running > purchased a new factory in memphis > building out a new data center > now they are buying a power plant and shipping it from overseas because you can’t get a new one in time in the US > Grok models are good > elon is hyping up next model > maybe it’ll be great / good > will it be openai level? or slightly behind? > we’ll see > he uses grok for current events > grok knows exactly what’s happening way better than google search, gemini or openai > every research lab is generally doing the same thing > even SSI

NIK

293,607 Aufrufe • vor 1 Jahr

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)
1:07

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

440,435 Aufrufe • vor 15 Tagen

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
1:10

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 Aufrufe • vor 3 Monaten

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.
4:38

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

Milk Road AI

34,378 Aufrufe • vor 1 Monat

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.
2:14

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.

Aish

334,732 Aufrufe • vor 1 Jahr

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓
49:52

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

Damir Akaza

164,950 Aufrufe • vor 1 Monat

GT Protocol AI Digest No.86: Claude Just Leveled Up Here are five stories worth your attention 👇 🧠 Claude Opus Gets an Upgrade Anthropic released a new Opus model with stronger performance in coding and cybersecurity. The update, alongside its Mythos Preview, points toward more advanced and autonomous AI systems. 🎨 Canva Goes Full Prompt Mode Canva’s AI 2.0 update expands prompt-based editing across the entire design process. Users can now generate and modify layouts, images, and text through simple instructions. 💻 Gemini Moves to Desktop Google launched a native Gemini app for macOS, bringing AI directly into the operating system. Assistants are becoming persistent tools, not just browser features. 🧬 AI Gets Specialized in Biology OpenAI introduced a biology-focused model designed for scientific workflows. AI is moving beyond general use into domain-specific applications. ⚙️ Intel Repositions in the AI Race Intel is aligning with Google’s infrastructure strategy and exploring ties to Musk’s chip projects. The competition is shifting toward hardware and large-scale compute. Read the full digest on our Medium 👉
0:50

GT Protocol AI Digest No.86: Claude Just Leveled Up Here are five stories worth your attention 👇 🧠 Claude Opus Gets an Upgrade Anthropic released a new Opus model with stronger performance in coding and cybersecurity. The update, alongside its Mythos Preview, points toward more advanced and autonomous AI systems. 🎨 Canva Goes Full Prompt Mode Canva’s AI 2.0 update expands prompt-based editing across the entire design process. Users can now generate and modify layouts, images, and text through simple instructions. 💻 Gemini Moves to Desktop Google launched a native Gemini app for macOS, bringing AI directly into the operating system. Assistants are becoming persistent tools, not just browser features. 🧬 AI Gets Specialized in Biology OpenAI introduced a biology-focused model designed for scientific workflows. AI is moving beyond general use into domain-specific applications. ⚙️ Intel Repositions in the AI Race Intel is aligning with Google’s infrastructure strategy and exploring ties to Musk’s chip projects. The competition is shifting toward hardware and large-scale compute. Read the full digest on our Medium 👉

GT Protocol

37,782 Aufrufe • vor 3 Monaten

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast
3:21

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

86,295 Aufrufe • vor 1 Monat