Latest / Core Memory / Her Multi-Billion Dollar Quest to End Disease - EP 25 Priscilla Chan

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Her Multi-Billion Dollar Quest to End Disease - EP 25 Priscilla Chan · Core Memory

0:00
SPEAKER_02: I am a tennis player.
0:01
SPEAKER_02: So we have one thing in common,
0:02
SPEAKER_02: at least,
0:03
SPEAKER_02: which was that we were both the captains of our tennis team.
0:07
SPEAKER_01: Oh, my God.
0:08
SPEAKER_01: You were?
0:08
SPEAKER_02: Yes.
0:09
SPEAKER_02: And you were the co-captain, I think?
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SPEAKER_01: I was the co-captain.
0:11
I played tennis in high school,
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partially because the tennis coach was my science teacher.
0:20
SPEAKER_02: I saw.
0:20
SPEAKER_01: And he was like, you have to do that.
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SPEAKER_01: I was like, I don't know how.
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SPEAKER_01: And so he's like, I'll teach you.
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SPEAKER_01: It's no problem.
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And I was my senior year co-captain with someone who's far
0:32
better than I would say
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SPEAKER_01: co-captain denotes no tennis skill on my on my part.
0:37
SPEAKER_02: Yeah.
0:37
SPEAKER_02: Well, I was looking at this because everything I could read.
0:41
SPEAKER_02: It seemed like you started playing your freshman year.
0:44
SPEAKER_02: It's a really hard sport to just pick up.
0:46
SPEAKER_01: It was really hard.
0:50
SPEAKER_01: It was really hard.
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SPEAKER_01: I worked hard at it.
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SPEAKER_01: I was never that good.
0:55
I will tell you the lasting legacy of my tennis career is I'm
1:00
now a mediocre pickleball player.
1:01
SPEAKER_02: Okay.
1:02
SPEAKER_02: Okay.
1:02
SPEAKER_02: Yes.
1:03
SPEAKER_02: So the pickleball people are taking over all our courts.
1:05
SPEAKER_01: It's true.
1:06
This is like the side part of my podcast where I hopelessly
1:10
fight against this
1:11
SPEAKER_02: because I'm not going to win.
1:14
SPEAKER_02: Yeah.
1:15
SPEAKER_02: So the tennis, it didn't stick with you.
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SPEAKER_01: I still played, but it's the thing.
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SPEAKER_01: I still do play tennis,
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SPEAKER_01: but like with tennis,
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SPEAKER_01: you really need someone who's like pretty well matched to you.
1:25
SPEAKER_01: Whereas pickleball, like it's like just giant pink.
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SPEAKER_01: I'm sorry.
1:29
SPEAKER_01: I don't know if I'm supposed to say that.
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It's giant ping pong and you can play with your friends and
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it's a great time.
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SPEAKER_01: And so I do both.
1:35
SPEAKER_02: Okay.
1:36
SPEAKER_02: We're going to talk science now.
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SPEAKER_01: Okay.
1:51
SPEAKER_02: Priscilla Chan.
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SPEAKER_02: Most recent title and current title is co-CEO and co-founder of CZI,
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SPEAKER_02: the Chan Zuckerberg Initiative.
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You're a doctor,
2:03
pediatrician,
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and obviously I read an academic all-star throughout your life.
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SPEAKER_02: So super impressive.
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As always,
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this podcast is made possible by the fine people at E1
2:18
Ventures.
2:19
SPEAKER_02: Thank you.
2:19
SPEAKER_02: You won ventures.
2:21
SPEAKER_02: So you started CZI about a decade, well, a decade ago.
2:24
SPEAKER_00: Yeah, exactly.
2:26
SPEAKER_02: with huge goals.
2:30
SPEAKER_02: I don't know if this has always been the same tagline,
2:35
SPEAKER_02: but supports the science and technology to make it possible to cure,
2:39
SPEAKER_02: prevent,
2:40
SPEAKER_02: and manage all disease in the century.
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SPEAKER_02: Was that from day one?
2:44
SPEAKER_01: That's us.
2:44
SPEAKER_01: That's from day one.
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SPEAKER_02: So this is a lofty, this is an ambitious, ambitious goal.
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SPEAKER_02: Although in this AI era, I feel like
2:54
SPEAKER_02: People say things like this quite seriously.
2:57
SPEAKER_02: Yeah.
2:57
SPEAKER_01: Yeah.
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SPEAKER_01: It has always been our goal.
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SPEAKER_01: We want to cure, prevent, or manage all disease.
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SPEAKER_01: And 10 years ago,
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SPEAKER_01: almost exactly 10 years from today,
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SPEAKER_01: Mark and I were deep in talking to scientists.
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SPEAKER_01: Like, what's possible?
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SPEAKER_01: What should we be working on?
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SPEAKER_01: What's feasible?
3:18
SPEAKER_01: And we're like, well, can we – we have –
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SPEAKER_01: we want to set a big audacious goal.
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SPEAKER_01: Can we just say we're going to cure all disease?
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SPEAKER_01: And they were like, well, no.
3:28
SPEAKER_01: And we're like, well, why not?
3:30
SPEAKER_01: And they were like, well, okay, but what about all these other things?
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SPEAKER_01: And like, what is even the pathway to doing that?
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SPEAKER_01: And we sort of pushed through.
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SPEAKER_01: And I will tell you,
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A lot of the scientists that we work with could not keep a
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straight face.
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SPEAKER_01: They're like, I have like, I have academic credibility to uphold.
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SPEAKER_02: When you wanted to use this.
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SPEAKER_01: To have that as our tagline.
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And they're like,
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and we're like,
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I,
3:55
we had this,
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we had an announcement,
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a public announcement.
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And like,
4:00
literally what some of the coaching was,
4:02
well,
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don't smirk when you're saying it.
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SPEAKER_01: Yeah.
4:06
SPEAKER_01: And then what's,
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SPEAKER_01: Totally crazy and unbelievable is exactly what you just said.
4:12
SPEAKER_01: Today, people are like, yeah.
4:14
SPEAKER_01: And probably not to leave.
4:15
People are saying that it's not even going to take the end of
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our century.
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SPEAKER_01: We have given ourselves too much runway.
4:21
And I think that's how we know that we are in just an
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incredibly exciting moment in
4:27
SPEAKER_01: science and in medicine.
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SPEAKER_02: Yeah, yeah.
4:29
SPEAKER_02: And so is this good or bad?
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SPEAKER_02: Now everybody's piggybacking on this.
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SPEAKER_02: It's great.
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SPEAKER_01: You think we're going to do this ourselves?
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SPEAKER_01: Of course not.
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SPEAKER_01: Like we depend on so many others to be able to do this work.
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SPEAKER_01: It's fantastic.
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SPEAKER_02: Yeah.
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SPEAKER_02: And I mean, we can, we'll get more into sort of CZI and what it's doing.
4:46
SPEAKER_02: But I mean,
4:47
obviously a lot of the motivations for this came from you being
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a doctor and what
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SPEAKER_02: I,
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SPEAKER_02: and an interest in science to begin with.
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SPEAKER_01: Yeah.
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SPEAKER_01: So I – oh, gosh, where do I start?
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SPEAKER_01: I'll start – I like to share this actually at our new hire orientation.
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SPEAKER_01: I am from Quincy, Massachusetts, and I'm the child of immigrants.
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SPEAKER_01: But I've also like, how do I explain this?
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SPEAKER_01: I'm 40 years old.
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SPEAKER_01: I've lived a lot of life.
5:21
SPEAKER_01: But the most important part of my life was actually before I was born.
5:27
SPEAKER_01: My grandparents, my two sets of my grandparents were from Saigon.
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SPEAKER_01: And during the Vietnam War, they needed to flee Saigon.
5:36
SPEAKER_01: And they couldn't get their kids out.
5:38
SPEAKER_01: And there's six on one side, nine on the other.
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SPEAKER_01: And they were buddies.
5:42
SPEAKER_01: And they were like, okay, we got to get our kids out of this country.
5:45
SPEAKER_01: There was a war.
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The only way they could get their kids out were these little
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boats that would float
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SPEAKER_01: out into the South China Sea.
5:52
SPEAKER_01: And they, but these boats would sink.
5:55
SPEAKER_01: They were just like crap boats.
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SPEAKER_01: They would sink.
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SPEAKER_01: And so they would hear stories of whole families sinking and dying.
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SPEAKER_01: And what they decided to do is pair up their buddies.
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Like my mom was paired with my aunt and we're like,
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okay,
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you guys take care of each other.
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And I mean,
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this sounds crazy to say,
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but if a boat sinks,
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we would only lose one or two kids.
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SPEAKER_00: Yeah.
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SPEAKER_01: And what happened is that,
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SPEAKER_01: and I try to hope that this is like a little spiritual,
6:25
SPEAKER_01: a little scientific.
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SPEAKER_01: I hope that's written into my epigenetics,
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SPEAKER_01: the belief that there's something better out there and it's audacious,
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SPEAKER_01: it's big,
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SPEAKER_01: it's scary,
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SPEAKER_01: you take a risk and you do it.
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SPEAKER_01: Like, that's in me.
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SPEAKER_01: because of my grandparents, my parents, and what they chose to do.
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SPEAKER_01: So I ended up in Quincy, Massachusetts.
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SPEAKER_01: And we were refugees.
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SPEAKER_01: We were immigrants.
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SPEAKER_01: We didn't know the system.
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SPEAKER_01: And so many people stepped up to make a difference in my life.
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SPEAKER_01: And that's when I realized, like, I want to help kids.
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SPEAKER_01: And I've taught,
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SPEAKER_01: I went to medical school,
7:00
SPEAKER_01: and I ended up at UCSF where I trained as a pediatrician.
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SPEAKER_01: And if you train as a pediatrician at UCSF,
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SPEAKER_01: it's like a little bit cute and cuddly,
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SPEAKER_01: but not that cute and cuddly.
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SPEAKER_02: I read some crazy stories of things you were doing, yes.
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SPEAKER_01: It's really about taking care of really sick kids with rare diseases.
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SPEAKER_01: And most of the time you're like, I don't know what that is.
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Or a parent slips you a PDF and they're like,
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this is everything that's known about our disease.
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SPEAKER_01: And you're scouring the paper and you're like,
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SPEAKER_01: Like, what do we even know?
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SPEAKER_01: What can I do to help this child and this family?
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SPEAKER_01: And like,
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that is how I learned that basic science is where hope comes
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from for medicine and
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SPEAKER_01: for patients.
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And if we don't invest in basic science,
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like there is no sort of hope for better in medicine.
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And sort of that's sort of like the really short version of
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sort of how I got to
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SPEAKER_01: really making CZI focused on basic science.
