September 8, 2025 • 57 mins
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On the Business of Biotech this week, Sahir Ali, Ph.D., founder and general partner at Modi Ventures, a family office investing at the intersection of technology and biology, talks about adapting the Markowitz model to improve returns and balance risk, his concept of the "biostack" for making direct investments into life sciences companies, and the revolutionary potential of scientific super intelligence. Ali explains why Houston, Texas is an underrated ecosystem for life sciences, and what AI will mean for medicine and the future of healthcare consumption.
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Episode Transcript
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Ben Comer (00:07):
Welcome back to the Business of Biotech.
I'm your host, Ben Comer, ChiefEditor at Life Science Leader,
and today I'm pleased to speakwith Sahir Ali, Ph.
D, founder and general partnerat Modi Ventures, a family
office focused on theintersection of technology and
biology.
Sahir is a young man to havealready led many professional
(00:28):
lives.
He worked on Wall Street duringhis undergraduate years.
He trained as a biomedicalresearcher, worked as a cancer
scientist and software engineer,became a sought-after
Salesforce architect and alsofound time to start a successful
e-commerce business with hisbrother, Amir Ali.
That doesn't cover everything.
(00:49):
We'll get some additional colorfrom Sahir about the
experiences that got him to thispoint, and we'll also learn
about Modi Ventures' uniqueinvestment model, how it builds
on the Markowitz model forportfolio organization and what
the future of tech, bio and AIin medicine might look like.
Sahir, thanks so much for beinghere.
(01:09):
I really appreciate it.
Sahir Ali (01:10):
Oh, happy to be here and thanks for all the
introduction there.
Ben Comer (01:15):
Yeah, absolutely.
I want to ask you a little bitabout some of that intro.
You were born in Pakistan andmoved to the U.
S.
-- Houston Texas, specifically-- as a child.
What sparked your interest inscience and technology initially
?
Sahir Ali (01:34):
Right.
Well, we moved here when I was10, 11 years old, and it was an
interesting time in late 90swhere, um, uh, I'd never seen a
computer before, in the sensethat it was never close by,
obviously seen a computer butnever had one in in my house.
(01:55):
So when we moved to houston, um, within a month, uh, we had a
computer, a compact presario andum so sort of.
It was an unattainable thingthat now was there.
It was gifted by my uncle.
That kind of just reallysparked curiosity on how it
actually worked, and so I thinkI went down the rabbit hole.
(02:17):
But one of the more, I would say, profound moments for me was
being part of FIRST Roboticsprogram that Dean Kamen started.
I don't know if many peoplewould recognize that program.
It was in late 80s.
Dean Kamen thought you know, inevery school all the athletes
are treated like stars.
You know you have pep rallies,you have cheerleaders, but what
(02:39):
about STEM kids?
They're the ones actually goingon changing the world in
science, technology, engineering, math.
We should treat them like stars.
So he started this programcalled FIRST Robotics and it's a
robotics competition.
It's the same concept as acompetition we should cheer the
kids on.
And so that's the program and Ibecame an alumni of that and in
many ways that was a big partof my growth and the curiosity
(03:05):
that led to being very technical, in fact, where where was that
program?
This program.
I mean the program was at, soif someone could be a coach that
could just recruit neighborhoodkids, it, it.
It was at school level.
In this particular case it wasat NASA level right as well,
like you know, just a communityservice.
(03:25):
It was different levels.
I was particularly going to anafter-school program at NASA,
but you know, in Clear Lake itwas walking distance.
But just so that everybody'saware, even today it's the
similar model Schools level.
Someone could become a coach.
You don't need to be atechnical, you just bring kids
together.
Yeah, so that's kind of thething and I always tell people
(03:46):
that by the time I was 15, I wasstill picking up English, but
my second language became C++.
So that's the kind of joke thatI tell people that the program
was quite influential for me.
But you know, that kind of gotme started on a technical
journey very early.
So that kind of got me startedon a technical journey very
early.
Ben Comer (04:04):
Do you think that you know?
There's been press reports andstuff about people coming out of
university with code, you knowextensive coding instruction,
having trouble finding a job, doyou?
I'm curious if you think thatyou know that interest in coding
early for you would have kindof the same results now.
(04:25):
Or do you think that we'regoing to get into AI later in
this conversation?
But do you think that codingyou know, just the technical
aspect and process of doing itwill become irrelevant as AI
gets stronger.
Sahir Ali (04:35):
Yeah, no, it's an interesting question.
So I don't describe what Ilearned during that time or one
of my formidable years.
It was not.
It was not coding, it was, Ithink, computational thinking
that's.
I would praise that and and yes, at the time it wasn't as
simple as even google was justaround, but it wasn't as easy as
(04:57):
just picking up just to writecode.
I think it was thecomputational thinking.
Where do you want it to applythat?
Um, the robotics, getting earlyin the robotics, you know,
brought that physical elementtogether where you can manifest
things.
But also on the software sideof things, you know, those days,
you know it.
Just, it was just a verydifferent time period where you
(05:17):
had to really go into thetechnical details to learn
anything.
For example, if you wanted tohost a website you wanted, you
had to understand how to telnet.
You know the term telnetdoesn't exist today.
For example, if you wanted tohost a website, you had to
understand how to telnet.
You know the term telnetdoesn't exist today.
You know SSH, most people wouldknow that.
So those things, you had to justbe very curious to go down in
the rabbit hole and really get afull stack, and so coding was
(05:38):
probably the end result of that,but you had to become very full
stack and so that sort ofcomputational mindset, being
able to very, very being curious, and had to find your own set
of knowledge or acquire set ofknowledge.
I think that was.
That is what we call humancuriosity and learning and all
of that.
So I mean, I don't think muchhas changed today.
Sure, you can vibe code and allof that, but just being able to
(06:00):
kind of go and go deep andunderstand, get yourself
technically well equipped to dosomething with it, I think that
still exists today, butdifferent times, I agree, of
course.
Ben Comer (06:11):
How did you end up on Wall Street as an undergraduate
?
Sahir Ali (06:15):
Yeah, so you know, before, a little before college,
you know, in high school, therewere a couple of interesting
things that I was alwaysinvolved with.
One was I had understood thatif I wanted to stand out,
technical chops were the way togo, clearly not a jock or
(06:35):
anything like that.
So that kind of was my moat, ifyou want to call it, and so I
was very curious on what all thetechnical applications were
there and I'd come across sortof quant as a field and you know
I Quantitative analysis is whatyou're referring to.
No, I'm actually particularlyreferring to be a quant as a
(06:58):
role on Wall Street.
Ben Comer (07:00):
Oh, okay.
Sahir Ali (07:01):
Yeah.
So, yes, quant is short forquantitative, but really quant
around 2006-ish time period wasreally referring to folks who
are just mathematical.
You know physics-oriented guyswho are creating mathematical
models at Wall Street and, youknow, at the time as a young in
my teenage years, to me itseemed very fascinating that
(07:22):
these guys were paid lots andlots of money and most people
didn't know who they were, andso there was an allure to that
and so I read a little bit andeventually, at Rutgers it turns
out Rutgers had a pretty prettywell-defined quant program for
grad school and engineeringelectrical engineering at some
point when I started tounderstand is, you know, it's
(07:42):
all signal.
This is pre-machine learningera.
So just, I think, knowingenough and knowing enough about
it at the freshman level and,you know, not many people even
knew what quants were.
I think that was kind of myforay into it and luckily, you
know, I ended up at an ultrainvestment group within Citi and
(08:02):
you know the rest of thehistory.
But that's kind of the sort ofprecursor to it.
It's just that again it comesdown to this just being
technical, full stack, just tobe able to know what I call
asymmetrical information.
What is it that you know andbeing able to use that that you
know, you can make yourselfstand out.
And for me, that just knowingenough about what a quant does,
(08:25):
what, what quant is, and beingable to position yourself as
your curiosity and interest is,at the end of the day, freshman
internships are all about justyou know how curious you are.
You're not supposed to knowmuch.
Ben Comer (08:37):
Yeah, yeah.
So you had some success, someearly success on Wall Street.
What?
What did you do next?
And we don't have to go throughyour entire bio, but I'm trying
to pull out some of the thingsthat you learned, some of the
experiences that you eventuallyare going to bring together to
create Modi.
Sahir Ali (08:55):
Yeah.
So a couple of things.
One is the quantitative mindsetwas developed in many ways
there.
How do you think about assetmanagement?
How do you think aboutportfolio constructions?
You know these sort of thingsearly on being in financial
(09:15):
services.
Also, it was an interesting timewhen I was there.
It was financial crisis in 2008and 2009.
And so that is a one.
It's a black swan moment, andso being in that in the space,
the kind of learnings you cansee and just just what you can
observe is is once in a lifetimeopportunity.
Yes, it was bad for the market,but, from a learning perspective
, what all that happened justbeing in that moment and there
(09:39):
and just you know fortunatelyfor me, I, my internship turned
into kind of a full-time gig.
So I was lucky enough that youknow, throughout my
undergraduate I had a BlackBerryand I was paid well, and so
just being in that environmentand just be able to learn and
the second thing is obviouslyquantitative trading, and all of
that had kind of come to a haltin that time period.
