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Greg Osuri built the case for decentralized AI in three layers in under 5 minutes. Layer one: access. Anthropic's Claude is the best coding model available. OpenClaw is the most successful open-source project in history. Anthropic just announced they're banning OpenClaw usage. The most powerful model cutting off the...

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The Rollup

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15,021 views • 6 months ago •via X (Twitter)

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Jerico6 months ago

Exactly why I don't understand why i can freely access @akashnet to host whatever but need to provide creditcard information when using Akash ML ... is that legal reasons? (Just trying to understand as it seems to be the same project that became so great thanks to @gregosuri)

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Void Logistics6 months ago

Not decentralised. Amazon. Decentralised. Hahaha

ncubelabs's profile picture
ncubelabs6 months ago

@akashnet 🎯

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The Relay6 months ago

Additionally, decentralized AI compute will undoubtedly see more success from it's centralized counterparts due to the incentive structure and cheaper costs Who wouldn't want to make some passive income with their GPU? Spot on analysis Greg 👌

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Jeff Nstyle6 months ago

Une belle tête de gagnant celui là

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48,010 views • 4 months ago

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20,739 views • 3 months ago

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138,165 views • 3 months ago

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BREAKING: Elon Musk says Chinese AI models are outstanding despite limited compute and expects China to address its chipmaking constraints in two or three years in his new interview with CCTV. “The Chinese AI models are generally outstanding actually. I mean you can see on the sort of intelligence rankings there, especially given the limited amount of compute that the Chinese AI companies have, the performance is excellent. Probably China's doing by far the best in terms of performance per unit of compute. And as I've said publicly, you know, I think China's going address the compute limitation in, I think, faster than most people expect. My rough guess would be that China addresses the compute constraints with lithography and chip making in about two or three years.”

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74,116 views • 21 days ago

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

Milk Road AI

34,720 views • 3 months ago

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.
6:01

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

Santiago

91,263 views • 11 months ago

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George
1:37

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George

a16z

39,947 views • 1 month ago

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.
2:51

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 views • 2 months ago

🚨David Sacks: There is no White House decision to ban open source models. Trump is listening to a chorus of voices — and Sacks says it would be a tragic mistake to take action against the open source ecosystem. His argument: Anthropic is the fastest growing tech company at scale in history. $10B ARR at the start of the year. $70B now. This is not a company that needs government protection. And yet they've spent enormous energy trying to panic Washington into banning American developers from using Chinese open source models. If distillation by Chinese companies was really their primary concern — a genuine national security threat — they would push to ban Chinese access to American models. Instead they're pushing to ban American access to Chinese models. Because this helps Anthropic's competitive position. Sacks: "That's the way you know the whole distillation thing is fake." He went further. If distillation is happening at industrial scale, it's obvious to see. Anthropic has 90% gross margins. Why aren't they stopping it at the source? Answer: because KYC'ing their customers would slow their growth. So instead of fixing their own house, they're asking the government to kneecap their competitors. "The question should be on Anthropic to explain why it's doing such a bad job — not on the whole American open source ecosystem to be punished for Anthropic's failure." Trump's AI Czar on The All-In Podcast.
4:30

🚨David Sacks: There is no White House decision to ban open source models. Trump is listening to a chorus of voices — and Sacks says it would be a tragic mistake to take action against the open source ecosystem. His argument: Anthropic is the fastest growing tech company at scale in history. $10B ARR at the start of the year. $70B now. This is not a company that needs government protection. And yet they've spent enormous energy trying to panic Washington into banning American developers from using Chinese open source models. If distillation by Chinese companies was really their primary concern — a genuine national security threat — they would push to ban Chinese access to American models. Instead they're pushing to ban American access to Chinese models. Because this helps Anthropic's competitive position. Sacks: "That's the way you know the whole distillation thing is fake." He went further. If distillation is happening at industrial scale, it's obvious to see. Anthropic has 90% gross margins. Why aren't they stopping it at the source? Answer: because KYC'ing their customers would slow their growth. So instead of fixing their own house, they're asking the government to kneecap their competitors. "The question should be on Anthropic to explain why it's doing such a bad job — not on the whole American open source ecosystem to be punished for Anthropic's failure." Trump's AI Czar on The All-In Podcast.

KanekoaTheGreat

48,392 views • 2 months ago

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.
2:50

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

Anthony Pompliano 🌪

76,176 views • 1 month ago