Scaling enterprise AI: Fireside chat with Eli Lilly’s Diogo Rau and Dario Amodei

Let's have faith in the pace of progress of the technology, because if the models get good enough to do it end-to-end a year from now, and only then you start deploying it, there'll be another two-year delay and that's two years during which all the work that you're doing to benefit patients is not happening. Hello, everyone. My name is Diogo Rao and I'm Chief Information and Digital Officer of Eli Lilly and Company. I'm joined here with Dario, who is the founder and CEO of Anthropic. Dario, thanks for joining me today. Thanks for having me, Diogo. I know you're spending a lot of time now thinking about how do we, how do you work better with enterprises? What's your enterprise strategy and how do you see Anthropic different from other providers? Yeah, I mean, you know, I think we've made a number of choices that are different, right? So if I think about the incentives given by consumer AI, folks are in a competition for engagement and growth, right? And so that drives a lot of behaviors of the AI that I think are not ideal from an enterprise perspective, right? For example, there's this idea of model sycophancy, where the model tells you whatever you say is a good idea, right? And even on the consumer side, that can, you know, cause problems. We've seen stories of people who are like, oh yeah, I've discovered a new fundamental theory of physics. And that's right. Models like, that's great. And maybe you don't want it to, maybe you don't want it to say that. But I think, you know, of course, on the enterprise side, the problem, you know, the problems are much greater and clearer with that, where, you know, you really don't want the model to say, oh yeah, this drug compound is great. You should spend millions of dollars to, you know, I just think this is, you know, I think your idea is great. I think it's really promising. Like you want truth. And so I think that incentive has led us to design our models in a different way, right? I think it's more compatible with making the model smarter, making them better at the wide variety of economically valuable tasks. And it causes us to put a premium on accuracy and reliability. One experiment, you know, that I give to everyone, although it's particularly relevant because I'm talking to you, is I say, you know, let's say I improve the model's knowledge of biochemistry from undergraduate level knowledge to graduate level knowledge. You know, if I go to consumers and say that, 99% of them are going to say, you know, I didn't know what you were talking about before. I don't know what you're talking about now. But if I go to you, like, you care about that a lot. That's very important. That's exactly right. Well, actually, that gets into something else that you've launched as well, which is skills, right? There are a lot of skills that you want in biology, or even just skills like as an enterprise, how you want to operate. Is that part of the future for you as well? Yeah, I definitely think so. I mean, things ranging from skills to, you know, we're in the process of launching various specialized clods, which are, you know, in some cases will be improvements to the model itself, fine tunings of the model. But in some cases, it'll be something that looks more like wrapping the model with access to particular types of information. So when we did Quad for Financial Services, you know, we connect it to a lot of the usual kind of indices and ratings. Right. And so, you know, you'd be surprised how much just making it easy to connect Quad to those things and kind of use it in a way that's aware of that knowledge is valuable. So I think, you know, we're working on a Quad for Life Sciences that will be some mixture of making the model inherently smarter and wrapping it with various things, right? I don't know exactly what the analogy will be here, but like, geez, there are zillions of databases of, you know, proteins, compounds, assays, like, you know, you probably want that at the model's fingertips. Well, any parting advice for those of us that are working in this world of drug discovery and development? You know, I would say there is a temptation, and I think it's hard to avoid starting this way of, you know, what are the small things we can do with AI? Like, and in a way, you just kind of have to start there. I think one of my pieces of advice is be very, very ambitious in terms of where the models are going. I think you can get Quad in a mode where there's an existing process. It has 20 parts. You want to swap in AI to part 5 and part 12. Right. And, you know, that can actually be hard because part 12 has to, you know, intersect with part 13 and part 11, which are not being done with AI. And, you know, you look at it and you're like, well, the AI models aren't where they could do, you know, part 0 to part 20 end-to-end. But in a year, they might be. That's right. And so you should start thinking now, don't get too seduced by, oh, we can make these little hill climbing gains by, you know, doing this part and that part. Let's start preparing to do the whole thing end-to-end. Let's have faith in the pace of progress of the technology because if the models get good enough to do it end-to-end a year from now and only then you start deploying it, there will be another two-year delay. And that's, you know, that's two years during which all the work that you're doing to benefit patients is not happening. Whereas if you go in parallel, if you start preparing now for the large change as the models are getting better, then, you know, you may save years of time. That's right. So don't do two-year-long projects and expect that it's going to be exactly the same way in two years from now. Yes, if you do two-year-long projects, plan for where the AI is going to, I mean, that sounds like an obvious thing to say, but I think it actually takes a lot of courage and foresight to do that. It does, for sure. Well, thanks a lot for taking the time to chat today. Really appreciate it. Yeah, yeah. And see you again soon.