Aaron Levie on Why Open AI Wins

Box co-founder and CEO Aaron Levie joins MTS hosts Theo Jaffee and Sofia Puccini to make the case for open-weight AI, unpack the economics of open versus closed models, and explain why he believes more openness could strengthen rather than undermine the U. S. AI ecosystem. Aaron argues that open models create more use cases, push closed labs to innovate faster, and don't fundamentally change where the economics of AI ultimately accrue. They debate model distillation, America's competition with China, why restricting access may simply accelerate competing AI ecosystems, and whether U. S. labs should begin releasing open-weight versions of previous-generation models. They also get into what the latest frontier models mean for knowledge work, how AI has changed software engineering at Box, and why Aaron believes companies cutting engineers may simply not be ambitious enough. Finally, they discuss why enterprises are unlikely to bet on a single model and why the layer that routes between models, data, and workflows could become increasingly valuable.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Erik Torenberg?

  Open weight AI is often for him is a threat to frontier labs. Aaron Levie thinks that gets the economics backwards. The Box co-founder and CEO joins Theo Jaffe and Sofia Puccini on MTS to discuss why open models could make the AI ecosystem more competitive. The debate around distillation in China and why America needs more open weight AI. They also get into the latest frontier models. How AI is expanding rather than shrinking Box's engineering roadmap and why model routing could become the default for enterprise AI.

• 0:35Speaker 01

  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, @Levy. 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?

• 1:01Aaron Levie?

  Astrology.

• 1:02Speaker 03

  Astrology. Of course astrology. Yeah. Huge acquisition in the astrology image gen space. Something we will be monitoring.

• 1:12Aaron Levie?

  Is it an open source play or not yet?

• 1:16Speaker 01

  I think not yet.

• 1:17Speaker 03

  I think astrology might remain closed source for the time being.

• 1:20Aaron Levie?

  Okay, that's too bad. We gotta fight that.

• 1:22Speaker 03

  Yeah, we really gotta 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 like your interpretation of this letter? What exactly is it aimed at? And like, what are you guys trying to shape? Who do you think Jensen was trying to shape?

• 1:41Aaron Levie?

  Yeah. Is the letter maybe like, it's like a Rorschach test of like, what do you see in this letter? Well, what we saw when we read it was, hopefully it's like the default stance of the folks that signed it, but basically kind of almost two fold sort of 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 like 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 kind of probably part one. And then part two, I think embedded in there was a little bit of a call to arms on the

• 2:22Aaron Levie?

  US actually needs to be probably even more invested in open weights models. And 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 US needs to continue to support open weights. We probably need even more innovation here. I know by the way, like distillation is actually not always bad and there's lots of use cases around it. So let's maybe like calm down on some of the FUD on that. We support every one of those stances, you know, open models are, I think, 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 kind

• 3:07Aaron Levie?

  of commercial and economic interest for a second of some of the closed players. I think there's a category that obviously has real arguments around the safety elements of open weight at some point in the future of capability. I don't agree with that point of view, but I think it's like 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.

• 3:29Speaker 01

  So diving into the distillation point, how would you separate distillation that is like just normal, basically normal economic activity from distillation that crosses a line into like some sort of civil or criminal violation?

• 3:43Aaron Levie?

  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 AI models should be trained on broadly the public internet, but another AI model can't be trained on the outputs of an AI model. I think it's 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 kind of credibly argue that there's some ethical kind of 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.

• 4:27Aaron Levie?

  Maybe it's in some kind of terms of service from the underlying provider somewhere in the fine print, but like, this is 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 is like, I don't see the distinction between those in any meaningful way.

• 4:46Speaker 03

  Yeah. I mean, we heard a take yesterday that was kind of exactly that. It was just like, we're focusing on the wrong part of the chain when we blame distillation. It should really be like, how is Anthropic thinking about like, how to restrict their API use or like how to very effectively like prevent the sort of, because we were also talking about how like distillation, they still have to pay for you for the API usage.

• 5:08Aaron Levie?

  Anthropic is getting paid more for distillation than most people have ever gotten paid for the original training run. So, and if you kind of like, you almost kind of do a thought experiment of like, well, 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, six and all that code ends up in public repos, what's the kind of compelling difference between those two scenarios that a model will get trained against? And I'm not sure, I at least understand why there'd be arguments, why 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 actually China in this case are doing kind of 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 kind of controversies and conspiracies about what's happening.

