April 18, 2022 • 46 mins
Francisco Gimenez is Partner at 8VC and focuses on Bio-IT investments. He received his Ph.D. from Stanford in Biomedical Informatics and B.S. in Electrical Engineering and Computer Sciences from UC Berkeley. In this episode, Francisco explains how breakthroughs in AI, gene editing, and cell therapies converged to jumpstart a new age in biology. He predicts that biomanufacturing platforms, armed with mountains of data and new tools, will bring down the costs of creating and commercializing drugs so smaller companies can treat rarer diseases and deliver more personalized cures. By decentralizing the pharma industry, Francisco is optimistic that the future of medicine will evolve from reactive care to preventative medicine that will help people fully self-actualize and lead their best, healthiest lives. [Joe is a founding partner of 8VC, his venture capital firm.]
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
if you think about it,
we have like this garage full of interesting parts now and we're mixing and matching and using all of them to try to cure diseases to run new experiments and all of that and every time we do,
we're just opening completely new fields of biology.
If we think about in this biotech landscape and so once we've opening these entirely new fields of biology then we're starting to see more and more we can take from what evolution is teaching us to build more tools.
(00:23):
What are a couple of the new fields of biology we've opened up.
Yeah.
So like I would say cell therapy being one of the sort of canonical textbook answers here,
right?
Using cells as medicine training ourselves to be medicine.
Yeah,
(00:44):
I'm joe Lonsdale,
welcome to american optimist Francisco.
Thanks for joining us.
Today is dr Francisco.
Jimenez,
your doctor from your stanford PhD in bioinformatics and you were born in Argentina Francisco.
But you came here when your family came here when you were quite young.
What brought you guys to the US first of all?
Yeah,
so my dad was an electrical engineer by training and they were in I was born in body luxury in Argentina and my family's from Mendoza and really,
(01:14):
you know,
my family just realized that the opportunity for kind of an electrical engineer was much greater in the United States than it was for Argentina.
And so they had kind of a a life and a path ahead of them,
financial opportunity,
business opportunity was bigger here.
Yeah,
absolutely,
it was kind of like the canonical case of the chasing the american dream,
right?
So they were doing well,
(01:34):
but there was just a ceiling to,
to mobility that my parents could see while they stuck around and they came to the midwest to phoenix.
You said he helped open the intel fab in Costa rica at some point I think.
Yeah,
so we went to uh the midwest was supposed to be a two year visa and then about 1.5 years into that,
my dad left the role and then just spam the entire country with his resume.
(01:57):
Uh and then for anything and they started out going like,
let's go to the floor,
let's go to Miami,
let's go to los Angeles,
go to san Francisco.
Uh we ended up in phoenix Arizona where he got his only interview was with Intel there and he said that he could program and see.
Uh and then with the sort of last money we had,
(02:17):
he bought cardigan and Richie's programming and see book and then learned it on the plane.
Uh and then I got the job at Intel uh and then we went to the phoenix Arizona site and were there for eight years and then after that when they were starting the fab in Costa rica,
He took us all to basically help open that fabulous in Costa Rica for two years and then we came back to the US to the folsom site for intel,
(02:41):
did you guys speak spanish at home because you're from Argentina,
that's already kind of knew it when you were in Costa rica?
Yeah,
yeah,
that was,
it was a big impetus.
We either had to go to Israel or Costa rica because we were kind of done with Arizona and decidedly better at spanish than Hebrew.
That's pretty good.
That's funny because it could have been in Israel and you went,
you went to Berkeley undergrad?
Yeah.
When did you apply to Berkeley?
(03:02):
Uh,
honestly,
I mean,
not to belabor the intel point,
but Andy Grove was the ceo of intel and my parents always said Andy Grove went to Berkeley,
it's the best school in the,
in the nation.
And so we were just like inception in my head,
in the weird way it does.
Andy Grove is a pretty cool guy standard.
I can forgive you for that mistake.
(03:22):
Well,
what I always like to say is like Berkeley is where I learned humility and stanford's,
where I learned entitlement,
which kind of angers both groups off,
which is why it's the best kind of joke,
but I think it's you need both of those two,
I think succeed in sort of entrepreneurship.
And so after Berkeley,
you got your PhD at stanford?
Yeah.
And how come you studied bioinformatics?
So I did research at UCSF in A gene therapy lab for Parkinson's disease and that's really where I fell in love with research and a lot of my work.
(03:51):
there was sort of analyzing images uh an M.
R.
I.
Is to see the distribution of of how injected gene therapies distribute as it pertains to gene expression.
Uh and just absolutely loved the idea of of at the time in 2010 was not a very loved field imaging informatics is kind of a backwater.
Uh and but that we could use what was a I at the time to kind of automate these human tasks.
(04:16):
That was so exciting.
And then I was kind of saved because around 2012 when deep learning came out all of a sudden my field became one of the most exciting research fields ever.
You find yourself in the middle of the hottest fields.
Exactly kind of by mistake,
exactly where just,
you know,
it's better to be smart than lucky,
right?
Sorry,
lucky than smart.
Probably if you're smart and you get lucky,
that's probably exactly what the best combination.
(04:38):
And you you were you were obviously one of the superstars there,
I think you gave the commencement address after you graduated.
And yeah,
it's plenty of wonderful things.
I gave the stanford,
that was this phds commencement speaker for 2015.
It was quite an honor,
awesome.
And so and so you you did really well there and you're in the middle of this hot new field maybe.
So,
you know,
a lot of people and we talked about earlier,
like with our friend rick that they were in a revolution in bio right now,
(05:00):
which I know you you agree with giving our audience some of the reasons one revolution by one of the key things that just started this revolution.
Why is this happening?
Right?
Yeah,
yeah.
So I think uh you know,
it's kind of like,
It's always hard,
I think we have to choose arbitrary dates for this because it's like at what point in an exponential growth graph,
did everything get exponential?
Right?
And the definition is that it's always always looks exponential along different scales,
(05:23):
but like one sort of window I love to think about is between 2011 and 2012,
right?
Like,
so I talked about how Alex net wins image net and that was really the sort of Fosbury flop for deep learning and,
and but at the same time within like the same sort of span of period is when jennifer,
Dowd nose work and functions work came out on CRISPR.
(05:45):
So my friends who were into genetic engineering working on like talons or zinc fingers all of a sudden saw their entire field upended at the same time,
I had a much better tool for genetic engineering could have used before.
Exactly.
And so it was like,
we all kind of joked that we had to take our qualifying exams,
which is like what's the state of the art and it was the fastest period of time for from your qualifying exams being taken towards being obsolete.
(06:09):
It was like the biggest waste of time ever because all of a sudden everything was neural nets and everything I'd learned up until that point was kind of obsolete.
And the same with the genetic engineering friends when CRISPR came out completely upended how we think about one year in 2012 had major advances in ai major advances in gene editing with this technology.
And we also have car T.
S come out right?
Carl june had his first paper come out showing that he was able to cure a pediatric oncology patient A.
(06:33):
L.
L.
With a car T.
Which was like 30 years of war effort.
But this was such a car T.
Being into american receptor T.
Cell.
This is when you basically program a T cell and tried to go after and kill the the cell cell.
Yeah,
exactly.
So in that you know,
you've talked about this with rick before but like it's it's and so I like to jokingly call it like this and you switch abilities because like so you think about Einstein in 1905,
(06:54):
he has four papers that completely changed physics,
right?
And he called us on his way to Belize.
And I think within that year we had sort of three fundamentally transformative technologies and you think about like they've completely changed the field,
like nobody can study ai without studying,
you know,
neural nets.
Deep learning,
nobody can study gene editing or or really symbiote at this point without using CRISPR and nobody can really think about like oncology without speaking about car T.
(07:22):
S.
And immuno oncology as all presidents,
they literally just transformed.
It's like why I like to think of them as like the Fosbury flop.
Like he did he did the back jump and all of a sudden everybody had to do that back jump forever and all eternity.
And why did that all happen then?
I think you said maybe nature published the first in bio paper like two thousand's we're thinking about that for a while.
We've been doing gene editing other ways.
(07:42):
Yeah for a while there's a coincidence that all this stuff happened sort of.
Yeah I don't I don't know I mean there's there's probably some some smarter like philosophy of science answer here.
I think maybe one way to to think about that is that at the very least when it comes to crisper and parties.
You know we had built the sort of enabling technologies and tools for both reading and writing DNA like sequencing obviously had been The human genome sequence in 2000.
(08:12):
That's kind of our break 20000.2000 2001.
But then actually having next gen sequencing really accelerate that allowed us to discover you know all the use cases behind crisper and do a lot of the enabling experiments.
Uh you know you need to both be able to read write and modulate a system to be able to kind of do real science behind it.
And so we had these great tools that we were able to do great experiments and you know,
(08:35):
the proliferation of those tools probably led to these experiments coming out seemingly simultaneously.
But if you think about like deep learning like similar,
we had like cloud computation,
we had Gpus,
we had all the sort of baseline substrate for this kind of research uh and just finally had that sort of spark that kind of set it all off and and and and right now we're still growing exponentially there's still there's still new tools we're creating all the time.
(08:59):
They're letting us modulate new ways.
They're teaching us new things which seem to be leading to us realizing we need better tools,
would you keep creating?
So it seems to be like this positive cycle and a little more like what are the key things are happening right now in the last few years?
Yeah,
so I would say that we are thinking a lot about um you know,
there's there's,
if you think about it,
we have like this garage full of interesting parts now and we're mixing and matching and using all of them to try to cure diseases to run new experiments and all of that.
(09:26):
Uh and every time we do,
we're just opening completely new fields of biology.
If we think about in this biotech landscape.
And so once we've opening these entirely new fields of biology,
uh then we're starting to see more and more we can take from what evolution is teaching us to build more tools,
what are a couple of the new fields of biology we've opened up.
Yeah,
so like I would say cell therapy being one of the sort of canonical textbook answers here,
(09:50):
right,
using cells as medicine,
training ourselves to be medicine.
So,
if you think about the evolution of how medicine happened,
it was like small molecules,
right?
It was kind of like our canonical early days uh german textile companies pivoting in a chemistry,
then we have biologics starting in the eighties,
(10:11):
the sort of initial biotech revolution.
And now we're really looking at cells themselves which are like the fundamental building blocks in our body.
How do we engineer them to fight disease or even better yet?
How do we think about our immune system,
which is really the best disease fighting mechanism in the world?
