OpenAI vs. Anthropic's Direct Faceoff + Future of Agents — With Aaron Levie

Yeah, good to be here. I certainly like the framing on the battle. You know, I think it's to some extent it was sort of an inevitable outcome because if you think about it, if you have this AI model that is super intelligence packed into a model, it eventually has to converge on all of the same use cases. And so then I think the labs eventually need to compete head-to-head for all those use cases.

Okay. I was like, your intro was basically a question so why not.

Yeah. So let me just inject a couple quick thoughts in your initial framing and then I'll answer the question more directly. I think probably to represent both sides of Anthropic and OpenAI on this, I think that probably the story might be even more kind of complicated than that initial framing because I actually think ChatGPT leaked into the enterprise and has had actually a lot of enterprise traction, of enterprise deployments, which is separate from the API business. And so if you go to a lot of enterprises, they actually will have ChatGPT as their corporate standard for their corporate LLM for employees to use. So, you know, it's hard to kind of decide what data you end up looking at, but I would generally argue that both have done actually extremely well in the enterprise. And ChatGPT obviously even more focused on the consumer historically and now obviously you have this increased battle for enterprise dominance both with coding, the APIs, and the end-user kind of corporate knowledge work use case. So kind of co-work. The co-work use case being that kind of third one. And the big breakthrough that has happened recently, literally just recently in the past few months, is this idea of what if you could give, what if an agent was really really good at coding but the use case wasn't to build software, the use case was to use its coding skills and general kind of tool calling skills and the ability to run scripts. What if the agent was really good at all of those capabilities but was applied to the rest of knowledge work and what kinds of use cases would that open up? And you know, kind of the mental model is like what if everybody was like truly an expert at using their computer and they could write code for any task they wanted to do. But that same person that was the expert at using their computer and writing code was a lawyer and they were a marketer and they were in life sciences and they did research. That's basically the power of agents today more and more in terms of where we're going. And so the idea, and co-work kind of best manifested this early on, I think we'll certainly see based on the rumors, OpenAI have a presence in the space and other players, is you know what if you had an agent that was your general purpose knowledge worker agent but again it could use every tool on your computer. It can write code on the fly for a new problem that it hasn't seen before. It can use things called skills to be able to leverage existing kind of ongoing scripts and code that it needs to be able to use. What kind of now superpower would that be? You know, to be able to have as this workhorse that you have next to you. That's kind of the next frontier of AI agents. And so, I think we're clearly moving from a world where you will use AI as this thing you chat back and forth with. And that was kind of the first manifestation of the chatbot to now a paradigm where the agent is given a task. It has a set of resources it has access to. It has access to maybe your data, your software, tools on your computer, tools in the cloud, and it can go off and work for minutes or hours or maybe even days and go and generate some effective work output that you can then go and use, review, and then incorporate into your broader work. So this is kind of the big prize because it goes from the TAM, the total addressable market being all of engineers to now the total addressable market is every knowledge worker and that's probably about a 30 to 50x larger market in terms of humans on the planet and their use cases.

This is going to be primarily business. I think...

Yeah. And I fully agree with that framing. And I actually think that will suck it into the enterprise. I think what we're going to see is that the value and the ROI on those tokens, the tokens are not going to be cheap anytime soon. And so the ROI on those tokens will just be much higher in the enterprise because it'll be generating something that impacts the GDP in some way. And so I think that we will probably prioritize a lot of these systems toward those types of activities. But I totally agree with his framing that you'll just use it in a general purpose way. And probably the more that you're the kind of person that already likes to automate your life and do a bunch of automation things in your personal life, you'll use this also in a personal capacity. But I think most of the true economic value of it will come from the enterprise.

Yeah, I think these are the new kind of personal evals or benchmarks that people have of like when would you be able to edit a video? And I think Dario, I think asked even Dario that question right, and he's, when can we just edit this whole thing?

Yeah, exactly. Exactly. This is primarily...

By the way, if that's what they prioritize, I would be thrilled. Get it done folks.