(10:00):
So I had an opportunity toadopt another technology.
At the time it was calledSalesforceforce completely
orthogonal.
However, salesforce 2008 and 9was sort of early days, and so I
picked up a new cloudtechnology and able to sort of
think about how do we buildpipelines and all of that, so.
So I think that so if you thinkabout, uh, how do you look at
the financial world, how do youthink portfolio, how do you
(10:22):
think about business and thenselling of that, the pipeline,
the lead to cash, and theneventually, at the same time,
when I went back to campus, Imean I was already doing some
computer vision research and allof that, and then I got really
interested in how do we applythe same quant mentality to, say
, cancer or medical imaging.
(10:44):
And that's where I joined a labthat, early on, one of the few
labs actually were looking atdigital pathology and such.
So anyways, that's where thethings start to sort of
intersect.
This idea of being sort ofquantitative stems from those
days.
Yeah, so you eventually reacheda point where you were in a
position to.
Ben Comer (11:00):
So you eventually reached a point where you were
in a position to establish ModiVentures.
Give us a sense this is afamily office.
You're making strategicinvestments.
Give us a sense of where themoney came from.
Was it just your successfulcareer up to that point to serve
as the basis for your initialinvestments, or what would you
(11:21):
say about that?
Sahir Ali (11:22):
Right, yes, we're lucky and blessed.
My family, which includes mybrothers, you know we're
entrepreneurial.
At the same time, it's veryacademic and we had companies in
the e-commerce space that didfantastically well, and again,
that's a story for some othertime.
However, that also includedbeing early in some technology,
(11:44):
looking at the world from a sortof a I like to call it
convergence what is reallyconverging and what the
derivatives will be that.
So we kind of set up a techplatform for that, had a couple
of other acquisitions that didwell, and especially in the year
2021, end of 2021, early 2022,there was a phenomenal shift
(12:09):
again in the financial andcapital markets where the
interest rates went up so muchthat it kind of disrupted the
capital markets.
(12:29):
But prior to that, duringpandemic, I was deeply thinking
about the venture capital world,because that is a world that I
was looking at it from anoutsider perspective.
We had made some investmentsbut really wasn't in the venture
capital world, if you want tosay.
I started to realize that Ithink it's become a pretty
mature asset class over the lastcouple of decades.
I think I'd written, I wasstaring at this chart where
somewhere in the orders of $30billion to $35 billion were
raised in totality in 2008 andin 2021, 190 billion dollars or
so in just that one year.
(12:50):
That was a phenomenal rise.
All this and that was sort ofgiven by what we call social
networks effect.
Right, you create an app andjust quickly distribute it and
you have users and the ad spaceand all of that, as I was, you
know, as a pandemic happened.
I said what's going to be?
What's going to be the nextthing for venture?
Obviously, the power law wasdriven by software.
(13:11):
That just scales very, veryquickly.
Obviously, the SaaS world andanything in the software world
is now saturated.
So what's going to come next?
And obviously AI was a thingthat was on top of my mind
because I was doing some deepresearch work using AI At the
time.
I was tracking what hadhappened with AlphaGo
reinforcement learning in 2021.
(13:33):
The AlphaFold had come out,obviously, 2017 paper attention
is all you need sort of thatencoder-decoder models we were
starting to go towards at thetime.
We were just calling itzero-shot learning, right, and
so everyone was very excitedabout that.
I thought that could be veryinteresting.
The second part I thought which,particularly in 2020, a chart
(13:53):
that started to float around wasthat how drastically the cost
of sequencing had come down.
This was perhaps the mostsignificant cost reduction of
any technology that we have seen.
It went from $3 billion, so tospeak, to finish human genome to
$300 by pandemic, which is 2020.
And it played a role.
You know, sequencing technology, the PCR, played a role in
(14:17):
overcoming the COVID pandemic.
And then the third part, youknow, which led to digital
biology and all of that, andwe'll talk about that more.
And the third is, you know, Ithink in the last 20 years,
we've had some phenomenalprogress in what we call
cellular programming, with thetools like CRISPR, base pair
editing, ips, stem cells, and soI thought, you know, this field
(14:38):
of biology is, in many ways,have started to become very deep
, tech-ish engineering, and Ithought that was a real
opportunity to be in that space,while everybody else would just
purely get excited aboutgenerative AI and all of that.
So, anyways, so I was thinkingabout that.
And then the other part of thequant mindset comes in is that
clearly there's a gap in venturecapital world where we can
create a different type ofportfolio.
(14:59):
So, anyways, that's the impetusto a lot of that, and Modi
Ventures really stems from that.
It is not yet another venturefund.
It thinks about the portfolioconstruction very differently,
in a quant manner.
It thinks about theintersection of technologies
that I just mentioned to you,which I've started to call
biostack, and we'll go into that.
And third part was how do weactually start to think about
(15:23):
risk modulation in venturecapital?
Because, as most of us know,that venture capital is very
volatile but it operates onpower law, which means you have
to be okay with extreme amountof volatility, which means 90%
of the companies might just goto zero and 10% successful.
So, anyways, you combine all ofthis.
That's kind of the impetus toModi Ventures and luckily we
(15:44):
were able to raise our firstfund very quickly and you know,
as you know, we just announcedour second fund two and a half
years later.
Ben Comer (15:52):
Yeah, and I want to.
I want to get into ModiVentures and your investment
framework in just a second.
But I'm curious about, you know, the the establishment of Modi
Ventures in Houston, aside fromyour childhood experiences there
, what kind of drew you back toHouston and maybe what would you
say about the Houston lifesciences ecosystem?
(16:15):
It's not, you know, it's notone that is often, you know,
talked about along the samelines of you know, san Diego or
Cambridge, or even New York.
Sahir Ali (16:26):
Yeah.
So I'm glad you actually askedthis question.
Why Houston, besides my ownpersonal connection to it,
family connection?
Well, houston, well, let's.
Let's start with the basic.
First of all, houston is hometo the largest medical complex
in the world Texas MedicalCenter.
I think most people may notrealize this, that 120,000
people go to work in that smalldistrict here which, if you have
(16:50):
an aerial view of Houston,you're going to start to see
three downtown looking areas.
One is the core downtown ofHouston.
The other is the financialdistrict where the which we call
the private equity world andeverything else called
Westheimer, and the other is TMC.
It's high rises in all hospitalsystems.
You know this area does moreheart surgeries than anybody
else.
It has the best cancer caretherapies.
(17:14):
You know, obviously, mdAnderson is part of this and
also, in many ways, one out ofthree approved oncology drugs in
FDA, the clinical trials happenin that area.
So, effectively, what you haveis a very deep, complex arc of
what we call applied medicine.
What it has lacked, typically,is the, I would say, the bottom
(17:39):
arc, which is company creation.
Innovation is there, it's justthat the capital may not be
there.
And so how do we turn this intoa full flywheel of life science
where you have the large humandata, you have the clinical
trials, you have the know-how ofpracticing medicine.
(17:59):
So the key thing is how do weturn that innovative wheel on,
how do we bring in the rightcapital, how do we start
companies here?
And that is really what Houstonoffers, and I think it is under
the radar and I think it'sgoing to do really well.
Lastly, the state of Texas hasalso contributed to sort of how
(18:21):
do we advance this?
For example, there's a grantsmechanism called SIPRIT, which
is for cancer and oncology.
It's a $3 billion pool of statemoney available to companies,
private companies in Texas $100million a year up to $100
million a year in funding.
They can also match privateinvestment two to one.
(18:42):
It similarly is proposed forDIPRIT, which is for dementia $3
billion again.
And so you also have that.
We have something called HelixPark, the Texas Medical Center
Initiative.
I don't know the acreage, butit's a multi, multi-billion
dollar development plan that'songoing.
At the moment we're sitting inone of those buildings.
It is all just about biotechinnovation here.
(19:05):
And also, lastly, I'll say I'mon the board of something called
Rebel, which is spun out of theRise Biotech Launchpad.
It's going to create incubatecompanies that are going to be
at the intersection of drugdelivery mechanisms with tech
bio.
So, anyways, there's lots goingon here and I thought this is a
real opportunity to buildsomething which is probably
(19:30):
going to have an inflectionpoint.
So, selfishly, this is not justa personal connection, houston,
but I think there's a lot herethat we could do with private
capital, innovation and turnthat into a flywheel, so I'm
very bullish on that.
Ben Comer (19:46):
Let's talk about Modi Ventures investment framework I
mentioned.
It's based, at least in part,on the Markowitz model.
You mentioned previously thatyou wanted to set something up
that was kind of fundamentallydifferent from other VC groups.
What could you say about ModiVentures investment framework, I
(20:07):
guess, and maybe how you thinkdifferently about risk and
return, especially in biotech?
Sahir Ali (20:14):
All right.
So you know, and again I'll goback to 2021.
So there was a couple ofinteresting observations for me.
One was that if you look at theventure capital world, everyone
talks about power law, whicheffectively just basically comes
(20:35):
down to is that there's goingto be a very handful of
companies that will drive thereturns.
Rest will just go to zero andthat's a very that, even as a
mathematical distribution,that's a very sort of a volatile
way of looking at things.