How are we giving it stronger and better tools to do what it's already doing?
Because the part of the immune system will some another part of the immune system to do certain things and we can maybe do that with cells cell therapy as well.
(10:36):
Exactly like how do we help our immune system recruit other things?
How do we help our immune system,
recognize cancer is like,
it's like vaccines,
right?
We're helping our immune system recognize something that's not there,
but innately the immune system is just doing exactly what it's always doing.
We're just kind of giving it hints on how to do it other than cell therapy.
Are there other areas of biology that have just opened up a lot recently?
What are some of the other things going on?
(10:57):
Yeah,
So I'd say outside of cell therapy thing we think about a lot is like bio manufacturing,
bio discovery,
advanced materials and biotech and whatnot.
So there's a human health care components.
Then there's this sort of manufacturing components,
engineering,
say yeast or e coli to produce things that we normally produce in other ways.
And this has been waves of this has been tried in the past and failed.
(11:19):
But we're actually starting to see a lot of this,
We're obviously seeing this and like food and food tech where we have all these vegan foods,
like impossible foods,
whatever.
And now we're seeing this with cheeses and really kind of a replacement space for for vegan and more sustainable foods there.
But then bio manufacturing of advanced materials.
Right,
So how do we create novel materials outside of petrochemicals with sort of interesting new biology,
(11:42):
biological uh inspiration that gives us new this is like how spiders make different types of silk,
exact close if you take the proteins and do that.
Yeah,
it's super cool.
And like we've you know,
if you think about it like so many of our chemicals derived from uh petroleum,
right?
Like we've we've really minded to death and all the synthetic chemistry and all that.
It's like like 50 to 100 years old.
(12:04):
But we're just at the early innings of saying like,
what are the properties of the biochemicals?
How do we make them?
What are some of the other ones?
I remember there's the there's this like leather that's made from mushroom.
Yeah,
right now,
which seems pretty cool.
Yeah,
there's so yeah,
both threads and micro works,
both are making vegan leather from from mushrooms and Stella.
(12:24):
McCartney has a purse,
you know with vegan leather,
which manufacturing exactly just mushrooms.
And uh not really even much too many mushrooms because they're growing it from scratch,
right?
Yeah,
they're growing the mushrooms from mushroom lover that you don't actually have to kill mushrooms to make the person.
I think I think we're comfortable with killing machine yard that's not canceling.
(12:45):
But then um yeah,
and on top of that,
you know,
we think about these from fibers,
both for sort of technical garment wear,
but also from like rope selection uh say threads and everything like this for like technical uh wear in sort of the field awesome.
Let's let's step back a little bit.
What are the big problems that we should be trying to solve with biology with this new science,
(13:11):
with all these tools were creating like as a society,
there's problems to solve.
I think curing diseases is a big and obvious one that's kind of a big broadway there are there are there are ways of looking digging into it,
like what are more specific problems we're trying to solve right now.
Um So you mean outside of the realm of human health care within human,
even even within human health care,
the ways of breaking it down better to like think about like what are we going after and what one of the ways in which in which business and science and policy like,
(13:36):
like what are you inspired just to solve and fix?
Yeah.
So I think I'll scope it's within human health care and I think what I'm very inspired by is that as we get more exquisite control over biology,
Much more tunable control.
Uh we get to stop thinking about diseases in these sort of broad population sense of the word and can start narrowing them down into individual uh diseases.
(14:04):
So like type two diabetes for example,
um you would need insulin to treat.
And this is how we treat everybody.
The type two diabetes is you need insulin to kind of manager glucose.
But you know,
what's the causal reason for Type two diabetes?
Right.
It's overeating and whatnot.
But where does that come from?
Could it be like depression and using food as kind of a crutch to help sort of self medicate.
(14:26):
Does it come from having some kind of physical injury that prevents you from being able to exercise.
Does it come from some kind of genetic predisposition here.
Like type two diabetes is so many different diseases.
If we think about on a causal spectrum as opposed to any symptomatic endpoint spectrum.
And so what I got excited about is how do we start breaking apart diseases on the individual basis?
(14:47):
And we can do that because it makes sense.
We can treat patients as individuals,
we can sequence them,
we can essay them.
We can look at them across all of these different dimensions and say,
here are the various reasons why you might have typed diabetes versus you have Type two diabetes and then intervene accordingly.
Right?
So we have personalized interventions,
personalized cures,
depending on what's going on.
(15:07):
Exactly.
So,
think about like a cell therapy company,
right?
Like The the the standard model would be cell therapy should not be done for type two diabetes because we have insulin.
And that's very cheap in a cell therapy to be hundreds of thousands of dollars.
But what about somebody who is scared of needles and cannot take their insulin?
Right?
Uh,
and all of a sudden there a huge sort of it's a threat to themselves.
(15:30):
Like they're obviously losing limbs.
It's a harsh,
harsh to the family.
It's hard on the medical system.
All of a sudden we start doing the math.
You're like a cell therapy might make sense because this might be a curative intervention that might be expensive up front,
but it helps you down the line and cell therapy eventually,
hopefully we get a lot cheaper for everyone.
Absolutely.
And so yeah,
there's this other part of this is like we can start thinking about individuals and hyper small populations of individuals disease.
(15:55):
But once we create the tools and technologies to cure those,
we have this infrastructure layer that allows us to expand it to so many others.
So we cure rare cancers of cell therapies today.
But we're looking and building all of the infrastructure and manufacturing systems and logistics and supply chain to be able to broaden that to so many other diseases.
And I think that's really exciting is you have to think about like curing patients at these very small population levels.
(16:19):
But building the infrastructure to broaden it distributed to everybody and then think about new population spaces that we can start curing those.
Why are we going after rare cancers right now versus really common cancers.
So rare cancers uh are generally have a much tighter sort of genomic signature that allow us to identify sort of causal reasons we understand it much better.
(16:40):
Common cancer might have lots of different types of things going on.
Exactly.
And then and then it just bears the question,
what does common cancer mean?
Right.
Like,
so we think of maybe breast cancer or something like cancer,
a lot of people will get right.
But we might we might start changing the definition of breast cancer to like you have a hair too positive uh tumor in your breast,
(17:00):
right?
And then we are treating hair to positive tumors.
So there might actually be 50 types of things people might call breast cancer that you have exactly to treat.
There's nothing about our medicines,
at least chemical medicines or biological,
whatever medicines that are that are necessarily about the part of the body it's in besides distribution and delivery.
But we're starting to rethink how we think about that and say like what's a pD one positive cancer?
(17:22):
It doesn't matter the tissue of origin.
So pD one might be is a is a signal that says essentially don't eat me.
Um so striking the immune system telling you not to eat needed.
Exactly.
Even though it's a bad thing.
Yeah,
exactly.
And so when we block that,
the cancer isn't able to sort of give the secret handshake with the immune system.
And so the immune system recognizes and kills it,
(17:43):
right?
Uh and so we're starting to think,
okay,
which kind of cancers has this marker on it that we can block that will allow our immune system coming in and you love yourself.
Exactly.
And when you when you break this roll down,
it seems to outsiders,
there's like infinite things going on.
It's just so complicated and what's the right way to think about it at a high level.
And there's just so many tools and there's so many different signals.
(18:04):
And there's so many different like you constantly from an outsider.
You hear about these things from the bio world and there's like 1000 different things people are doing.
Like how like how do you keep this organized in your head and how should we should be thinking about this space?
I would love to say if I had it like deeply organized in my space in my head,
I think.
Um,
so are we talking about this from,
from somebody who's maybe for example a patient who wants to learn more?
(18:27):
We're talking about general public.
Are we talking about how,
how many are smart friends in general public?
Maybe they want to eventually learn how to how to be helpful to some of these companies or maybe they're just curious for their family.
Yeah,
I think um what the most helpful thing in my mind is understanding how value is created and how these things come to fruition.
Right.
How does medicine happen?
(18:49):
Right.
Uh,
and we're starting to see this and sort of accelerated timeline under a microscope with,
you know,
obviously covid vaccines.
But going from sort of academic research,
fundamental biological research and then seeing how this gets spun into biotechs,
which the venture community funds and then using those biotechs running sort of early clinical trials to get enough excitement that big farmers are like,
(19:14):
we can take this and commercialize this and bring this to large populations.
Let's let's,
let's let's drill down a little bit then.
So so we so we have this university ecosystem.
Yeah.
Uh the government sponsors a lot of that um and just some private sponsorships as well of research but the government does a lot and then you have these people will spin out and they'll and they'll take breakthroughs,
they'll start biotech companies and these companies will raise tens hundreds of millions of dollars,
(19:37):
sometimes billions of dollars and they're doing something with that and then they're and they're proving it out and then their their partner and usually with farmers distributed and commercialized and sell the patients.
I think there's a view for a lot of people that you kind of have this breakthrough to university where it's like oh we've cured this form of cancer and then some business comes along and takes it and makes a lot of money off it and kind of screws everyone.
That's maybe the anti business view here.
(19:58):
Is there is there another view of what's actually happening?
Like what's the value out of all these people working at the startup companies from on these breakthroughs like what are they doing?
Yeah.
I mean like historically that happens because of the Bay Dole act which is basically I.
P.
Licensing and uh that literally is why we have a biotech ecosystem and so many curative things.
So it's like that has proven out to be the mechanism that's great you know personally I think that this is valuable because governments get to think on different timescales than say venture capitalists and,
(20:31):
and,
and you know,
startups,
right.
A government can think on like this sort of 2030 50 year timescale to say,
let's fund exciting biology just for the,
you know,
interest and pleasure of finding things out here.
Uh,
that might turn into something that might lead to a breakthrough that that might be exactly something.
(20:52):
And so this,
like,
I actually really love the separation of allowing government to sort of sponsor things that wouldn't necessarily have a clear commercial outcome today because you know,
like venture capital is like uh,
startups,
the entrepreneurship ecosystems.
These are the people who are like custom made ready and able to sort of take things and move them very quickly.
(21:15):
Just basically research funding someone like jennifer down,
no studying bacterial immune systems,
turns out she discovers a mechanism that makes gene anything much better.
Yeah.
And then someone and then,
and then someone can now take that and,
and,
and then,
and,
and so what,
what,
what,
what are,
what are these companies doing?
Like how,
like how do they think about it?
Cause I literally have,
you know,
friends who are progressive people in congress,
like shouldn't we just be taxing the companies were taking recipe and making all this money off,
(21:37):
like what are they,
what are they doing with with this work and effort?
