If you kind of play out the alternative, then all you would basically be arguing is America should have really expensive AI and the rest of the world should have very cheap AI, and then we'll see how kind of, you know, that works competitively over the next kind of 5 or 10 years. You can disagree with it, but he's obviously right that the following will happen: if you block off China, China is like not going to give up on AI. They actually have a lot of the core raw materials needed between raw human talent and kind of industrial might. I just don't think there's a scenario here where you can kind of close off completely to China and somehow you dramatically slow them down, and then kind of handwavy we just win. I just don't think that plays out in this space.
We are back live. We are live with Aaron Levie, the co-founder and CEO of Box, which does all kinds of things: cloud content management, enterprise documents, permissions, collaboration, a lot of different AI functions. He has an incredible Twitter account, @levie. He's been around on the website forever, and he writes about AI and many other topics. We have two huge stories today. I wonder which should we start with?
Yeah, huge acquisition in the astrology image gen space.
Something we will be monitoring. Is it an open source play or not yet?
I think astrology might remain closed source for the time being.
Okay, that's too bad. We got to fight that.
Yeah, we really got to fight that. But going to the real story. So, today there was an open weights letter that Jensen Huang wrote and Box signed. So, can you tell us sort of about your interpretation of this letter? What exactly is it aimed at? And yeah, what are you guys trying to shape? What do you think Jensen was trying to shape?
Yeah. Is the letter maybe like a Rorschach test of like what do you see in this letter? What we saw when we read it was hopefully the default stance of the folks that signed it. But basically twofold main points: one, the reason why open weights AI is super important is because it actually drives AI progress. We get more innovation, we get more options, you can build on top of these models, you can train them for your own use cases. So open weights in general, I would argue, is actually a very important part of the AI ecosystem—so much so that I think it's actually kind of misframed as zero-sum with closed weights. It actually just adds to the number of use cases that people then do with AI. So that's probably part one.
And then part two, I think embedded in there was a little bit of a call to arms on the US actually needs to be probably even more invested in open weights models. We probably want even more companies kind of showing up to the table with this innovation. And so it didn't seem like it was directly about we must support all of the things that are happening in China as much as the US needs to continue to support open weights, we probably need even more innovation here. And, oh, by the way, distillation is actually not always bad and there's lots of use cases around it. So let's maybe calm down on some of the FUD on that. So we support every one of those stances. Open models are very important to the AI ecosystem, and I actually think it pushes even the closed model providers to innovate faster and be able to deliver more for the market. So I think everybody is sort of a winner as a result of open weights.
To be fair to the other side of the argument, I think there's a category—forgetting commercial and economic interests for a second of some of the closed players—that obviously has real arguments around the safety elements of open weights at some point in the future of capability. I don't agree with that point of view, but I think it's a healthy debate to have and to have conversations around it. But I almost unequivocally take the stance that more open innovation and open weights innovation is good for AI broadly and the diffusion of AI.
So diving into the distillation point, how would you separate distillation that is just normal economic activity from distillation that crosses a line into some sort of civil or criminal violation?
Well, I don't know if I would make that argument. I don't know where that line would exist. I'm almost deeply on the side of I think it's very hard to make the argument that an AI model should be trained broadly on the public internet, but another AI model can't be trained on the outputs of an AI model. I think it's kind of a very tenuous argument to make. I don't see how you can kind of pull it off. At the exact same time, I understand—and if I were running a lab, I would want to lock these things down as much as humanly possible, so I'd be trying to find every defensive mechanism to prevent the distillation of my models. But I don't know that you'd be able to credibly argue that there's some ethical line that is crossed, simply because then you would be basically arguing against the underlying training runs of these models.
Most people didn't get to opt in or out of their data being trained on. Maybe it's in some kind of terms of service from the underlying provider somewhere in the fine print, but this was a thing that is sort of like we are assuming that the general knowledge of the world is going to be trained into these models. And that general knowledge, whether it comes from Reddit or it comes from Anthropic, I don't see the distinction between those in any meaningful way.
Yeah. I mean, we heard a take yesterday that was kind of exactly that. It was just like that we're focusing on the wrong part of the chain when we blame distillation. It should really be like how is Anthropic thinking about how to restrict their API use or how to very effectively prevent the sort of... Yeah. Because we're also talking about how like distillation, they still have to pay for the API usage.
Anthropic is getting paid more for distillation than most people have ever gotten paid for the original training run. And if you almost do a thought experiment of what's the difference between distillation in a very direct, like 'I'm using the API across 10,000 accounts,' versus just quite literally if we all generate code from Fable or GPT-5.6 and all that code ends up on GitHub in public repos, what's the compelling difference between those two scenarios that a model would get trained against? I'm not sure I at least understand why there'd be arguments that there'd be some kind of ethical line between those two.