In fact, you look at overallevery asset class available in
the market.
Venture capital tends to behigh risk, high reward, and so
(20:57):
the couple of things I thoughtof was that, if you think about
90% of the money that actuallyfunds venture capital, that
those entities are not generallywhat you would associate with
high risk, high return sort ofprofile, like endowments and
pensions 401ks yeah.
Yeah, they.
You know it's public money.
However, they do have exposureto venture capital.
(21:18):
I mean, in fact, some of thecollege endowments, the Yale
model.
They're up to 10%, 14%.
So I was like, okay, well, howdo they think about this space?
And turns out that they may notassociate such high volatility
to venture capital is becausethey don't invest directly into
companies.
They invest into fund managerswho are extremely established.
In fact, if you want to get 50,$60 million from a pension fund
(21:41):
or endowment fund, you got tobe on your sixth or seventh or
eighth fund.
You know you got to be veryestablished funds to be able to
show extremely tight record.
So I thought they're looking atthis space as in the venture
capital world lower risk andperhaps also lower return,
because as the funds go, youknow the larger funds you're not
(22:03):
returning, the proverbial 10Xers, right, it's just a law of
large numbers and also Cambridgereport, year after year,
identifies that.
So that's one 90% of the moneythat funds the venture capital
are in established funds andthose.
The second is there's anothersort of what I call an asset
within the VC world, which isdirect investments.
You can go invest directly intoa company and if that company
(22:26):
goes on to become a verysuccessful one, you can find
yourself into 100x or 200x mixes, but of course the outcome is
very binary there, and you could.
You know that's where the powerlaw really comes in.
And so I thought you know whenyou think about.
What Markowitz was talkingabout is that if you look at the
various assets that areavailable in the market and you
(22:49):
look at the base case, which isa stock and a bond problem, a
bond tends to be riskless butyou know very low risk but low
return, stock volatile, but itcould give you a high return.
But if you have sort of thethis, if the equation of market
is based on correlation and youknow the risk return profile, if
you could create anuncorrelated baskets of those,
(23:09):
you could technically bend thecurve.
What that means is, if you canmix them up, you could take less
risk than your lowrisk assetand get a higher return.
And so that's where I thoughtif we could do Modi Ventures in
a way that we could mix theright limited partnership
positions with directinvestments in a unified
(23:31):
framework and we'll come to itwhat that framework is from
investing on a biostack then Ithought there was an opportunity
to apply a bit of quantitativemodel where we can modulate the
risk and get a pretty decentreturn and don't have to play
what I call in a power law spacewhere plenty of people are.
So that is the fundamentalframework, without getting into
a lot of the more proprietarydetails, but that's the mindset
(23:53):
and the framework.
So it's not a fund of funds assuch, but it's also not a
co-investment in such.
There's a fundamentalquantitative way of sort of
looking at this and inspired bythe Markowitz or the Fish and
Frontier in the venture world.
And then I thought if you havethis sort of framework, what's
worthwhile to look at is thebiospace.
(24:14):
Why is that?
Because I don't want to use theword biotech, but when it comes
to bio and medicineparticularly, it has a very
interesting spectrum, right?
I'll give an example here.
When you look at any techcompany, which is primarily what
venture capital is driven by,it has seed series A, series B,
series C.
These are sort of series thatdefine some kind of product
(24:37):
market fit tied to how muchmoney can you raise.
However, if you think about thebio world, some of the
attractions defined by yourexperimentations and clinical
outcomes, right.
So you go from lab to IND, youput your lead candidate in, then
you have phase one, you havephase two, phase three and then
market approval.
Actually, it turns out at phaseone, phase two, phase three you
(24:57):
can actually have somedeterministic outcomes.
You can actually say at phasetwo who could be a potential
buyer, what could be the peaksales.
You could do thosedeterministic sort of outcomes,
and so that actually is veryinteresting than just purely
tech, where series A doesn'tmean much in terms of what your
outcome could be.
And then, if you look at phasethree plus, you have royalty
(25:17):
funds right.
There are ideas that you canactually partner with a biotech
company in exchange forroyalties rather than equities.
So I thought you take thisbiospectrum and then you apply
that framework that I just saida mix of LP positions and
directs and how do you capturethe entire spectrum?
That's where the bending of thecurve happens, and I thought
(25:38):
this framework just lent itselfvery well because you have
different assets, a differentvolatility, different and could
be uncorrelated.
Ben Comer (25:46):
Now, to do that, did you have to create it's part of
Modi Ventures a proprietarysoftware that you're able to do
that kind of analysis across theindustry?
Sahir Ali (25:56):
Yes, so I wouldn't call it a software.
It's not like packaged thingwhere I can just give it to
somebody, but there is some deepanalysis and modeling as such.
So I'll give you one example.
When you think about, there arehundreds of funds.
In fact, last I looked at itthere are close to a thousand
biotech funds even right Around.
(26:18):
And the key thing is for me, howdo you find uncorrelated funds
that you want to?
So, for example, typically whathappens with fund of funds, the
asset managers who run fund offunds, they go after the top
managers, and my sort ofhypothesis there was that these
fund of funds run by, say, largebanks or the asset managers,
they tend to be in verycorrelated funds.
(26:39):
What I mean is they tend to bein very correlated funds.
What I mean is they tend to goafter the top funds and they
kind of end up investing in eachother's syndicates.
Someone led a series B, someonewill come after series C, and
that I'd call it sort of acorrelated sort of outcome.
So how do you actually modelsomething where you can start to
see uncorrelated funds?
And one thing that has what Ihaven't talked about is one data
(27:01):
structure, as we call it incomputer science or
computational algorithm that hasdriven everything I've done
across I would say the work atWall Street, in Salesforce, in
cancer research and AI has beengraph algorithm.
How do you think about theworld in a network graph?
So we so the proprietary thingI'm talking about is that we did
.
We looked at all the venturecapital funding in the last 10
(27:24):
years at the time in 2021 andgot our hands on some data and
we looked, we mapped it out andwe created a graph network of
that and applied a graphalgorithm and we start to see
where the uncorrelated nodes areand the node is represented by
a fund.
To see where the uncorrelatednodes are and the node is
represented by a fund.
And so that's where aninteresting way of actually
looking at okay, well, if a fundthat is clustered together with
(27:48):
other funds, we don't want togo and invest in more of those,
we can just invest on one ofthose and then go after an
uncorrelated node which is in aspatial distance away from that,
and so what that allows you todo is get uncorrelated sort of
basket of these funds.
So that's one example I cangive you on how the quantitative
analysis and modeling actuallycomes in handy.
(28:09):
And looking at even just thefunds world, which one, which
funds, we want to invest in?
Yeah, and what's the outcome ofthat?
I'll give you a more tangibleoutcome of that.
So, for example, we invested ina fund that purely only looks
at phase two plus assets.
We also have a fund that onlydoes sort of royalty investments
(28:32):
, meaning after phase three.
We have a fund that looks atcompletely probably the most
earliest investments that arespinning out of colleges and
universities and pre-IND, so tospeak.
We have a fund that is quitegeneralist but 28% of their
portfolio looks at digitalhealth and medtech and all of
that which is not governed bythat biospectrum, but it is in
(28:54):
the biospace.
We have a fund that 33% oftheir allocations are in
computational bio, the techbiospace.
We call it the AI, drugdiscovery, the computational
nature of those things.
So you can start to see how doyou find those funds that are
uncorrelated, and that one partis it allows us to look at where
they fit into the grand schemewhich I call a network.
Ben Comer (29:14):
So by cobbling together these uncorrelated
funds.
That's how you get to whatyou've described as bending the
curve, meaning that it's asimilar amount of risk.
However, you potentially have agreater endpoint.
You have a better result.
Is that what you're saying?
Sahir Ali (29:35):
Yes, partially correct.
However, the bending of thecurve happens by mixing lower
risk assets, which I will bundleas limited partnership
positions, with directinvestments, where we're also
making direct investments.
So think about it like a bondin a stock.
That's where the so modern dayportfolio, the wealth manager,
will say we'll do 60% in stocks,40% in bonds or 20% bond.
(29:57):
It allows you to modulate yourportfolio to say is it a low
risk?
It's a moderate risk, a highrisk, right?
So that's one framing, evenwithin the LP positions that I
described to you.
Not every fund carries the samelevel of risk.
For example, we are LPs inCoastal Ventures.
I would consider that as alower risk investment
established funds.
However, we do have a couple ofemerging managers.
(30:19):
I would consider them a littlebit higher risk.
However, if you compare theboth, one has a higher
propensity, if they'resuccessful, to deliver more
higher returns and outsizedreturns than, say, a
billion-dollar fund.
It's a reality and so it's arisk-return profile, even within
the limited-partisan position.
But we also take that as a fullportfolio approach of LP
(30:42):
positions and directs.
Typically, what you see in theindustry is a fund of funds that
might just offer co-investmentswith the funds they work with.
In fact turns out our directinvestments are not correlated
with the funds we're in.
We don't go and double click ontheir companies as much we have
our own views of the world.
Yes, once in a while there isan overlap because that's the
relationships we have.
(31:02):
But it turns out in fund one weonly were correlated with the
funds we invested in.