I mean you could tax the companies,
but just imagine like if we didn't give people upside to translate these things there would not be a system to actually sort of commercialize these things in a sort of fast turnaround biotech ecosystem.
We would be dependent,
it's not like that that innovation would go away but we would be dependent on large incumbents like large pharma's or large,
(22:01):
you know agriculture companies or whatever to out license this stuff.
You would only be entrenching incumbents if you didn't allow sort of a startup ecosystem,
you built around one of these people do this start because when they take the technology,
what are they,
what are they doing?
And they built the company around it and then they we sort of define the use cases that it's gonna be applied to?
So there might be some novel I.
P.
Around a molecule that works for treating cancer.
(22:23):
Right?
Uh They might even do some initial work in humans at the academic level.
But then when we want to be able to say let's put all the money behind all the enabling sort of science to approve it with the FDA.
Run an appropriate clinical trial that's evenly powered has a randomized controlled trial.
How do we do this in a way that we make it manufacturing at scale and sort of approachable by many many people uh what are all of these mechanisms that like take it from?
(22:52):
What is essentially a small scientific experiment or small scientific like study to a commercial sort of industrial level drug that can be given to hundreds of millions of people.
And a lot of,
a lot of these companies are our platform companies and bio as well where they're developing some new technology or they're building it out.
I know a lot of our smart computer science friends go work on some of these companies like so so so so I mean what's,
(23:15):
what's going,
what's going on with that?
So there are people coming in and they're solving data problems that are relevant to these things like what are the data problems?
So the data problems are really like when we have these platform companies and we were talking about earlier about,
you know,
this,
this read write modulate loop,
right?
Or or a sort of standard design test build loop right?
Um that creates data every iteration of that creates data that we're trying to learn from.
(23:39):
And what's really exciting today is we're trying to shorten the cycle time of those loops,
right,
john Boyd's famous result from like you do loops,
Whoever can do an oodle loop faster is going to succeed and I forgot what the acronym stands for.
But uh but it was observed,
I don't evaluate something like that.
(23:59):
But it was,
it was the studies in the Air Force and he's basically saying like what makes some fighter programs or fighter jets better than others and it was really their ability to observe an event and then process it,
analyze it and then react to it and his sort of fundamental contribution to the Air Force was whoever can run this iteration faster is going to win in dogfights.
And so uh this is kind of like bread and butter to us today in startup land.
(24:23):
Whoever iterate faster learns faster because we just assume our y intercept can be anywhere along learning.
But it's really about like kind of that that derivative like how fast are we improving And as we build these platforms that can move so much quickly and do so many experiments.
We got robotics.
Ization,
we've got,
you know,
high throughput experiments in biology,
got micro fluids.
That means I can do a billion experiments at once.
(24:45):
Um We generate a ton of data,
Micro flip micro flicks,
you build experiments and once that means you're taking like tiny bits of of something,
lots of different time plating it all out.
Or even just doing crazy things like,
you know,
doing small experiments in each individual cell in the sample over like a billion cells and then sequencing it out to see like what responded and didn't respond to some kind of outcome.
(25:05):
Is that really expensive to do that many cells Now we've got that cheaper.
That's pretty cheap.
The expensive part is just like to say all the chemicals and reagents and just the time to do these kinds of things,
especially if we start doing at high precision.
But uh that that kind of system though um means we generate data right?
And we have to analyze it appropriately.
(25:26):
This is where we need like you can now generate data from literally millions or billions of experiments and you really do need computer scientists to map it out and to and to analyze it.
Yeah.
And then and that's like I would say Phase one right?
Like let's let's go back and re analyze data that are analyzed data that's generating high throughput.
But how do we start designing the experiments program progressively?
Right?
So like and and so and so these experiments are happening in the company with all this talent but they're not really happening as much in the university or the university of doing these things to what's what's the what's the difference,
(25:55):
the the universities are doing this right and some are doing the methodological work.
They just don't have the resources to really attract many of the top people and teams to work really hard on things.
It's like equity driven cultures and companies are better at some of these types of systems.
And it's definitely like upside,
you know,
financial upside definitely motivates a lot of people here,
especially like if you're making,
I don't know a million dollars at google as a software engineer.
(26:16):
It's very hard to go into making like 70 K.
As a post doc.
But I think in addition to that,
I think the most addictive part is the speed of this in a,
in a laboratory setting,
you might not have the resources,
you can do the experiment.
We know how to do the experiments frequently,
but it's like I just don't have like the robots to do say a zillion experiments in parallel.
I don't have all the gps.
(26:37):
I would want to run like a full moon and the culture is probably not as obsessed with speed in the university versus because you're not gonna die if you don't go fast enough.
Exactly.
Like you do need to publish right under some sort of defined timeframe,
but the mission is different.
The output is different than I think in a startup.
Yes,
(26:57):
you're basically running against the clock all the time and generally that clock is two years.
Right?
And you need meaningful advances to be able to raise more capital to be able to continue to.
Do you think because of this dynamic,
the startups sometimes have attracted the most talented people or the university still have the most talented people or there's different types of talent.
How do you think about that?
I think it's different types of talent,
startups and industry at the very least in the past five years has been some of the most insane amount of,
(27:22):
of poaching away from an academic university system.
Yeah,
that seems like a lot of good people are leaving universities to work on really exciting,
fast moving startups that are solving their curing disease and solving hard problems.
Well,
so think about like the bread and butter grant for like a biomedical researchers innaro one,
right?
That's like,
you know,
on the order of $5 million you can cover your lab for five years.
It's super prestigious,
(27:43):
getting a $5 million grants,
a big deal for us.
And this is like,
it could be 18 years.
Like when I just submit like an nrsc after everyone,
it was like uh sorry,
is it was 18 months,
right?
You know,
one rejection,
which is pretty standard.
And then optimization is like a 200 150 page grant that I had to submit.
Uh And and then I ended up with on the order of like 100 150 grand.
(28:05):
Right?
And so uh whereas now you go get a term sheet for for $10 million for which or $50 million.
And it's,
you know,
there's it's not unheard of to pitch for a month and get a $5 million term sheet right to be able to build these really focused things and super quickly and you don't have restrictions,
right?
And the university takes 50% of the overheads.
(28:26):
If you make $5 million.
University takes 2.5 of its university,
the university needs that to to run all of their departments of diversity and stuff,
you know,
I don't,
I don't know if I would say that,
but what are they using it for?
Well,
you have obviously centralized and shared services.
This covers like rent on your lab space.
Right,
okay.
So there's there's there's like real overhead and then there's also maybe 1500 administrators,
(28:46):
they're making sure the university yeah.
There's there's not,
there's just some stuff definite inefficiencies that you have no control over when you take that overhead,
but there's value to it as well.
I'm not going to like,
of course to say yes or no,
but At the very least we get the five million for a startup.
Every one of those dollars,
You know,
we're gonna all use them towards the things you're actually getting done.
Yeah.
Yeah,
that's true.
That's interesting.
Like big institution versus startup also,
(29:08):
you're more efficient that way.
And you probably have to ask permission every time you want to build something or do something,
you probably should go much faster.
I wasn't allowed to expense any amount of alcohol for any meal when I had grants,
things.
Things have definitely changed.
I mean,
you know,
in the university should shouldn't,
maybe,
maybe if someone running A.
B.
C.
I should talk to them about this policy,
(29:29):
I can probably learn a few things from stanford save save some budget.
So stepping back.
But again,
you're now working on investing in a lot of companies,
you're helping build companies as well.
What's,
what's,
what's the most fun part of what you do,
uh,
the most fun part I would say this is tough.
I think it's basically getting to actually turn ideas into action.
(29:52):
Um It's you know in grad school or just honestly in in many,
many slow moving institutions you talk a lot about really cool stuff.
There's very brilliant people and great ideas and it kind of ends with like a yeah,
that'd be cool.
And I think one of the most exciting parts is like you can finish an exciting hour long conversation and have a follow up that means like writing a check,
(30:16):
starting a lab,
you know hiring some people,
we helped come up with this idea for this giant bio manufacturer resilience last year.
I guess you probably couldn't have done that before in any other context.
Resilience was really hilarious because like we raised what like say $50 million when the idea was like on the back of a napkin and we had like three people but we just knew it was a big problem to solve for Right?
(30:37):
And then and then use Bob's reputation to raise 750 million off the company and get going.
Yeah it was it was you know so crazy to think that that we were Able to basically put together $800 million dollars in the span of like what five months?
Uh that would that is actually imposed.
I couldn't think of anything like that in in academia right?
(31:00):
And Francisco just really quickly explain what is resilience,
bio.
Yeah,
so resilience,
bio is basically a bio manufacturing company focusing on manufacturing medicines And so we are using this platform to be able to build and and manufacture all the medicines that that we will need broadly in the United States which so frequently are manufactured outside of the United States.
(31:25):
Uh and the impetus for building this was around vaccines which we knew if the M.
RNA vaccines worked we would not be able to make them.
We need manufacturing for that.
And we also it's also we started it of course to help make sure there's enough manufacturing in the U.
S.
And when our allied countries and not just other parts of the world.
Yeah I mean if you look at like what happened when Wuhan shut down and basically all of china shut down.
(31:49):
It wasn't just medicines but it was entire sort of our biotech supply chain.
So many of our companies had huge delays in the sort of re agents.
They were able to get deals,
they were able to close or whatever because there's so many cros out there and it kind of was like you know whether or not you you you feel discomfort around china owning it.
I definitely think we should feel discomfort about not owning our own sort of medical manufacturing supply chain.
(32:14):
It's good for resilience South America's back.
Well I think any any company should have you know their ability to manufacture their own medicines.
And so so what what are some of the other things that you think,
I mean,
what do you think happens thanks to you being doing what you're doing today?
That wouldn't have happened otherwise.
How do you,
do you think about it like that?
Like what's my deletion deletion criteria?
Are there,
are there like their companies with a better strategy?
(32:36):
There's companies that exist,
there's,
there's companies that you have to put them higher talent,
they need to do things.
It's kind of,
it's kind of fun to think about cause you're probably ultimately probably helping cure a lot of diseases if you're doing your job right.
I think the things that I'm most proud of are actually putting incredibly brilliant uh,
and complementary skill sets in a room and seeing what happens there.
(32:58):
I think,
you know,
I would love to think that I have great ideas and contribute or whatever,
but,
and I'm sure startup founders not and politely to my ideas and board meetings,
but like,
it's very clear when we get like two or 3 Amazing minds in a room and sparks start to fly that stuff happens at unprecedented scale.
