Latest / Core Memory / Joe Betts LaCroix on Longevity and Building and Destroying Things

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Joe Betts LaCroix on Longevity and Building and Destroying Things · Core Memory

0:02
SPEAKER_02: If someone's dying of Alzheimer's, no, we're just dumb.
0:05
SPEAKER_02: We don't know what to do about it.
0:06
SPEAKER_02: There's just no solution in nowhere in humanity.
0:09
SPEAKER_02: It's not poorly distributed or anything.
0:11
SPEAKER_02: It's just, we just don't know what to do.
0:13
SPEAKER_02: And it turns out that's true for a huge range of different horrific,
0:18
SPEAKER_02: slowly killing you,
0:19
SPEAKER_02: miserably degenerative age-related diseases.
0:22
SPEAKER_02: We don't know what to do.
0:23
SPEAKER_02: So like,
0:24
there's a technological realm that I can dive into and make a
0:27
huge difference for people.
0:44
SPEAKER_01: Hello, everyone.
0:46
This is Ashley Vance,
0:47
and welcome to the very first episode of the Core Memory Podcast.
0:53
SPEAKER_01: Have a treat for you today.
0:56
SPEAKER_01: Our first guest is Joe Betts-Lacroix.
1:00
SPEAKER_01: the CEO and founder of RetroBio.
1:04
SPEAKER_01: He is a longevity guru,
1:06
SPEAKER_01: a polymath,
1:08
SPEAKER_01: and just an interesting,
1:10
SPEAKER_01: thoughtful guy that I've gotten to know over the last couple of years.
1:14
SPEAKER_01: I think you'll enjoy our chat.
1:17
SPEAKER_01: And with that, let's go talk to Joe.
1:23
SPEAKER_01: Ashley.
1:24
SPEAKER_01: Hello, Joe.
1:25
SPEAKER_01: Thank you for being the first victim of my Poor Memory podcast.
1:29
SPEAKER_01: I used to be a podcaster, took a break for many years, and we're back.
1:33
SPEAKER_01: Psyched to be here.
1:34
SPEAKER_01: Well,
1:35
SPEAKER_01: we are here at RetroBios headquarters in Redwood City,
1:39
SPEAKER_01: California,
1:40
SPEAKER_01: and I am with Joe Betts Lacroix,
1:43
SPEAKER_01: the CEO and co-founder of the company.
1:45
SPEAKER_01: I have all kinds of things I want to ask you about,
1:49
SPEAKER_01: but maybe for people who haven't heard of Retro,
1:51
SPEAKER_01: we should start there and give them an idea of what you guys do.
1:55
SPEAKER_02: We're a pharma company,
1:57
SPEAKER_02: an aspiring pharma company,
1:59
SPEAKER_02: working to extend healthy human lifespan by 10 years.
2:04
SPEAKER_01: And you've been at it, what is it, like four years?
2:07
SPEAKER_01: Is that right?
2:07
SPEAKER_01: Off the top of my head?
2:09
SPEAKER_02: Yeah, we've been operating for about three years.
2:11
SPEAKER_01: Quite famously,
2:12
one of the biggest names in the longevity world in this new
2:15
wave of longevity startups.
2:18
And wrote a big story about you guys in Bloomberg Business
2:21
Magazine a little while ago.
2:23
SPEAKER_01: Sam Altman was the original backer of the company.
2:28
SPEAKER_01: And you guys have been looking at a variety of different areas.
2:31
One that I want to get into in particular a little bit later is
2:34
this partial cell reprogramming.
2:36
SPEAKER_01: This idea of taking...
2:38
SPEAKER_01: ourselves back to a healthier, younger state throughout the body.
2:42
SPEAKER_01: But we'll get into that in a little second.
2:45
SPEAKER_01: I've known you for a long time.
2:46
SPEAKER_01: You've got this very interesting,
2:48
SPEAKER_01: slightly unusual career path,
2:49
SPEAKER_01: although nothing's normal really,
2:51
SPEAKER_01: I guess,
2:52
SPEAKER_01: in Silicon Valley.
2:53
SPEAKER_01: But, you know, you've worked in a variety of different areas.
2:55
SPEAKER_01: You've got a super interesting story growing up.
2:59
SPEAKER_01: Take me back a little bit to where young Joe comes from.
3:03
SPEAKER_01: Do I start at birth?
3:05
SPEAKER_01: No.
3:08
SPEAKER_02: I don't remember that, but the birth is a little fuzzy still.
3:11
SPEAKER_02: What do I start remembering?
3:13
Yeah,
3:13
I grew up in a community of people who were starting over with society.
3:19
SPEAKER_02: That's what they thought.
3:20
SPEAKER_02: They were like, we're going to make a great new world.
3:22
We don't have to be constrained by basically the 50s was the
3:26
counterpart in their
3:27
SPEAKER_02: head of the thing that they didn't want to emulate.
3:28
SPEAKER_02: It was the so-called hippies.
3:31
SPEAKER_02: And let's create a better world.
3:32
SPEAKER_02: And this is up in Oregon, if I remember right.
3:35
SPEAKER_02: What did your parents ostensibly do?
3:37
SPEAKER_02: I guess various things.
3:39
SPEAKER_02: My dad has done a number of professions,
3:43
SPEAKER_02: starting off English professor at various colleges around Oregon.
3:48
SPEAKER_02: And then from there moved to concrete construction.
3:52
So building foundations for houses,
3:54
concrete stairways,
3:55
sidewalks,
3:56
that kind of thing.
3:56
SPEAKER_02: And then for a pretty good stint, ran a software development group.
4:00
SPEAKER_02: Also for a while, ran a robotics startup.
4:04
SPEAKER_02: Also like a fine glass bead importing business from Eastern Europe,
4:08
SPEAKER_02: especially the Czech Republic.
4:09
SPEAKER_01: I lived in the Czech Republic there, end of the glass there.
4:11
SPEAKER_01: It's an eclectic range of jobs that your dad was doing.
4:16
SPEAKER_02: Yeah, you might call it dilettante-ish.
4:19
SPEAKER_02: And your mom?
4:21
I think mostly keeping me sane and my sister during that period,
4:25
at least the earliest parts.
4:26
SPEAKER_02: I don't remember her professionally engaged.
4:28
SPEAKER_02: And then after that,
4:29
she worked in a plant nursery for a while and then started on
4:32
the administrative
4:33
SPEAKER_02: side in various organizations.
4:35
SPEAKER_02: Nature Conservancy,
4:37
SPEAKER_02: some other natural resources advocacy groups,
4:40
and for quite a while in the editorial department of the
4:43
Oregonian newspaper.
4:44
SPEAKER_01: Not that it breaks this way every time,
4:46
SPEAKER_01: but in technology land,
4:47
I usually find people that were quite driven and possessed
4:51
academically or were
4:52
SPEAKER_01: more in the camp of,
4:53
SPEAKER_01: I'm only interested in what I'm interested in and the rest can be damned.
4:57
SPEAKER_01: I don't know,
4:58
did you fall into either one of those camps or what was
5:01
interesting to you when you
5:02
SPEAKER_01: were growing up?
5:04
SPEAKER_02: I love building things and destroying things, I think, especially.
5:07
SPEAKER_02: Yeah, so I liked making things out of almost anything that's at hand.
5:12
SPEAKER_02: And then pretty young,
5:14
SPEAKER_02: maybe around nine,
5:16
I got really interested in electronics and started building
5:20
random things for fun.
5:21
SPEAKER_02: Intercom systems between houses.
5:24
One of the things that was interesting that I really liked
5:27
during a pretty
5:27
significant period while I was growing up was that a bunch of
5:30
us all made friends.
5:32
SPEAKER_02: on the same block.
5:33
SPEAKER_02: And we're reasonably like-minded enough that we decided,
5:37
SPEAKER_02: why don't we just share all the space and tore down the walls,
5:41
the various fences and so on in the backyards and formed one
5:45
giant unified back of
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SPEAKER_02: the block area.
5:48
and then traded having dinner at various people's houses and
5:51
had workshops and
5:52
SPEAKER_02: there's a welding workshop and a woodworking workshop and they built huge
5:55
SPEAKER_02: playground structures made of steel.
5:57
At one point they also helped us build an underground fort
6:01
which had a tunnel that
6:03
we would enter through like made out of welded together 55
6:06
gallon drums so you
6:07
could crawl down through this steel tunnel into an underground
6:10
space which is a
6:11
SPEAKER_02: very very bad idea for them to support.
6:14
SPEAKER_02: Wait, this was all the kids or the parents?
6:16
SPEAKER_02: The parents enabled this.
6:18
SPEAKER_02: I did learn to weld when I was young,
6:20
SPEAKER_02: maybe 10 or 11,
6:21
SPEAKER_02: but I'm not sure that I did all the welding on it.
6:26
But there's just enough resources that the parents were full of
6:28
energy and creative
6:29
SPEAKER_02: and like to build stuff.
6:30
SPEAKER_02: So they just kind of helped us if we had a spark of executive energy.
6:35
SPEAKER_02: So I think there's probably some help across the board.
6:38
SPEAKER_02: I mean, it sounds very idyllic.
6:41
SPEAKER_02: It was pretty good.
6:41
SPEAKER_02: Yeah, I liked it.
6:42
It was a very fertile,
6:45
supportive,
6:46
creative,
6:47
emotionally encouraging place.
6:49
SPEAKER_02: I built my first rocket there.
6:50
SPEAKER_02: I made napalm, which was kind of fun, except it hurt a lot.
6:55
SPEAKER_02: I did so many things.
6:56
SPEAKER_02: Just built various random electronic, fun electronic things.
7:00
Did you have any clue in your mind's eye of what you wanted to
7:03
be when you grew up,
7:04
SPEAKER_01: when you're doing all these different things?
7:05
SPEAKER_02: It's a little fuzzy.
7:07
SPEAKER_02: I did have a pretty large,
7:09
SPEAKER_02: like a nine-foot-high Van de Graaff generator also,
7:11
which was just very inspiring just because of the sparks and it
7:14
was very sci-fi-ish.
7:16
SPEAKER_02: I definitely remember wanting to have a scientific laboratory in a tower,
7:21
SPEAKER_02: which I haven't fully realized yet in my adult life.
7:25
SPEAKER_02: Definitely going very heavy on the scientific research part.
7:28
SPEAKER_01: Getting close, getting close.
7:29
SPEAKER_01: Yeah.
7:30
And then there's a particular building that has a tower on it
7:32
that I kind of have my eye on.
7:35
But no clear idea of what you were going to be later on or what
7:39
was calling to you the most.
7:41
I mean,
7:41
in general,
7:42
the idea of inventing and building things just seemed obvious.
7:46
SPEAKER_02: Okay.
7:46
SPEAKER_02: And so, like, inventor was sort of a title.
7:49
SPEAKER_02: I don't know if it's really a title anymore.
7:51
SPEAKER_02: It seemed like more of a 20th century kind of thing.
7:54
SPEAKER_02: I am now,
7:54
SPEAKER_02: like,
7:54
SPEAKER_02: I guess an inventor on maybe 210 or 220,
7:57
SPEAKER_02: somewhere in there,
7:59
SPEAKER_02: patents and patent applications.
8:00
SPEAKER_02: So I've sort of been fulfilling that itch of, like, invent.
8:03
SPEAKER_02: But these days,
8:04
SPEAKER_02: I've kind of drifted away from specifically thinking in terms of,
8:07
SPEAKER_02: like,
8:07
SPEAKER_02: I want to conceive a new device.
8:08
SPEAKER_02: Yeah.
8:09
as my creative outlet and more of like,
8:12
I want to change the human condition.
8:14
I'm going to fast forward a little bit and I'm going to mess up
8:16
some of the details
8:17
SPEAKER_01: because you and I have talked about some of this in the past,
8:20
SPEAKER_01: but the exact sequence of events is in my notebooks somewhere.
8:23
SPEAKER_01: I remember you didn't go straight from high school to college,
8:28
SPEAKER_01: at least not on the same cadence that a lot of people do.
8:33
You took this lifestyle that you were leading as a kid into
8:37
your teenage years and
8:38
SPEAKER_01: early 20s,
8:39
SPEAKER_01: didn't you?
8:39
SPEAKER_01: If I remember right,
8:40
it was a science house that you got started or an electronics
8:43
house or something
8:44
SPEAKER_01: like that.
8:45
SPEAKER_02: So it was a mixture of arts and science.
8:48
SPEAKER_02: There's a lot of universities that refer to themselves as,
8:51
SPEAKER_02: for instance,
8:52
SPEAKER_02: the Faculty of Arts and Sciences or whatever it looks.
8:54
arts used to really mean something other than just screwing
8:57
around with charcoal.
8:58
SPEAKER_02: It really meant something to me then too.
9:00
SPEAKER_02: This is early to mid 80s.
9:02
SPEAKER_02: To me,
9:03
it seemed really important that one should get in touch with
9:07
where their creative
9:08
impulses are coming from and like nurture a whole self and also
9:11
nurture your
9:12
SPEAKER_02: artistic side.
9:13
And maybe I just read too much like merchant ivory British
9:15
literature or something,
9:16
SPEAKER_02: you know,
9:17
SPEAKER_02: where everybody's supposed to
9:18
SPEAKER_02: be playing Chopin at the same time that they're also,
9:20
SPEAKER_02: whatever,
9:21
SPEAKER_02: designing the next fancy calculation machine or something.
9:25
SPEAKER_02: So this household that I ran had musicians living there,
9:30
SPEAKER_02: artists of various kinds,
9:32
and then I took over pretty much the whole basement and made it
9:34
into a sort of
9:36
SPEAKER_02: research lab of sorts.
9:38
You were building electronics,
9:40
things that people in town were using,
9:42
if I remember right?
9:43
SPEAKER_01: Yeah.
9:43
SPEAKER_02: Yeah,
9:44
SPEAKER_02: I did a bunch of work for sculptors,
9:45
SPEAKER_02: like in conjunction with,
9:47
SPEAKER_02: I would do the electronic aspect of some multimedia,
9:52
SPEAKER_02: mixed media sculptures that are in various places in the Portland,
9:55
SPEAKER_02: Oregon area.
9:56
SPEAKER_01: When you reflect back on this now, does this all seem unusual to you?
10:00
SPEAKER_01: I guess it was just your life, but it is an unusual story.
10:04
SPEAKER_02: I guess so.
10:05
I think that at that time,
10:07
electronics and computers were more of a frontier thing.
10:11
SPEAKER_02: It was much more of an area to be sort of a creative hacker in.