The companies they have lessthan 12%.
Ben Comer (31:11):
Yeah, that's really interesting because, of course,
you do also have the directinvestment in exceedingly risky
assets in the you know meaningcompanies, early stage biotech
companies are.
Well, let me ask you this youobjected to the word biotech.
Do you prefer tech, bio, orwhat do you dislike about
biotech?
No objection, it's just that.
Sahir Ali (31:31):
You know.
I think it's just all aboutframing.
I'm starting to call itinvesting on the biostack rather
than biotech or tech bio, andI'll tell you why.
So the way I describe biostack,like anybody else would, is the
DNA, rna, protein, cells,tissues, organs and organism.
So that's your stack and I'mtaking a page sort of a tech
(31:58):
stack, right From electrons tosilicon wafers, to the
transistors, to the chip, to themotherboard, the operating
system, the computers and thecloud, and you know the whole
stack.
In fact, if you were a venturecapitalist, you invested in some
parts of that stack to get somediversification and all.
(32:18):
So you know you made someinvestments in the
infrastructure layer of thecloud.
You made some investments inthe software layer, you made
some investments.
So I think, although you couldmake that separate layers here,
I feel like because the space ofbio and medicine is having a
converging moment, the betterframework is the bio stack.
(32:39):
I'm saying this is not true foreverybody else the better
framework is the biostack.
This is I'm saying this is nottrue for everybody else, but at
least for us.
So if you looked at the stack,we have profound sort of
convergence and breakthroughs ateach of the stacks.
So, for example, at the DNAlevel, we call it genomics,
being able to read what's in theDNA.
That's a read breakthrough.
That happened, you know, withthe rise of DNA sequencing, ngs
(33:02):
and all of that.
But we can also write on thatstack now through CRISPR.
So we have a read and writebreakthrough.
Now we go up the stack on RNA.
We have RNA sequencing, quantumscience, a bunch of companies
being able to read, but we alsohave companies who have
foundational models that canpredict the structures of the
RNA.
But we can write at the RNAstack as well.
By the way, most people knowthat technology, that was mRNA.
So I can make a case that theentire stack has now read and
(33:26):
write breakthroughs.
We can actually create digitalbiology, use digital biology and
at each stack you have massiveamount of sequences of data
coming out.
This is where LLM technology isextremely exciting to say.
Well, we havesequence-to-sequence
breakthrough in computer science.
We have digital biologybreakthroughs.
We're understanding.
We're starting to understandthe cell.
You know there are a bunch ofcompanies pursuing virtual cell
(33:47):
concept.
We can start to think aboutbiology as an engineering
principle, systems biology.
The stack is the guidingframework.
Now I'll give you one lastexample, in the protein layer.
So some of the investmentswe've done so at the protein
layer, so some of theinvestments we've done so at the
protein stack.
We have a company calledgenerate biomedicine right, it's
pursuing the novel proteindesigns, generative, ai based,
um uh therapeutics that are atthe protein level, right.
(34:10):
So so that's, that's, that'syour traditional tech, biocon
kind of company.
But we also have an investmentin a company called unnatural
product that looks atmacrocyclic peptides.
These are peptides that arebetween small and large
molecules, but you can engineerthem to have a permeability in
the cell like a small molecule,or they can bind like a large
molecule.
(34:31):
That's a new sort of modality ofdrugs, as Merck is in there and
in fact the CEO of Merck abouta year and a half ago said this
will be the new class of drugs.
So you can see that they'reboth kind of uncorrelated, they
both kind of use AI, but verydifferent to each other.
The last example I'll say onthe protein is we have an
investment in a company calledAtherBio which looks at the
enzyme space and say how do wereduce it down using
(34:52):
computational techniques to dosomething in manufacturing space
?
Because enzymes are thebuilding blocks for us.
We can take that down inmanufacturing, say for removal
of PFS, low-grade brimes oflithium to high-grade, that sort
of speak.
So that allows you a frameworkto say I'm going to target a
layer and make some uncorrelatedbets.
So that's how we think about itand at the organism level, by
(35:16):
the way, we call it the health.
Ben Comer (35:18):
So some digital health and medtech fits there,
got it.
So, in thinking about thecompanies that you're making
direct investments into, are you, do you see those companies as
being, you know, at one layer oranother of the bio stack?
Is that you know how?
Yeah, and that's how you areyou trying to balance it
throughout the stack, or arethere, you know, certain parts
of the stack that you're moreinterested than others?
You know?
Sahir Ali (35:38):
that's a good question of the stack that
you're more interested thanothers.
You know that's a good question.
I don't think I was.
It's, ultimately what we haveto see is where the returns are
going to come from at the end ofthe day.
And so I didn't really, I don'treally think about what part of
the stack that I think it'sgoing to be.
It's not like the tech stackwhere the software just made a
lot of sense.
Now you know it had thedistribution, so what this
(35:59):
framework is.
In the hindsight it seemed likeour stack turned into more of a
protein sort of layer, becamelike the bell, if you were to
imagine the bell curve and theorganism level where we start to
make some deep tech investmentsinto, say, a dental robotics
company or a company that cantake digital auscultation and
turn it into ambient capabilityand things like that, right.
(36:22):
So I don't have to necessarilysee which stack becomes more
proliferating, but that stack iswhere we want to be able to
deploy, which also allows us tobe uncorrelated.
That's what I keep highlighting, that term which, by the way,
if you are, by virtue of that,you're also diversified.
Now one could also say that,hey, you're just diversified,
now it's the one.
One could also say that, hey,you're just too broad.
It's a fair thing, but our mathmodel is also very different
(36:45):
and so in that sense, it's okayfor us to be quite broad.
Uh, and you know, I'll also saythat we don't have to worry too
much about the market trends.
For example, you know funds whojust do a lot of therapeutics.
They tend to be very muchfocused on what a pharma is
going to acquire in next coupleof years, and so you get very
correlated to that.
So in and one bad phase threethat failed, and then the entire
(37:09):
field sort of takes a setback.
What we have here is it allowsus to be resilient, a portfolio,
to be resilient to to some ofthose forces, because you know,
as we both know and everybodyknows, that biology, investing
in bio world and biomedicines isnot just risky but also
uncertain.
Right Investors know how tomanage the risk.
(37:29):
It's not the risk part thatscares people away from this
space, it's the uncertainty part, because biology is uncertain.
And so this allows us, thisbiostack framework, allows us to
be a bit, build a resilientportfolio, other than you know
the mix, the quantitativeframework of LP positions and
directs, which takes the wholething and takes this risky space
(37:50):
and gives a basket of low-riskassets which are limited
partnership positions.
Ben Comer (37:55):
Right, so here is there anything else you could
say about the criteria that youuse to select the companies that
you're going to make directinvestments into?
Sahir Ali (38:05):
Yes.
So this is where I would liketo redefine what I call tech bio
a little bit.
What I say, that I like to seehow our companies are at the
intersection of these threefundamental technologies that
are converging.
So one is, as I mentioned, Icall it sequencing-based digital
(38:27):
biology.
Right, so we can now digitizethe biostack, as I said.
And so anyone that's buildingsomething deeper in that tech
for example, one of thecompanies we're invested in is
called Glyphic Bio.
That is built on top ofnanopores, that can do more
accurate protein sequencingworthwhile building that space,
because protein sequencing isgoing to have a huge sort of
market, um, and so that's that'ssort of uh, anything that is
(38:51):
touching the sequencing anddigital biology.
Uh, it could be a softwarecompany, it could be an
analytics company, it could besomeone's that doing something
in liquid biopsy and so thosesort of things.
But once you have thosesequences one of the biggest
obviously not many people arenot convinced that there's a
role of artificial intelligenceon that biostack, a profound one
(39:12):
, right From not just drugdiscovery and design but being
able to understand causality ofdisease, for example, one thing
we've known is from Alzheimer's.
What are some of the variants atthe genetics level right.
Machine learning has played akey role to understand APOE
variants.
We have AI in medicine,radiology, pathology.
(39:33):
We're starting to use AI inphysical settings that we never
thought was possible or we havenever done but we're not
possible, but haven't doneambient AI between the doctor
and a patient, thoseconversations.
So AI has a profound role inthe entire biostack, from
organism down to DNA.
So that's the second sort of ifI was thinking about, if you
could visualize a Venn diagram,so sequencing artificial
(39:56):
intelligence.
And the third intersectingcircle is that, for the first
time you know since we haveunderstood in mid-1800s that
human disease effectively isabout cells being lost and
malfunctioning and theunderstanding that we need to
control the cell.
And if you can control the cell, we can control the system's
(40:16):
biology with it.
The cell programming space isvery, very interesting If it
combines with digital biology,ai, the idea that we can now
have tooling to go andmanipulate and control the cells
, such as the CRISPR, the geneediting, the viral vectors, gene
therapies, ips, stem cells.
So I'd like to invest in thatsort of intersection.
(40:37):
So the criteria could be thatyou could be creating new
therapeutics modality it's fine.
You could be a platform companythat brings a certain platform.
But again, you've got to builda platform, not for the sake of
building a platform, but thinkabout who those partnerships are
very early on.
Is there a partnership that youcan think about?
(40:58):
Who can help build you withthat?