And that's,
that's,
you know,
(33:19):
what I think has been sort of the most value generated is bringing in,
say amazing software engineers into biotech companies when neither group really realized that they could be talking to each other and gaining so much from each other or sort of helping,
you know,
like large pharma's talked to early biotechs with skill sets that they just didn't even realize they wanted and like helping do that.
(33:43):
And I think it's just the ability to connect people with deep problems and figure out how to join bringing the right resources and people together to solve the problems.
And if you,
in terms of like falsify a ble ble optimistic positions in the last couple of years,
I understand we've had more drugs approved than previously,
which is probably tied to a lot of what's going on.
And there's obviously always new tools.
(34:04):
There's so much more money going into this space right now.
Like like do you see like the amount of drugs and therapies being approved actually going up a lot on average over the next five years because of this or what's or what should the optimism reflect in the real world?
Yeah.
Okay.
So I don't know.
I'm sure we will see more and more drug candidates come through.
(34:24):
The problem of drugs being approved is it's it's very dependent on FDA.
Right?
And so we've had an FDA that's very excited about innovation for several years.
Scott Gottlieb was fantastic.
Uh And I think even now the FDA is starting to want more and more innovation to come out.
So approval is a little bit dependent on them.
Uh but we should see more very promising therapies that had their data look good.
(34:49):
It's it's depending on the government what they approved,
but but overall there's more good stuff being created.
Yes,
I would say my sort of false viable prediction would be that we're going to start seeing more and more biotechs make drugs that they try to commercialize themselves as opposed to selling to pharma to do the commercialization,
maybe new,
(35:09):
there's gonna be new big pharma companies and and why?
Why is that?
So I think the case study of Moderna is amazing right?
Madonna was a traditional biotech and is now actually commercializing its own drugs was able to raise enough money and stay independent.
Yeah.
Now they they control their destiny because they make revenue at a prophet who would have thought that that's like right now they're going to make all of us get booster shots and makes so much more money.
(35:32):
Yes.
Yes.
And so but because of that,
Madonna is very smart and they're being very aggressive about saying we have this golden goose,
we have this thing that makes a bunch of money,
how can we use all of our brilliant scientific minds are brilliant scientific platform.
And I heard this week they talked about there's going to be a shot for both.
Covid and the flu or something like that.
(35:53):
And they're like they can do this and they can start thinking about using this for you know uh for cancer.
You know as we start thinking about analytic viruses or even in vivo cars where we should live our M RNA car car constructs into our T cells.
And so what I think this is going to give more biotechs courage to do this.
But I also think that uh this allows us to cure more and more rare diseases.
(36:19):
Right?
So a big thing about farmers like when they acquire a company they really need this to be like a billion dollar a year drug almost justify a lot of the expenditures.
Um And so if you only made $100 million a year on your drug this would not be like that big of a needle.
So it's really hard to raise money for something that's only gonna make $100 million a year.
So there's all these rare diseases that's just hard to get the incentives secure.
(36:39):
But but these newer companies why can they do it?
Because the my my hope my prediction what we're trying to build towards is that we dropped the cost to actually make these drugs.
So you don't need to raise a billion dollars because they already have platforms,
you can already make the drugs pretty quickly and easily and therefore they might as well go and cure these 500 diseases which is now we're not worth doing.
Exactly.
And then if we if we enable them if we create infrastructure for both running the clinical trial,
(37:04):
we've done this with resilience to help with the manufacturing,
help them with commercialization.
These companies can go out and we can have many companies sort of I.
P.
O.
And be profitable.
Makings quote like only 100 $200 million.
Which like in any software sense would be an exciting outcome but just doesn't you know correct the incentives and I think what's really important about that is right now we're a little bit dependent on curing diseases in areas that pharma wants to cure.
(37:31):
So we've got like on the order of what 100 maybe 200 farmers looking to make these M.
And S.
So it means 100 and 200 chief scientific officers are dictating what does?
There's a little bit of groupthink amongst just like in any other field they're gonna be thinking about the same things a little too much.
Yeah and so but think about that like we have Maybe at best 200 people saying what diseases we should care in the United States right?
(37:55):
Like there's a lot of diseases we need to cure that not all of them are focused on.
And I think decentralizing and pulling that out and allowing sort of individual companies to go out and cure the diseases on their own is really where I think in 10 years I'm so optimistic is to create these businesses that can go after.
I know I know we don't we don't always share agreement on all things political which I respect but but I think this is kind of cool because you're basically giving like a version of like the pro liberty thesis,
(38:20):
which is that the world should be like more distributed and more more decision makers bottom up versus fewer top down because I don't yeah,
I definitely I don't I don't agree with top down other parts to this,
but I'm just saying like in general,
and it's a general theme,
it's good.
There's more people can make more decisions specifically because they're going to find things that there's fewer people on top are missing.
Look,
(38:40):
we've actually like as a weird analogy,
like think about the internet and what Shopify does,
right?
Um where before,
if you wanted to buy anything,
it was basically what is offered at walmart and and like the sixties,
seventies or whatever.
And and that's the thing I bought,
right?
So and now like if I'm really into,
(39:01):
I don't know,
some esoteric trading card uh like that,
I want to collect,
it's not available at walmart,
but it's available on the internet that walmart has like 100,000 at most 200,000 skews,
I think wish has 100 million skews on the internet has a billion skews.
Exactly.
And so if you think about that same model of like this long tail,
it can be profitable if you think at a global scale and we actually reduce the cost to actually start these kinds of innovations and distribute them to people and think about that in the context of diseases rather than skews right?
(39:29):
There were,
you know,
hundreds of thousands,
if not millions of rare diseases that if we can think about this on a global scale to cure them and allow smaller entities to cure them more quickly and cheaply,
then we can actually start hitting this long tail.
That doesn't make sense of these centralized systems.
That's awesome.
So we started this podcast because there's a lot of pessimism going on right now and I want to educate people about all these awesome,
(39:54):
optimistic things and share this and get them hopefully more excited and helping with the positive parts of our society.
Um,
are you still glad that your parents came to the United States as a place you're proud to be?
And what's your view on the future of America?
Of course I'm excited about like,
I feel like I,
I should be like on the,
on the front uh,
photo of the pamphlet of the american dream right for us to,
(40:15):
you know,
come to the US,
for me able to get like an amazing education at public schools and then go to stanford and get like amazing PhD and be able to work in venture capital and bring innovation,
you know,
to people,
is like what,
like that's literally what,
what you come to America to do.
So how do we make sure things keep getting better in in this country?
I think I think um uh what America has that?
(40:42):
Sometimes I think we forget is we have first round draft on the greatest talent in the world right?
If there is a smart person anywhere in the world we could find a way to get the new America.
So I think immigration is core to this right and then creating cultures that like allow for this.
This is maybe I don't think we disagree here.
But maybe my more liberal sentiments is like how do we create an environment that allows for so many different minds and perspectives and cultures to come in and feel welcome and able to succeed.
(41:11):
Um And I think that's to being a pluralistic society is tolerant of everyone as opposed to society where people are trying to destroy anyone's reputation when they disagree with them.
Yeah I mean like like I just what I really dislike is tribalism right?
And I think that can be attributed to any kind of these groups on both sides.
And it doesn't even matter like what's like there's no definition of side,
(41:35):
there's just tribalism and we decided like here's our group,
here's my in group here's the out group and um the the ability to come into the U.
S.
And feel safe and welcomed means finding your people right?
It doesn't mean there aren't.
Do you think academics have gotten more tribal or less tribal though over the last decade is that is the tribalism like affecting that part of society potentially as well in some ways.
(41:57):
Um I don't know if I would call it tribalism,
I would say that,
um,
what we are saying is is people who are more comfortable having say black and white views may be divorced of the nuance within there because they are divorced enough from situations where nuance is important.
(42:25):
So to maybe make that abstract statement a little more concrete,
like if you are in a tenured position in academia and you consider moving to industry,
the dark side,
which is always considered like the phrase that they tell you.
Um,
you have like the privilege of principle that many other people don't write,
(42:48):
you don't have to make trade offs to function in society.
You get to spout and like have the opinions that you want and that's wonderful,
you know,
trying to make a business work,
which requires some kind of trade off,
you're saying exactly like anybody who's ever run a business or been in a large organization has seen like,
like gray area HR situations manifest right?
(43:11):
Where on one hand somebody might say or do something that is,
that is reprehensible.
Uh,
and they and they should be let go.
But so frequently there's just ways to navigate people to better behavior and I think,
you know,
operating in a realm of forgiveness is so clutch and important,
(43:34):
both,
you know,
within,
you know,
conservative ideologies or liberal ideologies,
if we,
if we want to paint that into groups.
Um,
and that's kind of how we function with our families,
right?
Like we are,
we are forgiving if our family members do us wrong or say awful things or whatever to to some degree.
Obviously we always in some of these places don't have,
don't have to be as forgiving or to get things done and work in the world.
(43:55):
I think when you get into these hyper tribal situations,
when we get into these situations where you're divorced from having to be,
you know,
functioning with people who are different than you might be less forgiving you you don't have to be forgiving,
you don't have to flex that muscle,
don't know that that's great.
111 last question to leave us with uh if the revolution of bio continues and a lot of times people don't understand exponential growth is not intuitive and this keeps going for 20 or 30 years where someone like the really crazy cool things we might see in our lifetime.
(44:25):
Oh man.
Uh yeah,
I think we would flip the model of what we think of in Bioko life sciences from amelioration of suffering,
which is essentially carrying diseases to sort of improvement of like human condition like this.
(44:45):
The trans humanist might call it something like that,
it's a thorny issue,
but I think like so basically helping everyone be like self actualize and be a better version of themselves,
healthier and higher energy every day.
Yeah,
so like think about maybe something we see today a little bit is like therapy is used because we're trying to treat conditions like treat depression,
(45:07):
anxiety so on and so forth.
But how much of like say mental health coaching can be done to actually help us achieve like a better version of ourselves and this is like considered coaching today.
But you know,
and psychology is about really sort of like managing sort of downside things And I think we can think about that.
Do you think about focusing on the upside instead of just the downside,
(45:27):
each one of us could have a ton more energy could be instead of being 80% on some metric,
we could be a percent.
Uh and so flipping that model,
it's like,
okay,
we're not,
we we've moved beyond just just curing diseases.