10:14
SPEAKER_02: You felt like you were doing something important.
10:17
So now maybe that would be like if you're inventing new crypto algorithms,
10:21
SPEAKER_02: you would feel like,
10:21
SPEAKER_02: oh,
10:22
SPEAKER_02: nobody else is doing this,
10:23
SPEAKER_02: and this is very interesting,
10:24
SPEAKER_02: and it's going to change the world in some way.
10:25
SPEAKER_02: Yeah.
10:26
SPEAKER_02: Or even more modern,
10:27
fast forward to like you're creating some new meta platform for
10:32
agentic AIs to
10:34
SPEAKER_02: modify them,
10:34
SPEAKER_02: improve themselves or whatever.
10:36
SPEAKER_02: That's what it is now.
10:37
SPEAKER_02: But back then it was like,
10:38
SPEAKER_02: wow,
10:39
if you could do stuff with electronics and integrated circuits
10:41
and that kind of stuff,
10:43
SPEAKER_02: then yeah,
10:43
SPEAKER_02: you can create.
10:45
SPEAKER_01: I miss some of this,
10:46
SPEAKER_01: and we'll get into it a little bit later with OQO,
10:49
SPEAKER_01: which was another company you ran,
10:50
but I sort of miss when the computers were different and the
10:54
software was a bit
10:55
SPEAKER_01: more different,
10:55
SPEAKER_01: and there was this artistry and point of view in some of this stuff.
11:00
SPEAKER_01: There's many fascinating tales,
11:01
SPEAKER_01: but there's a lot of ground I want to cover with you,
11:02
SPEAKER_01: so we'll march forward a bit.
11:05
SPEAKER_01: You go from this,
11:06
SPEAKER_01: what feels like a freewheeling,
11:08
SPEAKER_01: unconventional youth,
11:09
SPEAKER_01: and then you end up at Harvard,
11:10
SPEAKER_01: and then I think...
11:13
SPEAKER_01: A mix with MIT and some Caltech in there.
11:16
SPEAKER_01: I mean,
11:17
SPEAKER_01: you know,
11:17
SPEAKER_01: as high echelon as you can get,
11:20
SPEAKER_01: what were your main focuses during these academic years?
11:23
So I went through this sort of six-year autodidactic period
11:26
because high school was
11:27
super boring and I just overgeneralized and decided that
11:30
probably all educational
11:32
SPEAKER_02: institutions are probably...
11:33
really slow and stuffy and bureaucratic and boring and
11:36
inefficient way of using one's time.
11:37
And then someone close to me was going off to Harvard and was
11:40
sort of describing
11:41
SPEAKER_02: the scene there.
11:42
And then you get to hang out with these professors and they're
11:44
utterly brilliant
11:44
and they teach you stuff like a hundred times faster and you
11:46
can teach yourself.
11:48
SPEAKER_02: Wait a minute, my universe is way too small here.
11:50
SPEAKER_02: I need to go off and work on this.
11:51
One of the things that I was doing during that autodidactic
11:53
period was reading
11:55
SPEAKER_02: about biology because some things were starting to happen.
11:58
SPEAKER_02: People were starting to be able to clone genes.
12:01
And I remembered reading in an article about protein dynamics
12:06
that they are
12:07
essentially performing things that are kind of like
12:10
computations in the gigahertz
12:12
SPEAKER_02: frequency range.
12:13
Like in terms of the rate at which they're bouncing against
12:14
each other and seeing
12:15
SPEAKER_02: if the confirmations stick.
12:17
SPEAKER_02: Does this number equal that number?
12:19
SPEAKER_02: Sort of like the computation.
12:21
SPEAKER_02: And at that time, computers were running at like a few megahertz.
12:25
SPEAKER_02: And this was sort of like a wake-up call for me.
12:28
SPEAKER_02: Like,
12:28
why are we messing around in the megahertz when there's this
12:30
stuff that knows how
12:32
SPEAKER_02: to operate in the gigahertz?
12:33
So I decided I'm going to go off to a world center of
12:36
biophysics and figure out how
12:38
SPEAKER_02: to make computing components out of these biological molecules.
12:42
And so they should be able to run at gigahertz,
12:43
which is what electronics should run at.
12:45
And I just,
12:46
it's a little before my time since that's now what they run at.
12:49
SPEAKER_02: But...
12:50
SPEAKER_02: Back then, they were just nauseating and slow.
12:53
SPEAKER_02: Yeah, so that's what I went off to do.
12:54
SPEAKER_02: And I jumped into this lab of Don Wiley,
12:58
SPEAKER_02: who's a biophysics professor there,
12:59
SPEAKER_02: who's a really cool guy,
13:01
SPEAKER_02: very brilliant,
13:02
SPEAKER_02: and took me under his wing.
13:03
SPEAKER_02: I mean,
13:03
I just wrote some software for him and helped him kind of
13:06
figure out how to do on
13:07
SPEAKER_02: the more computing side of the structural biology he was working on.
13:10
SPEAKER_02: And then I just started asking him questions, and he just kept answering.
13:14
SPEAKER_02: Nobody knows that yet.
13:17
SPEAKER_02: Okay,
13:17
SPEAKER_02: but like,
13:18
SPEAKER_02: surely we understand like,
13:20
SPEAKER_02: you know,
13:21
SPEAKER_02: how this confirmation changes when this other protein,
13:22
SPEAKER_02: no,
13:23
SPEAKER_02: nobody knows.
13:23
SPEAKER_02: And just like over and over again, that was the answer.
13:26
SPEAKER_02: And I was just like, okay, this is not happening.
13:30
SPEAKER_02: We don't understand biology.
13:33
I'm not going to be building a computer during my time at
13:35
Harvard out of biological components.
13:38
SPEAKER_02: Damn.
13:39
SPEAKER_02: Okay, I might as well do something like really fun.
13:41
SPEAKER_01: As far as I know, nobody has done that yet.
13:44
SPEAKER_02: Not really.
13:45
SPEAKER_02: No, people have like done some stuff with DNA.
13:49
SPEAKER_02: You know,
13:49
you can grow it into like particular shapes and DNA origami and
13:52
then you can sort
13:53
SPEAKER_02: of functionalize other stuff onto it.
13:54
SPEAKER_02: But I haven't heard of anything.
13:56
SPEAKER_02: Yeah.
13:56
SPEAKER_02: Okay.
13:57
SPEAKER_02: And now like the other stuff through the normal methods,
14:00
SPEAKER_02: like transistors and so on,
14:02
SPEAKER_02: they operate at gigahertz.
14:02
SPEAKER_02: So like, eh, you don't need biology anymore.
14:05
SPEAKER_01: You clearly always been drawn to this mix of biology and computation.
14:11
SPEAKER_02: Yeah.
14:12
SPEAKER_02: I mean, you were doing electronics.
14:14
I did a fellowship at Caltech around this period and Professor
14:20
Harry Gray's lab at Caltech,
14:21
SPEAKER_02: who's amazing.
14:22
And I ended up doing the three-dimensional modeling and graph
14:25
search in proteins
14:26
based on these quantum mechanical transfer functions that one
14:31
of the other two
14:32
SPEAKER_02: people I was working with on this project came up with.
14:34
So again,
14:35
I was sort of like the computation person involved in the biology.
14:40
SPEAKER_01: Pretty heady stuff.
14:40
SPEAKER_01: Are you picturing you're going to be an academic?
14:43
Or you're just going from one interesting idea to the next and
14:47
not even thinking about that?
14:48
SPEAKER_02: I'm not even thinking about that.
14:49
In fact,
14:50
they were all excited because the three of us hit this pretty
14:53
awesome discovery.
14:54
SPEAKER_02: Basically,
14:54
SPEAKER_02: we figured out how electrons tunnel through proteins,
14:56
SPEAKER_02: which is one of the most fundamentally important biological processes,
15:00
SPEAKER_02: period,
15:00
because it governs little synthesis and how energy is processed
15:03
in mitochondria and
15:04
SPEAKER_02: all that.
15:05
SPEAKER_02: all these other enzymatic processes.
15:06
SPEAKER_02: So it's pretty fundamental.
15:09
SPEAKER_02: They're like, oh my God, this is amazing.
15:10
SPEAKER_02: I was like, well, what's so amazing about it?
15:11
SPEAKER_02: It's cool.
15:12
SPEAKER_02: Like the software worked and we made some good predictions,
15:14
but they wrote up a paper of it and it got immediately snatched
15:17
up by science.
15:18
And then now it has like,
15:19
I don't know,
15:19
almost a thousand citations or something.
15:21
SPEAKER_02: And then a bunch of other articles at the same time.
15:24
SPEAKER_02: They're like, this is what's great about it.
15:25
SPEAKER_02: I'm like, what do you mean?
15:26
SPEAKER_02: I was like, papers.
15:27
SPEAKER_02: I was like,
15:29
SPEAKER_02: Who cares?
15:30
SPEAKER_02: I just didn't get it.
15:32
SPEAKER_02: To me,
15:33
it was about the thrill of the building and the exploration and
15:37
see if we can make
15:38
SPEAKER_02: it work.
15:39
SPEAKER_01: Okay.
15:40
SPEAKER_01: If you pull up your LinkedIn page,
15:43
SPEAKER_01: you end up at Almaden and then IBM Almaden Research Center,
15:47
SPEAKER_01: which is amazing,
15:48
SPEAKER_01: and then as the chief technology officer at Analog Devices.
15:51
SPEAKER_01: So at some point, you get the siren call of Silicon Valley.
15:56
SPEAKER_02: Yeah.
15:57
SPEAKER_02: I mean, I'd like to ascribe more agency to myself.
16:02
SPEAKER_02: Realistically,
16:03
SPEAKER_02: a lot of my very profound,
16:06
highly reasoned life moves end up being me chasing a woman of
16:10
one sort or another.
16:12
SPEAKER_02: This happens.
16:15
SPEAKER_02: But when I was at MIT, my girlfriend decided to move to San Francisco.
16:17
SPEAKER_02: And I was like, hmm, she stayed at MIT or...
16:21
isn't super exciting here um san francisco seems cool so i
16:25
followed her i got here
16:27
and then i thought okay cool maybe i'll make some everybody
16:31
does companies here so
16:31
i'll do some kind of company uh maybe i'll make some kind of
16:34
brand new sensor or
16:35
something and then a friend of a friend heard that i was there
16:38
and like oh you
16:39
should come work for ivm moment in research i'll hire you into
16:42
my group like uh
16:44
SPEAKER_02: okay i'll check it out so i went down there i was like this is
16:49
SPEAKER_01: For people who don't know,
16:50
SPEAKER_01: I mean,
16:51
SPEAKER_01: in its heyday,
16:53
SPEAKER_01: first of all,
16:54
just a beautiful building that's nestled into the hillside of
16:58
San Jose and was
16:59
SPEAKER_01: designed to really blend in with its surroundings.
17:02
SPEAKER_02: Yeah,
17:02
SPEAKER_02: it was a bit of a concession,
17:04
SPEAKER_02: I think,
17:04
from a planning commission perspective to even let IBM build a
17:08
building out there
17:09
SPEAKER_02: in the middle of wilderness.
17:10
SPEAKER_01: It's a nature preserve, pretty much.
17:13
SPEAKER_01: There's all kinds of animals running around, but
17:15
SPEAKER_01: Yeah, I mean, look, this is going back a few years.
17:18
SPEAKER_01: This is IBM's biggest computer company in the world in Albany.
17:23
SPEAKER_01: At least to me,
17:23
SPEAKER_01: when I would go there,
17:24
you get a special feeling people were really working on very
17:28
difficult problems.
17:29
SPEAKER_01: This is before the services division took over.
17:33
SPEAKER_02: Yeah, it's where they first wrote IBM in individual xenon atoms.
17:39
I think that's the first time anybody had ever done single atom
17:43
positioning like that.
17:44
SPEAKER_01: Okay,
17:44
SPEAKER_01: so you're there,
17:45
SPEAKER_01: and instead of doing the startup thing,
17:47
SPEAKER_01: you're nestled into one of the research centers in the world,
17:50
SPEAKER_01: so that must have felt good.
17:52
SPEAKER_02: It was really fun.
17:53
SPEAKER_02: It was very weirdly bureaucratic.
17:55
It was like everything sci-fi said that being in a huge
18:00
bureaucracy would be like.
18:02
SPEAKER_02: But it wasn't that hard to reverse engineer and get stuff done.
18:06
SPEAKER_02: Mostly if you needed something done,
18:07
SPEAKER_02: then you fill out the form,
18:08
SPEAKER_02: you send it in,
18:09
SPEAKER_02: then you go just figure out whose desk it's going to land at first,
18:11
SPEAKER_02: and you just walk over there and stand next to their desk until they
18:14
pull your thing off the stack and then you walk to the next
18:16
office and then they
18:17
assign it and then maybe there's a second or maybe there's a
18:20
third maybe there
18:21
isn't and then pretty much your thing is off and running it was
18:23
it was pretty fun
18:24
my friend was reasonably hands-off as a manager so I I just
18:30
invented a bunch of
18:31
stuff wrote some patents or they have a team that writes
18:34
patents for you but I
18:35
SPEAKER_02: invented some things and then the the patent attorneys came and
18:39
SPEAKER_02: hanging around and said, how does this work?
18:41
SPEAKER_02: And what's it for?
18:42
SPEAKER_02: And whatever.
18:43
SPEAKER_02: And created a new, like, a new kind of keyboard that's extremely thin.
18:46
SPEAKER_02: At this point,
18:47
they still had the Lenovo laptop division inside IBM before it
18:51
spun off into its
18:52
SPEAKER_02: own Chinese company.
18:53
SPEAKER_02: They called it ThinkPad at that time.
18:55
SPEAKER_02: Oh, yes.
18:56
SPEAKER_02: Loved it, ThinkPad.
18:57
SPEAKER_02: Had this little, like,
18:58
SPEAKER_02: orange pointing device in the middle of the keyboard.
19:00
SPEAKER_02: So I co-invented and improved sort of electronics for that.
19:05
SPEAKER_02: That saved a bunch of money.
19:06
SPEAKER_02: I used to love that thing.
19:08
SPEAKER_01: It was good.
19:08
SPEAKER_01: Yeah, it was actually kind of nice.
19:10
SPEAKER_01: Yeah, it was like a whole mouse.
19:11
SPEAKER_01: Yeah.
19:11
SPEAKER_01: I think it was Don Igler.
19:14
SPEAKER_01: I'm going to go with that.
19:14
SPEAKER_01: I'm putting my money on Don Igler with the atoms.