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SPEAKER_02: I mean, I read, well, okay, so totally appreciate that sentiment.
8:05
SPEAKER_02: And then,
8:05
SPEAKER_02: yeah,
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SPEAKER_02: I mean,
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SPEAKER_02: I read,
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SPEAKER_02: I mean,
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SPEAKER_02: this is more specific,
8:08
SPEAKER_02: but a story about two premature children that you,
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SPEAKER_02: got in the parking lot and yeah, they were breathing and you had to.
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SPEAKER_01: What internet are you on?
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SPEAKER_01: This is true.
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SPEAKER_01: We would train at San Francisco General Hospital and UCSF.
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SPEAKER_01: And in San Francisco General Hospital,
8:33
SPEAKER_01: you would take care of anything sort of,
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SPEAKER_01: we call it like that comes off the street life.
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SPEAKER_01: And at UCSF,
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SPEAKER_01: you did the super high-end sort of like bone marrow transplant,
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SPEAKER_01: specific genetic diagnoses.
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SPEAKER_01: And at the general,
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SPEAKER_01: when you were on call,
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SPEAKER_01: you would take care of any kid that sort of came off the street.
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And one of the most memorable cases I ever took care of was a
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mom who was really
9:02
SPEAKER_01: sick and pregnant.
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SPEAKER_01: And we couldn't even get her past the emergency room.
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SPEAKER_01: They sort of...
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did a emergency c-section right there in the middle of the
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emergency room and i had
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to grab a baby not knowing how old the baby was and revive the
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baby um right
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everything all of my skills had to come into play yeah yeah um
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i'm making you teary
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SPEAKER_02: at the beginning i feel bad it's
9:31
SPEAKER_02: And I know I'm going to take us forward for a second.
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There was something just from when you were growing up that I
9:36
was so curious about.
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SPEAKER_02: I mean,
9:39
SPEAKER_02: could you paint a picture of,
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SPEAKER_02: because I read some of these stories that you were talking about,
9:43
SPEAKER_02: like what your house looked like.
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SPEAKER_02: Do you have two sisters?
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SPEAKER_01: I have two younger sisters.
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SPEAKER_01: Two younger sisters.
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SPEAKER_02: And so there's the grandparents, the parents in the house.
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SPEAKER_02: People are speaking Cantonese in the house.
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SPEAKER_02: And you're this kind of bridge between the outside world and the house.
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SPEAKER_02: But yeah, I was trying to picture what it looked like in my head.
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SPEAKER_01: Okay.
10:03
SPEAKER_01: I grew up in a yellow house.
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SPEAKER_01: It had three bedrooms and one and a half baths.
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SPEAKER_00: Okay.
10:11
SPEAKER_01: And my grandparents shared a room.
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SPEAKER_01: My three sisters and I shared a room.
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SPEAKER_01: And my parents shared a room.
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SPEAKER_01: And we only had one bathroom.
10:23
SPEAKER_01: We took turns.
10:25
SPEAKER_01: And all of my cousins on my dad's side of the family lived within a mile.
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SPEAKER_01: And my grandparents have 11 grandchildren.
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SPEAKER_01: So that little house, I think I've looked up recently.
10:36
SPEAKER_01: It's something like 1,800 square feet.
10:39
SPEAKER_01: would packed with all my aunts and uncles and all my cousins.
10:43
SPEAKER_01: And so we didn't have much.
10:46
SPEAKER_01: But we had a big extended family.
10:51
SPEAKER_01: And my parents each worked two plus jobs my entire childhood.
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SPEAKER_02: Yeah, were you there from grade school through high school?
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SPEAKER_01: I was born there.
11:00
SPEAKER_01: And then we moved when I was like a sophomore or a junior.
11:06
SPEAKER_01: But there was something awesome about this house.
11:09
SPEAKER_01: I don't know how this happened because it's kind of on a big street.
11:14
But the bookmobile came every like...
11:16
Now,
11:16
this is new information,
11:17
I think.
11:18
SPEAKER_01: I don't think you found this on your internet.
11:19
SPEAKER_02: I haven't seen this.
11:21
SPEAKER_01: But the bookmobile came every like Tuesday or Wednesday.
11:26
SPEAKER_01: And that was my access to books and the world.
11:30
SPEAKER_01: And it literally stopped right in front of my house.
11:33
SPEAKER_02: What were you – I mean, were you fiction?
11:36
SPEAKER_02: Were you nonfiction?
11:37
SPEAKER_01: Were you – I would read everything, but mainly nonfiction.
11:45
SPEAKER_01: And then I went to a vo-tech high school.
11:49
SPEAKER_01: And about half the kids in my high school went to college.
11:53
SPEAKER_01: But you could also be a plumber, a carpenter, an electrician.
11:57
SPEAKER_01: I'm very handy, I think, because of that.
12:00
SPEAKER_01: And I took every single science class that was available.
12:04
SPEAKER_01: And when I was done, I got permission.
12:07
I bought myself a cancer biology textbook and I taught myself
12:11
cancer biology.
12:13
SPEAKER_02: So you were always...
12:15
SPEAKER_02: I saw you did the robotics club.
12:17
SPEAKER_02: Okay, you struck me as a very high achiever.
12:20
SPEAKER_02: With science,
12:22
I have to assume that was what you were most interested in and
12:26
called to you,
12:27
SPEAKER_02: or were you seeing that as this path to success and changing your life,
12:35
SPEAKER_02: I guess?
12:37
SPEAKER_01: It was my passion.
12:39
The cancer biology textbook was because I lost my grandfather
12:42
to cancer when I was
12:43
SPEAKER_01: in middle school.
12:45
SPEAKER_01: And so I just wanted to understand and learn more.
12:47
SPEAKER_01: But I loved sort of learning about how the world works.
12:51
SPEAKER_01: And so I had two passions.
12:54
SPEAKER_01: I loved kids.
12:55
I always loved kids because I felt like so many people growing
12:58
up had given me a chance.
13:00
SPEAKER_01: And like my way of paying it forward was going to be to work with kids.
13:05
um and i love science i was really really nerdy and like i love
13:10
the logic and the
13:11
beauty of how biology and science works did you think you'd be
13:15
a doctor or a
13:16
scientist what did you picture in your head well i actually
13:19
taught um elementary
13:21
school science before i went to medical school at harker at
13:24
harker yeah um and like
13:26
SPEAKER_02: when you were growing up what were you picturing you would
13:29
SPEAKER_01: I probably thought I would be a doctor,
13:34
SPEAKER_01: but it was really most important to me that I work with kids.
13:36
SPEAKER_02: Okay.
13:37
SPEAKER_02: Okay.
13:39
SPEAKER_02: I always wonder about Harker.
13:41
SPEAKER_02: Do you have thoughts on Silicon Valley schools?
13:44
SPEAKER_01: Oh, I have.
13:46
SPEAKER_01: You know why I did Harker?
13:48
I,
13:49
well,
13:50
intersecting into the part of my life that more people know
13:52
about.
13:53
SPEAKER_01: I met Mark in 2003, 22 years ago.
13:59
SPEAKER_01: And he moved out to Silicon Valley in 2004.
14:04
SPEAKER_01: And I was still just finishing my freshman year of college.
14:09
SPEAKER_01: And we were like, we continued dating long distance.
14:15
SPEAKER_01: Thank you, JetBlue.
14:17
SPEAKER_01: And then we're like, okay, are we going to make this work?
14:21
SPEAKER_01: We're going to try to live in the same place.
14:23
SPEAKER_01: And what I really wanted to do was teach for America.
14:27
SPEAKER_01: And they couldn't guarantee that I could come to the Bay Area.
14:31
SPEAKER_01: So I ended up at Harker instead.
14:33
SPEAKER_02: Okay, great.
14:34
SPEAKER_02: Okay.
14:34
SPEAKER_02: I mean, it's a fantastic school.
14:36
SPEAKER_02: Well, at least it has a fantastic reputation.
14:38
SPEAKER_01: Yeah.
14:38
SPEAKER_02: Yeah.
14:39
SPEAKER_02: Okay.
14:40
SPEAKER_02: All right.
14:40
SPEAKER_02: Sorry.
14:40
SPEAKER_02: I want to,
14:41
these are all things I've shared a picture in my head when I
14:43
was reading some of
14:45
SPEAKER_02: your background.
14:45
SPEAKER_02: So that's, that's really helpful.
14:48
SPEAKER_02: When you, when you start something like CZI, um,
14:53
SPEAKER_02: You know,
14:54
SPEAKER_02: we have seen in the past,
14:55
SPEAKER_02: I mean,
14:55
SPEAKER_02: going back even to,
14:57
SPEAKER_02: well,
14:57
SPEAKER_02: probably even further back,
14:58
SPEAKER_02: but like Howard Hughes,
14:59
SPEAKER_02: you know,
14:59
SPEAKER_02: has,
15:00
has gave all this money to science and medicine has had this
15:04
tremendous legacy.
15:05
SPEAKER_02: More recently,
15:06
SPEAKER_02: we've seen some things that came after CZI,
15:09
SPEAKER_02: like arc institutes where,
15:11
SPEAKER_02: um,
15:12
it's a group of wealthy people trying to go after science in
15:15
their particular way.
15:18
SPEAKER_02: And I know you consulted with people before you started CZI,
15:21
SPEAKER_02: but like,
15:22
how would you define how your model is different to other
15:26
people trying to approach
15:28
SPEAKER_02: furthering science?
15:30
SPEAKER_01: Yeah, for me, I'll start with me personally.
15:33
SPEAKER_01: Like I was going to be doing this work regardless.
15:39
I was ready to practice and serve and giving back was not a
15:47
career shift for me.
15:48
SPEAKER_01: This was my career.
15:51
And I think we're at a really cool moment where my interests
15:58
and Mark's interests
15:59
SPEAKER_01: and my skills and his skills came to a really interesting intersection.
16:04
SPEAKER_01: We started from that.
16:06
SPEAKER_01: We were like 10 years ago, we want to give back.
16:09
SPEAKER_01: But like, you know, we have resources.
16:11
SPEAKER_01: Money is obviously part of this.
16:13
SPEAKER_01: But what else can we bring to the table?
16:15
SPEAKER_01: And for me,
16:15
SPEAKER_01: it was my experience as a practitioner of medicine,
16:19
SPEAKER_01: from biology,
16:21
SPEAKER_01: and for Mark,
16:22
SPEAKER_01: was about building technology.
16:24
SPEAKER_01: And we were like, okay, those are the three things that we have.
16:29
SPEAKER_01: And so it was never just resources.
16:31
It was about sort of other skills,
16:34
knowledge that we could bring to the table.