We're using this for like manufacturing,
we're using this to help like vegans be able to like eat sustainable,
you know,
meets or whatever.
And even like say people who eat meat to eat less meat or whatever.
(45:50):
Uh those are all things that are beyond sort of necessarily human health care,
but really about kind of like growing ourselves as people in a society awesome.
Thanks Francisco.
Absolutely.
Thanks joe.
Mhm mm hmm.
if you think about it,
we have like this garage full of interesting parts now and we're mixing and matching and using all of them to try to cure diseases to run new experiments and all of that and every time we do,
we're just opening completely new fields of biology.
If we think about in this biotech landscape and so once we've opening these entirely new fields of biology then we're starting to see more and more we can take from what evolution is teaching us to build more tools.
(00:23):
What are a couple of the new fields of biology we've opened up.
Yeah.
So like I would say cell therapy being one of the sort of canonical textbook answers here,
right?
Using cells as medicine training ourselves to be medicine.
Yeah,
(00:44):
I'm joe Lonsdale,
welcome to american optimist Francisco.
Thanks for joining us.
Today is dr Francisco.
Jimenez,
your doctor from your stanford PhD in bioinformatics and you were born in Argentina Francisco.
But you came here when your family came here when you were quite young.
What brought you guys to the US first of all?
Yeah,
so my dad was an electrical engineer by training and they were in I was born in body luxury in Argentina and my family's from Mendoza and really,
(01:14):
you know,
my family just realized that the opportunity for kind of an electrical engineer was much greater in the United States than it was for Argentina.
And so they had kind of a a life and a path ahead of them,
financial opportunity,
business opportunity was bigger here.
Yeah,
absolutely,
it was kind of like the canonical case of the chasing the american dream,
right?
So they were doing well,
(01:34):
but there was just a ceiling to,
to mobility that my parents could see while they stuck around and they came to the midwest to phoenix.
You said he helped open the intel fab in Costa rica at some point I think.
Yeah,
so we went to uh the midwest was supposed to be a two year visa and then about 1.5 years into that,
my dad left the role and then just spam the entire country with his resume.
(01:57):
Uh and then for anything and they started out going like,
let's go to the floor,
let's go to Miami,
let's go to los Angeles,
go to san Francisco.
Uh we ended up in phoenix Arizona where he got his only interview was with Intel there and he said that he could program and see.
Uh and then with the sort of last money we had,
(02:17):
he bought cardigan and Richie's programming and see book and then learned it on the plane.
Uh and then I got the job at Intel uh and then we went to the phoenix Arizona site and were there for eight years and then after that when they were starting the fab in Costa rica,
He took us all to basically help open that fabulous in Costa Rica for two years and then we came back to the US to the folsom site for intel,
(02:41):
did you guys speak spanish at home because you're from Argentina,
that's already kind of knew it when you were in Costa rica?
Yeah,
yeah,
that was,
it was a big impetus.
We either had to go to Israel or Costa rica because we were kind of done with Arizona and decidedly better at spanish than Hebrew.
That's pretty good.
That's funny because it could have been in Israel and you went,
you went to Berkeley undergrad?
Yeah.
When did you apply to Berkeley?
(03:02):
Uh,
honestly,
I mean,
not to belabor the intel point,
but Andy Grove was the ceo of intel and my parents always said Andy Grove went to Berkeley,
it's the best school in the,
in the nation.
And so we were just like inception in my head,
in the weird way it does.
Andy Grove is a pretty cool guy standard.
I can forgive you for that mistake.
(03:22):
Well,
what I always like to say is like Berkeley is where I learned humility and stanford's,
where I learned entitlement,
which kind of angers both groups off,
which is why it's the best kind of joke,
but I think it's you need both of those two,
I think succeed in sort of entrepreneurship.
And so after Berkeley,
you got your PhD at stanford?
Yeah.
And how come you studied bioinformatics?
So I did research at UCSF in A gene therapy lab for Parkinson's disease and that's really where I fell in love with research and a lot of my work.
(03:51):
there was sort of analyzing images uh an M.
R.
I.
Is to see the distribution of of how injected gene therapies distribute as it pertains to gene expression.
Uh and just absolutely loved the idea of of at the time in 2010 was not a very loved field imaging informatics is kind of a backwater.
Uh and but that we could use what was a I at the time to kind of automate these human tasks.
(04:16):
That was so exciting.
And then I was kind of saved because around 2012 when deep learning came out all of a sudden my field became one of the most exciting research fields ever.
You find yourself in the middle of the hottest fields.
Exactly kind of by mistake,
exactly where just,
you know,
it's better to be smart than lucky,
right?
Sorry,
lucky than smart.
Probably if you're smart and you get lucky,
that's probably exactly what the best combination.
(04:38):
And you you were you were obviously one of the superstars there,
I think you gave the commencement address after you graduated.
And yeah,
it's plenty of wonderful things.
I gave the stanford,
that was this phds commencement speaker for 2015.
It was quite an honor,
awesome.
And so and so you you did really well there and you're in the middle of this hot new field maybe.
So,
you know,
a lot of people and we talked about earlier,
like with our friend rick that they were in a revolution in bio right now,
(05:00):
which I know you you agree with giving our audience some of the reasons one revolution by one of the key things that just started this revolution.
Why is this happening?
Right?
Yeah,
yeah.
So I think uh you know,
it's kind of like,
It's always hard,
I think we have to choose arbitrary dates for this because it's like at what point in an exponential growth graph,
did everything get exponential?
Right?
And the definition is that it's always always looks exponential along different scales,
(05:23):
but like one sort of window I love to think about is between 2011 and 2012,
right?
Like,
so I talked about how Alex net wins image net and that was really the sort of Fosbury flop for deep learning and,
and but at the same time within like the same sort of span of period is when jennifer,
Dowd nose work and functions work came out on CRISPR.
(05:45):
So my friends who were into genetic engineering working on like talons or zinc fingers all of a sudden saw their entire field upended at the same time,
I had a much better tool for genetic engineering could have used before.
Exactly.
And so it was like,
we all kind of joked that we had to take our qualifying exams,
which is like what's the state of the art and it was the fastest period of time for from your qualifying exams being taken towards being obsolete.
(06:09):
It was like the biggest waste of time ever because all of a sudden everything was neural nets and everything I'd learned up until that point was kind of obsolete.
And the same with the genetic engineering friends when CRISPR came out completely upended how we think about one year in 2012 had major advances in ai major advances in gene editing with this technology.
And we also have car T.
S come out right?
Carl june had his first paper come out showing that he was able to cure a pediatric oncology patient A.
(06:33):
L.
L.
With a car T.
Which was like 30 years of war effort.
But this was such a car T.
Being into american receptor T.
Cell.
This is when you basically program a T cell and tried to go after and kill the the cell cell.
Yeah,
exactly.
So in that you know,
you've talked about this with rick before but like it's it's and so I like to jokingly call it like this and you switch abilities because like so you think about Einstein in 1905,
(06:54):
he has four papers that completely changed physics,
right?
And he called us on his way to Belize.
And I think within that year we had sort of three fundamentally transformative technologies and you think about like they've completely changed the field,
like nobody can study ai without studying,
you know,
neural nets.
Deep learning,
nobody can study gene editing or or really symbiote at this point without using CRISPR and nobody can really think about like oncology without speaking about car T.
(07:22):
S.
And immuno oncology as all presidents,
they literally just transformed.
It's like why I like to think of them as like the Fosbury flop.
Like he did he did the back jump and all of a sudden everybody had to do that back jump forever and all eternity.
And why did that all happen then?
I think you said maybe nature published the first in bio paper like two thousand's we're thinking about that for a while.
We've been doing gene editing other ways.
(07:42):
Yeah for a while there's a coincidence that all this stuff happened sort of.
Yeah I don't I don't know I mean there's there's probably some some smarter like philosophy of science answer here.
I think maybe one way to to think about that is that at the very least when it comes to crisper and parties.
You know we had built the sort of enabling technologies and tools for both reading and writing DNA like sequencing obviously had been The human genome sequence in 2000.
(08:12):
That's kind of our break 20000.2000 2001.
But then actually having next gen sequencing really accelerate that allowed us to discover you know all the use cases behind crisper and do a lot of the enabling experiments.
Uh you know you need to both be able to read write and modulate a system to be able to kind of do real science behind it.
And so we had these great tools that we were able to do great experiments and you know,
(08:35):
the proliferation of those tools probably led to these experiments coming out seemingly simultaneously.
But if you think about like deep learning like similar,
we had like cloud computation,
we had Gpus,
we had all the sort of baseline substrate for this kind of research uh and just finally had that sort of spark that kind of set it all off and and and and right now we're still growing exponentially there's still there's still new tools we're creating all the time.
(08:59):
They're letting us modulate new ways.
They're teaching us new things which seem to be leading to us realizing we need better tools,
would you keep creating?
So it seems to be like this positive cycle and a little more like what are the key things are happening right now in the last few years?
Yeah,
so I would say that we are thinking a lot about um you know,
there's there's,
if you think about it,
we have like this garage full of interesting parts now and we're mixing and matching and using all of them to try to cure diseases to run new experiments and all of that.
(09:26):
Uh and every time we do,
we're just opening completely new fields of biology.
If we think about in this biotech landscape.
And so once we've opening these entirely new fields of biology,
uh then we're starting to see more and more we can take from what evolution is teaching us to build more tools,
what are a couple of the new fields of biology we've opened up.
Yeah,
so like I would say cell therapy being one of the sort of canonical textbook answers here,
(09:50):
right,
using cells as medicine,
training ourselves to be medicine.
So,
if you think about the evolution of how medicine happened,
it was like small molecules,
right?
It was kind of like our canonical early days uh german textile companies pivoting in a chemistry,
then we have biologics starting in the eighties,
(10:11):
the sort of initial biotech revolution.
And now we're really looking at cells themselves which are like the fundamental building blocks in our body.
How do we engineer them to fight disease or even better yet?
How do we think about our immune system,
which is really the best disease fighting mechanism in the world?
How are we giving it stronger and better tools to do what it's already doing?
Because the part of the immune system will some another part of the immune system to do certain things and we can maybe do that with cells cell therapy as well.
(10:36):
Exactly like how do we help our immune system recruit other things?
How do we help our immune system,
recognize cancer is like,
it's like vaccines,
right?
We're helping our immune system recognize something that's not there,
but innately the immune system is just doing exactly what it's always doing.