One of the learnings forTechBio world has been is that
platform can be very exciting,hugely exciting, but if there
aren't any partners and at theend of the customers, it's a.
It's an interesting learning.
So you know we all adopt fromthat we can look at.
You know, how do we actuallyhave curative things, for
(41:18):
example?
You know how do we start tothink about cures?
How do we think about reversingdisease?
How do we think about diseasemodifying stuff?
So that is again part of thebiostack.
And lastly, I'll say that AI inmedicine and AI in healthcare is
(41:39):
very different than AI inmedicine.
In my opinion, healthcare iswhen you have to deliver care
and all.
It's a very difficult space.
We tend to stay away from that,not saying we don't do it.
But AI in medicine what can wedo from troves of data in
medical imaging and everythingelse?
What can we actually turn theminto digital biomarkers?
(42:00):
How do we enable oncologists tosay here's a risk score, here's
how you better risk stratifythese patients.
How do we identify forpharmaceuticals that companion
diagnostics, that works onrudimentary imaging HNE images,
radiological images right, theseare some real opportunities to
(42:21):
have and real marketopportunities as well.
So this is worth deploying for.
Yeah, so these are some of thecriteria.
I mean it's a very broad sport,but again, you need something
like a biostack to kind of helpyou frame that.
So it all comes down to thatsort of stack.
Ben Comer (42:36):
You mentioned Rebel a minute ago and I did want to
just ask a quick question aboutthat organization.
I saw the announcement.
Modi made a strategicinvestment in partnership with
Rice University.
What caught my eye was that thecompany is developing
intelligent bioelectronictherapeutics.
I wanted to get you to tell mewhat those might be.
Sahir Ali (42:59):
Yes, well, you know it's offensive word saying
combination therapies, and sohere's, you have a therapeutic
along with something that couldbe a device, a digital device,
and these are, this could be acombination of those.
So, drug delivery mechanisms.
And how do we, how do we embed,maybe, a device that can
control regulation of the blood?
(43:20):
Uh, sorry, a drug in turn?
Uh, I you know if we could, ifwe could have interesting
devices that control, uh, how dowe actually start creating new
proteins within in vivo?
I think these are, these areall the sort of purview of of
that incubation in the venturestudio, and I think these are
all the sort of purview of thatincubation in the venture studio
, and I think these are thingsthat are worth building for,
(43:42):
because this could have a huge,tremendous impact on some of the
very aggressive diseases andareas today that we just perhaps
don't even have righttreatments.
And it's a bold one.
It comes from Rice.
Omid is a fantastic scientistand a PI at RISE.
Some of the technologies thatthey've developed, you know 100
(44:06):
plus patents.
How do we turn those intocompanies?
And that's going back to what Iwas saying the opportunity
space in Houston.
There's fantastic scientistshere.
In fact, there are institutionswho have done over the lifetime
100 plus INDs.
It's just that I think we'rejust trying to put together an
innovative sort of factory orlifecycle or a flywheel, and so
(44:27):
I'm just playing my small partin this and I think you'll see
some exciting things come out ofRebel.
So very excited about that andexcited about our investment
there as well.
And plus I'm very committed tobuilding the ecosystem in
Houston in ways we can, and Ithink Rebel in many ways is
thinking about very similar tohow Boston biotechs have in the
(44:49):
last couple of decades right theflagships and the third rock of
the world.
I think there's opportunity todo something here, but also take
some learnings along the way todo something here, but also
take some learnings along theway, I wanted to ask you about
one of the other companies thatyou've invested in Lila Sciences
.
Ben Comer (45:04):
They're working to develop scientific super
intelligence.
What would you say about that?
Sahir Ali (45:14):
Yeah, that's probably the most interesting and
exciting investment, just givenwhere the markets, where the I
guess the general exuberance is.
I mean, if you think about thecurrent state of AI, right since
2020, when the chat GPT sort oftook everybody's imagination,
what we have done very well isin what I call in the in silico
(45:35):
world, in the software softwareworld, if someone was to prompt
something magically, you know, aset of sequences come out that
appear like an essay, it appearlike an analysis of something,
whatever you want to frame it,as it can generate a movie, it
can generate images, it canoperate on images and
understands again, from pixel toto text, to everything,
(45:59):
anything that can turn into abunch of sequences.
Great, either llms or thesediffusion models just tend to do
very well science.
So, if you want to make a superintelligence, on the science
side of things, though, sciencehas a different sort of a
flywheel or a path where someonehas to create a hypothesis,
that has to go and experimentphysical experimentation,
(46:20):
physical access to the world tobe able to do that, and then you
collect data, you analyze itand then you repeat that loop.
So if we were to think aboutwhat is the role of agentic AI
or any sort of AI framework increating science.
So you have to create somethingcalled a super intelligence,
and that super intelligence hasto have access to the physical
world.
And that's what Laila's sort ofcore thesis is that if we have a
(46:45):
super intelligence layer or AIlayer, which is a software layer
, but what if it had access tosomething called AI factories
where it can actually conductexperimentations on that stack
from maybe DNA RNA materialsciences, on that stack from
maybe DNA RNA material sciences,how do we turn that into sort
of a full, sort of an AI factory?
(47:07):
And so what could be thebusiness models of that?
I mean, I don't want to go intodetails of that, but one couple
of things that could happen iswe could improve therapeutic
indexes of existing therapies.
We could find new materialsthat could be better soluble, or
it could have different sort ofZ index, as we call it in
(47:28):
chemistry, which would require,say, 300 scientists or 100
scientists.
Here you could just have twoscientists who can do proper
prompt and let theexperimentation loop happen.
Right?
This is a fundamental sort ofshift in thinking about how we
do AI science, so AI-basedscience.
So that's one, and the secondpart I'll say is that I think
(47:53):
it's a bigger, bolder sort ofplan to what I think seven or
eight years ago, openai andDeepMind were saying is that if
we could tie reinforcementlearning with sort of a model
that can just generalize, well,on multimodality, what can we do
and I think it's a similar sortof question here is that if we
(48:13):
were to build this sort ofplatform and sort of create that
loop, what could happen?
So it's a very exciting thing.
I think you'll hear some reallywonderful things coming out of
this one, but I'm very superexcited about Lila.
Ben Comer (48:29):
Well, I'm not going to ask you to put a specific
timeline on when that superintelligence might emerge here,
but I do want to find out whatyou think AI-driven science, or
just new applications of AI,might be capable of in, say, the
next five years.
Sahir Ali (48:48):
Yeah, so in science, one of the things that, if you
think about the past, scientistsused to use rulers and then the
microscope, then increasinglyelectronic sort of tooling,
right the sequencing and all ofthat what is the scientists of
(49:09):
today and future is going to beable to do?
They will be able to use thesesort of platforms where they can
quickly iterate on an idea,quickly experiment.
They don't have to keep havingto pipe it, you know that sort
of the boreas sort of things.
So I think we're sort of at anearly stage of can we, if it has
(49:32):
taken humanity to go fromdiscovery of electrons to
electricity, to say transistors,60 years, can we sort of use
our creativity in the same waybut reduce that timeline by, say
, 10 years?
And that's what scientificsuperintelligence allowed us to
do.
That's my hope, that's the,that's the opportunity.
(49:55):
Space is that, yes, we, we havedone phenomenal progress, but it
comes with the time and lots ofsort of human skills and all of
that.
Scientific superintelligencejust really accelerates all of
that.
I think it could perhaps put usin a whole different path that
we just haven't.
And also remember, science is aunique field where it's not
(50:15):
about what the public data isright.
It's also about proprietary anddata that doesn't exist today
perhaps, and that's where Ithink that intersection of
automated labs and being able tohave an LLM capability, the
prompting to be able to do that,create that flywheel.
I think there's a lot there,but I think you know the market
Synthetic data will be importantyou think, yeah, synthetic data
(50:37):
will be important.
Yeah, synthetic data is, is, isextremely important.
I think you know you can seesome examples in pathology and
and and especially what we callimbalanced, uh areas of of data,
where you know there's only one, uh one case that is positive
out of you know, say, a hundredthousand, you know,
glioblastomas of the world.
Right, synthetic data could bevery supremely useful in
(50:58):
medicine, but in many other ways.
So I think, uh, that's that'skind of my hope, is that, um, I
think, and also there's a clearspace to build.
Right, while everybody'sexcited about generative ai in
general sense, and enterpriseand consumer and all, I think
this is a space ripe forinnovation.
Uh, capital markets.
I think, uh, what I callengineering of life has always
(51:19):
been the most important part tous.
Ben Comer (51:22):
So super intelligence in five years.
Sahir Ali (51:26):
Yeah, I mean, I don't know what to say about
predictions.
All predictions are bad.
So I will say that I thinkmaybe in five years we might
hopefully have a moment where wecan start to capture people's
imaginations and say that thisis something that ultimately
will make a material differenceto people.
(51:48):
Ultimately, this space thatmost of us are excited about,
investment, is because we haveto remember the direct
implications are that people'slives.
We have to remember the directimplications are the people's
lives.
We can save lives.
We can alleviate pain andsuffering that comes about with
these aggressive diseases, andit's not the person with the
disease but there's an entiresort of society, family that
(52:11):
suffers with it.