16:36
SPEAKER_01: And we looked at the problem of basic science.
16:38
SPEAKER_01: We were like, okay, what can we do?
16:40
SPEAKER_01: And I think we were the first to really,
16:42
SPEAKER_01: in a serious way,
16:43
SPEAKER_01: bring together biology and technology.
16:46
And we said,
16:48
okay,
16:49
there's a few core principles we want to really focus on.
16:51
SPEAKER_01: One, we want to fund unmet needs.
16:55
SPEAKER_01: Two,
16:55
SPEAKER_01: we want to really build a way for people from different disciplines,
17:01
SPEAKER_01: starting,
17:01
SPEAKER_01: you know,
17:02
SPEAKER_01: like from our two different backgrounds.
17:04
SPEAKER_01: Bring people from different disciplines together to solve heart problems.
17:09
SPEAKER_01: And three, we want to build technology.
17:12
SPEAKER_01: Like there's got to be cool, interesting things to build.
17:15
SPEAKER_01: that can help accelerate science.
17:17
SPEAKER_01: All of this,
17:18
SPEAKER_01: when we say we want to cure,
17:19
SPEAKER_01: prevent,
17:20
SPEAKER_01: and manage all disease,
17:21
SPEAKER_01: our strategy is not disease by disease.
17:23
SPEAKER_01: Our strategy is how do we make all of science faster,
17:28
SPEAKER_01: accelerate the path to impact so that we actually can get to that goal.
17:34
Okay,
17:34
so then when we started,
17:35
we were like,
17:35
okay,
17:36
well,
17:36
we're going to build a biohub.
17:38
SPEAKER_01: Biohub is super cool.
17:39
SPEAKER_01: And our first one was in San Francisco,
17:41
SPEAKER_01: where it was a collaboration between UCSF,
17:43
SPEAKER_01: UC Berkeley,
17:44
SPEAKER_01: and Stanford.
17:45
SPEAKER_01: And we got these universities to partner together,
17:49
bring their best and brightest to work on a problem that's
17:55
going to take 10 years
17:56
SPEAKER_01: to solve.
17:56
SPEAKER_01: We gave them a long runway.
17:58
SPEAKER_02: Which is?
17:58
SPEAKER_02: Sorry.
18:01
SPEAKER_02: Just the particular problem.
18:03
SPEAKER_01: Oh,
18:03
SPEAKER_01: in San Francisco,
18:05
the particular problem was really understanding the cell at the
18:08
single cell level,
18:10
SPEAKER_01: both sort of in its innate state and under sort of perturbations,
18:14
SPEAKER_01: including infectious disease.
18:17
SPEAKER_01: And so there's the biohub.
18:18
SPEAKER_01: And the reason why that collaboration is so cool,
18:21
SPEAKER_01: like UC Berkeley,
18:23
SPEAKER_01: fantastic biology bioengineering department,
18:28
SPEAKER_01: home to Jennifer Doudna.
18:30
SPEAKER_02: Pomoda College grad.
18:31
SPEAKER_01: Yeah.
18:31
SPEAKER_02: There we go.
18:32
SPEAKER_01: And then we have UCSF, one of the best medical systems in the world.
18:36
SPEAKER_01: Like, we were able to bring those groups together.
18:39
SPEAKER_01: Stanford Engineering came together to work on that problem.
18:42
SPEAKER_01: Super cool.
18:44
SPEAKER_01: We wanted to fund unmet needs, so we did grant funding.
18:48
SPEAKER_01: And we funded things.
18:49
SPEAKER_01: We knew that...
18:51
SPEAKER_01: The academic system is really good at funding the first,
18:53
SPEAKER_01: getting that first cool paper in,
18:55
SPEAKER_01: nature,
18:56
SPEAKER_01: science,
18:56
SPEAKER_01: cell,
18:57
SPEAKER_01: like that works and it's rewarded.
19:00
SPEAKER_01: But no one was really rewarding finishing complex problems.
19:04
SPEAKER_02: I see.
19:04
SPEAKER_02: Okay.
19:04
SPEAKER_02: Okay.
19:05
SPEAKER_01: And so human cell atlas falls into that category.
19:08
SPEAKER_01: The methodologies were out there,
19:11
SPEAKER_01: but no one was excited to fund or incentivize completing an atlas,
19:16
SPEAKER_01: which is like millions and millions of cells.
19:20
And how could we support a burgeon community to complete these
19:26
atlases so they can
19:27
SPEAKER_01: be useful artifacts for everyone to use?
19:32
SPEAKER_01: We funded patients to help make biologists better.
19:35
SPEAKER_01: And then we built tools.
19:38
SPEAKER_01: We built a sort of industry standard software engineering team,
19:44
SPEAKER_01: and we built tools like Cell by Gene,
19:47
and we supported open source tools that help scientists do
19:51
their work faster and
19:52
SPEAKER_01: better.
19:52
And then we learned that,
19:55
first of all,
19:55
the biohubs were awesome,
19:57
and now we have three more.
19:59
SPEAKER_02: Chicago, New York.
20:00
SPEAKER_01: Chicago,
20:01
SPEAKER_01: New York,
20:01
SPEAKER_01: and then imaging,
20:02
SPEAKER_01: which is sort of not,
20:05
SPEAKER_01: it sort of supports the other endeavors.
20:09
SPEAKER_01: We learned that we could build cool, big data sets.
20:13
Cell by Gene was both a useful tool used by thousands of
20:16
scientists on a weekly basis.
20:19
And it has a data set that people used to build a lot of these
20:22
models on top of now.
20:26
SPEAKER_01: And we could build useful technology.
20:29
SPEAKER_01: So we're like, okay, there's a little bit of flywheel going here.
20:33
SPEAKER_01: And then we're like, LLMs come on the scene.
20:37
SPEAKER_01: And we're like, holy cow.
20:39
SPEAKER_01: That was the moment where people went from being like,
20:42
SPEAKER_01: we can't even say your mission with a straight face,
20:45
SPEAKER_01: to being like,
20:45
SPEAKER_01: well,
20:46
SPEAKER_01: probably 20 years,
20:47
SPEAKER_01: you think?
20:48
SPEAKER_01: Why are we dilly-dallying?
20:51
SPEAKER_01: And we're like, OK, OK now.
20:53
And then we realized that between our ability to do science in
20:57
our biohubs,
20:58
SPEAKER_01: our ability to bring the community together to build assets,
21:01
SPEAKER_01: we could build data sets.
21:03
SPEAKER_01: We could build the compute that's necessary.
21:06
SPEAKER_01: We have one of the largest compute clusters for the life sciences.
21:13
SPEAKER_01: And we also had the scientists,
21:15
SPEAKER_01: the patients,
21:16
the doctors to make sure that what are the models that we're
21:19
building using large
21:21
SPEAKER_01: language models to elucidate
21:23
interesting questions and answers in biology apply and have
21:28
human impact.
21:29
SPEAKER_01: And so that's sort of where we are today.
21:31
SPEAKER_01: And,
21:32
SPEAKER_01: you know,
21:33
SPEAKER_01: in science,
21:34
SPEAKER_01: there's all sorts of the fact that there are weak,
21:38
SPEAKER_01: you know,
21:39
SPEAKER_01: we're not an entirely original idea.
21:41
We sort of come from inspiration from others and that there's
21:43
others following us.
21:45
SPEAKER_01: Like, that's awesome.
21:46
SPEAKER_01: Yeah.
21:46
SPEAKER_01: Yeah.
21:46
SPEAKER_02: Yeah,
21:47
SPEAKER_02: I think,
21:47
SPEAKER_02: I mean,
21:47
I don't really want to create like a science on science war or
21:51
anything like that.
21:51
And I think you're probably at least vaguely friends with
21:55
Patrick Collison and some
21:57
SPEAKER_02: of the people behind the Ark Institute.
21:59
SPEAKER_02: I mean, there's some similarities, right?
22:00
SPEAKER_02: Because they were trying to...
22:02
SPEAKER_02: They came after you guys,
22:03
but now they're trying to create this link between the
22:06
universities and apply AI
22:08
SPEAKER_02: to,
22:08
SPEAKER_02: to.
22:09
SPEAKER_00: Well, you know, Silvana was a biohub investigator.
22:11
SPEAKER_02: Okay.
22:11
SPEAKER_02: Okay.
22:12
SPEAKER_00: Um, and.
22:13
You don't feel,
22:14
you don't feel like they,
22:16
you guys,
22:16
you're not upset or anything.
22:17
SPEAKER_01: No, no.
22:19
SPEAKER_01: Well,
22:19
SPEAKER_01: Silvana was a biohub investigator and I think a lot,
22:22
SPEAKER_01: she's like,
22:23
she's able to take things that she like feels like works well
22:26
and build upon that.
22:28
SPEAKER_01: And like, we're, we are friends actually.
22:30
SPEAKER_01: Okay.
22:31
SPEAKER_01: Yeah.
22:31
SPEAKER_01: Right.
22:31
Well,
22:31
and then I talked to the people behind Convergent Research a
22:35
couple weeks ago.
22:38
They sort of have this – they saw these gaps where things that
22:44
might – research
22:45
SPEAKER_02: that might not be clear enough to become a startup.
22:47
SPEAKER_02: It may or may not have a business model around it.
22:49
SPEAKER_02: And it's also maybe like too expensive for a university to keep funding.
22:53
SPEAKER_02: I mean you outlined very clearly your attack.
22:57
SPEAKER_02: I was just wondering if you also saw these gaps where –
23:01
SPEAKER_02: Something like NIH funding gets you,
23:03
SPEAKER_02: like,
23:03
SPEAKER_02: so far,
23:04
but there's always going to be these massive projects that just
23:09
require more
23:09
SPEAKER_02: capital than probably makes sense unless that's just what you want to do.
23:13
SPEAKER_01: Well,
23:13
SPEAKER_01: I think the point at which biology is at,
23:16
SPEAKER_01: like,
23:16
SPEAKER_01: there is a lot more sort of open discovery science that needs to happen.
23:22
SPEAKER_01: But biology is actually kind of merging towards where physics is.
23:26
And you start needing sort of larger scale teams,
23:30
interdisciplinary teams.
23:32
SPEAKER_01: The equipment is getting very serious.
23:35
SPEAKER_01: We're not at the point of like LIGO,
23:38
SPEAKER_01: but I think we're getting closer and closer to being a team science,
23:42
SPEAKER_01: shared resources.
23:43
SPEAKER_01: And so my sense is our group, other groups that you're talking about,
23:50
SPEAKER_01: are trying different ways to attack that problem.