19:16
SPEAKER_01: Ah, it's coming to you.
19:18
SPEAKER_01: We'll see if I'm right.
19:18
SPEAKER_01: We'll see if I'm right.
19:21
SPEAKER_01: And then you go to analog design.
19:24
SPEAKER_01: Yeah.
19:24
SPEAKER_02: At IBM,
19:26
SPEAKER_02: I noticed that,
19:26
SPEAKER_02: you know,
19:27
I could save them a few million dollars a year and they'd give
19:29
me a pat on the back
19:29
SPEAKER_02: and say,
19:30
SPEAKER_02: you're great.
19:31
SPEAKER_02: Keep going.
19:32
SPEAKER_02: And I was like, huh, wait a minute.
19:33
SPEAKER_02: Shouldn't you give me some of that?
19:36
SPEAKER_02: Or like, what incentivizes me to do the next thing?
19:38
SPEAKER_02: They're like, no, that's not how we work.
19:40
SPEAKER_02: So I decided to just go off on my own, start designing electronics.
19:44
SPEAKER_02: So I just,
19:45
I guess I developed a reputation for being one of those people
19:49
you could go to when
19:50
SPEAKER_02: you had some electronics to design that were kind of hard.
19:53
SPEAKER_02: Maybe Joe can do it or whatever.
19:54
SPEAKER_02: Um,
19:56
SPEAKER_02: so I just ended up doing a bunch of designing and building of like weird,
19:59
SPEAKER_02: hard,
19:59
SPEAKER_02: challenging electronic stuff,
20:01
including some stuff for the hard drive part research and
20:03
manufacturing industry.
20:05
SPEAKER_01: What were you, what were you particularly good at?
20:08
SPEAKER_01: You saw the architecture differently than other people did.
20:13
SPEAKER_01: What's the craft to be able to do that in a different way?
20:17
SPEAKER_02: Part of it,
20:18
SPEAKER_02: I ascribe to this,
20:21
SPEAKER_02: you know,
20:21
SPEAKER_02: going back to my childhood,
20:22
SPEAKER_02: just like growing up and redefining everything.
20:24
SPEAKER_02: And then part of that is being out in the simply farm,
20:27
SPEAKER_02: out in the woods in the middle of nowhere.
20:28
SPEAKER_02: Whatever you need to get done, you just build it.
20:30
You figure out the materials that are at hand and you just use
20:32
them somehow.
20:32
SPEAKER_02: So I think most of my buildings kind of like that, kind of...
20:36
SPEAKER_02: There's probably some way to make this work using the materials at hand.
20:39
So you just fill up your brain with the specifications of a
20:42
bunch of the components
20:44
SPEAKER_02: and the integrated circuits and so on that are of the day.
20:46
SPEAKER_02: And then somebody proposes to build something and you're like,
20:50
SPEAKER_02: hmm,
20:50
I think I can figure out a way to do that based on the ones
20:52
that are in my brain
20:53
SPEAKER_02: right now.
20:54
that there's like cycles of that i guess when i was a little
20:57
kid i read this thing
20:57
called the ttl data book which had most of the chips in it now
21:01
they're all numbered
21:02
really beautifully like there's the 7404 and 7416 and the four
21:07
you know they all
21:08
had these nice 74 numbers they all these beautiful logical
21:10
digital functions and i
21:12
just learned them all and it was just really fun if somebody
21:15
said hey could you do
21:16
SPEAKER_02: a device make a device that does x like oh i could use a
21:21
a 7404 for this and then a 7408 hand gate and so on and just
21:25
like start kind of
21:26
SPEAKER_02: building it mentally.
21:28
SPEAKER_02: Just fun to build out of pieces like that.
21:30
SPEAKER_02: I guess that's probably also influential for doing that kind of thing.
21:33
SPEAKER_01: When you were talking,
21:33
I was thinking a little bit about Peter Back from New Zealand
21:36
who started Rocket
21:37
SPEAKER_01: Lab and...
21:38
SPEAKER_01: Yeah,
21:39
SPEAKER_01: yeah.
21:39
SPEAKER_01: He seems really cool.
21:40
SPEAKER_01: I'd love to meet him.
21:40
SPEAKER_01: Yeah.
21:41
SPEAKER_01: Didn't go to college.
21:41
Was definitely not a MIT aerospace PhD,
21:45
which is usually sort of the prerequisite for the field.
21:49
SPEAKER_01: And he seems to...
21:51
SPEAKER_01: Well, he's proven out.
21:52
He's able to approach some of these problems in aerospace just
21:56
in a slightly
21:57
SPEAKER_01: different way,
21:58
SPEAKER_01: but very much had that not a wealthy background,
22:01
had to figure out a lot of the stuff on his own and seems to
22:04
come at it from a
22:06
SPEAKER_01: different angle that works.
22:08
SPEAKER_02: Yeah,
22:08
SPEAKER_02: I think I also like the process of,
22:10
and this just translates absolutely directly onto how we
22:13
operate here at Retro.
22:14
SPEAKER_02: I love to iterate my way toward designs that work.
22:17
So I think a lot of things that get built in organizations
22:20
often spend too much
22:21
SPEAKER_02: time planning the perfect way to do it.
22:22
They'll have a one-year planning process before they get going
22:26
on building whatever device.
22:28
Whereas I immediately think too,
22:31
what's the riskiest little piece of this?
22:33
SPEAKER_02: What if I could just prototype that in my lab right now?
22:35
SPEAKER_02: Just break it down and build that and see if it works.
22:39
SPEAKER_02: Oh, it doesn't work.
22:40
SPEAKER_02: What did I do wrong?
22:41
SPEAKER_02: Rip it apart, probe it, measure it.
22:44
SPEAKER_02: Ah, okay, that's what I wasn't getting.
22:46
SPEAKER_02: I need to change this part.
22:48
SPEAKER_02: Okay, the core is working.
22:49
SPEAKER_02: Okay, now I'll start building the rest around it.
22:51
SPEAKER_02: There's this sort of,
22:51
SPEAKER_02: like,
22:52
SPEAKER_02: my version of dopamine scrolling,
22:54
SPEAKER_02: I guess,
22:54
SPEAKER_02: is like building the chunks and seeing one little piece of it work.
22:58
Like building something and seeing it work is one of the core
23:02
sort of dopamine
23:02
SPEAKER_02: drivers of my brain.
23:05
SPEAKER_02: Somebody says,
23:05
SPEAKER_02: you know,
23:05
SPEAKER_02: build us this low noise,
23:08
SPEAKER_02: high bandwidth,
23:09
SPEAKER_02: laser scanning power controller thing for...
23:11
SPEAKER_02: Hard disk drive scanning or something like that.
23:14
SPEAKER_02: I'll just immediately think, okay, what's the hard part of that?
23:16
SPEAKER_02: Controlling probably laser intensity to really low noise.
23:19
SPEAKER_02: Well, do you have one of the lasers?
23:21
SPEAKER_02: Yeah, here you go.
23:22
SPEAKER_02: I'm just going to see if I can get the noise that low.
23:25
SPEAKER_02: All right, that's working.
23:27
SPEAKER_02: Now, what's the rest of it?
23:28
SPEAKER_02: I'm supposed to build around this and then just keep, okay.
23:30
SPEAKER_02: But that first chunk of it's working, that's super fun.
23:33
SPEAKER_02: That keeps me coming back for more.
23:34
SPEAKER_01: When you get to this point of running a company of a decent size,
23:37
SPEAKER_01: do you still get to do stuff like that?
23:41
SPEAKER_02: Yes.
23:42
SPEAKER_02: It's kind of a guilty pleasure.
23:43
SPEAKER_02: I have this ethos that I don't,
23:45
SPEAKER_02: want any peer managers of the company,
23:47
and I am anticipating that as it becomes many hundreds and then
23:51
many thousands,
23:52
SPEAKER_02: hopefully tens of thousands and more employees,
23:54
SPEAKER_02: that there still won't be any peer managers.
23:56
SPEAKER_02: I always want somebody to still have their feet on the ground somewhere,
24:01
SPEAKER_02: even if the ledge is fairly thin,
24:02
because I think that people have a tendency to lose touch with
24:07
what's actually
24:07
going on and make stupid decisions that a bunch of other people
24:10
have to follow if
24:12
SPEAKER_02: they get too disconnected.
24:13
SPEAKER_02: So I think from the perspective of it being a guilty pleasure,
24:16
SPEAKER_02: I think if it's like 5% of my time,
24:19
SPEAKER_02: it's fine.
24:20
It's not getting in the way of one of the main things which I
24:25
need to do of staying
24:27
SPEAKER_02: abreast of what's going on strategically in the industry,
24:31
SPEAKER_02: make sure the company's pointed in the right direction in the long range,
24:34
working with the teams and they're complementing each other and
24:38
sharing resources
24:39
SPEAKER_02: on the right things.
24:41
SPEAKER_02: You know, all those other sort of CEO-ish things.
24:44
SPEAKER_02: So 95% of my time.
24:46
SPEAKER_02: That's fine for that.
24:47
So the other five,
24:48
I'm always looking for like little projects that I can jump into.
24:51
SPEAKER_02: Lately,
24:51
SPEAKER_02: for instance,
24:52
SPEAKER_02: in the automation group,
24:53
SPEAKER_02: you know,
24:53
I built this machine that like swirls this sort of quarter ton
24:58
incubator device at
24:59
SPEAKER_02: a certain frequency and radius.
25:01
SPEAKER_02: I don't know, just like weird little projects.
25:04
SPEAKER_02: Keeps me alive.
25:05
SPEAKER_01: When I first met you,
25:07
SPEAKER_01: I think it was a couple of years ago,
25:08
SPEAKER_01: I walked in,
25:09
SPEAKER_01: not the building we're currently in,
25:11
SPEAKER_01: but one that's next door.
25:13
SPEAKER_01: This is a nice office for people who can't see it.
25:15
SPEAKER_01: The other one is,
25:16
SPEAKER_01: it's a building that's been in Redwood City,
25:18
SPEAKER_01: I'm sure,
25:18
SPEAKER_01: for many decades and was some other kind of shop in a previous life.
25:22
SPEAKER_01: But I walk in,
25:24
SPEAKER_01: meet you,
25:24
the decor was a little bit different than we get into the more
25:29
warehouse-y part of
25:31
SPEAKER_01: the building.
25:32
SPEAKER_01: And you guys have all these shipping containers everywhere.
25:34
SPEAKER_01: There's
25:35
SPEAKER_01: HVAC systems that clearly had looked different,
25:39
SPEAKER_01: a little more handmade than you would see at a run-of-the-mill company.
25:43
It struck me visually,
25:45
and then the story behind it was pretty fascinating.
25:48
SPEAKER_01: So walk me back to sort of the founding of Retro.
25:51
SPEAKER_01: Let's get to the part I described,
25:52
but let's start with what the mission was and then why I ended
25:57
up in this room full
25:58
SPEAKER_01: of shipping containers.
26:00
SPEAKER_02: Yeah, so the mission or extending a healthy human lifespan.
26:02
Why did I do that as opposed to making a company to make
26:07
high-tech AI-enabled
26:09
SPEAKER_02: electric scooters or something?
26:11
SPEAKER_02: After my first company,
26:13
SPEAKER_02: which made these tiny little laptop computers,
26:15
SPEAKER_02: arguably the smallest laptops of the day.
26:18
SPEAKER_02: Okiwo.
26:18
SPEAKER_02: Yeah.
26:19
SPEAKER_02: It was a phenomenon in its day.
26:21
SPEAKER_02: Mm-hmm.
26:22
SPEAKER_02: Yeah, it was really fun to do.
26:26
But after that,
26:27
and...
26:29
Like,
26:29
basically,
26:29
the smartphone for the smartphone.
26:32
SPEAKER_01: It was like a souped-up PDA.
26:35
SPEAKER_02: Yeah.
26:35
SPEAKER_02: I mean,
26:36
SPEAKER_02: at that point,
26:36
SPEAKER_02: I called it the Race to the Middle because there were...
26:38
SPEAKER_02: And the site I was on were...
26:41
SPEAKER_02: the computers getting smaller and smaller.
26:43
So there's,
26:44
you know,
26:44
Univac and then the mini computers and desktop computers and so on.
26:48
SPEAKER_02: They were just getting smaller, smaller, smaller, smaller.
26:49
And then there were laptops for a while and the laptops were
26:52
kind of getting smaller.
26:53
And then OQO jumped ahead and basically took the laptop into
26:57
like roughly kind of
26:58
SPEAKER_02: big smartphone size.
27:00
SPEAKER_02: maybe about the size of this Google Pixel 9 Pro Fold I'm holding here.
27:04
SPEAKER_02: And why do it?
27:06
SPEAKER_02: Because it's cool.
27:07
That was pretty much the motive power that got me out of bed
27:10
and that probably
27:11
SPEAKER_02: motivated most of the engineers to come.
27:12
SPEAKER_02: Oh, I work in this company.
27:13
SPEAKER_02: Why are you doing it?
27:14
SPEAKER_02: Because it's really cool.
27:15
SPEAKER_02: Because it's the smallest in the world.
27:17
SPEAKER_02: We got in the Guinness World Records, 2006, I think.
27:19
SPEAKER_02: But after that, Google thought it was cool, too, and they bought it.
27:23
SPEAKER_02: And so I had a few million dollars and was just kind of like,
27:28
SPEAKER_02: wait a minute,
27:28
SPEAKER_02: I should stop and reconsider.
27:29
SPEAKER_02: What the hell am I doing with my life?
27:32
I tried retiring and going and sitting on a beach for a while,
27:35
which I lasted about nine days.
27:37
SPEAKER_02: It's like,
27:38
this is incredibly boring and it's going to be depressing just
27:41
to sit here and not
27:41
SPEAKER_02: build anything.
27:43
SPEAKER_02: I need to do something,
27:44
SPEAKER_02: but I should do something that matters more than making cute gadgets for
27:49
SPEAKER_02: salespeople or various industrial applications.
27:52
SPEAKER_02: And I don't know what matters.
27:53
SPEAKER_02: It was like a soul searching time.
27:54
SPEAKER_02: What does matter?
27:55
SPEAKER_02: One of the conclusions I came to is that like people really matter.
27:58
SPEAKER_02: They're everything.
27:59
SPEAKER_02: It weirdly took me a while to come to that conclusion.
28:01
SPEAKER_02: Like as a kid, I thought I was an environmentalist.
28:04
As I sort of extrapolated,
28:05
like,
28:06
oh,
28:06
so how do I completely max on environmentalism?
