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Mark Zuckerberg & Priscilla Chan: How AI Will Cure All Disease

Mark Zuckerberg & Priscilla Chan: How AI Will Cure All Disease

18 segments available

Priscilla Chan and Mark Zuckerberg join a16z’s Ben Horowitz, Erik Torenberg, and Vineeta Agarwala to share how the Chan Zuckerberg Initiative is building the computational tools that will accelerate the cure, prevention, and management of all disease by century's end. They explain why basic science needs $100 million-scale projects that traditional NIH grants can't fund, how their Cell Atlas became biology's missing periodic table with millions of cells catalogued in open-source format, and why their new virtual cell models will let scientists test high-risk hypotheses in silico before investing in expensive wet lab work. Plus: the organizational shift unifying the Biohub under AI leadership, what happens when biologists and engineers sit side-by-side, and why modern biology labs are expanding compute instead of square footage. Timestamps 00:00 Introduction 03:42 Building tools to accelerate scientific discovery 05:26 The credible path to funding basic science 07:03 Biohub = Frontier Biology + Frontier AI 08:58 Challenges building on a 10-15 year timeline 09:39 How CZI chooses what to work on 11:17 Making sense of science with LLMs 11:32 Measuring success in the therapeutic realm 13:32 "Most diseases should be thought of as rare diseases” 15:39 Inspiration: building a periodic table for biology 19:27 Why virtual cells? 21:17 The Biohub Master Plan 21:51 How virtual cell models allow more risk taking 28:15 Bringing CZI & Biohub together 30:32 Why Biohub matters 33:36 The importance of interface design in democratizing scientific discovery 35:34 How Biohub encourages cross-functional collaboration 40:38 Looking ahead: the broader impact of AI on biotech Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Segments Timeline

1
0:00 - 3:44
3:44 duration746 words

Introduction

"this is a a a space that I mean that there's just going to be a huge amount of leverage with AI. It still seems like there could be a lot more effort in this space around building tools and it's kind ..."

2
3:44 - 5:26
1:42 duration322 words

Building tools to accelerate scientific discovery

"basically let's help build tools that will accelerate the pace of the whole field and I think that that there's a niche that I think fits that because if you look at how funding works in science you k..."

3
5:26 - 7:04
1:37 duration344 words

The credible path to funding basic science

"when we first set out that the goal to cure and prevent disease by the end of the century, people like honestly most scientists couldn't look at us with a straight face [laughter] >> and crazy. >> Yes..."

4
7:04 - 8:59
1:54 duration379 words

Biohub = Frontier Biology + Frontier AI

"ourselves and the work that we're doing at the Biohub as frontier biology paired with frontier AI, right? So, there's there are labs that do frontier AI that uh basically, you know, are building the m..."

5
8:59 - 9:39
0:40 duration135 words

Challenges building on a 10-15 year timeline

"to 15 is kind of an interesting time horizon sort of like similar to the time horizon of a venturebacked company similar to the time horizon on which a team can work together for that period of time I..."

6
9:39 - 11:17
1:38 duration278 words

How CZI chooses what to work on

">> right >> not everything needs to be solved for us to take it on in fact if everything's solved then that feels like that should just go >> ambitious enough >> yeah like you like we we have we have ..."

7
11:17 - 11:35
0:17 duration51 words

Making sense of science with LLMs

"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. Um and large language models coming onto the scene we're l..."

8
11:35 - 13:34
1:59 duration374 words

Measuring success in the therapeutic realm

"success as in the therapeutic realm. So, you know, we think a lot about understanding biology and sometimes we bet on startups that want to unlock completely new biological areas, diseases where we do..."

9
13:34 - 15:40
2:05 duration408 words

"Most diseases should be thought of as rare diseases”

"most diseases should be thought of as rare diseases because each one of our biology is different and right now we just get lumped right we get lumped based on age demographics ancestry if we're lucky ..."

10
15:40 - 19:27
3:47 duration771 words

Inspiration: building a periodic table for biology

"it's kind of this crazy thing that we're, you know, here in, you know, 2025 and there's not the kind of periodic table of elements equivalent for biology, right? So, that was sort of a lot of the insp..."

11
19:27 - 21:17
1:49 duration367 words

Why virtual cells?

"we think that this is like probably one of the most important sets of tools that you need to build um and it's not a single thing right so there's different angles to to come at this from the cell atl..."

12
21:17 - 21:52
0:34 duration109 words

The Biohub Master Plan

"we'll we'll build the biohubs around grand biological challenges. The biohubs will build tools that will generate novel data sets. We will build models based on those and then eventually combine the m..."

13
21:52 - 28:16
6:24 duration1269 words

How virtual cell models allow more risk taking

"promise of being able to do virtual biology using a virtual cell model is you can actually take on riskier ideas. right now like grant funding can be hard to come by and the wet lab work is expensive ..."

14
28:16 - 30:32
2:15 duration431 words

Bringing CZI & Biohub together

"uh thinking about how we are going to be coming together as one team. Um and you know in the past we have done we've run biohubs and we've done built software we've done some AI research but all of it..."

15
30:32 - 33:36
3:04 duration589 words

Why Biohub matters

"are we've funded data sets we've built data sets we're like building the instrumentation now to be able to look at the cell whether it's you know for at the tissue cell communication our cryoEM where ..."

16
33:36 - 35:35
1:58 duration363 words

The importance of interface design in democratizing scientific discovery

">> And the user interface is actually really important. Um you talked about uh you guys have a founder who's using Cell by Gene. That user interface was intentionally designed to not need to have a co..."

17
35:35 - 40:39
5:04 duration1002 words

How Biohub encourages cross-functional collaboration

"about science as it's this portfolio right society has a portfolio of stuff that it's trying to do and as in terms of philanthropy you want to >> be the most additive that you can be by trying to figu..."

18
40:39 - 44:33
3:54 duration740 words

Looking ahead: the broader impact of AI on biotech

"even principles or a northstar that's going to guide how you guys grow and evolve going forward? You know, it's been really interesting in the past 10 years because I actually spent the first few year..."