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SITUATION EXPLAINED: Applied Compute, started by 3 ex-OpenAI researchers, hit a $100M revenue run rate in under 18 months. • It went from a $20M seed in June 2025 to an $80M round last October to this $350M raise • Gil also backed the $80M round, and NVIDIA, Blackstone,... Kleiner Perkins and Lux joined this one • Co-founders Yash Patil, Rhythm Garg and Linden Li are 23, 24 and 25 and became friends at Stanford • At OpenAI, Patil worked on what became Codex, Garg on reasoning models and Li on machine learning systems • It builds custom models on top of open-weight models and says they can be about 10x cheaper than frontier models in some cases • A custom model for Harvey improved answer and citation quality at under half the inference cost of a frontier model • Enterprise customers have more than doubled since the last raise, and its inference platform now serves over 1T tokens a day sophia dew: "The big prediction that Elad had is that open models didn't have to defeat OpenAI for a market to become enormous. You're going to see the frontier labs dominate, but you're also going to see that wider ecosystem develop as well."show more

MTS

581,584 subscribers

10,671 Aufrufe • vor 7 Tagen •via X (Twitter)

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4 Kommentare

Profilbild von OSINT Capital

OSINT Capitalvor 7 Tagen

Is it your voice or is something wrong with the sound? @sophiadew

Profilbild von Adam Milan Malherbe

Adam Milan Malherbevor 7 Tagen

Wild that the business isn't beating the frontier models, it's being good enough at one narrow job for half the price. Most companies don't need the smartest model, just the right one

Profilbild von Finn Puklowski

Finn Puklowskivor 6 Tagen

Monsterrrrrs

Profilbild von General Compute

General Computevor 6 Tagen

Nice one @appliedcompute pushing the frontier of inference demand! 🥳

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Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

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As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

NVIDIA AI

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.Sigil Wen on "Underdog’s Law," where local AI on your MacBook can already outperform frontier models OpenAI and Anthropic released just 6 months ago: "Underdog is your private personal AI that runs on devices that you already own. There should be no trade-off between privacy and capability." "I've really been tracking the Pareto frontier of AI models, and I've noticed this pattern where now the frontier models that could run on your devices and can fit on your MacBook can be more powerful than the frontier models that OpenAI, Anthropic released from more than six months ago." "I call that Underdog's Law, where the frontier models from today you will be able to bring to devices that run fully privately and locally." Conway Research

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MTS

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When is harness optimization enough? When does post-training make sense? What will determine who becomes the new AI hyperscaler? Yash Patil, co-founder of Applied Compute, was the perfect person to discuss this and more. Yash worked on Codex at OpenAI before leaving to co-found Applied Compute with fellow OpenAI researchers Rhythm Garg and Linden Li. Sixteen months in, the company is a unicorn, and is serving enterprises including DoorDash and Cognition. We cover: 0:00 Intro 1:42 The Case for Owning Your Intelligence 4:35 OpenAI x Baseten and the Multi-Model Future 7:19 Can Post-Training Beat Frontier Models? 9:03 What Data Stays Out of Distribution 11:51 Data Efficiency and Continual Learning 14:31 When Updating Model Weights Makes Sense 16:01 Online Training in Practice 18:16 Lowering the Cost of Post-Training 19:48 Does Every Company Need Its Own Model? 24:06 RL in Non-Verifiable Domains 26:59 How Applied Compute Picks Customers 28:46 Building an Inference Business 34:43 The Case for a New AI Hyperscaler 38:21 Building RL Environments 41:33 Quickfire YouTube: Spotify: Apple:

When is harness optimization enough? When does post-training make sense? What will determine who becomes the new AI hyperscaler? Yash Patil, co-founder of Applied Compute, was the perfect person to discuss this and more. Yash worked on Codex at OpenAI before leaving to co-found Applied Compute with fellow OpenAI researchers Rhythm Garg and Linden Li. Sixteen months in, the company is a unicorn, and is serving enterprises including DoorDash and Cognition. We cover: 0:00 Intro 1:42 The Case for Owning Your Intelligence 4:35 OpenAI x Baseten and the Multi-Model Future 7:19 Can Post-Training Beat Frontier Models? 9:03 What Data Stays Out of Distribution 11:51 Data Efficiency and Continual Learning 14:31 When Updating Model Weights Makes Sense 16:01 Online Training in Practice 18:16 Lowering the Cost of Post-Training 19:48 Does Every Company Need Its Own Model? 24:06 RL in Non-Verifiable Domains 26:59 How Applied Compute Picks Customers 28:46 Building an Inference Business 34:43 The Case for a New AI Hyperscaler 38:21 Building RL Environments 41:33 Quickfire YouTube: Spotify: Apple:

Jacob Effron

27,267 Aufrufe • vor 1 Monat

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

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

Jawwwn

181,054 Aufrufe • vor 3 Monaten

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

The All-In Podcast

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Open weight models are a genius GTM strategy. If you're the third, fourth, or fifth lab chasing OpenAI and Anthropic, giving away the weights builds trust. Open weight models are cheaper, transparent, and easy to run on your own systems. You don't have to trust the lab at all. Commercialization comes from the services that sit on top: consulting, training, and customizing the models. After all, who understands DeepSeek better than the DeepSeek team? Shiv Shankar (Shiv Shankar) on Dry Run

Open weight models are a genius GTM strategy. If you're the third, fourth, or fifth lab chasing OpenAI and Anthropic, giving away the weights builds trust. Open weight models are cheaper, transparent, and easy to run on your own systems. You don't have to trust the lab at all. Commercialization comes from the services that sit on top: consulting, training, and customizing the models. After all, who understands DeepSeek better than the DeepSeek team? Shiv Shankar (Shiv Shankar) on Dry Run

khushi

12,697 Aufrufe • vor 21 Tagen

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 Aufrufe • vor 3 Monaten

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,392 Aufrufe • vor 5 Monaten

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

141,614 Aufrufe • vor 4 Monaten

Most inference clouds have moved away from custom engines toward open-source ones like vLLM and SGLang, which makes GPU capacity the real constraint. Yash Patil worked on Codex at OpenAI before co-founding Applied Compute. Building on a point from Dylan Patel he said: "If you have some GPUs and you put a vLLM on it and you throw it up on OpenRouter, you kind of have a production inference offering."

