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a16z Podcast · 3 min · Nov 6, 2025
CZI takes on long-term projects with a credible path
Priscilla Chan says a 10-to-15-year project needs a credible route, a capable leader, and enough uncertainty to justify taking risk. The three Biohubs focus on cell engineering, tissue research, and imaging.
Chan Zuckerberg Initiative
Erik Torenberg, Host · General partner, Andreessen Horowitz
Helped build Product Hunt; founded On Deck, Village Global and Turpentine.
About this segment
Priscilla Chan says a 10-to-15-year project needs a credible route, a capable leader, and enough uncertainty to justify taking risk. The three Biohubs focus on cell engineering, tissue research, and imaging.
From “Mark Zuckerberg & Priscilla Chan: How AI Will Cure All Disease”, Nov 6, 2025. Watch the full episode
Thanks. We'll look at and fix what's off.
“there's a credible pathway, someone who is at the helm who can do this, and there's enough ambiguity where we feel like we could take on that risk.”
Takeaways
- Choose ambitious projects with a plausible route to progress.
- Look for a capable lead and uncertainty with potential upside.
- Match Biohub sites to distinct biological challenges.
In this episode
Full episode- 3:23Shared tools can tackle science NIH grants leave unfunded5:20
- 15:18A shared annotation tool helped create a cell atlas3:16
- 21:34Virtual cells could make risky experiments cheaper to test4:02
- 11:22Most diseases need treatment tailored to individual biology2:58
- 28:00A shared data-and-model loop is driving Biohub's reorganization3:59
8:50CZI takes on long-term projects with a credible path2:37
- 38:17Biohub is scaling compute instead of lab square footage2:17Up next
- 18:33Virtual cells will combine specialized models into broader systems2:53
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 8:56
between. They're 10 to 15 year horizons, at least per kind of the way you communicate about them and the way you energize the scientific community about them. 10 to 15 is kind of an interesting time horizon, sort of like similar to the time horizon of a venture-backed company, similar to the time horizon on which a team can work together for that period of time. How did you get to that number? And then how are you thinking about the challenges that you take on in each 10 to 15 year wave, because that's concrete, achievable. You build a lot of credibility around it the way that you've announced those challenges. Well, I'm curious how you guys think about it. But for us, when we looked at the grand challenges on the 10 to 15 year time horizon, it needs to be like, when you look at it, you're like, I see a path. Not everything needs to be solved for us to take
- 9:46
it on. In fact, if everything's solved, then that feels like that should just go. Ambitious enough. Yeah, like we have some risk appetite, right? So we want things where we're like, there's a credible pathway, someone who is at the helm who can do this, and there's enough ambiguity where we feel like we could take on that risk. And if we do it, like the returns could be higher than even expected. And the way we modeled that in the biohubs is we have three biohubs.
- 10:14
We have one in San Francisco, one in Chicago, one in New York. The one in New York works on cell engineering. Can we engineer cells to go in and detect signals, read it out, or to take certain actions? In Chicago, we're building tissues and looking at cell communications within tissues. And then in San Francisco, we're looking at deep imaging and transcriptomics. And that work, the locations are not by accident.
- 10:39
We also look at the partner universities because we have folks who come to the biohubs to do this work, collaborative, interdisciplinary, and sort of unconstrained by the traditional lab. But we also build off of the labs at these academic institutes that support the work. And so that's how we sort of choose the grand challenge and the locations. And then the sort of layering and the large language models and AI coming into the picture has been so interesting because we were already building tools to measure interesting data, building the data sets, but we didn't really know what to do with them yet.