We're just kind of giving it hints on how to do it other than cell therapy.
Are there other areas of biology that have just opened up a lot recently?
What are some of the other things going on?
(10:57):
Yeah,
So I'd say outside of cell therapy thing we think about a lot is like bio manufacturing,
bio discovery,
advanced materials and biotech and whatnot.
So there's a human health care components.
Then there's this sort of manufacturing components,
engineering,
say yeast or e coli to produce things that we normally produce in other ways.
And this has been waves of this has been tried in the past and failed.
(11:19):
But we're actually starting to see a lot of this,
We're obviously seeing this and like food and food tech where we have all these vegan foods,
like impossible foods,
whatever.
And now we're seeing this with cheeses and really kind of a replacement space for for vegan and more sustainable foods there.
But then bio manufacturing of advanced materials.
Right,
So how do we create novel materials outside of petrochemicals with sort of interesting new biology,
(11:42):
biological uh inspiration that gives us new this is like how spiders make different types of silk,
exact close if you take the proteins and do that.
Yeah,
it's super cool.
And like we've you know,
if you think about it like so many of our chemicals derived from uh petroleum,
right?
Like we've we've really minded to death and all the synthetic chemistry and all that.
It's like like 50 to 100 years old.
(12:04):
But we're just at the early innings of saying like,
what are the properties of the biochemicals?
How do we make them?
What are some of the other ones?
I remember there's the there's this like leather that's made from mushroom.
Yeah,
right now,
which seems pretty cool.
Yeah,
there's so yeah,
both threads and micro works,
both are making vegan leather from from mushrooms and Stella.
(12:24):
McCartney has a purse,
you know with vegan leather,
which manufacturing exactly just mushrooms.
And uh not really even much too many mushrooms because they're growing it from scratch,
right?
Yeah,
they're growing the mushrooms from mushroom lover that you don't actually have to kill mushrooms to make the person.
I think I think we're comfortable with killing machine yard that's not canceling.
(12:45):
But then um yeah,
and on top of that,
you know,
we think about these from fibers,
both for sort of technical garment wear,
but also from like rope selection uh say threads and everything like this for like technical uh wear in sort of the field awesome.
Let's let's step back a little bit.
What are the big problems that we should be trying to solve with biology with this new science,
(13:11):
with all these tools were creating like as a society,
there's problems to solve.
I think curing diseases is a big and obvious one that's kind of a big broadway there are there are there are ways of looking digging into it,
like what are more specific problems we're trying to solve right now.
Um So you mean outside of the realm of human health care within human,
even even within human health care,
the ways of breaking it down better to like think about like what are we going after and what one of the ways in which in which business and science and policy like,
(13:36):
like what are you inspired just to solve and fix?
Yeah.
So I think I'll scope it's within human health care and I think what I'm very inspired by is that as we get more exquisite control over biology,
Much more tunable control.
Uh we get to stop thinking about diseases in these sort of broad population sense of the word and can start narrowing them down into individual uh diseases.
(14:04):
So like type two diabetes for example,
um you would need insulin to treat.
And this is how we treat everybody.
The type two diabetes is you need insulin to kind of manager glucose.
But you know,
what's the causal reason for Type two diabetes?
Right.
It's overeating and whatnot.
But where does that come from?
Could it be like depression and using food as kind of a crutch to help sort of self medicate.
(14:26):
Does it come from having some kind of physical injury that prevents you from being able to exercise.
Does it come from some kind of genetic predisposition here.
Like type two diabetes is so many different diseases.
If we think about on a causal spectrum as opposed to any symptomatic endpoint spectrum.
And so what I got excited about is how do we start breaking apart diseases on the individual basis?
(14:47):
And we can do that because it makes sense.
We can treat patients as individuals,
we can sequence them,
we can essay them.
We can look at them across all of these different dimensions and say,
here are the various reasons why you might have typed diabetes versus you have Type two diabetes and then intervene accordingly.
Right?
So we have personalized interventions,
personalized cures,
depending on what's going on.
(15:07):
Exactly.
So,
think about like a cell therapy company,
right?
Like The the the standard model would be cell therapy should not be done for type two diabetes because we have insulin.
And that's very cheap in a cell therapy to be hundreds of thousands of dollars.
But what about somebody who is scared of needles and cannot take their insulin?
Right?
Uh,
and all of a sudden there a huge sort of it's a threat to themselves.
(15:30):
Like they're obviously losing limbs.
It's a harsh,
harsh to the family.
It's hard on the medical system.
All of a sudden we start doing the math.
You're like a cell therapy might make sense because this might be a curative intervention that might be expensive up front,
but it helps you down the line and cell therapy eventually,
hopefully we get a lot cheaper for everyone.
Absolutely.
And so yeah,
there's this other part of this is like we can start thinking about individuals and hyper small populations of individuals disease.
(15:55):
But once we create the tools and technologies to cure those,
we have this infrastructure layer that allows us to expand it to so many others.
So we cure rare cancers of cell therapies today.
But we're looking and building all of the infrastructure and manufacturing systems and logistics and supply chain to be able to broaden that to so many other diseases.
And I think that's really exciting is you have to think about like curing patients at these very small population levels.
(16:19):
But building the infrastructure to broaden it distributed to everybody and then think about new population spaces that we can start curing those.
Why are we going after rare cancers right now versus really common cancers.
So rare cancers uh are generally have a much tighter sort of genomic signature that allow us to identify sort of causal reasons we understand it much better.
(16:40):
Common cancer might have lots of different types of things going on.
Exactly.
And then and then it just bears the question,
what does common cancer mean?
Right.
Like,
so we think of maybe breast cancer or something like cancer,
a lot of people will get right.
But we might we might start changing the definition of breast cancer to like you have a hair too positive uh tumor in your breast,
(17:00):
right?
And then we are treating hair to positive tumors.
So there might actually be 50 types of things people might call breast cancer that you have exactly to treat.
There's nothing about our medicines,
at least chemical medicines or biological,
whatever medicines that are that are necessarily about the part of the body it's in besides distribution and delivery.
But we're starting to rethink how we think about that and say like what's a pD one positive cancer?
(17:22):
It doesn't matter the tissue of origin.
So pD one might be is a is a signal that says essentially don't eat me.
Um so striking the immune system telling you not to eat needed.
Exactly.
Even though it's a bad thing.
Yeah,
exactly.
And so when we block that,
the cancer isn't able to sort of give the secret handshake with the immune system.
And so the immune system recognizes and kills it,
(17:43):
right?
Uh and so we're starting to think,
okay,
which kind of cancers has this marker on it that we can block that will allow our immune system coming in and you love yourself.
Exactly.
And when you when you break this roll down,
it seems to outsiders,
there's like infinite things going on.
It's just so complicated and what's the right way to think about it at a high level.
And there's just so many tools and there's so many different signals.
(18:04):
And there's so many different like you constantly from an outsider.
You hear about these things from the bio world and there's like 1000 different things people are doing.
Like how like how do you keep this organized in your head and how should we should be thinking about this space?
I would love to say if I had it like deeply organized in my space in my head,
I think.
Um,
so are we talking about this from,
from somebody who's maybe for example a patient who wants to learn more?
(18:27):
We're talking about general public.
Are we talking about how,
how many are smart friends in general public?
Maybe they want to eventually learn how to how to be helpful to some of these companies or maybe they're just curious for their family.
Yeah,
I think um what the most helpful thing in my mind is understanding how value is created and how these things come to fruition.
Right.
How does medicine happen?
(18:49):
Right.
Uh,
and we're starting to see this and sort of accelerated timeline under a microscope with,
you know,
obviously covid vaccines.
But going from sort of academic research,
fundamental biological research and then seeing how this gets spun into biotechs,
which the venture community funds and then using those biotechs running sort of early clinical trials to get enough excitement that big farmers are like,
(19:14):
we can take this and commercialize this and bring this to large populations.
Let's let's,
let's let's drill down a little bit then.
So so we so we have this university ecosystem.
Yeah.
Uh the government sponsors a lot of that um and just some private sponsorships as well of research but the government does a lot and then you have these people will spin out and they'll and they'll take breakthroughs,
they'll start biotech companies and these companies will raise tens hundreds of millions of dollars,
(19:37):
sometimes billions of dollars and they're doing something with that and then they're and they're proving it out and then their their partner and usually with farmers distributed and commercialized and sell the patients.
I think there's a view for a lot of people that you kind of have this breakthrough to university where it's like oh we've cured this form of cancer and then some business comes along and takes it and makes a lot of money off it and kind of screws everyone.
That's maybe the anti business view here.
(19:58):
Is there is there another view of what's actually happening?
Like what's the value out of all these people working at the startup companies from on these breakthroughs like what are they doing?
Yeah.
I mean like historically that happens because of the Bay Dole act which is basically I.
P.
Licensing and uh that literally is why we have a biotech ecosystem and so many curative things.
So it's like that has proven out to be the mechanism that's great you know personally I think that this is valuable because governments get to think on different timescales than say venture capitalists and,
(20:31):
and,
and you know,
startups,
right.
A government can think on like this sort of 2030 50 year timescale to say,
let's fund exciting biology just for the,
you know,
interest and pleasure of finding things out here.
Uh,
that might turn into something that might lead to a breakthrough that that might be exactly something.
(20:52):
And so this,
like,
I actually really love the separation of allowing government to sort of sponsor things that wouldn't necessarily have a clear commercial outcome today because you know,
like venture capital is like uh,
startups,
the entrepreneurship ecosystems.
These are the people who are like custom made ready and able to sort of take things and move them very quickly.
(21:15):
Just basically research funding someone like jennifer down,
no studying bacterial immune systems,
turns out she discovers a mechanism that makes gene anything much better.
Yeah.
And then someone and then,
and then someone can now take that and,
and,
and then,
and,
and so what,
what,
what,
what are,
what are these companies doing?
Like how,
like how do they think about it?
Cause I literally have,
you know,
friends who are progressive people in congress,
like shouldn't we just be taxing the companies were taking recipe and making all this money off,
(21:37):
like what are they,
what are they doing with with this work and effort?
I mean you could tax the companies,
but just imagine like if we didn't give people upside to translate these things there would not be a system to actually sort of commercialize these things in a sort of fast turnaround biotech ecosystem.
We would be dependent,
it's not like that that innovation would go away but we would be dependent on large incumbents like large pharma's or large,
(22:01):
you know agriculture companies or whatever to out license this stuff.