23:53
SPEAKER_01: Like,
23:54
what does biology look like beyond the individual wet lab to
23:58
solve modern biology
24:00
SPEAKER_01: questions?
24:01
SPEAKER_02: Okay.
24:02
SPEAKER_02: And you... I think it's about $7 billion to date that have gone into CZI.
24:07
SPEAKER_02: And I ask this next question with all due respect.
24:11
SPEAKER_02: I mean, you guys have pledged, I think, 99% of your wealth to this.
24:15
SPEAKER_02: How do you...
24:18
SPEAKER_02: Sometimes I look at something like the connectome,
24:20
which is this quest to make a map of the brain on this very
24:25
detailed level,
24:26
SPEAKER_02: every neuron,
24:26
SPEAKER_02: synapse,
24:27
SPEAKER_02: axon,
24:27
SPEAKER_02: and something I've been fascinated by for many years.
24:30
SPEAKER_02: I think there's open,
24:33
SPEAKER_02: fair debate as to how useful it may or may not be,
24:36
SPEAKER_02: which is maybe part of the problem.
24:37
SPEAKER_02: But,
24:38
SPEAKER_02: you know,
24:38
I go to these labs and I'm at Harvard and I'm with this Jeff
24:41
Lickman and he's got
24:43
SPEAKER_02: one brain slicer.
24:44
SPEAKER_01: Do you know I worked in Jeff Lickman's lab?
24:46
SPEAKER_01: In Jeff's lab?
24:46
SPEAKER_02: Oh, my God.
24:47
SPEAKER_02: He's one of my favorite people.
24:49
SPEAKER_01: As an undergrad.
24:50
SPEAKER_01: Did you?
24:50
SPEAKER_02: Okay.
24:51
SPEAKER_02: So did you work?
24:52
SPEAKER_02: Was he doing?
24:53
SPEAKER_02: Yeah, he would have been doing...
24:55
SPEAKER_01: I mean, I don't know.
24:56
SPEAKER_01: I was 19 years old.
24:58
SPEAKER_01: My job was to dissect individual fluorescent axons in mice.
25:03
SPEAKER_02: In mice.
25:04
SPEAKER_02: Okay.
25:04
SPEAKER_02: Okay.
25:04
SPEAKER_02: Well, so, you know, he's one of my favorite people.
25:07
SPEAKER_02: He's got this brain slicer.
25:10
SPEAKER_02: I think he has two maybe.
25:12
SPEAKER_02: And I'm like,
25:12
SPEAKER_02: Jeff,
25:12
SPEAKER_02: what would it take to like,
25:14
SPEAKER_02: you know,
25:15
we've done a worm's brain and we just did a fly and the quest
25:19
is to get to a human.
25:20
SPEAKER_02: And I'm like, what would it take?
25:22
SPEAKER_02: You know, he's like, well...
25:24
We'd have to have like a thousand of these brain slicers and
25:27
then just a ton of computing.
25:29
SPEAKER_02: And like at some point it's a resource problem.
25:31
SPEAKER_02: And then I'm like, well, it's the brain.
25:34
SPEAKER_02: Why don't, there's so many rich people in the world.
25:36
Like how do we not want to know like what the,
25:39
somebody must want to fund this.
25:41
SPEAKER_02: And so,
25:41
SPEAKER_02: yeah,
25:41
SPEAKER_02: I'm just,
25:41
SPEAKER_02: this is a very long winded question of like,
25:44
SPEAKER_02: how do you decide how much you're going to give away when,
25:48
SPEAKER_02: and some of these things might be like $10 billion projects on their own.
25:52
SPEAKER_02: Yeah.
25:53
SPEAKER_01: You know, we, for us, we started with a big portfolio.
26:01
SPEAKER_01: And I don't have a metric that tells me, like, that's a winner, right?
26:05
SPEAKER_01: But what you do is you look at, like, why us?
26:09
And,
26:09
like,
26:10
resources,
26:11
money itself is not enough to be,
26:13
like,
26:13
we are differentiated to do this.
26:15
We have to look at what else do we bring to the table and how
26:19
does it make the sum
26:20
SPEAKER_01: of the work better?
26:21
SPEAKER_01: And so we started really broad and we've been looking at like,
26:25
SPEAKER_01: okay,
26:25
SPEAKER_01: some of the stuff is like,
26:26
SPEAKER_01: you know,
26:27
SPEAKER_01: pushing a rock uphill that's like not going well.
26:29
SPEAKER_01: You've got to be real.
26:30
SPEAKER_01: And it's good work and good work that we care about.
26:34
SPEAKER_01: But it's like, we are not winning at this.
26:36
SPEAKER_01: And then some stuff you're like,
26:38
SPEAKER_01: oh my God,
26:38
SPEAKER_01: this flywheel,
26:39
SPEAKER_01: like the human cells,
26:40
SPEAKER_01: that flywheel just went.
26:42
And so that's a consortium of people that have been trying to
26:45
build a – it's like a virtual –
26:51
SPEAKER_02: Like a complete body at the cellular level, right?
26:54
SPEAKER_02: Of a map.
26:56
SPEAKER_02: I mean, sort of like the connectome, but for all cells.
26:59
SPEAKER_01: And yeah, that worked.
27:02
SPEAKER_01: And so when we see things that work, we should do more of them.
27:04
SPEAKER_01: Can I take an aside on human cell atlas?
27:06
SPEAKER_02: Yeah, 100%.
27:08
SPEAKER_01: So I'm going to knock it down to pretty basic for a second.
27:13
SPEAKER_01: So we've understood the human genome for decades now.
27:16
The human genome,
27:17
you know,
27:18
the way to think about it is that it's like you have kids.
27:23
SPEAKER_01: You're familiar with Legos.
27:24
SPEAKER_02: Yes.
27:25
SPEAKER_02: Yeah.
27:25
SPEAKER_02: In my foot.
27:27
SPEAKER_01: Yeah.
27:27
SPEAKER_01: Yeah.
27:27
SPEAKER_01: Deeply familiar.
27:28
SPEAKER_01: All the shapes by touch.
27:30
SPEAKER_01: So the human genome basically tells you,
27:33
SPEAKER_01: like,
27:33
SPEAKER_01: for you,
27:34
SPEAKER_01: what are all the possible Lego pieces that are available to create you?
27:38
SPEAKER_01: And we have been able to map that for a long time.
27:42
But we have never been able to say,
27:44
how do these pieces come together to create you?
27:48
SPEAKER_01: And that is actually really important because when you get sick,
27:51
SPEAKER_01: it's because some parts of you,
27:52
SPEAKER_01: some Lego pieces are broken,
27:54
SPEAKER_01: missing in the wrong places.
27:56
And what the Human Cell Atlas does,
27:59
we have trillions of cells in our body.
28:01
And it turns out each cell expresses a different subset of the
28:06
Lego pieces.
28:08
SPEAKER_01: And that makes a certain pack of Legos will create a heart cell.
28:14
SPEAKER_01: A certain pack will create a kidney cell, a skin cell.
28:17
SPEAKER_01: And then you can start seeing,
28:19
SPEAKER_01: oh,
28:19
SPEAKER_01: this is what makes each one of these cells unique and different.
28:23
SPEAKER_01: And then the next step will be to look at those spatially.
28:25
SPEAKER_01: Where are they in the cell?
28:27
SPEAKER_01: How do they come together?
28:28
SPEAKER_01: And then you start getting hopefully video too in live cells.
28:33
Then you start understanding sort of what the molecular
28:37
machinery inside of you and
28:39
SPEAKER_01: how all of that works.
28:41
SPEAKER_01: Then you can start understanding like,
28:43
SPEAKER_01: sometimes what is even that cell?
28:45
SPEAKER_01: What does it do?
28:46
SPEAKER_01: And where has it broken down?
28:48
And how can we design a therapeutic,
28:50
a drug,
28:52
a protein to actually change that cell state?
28:56
So suddenly we went from static to more and more dynamic
29:02
through the cell atlas.
29:04
SPEAKER_01: And sometimes the question I get about the human cell is just like,
29:09
SPEAKER_01: is this a stamp collecting activity?
29:11
SPEAKER_01: Is it worthwhile?
29:12
SPEAKER_01: Like, what is the impact on this?
29:15
And I think the thing that unlocks the power of these bodies
29:22
are the large language models.
29:24
SPEAKER_01: And we can start looking at differences.
29:26
SPEAKER_01: And turns out all your cells are kind of different.
29:29
SPEAKER_01: And you want to know what cells are doing related activities.
29:34
SPEAKER_01: For instance, CD47, very active in a number of cancers.
29:39
SPEAKER_01: And you can try to drug and target CD47, right?
29:44
SPEAKER_01: But the problem is, who else expresses CD47 and is necessary?
29:49
And the cool thing someone figured out by looking at these
29:54
atlases is that
29:56
SPEAKER_01: It's true.
29:57
SPEAKER_01: There are normal cells that need CD47, and there's the hijacked cells.
30:02
But the normal cells are expressing a variant that looks
30:05
slightly different.
30:06
So can you take advantage of that difference and design a
30:09
cancer drug that is
30:11
SPEAKER_01: specific with lower side effects than,
30:15
SPEAKER_01: you know,
30:15
SPEAKER_01: a molecule that is looking at the CD47 in a more general way?
30:20
SPEAKER_01: So that's sort of how to think about the human cell atlas.
30:26
And I was just looking,
30:27
because you have a software tool that does exactly that,
30:30
right?
30:30
SPEAKER_02: I thought I found the name here somewhere.
30:32
SPEAKER_01: Yeah, it's SellbyGene.
30:33
SPEAKER_02: SellbyGene.
30:34
SPEAKER_02: I was looking at,
30:34
SPEAKER_02: oh,
30:34
there was a different thing that I was checking last night that
30:37
I wanted to ask you
30:38
SPEAKER_02: about.
30:40
SPEAKER_02: Okay, I'll find it.
30:41
SPEAKER_02: We'll come back.
30:42
SPEAKER_01: Well, are you talking about some of the models?
30:45
SPEAKER_01: Yeah.
30:45
SPEAKER_02: It was, oh, here it is, transcriptformer.
30:48
SPEAKER_01: Oh, yes.
30:48
SPEAKER_02: Is that?
30:49
SPEAKER_01: Transcriptformer is great.
30:51
SPEAKER_01: And our ultimate goal at CZI is to build a virtual cell.
30:56
SPEAKER_01: And I'm super passionate about that.
30:58
SPEAKER_01: We can talk about that in general in a sec.
31:00
But the thing that transcriptformer does is that it allows you
31:03
to look at the
31:04
SPEAKER_01: transcripts,
31:04
SPEAKER_01: which are the mRNAs that we're looking at,
31:07
SPEAKER_01: and compare across species.