If we could create preventativeand curative medicines, it can
also lessen the burden onsociety, the financial toxicity,
and there's lots at play.
And so, again, we don't want tocreate technology for the sake
of technology, but what is itultimately going to help?
So I don't have a five-yearprediction or 10-year.
I'm hoping that in five to 10years we are going to start to
(52:34):
shift towards precision medicine.
Again, precision medicine isabout right data, right target,
right patient, and all of thathas to do with being very
precise.
Personalized, generative is theterm in there as well.
So platforms like Lila can gobeyond just medicine, but also
(52:55):
in material sciences.
But I'm more excited about Liladoing something in the space
that I'm more excited about andthen other companies that are
building not just ours but thewhole ecosystem.
I think we're in an inflectionpoint of what I think truly the
intersection of tech, bio andmedicine can do for us.
Ben Comer (53:14):
Yeah, and we're running short on time here, but
maybe my final question for you,sahir, is just about that the
future of tech and bio and maybe, getting back to Modi, why you
think new financial engineeringwill different approaches to
(53:37):
strategic investing as tech bio,you know as a kind of
individual industry, you knowcontinues to progress.
Sahir Ali (53:47):
Yeah, well, you know, as I said, the space has become
mature.
So now you can actually havesome interesting financial
engineering.
For example, private credits,royalty plays are sort of 10,
15-year concepts.
There are funds who are justlooking to underwrite the entire
phase three with differentfinancial stacks offering mix of
private credits, mix ofroyalties.
(54:11):
You come into early stage.
States like Texas, maybe someNorth Carolina, california, led
the way with stem cells back inthe day.
What roles do state play infunding while the national
funding is in some kind of adisarray?
So that has changed.
In fact, we are forced to thinkabout what does curative
(54:36):
medicine look like and how do weprice those right?
So the crispers of the you knowthe sickle cell and crisper,
one, treatment that can justreverse the disease, our, our
financial models forreimbursements and everything
else is focused on not curingbut managing the disease.
So so new ways of new.
(54:57):
We have to think about new ways.
Even in the venture capitalworld we have not had technology
that was moving at pace that is, in this space.
We've had the breakthroughs ofAlphaFold and we're building on
top of that.
We have artificial intelligencethat's just pretty much applied
across the board, everywhere.
So that is going to have a bigcompounding effect.
(55:19):
We're going to need to figureout how do we think about the
value of some of these assets ininteresting ways.
I mean, these are platformsthat we've never seen before.
Traditionally, we think ofbiotech as a certain molecule
and what kind of peak sales it'sgoing to have and a few other
things, but beyond that we'regoing to have we have new
(55:42):
technologies and new sort ofplatforms.
So there's an exciting,exciting space.
Also, I don't want to underscore, I want to underscore the
consumerization of health.
It is, it is absolutely here.
There is a generational shiftbetween Gen Zs and millennials.
You know, as myself being amillennial, we were considered
(56:02):
the online first.
Gen Zs are digital, everything.
They may be okay with the ideaof an AI doctor, than say you
and I, you know that's not theera, that's not what we've sort
of mentally tuned ourself.
But you think about the alpha.
I don't know what thegeneration is called before, but
after.
Gen Zs yeah, gen Alpha, yeah,gen Alpha.
(56:23):
So Gen Zs are now enteringworkspace and in the next five,
six years they will be inpositions where they're going to
start to make a decent amountof money.
There will be a big consumermarket.
Alpha Gen similarly.
So I think, if you think aboutthe social media's rise, it was
(56:44):
millennials and then taken overby Gen Zs.
So what is?
And I think in this space Imean I was reading a report just
two weeks ago that in Gen Zsand some later millennials
they're not drinking alcohol asmuch and this is the first time
in 90 years we've seen that sortof data- so, there's
consciousness of being healthy.
There is a digital first rise offunction health of the world.
I think consumerization ofhealth is a big part of my
prediction of the next five to10 years, and I'm taking some
(57:07):
bets there as well.
So let's not forget aboutconsumerization of health.
Ben Comer (57:10):
Well, I really enjoyed speaking with you.
Sahir, Thanks for being here.
Sahir Ali (57:15):
No, thanks for having me.
This was a great conversation,really enjoyed it, thanks for
being here.
Ben Comer (57:18):
No thanks for having me.
This was a great conversation.
Really enjoyed it.
We've been speaking with SahirAli, phd founder and general
partner at Modi Ventures.
I'm Ben Comer and you've justlistened to the Business of
Biotech.
Find us and subscribe anywhereyou listen to podcasts, and be
sure to check out our new weeklyvideocast of these
conversations every Monday underthe Business of Biotech tab at
Life Science Leader.
We'll see you next week andthanks, as always, for listening
(57:41):
.
Welcome back to the Business of Biotech.
I'm your host, Ben Comer, ChiefEditor at Life Science Leader,
and today I'm pleased to speakwith Sahir Ali, Ph.
D, founder and general partnerat Modi Ventures, a family
office focused on theintersection of technology and
biology.
Sahir is a young man to havealready led many professional
(00:28):
lives.
He worked on Wall Street duringhis undergraduate years.
He trained as a biomedicalresearcher, worked as a cancer
scientist and software engineer,became a sought-after
Salesforce architect and alsofound time to start a successful
e-commerce business with hisbrother, Amir Ali.
That doesn't cover everything.
(00:49):
We'll get some additional colorfrom Sahir about the
experiences that got him to thispoint, and we'll also learn
about Modi Ventures' uniqueinvestment model, how it builds
on the Markowitz model forportfolio organization and what
the future of tech, bio and AIin medicine might look like.
Sahir, thanks so much for beinghere.
(01:09):
I really appreciate it.
Sahir Ali (01:10):
Oh, happy to be here and thanks for all the
introduction there.
Ben Comer (01:15):
Yeah, absolutely.
I want to ask you a little bitabout some of that intro.
You were born in Pakistan andmoved to the U.
S.
-- Houston Texas, specifically-- as a child.
What sparked your interest inscience and technology initially
?
Sahir Ali (01:34):
Right.
Well, we moved here when I was10, 11 years old, and it was an
interesting time in late 90swhere, um, uh, I'd never seen a
computer before, in the sensethat it was never close by,
obviously seen a computer butnever had one in in my house.
(01:55):
So when we moved to houston, um, within a month, uh, we had a
computer, a compact presario andum so sort of.
It was an unattainable thingthat now was there.
It was gifted by my uncle.
That kind of just reallysparked curiosity on how it
actually worked, and so I thinkI went down the rabbit hole.
(02:17):
But one of the more, I would say, profound moments for me was
being part of FIRST Roboticsprogram that Dean Kamen started.
I don't know if many peoplewould recognize that program.
It was in late 80s.
Dean Kamen thought you know, inevery school all the athletes
are treated like stars.
You know you have pep rallies,you have cheerleaders, but what
(02:39):
about STEM kids?
They're the ones actually goingon changing the world in
science, technology, engineering, math.
We should treat them like stars.
So he started this programcalled FIRST Robotics and it's a
robotics competition.
It's the same concept as acompetition we should cheer the
kids on.
And so that's the program and Ibecame an alumni of that and in
many ways that was a big partof my growth and the curiosity
(03:05):
that led to being very technical, in fact, where where was that
program?
This program.
I mean the program was at, soif someone could be a coach that
could just recruit neighborhoodkids, it, it.
It was at school level.
In this particular case it wasat NASA level right as well,
like you know, just a communityservice.
(03:25):
It was different levels.
I was particularly going to anafter-school program at NASA,
but you know, in Clear Lake itwas walking distance.
But just so that everybody'saware, even today it's the
similar model Schools level.
Someone could become a coach.
You don't need to be atechnical, you just bring kids
together.
Yeah, so that's kind of thething and I always tell people
(03:46):
that by the time I was 15, I wasstill picking up English, but
my second language became C++.
So that's the kind of joke thatI tell people that the program
was quite influential for me.
But you know, that kind of gotme started on a technical
journey very early.
So that kind of got me startedon a technical journey very
early.
Ben Comer (04:04):
Do you think that you know?
There's been press reports andstuff about people coming out of
university with code, you knowextensive coding instruction,
having trouble finding a job, doyou?
I'm curious if you think thatyou know that interest in coding
early for you would have kindof the same results now.
(04:25):
Or do you think that we'regoing to get into AI later in
this conversation?
But do you think that codingyou know, just the technical
aspect and process of doing itwill become irrelevant as AI
gets stronger.
Sahir Ali (04:35):
Yeah, no, it's an interesting question.
So I don't describe what Ilearned during that time or one
of my formidable years.
It was not.
It was not coding, it was, Ithink, computational thinking
that's.
I would praise that and and yes, at the time it wasn't as
simple as even google was justaround, but it wasn't as easy as
(04:57):
just picking up just to writecode.
I think it was thecomputational thinking.
Where do you want it to applythat?
Um, the robotics, getting earlyin the robotics, you know,
brought that physical elementtogether where you can manifest
things.
But also on the software sideof things, you know, those days,
you know it.
Just, it was just a verydifferent time period where you
(05:17):
had to really go into thetechnical details to learn
anything.