• 5:54Aaron Levie?

  And I don't know enough to be able to kind of credibly argue that other things are or are not happening and why these models are progressing. I'll let obviously the labs to make those cases and the government to kind of 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? I think the best case scenario personally 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, you know, moonshot. Like, 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, you know, somebody does that, but you know, for now, still waiting.

• 6:35Speaker 03

  We're going to attack the Gates Foundation under this post.

• 6:39Speaker 01

  They might do it. They might do it. There's another angle here, which is, you know, 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 like 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.

• 7:11Aaron Levie?

  And I think those are great arguments, but it's like, okay, so what do you do about that? You know, some people say that the iPhone should cost $5,000 instead of $1,000 because we shouldn't, you know, rely on manufacturing advancements that exist in China. Like, I don't know, 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 five or 10 years.

• 7:34Speaker 03

  Yeah.

• 7:35Aaron Levie?

  So it just may be one of those things, which is, you know, the cat's out of the bag on that. We can't unwind that dynamic, like open weights models exist, China knows how to produce them. I lean more on the Jensen side of the argument, you know, everybody had this moment where they watched the Jensen Dorakesh podcast and that was like the original kind of Rorschach test of like, you know, some people parse Dorakesh as like, obviously like that's the exact right position to be taking and you know, you should be interrogating Jensen. And then some other people watched it and was like, why is Dorakesh not understanding Jensen's point and kind of letting him kind of, you know, sort of expand on that? I was like, watching it and just was like, yeah, Jensen's obviously right. You can disagree with it, but he's obviously right that the falling will happen. If you block off China, China is like not going to give up on AI.

• 8:18Aaron Levie?

  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, if you just assume that China considers AI to be very strategic, you know, strategically important and oh, by the way, you know, you know, all these funny meme, you know, tweets of like, like we have more Chinese AI researchers than the Chinese labs do in the US. Like obviously they can produce incredible talent working on AI. So this is not something that we own, you know, some impenetrable, you know, kind of talent, you know, base to, 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, you know, raw human talent and in kind of industrial might for building chips and fabs and whatnot over time.

• 9:03Aaron Levie?

  You know, like the, getting the training data, you could just, you know, go hire 100,000 people in China to go generate an insane amount of data. Like there's like ways of getting the data. So at some point you can, it's not that complex to think through China becoming a very, very strong, you know, 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, is going to be bad for the, 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 build out that, that's sort of going on. And I, I actually generally kind of land on that because what's going to happen is the alternative is, is that these models are still going to get trained one way or

• 9:48Aaron Levie?

  another, but now they're going to be, you know, trained on a different hardware stack. Ultimately, over time, that hardware stack can be the one that, that gets deployed in sovereign clouds and whatnot. And so, so I think it's, 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, and then kind of hand wavy, we just win. I just don't think that that plays out in this space. So, so you know, all of the, you know, kind of a lot of the, the sort of theoretical risks of either open weights or, or, or, you know, the infrastructure side have to, have to assume that we have such an insurmountable lead against China and that only accelerates

• 10:31Aaron Levie?

  past the point where like, there's just simply no catching up. And I think as we've seen recently with, with, you know, K3 and, and other models, you know, that gap is maybe narrowing as opposed to expanding over time.

• 10:43Speaker 01

  Yeah. That Dorakesh Jensen interview, by the way.

• 10:46Speaker 03

  Generational.

• 10:47Speaker 01

  They had such gems in it. Like when Jensen was like, I'm not a loser, you know, you're not talking to someone who woke up a loser. We're not a car. Yeah. That losing mindset makes no sense.

• 10:58Aaron Levie?

  No, that was some of the best content ever produced. So I don't know why they didn't charge for that one.

• 11:04Speaker 03

  Yeah, it felt like tech reality TV. Like, he was actually just getting into it. It was awesome.

• 11:09Speaker 01

  Absolute cinema.

• 11:11Speaker 03

  Yeah.

• 11:13Speaker 03

  Well, OK, what do you think would have happened if, like, let's say, the US would have open sourced, like, beat China to open sourcing, like, a Kimi K3 level capability model? Like, because I understand the argument of, like, OK, we need to deploy, like, American open source models that are the same, if not better, than the Chinese open source models.

• 11:33Speaker 03

  But, like, what do you think are the trade offs of, like, completely open sourcing it to the point where, like, obviously Chinese labs would be able to, like, have access to it?

• 11:41Aaron Levie?