28:08
SPEAKER_02: How do I be the best environmentalist the world has ever known?
28:11
SPEAKER_02: And it just kept coming back to easy, just extinguish all the humans.
28:14
SPEAKER_02: Wait, no, I don't want to extinguish the humans.
28:16
SPEAKER_02: Humans are great.
28:17
SPEAKER_02: And like perfect environmentalism is at odds with human existence.
28:22
SPEAKER_02: So I just decided I'm all in on human.
28:25
SPEAKER_02: Great.
28:25
SPEAKER_02: So how do I max out on making human better, making human life better?
28:30
SPEAKER_02: So I could enable more creativity.
28:32
SPEAKER_02: I could enable humans to reach their potential more.
28:35
SPEAKER_02: I didn't really know how to do any of that.
28:36
SPEAKER_02: Or I could do the negative version of it.
28:38
SPEAKER_02: Like, how do I keep the bad things from happening to people?
28:41
SPEAKER_02: That seems somehow more tangible and more approachable.
28:44
SPEAKER_02: So what are all the bad things that happen to people?
28:45
SPEAKER_02: There's like, I don't know, war?
28:48
SPEAKER_02: Well, that's a political thing.
28:49
SPEAKER_02: I'm not really into politics.
28:50
SPEAKER_02: I'm terrible at politics.
28:51
SPEAKER_02: So, nah.
28:52
SPEAKER_02: I'm not going to stop war from happening.
28:54
SPEAKER_02: I can help war really easily because I love building machines.
28:57
SPEAKER_02: But no, I'm not really into that.
28:59
SPEAKER_02: Poverty.
29:00
SPEAKER_02: Wait, we have enough wealth.
29:02
SPEAKER_02: It's just not evenly distributed.
29:03
SPEAKER_02: And the distribution process is called politics, basically.
29:06
SPEAKER_02: So I'm not going to do anything about that.
29:07
SPEAKER_02: And then there's like hunger, which is basically the same.
29:10
SPEAKER_02: And all these things.
29:11
It's just like,
29:11
there's nothing I can do except what are the other sources of misery?
29:15
SPEAKER_02: Oh, the horrific process of dying of diseases.
29:19
SPEAKER_02: That's like really fundamentally bad.
29:22
SPEAKER_02: Is that a politics problem?
29:23
SPEAKER_02: Oh, not as much.
29:25
SPEAKER_02: It's kind of like a technological problem.
29:27
SPEAKER_02: It's not like,
29:28
SPEAKER_02: you know,
29:28
SPEAKER_02: housing,
29:29
SPEAKER_02: like someone's homeless and like,
29:30
SPEAKER_02: well,
29:30
SPEAKER_02: there's a bunch of extra homes,
29:31
SPEAKER_02: you know,
29:31
SPEAKER_02: down in Ventura or something.
29:32
SPEAKER_02: Why don't they just, well, we just move them down there.
29:34
Like it's easy to solve the housing problem if you can solve
29:37
the political problem.
29:38
SPEAKER_02: If someone's dying of Alzheimer's, no, we're just dumb.
29:42
SPEAKER_02: We don't know what to do about it.
29:45
SPEAKER_02: There's no solution in nowhere in humanity.
29:48
SPEAKER_02: It's not poorly distributed or anything.
29:50
SPEAKER_02: We just don't know what to do.
29:51
SPEAKER_02: And it turns out that's true for a huge range of different horrific,
29:57
SPEAKER_02: slowly killing you,
29:59
SPEAKER_02: miserably degenerative,
30:01
SPEAKER_02: age-related diseases.
30:02
SPEAKER_02: We don't know what to do.
30:03
So there's a technological realm that I can dive into and make
30:07
a huge difference for people.
30:09
A difficult one and one that this whole,
30:12
the medical field can have a good reputation.
30:14
The longevity field,
30:15
part of it has had a mixed reputation over the years and mixed pursuits.
30:22
SPEAKER_01: But that becomes your calling and finding your way into that.
30:25
SPEAKER_01: I remember you would host longevity events and gatherings.
30:29
And so you were doing a searching of sorts for a little while
30:33
to figure out what
30:34
SPEAKER_01: angle or avenue in particular you wanted.
30:37
SPEAKER_01: For sure, yeah.
30:38
SPEAKER_00: Yeah,
30:39
SPEAKER_02: I think that people over the years,
30:43
SPEAKER_02: for a long time,
30:44
SPEAKER_02: it's been clear that,
30:45
SPEAKER_02: well,
30:45
SPEAKER_02: doing something about this aging problem,
30:47
SPEAKER_02: it would be important.
30:49
SPEAKER_02: They're just like very technologically impoverished eras, mostly.
30:53
SPEAKER_02: You know,
30:54
if like thousands of years ago and some assistant to a king
30:58
gets it in his head that,
30:59
SPEAKER_02: yeah,
30:59
SPEAKER_02: I can cure aging,
31:00
SPEAKER_02: and the king's like,
31:01
SPEAKER_02: great,
31:01
SPEAKER_02: you know.
31:02
SPEAKER_02: Yeah, we already know that we want that.
31:04
SPEAKER_02: Go do that.
31:05
SPEAKER_02: Here's a whole bunch of resources.
31:06
SPEAKER_02: And then, of course, they fail because they don't know how biology works.
31:10
SPEAKER_02: I think that's happened over and over again a bunch of times.
31:13
SPEAKER_02: What's different now?
31:14
SPEAKER_02: Largely,
31:16
SPEAKER_02: fittingly,
31:16
SPEAKER_02: I guess,
31:17
SPEAKER_02: for this story arc,
31:18
SPEAKER_02: due to technology,
31:20
biology has been hitching a ride on information science ever
31:23
since the advent
31:24
SPEAKER_02: especially of the gene sequencer,
31:26
SPEAKER_02: which turns biology into a bunch of ones and zeros.
31:29
SPEAKER_02: And so Moore's Law then is essentially driving biological understanding.
31:36
SPEAKER_02: And so computers,
31:37
SPEAKER_02: you know,
31:37
SPEAKER_02: keep getting better,
31:38
SPEAKER_02: you know,
31:39
SPEAKER_02: doubling every year-ish depending on what metric you're using.
31:42
That's massively helping us understand what biology is and how
31:45
it works and why it breaks.
31:47
SPEAKER_02: Now's really the time.
31:49
SPEAKER_01: What's in the sense of you could gather and analyze more data.
31:53
SPEAKER_01: You have gene sequencers that are cheaper, faster, better.
31:57
SPEAKER_01: You have things like single cell sequencers.
32:00
SPEAKER_01: Now it's that combination of Moore's law affecting the software side,
32:04
SPEAKER_01: the data side,
32:05
and this hardware side just to gather more information about
32:08
what's going on in the body.
32:09
SPEAKER_01: Is that the general idea?
32:12
SPEAKER_01: That's the general sense.
32:13
SPEAKER_02: Yeah, biology has become extremely parallel.
32:15
SPEAKER_02: The human brain, in some ways, is terrible at doing things in parallel.
32:20
SPEAKER_02: Like,
32:20
SPEAKER_02: I don't know,
32:20
SPEAKER_02: I really loved Kahneman's Thinking Fast,
32:22
SPEAKER_02: Thinking Slow,
32:23
SPEAKER_02: and his paradigm of System 1,
32:25
SPEAKER_02: System 2.
32:25
SPEAKER_02: And if we're doing stuff on System 1, it is pretty parallel.
32:29
You're just walking along,
32:30
things just pop up,
32:31
you don't even have to try.
32:32
SPEAKER_02: You're just looking for something red that you lost in the forest floor,
32:35
SPEAKER_02: and you're just walking along,
32:36
SPEAKER_02: hey,
32:36
SPEAKER_02: there it is.
32:37
SPEAKER_02: Very parallel things were happening.
32:39
SPEAKER_02: But it wasn't that hard, just the red happens to pop.
32:42
But when it comes to System 2 things,
32:44
where you're thinking about something very complicated,
32:48
SPEAKER_02: And you're having to slow down and be logical and step by step about it.
32:52
SPEAKER_02: Then we're just terrible at being parallel.
32:53
SPEAKER_02: Like,
32:54
SPEAKER_02: okay,
32:54
I'm going to keep in mind a thousand different genes and gene
32:57
circuits and then
32:58
compare all those patterns to the five dozen gene circuits that
33:02
I've sequenced that
33:03
SPEAKER_02: seem turned on in this chip and in this cell.
33:07
And that'll give me some inspiration about what the effect of
33:09
this particular perturbation is.
33:12
SPEAKER_02: No.
33:12
SPEAKER_02: Humans don't do that.
33:13
SPEAKER_02: They're terrible at that.
33:15
SPEAKER_02: So that means that now that we have computers that can do it,
33:19
SPEAKER_02: it's massively speeding everything up.
33:20
SPEAKER_02: So we can sequence the genome,
33:24
SPEAKER_02: say the RNA component of the genome,
33:27
SPEAKER_02: in each cell for 100,000 cells in a single experiment.
33:31
So you're getting like thousands of genes per cell times
33:34
thousands of cells.
33:35
SPEAKER_02: Uh-oh.
33:37
SPEAKER_02: and just get one data set like that.
33:39
So it's extremely parallel in that way and then the computers
33:43
can easily think
33:43
SPEAKER_02: about that and draw conclusions like that.
33:46
SPEAKER_02: What's going on in this?
33:47
What's different about this set of cells than this other set of
33:49
cells in this
33:50
SPEAKER_02: gigabytes information?
33:51
SPEAKER_02: And
33:53
SPEAKER_02: Then in the last couple of years,
33:55
it's getting even more exciting because these large models are
34:00
starting to be able
34:00
SPEAKER_02: to reason about that data in an even different way.
34:03
Less just comparing two patterns kind of way or subtracting
34:07
these things and
34:07
SPEAKER_02: finding the difference and seeing the difference pop out,
34:09
SPEAKER_02: but more of like a multi-step fashion.
34:12
SPEAKER_02: This implies that, implies this other thing.
34:15
SPEAKER_01: I think people,
34:17
SPEAKER_01: in the broadest of strokes,
34:18
we sequenced the human genome and people felt like overnight
34:22
there was going to be
34:22
SPEAKER_01: massive cures to all sorts of things.
34:25
SPEAKER_01: We had the whole DIY bio movement.
34:29
SPEAKER_01: We were turning...
34:31
SPEAKER_01: cells in the software.
34:32
SPEAKER_01: We're going to reprogram everything, CRISPR right on the heels of that.
34:37
SPEAKER_01: Now we have this great editing tool.
34:40
SPEAKER_01: And then I think the public hears about things like this,
34:42
and then they have a little bit of a sense of letdown that
34:45
these things all take
34:46
SPEAKER_01: time and not as easy as first build.
34:50
SPEAKER_01: So when you say you feel like this time things are a bit different,
34:55
SPEAKER_01: we have better tools,
34:57
SPEAKER_01: we're at a different stage,
34:59
SPEAKER_01: that's the culmination of these...
35:01
SPEAKER_01: 20 years of things I just listed,
35:03
SPEAKER_01: and people should be cautioned that things will still take time,
35:09
SPEAKER_01: or you feel like we are at the cusp of profound near-term breakthroughs.
35:13
SPEAKER_02: I sequenced the human genome in 2001,
35:16
SPEAKER_02: because that was meant to be the genome of all of humanity.
35:21
SPEAKER_02: Of course, we all have different genomes.
35:23
SPEAKER_02: And then...
35:24
It took a bunch of engineering to get it cheap enough that we
35:27
could then start
35:28
SPEAKER_02: sequencing the genome of individual people or groups of people.
35:33
SPEAKER_02: Okay,
35:34
SPEAKER_02: that's making more sense because it's interesting,
35:37
SPEAKER_02: like one person lives to 100 and some other person dies at 70.
35:41
There must be differences between people so that,
35:43
you know,
35:44
it's not just the human genome.
35:45
It's the differences between the genome are extremely
35:48
interesting and important.
35:50
But it turns out that not all the cells in your body are doing
35:53
the same thing.
35:54
So then we started being able to sequence the genomes in each
35:57
individual cell to
35:58
SPEAKER_02: see which RNAs are being expressed.
36:00
SPEAKER_02: And of course, this is different in all different tissues in your body.
36:05
SPEAKER_02: So if you plot this and then partway through,
36:07
SPEAKER_02: we started getting like proteomics started getting better and better.
36:11
SPEAKER_02: And proteomics is just starting to be sequenced now.
36:16
Just like in the last year or so,
36:18
you're starting to see some of the early academic stuff.
36:20
SPEAKER_02: So it's going to be product and probably not too much longer.
36:23
SPEAKER_02: But if you plot this all out,
36:25
SPEAKER_02: you see the steps coming closer,
36:28
SPEAKER_02: like sooner and sooner to each other.
36:30
SPEAKER_02: You know,
36:30
maybe from the time the whole humanities genome was sequenced
36:34
until you had
36:36
SPEAKER_02: individuals could be sequenced.
36:38
SPEAKER_02: You know, maybe that was, I don't know, what was that, 10 years maybe?
36:41
SPEAKER_02: And then from there to the point where you could, you know, sequence
36:46
SPEAKER_02: a bunch of individual cells,
36:47
SPEAKER_02: probably like another five years,
36:49
SPEAKER_02: and then putting together and understanding networks,
36:53
seeing the interactions between cells that you're sequencing in
36:56
other cells or
36:57
SPEAKER_02: being able to,
36:58
like just sequencing the cells in a tissue is also a little bit
37:01
blind to the actual
37:03
SPEAKER_02: biology because it actually really matters where the cells are.
37:05
SPEAKER_02: So like a couple of years after that, we started getting
37:08
SPEAKER_02: spatial DNA sequencing.
37:09
So it could show you the entire genome of a cell at this spot
37:14
in an image.
37:14
SPEAKER_02: And then you have like thousands of cells,
37:16
like pixels in an image of where you're actually looking at a
37:18
picture of the tissue.
37:19
SPEAKER_02: And then, so that was just a couple of years.
37:22
SPEAKER_02: Now the changes, the big changes are coming at us like every year.
37:26
SPEAKER_02: It's sort of like what's happening in AI right now.
37:28
SPEAKER_02: You know,
37:28
we had these large models kind of start to debut to the public
37:32
maybe what,
37:33
SPEAKER_02: two years ago.
37:34
SPEAKER_02: And then, then they've just been getting better at a rate where
37:38
SPEAKER_02: It's not like you're thinking,
37:39
SPEAKER_02: geez,
37:40
SPEAKER_02: I wonder in two years how good this is going to be anymore.