For example, if you wanted tohost a website you wanted, you
had to understand how to telnet.
You know the term telnetdoesn't exist today.
For example, if you wanted tohost a website, you had to
understand how to telnet.
You know the term telnetdoesn't exist today.
You know SSH, most people wouldknow that.
So those things, you had to justbe very curious to go down in
the rabbit hole and really get afull stack, and so coding was
(05:38):
probably the end result of that,but you had to become very full
stack and so that sort ofcomputational mindset, being
able to very, very being curious, and had to find your own set
of knowledge or acquire set ofknowledge.
I think that was.
That is what we call humancuriosity and learning and all
of that.
So I mean, I don't think muchhas changed today.
Sure, you can vibe code and allof that, but just being able to
(06:00):
kind of go and go deep andunderstand, get yourself
technically well equipped to dosomething with it, I think that
still exists today, butdifferent times, I agree, of
course.
Ben Comer (06:11):
How did you end up on Wall Street as an undergraduate
?
Sahir Ali (06:15):
Yeah, so you know, before, a little before college,
you know, in high school, therewere a couple of interesting
things that I was alwaysinvolved with.
One was I had understood thatif I wanted to stand out,
technical chops were the way togo, clearly not a jock or
(06:35):
anything like that.
So that kind of was my moat, ifyou want to call it, and so I
was very curious on what all thetechnical applications were
there and I'd come across sortof quant as a field and you know
I Quantitative analysis is whatyou're referring to.
No, I'm actually particularlyreferring to be a quant as a
(06:58):
role on Wall Street.
Ben Comer (07:00):
Oh, okay.
Sahir Ali (07:01):
Yeah.
So, yes, quant is short forquantitative, but really quant
around 2006-ish time period wasreally referring to folks who
are just mathematical.
You know physics-oriented guyswho are creating mathematical
models at Wall Street and, youknow, at the time as a young in
my teenage years, to me itseemed very fascinating that
(07:22):
these guys were paid lots andlots of money and most people
didn't know who they were, andso there was an allure to that
and so I read a little bit andeventually, at Rutgers it turns
out Rutgers had a pretty prettywell-defined quant program for
grad school and engineeringelectrical engineering at some
point when I started tounderstand is, you know, it's
(07:42):
all signal.
This is pre-machine learningera.
So just, I think, knowingenough and knowing enough about
it at the freshman level and,you know, not many people even
knew what quants were.
I think that was kind of myforay into it and luckily, you
know, I ended up at an ultrainvestment group within Citi and
(08:02):
you know the rest of thehistory.
But that's kind of the sort ofprecursor to it.
It's just that again it comesdown to this just being
technical, full stack, just tobe able to know what I call
asymmetrical information.
What is it that you know andbeing able to use that that you
know, you can make yourselfstand out.
And for me, that just knowingenough about what a quant does,
(08:25):
what, what quant is, and beingable to position yourself as
your curiosity and interest is,at the end of the day, freshman
internships are all about justyou know how curious you are.
You're not supposed to knowmuch.
Ben Comer (08:37):
Yeah, yeah.
So you had some success, someearly success on Wall Street.
What?
What did you do next?
And we don't have to go throughyour entire bio, but I'm trying
to pull out some of the thingsthat you learned, some of the
experiences that you eventuallyare going to bring together to
create Modi.
Sahir Ali (08:55):
Yeah.
So a couple of things.
One is the quantitative mindsetwas developed in many ways
there.
How do you think about assetmanagement?
How do you think aboutportfolio constructions?
You know these sort of thingsearly on being in financial
(09:15):
services.
Also, it was an interesting timewhen I was there.
It was financial crisis in 2008and 2009.
And so that is a one.
It's a black swan moment, andso being in that in the space,
the kind of learnings you cansee and just just what you can
observe is is once in a lifetimeopportunity.
Yes, it was bad for the market,but, from a learning perspective
, what all that happened justbeing in that moment and there
(09:39):
and just you know fortunatelyfor me, I, my internship turned
into kind of a full-time gig.
So I was lucky enough that youknow, throughout my
undergraduate I had a BlackBerryand I was paid well, and so
just being in that environmentand just be able to learn and
the second thing is obviouslyquantitative trading, and all of
that had kind of come to a haltin that time period.
(10:00):
So I had an opportunity toadopt another technology.
At the time it was calledSalesforceforce completely
orthogonal.
However, salesforce 2008 and 9was sort of early days, and so I
picked up a new cloudtechnology and able to sort of
think about how do we buildpipelines and all of that, so.
So I think that so if you thinkabout, uh, how do you look at
the financial world, how do youthink portfolio, how do you
(10:22):
think about business and thenselling of that, the pipeline,
the lead to cash, and theneventually, at the same time,
when I went back to campus, Imean I was already doing some
computer vision research and allof that, and then I got really
interested in how do we applythe same quant mentality to, say
, cancer or medical imaging.
(10:44):
And that's where I joined a labthat, early on, one of the few
labs actually were looking atdigital pathology and such.
So anyways, that's where thethings start to sort of
intersect.
This idea of being sort ofquantitative stems from those
days.
Yeah, so you eventually reacheda point where you were in a
position to.
Ben Comer (11:00):
So you eventually reached a point where you were
in a position to establish ModiVentures.
Give us a sense this is afamily office.
You're making strategicinvestments.
Give us a sense of where themoney came from.
Was it just your successfulcareer up to that point to serve
as the basis for your initialinvestments, or what would you
(11:21):
say about that?
Sahir Ali (11:22):
Right, yes, we're lucky and blessed.
My family, which includes mybrothers, you know we're
entrepreneurial.
At the same time, it's veryacademic and we had companies in
the e-commerce space that didfantastically well, and again,
that's a story for some othertime.
However, that also includedbeing early in some technology,
(11:44):
looking at the world from a sortof a I like to call it
convergence what is reallyconverging and what the
derivatives will be that.
So we kind of set up a techplatform for that, had a couple
of other acquisitions that didwell, and especially in the year
2021, end of 2021, early 2022,there was a phenomenal shift
(12:09):
again in the financial andcapital markets where the
interest rates went up so muchthat it kind of disrupted the
capital markets.
(12:29):
But prior to that, duringpandemic, I was deeply thinking
about the venture capital world,because that is a world that I
was looking at it from anoutsider perspective.
We had made some investmentsbut really wasn't in the venture
capital world, if you want tosay.
I started to realize that Ithink it's become a pretty
mature asset class over the lastcouple of decades.
I think I'd written, I wasstaring at this chart where
somewhere in the orders of $30billion to $35 billion were
raised in totality in 2008 andin 2021, 190 billion dollars or
so in just that one year.
(12:50):
That was a phenomenal rise.
All this and that was sort ofgiven by what we call social
networks effect.
Right, you create an app andjust quickly distribute it and
you have users and the ad spaceand all of that, as I was, you
know, as a pandemic happened.
I said what's going to be?
What's going to be the nextthing for venture?
Obviously, the power law wasdriven by software.
(13:11):
That just scales very, veryquickly.
Obviously, the SaaS world andanything in the software world
is now saturated.
So what's going to come next?
And obviously AI was a thingthat was on top of my mind
because I was doing some deepresearch work using AI At the
time.
I was tracking what hadhappened with AlphaGo
reinforcement learning in 2021.
(13:33):
The AlphaFold had come out,obviously, 2017 paper attention
is all you need sort of thatencoder-decoder models we were
starting to go towards at thetime.
We were just calling itzero-shot learning, right, and
so everyone was very excitedabout that.
I thought that could be veryinteresting.
The second part I thought which,particularly in 2020, a chart
(13:53):
that started to float around wasthat how drastically the cost
of sequencing had come down.
This was perhaps the mostsignificant cost reduction of
any technology that we have seen.
It went from $3 billion, so tospeak, to finish human genome to
$300 by pandemic, which is 2020.
And it played a role.
You know, sequencing technology, the PCR, played a role in
(14:17):
overcoming the COVID pandemic.
And then the third part, youknow, which led to digital
biology and all of that, andwe'll talk about that more.
And the third is, you know, Ithink in the last 20 years,
we've had some phenomenalprogress in what we call
cellular programming, with thetools like CRISPR, base pair
editing, ips, stem cells, and soI thought, you know, this field
(14:38):
of biology is, in many ways,have started to become very deep
, tech-ish engineering, and Ithought that was a real
opportunity to be in that space,while everybody else would just
purely get excited aboutgenerative AI and all of that.
So, anyways, so I was thinkingabout that.
And then the other part of thequant mindset comes in is that
clearly there's a gap in venturecapital world where we can
create a different type ofportfolio.
(14:59):
So, anyways, that's the impetusto a lot of that, and Modi
Ventures really stems from that.
It is not yet another venturefund.
It thinks about the portfolioconstruction very differently,
in a quant manner.
It thinks about theintersection of technologies
that I just mentioned to you,which I've started to call
biostack, and we'll go into that.
And third part was how do weactually start to think about
(15:23):
risk modulation in venturecapital?
Because, as most of us know,that venture capital is very
volatile but it operates onpower law, which means you have
to be okay with extreme amountof volatility, which means 90%
of the companies might just goto zero and 10% successful.
