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Kevin explains how frontier AI labs could benefit from open-sourcing models. “If they were to verticalize more quickly... there’s a tipping point where open-sourcing some of their models can make sense because they have a way to potentially capture more value.”

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Delphi Digital

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28,682 просмотров • 1 месяц назад •via X (Twitter)

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Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.
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Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

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Is compute scarcity masking the real economics of AI? interesting point from Peter Fenton: open-weight models are still complex to run well. For now, the big labs have the bundle: model + compute + reliability + access... and thus pricing power. But if compute supply loosens and open model tooling gets easier, routine AI work can more easily move to cheaper/open models.
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Is compute scarcity masking the real economics of AI? interesting point from Peter Fenton: open-weight models are still complex to run well. For now, the big labs have the bundle: model + compute + reliability + access... and thus pricing power. But if compute supply loosens and open model tooling gets easier, routine AI work can more easily move to cheaper/open models.

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Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."
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Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."

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Delphi AI researcher Accelxr 👾 explains why Nvidia’s move into open models could make it a competitor to its own customers. “Nvidia is best positioned to extract maximum throughput for their models out of their own tech stack.”
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Delphi AI researcher Accelxr 👾 explains why Nvidia’s move into open models could make it a competitor to its own customers. “Nvidia is best positioned to extract maximum throughput for their models out of their own tech stack.”

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Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg
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Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

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Five open questions from Naval about the future of AI: - Do the models top out, or do we go all the way to AGI? - Do 2-4 companies continue to swallow all the value? - Do we end up more consolidated than even the Mag 7, where there’s just Mag 2 or Mag 1? - Do open source and privacy have a chance, or do people always want the smartest model? - Can you train AI in a distributed way or will it centralize more and more?
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NVIDIA AQCURIES HUGGING FACE FOR $12.9B. Jensen on the value of open source models: "I recommended that people use closed models as much as they can because they are developing rapidly. We use Claude, Codex, Perplexity, and Cursor at Nvidia. However, there are so many companies that have domain expertise, companies that have sovereignty reasons, they do their own research, those companies need to have control and ownership over their own intellectual proprietary. Open sourced models have become sufficiently good to allow companies to build their own AI."

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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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SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

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Congrats to our partners Together AI on their $800M Series C fundraising round. Few teams have done more to make open models fast, accessible, and production-ready. We’re excited to keep working with Vipul Ved Prakash and the team on the next frontier of inference: lower costs, better performance, and open models at real scale.
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Congrats to our partners Together AI on their $800M Series C fundraising round. Few teams have done more to make open models fast, accessible, and production-ready. We’re excited to keep working with Vipul Ved Prakash and the team on the next frontier of inference: lower costs, better performance, and open models at real scale.

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David Sacks: Anthropic Wants to Ban Open Source AI in America Listen to All-In to stay a few weeks (or months) ahead of everyone else David Sacks back in May: “I think where it's all leading to is an effort to ban open source models. There's a lot of breadcrumbs leading here. You look at a lot of the rhetoric around how models need to have guardrails, and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropic's blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. They're trying to create ideas or put predicate facts in the public record to justify an action later on. I think it's just a matter of time before they feel like they're at a position where maybe they can push for that type of ban directly.”

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Open source powers every layer of the AI software stack—from agents and models to inference, training and cloud infrastructure. At #OSSummit Korea, Linux Foundation CEO Jim Zemlin explains how open collaboration is making AI faster, more affordable and more accessible. Watch:
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Open source powers every layer of the AI software stack—from agents and models to inference, training and cloud infrastructure. At #OSSummit Korea, Linux Foundation CEO Jim Zemlin explains how open collaboration is making AI faster, more affordable and more accessible. Watch:

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Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.
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Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

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Sam Altman on Open Source AI's Future⁣ ⁣ "Right now, what everybody wants is just the most capable frontier coding model they can have. The big frontier models, even if we made them open source, are hard to run." — #sama
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Sam Altman on Open Source AI's Future⁣ ⁣ "Right now, what everybody wants is just the most capable frontier coding model they can have. The big frontier models, even if we made them open source, are hard to run." — #sama

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