  Yeah, I, you know, first of all, even the open versus closed, I actually appreciate all the debate that happens on this. So I'm very passionate about the topic, but I also totally appreciate all of the arguments on the other side, even, you know, though I probably lean the other way in some of them, but some of them do inform, informs some of my views and I kind of update maybe then to be a little bit more nuanced on my end. But, but, you know, on the open source US side, I generally think that the moneymaker in AI is inference. And so ultimately, the dollars are going to flow to the infrastructure stack one way or another.

• 12:22Speaker 03

  Yeah.

• 12:22Aaron Levie?

  I think in a world where you only had one or two labs, and somehow there were these insanely kind of closed secrets about training, and you had, like, real intellectual property that was protectable and patentable and nobody ever could know about, and it's, like, you know, totally locked down. Then, in that world, I think you could probably argue that, like, you know, there's another couple of layers of the stack that you could kind of close off. But in a world where we're going to have, you know, three to five players in the US that consider it sort of existential to them to have leading models, then you have enough of a competitive dynamic in the market where you have to expect that the cost of tokens converge closer and closer to the, to the cost of the infrastructure over time. Not to zero.

• 13:06Aaron Levie?

  I'm definitely not a believer that there's no margin there, but closer. So, you know, in the 20, 30, 40% range on top of the cost of infrastructure, which is different from 70 or 80 or 90%, let's say. So if that's the case, then actually, if you had an open model and you powered, let's say, the preferred infrastructure for that open model, or you created the post-training environment for that open model, or you were the, the kind of considered the safer brand for deploying that open model by enterprises.

• 13:33Speaker 03

  Yeah.

• 13:33Aaron Levie?

  I actually would argue, make almost the same amount of revenue, just by, again, driving the inference of that model. And so if you assume that with things like, you know, I would actually make the case that even for something like an OpenAI, if you fast-followed your frontier models with open source versions of, let's say, the prior generation at a more consistent, you know, pace, I actually think you would, you would be even more competitive with your frontier models, because it would keep more and more of the use cases within your ecosystem and your family, and you probably could just power a lot of the inference of those open models as well. So I would, I would just argue that these are, these are kind of flavors of, of ways of enabling different kinds of use cases with AI, as opposed to entirely different economic structures of AI.

• 14:17Aaron Levie?

  Because at the end of the day, you still are, like, like, you're going to be paying for large GPU clusters no matter what. Open source AI does not mean that somehow I'm going to be running Fable on my lap, you know, my laptop, and I'm able to kind of circumvent the need to have cloud infrastructure. Like, the dollars are going to eventually flow.

• 14:34Speaker 03

  We did, yeah.

• 14:35Aaron Levie?

  Oh, OK. Yeah, so the dollars are going to flow into some infrastructure provider, and there's no reason that OpenAI can't be that infrastructure provider, or Anthropic can't be that infrastructure provider, you know, obviously Meta and SpaceX now are going to be getting in that space. So I'm not convinced that open weights AI dramatically changes the economic structure of AI, other than to just provide even more avenues to innovation and more avenues of use cases that begin to emerge. And that, I think, would actually be a good thing for, for the ecosystem broadly. So I'm pro, you know, US open weights. I actually think a lot of the closed labs should, should consider having kind of fast or follow open weights models.

• 15:11Speaker 03

  Yeah.

• 15:12Aaron Levie?

  You know, to be able to handle the post-training use cases and some of the sovereign use cases. And I don't think it would actually be as bad as probably is sort of perceived from a market standpoint.

• 15:22Speaker 01

  Yeah, this is a really interesting thread. I'm curious, you know, if this would be good for the business model, the labs, like, why don't the labs do it?

• 15:29Speaker 03

  Yeah.

• 15:29Speaker 01

  You know, Anthropic has never open sourced anything. Google open sources Gemma, which is like something, but it's not like the last generation of Gemini.

• 15:36Aaron Levie?

