“The challenge will be that the things that we most want to know about biology, they're not represented in text. And they cannot be derived from first principles or from what we already know. So they're empirical facts that can only be derived by performing experiments.”
The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · AI Research & Frontier Labs · October 2026
Nathan Labenz asked whether language models and biology models will eventually be trained into one shared latent space, the way text and images have been. Rives said language is powerful for reasoning, but that this gap is why predictive models of biology, trained on measurements, are critical.
Transcript
…is no disease anymore. And that's that, you know, the only way to get there is going to be by really focusing on the kind of the deep biology and kind of building the technology that can allow us to understand how that is determining for health and disease and how we can effectively intervene.
…to an image generation model like couldn't quite do. So do you think that reasoning through language and kind of biology models stay arm's length tool call relationships long term? Or do we ultimately see like bioencoders that feed into the same reasoning space that we're developing in language?
Yeah, it's a really interesting question. I think that the truth is, I'm not sure. I think that language is really powerful for reasoning. It's a substrate for reasoning, for thinking. And the power that language also gives us is to tap into the model of biology that's been created through centuries of kind of human empirical investigation. And all of that is represented in language. It's learnable through language. And I think this very, let's say, expressive, full system of knowledge can, I think, really be materialized and leveraged through reasoning systems. I think the challenge will be that the things that we most want to know about biology, they're not represented in text. And they cannot be, you know, derived from first principles or from what we already know. So they're empirical facts that can only be derived by performing experiments. And so it's from thinking about this, I think this core aspect of what makes biology different, I think, than other areas where we're building AI that I think will make predictive models of biology critical…
Okay. No, we're almost out of time. I'll do two more quick ones if we can. One, not to be a dog with a bone on this Brian Johnson's question, but that would be pretty expensive. But if money were no object and, you know, for some rare philanthropists, money is maybe close to no object in pursuit of…
I mean, again, I think I just come at it. We just come at it from this kind of frame of really thinking about the fundamental mechanisms. But I think that data would be immensely valuable. I just see that, I think, as more likely to happen. I think over time that will happen. I mean, there are some…
Yeah, interesting. Maybe less neglected than it seems to be from my outside position. All right, last one, personal health advice. What are you doing to build up the data set that you need for yourself to be able to take advantage of these future technologies? And what, if anything, do you feel is…
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