# Virtual cells will combine specialized models into broader systems

Markdown copy of https://realalpha.app/s/virtual-cells-will-combine-specialized-models-into-broader/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Mark Zuckerberg](https://realalpha.app/builders/mark-zuckerberg/) · Founder & CEO, Meta · Launched Facebook at Harvard with his roommates in 2004, and co-founded the Chan Zuckerberg Initiative.
- Host: [Erik Torenberg](https://realalpha.app/people/erik-torenberg/) · General partner, Andreessen Horowitz · Helped build Product Hunt; founded On Deck, Village Global and Turpentine.
- Show: a16z Podcast
- Episode: [Mark Zuckerberg & Priscilla Chan: How AI Will Cure All Disease](https://realalpha.app/v/YnV8pgHtO5Y/), Nov 6, 2025
- Moment: 18:33 to 21:26 of the episode (3 min), playing on https://realalpha.app/s/virtual-cells-will-combine-specialized-models-into-broader/; the episode from there: https://realalpha.app/v/YnV8pgHtO5Y/?t=1113

## Quote

> “you have different capabilities and then over time you **train them into models** and it gets more and more general”
> — [Mark Zuckerberg](https://realalpha.app/builders/mark-zuckerberg/) at [21:00](https://realalpha.app/v/YnV8pgHtO5Y/?t=1260) · a16z Podcast

## Takeaways

1. Build models at several biological scales.
2. Generate datasets around specific biological challenges.
3. Combine specialized models into more general ones.

## About this segment

Mark Zuckerberg describes building from protein and cell models toward systems such as the immune system. Biohubs would generate purpose-built datasets and combine models into increasingly general simulations.

From “Mark Zuckerberg & Priscilla Chan: How AI Will Cure All Disease”, Nov 6, 2025. [Watch the full episode](https://realalpha.app/v/YnV8pgHtO5Y/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[18:40](https://realalpha.app/v/YnV8pgHtO5Y/?t=1120) Yeah, I mean, we think that this is going to be one of the most important tools at this point is basically building up the kind of hierarchy from proteins to just different structures within the cell to a whole virtual immune system or different levels of hierarchy. And we think this is going to end up being a very important set of tools for people to effectively generate hypotheses for different science work.

[19:09](https://realalpha.app/v/YnV8pgHtO5Y/?t=1149) Even before you get to the point where you're really running full experiments in it, you can come up with some estimate of how that might run. It will be useful for some of the precision medicine type examples that Priscilla was talking about a few minutes ago. But we think that this is probably one of the most important sets of tools that you need to build. And it's not a single thing, right? So there's different angles to come at this from.

[19:36](https://realalpha.app/v/YnV8pgHtO5Y/?t=1176) The Cell Atlas data is helpful for understanding things on a cellular level. One of the kind of most important things that we're doing right now, how there's this great company, Evolutionary Scale, who actually had a bunch of researchers who'd formerly worked at Meta on protein folding models, is joining a biohub, and Alex Reeves, the leader of it, is actually going to... be the kind of head of the whole science program, which is actually kind of interesting when you think about it, where it's like you have AI and biology coming together, and really it's like an AI person who understands biology as running it rather than a biologist who has some understanding of AI, I think just kind of speaks a little bit to where we think the relative weight of these things is.

[20:20](https://realalpha.app/v/YnV8pgHtO5Y/?t=1220) But, I mean, we basically view, you know, like Priscilla was saying with the different biohubs, New York doing cellular engineering will basically make it so that you can have cells that can record different things that are going on around the body and share that data, and then you can build that into models. The Chicago Biohub being able to record inflammation and basically study that in order to kind of help understand, like, that's a different data set.

[20:47](https://realalpha.app/v/YnV8pgHtO5Y/?t=1247) We have the Imaging Institute, which is what we just trained our first set of models around that, which are the first, like, spatial models around understanding like the way that that kind of cells look in different states and eventually just like you have this analogy on the kind of the industry side around language models where you have different capabilities and then over time you train them into models and it gets more and more general that's kind of the idea here so we'll we'll build the biohubs around grand biological challenges the biohubs will build tools that will generate novel data sets