  I think there's two categories that people fall in. One is, you sort of, you have to have a, you know, your time horizon has to be longer to believe that you can monetize open weights, because like, tomorrow, if I release a closed model, I just will literally make more money if I just keep it closed and I force everything to route through my API. That, on paper, I'm going to make more money, no question. But over the long arc of time, where, where no matter what, you have to assume that the cost of token per, per task, you know, gets driven down regardless, then, you know, you kind of play things out two or three stages and it's sort of, it's almost, you know, kind of a wash whether, whether you were closed or open, because again, it's, it's really just the inference cost that, that ends up mattering. That's, that's the first part. And then the second part for Anthropic specifically, let's say, is I actually just believe that

• 16:20Aaron Levie?

  they, they consider this to be a major safety risk. So, so to them, they're not, like, thinking about this. I'm guessing they're not thinking about this as like an economic, you know, argument that they should be open sourced for market share reasons. I actually just think that they, they fundamentally believe, no, you actually need one entity to control the, the flow of the tokens, to be able to do, you know, kind of, prevent, you know, prompt injection and, and make sure that you can route to different models based on the kinds of queries people are doing. You can't do that if, in an open weights environment. So, so I would just almost consider them to likely never to open, open source their models for, you know, sort of like, you know, kind of reasons that, you know, are tied to just the creation of Anthropic in the first place.

• 17:02Speaker 03

  Yeah. I feel like, well, with regards to, like, Frontier Labs, this is going to be a segue to Claude Opus 4.5, or Claude Opus 5 being out now. But, yeah, with regards to, like, the timing of the release of the models in Frontier Labs, it just feels like a timing thing now. Like, we know they're cooking good models internally. So with that being said, what were your thoughts on Opus 5? Especially from like a, you know, knowledge work perspective, because you have that perspective at Box.

• 17:30Aaron Levie?

  Yeah, so we, we've been running evals on Opus, Opus 5 the past, kind of, maybe week or week and a half or so, a couple of weeks, and it's, it's a fantastic model. It meaningful jumps over Opus 4.8, which already was kind of best in class at, at the time period that it was launched. And so, you know, the way this shows up in our world, so we deal with, with kind of, you know, corporate data, documents, financial, you know, documents, contracts, marketing assets, research materials across every single industry. So this can be life sciences companies, law firms, large banks. And so you can imagine that, that, that the things that you're, if you're a large enterprise, you kind of need two major aspects within AI model.

• 18:13Aaron Levie?

  You want a deep domain understanding, like, you have to understand life sciences, you have to understand law, like, that has to be packed into the model. And then you have to be extremely good at just being able to kind of work with large amounts of data, process it, deal with tools, analytical capabilities that are kind of, you know, horizontal. So you have kind of general intelligence, you know, going up that matters, and then you have very specific kind of industry intelligence. And Opus, Opus 5, you know, kind of just represents an improvement on both those axes. So we tried it against, you know, a variety of, of different industry tests that we do. Again, kind of meaningful jumps over, over 4.8. I'd expect that this, you know, becomes, you know, again, a very compelling model

• 18:53Aaron Levie?

  across knowledge work, as, you know, GPT-5.6, you know, I think equally has. So definitely great kind of work on the, on the Claude front. And, and I think, you know, again, like, what's exciting is, and this is why I don't think it's zero sum, like, like, the closed labs continue to stay ahead on the frontier. And, and I think the vast majority of the dollars in profit will still go to the closed labs in almost all scenarios that this plays out.

• 19:21Aaron Levie?

  And so, you know, I think it's been a great month for both OpenAI and Anthropic on that front.

• 19:26Speaker 01

  Are you finding Fable better for any tasks than Opus? You know, like, it is, it seems on Vibes, like, very slightly better, but also twice as expensive, so Opus seems like Pareto superior there.

• 19:38Aaron Levie?

  Yeah, so I don't think we've like, so we have internal benchmarks that deal with kind of general knowledge work. And I would actually agree with you that that probably due to the cost difference, you probably argue that that that Opus 5 is now kind of an improvement over Fable because of the cost delta. On certain coding tasks that we had tested internally, Fable completely outmatched 4.8 like in a, in a, in a way that you couldn't make up for by the, by the, you know, token cost difference. Just like, like being able to have superior solutions to hard technical problems.

• 20:13Speaker 01

  Sorry, 4.8 or Opus 5?

• 20:15Aaron Levie?