And then you kind of layer in another element, which is I think there's a lot of people that argue that China in this case is doing breakthrough innovation in general independent of distillation that's causing models to progress. But also I'm sure that there's going to be other controversies and conspiracies about what's happening, and I don't know enough to be able to credibly argue that other things are or are not happening in why these models are progressing. I'll let the labs make those cases and the government pursue that as they see fit. But all of that to me is independent of: do we want open weights models and do we want this innovation in general from the ecosystem? Personally, the best-case scenario would be you get a few people that are at a stage in life where they can just be like, 'Yep, I'm going to drop $10 billion on building an open lab in America,' and we have our own version of Moonshot. I think that would be a great service to America and to innovation in general. So I've been kind of waiting for the day that somebody does that, but for now, still waiting.
Decades foundation [laughter] under this post.
They might do it. They might do it.
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There's another angle here, which is most open weights models are coming from China, and some people worry that if we become too dependent—if the American startup ecosystem becomes too dependent on Chinese open weight models—and then they shut off the open weights, they stop releasing new open weights models, that this could be very bad for the American tech industry, or that because these are Chinese open weight models, they undercut the margins of American companies. And it's a national security concern, say some people.
Well, and I think those are great arguments, but it's like, okay, so what do you do about that? Some people say that the iPhone should cost $5,000 instead of $1,000 because we shouldn't rely on manufacturing advancements that exist in China. If you kind of play out the alternative, then all you would basically be arguing is America should have really expensive AI and the rest of the world should have very cheap AI, and then we'll see how that works competitively over the next kind of 5 or 10 years.
So it just might be one of those things where the cat's out of the bag on that. We can't unwind that dynamic. Open weights models exist, and China knows how to produce them. I lean more on the Jensen side of the argument. Everybody had this moment where they watched the Jensen-Dwarkesh podcast, and that was like the original Rorschach test of like... Some people parsed Dwarkesh as obviously that's the exact right position to be taking and you should be interrogating Jensen, and then other people watched it and were like, 'Why is Dwarkesh not understanding Jensen's point and letting him expand on that?' I was watching it and was just like, yeah, Jensen's obviously right. You can disagree with it, but he's obviously right that the following will happen.
If you block off China, China is not going to give up on AI. They're not going to just decide that this is not that important of a technology category for them to play in. So if you just assume that China considers AI to be strategically important—and, oh, by the way, all these funny meme tweets of like, 'We have more Chinese AI researchers than the Chinese labs do in the US.' Obviously, they can produce incredible talent working on AI. So this is not something that we own some impenetrable talent base to be able to compete against. So it's incredibly important strategically for China. They actually have a lot of the core raw materials needed between raw human talent and industrial might for building chips and fabs and whatnot over time. Getting the training data: you could just go hire 100,000 people in China to go generate an insane amount of data. There's ways of getting the data.
So at some point, it's not that complex to think through China becoming a very strong player in AI. And if they're a very strong player in AI and the world adopts their models and adopts their architecture, that obviously is going to be bad for the US economically over the long run. So Jensen's point is, instead of sort of forcing that and catalyzing that to happen even faster by the constraints that you impose, maybe we should actually be a part of the infrastructure buildout that's already going on. And I actually generally kind of land on that, because the alternative is that these models are still going to get trained one way or another, but now they're going to be trained on a different hardware stack. Ultimately over time, that hardware stack can be the one that gets deployed in sovereign clouds and whatnot. I just don't think there's a scenario here where you can kind of close off completely to China and somehow you dramatically slow them down, and then kind of handwavy we just win. I just don't think that that plays out in this space.
A lot of the sort of theoretical risks of either open weights or the infrastructure side have to assume that we have such an insurmountable lead against China, and that only accelerates past the point where there's just simply no catching up. And I think as we've seen recently with Kimi K3 and other models, that gap is maybe narrowing as opposed to expanding over time.
Yeah, that Dwarkesh-Jensen interview, by the way, generational.
They had such gems in it. Like when Jensen was like, 'I'm not a loser. You're not talking to someone who woke up a loser. We're not a car.'
Yeah. 'That losing mindset makes no sense.'
No, that was some of the best content ever produced. I don't know why they didn't charge for that one.
Yeah, it felt like tech reality TV. Like he was actually just getting into it. It was awesome.
Yeah. Well, okay. What do you think would have happened if, let's say, the US would have open sourced, like beat China to open sourcing a Kimi K3-level capability model? Because I understand the argument of, okay, we need to deploy American open source models that are the same, if not better, than the Chinese open source models. But what do you think are the trade-offs of completely open sourcing it to the point where obviously Chinese labs would be able to have access to it?