You would only be entrenching incumbents if you didn't allow sort of a startup ecosystem,
you built around one of these people do this start because when they take the technology,
what are they,
what are they doing?
And they built the company around it and then they we sort of define the use cases that it's gonna be applied to?
So there might be some novel I.
P.
Around a molecule that works for treating cancer.
(22:23):
Right?
Uh They might even do some initial work in humans at the academic level.
But then when we want to be able to say let's put all the money behind all the enabling sort of science to approve it with the FDA.
Run an appropriate clinical trial that's evenly powered has a randomized controlled trial.
How do we do this in a way that we make it manufacturing at scale and sort of approachable by many many people uh what are all of these mechanisms that like take it from?
(22:52):
What is essentially a small scientific experiment or small scientific like study to a commercial sort of industrial level drug that can be given to hundreds of millions of people.
And a lot of,
a lot of these companies are our platform companies and bio as well where they're developing some new technology or they're building it out.
I know a lot of our smart computer science friends go work on some of these companies like so so so so I mean what's,
(23:15):
what's going,
what's going on with that?
So there are people coming in and they're solving data problems that are relevant to these things like what are the data problems?
So the data problems are really like when we have these platform companies and we were talking about earlier about,
you know,
this,
this read write modulate loop,
right?
Or or a sort of standard design test build loop right?
Um that creates data every iteration of that creates data that we're trying to learn from.
(23:39):
And what's really exciting today is we're trying to shorten the cycle time of those loops,
right,
john Boyd's famous result from like you do loops,
Whoever can do an oodle loop faster is going to succeed and I forgot what the acronym stands for.
But uh but it was observed,
I don't evaluate something like that.
(23:59):
But it was,
it was the studies in the Air Force and he's basically saying like what makes some fighter programs or fighter jets better than others and it was really their ability to observe an event and then process it,
analyze it and then react to it and his sort of fundamental contribution to the Air Force was whoever can run this iteration faster is going to win in dogfights.
And so uh this is kind of like bread and butter to us today in startup land.
(24:23):
Whoever iterate faster learns faster because we just assume our y intercept can be anywhere along learning.
But it's really about like kind of that that derivative like how fast are we improving And as we build these platforms that can move so much quickly and do so many experiments.
We got robotics.
Ization,
we've got,
you know,
high throughput experiments in biology,
got micro fluids.
That means I can do a billion experiments at once.
(24:45):
Um We generate a ton of data,
Micro flip micro flicks,
you build experiments and once that means you're taking like tiny bits of of something,
lots of different time plating it all out.
Or even just doing crazy things like,
you know,
doing small experiments in each individual cell in the sample over like a billion cells and then sequencing it out to see like what responded and didn't respond to some kind of outcome.
(25:05):
Is that really expensive to do that many cells Now we've got that cheaper.
That's pretty cheap.
The expensive part is just like to say all the chemicals and reagents and just the time to do these kinds of things,
especially if we start doing at high precision.
But uh that that kind of system though um means we generate data right?
And we have to analyze it appropriately.
(25:26):
This is where we need like you can now generate data from literally millions or billions of experiments and you really do need computer scientists to map it out and to and to analyze it.
Yeah.
And then and that's like I would say Phase one right?
Like let's let's go back and re analyze data that are analyzed data that's generating high throughput.
But how do we start designing the experiments program progressively?
Right?
So like and and so and so these experiments are happening in the company with all this talent but they're not really happening as much in the university or the university of doing these things to what's what's the what's the difference,
(25:55):
the the universities are doing this right and some are doing the methodological work.
They just don't have the resources to really attract many of the top people and teams to work really hard on things.
It's like equity driven cultures and companies are better at some of these types of systems.
And it's definitely like upside,
you know,
financial upside definitely motivates a lot of people here,
especially like if you're making,
I don't know a million dollars at google as a software engineer.
(26:16):
It's very hard to go into making like 70 K.
As a post doc.
But I think in addition to that,
I think the most addictive part is the speed of this in a,
in a laboratory setting,
you might not have the resources,
you can do the experiment.
We know how to do the experiments frequently,
but it's like I just don't have like the robots to do say a zillion experiments in parallel.
I don't have all the gps.
(26:37):
I would want to run like a full moon and the culture is probably not as obsessed with speed in the university versus because you're not gonna die if you don't go fast enough.
Exactly.
Like you do need to publish right under some sort of defined timeframe,
but the mission is different.
The output is different than I think in a startup.
Yes,
(26:57):
you're basically running against the clock all the time and generally that clock is two years.
Right?
And you need meaningful advances to be able to raise more capital to be able to continue to.
Do you think because of this dynamic,
the startups sometimes have attracted the most talented people or the university still have the most talented people or there's different types of talent.
How do you think about that?
I think it's different types of talent,
startups and industry at the very least in the past five years has been some of the most insane amount of,
(27:22):
of poaching away from an academic university system.
Yeah,
that seems like a lot of good people are leaving universities to work on really exciting,
fast moving startups that are solving their curing disease and solving hard problems.
Well,
so think about like the bread and butter grant for like a biomedical researchers innaro one,
right?
That's like,
you know,
on the order of $5 million you can cover your lab for five years.
It's super prestigious,
(27:43):
getting a $5 million grants,
a big deal for us.
And this is like,
it could be 18 years.
Like when I just submit like an nrsc after everyone,
it was like uh sorry,
is it was 18 months,
right?
You know,
one rejection,
which is pretty standard.
And then optimization is like a 200 150 page grant that I had to submit.
Uh And and then I ended up with on the order of like 100 150 grand.
(28:05):
Right?
And so uh whereas now you go get a term sheet for for $10 million for which or $50 million.
And it's,
you know,
there's it's not unheard of to pitch for a month and get a $5 million term sheet right to be able to build these really focused things and super quickly and you don't have restrictions,
right?
And the university takes 50% of the overheads.
(28:26):
If you make $5 million.
University takes 2.5 of its university,
the university needs that to to run all of their departments of diversity and stuff,
you know,
I don't,
I don't know if I would say that,
but what are they using it for?
Well,
you have obviously centralized and shared services.
This covers like rent on your lab space.
Right,
okay.
So there's there's there's like real overhead and then there's also maybe 1500 administrators,
(28:46):
they're making sure the university yeah.
There's there's not,
there's just some stuff definite inefficiencies that you have no control over when you take that overhead,
but there's value to it as well.
I'm not going to like,
of course to say yes or no,
but At the very least we get the five million for a startup.
Every one of those dollars,
You know,
we're gonna all use them towards the things you're actually getting done.
Yeah.
Yeah,
that's true.
That's interesting.
Like big institution versus startup also,
(29:08):
you're more efficient that way.
And you probably have to ask permission every time you want to build something or do something,
you probably should go much faster.
I wasn't allowed to expense any amount of alcohol for any meal when I had grants,
things.
Things have definitely changed.
I mean,
you know,
in the university should shouldn't,
maybe,
maybe if someone running A.
B.
C.
I should talk to them about this policy,
(29:29):
I can probably learn a few things from stanford save save some budget.
So stepping back.
But again,
you're now working on investing in a lot of companies,
you're helping build companies as well.
What's,
what's,
what's the most fun part of what you do,
uh,
the most fun part I would say this is tough.
I think it's basically getting to actually turn ideas into action.
(29:52):
Um It's you know in grad school or just honestly in in many,
many slow moving institutions you talk a lot about really cool stuff.
There's very brilliant people and great ideas and it kind of ends with like a yeah,
that'd be cool.
And I think one of the most exciting parts is like you can finish an exciting hour long conversation and have a follow up that means like writing a check,
(30:16):
starting a lab,
you know hiring some people,
we helped come up with this idea for this giant bio manufacturer resilience last year.
I guess you probably couldn't have done that before in any other context.
Resilience was really hilarious because like we raised what like say $50 million when the idea was like on the back of a napkin and we had like three people but we just knew it was a big problem to solve for Right?
(30:37):
And then and then use Bob's reputation to raise 750 million off the company and get going.
Yeah it was it was you know so crazy to think that that we were Able to basically put together $800 million dollars in the span of like what five months?
Uh that would that is actually imposed.
I couldn't think of anything like that in in academia right?
(31:00):
And Francisco just really quickly explain what is resilience,
bio.
Yeah,
so resilience,
bio is basically a bio manufacturing company focusing on manufacturing medicines And so we are using this platform to be able to build and and manufacture all the medicines that that we will need broadly in the United States which so frequently are manufactured outside of the United States.
(31:25):
Uh and the impetus for building this was around vaccines which we knew if the M.
RNA vaccines worked we would not be able to make them.
We need manufacturing for that.
And we also it's also we started it of course to help make sure there's enough manufacturing in the U.
S.
And when our allied countries and not just other parts of the world.
Yeah I mean if you look at like what happened when Wuhan shut down and basically all of china shut down.
(31:49):
It wasn't just medicines but it was entire sort of our biotech supply chain.
So many of our companies had huge delays in the sort of re agents.
They were able to get deals,
they were able to close or whatever because there's so many cros out there and it kind of was like you know whether or not you you you feel discomfort around china owning it.
I definitely think we should feel discomfort about not owning our own sort of medical manufacturing supply chain.
(32:14):
It's good for resilience South America's back.
Well I think any any company should have you know their ability to manufacture their own medicines.
And so so what what are some of the other things that you think,
I mean,
what do you think happens thanks to you being doing what you're doing today?
That wouldn't have happened otherwise.
How do you,
do you think about it like that?
Like what's my deletion deletion criteria?
Are there,
are there like their companies with a better strategy?
(32:36):
There's companies that exist,
there's,
there's companies that you have to put them higher talent,
they need to do things.
It's kind of,
it's kind of fun to think about cause you're probably ultimately probably helping cure a lot of diseases if you're doing your job right.
I think the things that I'm most proud of are actually putting incredibly brilliant uh,
and complementary skill sets in a room and seeing what happens there.
(32:58):
I think,
you know,
I would love to think that I have great ideas and contribute or whatever,
but,
and I'm sure startup founders not and politely to my ideas and board meetings,
but like,
it's very clear when we get like two or 3 Amazing minds in a room and sparks start to fly that stuff happens at unprecedented scale.
And that's,
that's,
you know,
(33:19):
what I think has been sort of the most value generated is bringing in,
say amazing software engineers into biotech companies when neither group really realized that they could be talking to each other and gaining so much from each other or sort of helping,
you know,
like large pharma's talked to early biotechs with skill sets that they just didn't even realize they wanted and like helping do that.