  Prior to having access to 5, if we kind of compared like how big of a leap 5 was on, on that, I don't have enough internal coding tests to kind of compare against. So, so I had to see if like, if how much is 4.8 meant to be, you know, kind of a superior coding model versus a lot of the benchmarks that they just came out with, where these kind of general knowledge work, kind of agentic tool use type benchmarks. So, so I think it kind of open question. The one problem that Fable had, and everybody's kind of tweeted about this already, is, is it will often, you know, again, kind of push you down to 4.8 for certain capabilities. And then that becomes a problem because you're not, you know, it's a kind of hard in advance to know, like, did the, did the

• 20:57Aaron Levie?

  thing kind of, you know, trigger some security warning because it was looking through permissions, you know, access control code. And so it kind of freaked out and, and, you know, isn't then giving you kind of Fable level of intelligence. So I do think that Anthropic's going to have to kind of work through that. I've, I've heard of, you know, folks in the biospace that that effectively can't use Fable because it just again pushes their queries down, you know, too frequently. So you're not getting the, the raw intelligence from the model. And so, you know, I think, I think we have to, like, I, I appreciated Anthropic's ultimate proposal on, on how we should kind of work through these types of dynamics with the government, have a, have a kind of a multi-point framework where we kind of agree

• 21:39Aaron Levie?

  on what, what are these kind of capabilities that are of different risk levels and test the models for them. But there's, there is kind of an interesting question though, which is like, well, what, who gets to decide that risk framework? And, and how do we agree that the risks are actually, you know, real risks that we perceive? Because I would argue that if, if, if you've kind of snapshot it in time, Fable, right now, the level of things that they, they push down and prevent you from doing, that would be untenable for the future of AI. People will, will simply not use AI if, if this is the kind of ongoing environment. And so, so like, how should we decide now? Like, like, maybe, maybe, they say these risks are very highly nuanced. And so, so I, that's why I generally lean

• 22:22Aaron Levie?

  toward like, it's all very, like way too early to be locking these models down, you know, given, given how early we are.

• 22:29Speaker 01

  So how is it affecting software engineering at Box? Like, are you hiring more or fewer people? Like, how do the skills that you're looking for in software engineers change over time? How do you expect them to change in the near future?

• 22:41Aaron Levie?

  Yeah, we're, I'm still very bullish on software engineering. We're, you know, the way that we've used these models is, is simply to just to do way more than we were before. The, the kinds of, and it's just like so qualitative and only, only people that are, you know, probably like in the product roadmap reviews each day can, you know, maybe kind of fully process. But like, there's something totally different when you're, when you're doing a product planning session, when you are thinking in a world of, of just sheer human, you know, base constraints versus, versus now what AI unlocks, like, it completely changes your mindset about the things that you'll go and tackle

• 23:21Aaron Levie?

  and take on. We have multiple dozens of, of, of projects right now that we absolutely would not be doing if AI didn't exist. We just, we would not have, we would not have lit up the projects. Like, we would have said, no, that's too complex. It's not worth it. Like, you actually have two interesting scenarios. You either say no to the very small things because it's not worth it, or you say no to the really big things because they're simply too hard. And so most of your software projects kind of stay in this middle band, which is like, okay, it feels like something that maybe you could do in one to six months. Like, those are the kind of things that we would use to kind of tackle pre-AI. Now what happens is you can tackle the multi-year projects because they're not multi-year anymore. And the things that are kind of like, well, that would take a week,

• 24:06Aaron Levie?

  but it's not that big of an issue. So we're just never going to do it. And so it just remains on the backlog, just very far down the backlog. You do those because now that takes two hours. So what, what happens is you end up basically being able to solve more and more problems that your customers have always had. And that actually just increases your ambition. So, you know, wherever possible, I'm, I'm generally in the mode of, of actually trying to add more human talent to this because, because there's actually just more things we want to go and take on. And now actually, the main problem is, is we have, you know, you just have financial constraints because there's other areas of the business that you want to grow and that you want to hire for. But, but I, I generally think if you're, if you, if you think that, that you've kind of eliminated the need for software engineers, you're, you're just, you're, there's just no chance you're being ambitious enough with

• 24:51Aaron Levie?

  your product roadmap. And, and so we're constantly pushing ourselves of like, now what more can we take on as a result of AI?

• 24:58Speaker 03

  Yeah, totally. Yeah, even like talking to some of these coding agents, they will overestimate the amount of time that it takes to do a project, like severely. They'll be like, this is a weekend long project.

• 25:10Speaker 01

  Sometimes they say it's a month.

• 25:12Speaker 03

  Yeah, they're like, this is a month long project. And I'm like, oh my God, like, this is, this is going to be so long. And then I knock it out in like three hours.

• 25:19Aaron Levie?

  Yeah, that's because it was trained on all of our Slack messages pre-AI. And so where the, where the engineer, you know, said that's going to be two years. So I think it's, it's, it's definitely trained to be highly, highly conservative on project timelines.