37:43
SPEAKER_02: It's like you're wondering every morning when you wake up,
37:46
SPEAKER_02: I wonder if there's something crazy going to land.
37:48
SPEAKER_02: And then it was like every morning,
37:49
SPEAKER_02: you know,
37:50
SPEAKER_02: it kind of reminded me of this,
37:51
SPEAKER_02: the 12 days of open AI,
37:52
SPEAKER_02: where it's just like,
37:54
SPEAKER_02: But wait, there's more.
37:55
SPEAKER_02: And biology is in that space too.
37:57
SPEAKER_02: And it's deeply intertwined with this so-called AI.
38:01
SPEAKER_02: And it's helping us understand it.
38:03
Because as I mentioned before,
38:04
humans are really bad at understanding biology.
38:06
SPEAKER_02: It's so parallel.
38:09
SPEAKER_02: So now we're building models
38:10
that help us predict and create new biological structures that
38:17
we just couldn't
38:17
SPEAKER_02: have come up with on our own.
38:19
SPEAKER_02: And so this convergence,
38:20
SPEAKER_02: I was saying,
38:21
SPEAKER_02: of biology kind of hitching a ride on Moore's Law is fully in play now.
38:26
SPEAKER_02: And then biology is like,
38:28
SPEAKER_02: It was like,
38:28
SPEAKER_02: I don't know,
38:29
SPEAKER_02: in Moore's law land,
38:30
SPEAKER_02: it was kind of doubling every two years or something.
38:32
SPEAKER_02: And then it's been speeding up.
38:34
SPEAKER_02: And now I think the AI Moore's law,
38:35
SPEAKER_02: I don't know if,
38:36
SPEAKER_02: is there a name for an AI Moore's law or a new exponent or something,
38:38
SPEAKER_02: but it is not doubling every two years.
38:41
SPEAKER_02: It's like doubling every like four months or something.
38:44
SPEAKER_02: I don't know what the actual number is, but it must be short.
38:46
SPEAKER_02: And biology is deeply tied up in that.
38:49
SPEAKER_01: Because it's scaling laws, Ilya's law, something like that.
38:52
SPEAKER_01: Something like that, yeah.
38:54
SPEAKER_01: Okay,
38:55
SPEAKER_01: on that point,
38:56
I think many biotech startups that I've encountered are really
39:03
fundamentally based
39:04
SPEAKER_01: around a single idea.
39:06
SPEAKER_01: In many cases, one big giant bet is
39:09
SPEAKER_01: The idea is to develop a single drug,
39:12
get it to a stage where then a bigger pharma company will come
39:16
in and fund things
39:17
SPEAKER_01: to the end,
39:19
SPEAKER_01: and you have your big,
39:20
SPEAKER_01: grand exit,
39:22
SPEAKER_01: and this thing either works or doesn't,
39:23
SPEAKER_01: and people either get rich or they don't.
39:26
SPEAKER_01: You guys, and tell me if I'm right or wrong, based on...
39:31
what you just laid out,
39:31
all this excitement in these different areas,
39:33
these different tools.
39:35
SPEAKER_01: A big thesis going into this was,
39:37
SPEAKER_01: and on your iteration principle,
39:39
SPEAKER_01: I think,
39:39
SPEAKER_01: let's place a handful of bets,
39:42
or let's at least begin with a handful of ideas and chase them
39:46
quickly and see
39:48
SPEAKER_01: which ones...
39:48
SPEAKER_01: pan out.
39:49
When you and I first started talking,
39:51
it was a pretty wide range of stuff.
39:53
SPEAKER_01: There were things like looking at autophagy,
39:56
SPEAKER_01: which is this process where your cells begin to kind of slough off dead,
40:00
harmful material ranging to what we discussed very briefly at
40:04
the beginning,
40:04
SPEAKER_01: this partial cell reprogramming,
40:06
SPEAKER_01: which is an entire different field of science.
40:08
SPEAKER_01: This is my very long, long-winded question.
40:11
SPEAKER_01: Am I right that it was this conversation?
40:12
SPEAKER_02: For sure.
40:13
SPEAKER_02: I'd say that retro
40:15
SPEAKER_02: It's not specifically designed as a money-making thing.
40:19
I think about it as money is a feedback system from society
40:26
that you're doing
40:27
SPEAKER_02: something really valuable.
40:29
So I think retro will generate a huge amount of revenue because
40:34
it will be like
40:36
SPEAKER_02: making a difference in people's health.
40:39
And that will enable us to continue to grow and to do a lot
40:42
more research and to
40:44
make a difference,
40:44
and a lot more people develop more and more and more medicines.
40:48
SPEAKER_02: That's how I want it to snowball.
40:49
SPEAKER_02: But the money itself isn't the primary goal.
40:54
The primary goal is to ameliorate this vast quantity of human
40:59
suffering from
41:00
SPEAKER_02: horrifically dying in pain or losing oneself to neurodegeneration,
41:04
SPEAKER_02: et cetera.
41:05
SPEAKER_02: So the mission is central,
41:07
SPEAKER_02: and anything that could possibly serve that mission,
41:10
SPEAKER_02: I'm willing to do.
41:11
SPEAKER_02: As long as it's legal, moral, involves consenting adults.
41:16
SPEAKER_02: So, so far we have driven pretty hard at about six things.
41:21
SPEAKER_02: And then last year,
41:22
I decided that we wanted to consolidate down to the ones that
41:27
we are absolutely
41:28
SPEAKER_02: most certain and most excited about and really get behind those.
41:32
SPEAKER_02: We end up with three of them.
41:34
SPEAKER_02: Biology.
41:34
SPEAKER_02: If we understood biology...
41:36
SPEAKER_02: we'd be done.
41:37
SPEAKER_02: Because we don't understand biology, it's very empirical.
41:40
SPEAKER_02: You have to try things.
41:41
SPEAKER_02: You can't know the answer ahead of time,
41:43
SPEAKER_02: which makes it exciting,
41:45
but also frustrating if you happen to be working on a project
41:48
that is trying to ask
41:49
SPEAKER_02: the question,
41:50
SPEAKER_02: can you dilute human blood and extend the life of that person,
41:54
SPEAKER_02: and then come to the conclusion of,
41:56
SPEAKER_02: this isn't really going to work.
41:57
SPEAKER_02: This isn't practical.
42:00
SPEAKER_02: And so, great.
42:02
Set it aside and work on the things that have the highest
42:04
chance of really making a difference.
42:06
SPEAKER_01: You were talking for a minute about sort of the plasma exchange.
42:09
My acquaintance,
42:11
Brian Johnson,
42:12
also shifted away from his regular Blood Boy excursions.
42:18
SPEAKER_01: I know some of this stuff is secretive,
42:20
SPEAKER_01: but to the extent that you can talk about the three different areas,
42:23
SPEAKER_01: could you break those down?
42:25
SPEAKER_01: Sure.
42:26
SPEAKER_02: Yeah,
42:27
SPEAKER_02: so we're still excited about partial reprogramming,
42:30
SPEAKER_02: and we're continuing to pursue that as a research program,
42:34
SPEAKER_02: as distinct from,
42:34
SPEAKER_02: say,
42:35
SPEAKER_02: a development program where we're going more directly to clinic.
42:38
SPEAKER_02: Because I think that in the long run,
42:42
there are going to be a large number of partial reprogramming
42:45
therapeutics that are
42:46
SPEAKER_02: targeted to specific tissues.
42:48
Each tissue has its own sort of little proclivities and quirks
42:52
about how it likes
42:53
SPEAKER_02: to be reprogrammed.
42:54
But it's a bit dangerous going because you're doing it in the
42:57
body and you don't
42:58
have very much control of the things going on in the body when
43:01
you send the
43:01
SPEAKER_02: medicine in there,
43:02
SPEAKER_02: especially a very powerful medicine that can reprogram cells.
43:05
So the first therapeutics we're going after are ones where we
43:08
can reprogram cells
43:09
SPEAKER_02: outside the body and then put them back in.
43:11
SPEAKER_02: And it's a paradigm we call replacement.
43:14
So we sort of divide down all sort of rejuvenation into either
43:18
repair slash
43:19
SPEAKER_02: rejuvenation or replacement.
43:21
SPEAKER_02: And the most fail-safe way of,
43:24
SPEAKER_02: you know,
43:25
SPEAKER_02: you can try to rebuild the transmission if you're feeling like a genius,
43:27
SPEAKER_02: especially automatic transmission,
43:29
SPEAKER_02: which has some unknown hundreds of numbers of parts.
43:31
SPEAKER_02: Or you can just replace the darn thing.
43:33
SPEAKER_02: Then you know you have a brand new transmission.
43:35
SPEAKER_02: So that's replacement.
43:36
If you're replacing some cells in an old body with cells that
43:39
are zero age,
43:40
SPEAKER_02: essentially,
43:41
SPEAKER_02: new,
43:42
SPEAKER_02: in the car metaphor,
43:43
SPEAKER_02: okay,
43:44
SPEAKER_02: that's very fundamental rejuvenation.
43:46
SPEAKER_02: The problem is how to get them in there.
43:48
SPEAKER_02: That's one of them.
43:49
SPEAKER_02: The other is how to get them.
43:51
But it's a very profound and powerful way of rejuvenating
43:53
because it's brand new cells.
43:55
Just super quick for someone who's maybe hearing about this for
43:59
the first time.
44:00
When you're talking about working on cells outside of the body
44:04
and then possibly
44:05
SPEAKER_01: getting them in,
44:06
SPEAKER_01: what types of things are you talking about?
44:08
SPEAKER_01: What would you be repairing?
44:09
SPEAKER_01: Just to put us in the ballpark of what's possible here.
44:13
SPEAKER_02: Sure.
44:13
SPEAKER_02: Well, one would be, let's say, some components of your blood system.
44:18
SPEAKER_02: You can take blood cells out.
44:21
SPEAKER_02: You can reprogram them.
44:23
You can re-differentiate that back into a particular blood cell
44:27
type that you want
44:28
SPEAKER_02: to have back in your body and then inject it back in.
44:32
The slightly scary part of that is that tissue is outside your
44:35
body for a while and
44:37
SPEAKER_02: it doesn't have the usual protection that you have.
44:40
You have this nice waterproof skin wrapped all around you with
44:45
an immune system all
44:45
SPEAKER_02: over it defending you from pathogens.
44:48
So it has to be done in a sterile setting,
44:50
carefully constructed sterile setting.
44:53
SPEAKER_02: But that's pretty well known how to do that.
44:55
There have been hundreds and hundreds of cell therapies,
44:58
types of cell therapies even.
45:00
There's like hundreds of clinical trials underway for various
45:02
types of cell
45:02
SPEAKER_02: therapies that process cells and put them back in human bodies.
45:05
It sounds kind of scary,
45:06
but it's actually getting to be a fairly mature technology.
45:10
SPEAKER_01: And sorry,
45:11
SPEAKER_01: and just partial,
45:12
SPEAKER_01: so reprogramming itself is,
45:15
SPEAKER_01: and please help me out here because I'll put my foot in my mouth,
45:17
SPEAKER_01: but it's this idea of about a decade or so or more ago,
45:22
we discovered a way to hit cells with a certain combination of
45:26
proteins or what
45:28
SPEAKER_01: often get called transcription factors.
45:31
SPEAKER_01: So backing up, around 2006, Nobel Prize,
45:35
by Yamanaka in Japan figured out that you can take pretty much
45:39
any cell from a body
45:40
SPEAKER_02: and express these four proteins in the cell,
45:43
SPEAKER_02: and it'll reprogram all the way back to a pluripotent stem cell.
45:46
So the cell goes from an old skin cell back to something like
45:51
representing like an
45:52
SPEAKER_01: embryonic cell,
45:53
SPEAKER_01: an original cell,
45:55
SPEAKER_01: undefined.
45:56
SPEAKER_01: Yeah, we all come from one of those.
45:58
SPEAKER_02: We started as a single cell,
46:01
SPEAKER_02: and then that divided and divided,
46:03
SPEAKER_02: and each time it divides,
46:03
SPEAKER_02: it also differentiates,
46:04
SPEAKER_02: and they start taking on separate identities.
46:07
And eventually,
46:08
you have billions of cells,
46:11
and how to be skin cells and lung cells and so on.
46:15
SPEAKER_02: Reprogramming is the reverse of that.
46:17
SPEAKER_02: You apply these incredibly brilliantly discovered transcription factors,
46:21
and the cell reverses back up that hierarchy and ends up as the
46:25
single cell again.
46:26
SPEAKER_02: And then you can make a new person out of it.
46:28
SPEAKER_02: which is not what we're doing.
46:30
SPEAKER_02: So partial reprogramming,
46:31
SPEAKER_02: which we are not taking to clinic right now,
46:33
SPEAKER_02: that's still in research stages,
46:35
SPEAKER_02: is the process of partially reprogramming the cell,
46:37
SPEAKER_02: but not going all the way back to this pluriputin stem cell stage.
46:40
then removing the reprogramming pressure and letting the cell
46:43
bounce back to its
46:44
SPEAKER_02: original cell identity and thereby basically answer the question.
46:48
SPEAKER_02: But our advisor,
46:49
SPEAKER_02: Alex Ocampo,
46:50
SPEAKER_02: figured out 10 years after this discovery,
46:52
SPEAKER_02: back in 2016,
46:54
SPEAKER_02: that you can tease apart these two phenomena that occur,
46:57
SPEAKER_02: which one is the loss of identity as it's of adult identity.
47:01
SPEAKER_02: Like, is it a liver cell, skin cell?
47:03
SPEAKER_02: as it's being reprogrammed.
47:04
SPEAKER_02: And the other is loss of age.
47:06
When you go back to pluripotent stem cells,
47:08
age features of the cell get wiped out.
47:11
SPEAKER_02: It reverts back to zero age again.
47:13
SPEAKER_02: Seems kind of hard to believe almost.
47:17
SPEAKER_02: Yet,
47:18
that's why two 30-year-olds can get together and have a
47:21
zero-year-old baby because
47:23
SPEAKER_02: that process happened in there.
47:24
One cell from each of them got together and then that cell
47:26
turned out young.
47:29
SPEAKER_02: So we know it works.
47:30
But it's pretty complicated to stop partway and then let them
47:33
bounce back.
47:34
SPEAKER_01: You want a liver cell that you're taking back to this healthier,
47:39
SPEAKER_01: more robust state,
47:40
SPEAKER_01: but it's still very much a liver cell.