(33:43):
And I think it's just the ability to connect people with deep problems and figure out how to join bringing the right resources and people together to solve the problems.
And if you,
in terms of like falsify a ble ble optimistic positions in the last couple of years,
I understand we've had more drugs approved than previously,
which is probably tied to a lot of what's going on.
And there's obviously always new tools.
(34:04):
There's so much more money going into this space right now.
Like like do you see like the amount of drugs and therapies being approved actually going up a lot on average over the next five years because of this or what's or what should the optimism reflect in the real world?
Yeah.
Okay.
So I don't know.
I'm sure we will see more and more drug candidates come through.
(34:24):
The problem of drugs being approved is it's it's very dependent on FDA.
Right?
And so we've had an FDA that's very excited about innovation for several years.
Scott Gottlieb was fantastic.
Uh And I think even now the FDA is starting to want more and more innovation to come out.
So approval is a little bit dependent on them.
Uh but we should see more very promising therapies that had their data look good.
(34:49):
It's it's depending on the government what they approved,
but but overall there's more good stuff being created.
Yes,
I would say my sort of false viable prediction would be that we're going to start seeing more and more biotechs make drugs that they try to commercialize themselves as opposed to selling to pharma to do the commercialization,
maybe new,
(35:09):
there's gonna be new big pharma companies and and why?
Why is that?
So I think the case study of Moderna is amazing right?
Madonna was a traditional biotech and is now actually commercializing its own drugs was able to raise enough money and stay independent.
Yeah.
Now they they control their destiny because they make revenue at a prophet who would have thought that that's like right now they're going to make all of us get booster shots and makes so much more money.
(35:32):
Yes.
Yes.
And so but because of that,
Madonna is very smart and they're being very aggressive about saying we have this golden goose,
we have this thing that makes a bunch of money,
how can we use all of our brilliant scientific minds are brilliant scientific platform.
And I heard this week they talked about there's going to be a shot for both.
Covid and the flu or something like that.
(35:53):
And they're like they can do this and they can start thinking about using this for you know uh for cancer.
You know as we start thinking about analytic viruses or even in vivo cars where we should live our M RNA car car constructs into our T cells.
And so what I think this is going to give more biotechs courage to do this.
But I also think that uh this allows us to cure more and more rare diseases.
(36:19):
Right?
So a big thing about farmers like when they acquire a company they really need this to be like a billion dollar a year drug almost justify a lot of the expenditures.
Um And so if you only made $100 million a year on your drug this would not be like that big of a needle.
So it's really hard to raise money for something that's only gonna make $100 million a year.
So there's all these rare diseases that's just hard to get the incentives secure.
(36:39):
But but these newer companies why can they do it?
Because the my my hope my prediction what we're trying to build towards is that we dropped the cost to actually make these drugs.
So you don't need to raise a billion dollars because they already have platforms,
you can already make the drugs pretty quickly and easily and therefore they might as well go and cure these 500 diseases which is now we're not worth doing.
Exactly.
And then if we if we enable them if we create infrastructure for both running the clinical trial,
(37:04):
we've done this with resilience to help with the manufacturing,
help them with commercialization.
These companies can go out and we can have many companies sort of I.
P.
O.
And be profitable.
Makings quote like only 100 $200 million.
Which like in any software sense would be an exciting outcome but just doesn't you know correct the incentives and I think what's really important about that is right now we're a little bit dependent on curing diseases in areas that pharma wants to cure.
(37:31):
So we've got like on the order of what 100 maybe 200 farmers looking to make these M.
And S.
So it means 100 and 200 chief scientific officers are dictating what does?
There's a little bit of groupthink amongst just like in any other field they're gonna be thinking about the same things a little too much.
Yeah and so but think about that like we have Maybe at best 200 people saying what diseases we should care in the United States right?
(37:55):
Like there's a lot of diseases we need to cure that not all of them are focused on.
And I think decentralizing and pulling that out and allowing sort of individual companies to go out and cure the diseases on their own is really where I think in 10 years I'm so optimistic is to create these businesses that can go after.
I know I know we don't we don't always share agreement on all things political which I respect but but I think this is kind of cool because you're basically giving like a version of like the pro liberty thesis,
(38:20):
which is that the world should be like more distributed and more more decision makers bottom up versus fewer top down because I don't yeah,
I definitely I don't I don't agree with top down other parts to this,
but I'm just saying like in general,
and it's a general theme,
it's good.
There's more people can make more decisions specifically because they're going to find things that there's fewer people on top are missing.
Look,
(38:40):
we've actually like as a weird analogy,
like think about the internet and what Shopify does,
right?
Um where before,
if you wanted to buy anything,
it was basically what is offered at walmart and and like the sixties,
seventies or whatever.
And and that's the thing I bought,
right?
So and now like if I'm really into,
(39:01):
I don't know,
some esoteric trading card uh like that,
I want to collect,
it's not available at walmart,
but it's available on the internet that walmart has like 100,000 at most 200,000 skews,
I think wish has 100 million skews on the internet has a billion skews.
Exactly.
And so if you think about that same model of like this long tail,
it can be profitable if you think at a global scale and we actually reduce the cost to actually start these kinds of innovations and distribute them to people and think about that in the context of diseases rather than skews right?
(39:29):
There were,
you know,
hundreds of thousands,
if not millions of rare diseases that if we can think about this on a global scale to cure them and allow smaller entities to cure them more quickly and cheaply,
then we can actually start hitting this long tail.
That doesn't make sense of these centralized systems.
That's awesome.
So we started this podcast because there's a lot of pessimism going on right now and I want to educate people about all these awesome,
(39:54):
optimistic things and share this and get them hopefully more excited and helping with the positive parts of our society.
Um,
are you still glad that your parents came to the United States as a place you're proud to be?
And what's your view on the future of America?
Of course I'm excited about like,
I feel like I,
I should be like on the,
on the front uh,
photo of the pamphlet of the american dream right for us to,
(40:15):
you know,
come to the US,
for me able to get like an amazing education at public schools and then go to stanford and get like amazing PhD and be able to work in venture capital and bring innovation,
you know,
to people,
is like what,
like that's literally what,
what you come to America to do.
So how do we make sure things keep getting better in in this country?
I think I think um uh what America has that?
(40:42):
Sometimes I think we forget is we have first round draft on the greatest talent in the world right?
If there is a smart person anywhere in the world we could find a way to get the new America.
So I think immigration is core to this right and then creating cultures that like allow for this.
This is maybe I don't think we disagree here.
But maybe my more liberal sentiments is like how do we create an environment that allows for so many different minds and perspectives and cultures to come in and feel welcome and able to succeed.
(41:11):
Um And I think that's to being a pluralistic society is tolerant of everyone as opposed to society where people are trying to destroy anyone's reputation when they disagree with them.
Yeah I mean like like I just what I really dislike is tribalism right?
And I think that can be attributed to any kind of these groups on both sides.
And it doesn't even matter like what's like there's no definition of side,
(41:35):
there's just tribalism and we decided like here's our group,
here's my in group here's the out group and um the the ability to come into the U.
S.
And feel safe and welcomed means finding your people right?
It doesn't mean there aren't.
Do you think academics have gotten more tribal or less tribal though over the last decade is that is the tribalism like affecting that part of society potentially as well in some ways.
(41:57):
Um I don't know if I would call it tribalism,
I would say that,
um,
what we are saying is is people who are more comfortable having say black and white views may be divorced of the nuance within there because they are divorced enough from situations where nuance is important.
(42:25):
So to maybe make that abstract statement a little more concrete,
like if you are in a tenured position in academia and you consider moving to industry,
the dark side,
which is always considered like the phrase that they tell you.
Um,
you have like the privilege of principle that many other people don't write,
(42:48):
you don't have to make trade offs to function in society.
You get to spout and like have the opinions that you want and that's wonderful,
you know,
trying to make a business work,
which requires some kind of trade off,
you're saying exactly like anybody who's ever run a business or been in a large organization has seen like,
like gray area HR situations manifest right?
(43:11):
Where on one hand somebody might say or do something that is,
that is reprehensible.
Uh,
and they and they should be let go.
But so frequently there's just ways to navigate people to better behavior and I think,
you know,
operating in a realm of forgiveness is so clutch and important,
(43:34):
both,
you know,
within,
you know,
conservative ideologies or liberal ideologies,
if we,
if we want to paint that into groups.
Um,
and that's kind of how we function with our families,
right?
Like we are,
we are forgiving if our family members do us wrong or say awful things or whatever to to some degree.
Obviously we always in some of these places don't have,
don't have to be as forgiving or to get things done and work in the world.
(43:55):
I think when you get into these hyper tribal situations,
when we get into these situations where you're divorced from having to be,
you know,
functioning with people who are different than you might be less forgiving you you don't have to be forgiving,
you don't have to flex that muscle,
don't know that that's great.
111 last question to leave us with uh if the revolution of bio continues and a lot of times people don't understand exponential growth is not intuitive and this keeps going for 20 or 30 years where someone like the really crazy cool things we might see in our lifetime.
(44:25):
Oh man.
Uh yeah,
I think we would flip the model of what we think of in Bioko life sciences from amelioration of suffering,
which is essentially carrying diseases to sort of improvement of like human condition like this.
(44:45):
The trans humanist might call it something like that,
it's a thorny issue,
but I think like so basically helping everyone be like self actualize and be a better version of themselves,
healthier and higher energy every day.
Yeah,
so like think about maybe something we see today a little bit is like therapy is used because we're trying to treat conditions like treat depression,
(45:07):
anxiety so on and so forth.
But how much of like say mental health coaching can be done to actually help us achieve like a better version of ourselves and this is like considered coaching today.
But you know,
and psychology is about really sort of like managing sort of downside things And I think we can think about that.
Do you think about focusing on the upside instead of just the downside,
(45:27):
each one of us could have a ton more energy could be instead of being 80% on some metric,
we could be a percent.
Uh and so flipping that model,
it's like,
okay,
we're not,
we we've moved beyond just just curing diseases.
We're using this for like manufacturing,
we're using this to help like vegans be able to like eat sustainable,
you know,
meets or whatever.
And even like say people who eat meat to eat less meat or whatever.
(45:50):
Uh those are all things that are beyond sort of necessarily human health care,
but really about kind of like growing ourselves as people in a society awesome.
Thanks Francisco.
Absolutely.
Thanks joe.
Mhm mm hmm.
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