Most inference clouds have moved away from custom engines toward open-source ones like vLLM and SGLang, which makes GPU capacity the real constraint. Yash Patil worked on Codex at OpenAI before co-founding Applied Compute. Building on a point from Dylan Patel he said: "If you have some GPUs and you put a vLLM on it and you throw it up on OpenRouter, you kind of have a production inference offering."

Jacob Effron

10,021 Aufrufe • vor 1 Monat

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 Aufrufe • vor 3 Monaten

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

252,612 Aufrufe • vor 2 Monaten

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

Karl Mehta

17,403 Aufrufe • vor 3 Monaten

Firelight's Jesus Rodriguez warns open-weight AI will fuel more frequent exploits of second-tier DeFi protocols "When the first announcement of Mythos came out, I spoke to some Anthropic researchers that weekend. And I wrote this long 3,000-word essay basically outlining that this risk is real." "The open-weight models, the Chinese models, at the time were about six months behind the frontier. Now they're closer, and the gap is closing." "Anthropic and OpenAI are going to be thoughtful about those. But now you're going to have open-weight models that have cyber-exploit capabilities." "Let's say second-tier protocols in DeFi. The teams are not on top of their cyber defenses because the protocol isn't doing well, they don't have high TVL. Those are going to get exploited." "And you're seeing the frequency of exploits. Maybe not the magnitude of the exploit, but the frequency is just increasing."

Firelight's Jesus Rodriguez warns open-weight AI will fuel more frequent exploits of second-tier DeFi protocols "When the first announcement of Mythos came out, I spoke to some Anthropic researchers that weekend. And I wrote this long 3,000-word essay basically outlining that this risk is real." "The open-weight models, the Chinese models, at the time were about six months behind the frontier. Now they're closer, and the gap is closing." "Anthropic and OpenAI are going to be thoughtful about those. But now you're going to have open-weight models that have cyber-exploit capabilities." "Let's say second-tier protocols in DeFi. The teams are not on top of their cyber defenses because the protocol isn't doing well, they don't have high TVL. Those are going to get exploited." "And you're seeing the frequency of exploits. Maybe not the magnitude of the exploit, but the frequency is just increasing."

The Wolf Of All Streets

22,333 Aufrufe • vor 29 Tagen

If I could invest into one private company right now it would be Fireworks Fireworks AI serves 40 trillion tokens a day. They run inference for open-weight and customer-tuned models. Over 95% of those tokens come from models tuned on a customer's own data rather than off-the-shelf frontier models. Revenue passed $1B annualized in July, up 5x year over year. They raised $1.505B at a $17.5B valuation on July 15. Eight months earlier they were valued at $4B. "Fireworks is the TSMC of AI Factories." Jensen Huang

If I could invest into one private company right now it would be Fireworks Fireworks AI serves 40 trillion tokens a day. They run inference for open-weight and customer-tuned models. Over 95% of those tokens come from models tuned on a customer's own data rather than off-the-shelf frontier models. Revenue passed $1B annualized in July, up 5x year over year. They raised $1.505B at a $17.5B valuation on July 15. Eight months earlier they were valued at $4B. "Fireworks is the TSMC of AI Factories." Jensen Huang

KawzInvests

19,940 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 2 Monaten

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -

Podcast Alpha

26,746 Aufrufe • vor 2 Monaten

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

Ben Pouladian

71,722 Aufrufe • vor 4 Monaten

"A modelless company is sitting on shifting sand." Yash Patil (Yash Patil) is the founder and CEO of Applied Compute, a company that trains custom models on company data and serves them in production. His conviction: every organization has its own definition of what good looks like, and a company that doesn't own its model is one system card away from finding out what it can no longer do. (0:00) Introduction (3:50) Betting on custom AI models (12:30) Yash's early influences and first projects (19:29) Inside OpenAI during Sam Altman's firing (28:18) What Yash admires about Sam Altman (29:43) Teaching models to reason (35:39) The core insight behind Applied Compute (45:55) Why model training never ends (51:25) The culture and people of Applied Compute (1:03:48) Final meditations Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru_HQ: The AI source of truth for work. Persona: Trusted identity verification for any use case.

"A modelless company is sitting on shifting sand." Yash Patil (Yash Patil) is the founder and CEO of Applied Compute, a company that trains custom models on company data and serves them in production. His conviction: every organization has its own definition of what good looks like, and a company that doesn't own its model is one system card away from finding out what it can no longer do. (0:00) Introduction (3:50) Betting on custom AI models (12:30) Yash's early influences and first projects (19:29) Inside OpenAI during Sam Altman's firing (28:18) What Yash admires about Sam Altman (29:43) Teaching models to reason (35:39) The core insight behind Applied Compute (45:55) Why model training never ends (51:25) The culture and people of Applied Compute (1:03:48) Final meditations Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru_HQ: The AI source of truth for work. Persona: Trusted identity verification for any use case.

Mario Gabriele

105,598 Aufrufe • vor 4 Monaten