47:42
SPEAKER_01: You haven't wiped it clean.
47:44
Yeah,
47:45
well,
47:45
in the intermediate phases,
47:46
it forgets it's a liver cell for a little while.
47:48
SPEAKER_02: So we've done this.
47:49
We've had beautiful results from doing partial reprogramming in
47:52
liver in particular,
47:53
SPEAKER_02: just coincidentally.
47:54
SPEAKER_02: For a little while, it kind of stops acting like a liver cell.
47:58
SPEAKER_02: Then you release the reprogramming pressure,
48:00
SPEAKER_02: and most of them bounce back and become liver cells again.
48:03
SPEAKER_02: And then by numerous measures, they are younger.
48:07
This is,
48:08
at the moment,
48:08
hard to make into a therapeutic,
48:10
but we'll get there for some tissues.
48:11
SPEAKER_01: Because it has a tendency to run out of control and create tumors and
48:16
SPEAKER_02: But there's a lot of work to be done.
48:18
SPEAKER_02: Yeah.
48:18
SPEAKER_02: There's like, well, what if you go too far?
48:19
SPEAKER_02: What if a few of the cells go a little too far and they form these little
48:21
SPEAKER_02: pluripotent stem cells inside your liver?
48:23
SPEAKER_02: And then you end up, as I mentioned earlier, pluripotent stem cells
48:28
SPEAKER_02: grow into people.
48:29
So then you have these little people trying to grow inside your liver,
48:31
which is awkward.
48:32
SPEAKER_02: And, you know, this happens.
48:35
SPEAKER_02: Unless you've heard that.
48:36
And they have,
48:37
like,
48:37
little bones and teeth,
48:38
and then,
48:38
like,
48:39
literally this happens.
48:40
SPEAKER_01: I've seen the photos.
48:42
SPEAKER_01: However,
48:43
SPEAKER_01: these Yamanaka factors,
48:45
SPEAKER_01: this whole field,
48:46
SPEAKER_01: when I heard about this for the first time,
48:48
SPEAKER_01: it is,
48:49
SPEAKER_01: like,
48:49
SPEAKER_01: the coolest thing.
48:50
SPEAKER_01: And...
48:51
SPEAKER_01: Some people listening to this will know all this and some will not.
48:53
SPEAKER_01: I mean, there's you guys, there's New Limit, there's Altos Labs.
48:58
This field of partial cell reprogramming,
49:02
this is of massive interest right now.
49:05
SPEAKER_01: There's a ton of potential.
49:08
SPEAKER_02: And I believe there will be many therapies eventually.
49:11
But it has to be approached very carefully and slowly and
49:14
safely to make sure we
49:15
SPEAKER_02: don't hurt people,
49:16
SPEAKER_02: because that's the opposite of our mission.
49:19
What we feel more confident in right now in terms of somebody
49:22
to go to clinic with
49:23
SPEAKER_02: is reprogramming it all the way back to pluripotent stem cell,
49:26
SPEAKER_02: which has this beautiful property of being completely young.
49:31
SPEAKER_02: And they also reproduce very nicely at that stage,
49:34
SPEAKER_02: so you can grow it up into billions of them,
49:37
SPEAKER_02: re-differentiating it into a cell type of interest.
49:42
and you can do a ton of quality control when it's outside the
49:44
body and make sure
49:45
they're all uniform and sterile and clean and nice and perfect
49:49
and that nothing
49:50
SPEAKER_02: untoward happened,
49:51
SPEAKER_02: and then you can put them all back in the body.
49:53
SPEAKER_02: And so we call this micro-replacement,
49:55
and there are a few different cell types that can afford of
49:58
this particular modality.
50:01
In that,
50:01
this is all for one of your research programs,
50:05
and then number two?
50:08
SPEAKER_02: So we have two different micro-replacement programs going on.
50:11
SPEAKER_02: One related to the blood system and the other in the brain.
50:14
SPEAKER_02: And then autophagy, I'm still excited about.
50:17
And so we have an autophagy program that is based on a small
50:23
molecule that
50:24
SPEAKER_02: upregulates autophagy,
50:26
SPEAKER_02: which will,
50:27
SPEAKER_02: if we're correct in our hypothesis,
50:30
SPEAKER_02: help with neurodegeneration.
50:32
SPEAKER_02: Because in multiple of these neurodegeneration phenomena,
50:37
SPEAKER_02: like Alzheimer's disease,
50:39
SPEAKER_02: there are unwanted proteins,
50:42
SPEAKER_02: like misfolded proteins that are accumulating over time in these cells,
50:46
SPEAKER_02: causing problems.
50:47
SPEAKER_02: Like a plaque.
50:48
SPEAKER_02: Correct.
50:48
And why aren't the cells doing what they're supposed to do with
50:53
accumulated gunk inside them?
50:56
SPEAKER_02: Most of the time,
50:57
their lysosomes will destroy them using the process of
51:01
autophagy and break them
51:02
SPEAKER_02: back down into individual
51:04
SPEAKER_02: amino acids so you can make new proteins out of them.
51:06
SPEAKER_02: But sometimes they get stuck.
51:08
SPEAKER_02: And Alzheimer's disease, they get stuck.
51:10
SPEAKER_02: The lysosomes stop functioning.
51:12
So what we're up to is bringing forth this small molecule drug
51:16
that fixes that process.
51:18
SPEAKER_01: In my idiot version of autophagy,
51:21
I just know from reading my fasting books that if you starve
51:26
your cells for a while,
51:28
it seems to kick off this autophagy process where I'm told you
51:33
start to slough off
51:34
SPEAKER_01: dead cells and waste.
51:36
SPEAKER_01: Is that the right general description?
51:38
SPEAKER_01: Yeah.
51:38
SPEAKER_02: Yeah, well, it's a subcellular process.
51:42
SPEAKER_02: I think there are some other things that happen outside cells,
51:46
SPEAKER_02: like dying cells,
51:47
SPEAKER_02: other cells can eat and kind of break them down.
51:50
SPEAKER_02: But autophagy specifically is intracellular, like inside the cell.
51:55
SPEAKER_02: And it's the process of proteins are made of amino acids.
51:59
SPEAKER_02: And for some proteins, they just get damaged.
52:03
SPEAKER_02: A ray of sunlight hits it and zaps part of it.
52:06
And so it's kind of odd shaped and has the wrong molecular
52:09
structure in one spot.
52:10
So they just send them off through the autophagy pathway and
52:13
break them down and
52:13
SPEAKER_02: get the amino acids back and we'll build it again.
52:16
SPEAKER_02: proteins are constantly being rebuilt.
52:18
It's like you have some like insane mega baby who's just like
52:22
building stuff out of Legos.
52:23
SPEAKER_02: And you have like,
52:24
SPEAKER_02: you know,
52:24
SPEAKER_02: this like Starship Enterprises that are built out of Legos,
52:27
SPEAKER_02: except one of those disks is pointed the wrong direction.
52:30
SPEAKER_02: And like, if the whole room is like filling up with
52:32
SPEAKER_02: broken,
52:33
SPEAKER_02: bad Starship Enterprises,
52:34
the kid's going to start getting very angry because there's no
52:37
more Legos to play with.
52:38
Um,
52:38
so somebody else has to kind of break them down and put them
52:40
back in the basket for him.
52:41
SPEAKER_02: And that's, that's what the autophagy is supposed to do.
52:43
SPEAKER_02: Just break them down back into the,
52:45
SPEAKER_02: like the canonical,
52:46
SPEAKER_02: like eight bump Lego pieces,
52:48
SPEAKER_02: um,
52:49
SPEAKER_02: and then build new stuff out of them.
52:50
The small molecule would be a drug that would stimulate that
52:54
process or activate it.
52:56
SPEAKER_01: Yep.
52:56
SPEAKER_01: And you can target something like that directly toward the brain.
53:01
Yeah,
53:02
this drug happens to have a very nice,
53:05
what they call,
53:06
CSF plasma ratio.
53:08
SPEAKER_02: It passes the blood-brain barrier fabulously well.
53:12
SPEAKER_02: which is often a highly sought after pharmacological property of a drug.
53:17
Other times people spend their entire career trying to stop it
53:20
so that the drug
53:21
SPEAKER_02: doesn't have the cognitive side effects.
53:24
SPEAKER_02: But in this case, it's a really good property.
53:27
SPEAKER_01: Okay,
53:28
SPEAKER_01: you look at you guys' new limit,
53:30
SPEAKER_01: which is backed by Brian Armstrong and a number of other people.
53:34
They have been quite open about pursuing cell reprogramming and
53:39
these transcription
53:39
factors and trying to find this right recipe of them to do
53:43
interesting things to cells.
53:45
SPEAKER_01: You probably know more than I do,
53:46
but the rumor from my perspective about Altos Lab is that
53:50
they're doing similar work,
53:52
SPEAKER_01: although from where I sit,
53:53
SPEAKER_01: it's hard to know the breadth of exactly what they're chasing after.
53:56
SPEAKER_01: They're a little more secretive.
53:58
SPEAKER_01: But,
53:58
SPEAKER_01: you know,
53:59
the upshot of what I'm getting at is there's a lot of money
54:01
that's been spent
54:03
SPEAKER_01: already and is going to continue to be spent on this field.
54:07
SPEAKER_01: When I talk to some people,
54:09
SPEAKER_01: everyone knows that this is cool and interesting,
54:12
but even my hardcore science friends who are into longevity and
54:16
into this field,
54:17
some of them voice skepticism around feeling like the
54:20
transcription factor stuff
54:22
SPEAKER_01: will ever...
54:24
kind of like pan out in the most idealistic version of this and
54:28
that there's going
54:30
SPEAKER_01: to be,
54:30
I don't want to put words in people's mouth and I'm not smart
54:32
enough to articulate
54:33
SPEAKER_01: exactly what they've said to me,
54:34
but there's more complexity in the body than these will be able
54:38
to deal with.
54:39
SPEAKER_02: Well, I for sure agree.
54:41
SPEAKER_02: And I think it's been kind of an annoying problem that some people,
54:44
SPEAKER_02: you know,
54:45
SPEAKER_02: when this was first discovered by Ocampo in 2016,
54:48
SPEAKER_02: there are a bunch of people out there who are just like,
54:51
SPEAKER_02: this is amazing, it's going to cure everything.
54:53
Let's put the Yamanaka factors in a virus and give it to every
54:56
single cell in the
54:56
SPEAKER_02: body and people are going to be rejuvenated.
54:58
And it's just obviously a really good way of hurting people
55:02
because different
55:03
tissues in your body are more amenable to reprogramming and
55:05
some of them reprogram
55:06
faster and some of them are already stem cells and so it
55:09
doesn't take as much of a
55:10
SPEAKER_02: bump to take them back to pluripotent stem cells.
55:12
So you'd mostly have no effect on the body while some of them
55:16
were getting fully
55:17
SPEAKER_02: reprogrammed and the person would have this...
55:20
SPEAKER_02: Horrible, horrible, slow death.
55:24
I think some people are just very irrational and wishful
55:27
thinking and gung-ho.
55:29
SPEAKER_02: And fortunately, mostly they don't tend to do anything anyway.
55:32
SPEAKER_02: I think biology is still really hard.
55:36
SPEAKER_02: And so the directions that we're going,
55:39
SPEAKER_02: we just wait until we're very,
55:40
SPEAKER_02: you know,
55:40
SPEAKER_02: like I said,
55:40
we've done a number of different therapeutic avenue exercises
55:44
over the last few years,
55:46
SPEAKER_02: looking for ones where there's a clear story for how this can be safe,
55:49
SPEAKER_02: how it's actually producible at a reasonable cost.
55:54
It's ultimately scalable so that we could make a difference in
55:58
millions and
55:58
SPEAKER_02: millions of people's lives and not just Brian Johnson.
56:01
SPEAKER_02: Yeah.
56:03
SPEAKER_01: He's going to beat us all.
56:06
SPEAKER_02: More power to him.
56:09
And that we can get a clear,
56:12
like,
56:12
mechanistic proof of concept in our labs.
56:15
SPEAKER_02: And just keep searching for these first opportunities to go after.
56:18
SPEAKER_02: And I think for partial in vivo reprogramming,
56:21
it's going to be longer like that's one where we have to be
56:23
really careful about it
56:25
and safe and it's going to because different tissues reprogram
56:28
at different rates
56:30
we're going to have to do it in a targeted tissue by tissue
56:33
basis with gene
56:35
delivery vectors that have a homing function to a specific
56:40
tissue and they're going
56:41
to have to have mechanisms that slow down the fast ones and
56:44
speed up the slow ones
56:46
SPEAKER_02: and even out the dose between cells and you know
56:49
SPEAKER_02: Things that are just really hard in biology.
56:52
SPEAKER_02: Actually,
56:52
SPEAKER_02: small molecules are fantastically good at that,
56:54
SPEAKER_02: but gene therapy delivery systems right now aren't yet.
56:58
SPEAKER_02: You can take a pill and modulo your stomach and your gut.
57:01
Most of the rest of your body can get a pretty evenly
57:03
distributed version of that.
57:05
SPEAKER_02: It's pretty impressive.
57:06
So that's why we're focusing right now on things that we can do
57:10
ex vivo other than
57:12
SPEAKER_02: for small molecules.
57:14
SPEAKER_01: Okay.
57:16
I realized I'd never finished the story on when I first walked
57:19
in here and we had
57:20
SPEAKER_01: the shipping containers and the unusual infrastructure.
57:25
Part of this was you were trying to build a biotech company in
57:30
a different way than
57:32
SPEAKER_01: people usually do.
57:34
SPEAKER_01: And in this case,
57:36
SPEAKER_01: some of these experiments have to be done on mice,
57:40
SPEAKER_01: on rodents,
57:41
SPEAKER_01: and the shipping containers were...
57:42
SPEAKER_01: How has your big experiment gone on running a biotech company?
57:48
SPEAKER_01: in a different way, trying to do things at least at the outset.
57:51
SPEAKER_02: So we learn faster and taking these other- We built labs.
57:54
SPEAKER_02: Well,
57:54
SPEAKER_02: one of the sort of,
57:55
SPEAKER_02: I guess,
57:55
creative constraints is that we started operating in 2021 when
58:01
all the labs were
58:01
SPEAKER_02: full of a thousand different COVID companies.
58:05
SPEAKER_02: That's great.
58:06
SPEAKER_02: I'm glad they were helping people.
58:08
SPEAKER_02: That was a horrible time.
58:10
SPEAKER_02: But the world probably didn't need like a thousand COVID companies.
