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Video · 2026-07-23 · 1h 28m · 5 moments

Frontier Labs Threatened by Kimi? Should the US Ban Chinese Open-Source Models & Stripe Buys PayPal

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01
Claim

There are real data export and security risks with Chinese open-weight models, and CIOs at large enterprises will be reluctant to adopt them regardless of hosting arrangements.

Jason argues that despite reassurances about on-prem hosting eliminating security risks, the historical pattern of Chinese technology products having data export issues, combined with the opacity of AI models, means most enterprise CIOs won't take the risk.

transcript

Jason: I don't think you're going to convince me there aren't some data export risks with China based models. You're just not going to convince me based on what I've done with all our agents in building. And if you're not going to convince me, I don't think you're going to convince 99% of the world that there isn't some security leakage issue. It's already scary how much of our data we put into these closed source models in the US. It is scary. Here's Elon saying scam altman every day to create distrust. Right? I there are we cannot understand what these models do. They are connected to the internet. We cannot even even if we have fable read it and have it read it itself. I don't think you're going to convince most of us there isn't data export risk. And so I think that's going to lead to tighter constriction than this you know leave everything open so we can compete in my portfolio company's benefit uh argument. I think every CIO is being told right now, oh don't worry if you hosted onrem, you remove any security risks and the back door then is removed that could potentially be there. Why would you not be alleviated by that reassurance of onrem would solve that solution? Why would you not be reassured?

provides context · 2rebuts · 1

02
Claim

The open-weight, low-cost LLM business is a real and growing category, and it's an open question why no US company has stepped up to compete with OpenAI and Anthropic in this space.

The hosts question why no US company has built a leading open-weight model business, given that Chinese companies like Kimi and Alibaba are doing it and getting $50-70B valuations, while US frontier labs remain premium-priced products.

transcript

Rory: Can you make money as a maybe not completely openweight but a lowcost US provider of these models and be competitive with those guys because you know the open weight models in China are getting 5070 billion valuations like it's not entropic but I wouldn't turn down a $50 billion outcome if someone could make a convincing case to me that a US company could do this. So I think that's one of the interesting questions here. Maybe it's because the dirty little secret is a lot of their advantage is distillation, which you can't legally do if you're US-based. So I do wonder if there is a market for 80% cheaper intelligence and that's roughly what we're looking at in terms of when you take into account the cost of inference the difference between the bundled product that is an frontier model you know IP plus inference and an open source model where you dissociate the IP from the inference cost if you're looking at 80% cheaper opportunity and there's the mass demand for that when is someone going to try and fill that demand in the US?

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03
Fact

The standalone inference business (Fireworks, Together, etc.) is exploding because the open-weight model trend is exploding, and these companies are seeing massive growth with expanding gross margins.

Rory and Jason discuss Fireworks' $1.5B round at 17.5B valuation, noting it's doing over $1B ARR in 3.5 years with 40T tokens/day, and the key insight is that inference providers benefit from the open-weight trend and can charge high margins on committed compute.

transcript

Rory: the story is inference. Yes. First of all I agree. Yeah inference is a hu it goes back ironically to the prior comment on openweight models. it this kind of standalone inference is a big business right and you know obviously you know inference is both something that's done within the the frontier model companies where they do their own inference and people like Microsoft and Google provide the capex and provide the compute for that but people like fireworks and base 10 they and file they all make their money offering a variety of these open openweight models to third party developers and enterprises that want to use open source models to do AI, right? And it, as I said, the two trends go together. They're exploding because the open source trend is exploding. So if you're base 10, if you're foul more media, if you're fireworks, if you're together, this is your marker than your moment, right?

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04
Data

The AI application layer is not generating significant revenue yet — infrastructure and foundation model companies dominate spending, making the app layer almost a rounding error in comparison.

Jason and Rory break down the AI market into three buckets: infrastructure ($800-900B), foundation models (~$100B), and apps (struggling to reach $40-50B), concluding that the volume of investment has been front-loaded on infrastructure and the application layer hasn't arrived yet.

transcript

Rory: I divide the AI world up into three buckets. It's the making AI, the infrastructure layer, right? And you're right, the spend there is 8 $900 billion a year. Then there's the two foundation model companies themselves and they're doing plus or minus hundred billion dollars a year, right? And then taking those guys out, rounding up every other apps company, right? You struggle to make 40 or 50 bill. You struggle. You start with cursor at four because I think coding is an app. You know, you by the time you're chucking in Harvey, you're adding two 300 million, right? It's amazing. I mean, just the difference in spend. And you know, at some point, the people spending a trillion dollars a year are going to want some apps to pay for all this, right? But right now the volume has it's been front end loaded on the infrastructure side and at some point the revenue has to match it.

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05
Prediction

The growth rate of OpenAI and Anthropic is the single most important variable for the entire US stock market, because all hyperscaler capex commitments are a function of their expected demand.

Rory argues that if the open-weight model competition reduces the frontier model companies' growth rate below 100% in 1-2 years, it would cause a massive dislocation in the US stock market because all hyperscaler commitments are based on the assumption that OpenAI and Anthropic's 10x growth continues.

transcript

Rory: If you know the answer to that question, that one question, you know the answer to the entire direction of the US stock market for the next two years because all the hyperscala RPO, all of it is a function of the commitments they've gotten from the the hype from the foundation model companies. And yeah, you can say if the open models, open weight models explode, there will be demand for inference. And yeah, you will have this kind of transition from oh, I sold it to open AAI but I should have sold it to um I don't know cursor or B base 10 or someone else and the capex will get repurposed, but it will be a big ass dislocation and I just genuinely don't know. I mean it's the million-dollar question.

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