• 25:33Speaker 01

  Same with math too. Like, if you ask an AI, like, what is your timeline to an AI model disproving an open conjecture in mathematics? It'll be like, maybe five years? Nope. It's already happened.

• 25:48Speaker 03

  Yeah, totally. I guess like a last question, because we were talking about this earlier, is like, since it looks like model releases are just happening way more frequently, for enterprises specifically, it seems like, you know, loyalty to one provider is not going to make it, basically. So what do you think is like the optimal strategy here? Like, we were talking about how model routing is the future and like all of these services that offer that sort of thing are the future.

• 26:14Aaron Levie?

  Yeah, I mean, kind of, that's, that's, that's my conclusion, but I'm also extremely biased in that conclusion. Like, like, like, the, the more that you need multiple models to, to do a task for a set of tasks, the more value accrues to the layer that can understand the task and get access to the data and handle the workflow, which obviously is, is a better outcome for the, let's say, applied AI layer, which is, which is where we tend to sit. And, and that, that's, that's a, that's a future that obviously a lot of companies are, are aligned to and, and, you know, strategically and kind of existentially in some cases. So if you're cognition and factory and cursor, you know, and rep lit,

• 26:57Aaron Levie?

  et cetera, obviously the outcome that you want to have happen is that you actually have many models, they're all good on different axes. Some are like really like the cost tuned, you know, kind of workhorse models. And some are like the super frontier, you know, orchestrators. And you want to have an outcome where you actually need multiple of those models to, to, to be able to complete the task effectively, or at least cost effectively. That's actually the, that, that appears to be the timeline that we're on. And, and now, again, I think you have basically five credible US players in, in, in model development between SpaceX, Google, Anthropic, OpenAI, and, and, who'd I forget, Meta. And so, like, those

• 27:42Aaron Levie?

  five players are all on a, on a warpath for both driving down the cost of intelligence and improving the frontier at the same time. So having a model router that can kind of be above that, and again, pick and choose at different points, you know, which model to use, I think creates a lot of value for enterprises and actually helps with, interestingly, it actually helps with the diffusion of AI. You know, one of the, the challenges that enterprises have is, is almost kind of like, um, analysis paralysis on, on how, if you have so many models that are, that are emerging and they're constantly leapfrogging each other, you actually have like a resistance to just landing on one model family or one

• 28:23Aaron Levie?

  partner. And so the applied layer effectively gives you that relief where it basically says, you know what? You don't have to make that choice. You can, you can start to kind of get your workflows going, get your data in the right setup. And then you want to use Fable one day, go for it. You want to use GPT 5.6, go for it. Or you want to route that to, you know, Grok 4.5, also, you know, go for it. We can lower the cost of the overall workflow. And so that's the, that's the layer that I think is, is going to become increasingly valuable over time. And that layer is, is actually kind of well tuned to understand the deep industry and vertical use cases that AI needs to be applied to. Whereas the pure horizontal models are, are, you know, it's much harder for them to go deep in, in

• 29:04Aaron Levie?

  legal and finance and healthcare, not because the model can't do it, but because the, the surrounding, the surrounding kind of, you know, apparatus that you need around the model is not there. It needs to get access to the right data, it needs to be implemented into the workflow in the right way. And that's why I think you're going to have just a tremendous amount of value creation at, at that layer of the stack.

• 29:23Speaker 03

  Yeah, totally. That does seem to be where we're heading. Well, thanks so much, Aaron. We are at time.

• 29:29Speaker 01

  It's been a crazy week.

• 29:31Aaron Levie?

  Crazy. I don't think it's over yet. We, yeah, we're not, we're, we have at least 10 more hours to go of, of what could happen today. So.

• 29:40Speaker 01

  Open source, open source, so much to talk about. We're so glad to have you on.

• 29:44Aaron Levie?

  Open AI, guys, OpenAI hacked Hugging Face just four days ago. So.

• 29:49Speaker 03

  That was four days ago.

• 29:50Speaker 01

  I thought that was a month ago.

• 29:52Speaker 03

  We have until 11:59 to figure out the next big story.

• 29:56Aaron Levie?

  So many things can happen today.

• 29:58Speaker 01

  So many things. Thank you so much, Aaron.

• 30:02Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening, and I'll see you in the next episode.

• 30:27Erik Torenberg?

  This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16Z, or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.