58:12
SPEAKER_02: It probably needs like 10 or maybe a hundred.
58:15
SPEAKER_02: But anyway, the labs were all full.
58:17
SPEAKER_02: And I need lab space to launch a wet lab biology company.
58:22
So having previously come in contact with the beauty of that
58:26
building block called
58:27
SPEAKER_02: the intermodal shipping container,
58:28
SPEAKER_02: I decided,
58:29
SPEAKER_02: okay,
58:29
SPEAKER_02: fine,
58:30
SPEAKER_02: we'll build labs.
58:31
SPEAKER_02: And that means we don't need a fancy building.
58:33
We can find just a raw warehouse just to kind of keep the rain
58:36
off of them.
58:37
And then we can do everything else inside these shipping
58:41
container-based labs.
58:43
So we got a place over in Oakland called American Steel where a
58:46
lot of the Burning
58:47
Man exhibits are a bit like sculptures and so on were built and
58:51
started modifying
58:52
shipping containers to be these rather actually functional and
58:56
beautiful biotech labs.
58:58
SPEAKER_01: Yeah, they're cool.
58:59
SPEAKER_01: They're well organized.
59:00
SPEAKER_01: They're all well lit and cooled and monitored.
59:06
And just trucked them over to Redwood City to our warehouse and
59:10
set them down,
59:10
SPEAKER_02: bolted them together as multiple shipping containers to make one lab.
59:14
SPEAKER_02: And we've had, I guess, maybe 14 of them inside there.
59:19
SPEAKER_02: And it just seemed to work.
59:21
Some of the scientists who are recruited initially are a bit skeptical,
59:24
like,
59:24
really?
59:24
SPEAKER_02: Are we going to be able to recruit top-notch scientists?
59:28
SPEAKER_02: But then after the first one showed up,
59:29
SPEAKER_02: plugged it in,
59:30
SPEAKER_02: turned it on,
59:31
SPEAKER_02: they walked in like,
59:32
SPEAKER_02: okay,
59:32
SPEAKER_02: well,
59:33
SPEAKER_02: this is a lab.
59:34
SPEAKER_02: This is like a perfectly fine habitat for Homo scientificus.
59:39
SPEAKER_01: Well, in my experience, it was the opposite effect.
59:42
SPEAKER_01: I kept hearing, have you seen what retro built?
59:46
SPEAKER_01: You got to go check this out.
59:47
SPEAKER_01: Nobody's ever done this before.
59:49
SPEAKER_01: And it seemed to be like a calling card.
59:53
SPEAKER_02: I think it's an interesting filter.
59:58
SPEAKER_02: It feels like the right kind of filter to me,
1:00:00
SPEAKER_02: but there could be sort of an anthropic effect there.
1:00:02
SPEAKER_01: Are you a traditionalist?
1:00:04
SPEAKER_01: Do you have to have things...
1:00:06
SPEAKER_02: If you're the kind of scientist who wants to go plod along with Pfizer,
1:00:10
SPEAKER_02: you're going to walk in and run shrieking.
1:00:18
SPEAKER_02: But if you're like,
1:00:18
SPEAKER_02: huh,
1:00:18
I wonder if we could change the world and design medicines that
1:00:21
alter the course of aging...
1:00:23
SPEAKER_02: Uh, which has never been done before.
1:00:25
SPEAKER_02: And then you walk into this lab that's kind of not been built before.
1:00:30
SPEAKER_02: They might, those two things might resonate a bit.
1:00:33
SPEAKER_01: You have this long experience with lab mice.
1:00:36
I think I've expressed my fascination to you before about maybe
1:00:38
wanting to do a,
1:00:40
SPEAKER_01: a documentary on this noble creature that,
1:00:44
SPEAKER_01: um,
1:00:45
SPEAKER_01: Gives so much and then also gives us false hope many times,
1:00:50
SPEAKER_01: you know,
1:00:50
SPEAKER_01: just as far as things moving from the rodents to humans and working.
1:00:55
SPEAKER_01: Strange question,
1:00:55
SPEAKER_01: I'll grant you,
1:00:56
SPEAKER_01: but just as somebody who's been in this field and dealt with them,
1:01:00
SPEAKER_01: I don't know,
1:01:00
what's the most interesting thing about lab mice that you have
1:01:05
encountered in your career?
1:01:06
SPEAKER_01: What should I know about these animals as I chase this film?
1:01:12
SPEAKER_02: That's a really cool idea for a film.
1:01:16
SPEAKER_02: They're fascinating little creatures.
1:01:18
SPEAKER_02: And there's quite a history of the science of it, too.
1:01:21
SPEAKER_02: I know some of the OGs, if you want to run after them.
1:01:25
SPEAKER_02: Like the people who started cultivating these?
1:01:28
SPEAKER_02: Yeah,
1:01:29
people who were involved in coming up with some of the big
1:01:32
innovations in the fields,
1:01:33
SPEAKER_02: like the so-called IVC cage.
1:01:36
It used to be that people would collect mice for research and
1:01:40
just put them in
1:01:41
SPEAKER_02: boxes with,
1:01:42
SPEAKER_02: like,
1:01:43
SPEAKER_02: a screen on the top,
1:01:45
SPEAKER_02: and that's that.
1:01:47
SPEAKER_02: Periodically change their bedding.
1:01:49
But that's open to the air,
1:01:50
and the cage on the left has an influence on the cage on the right.
1:01:58
SPEAKER_02: They can hear each other and, like, various...
1:02:02
SPEAKER_02: cells and stuff and can pass,
1:02:04
SPEAKER_02: you know,
1:02:05
different pathogens can pass and forth across between the cages
1:02:08
and so on.
1:02:09
SPEAKER_02: So the intellectually,
1:02:10
SPEAKER_02: individually ventilated cage was a big step forward when they said,
1:02:14
SPEAKER_02: okay,
1:02:14
we're going to put these cages all on this carefully designed
1:02:17
rack and we're going
1:02:18
to have a single source of air and then it's going to be
1:02:20
individually routed to
1:02:21
SPEAKER_02: each of the cages.
1:02:22
SPEAKER_02: For the first time meant that they're actually individual experiments,
1:02:25
SPEAKER_02: like more isolated individual experiments.
1:02:28
SPEAKER_02: And then the way I do it,
1:02:28
SPEAKER_02: I also individually light them,
1:02:30
SPEAKER_02: which still,
1:02:31
SPEAKER_02: as far as I can tell them,
1:02:32
SPEAKER_02: 5-year-olds don't do.
1:02:34
SPEAKER_02: Like, I don't know why.
1:02:35
SPEAKER_02: There's like 100 million mice a year using research,
1:02:37
SPEAKER_02: and they still don't individually light their cages.
1:02:38
SPEAKER_01: Just because you're not getting this consistent,
1:02:40
SPEAKER_01: well,
1:02:41
SPEAKER_01: first of all,
1:02:41
SPEAKER_01: for the mice,
1:02:42
SPEAKER_01: a consistent experience,
1:02:43
SPEAKER_01: and then for your results?
1:02:45
SPEAKER_02: Yeah, I mean, they're incredibly photosensitive creatures.
1:02:48
SPEAKER_02: Like, you can turn on the light in the middle of the night.
1:02:52
SPEAKER_02: If you're a cruel grad student at a university or whatever,
1:02:55
SPEAKER_02: you don't listen to the rules and you turn on the light.
1:02:57
Because you happen to be working at four in the morning and
1:03:00
they flip out.
1:03:00
SPEAKER_02: This is Armageddon basically.
1:03:02
SPEAKER_02: This never happened in millions of years of evolution, right?
1:03:04
SPEAKER_02: It's the middle of the night.
1:03:04
SPEAKER_02: It's the middle of the night.
1:03:05
SPEAKER_02: It's not like there's anti-eclipses, right?
1:03:08
SPEAKER_02: Where like the earth suddenly turns transparent and everything's bright.
1:03:11
SPEAKER_02: So the mothers will just,
1:03:13
they'll eat their babies at that point and run because like the
1:03:16
world was on fire,
1:03:16
SPEAKER_02: like something horrible.
1:03:18
um they're extremely photosensitive animals and so in a normal
1:03:21
vivarium the cages
1:03:23
at the bottom of a rack get about a hundred times less light
1:03:26
than the cages at the
1:03:26
top of the rack but everybody gnaws this like why i don't
1:03:29
understand anyway i have
1:03:30
them all modified at retro so that they all get exactly 100 lux
1:03:35
regardless of their
1:03:36
SPEAKER_02: position
1:03:37
SPEAKER_02: Just many details like that.
1:03:39
SPEAKER_02: Mice, most of their brain is devoted to smell.
1:03:42
SPEAKER_02: I don't know, it's like two-thirds of their sensory cortex or something.
1:03:46
SPEAKER_02: There's incredibly smell-oriented animals.
1:03:51
SPEAKER_02: Most of the research is done on this
1:03:53
SPEAKER_02: weird strain called the so-called C57BL6,
1:03:59
abbreviated Black 6 animal that has been bred over and over and
1:04:03
over again,
1:04:05
hundreds and hundreds of times sequentially to be these
1:04:07
relatively docile,
1:04:09
SPEAKER_02: very biologically odd.
1:04:10
SPEAKER_02: They're very biologically odd mammals,
1:04:12
and yet now they're the standard mammal on which almost all
1:04:16
human medicines are developed,
1:04:17
SPEAKER_02: which is
1:04:19
SPEAKER_02: Kind of bad.
1:04:20
SPEAKER_01: Super weird, right?
1:04:21
SPEAKER_02: Yeah.
1:04:21
SPEAKER_02: One of the weird things about them is that they are,
1:04:24
SPEAKER_02: they're all sort of like identical twins of each other.
1:04:26
SPEAKER_02: So genetically identical.
1:04:28
SPEAKER_02: I mean, that's fine from one to the next.
1:04:30
SPEAKER_02: But all of them are homozygous at every location in their genome,
1:04:35
SPEAKER_02: which that is like very,
1:04:36
SPEAKER_02: very weird.
1:04:37
SPEAKER_02: It doesn't exist anywhere in nature.
1:04:38
It makes it very easy to breed them so they stay the same over
1:04:41
and over again because...
1:04:42
SPEAKER_02: The genome,
1:04:44
SPEAKER_02: every reproductive cycle,
1:04:45
the genome tends to has this so-called crossover during meiosis
1:04:50
where the sides,
1:04:50
SPEAKER_02: which side of the two different chromosomes flips,
1:04:54
SPEAKER_02: but that doesn't change them because they're identical on both sides.
1:04:57
But this means they've had to be bred so that the typical
1:05:00
recessive diseases from
1:05:01
SPEAKER_02: having the same allele on both chromosomes don't kill them.
1:05:07
SPEAKER_02: or make them sick.
1:05:08
So that pretty much all die of like particular kind of obscure
1:05:14
flavors of lymphoma.
1:05:16
SPEAKER_02: So when people talk about increasing mouse lifespan by doing X or Y,
1:05:23
SPEAKER_02: largely this is the process of making a difference in mouse lymphoma.
1:05:29
That's why I tend to really put a lot of credence on like total
1:05:34
mouse lifespan as a readout.
1:05:38
Given all these oddities and general problems of translation,
1:05:43
why don't we change this?
1:05:44
SPEAKER_01: Why don't we have a lot more rodent variety?
1:05:50
SPEAKER_02: I tried.
1:05:52
There have been some really valiant attempts to create a more
1:05:54
genetically diverse
1:05:57
SPEAKER_02: different types of genetically diverse pools of mice.
1:06:01
One of them is the so-called HET3 strain which was developed by
1:06:06
Rich Miller and a
1:06:08
few other folks that contains a bunch more variability and was
1:06:14
used by the National
1:06:15
SPEAKER_02: Institutes of Aging for the last
1:06:18
SPEAKER_02: I don't know,
1:06:19
SPEAKER_02: 10 or maybe even 20 years now,
1:06:22
to do their studies to see if there are things that can affect
1:06:25
mouse lifespan.
1:06:26
And it's thought that lifespan in this more genetically diverse
1:06:29
creature is
1:06:29
SPEAKER_02: probably a more indicative readout of how it might translate to human.
1:06:33
SPEAKER_02: And a bunch of interesting things were discovered that way.
1:06:37
SPEAKER_02: Rapamycin, for instance, extends mouse lifespan.
1:06:41
SPEAKER_02: Unclear whether it extends human lifespan.
1:06:43
SPEAKER_02: The jury is still out.
1:06:45
SPEAKER_01: Brian Johnson not taking it anymore.
1:06:46
SPEAKER_02: Not anymore.
1:06:47
SPEAKER_02: Yeah, right.
1:06:48
SPEAKER_02: I think it's also probably some side effects.
1:06:53
SPEAKER_01: He's our human mouse.
1:06:57
SPEAKER_01: Yeah, that's right.
1:06:58
SPEAKER_02: Yeah, Brian's a very normal person.
1:07:01
SPEAKER_02: So whatever works for him probably works for everybody.
1:07:02
SPEAKER_01: Yeah.
1:07:06
SPEAKER_01: Brand's a trip.
1:07:07
SPEAKER_01: We love it.
1:07:08
SPEAKER_01: Watch Don't Die on Netflix.
1:07:10
SPEAKER_01: Yeah.
1:07:10
SPEAKER_01: So, yeah, you had a company, Viome.
1:07:14
SPEAKER_01: You said, I've tried this.
1:07:15
SPEAKER_02: You were trying out some of these ideas.
1:07:17
SPEAKER_02: Yeah,
1:07:17
SPEAKER_02: I was running this nonprofit for a while,
1:07:19
wanting to learn about a lot of the science going on and
1:07:23
longevity research,
1:07:26
SPEAKER_02: and also the conversation generally around
1:07:31
Longevity biology in Silicon Valley was not satisfying to me
1:07:37
because it was mostly
1:07:38
driven by sort of the snake oil salespeople and their standard
1:07:43
of evidence were poor.
1:07:44
So the idea of what piece of evidence led to and therefore you
1:07:49
should take this or
1:07:50
SPEAKER_02: buy this as the case may be.
1:07:52
SPEAKER_02: what didn't logically follow to me.
1:07:54
SPEAKER_02: You do that if you have a statistical significance above some threshold,
1:07:59
or you can somehow prove with reasonable scientific evidence
1:08:04
that this thing
1:08:05
SPEAKER_02: matters or not.
1:08:06
SPEAKER_02: And 99.9% of the time, no such evidence existed.
1:08:10
So I wanted to up-level the dialogue in general to a more
1:08:14
scientific footing.