31:10
SPEAKER_01: And look, there's lots of model organisms you were talking about.
31:14
SPEAKER_01: Worms, flies, mice.
31:16
SPEAKER_01: How many times have we solved diabetes in mice?
31:19
SPEAKER_01: So many.
31:20
SPEAKER_01: They are diabetes free.
31:23
SPEAKER_01: But we are not.
31:24
SPEAKER_01: So what's the gap?
31:25
And so you can use a tool like TranscriptFormer to start
31:29
mapping model organisms,
31:31
SPEAKER_01: their cell activity,
31:33
SPEAKER_01: to understand,
31:33
SPEAKER_01: like,
31:34
SPEAKER_01: does that really map to the human?
31:36
SPEAKER_01: And actually vice versa.
31:38
SPEAKER_01: So only two species in the world get leprosy.
31:42
SPEAKER_01: Humans and armadillos.
31:45
SPEAKER_02: Oh, right.
31:45
SPEAKER_02: Yeah, okay.
31:46
SPEAKER_01: Okay.
31:46
SPEAKER_01: So how can we, what is it about these two that make them in common?
31:51
SPEAKER_01: And how do you use, say, armadillo as a model organism?
31:55
So the transcript form allows you to map across species because
32:01
it's obviously not
32:02
SPEAKER_01: always the best to study in humans.
32:04
So how do you map across species and make sure that you're
32:07
doing science that is
32:08
SPEAKER_01: meaningful and applicable to humans later on?
32:12
And so it's some sort of tool that is like taking in these
32:14
databases and then
32:16
SPEAKER_02: letting you,
32:16
SPEAKER_02: I mean,
32:17
SPEAKER_02: it seemed like it was playing off LLMs.
32:19
SPEAKER_01: It's based on an LLM architecture.
32:21
SPEAKER_01: It is a large language model.
32:23
And in the sense that it's also letting you ask like natural
32:26
language queries of
32:27
SPEAKER_02: this stuff,
32:28
SPEAKER_02: or do you have to know what you're doing when you...
32:31
SPEAKER_01: I think you can use it as sort of a biologist.
32:36
SPEAKER_01: I would say I'm not prepared to ask it specific questions,
32:41
SPEAKER_01: but in the context of biology,
32:43
SPEAKER_01: you don't need to be a computer scientist to use the tool.
32:46
SPEAKER_01: It's meant for biologists to use it.
32:49
SPEAKER_02: How much,
32:49
SPEAKER_02: like,
32:49
SPEAKER_02: on the LLM stuff,
32:50
SPEAKER_02: because it is so exciting,
32:52
SPEAKER_02: and I think there was,
32:54
SPEAKER_02: like,
32:54
SPEAKER_02: a thing last week,
32:55
SPEAKER_02: this Chai 2 came out,
32:57
SPEAKER_02: where they were coming up with antibodies on these,
33:01
SPEAKER_02: like,
33:01
SPEAKER_02: one-shot runs.
33:02
SPEAKER_02: I mean, stuff that, like, seems almost hard to believe.
33:05
You know,
33:06
I looked at what happened when Greg Brockman had his sabbatical
33:10
from OpenAI.
33:11
He went to Arc Institute,
33:13
and he helped fix some of their models,
33:15
which was awesome.
33:16
SPEAKER_02: But the one thing that was, like...
33:18
SPEAKER_02: Triggering in my head is like,
33:19
SPEAKER_02: you have this massive bidding war for talent among all these companies,
33:24
SPEAKER_02: as you would know,
33:26
SPEAKER_02: for AI talent.
33:27
And,
33:28
you know,
33:29
they're getting gobbled up by like all the big foundational
33:33
model companies.
33:34
Like,
33:34
are the science people going to be able to get the quality of
33:39
AI technology?
33:40
SPEAKER_02: talent to push all this forward.
33:42
SPEAKER_02: You know what I mean?
33:42
Like we're kind of using all our brain power on this language
33:46
coding quest over here.
33:48
SPEAKER_01: Well, I see what you're saying.
33:50
SPEAKER_01: So I look at the market.
33:51
SPEAKER_01: First of all, it's not easy.
33:52
SPEAKER_01: I'm not going to pretend like I have the answer.
33:55
SPEAKER_01: And there are folks who want to build things like superintelligence.
33:59
SPEAKER_01: And we're not that.
34:01
We're not even sort of building these large language models
34:03
from scratch,
34:04
right?
34:05
SPEAKER_01: And so we are using fine-tuning.
34:09
SPEAKER_01: We're building off of other models.
34:11
SPEAKER_01: We're building upon those that have done the work ahead of us.
34:15
SPEAKER_01: So what we do have is mission.
34:20
So people who want to work on biology,
34:22
there's a much smaller subset of places to go.
34:25
SPEAKER_01: So if you want to work on biology,
34:27
there are a smaller group that we're really discussing and
34:31
competing with on
34:32
SPEAKER_01: talent.
34:33
SPEAKER_01: And then the other thing is there's compensation,
34:36
SPEAKER_01: which is obviously important,
34:38
SPEAKER_01: and we cannot compete with the tech companies on this.
34:42
SPEAKER_01: But the other thing researchers really care about is access to GPUs.
34:47
Like you're not going to make the most of someone if you don't
34:50
actually have the
34:51
SPEAKER_01: GPUs for them to work from.
34:53
SPEAKER_01: So we have that at CCI.
34:56
SPEAKER_01: Our cluster is about 1,000.
34:59
SPEAKER_01: And we have goals to grow it larger.
35:00
SPEAKER_01: And that's what we have.
35:05
Like come work with us on biology and come work with us because
35:08
we're going to have
35:09
SPEAKER_01: the compute to sort of support the research that you want to do.
35:13
We should be forcing Jensen to keep 50,000 GPUs for science
35:17
every year or something like that.
35:23
SPEAKER_02: Okay.
35:24
SPEAKER_02: I noticed the areas that
35:28
And this was taken from my rushing,
35:32
not rushing,
35:32
but going through your website.
35:33
SPEAKER_01: I don't think you rushed.
35:35
SPEAKER_01: You know about my tennis team.
35:36
SPEAKER_02: At the different spots you're investing in.
35:38
I mean,
35:38
I spend so much of my time on brain-computer interface stuff,
35:43
on...
35:45
SPEAKER_02: epigenetic reprogramming.
35:47
SPEAKER_02: I don't know.
35:47
SPEAKER_02: These are just two of the things that have really grabbed me lately.
35:51
And so my worldview is a little skewed just because I've been
35:55
in those two worlds so much.
35:57
SPEAKER_02: I didn't see a lot of BCI stuff that you guys were looking at.
36:01
I mean,
36:01
it seemed a lot around building these foundational models of
36:07
the body.
36:08
I think in Chicago,
36:09
the focus is on inflammation or maybe that's just part of the
36:13
quest.
36:13
SPEAKER_02: Yeah, so...
36:16
SPEAKER_02: Yeah.
36:16
SPEAKER_02: I mean,
36:17
SPEAKER_02: this was,
36:17
SPEAKER_02: it's a long,
36:18
SPEAKER_02: another long winded question,
36:19
SPEAKER_02: but,
36:19
SPEAKER_02: but it felt to me like you guys were not following the,
36:24
SPEAKER_02: um,
36:25
SPEAKER_02: some of these areas that the rest of the world longevity,
36:28
SPEAKER_02: all these things that people seem to be chasing.
36:30
SPEAKER_02: And you were,
36:31
you were kind of doing your own thing on these very like deep
36:34
foundational
36:35
SPEAKER_02: technologies that unlock,
36:36
SPEAKER_02: um,
36:37
SPEAKER_02: like unlock things.
36:39
SPEAKER_01: Yeah.
36:39
So that comes back to the strategy of we're not trying to solve
36:44
one particular health problem.
36:47
We are really trying to build tools that makes everyone better
36:49
at their work.
36:50
SPEAKER_01: And so if you look at the things we're trying to do,
36:53
it really is about tool building that's going to be open to the
36:56
entire scientific
36:57
SPEAKER_01: community to build.
36:58
SPEAKER_01: On the virtual cell model,
37:00
we really believe that biology is going to be more creative and
37:04
faster if you build
37:06
SPEAKER_01: an in silico cell that biologists can flip the ratio on.
37:09
SPEAKER_01: Right now, it's 90% wet lab, 10% computational.
37:13
SPEAKER_01: And it's slow and it's hard.
37:15
SPEAKER_01: And it makes people risk averse.
37:18
SPEAKER_01: It's going to take so long to do that.
37:20
SPEAKER_01: I'm not going to test it unless I'm really convinced.
37:23
If you can build an in silico model and flip the ratio,
37:26
they're 90% computational,
37:26
10% wet lab.
37:28
SPEAKER_02: So you want to make a cell model that is so rich,
37:33
SPEAKER_02: a virtual cell that I can run experiments on it.
37:36
SPEAKER_02: I can test molecules.
37:38
SPEAKER_01: Exactly.
37:39
SPEAKER_01: I want to be able to de-risk that,
37:41
SPEAKER_01: do that in silico so that in the wet lab,
37:45
SPEAKER_01: I can just confirm it.
37:46
SPEAKER_01: That's going to make science faster.
37:48
SPEAKER_01: That's going to make people willing to test riskier ideas faster.
37:53
SPEAKER_01: And that is going to be something that accelerates a lot of science.
37:58
SPEAKER_01: It's going to change the paradigm.
38:00
SPEAKER_01: And it's actually going to very directly impact human health.
38:05
SPEAKER_01: And then at our biohubs,
38:08
we do a lot of work around engineering tools in addition to
38:13
sort of the large
38:15
SPEAKER_01: language models and modeling work that we're doing.
38:18
SPEAKER_01: In Chicago,
38:20
SPEAKER_01: what Shana is doing,
38:22
SPEAKER_01: which is incredible,
38:23
SPEAKER_01: is that she wants to understand inflammation at the tissue level,
38:27
SPEAKER_01: at the organ level.
38:28
SPEAKER_01: And,
38:29
SPEAKER_01: you know,
38:30
SPEAKER_01: right now,
38:30
SPEAKER_01: you tell a doctor,
38:32
SPEAKER_01: you're like,
38:33
SPEAKER_01: I don't know,
38:34
SPEAKER_01: like something my grandma would say,
38:36
SPEAKER_01: you know,
38:36
SPEAKER_01: my joints hurt when I eat cucumbers.
38:37
SPEAKER_01: And you're like, what?
38:40
SPEAKER_01: And like, you know, I'm a doctor.
38:41
SPEAKER_01: I'm like, I don't know what to do with that.