It's good to have that as like a scarce activity. So I'm not worried so much about will people want this in the sense of because I think that's kind of like a fax machine argument and yes, there will always be hold outs, but I think efficiency generally always prevails simply because you end up prioritizing your time and the value of your time as a new technology emerges and you're like, well, yeah, I probably don't want to literally go to a fax machine, you know, have to put a piece of paper in this thing, type in a bunch of numbers if it just is an attachment and I send it to an email address. Like, it's like 10 times easier. So I think that will happen to a large set of areas of work and we'll look back and we'll just consider it laughable that like we spent two and a half hours going and like reading some research paper just to find one fact because previously we didn't know where that fact might be in the paper and so we like, we all have our own little tricks like we do some skimming and we kind of look roughly spatially for the area but it still takes like an hour like an AI agent just does that literally for us in 3 seconds and there's no going back like we don't want to do that anymore. So the question is like how deep can that go into work? How long running can those agents be across work before you have to sort of review the output that the agent is doing? How well do these models work on much more subjective tasks like editing a video is going to be actually in many cases a harder task than coding because the code right now is like it has this great property of in the eval process, in the training process rather, you can instantly evaluate did the code run, how clean was the code. We have a bunch of areas of work that don't have that ability to instantly sort of verify. So the reward function is a lot trickier for the agent. And then thus in the real life workflow, it's kind of hard to then go and automate that task. So I think this is actually going to take a lot longer to play out than maybe what we and some think in Silicon Valley because what's happened in Silicon Valley is we sort of look at all of the power of AI coding and because that's like the most economically useful task within Silicon Valley, we sort of extrapolate most things from like how good AI coding is. And because that, then we're like well if AI can do code really well then it probably can do legal and medical and life sciences and architecture and design all of those other tasks because we're kind of extrapolating the automation gains that we're seeing in AI and in coding. And the challenges that, and this has been talked about by a bunch of folks at different times but just to kind of share a few of the big buckets that I think everybody has kind of come down on. In coding you have, it's entirely text-based. You have access to the entire codebase. The agent generally has access to the entire codebase. The models are really really trained on coding because again, it's sort of verifiable. You can test the code and see if it works. The users of the agents in these cases are highly technical. So, they know their way around these systems. They know when the agent goes kind of crazy how to put it back on track. They know how to install the latest plugins that it needs. Now you compare to the rest of knowledge work where it's just somebody doing their daily marketing job and the context the agent needs is in 20 different systems and so each of those systems have to be individually wired up or you have to consolidate a bunch of data. The users maybe is not insanely technical and so they have got to go spend a bunch of time learning this stuff and the learning of a new tool is just generally not that much fun for people that aren't in tech because it's just like that's just like a pain. They don't get the same benefit of the verifiability of the coding agent. And so even when the agent goes and does a bunch of work, they have to have to go review the whole thing at the end of it because they have to make sure everything is sort of factually correct or has the right kind of sensibilities in what they produced. So all of those things are, and we haven't even gotten into like the governance policies, the compliance policies of that company. So all of those things add up to actually just meaning that the diffusion of these types of technologies will take many many years as they go through the rest of the world. And that's the part that I think Silicon Valley is going to have to be a bit patient on. And actually that conversely is why I think there's so much opportunity right now is because if you can build products and platforms that are sort of the bridge to that end state and make it as easy as possible for enterprises to go down that journey that's just a tremendous amount of opportunity. So the labs are going to do that and OpenAI will do that, Anthropic will do that, there'll be a bunch of startups that do it in either vertical categories or horizontals like what we're working on but that's sort of the big opportunity is can you bridge how the world works today to that end state. But I think that I would expect most people have agents running in their daily life from a workplace standpoint over the coming years just because the efficiency will just be too strong to kind of avoid.

But here's what's going to happen. Here's what happen. And I used, you know, I use sort of maybe like lightweight AI video editing. I don't know how much AI is in there. But there's always this part where you're like, actually, no, that's the moment you want to go and look at the reaction of the other person. Even though somebody else is talking, we should kind of make sure we cut to the other participant.

Yes. And then I think what will happen then is you know if you look at a sophisticated production in Hollywood, they have layers and layers of editors and then producers and there's like, I don't even know all the names but like there's somebody who oversees the editors and they look at the final set of edits and then there's the ultimate producer and the director and so on. I think that what will happen is the video editor of the future just compresses all of those roles and the agent is doing the cutting part in an automated fashion, but I actually think that you'll still have that ultimate person maybe what they'll review is five different cuts as options and they are now playing the role of the most senior editor in a TV show that would have happened in the past but now you bring that same capability to every podcaster like that was never possible before.

No, but so then it's like the editor didn't really go away. What they are just doing is a completely different activity than what they did before. They have five agents producing a bunch of examples and then they are doing some kind of final synthesis of that work into some final output.

Because you'll just feel it like you'll watch a podcast and you'll be like ah that was like really janky how they cut that thing and then they'll be like ah they probably just used AI only.

Well, I just don't agree that that'll happen. So I'm not, I can't defend do we want to be in that world because I actually don't think that plays out.

I think it will but I think it's going to be more for probably economically much more testable outcomes. Like I just don't think that of all the compute supply in the world that what we're going to do is spend our compute on editing podcasts 10 different ways and running those.

I was sort of just specifically reflecting on your one example. I think this will exactly happen in a bunch of other areas. It's going to happen in finance. It's going to happen in marketing. It's going to happen in healthcare. It's going to happen in life sciences. We're going to use it for drug discovery. I was talking to a life sciences CEO. And what we're going to now be able to do is we will be able to run, on the order of 10 to 100 times more experiments across everything that we want to go detect. And then you'll sort of narrow those experiments down to the ones that you actually want to do the full clinical trial process on and the full level of experimentation on. But our ability to experiment and have agents run in parallel across all areas of economically valuable work is only going to be a boon to society. We will discover drugs that we wouldn't have discovered before. You'll certainly get much more novel, maybe you could debate if this is good or bad, but you'll get more novel ways of doing financial services because you'll be able to be even more hyper tuned to market trends and what's happening in the market. Certainly marketing, I just think it's only a good thing if marketers can find their customers better. And so to me like algorithmically driven advertising is just a corollary to being able to better find customers that want your services. And that is just only a good thing if you're a small business and I can only find the people from my coffee shop that drink coffee in this neighborhood and I can target them and I can now spend money to get those customers and instead of just blasting dollars and then not getting any efficacy that's only a good thing right. So I think that the idea of agents being able to do so much more of this is a completely net positive for society and I think there's other areas where algorithms can be tricky. But I'm not worried about the ones where it's sort of like agents running in parallel doing work for us in the background. I think the dollars will generally flow to the areas where that ends up being useful for society.