1:08:16
So I'd started bringing in professors from Stanford and UCSF
1:08:20
and Berkeley and the
1:08:22
SPEAKER_02: Buck Institute.
1:08:23
SPEAKER_02: There's a research institute up in Nevada,
1:08:25
California that studies aging to give these sort of open forum
1:08:30
sort of lecture and
1:08:31
SPEAKER_02: lecture discussion events I called salons.
1:08:34
And I did like many dozens of these,
1:08:37
like lots and lots of events and met a lot of people.
1:08:40
One of the themes I started noticing was that these research
1:08:45
efforts would go a
1:08:46
SPEAKER_02: certain distance.
1:08:47
SPEAKER_02: And there's this sort of typical echelon that research would step up.
1:08:51
SPEAKER_02: You know,
1:08:51
SPEAKER_02: they'd start off in,
1:08:52
SPEAKER_02: say,
1:08:53
SPEAKER_02: yeast or something,
1:08:54
SPEAKER_02: find something that looks kind of interesting and then try it.
1:08:56
SPEAKER_02: worms for work there,
1:08:58
SPEAKER_02: maybe they try and flies,
1:08:59
SPEAKER_02: we just kind of go up levels of sophistication,
1:09:01
SPEAKER_02: biological sort of following evolution,
1:09:04
essentially from single cells and multiple cells and more and
1:09:06
more complex creatures.
1:09:07
I kept noticing sometimes they would just skip mouse or they'd
1:09:11
just stop at mouse
1:09:12
SPEAKER_02: and say,
1:09:13
SPEAKER_02: okay,
1:09:13
SPEAKER_02: I published a paper.
1:09:14
SPEAKER_02: Now I'm going to go back and like look at some other phenomenon in aging.
1:09:17
SPEAKER_02: I'm like,
1:09:18
SPEAKER_02: wait,
1:09:18
but mouse is like starting to become close to human because
1:09:21
then it's a mammal and
1:09:23
SPEAKER_02: So many,
1:09:23
SPEAKER_02: you know,
1:09:23
SPEAKER_02: has a similar immune system and like,
1:09:25
SPEAKER_02: and then like,
1:09:26
SPEAKER_02: then you can go to human and they go,
1:09:27
SPEAKER_02: don't,
1:09:27
SPEAKER_02: you know,
1:09:27
SPEAKER_02: people matter,
1:09:28
SPEAKER_02: but you know,
1:09:29
SPEAKER_02: academia,
1:09:29
SPEAKER_02: as I mentioned earlier,
1:09:30
SPEAKER_02: is not,
1:09:30
SPEAKER_02: it thinks different.
1:09:32
SPEAKER_02: But the response I got was that,
1:09:35
SPEAKER_02: yeah,
1:09:35
SPEAKER_02: but like,
1:09:35
SPEAKER_02: ugh,
1:09:36
my house research is so hard and expensive and I bring all my
1:09:39
grad students on and
1:09:40
SPEAKER_02: they don't want to join my group and like,
1:09:42
SPEAKER_02: eh,
1:09:43
SPEAKER_02: it's not worth it.
1:09:44
SPEAKER_02: I just kept thinking,
1:09:45
SPEAKER_02: and I was hosting these at Y Combinator at the time,
1:09:50
SPEAKER_02: because a friend of mine was a founder of it.
1:09:53
SPEAKER_02: And I was like, what should I do with this mouse thing?
1:09:55
SPEAKER_02: Like,
1:09:56
SPEAKER_02: it seems to be stopping some of the researches,
1:09:57
SPEAKER_02: like mouse research is tough and unattractive.
1:10:01
SPEAKER_02: He's like, well, you should, you know, do a company there.
1:10:05
SPEAKER_02: That's how you solve all problems.
1:10:06
SPEAKER_02: Yeah.
1:10:09
SPEAKER_02: Yeah, so that was Trevor Blackwell.
1:10:13
SPEAKER_02: So I thought, huh, how hard could that be?
1:10:14
SPEAKER_02: I'll just automate the whole process.
1:10:16
SPEAKER_02: I'll make like mouse vending machines.
1:10:18
SPEAKER_02: That's the last word.
1:10:21
SPEAKER_02: Yeah,
1:10:22
SPEAKER_02: so I decided to,
1:10:23
SPEAKER_02: as in the sort of hubris of the Silicon Valley startup world,
1:10:28
SPEAKER_02: quote,
1:10:28
SPEAKER_02: fix mouse research,
1:10:30
SPEAKER_02: unquote.
1:10:31
SPEAKER_02: Yeah,
1:10:31
it turned out some of the hardest things for people to do was
1:10:34
to actually measure
1:10:36
SPEAKER_02: the data points,
1:10:36
SPEAKER_02: like actually giving them injections or so on.
1:10:38
SPEAKER_02: That wasn't that hard.
1:10:40
SPEAKER_02: But then seeing what the result was is hard.
1:10:43
SPEAKER_02: They get tired afterward.
1:10:45
SPEAKER_02: They get anxious.
1:10:47
SPEAKER_02: What kind of side effects are they experiencing?
1:10:48
SPEAKER_02: That's what we care about for humans.
1:10:50
SPEAKER_02: Is that what's happening to these mice?
1:10:51
SPEAKER_02: I don't know,
1:10:52
you have a grad student sit around next to the cage for a month
1:10:55
and watch them
1:10:56
SPEAKER_02: every day.
1:10:56
SPEAKER_02: Like that's just not gonna happen.
1:10:58
SPEAKER_02: So that seemed to be the hardest problem.
1:11:00
So we put HD video cameras in every single mouse cage and then
1:11:04
used computer vision,
1:11:06
SPEAKER_02: which would be called AI now.
1:11:08
SPEAKER_02: against those data streams.
1:11:09
SPEAKER_02: So we had like thousands and thousands of HD video cameras,
1:11:13
SPEAKER_02: video streams coming up to the cloud.
1:11:15
I think our upload rate was not far behind Netflix,
1:11:20
or YouTube,
1:11:21
sorry,
1:11:21
at that time.
1:11:22
SPEAKER_02: Of just mice scratching themselves.
1:11:26
SPEAKER_02: Yeah,
1:11:26
SPEAKER_02: just kind of hanging out,
1:11:28
SPEAKER_02: doing their mouse thing,
1:11:29
SPEAKER_02: chasing each other around,
1:11:30
SPEAKER_02: trying to make new mice.
1:11:32
SPEAKER_01: Was there a Mr. Beast of the mice?
1:11:37
SPEAKER_02: Yeah,
1:11:37
people have done some experiments where they just put mice in
1:11:40
an environment and
1:11:41
SPEAKER_02: just let them go.
1:11:43
SPEAKER_02: Just keep pouring in more food periodically.
1:11:45
SPEAKER_02: You know, it's Lord of the Flies, mouse land.
1:11:50
SPEAKER_01: And so it was this great idea,
1:11:51
SPEAKER_01: and then in the end,
1:11:53
SPEAKER_01: it was just a hard business to keep going and find its...
1:11:57
SPEAKER_02: Yeah,
1:11:58
SPEAKER_02: I mean,
1:11:58
SPEAKER_02: it was like...
1:12:01
SPEAKER_02: It was technically very successful.
1:12:03
We really made the most technologically advanced beautiful
1:12:07
mouse cage that just
1:12:08
SPEAKER_02: streams like billions of data points off of each cage.
1:12:12
And it was possible for us to read out which therapeutics were
1:12:17
working and which ones weren't.
1:12:17
SPEAKER_02: I mean, that's the ultimate.
1:12:19
SPEAKER_02: Goal is like, you know, what you want to try to like cure like COPD.
1:12:23
SPEAKER_02: And we have these 10 medicines.
1:12:24
SPEAKER_02: We're wondering which ones like will cure symptoms of COPD.
1:12:27
SPEAKER_02: And so you were doing this for other companies.
1:12:29
SPEAKER_01: Right.
1:12:29
SPEAKER_01: Yeah.
1:12:30
SPEAKER_01: And they want to control.
1:12:31
SPEAKER_01: For sure.
1:12:32
Even though they had crappier stuff,
1:12:34
they still want their mice in their building.
1:12:37
Yeah,
1:12:38
so...
1:12:39
It turns out building,
1:12:40
like,
1:12:40
a B2B services business is really hard.
1:12:42
SPEAKER_02: Or it's just not as interesting,
1:12:46
SPEAKER_02: I guess,
1:12:47
SPEAKER_02: as,
1:12:47
SPEAKER_02: like,
1:12:47
SPEAKER_02: building something amazing and having it work.
1:12:50
SPEAKER_02: We made thousands of them...
1:12:52
SPEAKER_02: In our facility, people really wanted to run them in their facility.
1:12:55
SPEAKER_02: So then we had to make the whole thing into a product,
1:12:57
which means we had to get FCC approval and UL and put it in a
1:13:03
nice package and
1:13:06
SPEAKER_02: maintain it and have people fly around and have sales technicians.
1:13:10
SPEAKER_02: I mean, we started getting a lot of adoption.
1:13:13
We had probably maybe six of the top 10 pharma had them going
1:13:17
in their vivariums.
1:13:18
SPEAKER_02: at some scale.
1:13:19
SPEAKER_02: Did they replace every single cage in their ribarium?
1:13:22
SPEAKER_02: No.
1:13:22
But it is a very high-tech,
1:13:25
high-data,
1:13:26
high-automation way of doing mouse experiments.
1:13:29
SPEAKER_02: And it turns out that fit perfectly with the sort of high-tech,
1:13:34
high-data pipeline that recursion pharma had been building at
1:13:38
the cellular level.
1:13:39
And then when they got hits from the cellular stage of their
1:13:43
drug discovery pipeline,
1:13:45
SPEAKER_02: They then wanted to then move them on into, into animals.
1:13:49
SPEAKER_02: So that, in that sense, it was, it was a marriage made in heaven.
1:13:52
SPEAKER_02: So that was, it was a great acquisition partner.
1:13:54
SPEAKER_01: Okay.
1:13:55
SPEAKER_01: Well,
1:13:56
SPEAKER_01: I'm going to take you up on these,
1:13:57
SPEAKER_01: these mouse tails and you're,
1:13:59
SPEAKER_01: uh,
1:13:59
SPEAKER_01: some of these experts undetermined this movie at some point.
1:14:03
SPEAKER_01: Um, Joe, I wanted to thank you so much.
1:14:07
SPEAKER_01: And if I can get permission from you,
1:14:10
SPEAKER_01: maybe we could find some photos just to,
1:14:11
SPEAKER_01: I just want to show people what this place is.
1:14:13
looks like on some level for people who don't get a chance to
1:14:17
come visit just
1:14:18
SPEAKER_01: because it's very cool.
1:14:20
SPEAKER_01: And,
1:14:21
SPEAKER_01: you know,
1:14:21
SPEAKER_01: when I was getting ready for this podcast,
1:14:23
SPEAKER_01: I went down my Joe Betts Lacroix rabbit hole on YouTube last night.
1:14:29
SPEAKER_01: I found some old...
1:14:31
SPEAKER_01: No, he's cringing, but I actually urge people to go look at him.
1:14:39
You've got this great energy and just like positivity when you
1:14:44
talk about all this stuff.
1:14:45
SPEAKER_01: And so for people who are interested...
1:14:47
SPEAKER_01: In longevity,
1:14:48
SPEAKER_01: I mean,
1:14:48
SPEAKER_01: you can find talks going back,
1:14:50
SPEAKER_01: I don't know,
1:14:50
SPEAKER_01: six,
1:14:51
SPEAKER_01: seven,
1:14:51
SPEAKER_01: eight,
1:14:51
SPEAKER_01: nine,
1:14:52
SPEAKER_01: 10 years,
1:14:53
SPEAKER_01: if I remember right from last night.
1:14:55
SPEAKER_02: I was in Kevin Kelly's house back in 2000 something.
1:15:04
SPEAKER_02: I can't remember what exact number.
1:15:06
When we came up with this idea of creating a movement called
1:15:09
the Quantified Self with Gary Wolf.
1:15:14
SPEAKER_02: Did you guys...
1:15:15
SPEAKER_01: You guys coined these terms?
1:15:18
SPEAKER_02: Yeah.
1:15:18
SPEAKER_02: Oh my God.
1:15:19
SPEAKER_02: And then there's sort of like,
1:15:20
SPEAKER_02: so we're going to create this community and people are going to work on,
1:15:23
like they're going to be quantifying themselves and it's going
1:15:27
to be like a whole movement.
1:15:28
SPEAKER_02: And like,
1:15:29
there's no reason that the N of one can't also have
1:15:31
significance if you have enough
1:15:32
SPEAKER_02: data points.
1:15:33
SPEAKER_02: So great.
1:15:34
SPEAKER_02: So we held this event, you know, the event, we call it quantified self.
1:15:37
SPEAKER_02: And then like, who's going to speak at it?
1:15:38
SPEAKER_02: And there's like, uh,
1:15:40
SPEAKER_02: So I ended up like being the speaker at like three of them for a while.
1:15:44
SPEAKER_02: You could probably find some really old quantified self videos if
1:15:49
SPEAKER_01: I know you cringe, but I do urge people to go do this.
1:15:51
SPEAKER_01: And it's funny,
1:15:52
SPEAKER_01: you bring up Kevin Kelly,
1:15:53
SPEAKER_01: and this is the first episode of the Core Memory Podcast.
1:15:56
SPEAKER_01: Kevin Kelly was about, I want to say, four months ago.
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SPEAKER_01: I ran into him at an event,
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SPEAKER_01: and we had sort of a heart-to-heart,
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and a couple words that he said pushed me over the edge to quit
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my job and go do
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SPEAKER_01: this thing.
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SPEAKER_01: So he's either to blame him or to think.
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SPEAKER_01: Love that guy.
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SPEAKER_01: Yeah.
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a legend well Joe thanks so much for your time and I'm sure
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people hopefully
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learned a lot and you just it's been really interesting knowing
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you over the last
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couple years I love people who are into so much different stuff
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and just have their
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lives go in unusual directions it's kind of my favorite people
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to hang out with so
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thank you for your time it's been a pleasure thanks so much
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Ashley music music
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SPEAKER_01: Thank you all so much for listening.
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SPEAKER_01: I'm your host, Ashley Vance.
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SPEAKER_01: Our guest today was Joe Betz-Lacroix.
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SPEAKER_01: The show is produced by me and David Nicholson.
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SPEAKER_01: Our theme music is by James Mercer and John Sortland,
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SPEAKER_01: and our sound editing is by John Sortland as well.