38:43
SPEAKER_01: But, you know, everyone's body works differently.
38:46
And there's probably truth in a lot of these things that cause
38:50
inflammatory states,
38:51
SPEAKER_01: whether it's arthritis like my grandmother,
38:54
SPEAKER_01: neurodegeneration,
38:55
SPEAKER_01: cardiac health,
38:57
SPEAKER_01: aging.
38:58
SPEAKER_01: And you want to be able to understand what the inflammatory patterns are.
39:02
But a lot of these changes are in a time scale that aren't
39:06
captured by blood tests
39:07
SPEAKER_01: that you get,
39:08
SPEAKER_01: however frequently you're taking them.
39:10
You're just not capturing these more fine changes that impact
39:15
your health in the way you feel.
39:18
Shayna developed a sensor that can be eventually used very
39:22
similar to a glucose
39:23
monitor that folks are using to actually look at these spikes
39:27
in inflammatory
39:28
SPEAKER_01: states and to be able to pick up super early signs of autoimmune flare,
39:35
SPEAKER_01: cancer recurrence,
39:36
SPEAKER_01: whatever it might be,
39:38
SPEAKER_01: and to be able to look at it with a fine temporal pattern.
39:43
SPEAKER_01: When you do this, this happens.
39:45
SPEAKER_02: I mean, I saw photos.
39:47
SPEAKER_02: It looked quite small in the photos.
39:49
SPEAKER_02: It's teeny tiny.
39:50
Does it get placed like an insulin sensor in one spot or you're
39:55
putting it near where you're?
39:57
SPEAKER_01: So right now it's mainly only in animals.
40:00
But the idea is that you could put it in your skin just like an
40:03
insulin sensor.
40:04
SPEAKER_01: But you can also in animal models use it,
40:07
SPEAKER_01: put it in the brain and look at inflammatory states in the brain.
40:12
And the novel part of that technology is that they use sort of
40:15
a physical vibration
40:16
SPEAKER_01: to be able to attract,
40:19
SPEAKER_01: record,
40:20
SPEAKER_01: and flick off the molecule so you get that fine pattern.
40:25
SPEAKER_01: Like when we tested it in mice,
40:27
SPEAKER_01: we could actually see when you poked,
40:30
SPEAKER_01: the animal did not like it when you used a needle to inject it.
40:35
SPEAKER_01: you could see the immune response just from that.
40:38
So you can start looking at much more clearer patterns and look
40:42
at immune profiles
40:44
SPEAKER_01: of different states of health.
40:47
SPEAKER_01: So that's Chicago.
40:48
SPEAKER_01: In New York, this is like the fantastic voyage.
40:52
SPEAKER_01: In New York,
40:53
what Andrea is trying to do is to be able to engineer immune
40:59
cells to say,
41:01
SPEAKER_01: go to Ashley's pancreas and tell me what's up.
41:04
SPEAKER_01: And you can give it instructions,
41:06
SPEAKER_01: and it can go to your pancreas,
41:09
SPEAKER_01: observe,
41:10
SPEAKER_01: and write into its own DNA what it observed,
41:14
SPEAKER_01: yes,
41:15
SPEAKER_01: no,
41:15
SPEAKER_01: of whatever signal you're looking for.
41:18
SPEAKER_01: And then it would lice and release cell-free DNA into your bloodstream.
41:22
SPEAKER_01: And then I could draw your blood,
41:25
actually much like a lot of the cell-free DNA that's used
41:27
around pregnancy now.
41:29
SPEAKER_01: And I could read out what that cell saw.
41:32
And then the next step would be to engineer immune cells to be
41:35
able to take an action.
41:39
SPEAKER_01: And so these are all, they're tools.
41:42
And they're tools that you can imagine using in a lot of
41:45
different contexts to
41:47
SPEAKER_01: accelerate science and biology.
41:48
SPEAKER_02: And so in each biohub just has a slightly different focus.
41:52
SPEAKER_01: Yeah.
41:52
SPEAKER_01: And then they come together.
41:54
SPEAKER_01: You know,
41:55
SPEAKER_01: there's sort of like an immune system theme because in San Francisco,
41:59
SPEAKER_01: we do a lot of transcriptomics and imaging and infection,
42:05
SPEAKER_01: which is another way to perturb the immune system.
42:08
And they're all generating interesting observations and data
42:12
sets that feed into
42:14
SPEAKER_01: our AI modeling work.
42:15
SPEAKER_02: Okay.
42:16
SPEAKER_02: I mean, it seemed overwhelming.
42:17
SPEAKER_02: You guys must be bombarded with so much information.
42:20
SPEAKER_02: And then,
42:21
SPEAKER_02: I mean,
42:23
SPEAKER_02: look,
42:23
I worked at Bloomberg for a while and I used to sit in the room
42:26
with some of the
42:27
people in charge of investing Mike's money from a philanthropic
42:32
perspective.
42:32
And I know he was always so concerned with doing it efficiently
42:35
and actually having
42:36
SPEAKER_02: the money do good.
42:37
SPEAKER_02: I mean...
42:38
SPEAKER_02: It's like,
42:38
SPEAKER_02: you know,
42:39
SPEAKER_02: it's like everybody's like,
42:41
SPEAKER_02: oh,
42:41
SPEAKER_02: I wish I had all the money I would fund this and this.
42:43
SPEAKER_02: You know, it's like a logistically hard thing to do.
42:46
How do you like what is your normal day like as you're trying
42:50
to triage both the
42:52
SPEAKER_02: science and then what you're going to back?
42:56
I spend a lot of time with the scientists to understand what
43:01
they're excited about,
43:03
SPEAKER_01: what's feasible,
43:04
SPEAKER_01: and what is the plan to do there.
43:06
SPEAKER_01: And the most common question I ask is, are we being ambitious enough?
43:11
SPEAKER_01: And where is the risk?
43:12
SPEAKER_01: How could we make this go faster?
43:13
SPEAKER_01: And I want to invest in a way that...
43:21
SPEAKER_01: optimizes our chance of doing something truly incredible.
43:23
SPEAKER_02: Yeah, so the big stuff, yeah.
43:25
SPEAKER_02: I'm just keeping an eye on time,
43:27
because there were actually a couple of things that have just
43:29
happened that I want
43:30
SPEAKER_02: to talk about.
43:31
SPEAKER_02: Yeah, I mean, so I've covered rare disease for a really long time.
43:37
SPEAKER_02: quite often children and it's just terrible on all fronts.
43:41
SPEAKER_02: I mean,
43:42
SPEAKER_02: often these genetic diseases that,
43:44
SPEAKER_02: um,
43:45
SPEAKER_02: are devastating children,
43:46
SPEAKER_02: you know,
43:47
SPEAKER_02: might,
43:48
SPEAKER_02: we'll have very often quite short lifespans.
43:50
SPEAKER_02: It's,
43:50
SPEAKER_02: uh,
43:52
SPEAKER_02: part of medicine where,
43:53
SPEAKER_02: um,
43:54
pharmaceutical companies tend not to be incented to pursue
43:58
something cause the
43:59
SPEAKER_02: market's not big enough.
44:00
SPEAKER_02: But then when you add up all the people with rare diseases,
44:03
SPEAKER_02: it's actually quite a large population.
44:05
SPEAKER_02: They just,
44:06
SPEAKER_01: Hundreds of millions.
44:06
SPEAKER_02: Right, but they just all happen to be quite different.
44:08
SPEAKER_02: And so we talked about Jennifer Doudna before.
44:11
SPEAKER_02: And what was it?
44:13
SPEAKER_02: It was like two months ago, I think, there was this baby.
44:16
SPEAKER_02: Is it KJ?
44:17
SPEAKER_01: Baby KJ.
44:17
SPEAKER_02: Baby KJ.
44:18
SPEAKER_02: So had one of these rare genetic diseases.
44:21
SPEAKER_02: We actually created a therapy after KJ was born to treat it.
44:26
SPEAKER_02: I think it was liver or kidney?
44:28
SPEAKER_02: Liver.
44:28
SPEAKER_02: Liver.
44:29
SPEAKER_02: And so this is just a little context of we just had this momentous
44:34
thing with this baby where regulators and the therapies all
44:39
came together quickly
44:40
SPEAKER_02: to help the child.
44:42
SPEAKER_02: And now it sounds like you guys are going to kind of broaden this out.
44:46
SPEAKER_01: Yeah.
44:47
SPEAKER_01: This is one I'm super excited about.
44:49
And it brings together all the different threads that we've
44:51
been talking about.
44:52
SPEAKER_01: Baby KJ had what's called a urea cycle disorder,
44:56
SPEAKER_01: which means that his liver is unable to process a byproduct of protein.
45:04
SPEAKER_01: And the byproduct is toxic.
45:06
And so every time his liver is unable to keep up with the
45:10
protein that he's
45:11
SPEAKER_01: ingested,
45:12
SPEAKER_01: he accumulates toxins.
45:14
SPEAKER_01: He would have.
45:14
SPEAKER_01: Sorry, he does not, which is the cool part.
45:17
SPEAKER_01: He would have accumulated toxins that led him to have brain damage.
45:22
And eventually this accumulates in a child to can be pretty
45:27
severe intellectual disability.
45:30
SPEAKER_01: And from there...
45:34
SPEAKER_01: When he was diagnosed,
45:37
SPEAKER_01: a team at CHOP,
45:38
SPEAKER_01: between CHOP,
45:39
SPEAKER_01: IGI,
45:40
SPEAKER_01: Jennifer Downis Group at Berkeley,
45:42
SPEAKER_01: and Danaher,
45:43
actually had all the parts already ready to go to understand
45:48
his specific mutation
45:50
SPEAKER_01: and to use CRISPR to go in and correct that mutation.
45:53
SPEAKER_01: Yeah.
45:53
SPEAKER_01: And he was given,
45:55
SPEAKER_01: within seven months of his life,
45:56
SPEAKER_01: he was given three doses of CRISPR,
45:59
SPEAKER_01: a CRISPR-powered therapeutic,
46:02
SPEAKER_01: to change that gene.
46:05
And he is now able to normally process protein with no fear of
46:10
going into these
46:10
SPEAKER_01: metabolic crises.
46:12
SPEAKER_01: That's incredible.
46:13
SPEAKER_02: Right.
46:13
SPEAKER_02: I mean,
46:14
SPEAKER_02: yes,
46:14
SPEAKER_02: because gene therapies are expensive,
46:16
SPEAKER_02: rare,
46:16
SPEAKER_02: and we hadn't tried these new techniques.
46:19
SPEAKER_01: KJ was the first.
46:21
SPEAKER_01: We've had gene therapies, and we have also used CRISPR.
46:24
KJ was the first one where we looked at his specific mutation
46:27
and designed a
46:28
SPEAKER_01: bespoke drug for him.
46:31
SPEAKER_01: And what we're excited to do is how do we...
46:36
continue going down this path and use the CRISPR-Cas9 platform
46:41
to use the molecules
46:42
that have been designed and to partnership with the FDA to
46:46
build this into a
46:47
platform where it becomes easier and faster to be able to
46:53
design a bespoke gene
46:56
SPEAKER_01: therapy to a patient,
46:58
SPEAKER_01: most often a baby,
47:00
and to correct a mutation that would have dramatically altered
47:03
their life.
47:04
And what we're doing in partnership with Jennifer and the IGI
47:10
is to create a center at UCSF.
47:13
SPEAKER_01: It's like this one I feel so passionate and sort of makes my heart.
47:20
SPEAKER_01: I don't know.
47:21
It's both excitement and sort of,
47:25
I don't know,
47:26
passion,
47:27
I guess is what I call it.
47:28
It's because we are literally going back to the teams that I
47:31
trained with as a pediatrician.
47:34
SPEAKER_01: that motivated me to invest in basic science.
47:38
And with this program to gene edit children who are born
47:45
without immune systems and
47:47
SPEAKER_01: without functioning livers to be able to process their food.
47:53
And these kids otherwise would have had progressive illnesses
47:58
and problems.
47:59
SPEAKER_01: really severe,
48:01
SPEAKER_01: unpleasant,
48:02
long-term years in the hospital and sort of never going back to
48:07
a normal baseline.
48:09
And so we're going to be able to really closely tie together
48:13
what we know from the
48:14
SPEAKER_01: basic science and deliver a therapeutic to impact people's lives.
48:19
SPEAKER_02: I don't want to give people...
48:22
SPEAKER_02: false hope or even set timelines on some of the stuff.
48:25
SPEAKER_02: Cause it's such difficult,
48:26
SPEAKER_02: but like,
48:26
SPEAKER_02: do we,
48:26
SPEAKER_02: so you've,
48:27
SPEAKER_02: we feel like what we saw with baby KJ,
48:31
SPEAKER_02: um,
48:35
It proved enough stuff out that we feel like we will be able to
48:38
do this for
48:39
SPEAKER_02: multiple other conditions on a – none of this is easy,
48:43
SPEAKER_02: but,
48:43
SPEAKER_02: like,
48:44
SPEAKER_02: I'm trying to find the right word.
48:45
SPEAKER_02: Feasible?
48:46
SPEAKER_02: Yeah, yeah.
48:47
SPEAKER_01: So, yes.
48:48
And I think you have to be super careful about the cases that
48:51
you choose because
48:52
SPEAKER_01: there are unknowns and there are risks.
48:55
SPEAKER_01: And so you want to choose a case in which it would be the risks –
49:02
SPEAKER_01: sort of are outweighed by the potential upside that the patient has.
49:05
And so we're going to be working with a clinical advisory group
49:07
to work on that.
49:09
You choose the cases carefully and you work with the families
49:13
and you work with the
49:14
physicians to figure out how to deploy this and you monitor
49:18
what's going on.
49:19
SPEAKER_01: But the interesting thing is, as you get to more and more...
49:25
SPEAKER_01: you understand sort of how long it lasts.
49:27
SPEAKER_01: What are the downstream effects?
49:29
SPEAKER_01: Like, what are we seeing?
49:30
SPEAKER_01: Because you're worried about off-target effects.
49:33
And this is where actually some of the cell modeling work comes
49:36
into play as well.
49:37
SPEAKER_01: Because you want to know, you know, in the case of baby KJ,
49:42
we edited his liver,
49:45
what are the other effects that this edit will have?
49:49
SPEAKER_01: And you test this in mice,
49:51
SPEAKER_01: you test this in non-human primates,
49:54
but what you ultimately would be incredibly helpful is to be
49:58
able to look at this
50:00
edit in a virtual setting and look at the downstream effects to
50:05
be able to predict
50:06
SPEAKER_01: what sorts of consequences,
50:08
SPEAKER_01: intended or not intended,
50:10
SPEAKER_01: you would have.
50:11
And is it going to be all monogenetic single gene conditions
50:15
for the foreseeable future?
50:16
SPEAKER_01: Yeah.
50:16
We're planning,
50:18
in this model and this investment,
50:19
we're targeting about eight patients.
50:21
SPEAKER_01: Okay.
50:22
SPEAKER_01: And we'll learn a lot from these eight patients,
50:25
SPEAKER_01: both from the regulatory perspective and the scientific perspective.
50:28
SPEAKER_01: But you really want to know the exact mutation.
50:31
So we're going after really well-characterized Mendelian
50:36
monogenetic diseases where
50:37
SPEAKER_01: we understand the pathway.
50:39
But the really cool thing and the other thing that's happening
50:42
for us this week is
50:44
SPEAKER_01: that we're releasing an AI model called Gremlin.
50:48
And the thing that Gremlin does is Gremlin looks at
50:52
non-monogenetic diseases.
50:55
It is looking at the protein expression of a constellation of
50:59
your gene profile and
51:01
looking at where a disease has sort of a lever or a sort of key
51:08
intersection that
51:10
SPEAKER_01: would help in sort of a lot of other diseases like ALS,
51:14
SPEAKER_01: Parkinson's,
51:16
SPEAKER_01: autoimmune disease.
51:18
A lot of factors come into play and you want to understand
51:20
where the linchpin is.
51:22
SPEAKER_01: So the one I find helpful to think about is, say, autoimmune disease.
51:28
SPEAKER_01: In autoimmune disease,
51:30
you have usually a genetic predisposition and the dreaded
51:35
variant of unknown
51:36
SPEAKER_01: significance.
51:37
It's great when it's a slam dunk,
51:39
but then there's usually a variant of unknown significance.
51:42
There's usually sort of an environmental factor,
51:45
whether it's an infection or pregnancy.
51:48
And then something happens where it triggers some people to
51:52
flip into illness and
51:53
SPEAKER_01: some people don't.
51:55
SPEAKER_01: And what Gremlin could do is look at all these pathways
51:59
SPEAKER_01: And instead of looking at a single pathway that you can correct,
52:01
SPEAKER_01: like we were talking about with the work that we'll do with Jennifer,
52:05
SPEAKER_01: Gremlin looks at the multiple pathways.
52:08
And individually,
52:09
it's unclear sort of at what point you should intervene.
52:12
SPEAKER_01: But together,
52:13
SPEAKER_01: you can look at the intersection and say,
52:15
SPEAKER_01: ah,
52:15
SPEAKER_01: it's this protein that goes wrong when that happens.
52:18
SPEAKER_01: And Gremlin is aimed to really help you understand these non-Mendelian
52:24
SPEAKER_01: multifactorial illnesses that so many of us deal with.
52:27
SPEAKER_02: And so it's – and I have that in my notes, but it's an application.
52:30
SPEAKER_02: I mean, what's the best way to describe it?
52:32
SPEAKER_02: A tool?
52:34
SPEAKER_01: It's like an AI-agentic model.
52:36
SPEAKER_02: It's a model.
52:36
SPEAKER_02: Right.
52:36
SPEAKER_02: Okay.
52:37
SPEAKER_02: And you guys open source like a ton of this stuff.
52:39
SPEAKER_01: All of it, yeah.
52:40
SPEAKER_02: I'm keeping an eye on time.
52:42
SPEAKER_02: I'm sorry to do this.
52:43
SPEAKER_02: I have to ask you a couple other questions.
52:45
SPEAKER_02: Sure.
52:45
SPEAKER_02: There's been a couple stories about you guys lately.
52:48
SPEAKER_02: And so you've been at this for 10 years.
52:51
SPEAKER_02: I think when you started, it was –
52:53
SPEAKER_02: It was health, well, science and education.
52:57
SPEAKER_02: And there were a couple of buckets.
52:59
SPEAKER_02: And as time has gone on,
53:01
SPEAKER_02: it feels very science is the priority,
53:05
SPEAKER_02: or at least that's my interpretation.
53:07
SPEAKER_02: And I think you wrote a newsletter to this effect.
53:11
SPEAKER_02: So the standard did a story, and then a couple of people followed.
53:14
SPEAKER_02: And I guess they were arguing in these pieces
53:17
SPEAKER_02: They felt like politics has come into play on some of CZI's mission and,
53:22
SPEAKER_02: um,
53:23
and that you guys had gotten rid of some of these other things
53:26
and then emphasized
53:27
SPEAKER_02: science.
53:28
SPEAKER_02: I mean, I don't actually care.
53:30
SPEAKER_02: Um, I'm pretty pro science and money going towards science.
53:33
Um,
53:34
but I,
53:34
you know,
53:34
I can see why some people would,
53:36
I know you wrote a newsletter of this.
53:37
SPEAKER_02: I just wanted,
53:38
SPEAKER_02: you know,
53:38
SPEAKER_02: if there was something you wanted to address on this,
53:42
SPEAKER_02: this front and,
53:43
and sort of how you guys are thinking about it and then some of
53:46
that criticism,
53:46
SPEAKER_02: I guess.
53:47
SPEAKER_01: Yeah.
53:48
SPEAKER_01: You mentioned this,
53:49
SPEAKER_01: but over the past 10 years,
53:51
SPEAKER_01: we've invested $7 billion in science,
53:55
SPEAKER_01: in the community,
53:56
SPEAKER_01: in all these different efforts.
53:58
SPEAKER_01: And a lot of good work has happened, and we're proud of that good work.
54:02
SPEAKER_01: And also, not all of it has gone the way we expected.
54:08
SPEAKER_01: And some of it has been sort of, it just hasn't hit that flywheel.
54:12
SPEAKER_01: And meanwhile,
54:15
SPEAKER_01: In science,
54:16
SPEAKER_01: we have hit an incredible,
54:17
interesting inflection point where putting more resources we
54:22
think actually can
54:23
SPEAKER_01: produce outside results.
54:26
SPEAKER_01: And it's something I care deeply about.
54:29
SPEAKER_01: This is not, I didn't stumble upon science yesterday.
54:33
SPEAKER_01: This is something, you know, I've been reading that biology textbook.
54:36
SPEAKER_01: I've been in medicine and I'm thrilled to be able to,
54:40
SPEAKER_01: with the work with Jennifer,
54:42
SPEAKER_01: to go back to thinking about patients.
54:44
SPEAKER_01: Like this is ultimately...
54:46
SPEAKER_01: my life's work.
54:47
And so when given the opportunity to focus our efforts and
54:52
invest more in something
54:53
SPEAKER_01: that is going to improve the lives of a lot of people,
54:56
SPEAKER_01: like I'm excited to do it.
54:59
SPEAKER_02: So when you,
55:00
SPEAKER_02: when you see criticism like that,
55:01
SPEAKER_02: I mean,
55:01
SPEAKER_02: does it,
55:02
SPEAKER_02: um,
55:03
SPEAKER_02: I don't know,
55:04
SPEAKER_02: does it,
55:05
SPEAKER_02: does it bother you?
55:06
SPEAKER_02: Does it not?
55:06
SPEAKER_02: Is it just the reality of the situation?
55:08
SPEAKER_02: Um, yeah, I don't know how you process it.
55:12
SPEAKER_01: Uh,
55:14
SPEAKER_01: I think in scenarios like that,
55:16
SPEAKER_01: you just,
55:17
SPEAKER_01: well,
55:17
SPEAKER_01: everyone's entitled to their opinion,
55:19
SPEAKER_01: especially when you do something so publicly.
55:21
SPEAKER_01: And I think the parts where I want criticism to make us better.
55:28
SPEAKER_01: And so when I get criticism, like, have they thought about this?
55:31
SPEAKER_01: What will this look like?
55:32
SPEAKER_01: Yeah, that's great.
55:33
SPEAKER_01: And I'm going to take that and internalize it and make the work better.
55:36
And then sometimes people are expressing their opinions and you
55:39
just have to
55:40
SPEAKER_01: respect it for that.
55:40
SPEAKER_02: I mean, I get some, it's like, you know,
55:44
Wealthy,
55:45
powerful people doing big things are always going to be under
55:48
the microscope.
55:49
You're doing big stuff in the world,
55:51
you know,
55:51
like in the arena,
55:52
I guess,
55:53
as people say.
55:54
SPEAKER_02: But then there's an element to some of this.
55:59
It's like,
55:59
God,
56:00
I mean,
56:00
you're putting like billions of dollars in the science.
56:02
SPEAKER_02: It's like, I don't know.
56:05
SPEAKER_02: I don't know.
56:06
seems like a decent thing to do um yeah the uh so you you just
56:11
came out with these
56:12
two big announcements um that'll probably be coming out just
56:16
like a couple days
56:17
before this podcast airs um what else are like i mean there's
56:21
it seems like there's
56:21
tons of priorities but um is there like one big project you're
56:26
really eyeing within
56:28
SPEAKER_02: the next year
56:29
SPEAKER_01: Yeah,
56:30
SPEAKER_01: I mean,
56:31
we're lining up all of our ducks to really accelerate science
56:36
at the intersection
56:38
SPEAKER_01: of AI and biology.
56:40
SPEAKER_01: Like, that's what we're going for.
56:42
We want to be able to build useful tools to make work like
56:47
Jennifer's better.
56:49
SPEAKER_01: We want to build tools that help us understand complex diseases.
56:53
SPEAKER_01: We want scientists to be using our tools,
56:56
SPEAKER_01: whether it's,
56:57
SPEAKER_01: you know,
56:57
SPEAKER_01: Shana's sensor or the engineered cells coming out of New York.
57:00
And we actually have a bunch of stuff that we're building out
57:03
of San Francisco that
57:04
SPEAKER_01: we didn't get to.
57:04
But,
57:05
like,
57:06
we want all those tools to come together to make science go
57:10
faster.
57:10
SPEAKER_02: Is there, like, any worry that we...
57:15
SPEAKER_02: I am bombarded by AI meets science startups, and everyone's so excited.
57:22
SPEAKER_02: And a lot of the medical stuff, this technology definitely seems to work.
57:25
SPEAKER_02: I don't know.
57:26
SPEAKER_02: Is there any worry we put too many eggs in this basket?
57:30
SPEAKER_02: Because,
57:30
SPEAKER_02: you know,
57:30
SPEAKER_02: there's still the fear that,
57:31
SPEAKER_02: like,
57:32
SPEAKER_02: oh,
57:32
SPEAKER_02: these LLMs are amazing,
57:34
SPEAKER_02: but,
57:34
SPEAKER_02: you know,
57:35
SPEAKER_02: we may be hitting a wall.
57:36
SPEAKER_02: We may...
57:38
There might be limits to this and that we go sort of too far
57:41
that direction.
57:42
And then it turns out these things can't do what we thought
57:45
they could do.
57:47
SPEAKER_01: I would,
57:48
SPEAKER_01: you know,
57:48
SPEAKER_01: my approach here and Mark's approach here is like,
57:51
SPEAKER_01: let's go all in to know whether or not it was possible and we'll learn.
57:55
And even if it wasn't possible,
57:57
great tools and great things will come out of it.
58:00
SPEAKER_01: And if, you know, if we actually get to the moon, that's awesome.
58:04
SPEAKER_02: Yeah.
58:05
SPEAKER_01: But it's better than sort of giving it a half-hearted,
58:07
SPEAKER_01: hedged effort because we'll never really know.
58:10
SPEAKER_02: Yeah.
58:11
SPEAKER_02: Yeah.
58:11
And I don't know if you get too into this stuff,
58:13
but like the state of U.S.
58:16
I mean,
58:16
obviously there has been some cuts to NIH,
58:18
like drastic cuts at some of these labs.
58:21
SPEAKER_02: Yeah, I don't know.
58:22
I mean,
58:22
how do you feel about the state of American science at the
58:24
moment?
58:26
SPEAKER_01: I think...
58:28
SPEAKER_01: The foundation,
58:29
the bedrock that we all get to build from in American science
58:32
is incredible because
58:34
SPEAKER_01: of the investment that our government has made.
58:36
SPEAKER_01: It's so important.
58:39
SPEAKER_01: And the reason why we're able to take these sort of high risk,
58:42
SPEAKER_01: high reward projects with it is because the broader ecosystem exists.
58:48
And I think we need to continue ensuring that like American
58:53
science is fantastic.
58:56
And I think sometimes we sort of lose the thread because the
59:00
time between like a
59:01
SPEAKER_01: basic science discovery and impact in medicine,
59:03
SPEAKER_01: for instance,
59:04
SPEAKER_01: is decades and decades.
59:05
SPEAKER_01: People just lose the thread.
59:07
SPEAKER_01: That's partially why this case CHOP with baby KJ is so inspirational.
59:11
SPEAKER_01: Because, you know, Jennifer got the Nobel Prize in 2015, or 2020.
59:14
SPEAKER_01: She won the Breakthrough Prize in 2015.
59:18
SPEAKER_01: She won the Nobel Prize in 2020.
59:19
SPEAKER_01: And today, in 2025, a baby has had its lives changed.
59:21
SPEAKER_01: His life changed.
59:22
SPEAKER_01: And so I think we've...
59:32
We've got to reignite people's imagination about how impactful
59:36
and cool science is.
59:39
SPEAKER_01: And I'm not saying the system's perfect.
59:42
There are imperfections in the system that I think we would all
59:46
like to see improved.
59:47
SPEAKER_01: Like right now we're funding scientists way too late in their career.
59:51
SPEAKER_01: You know this, but the average R01 goes to a 45-year-old.
59:55
And I would venture to say our younger people are more
59:59
creative,
59:59
bolder,
1:00:00
and want to take risks.
1:00:03
SPEAKER_01: The system is imperfect,
1:00:04
SPEAKER_01: and so we need to sort of give it a boost,
1:00:08
SPEAKER_01: but it's what incredible breakthroughs are built off of.
1:00:12
SPEAKER_02: Okay.
1:00:12
SPEAKER_02: Well, I know, I think you've got to go.
1:00:16
SPEAKER_02: And I know you're on a busy schedule.
1:00:18
SPEAKER_02: Thank you so much for coming.
1:00:19
SPEAKER_02: We have a story coming on baby KJ Ashley, written by a doctor.
1:00:23
SPEAKER_02: I don't know when it's going to publish,
1:00:25
but it's kind of the story behind the story on the regulatory
1:00:29
journey that
1:00:30
SPEAKER_02: happened.
1:00:31
SPEAKER_02: And then I know I'm determined.
1:00:35
SPEAKER_02: Well,
1:00:35
SPEAKER_02: I live here,
1:00:35
SPEAKER_02: so we'll do some UCSS stuff,
1:00:37
SPEAKER_02: but I want to go to Chicago and check out the inflammation work for sure.
1:00:42
And our imaging institute where we have our cryo-EM is actually
1:00:47
just in Redwood City.
1:00:49
And I'm super into that because it feels like we're on the
1:00:51
verge of so many
1:00:52
SPEAKER_02: breakthroughs on imaging.
1:00:54
SPEAKER_01: Imaging is going to be huge.
1:00:57
SPEAKER_01: And they'll show, I mean, they'll nerd out with you.
1:01:00
SPEAKER_01: With Holger Mueller from UC Berkeley,
1:01:02
SPEAKER_01: they just built the laser face plate and lots of really tiny mirrors.
1:01:06
SPEAKER_01: Yeah.
1:01:07
SPEAKER_01: That has sort of blown the contrast in resolution.
1:01:10
SPEAKER_01: through the roof, even for the latest cryos.
1:01:12
SPEAKER_02: Okay.
1:01:12
SPEAKER_02: Well, I'm into optics.
1:01:13
SPEAKER_02: All right.
1:01:14
SPEAKER_02: My very last question is a very quick one.
1:01:15
SPEAKER_02: On these famous 4th of July videos,
1:01:18
SPEAKER_02: I mean,
1:01:19
SPEAKER_02: is that like just a dude's thing or are you behind the scenes?
1:01:24
SPEAKER_01: I'm there.
1:01:25
SPEAKER_01: There's shenanigans available for everyone.
1:01:28
SPEAKER_01: We even just filmed the Avengers video with our kids.
1:01:33
SPEAKER_01: So it's a whole family affair.
1:01:35
SPEAKER_02: Okay.
1:01:35
SPEAKER_02: Okay.
1:01:36
SPEAKER_02: All right.
1:01:36
SPEAKER_02: I thought it might be.
1:01:37
SPEAKER_02: I was just checking.
1:01:38
SPEAKER_02: Well, thank you so much for coming in.
1:01:39
SPEAKER_02: It was such a pleasure.
1:01:40
SPEAKER_02: And thank you.
1:01:42
SPEAKER_01: Thank you for having me.
1:01:46
core memory is hosted by me ashley vance the show is produced
1:01:51
by david nicholson
1:01:53
and me our theme music is by james mercer and john sortland and
1:01:58
the show is edited
1:02:00
by the john sortland thank you all for listening as mentioned
1:02:06
the show is sponsored
1:02:07
SPEAKER_02: by you on ventures thanks