So, anyways, you combine all ofthis.
That's kind of the impetus toModi Ventures and luckily we
(15:44):
were able to raise our firstfund very quickly and you know,
as you know, we just announcedour second fund two and a half
years later.
Ben Comer (15:52):
Yeah, and I want to.
I want to get into ModiVentures and your investment
framework in just a second.
But I'm curious about, you know, the the establishment of Modi
Ventures in Houston, aside fromyour childhood experiences there
, what kind of drew you back toHouston and maybe what would you
say about the Houston lifesciences ecosystem?
(16:15):
It's not, you know, it's notone that is often, you know,
talked about along the samelines of you know, san Diego or
Cambridge, or even New York.
Sahir Ali (16:26):
Yeah.
So I'm glad you actually askedthis question.
Why Houston, besides my ownpersonal connection to it,
family connection?
Well, houston, well, let's.
Let's start with the basic.
First of all, houston is hometo the largest medical complex
in the world Texas MedicalCenter.
I think most people may notrealize this, that 120,000
people go to work in that smalldistrict here which, if you have
(16:50):
an aerial view of Houston,you're going to start to see
three downtown looking areas.
One is the core downtown ofHouston.
The other is the financialdistrict where the which we call
the private equity world andeverything else called
Westheimer, and the other is TMC.
It's high rises in all hospitalsystems.
You know this area does moreheart surgeries than anybody
else.
It has the best cancer caretherapies.
(17:14):
You know, obviously, mdAnderson is part of this and
also, in many ways, one out ofthree approved oncology drugs in
FDA, the clinical trials happenin that area.
So, effectively, what you haveis a very deep, complex arc of
what we call applied medicine.
What it has lacked, typically,is the, I would say, the bottom
(17:39):
arc, which is company creation.
Innovation is there, it's justthat the capital may not be
there.
And so how do we turn this intoa full flywheel of life science
where you have the large humandata, you have the clinical
trials, you have the know-how ofpracticing medicine.
(17:59):
So the key thing is how do weturn that innovative wheel on,
how do we bring in the rightcapital, how do we start
companies here?
And that is really what Houstonoffers, and I think it is under
the radar and I think it'sgoing to do really well.
Lastly, the state of Texas hasalso contributed to sort of how
(18:21):
do we advance this?
For example, there's a grantsmechanism called SIPRIT, which
is for cancer and oncology.
It's a $3 billion pool of statemoney available to companies,
private companies in Texas $100million a year up to $100
million a year in funding.
They can also match privateinvestment two to one.
(18:42):
It similarly is proposed forDIPRIT, which is for dementia $3
billion again.
And so you also have that.
We have something called HelixPark, the Texas Medical Center
Initiative.
I don't know the acreage, butit's a multi, multi-billion
dollar development plan that'songoing.
At the moment we're sitting inone of those buildings.
It is all just about biotechinnovation here.
(19:05):
And also, lastly, I'll say I'mon the board of something called
Rebel, which is spun out of theRise Biotech Launchpad.
It's going to create incubatecompanies that are going to be
at the intersection of drugdelivery mechanisms with tech
bio.
So, anyways, there's lots goingon here and I thought this is a
real opportunity to buildsomething which is probably
(19:30):
going to have an inflectionpoint.
So, selfishly, this is not justa personal connection, houston,
but I think there's a lot herethat we could do with private
capital, innovation and turnthat into a flywheel, so I'm
very bullish on that.
Ben Comer (19:46):
Let's talk about Modi Ventures investment framework I
mentioned.
It's based, at least in part,on the Markowitz model.
You mentioned previously thatyou wanted to set something up
that was kind of fundamentallydifferent from other VC groups.
What could you say about ModiVentures investment framework, I
(20:07):
guess, and maybe how you thinkdifferently about risk and
return, especially in biotech?
Sahir Ali (20:14):
All right.
So you know, and again I'll goback to 2021.
So there was a couple ofinteresting observations for me.
One was that if you look at theventure capital world, everyone
talks about power law, whicheffectively just basically comes
(20:35):
down to is that there's goingto be a very handful of
companies that will drive thereturns.
Rest will just go to zero andthat's a very that, even as a
mathematical distribution,that's a very sort of a volatile
way of looking at things.
In fact, you look at overallevery asset class available in
the market.
Venture capital tends to behigh risk, high reward, and so
(20:57):
the couple of things I thoughtof was that, if you think about
90% of the money that actuallyfunds venture capital, that
those entities are not generallywhat you would associate with
high risk, high return sort ofprofile, like endowments and
pensions 401ks yeah.
Yeah, they.
You know it's public money.
However, they do have exposureto venture capital.
(21:18):
I mean, in fact, some of thecollege endowments, the Yale
model.
They're up to 10%, 14%.
So I was like, okay, well, howdo they think about this space?
And turns out that they may notassociate such high volatility
to venture capital is becausethey don't invest directly into
companies.
They invest into fund managerswho are extremely established.
In fact, if you want to get 50,$60 million from a pension fund
(21:41):
or endowment fund, you got tobe on your sixth or seventh or
eighth fund.
You know you got to be veryestablished funds to be able to
show extremely tight record.
So I thought they're looking atthis space as in the venture
capital world lower risk andperhaps also lower return,
because as the funds go, youknow the larger funds you're not
(22:03):
returning, the proverbial 10Xers, right, it's just a law of
large numbers and also Cambridgereport, year after year,
identifies that.
So that's one 90% of the moneythat funds the venture capital
are in established funds andthose.
The second is there's anothersort of what I call an asset
within the VC world, which isdirect investments.
You can go invest directly intoa company and if that company
(22:26):
goes on to become a verysuccessful one, you can find
yourself into 100x or 200x mixes, but of course the outcome is
very binary there, and you could.
You know that's where the powerlaw really comes in.
And so I thought you know whenyou think about.
What Markowitz was talkingabout is that if you look at the
various assets that areavailable in the market and you
(22:49):
look at the base case, which isa stock and a bond problem, a
bond tends to be riskless butyou know very low risk but low
return, stock volatile, but itcould give you a high return.
But if you have sort of thethis, if the equation of market
is based on correlation and youknow the risk return profile, if
you could create anuncorrelated baskets of those,
(23:09):
you could technically bend thecurve.
What that means is, if you canmix them up, you could take less
risk than your lowrisk assetand get a higher return.
And so that's where I thoughtif we could do Modi Ventures in
a way that we could mix theright limited partnership
positions with directinvestments in a unified
(23:31):
framework and we'll come to itwhat that framework is from
investing on a biostack then Ithought there was an opportunity
to apply a bit of quantitativemodel where we can modulate the
risk and get a pretty decentreturn and don't have to play
what I call in a power law spacewhere plenty of people are.
So that is the fundamentalframework, without getting into
a lot of the more proprietarydetails, but that's the mindset
(23:53):
and the framework.
So it's not a fund of funds assuch, but it's also not a
co-investment in such.
There's a fundamentalquantitative way of sort of
looking at this and inspired bythe Markowitz or the Fish and
Frontier in the venture world.
And then I thought if you havethis sort of framework, what's
worthwhile to look at is thebiospace.
(24:14):
Why is that?
Because I don't want to use theword biotech, but when it comes
to bio and medicineparticularly, it has a very
interesting spectrum, right?
I'll give an example here.
When you look at any techcompany, which is primarily what
venture capital is driven by,it has seed series A, series B,
series C.
These are sort of series thatdefine some kind of product
(24:37):
market fit tied to how muchmoney can you raise.
However, if you think about thebio world, some of the
attractions defined by yourexperimentations and clinical
outcomes, right.
So you go from lab to IND, youput your lead candidate in, then
you have phase one, you havephase two, phase three and then
market approval.
Actually, it turns out at phaseone, phase two, phase three you
(24:57):
can actually have somedeterministic outcomes.
You can actually say at phasetwo who could be a potential
buyer, what could be the peaksales.
You could do thosedeterministic sort of outcomes,
and so that actually is veryinteresting than just purely
tech, where series A doesn'tmean much in terms of what your
outcome could be.
And then, if you look at phasethree plus, you have royalty
(25:17):
funds right.
There are ideas that you canactually partner with a biotech
company in exchange forroyalties rather than equities.
So I thought you take thisbiospectrum and then you apply
that framework that I just saida mix of LP positions and
directs and how do you capturethe entire spectrum?
That's where the bending of thecurve happens, and I thought
(25:38):
this framework just lent itselfvery well because you have
different assets, a differentvolatility, different and could
be uncorrelated.
Ben Comer (25:46):
Now, to do that, did you have to create it's part of
Modi Ventures a proprietarysoftware that you're able to do
that kind of analysis across theindustry?
Sahir Ali (25:56):
Yes, so I wouldn't call it a software.
It's not like packaged thingwhere I can just give it to
somebody, but there is some deepanalysis and modeling as such.
So I'll give you one example.
When you think about, there arehundreds of funds.
In fact, last I looked at itthere are close to a thousand
biotech funds even right Around.
(26:18):
And the key thing is for me, howdo you find uncorrelated funds
that you want to?
So, for example, typically whathappens with fund of funds, the
asset managers who run fund offunds, they go after the top
managers, and my sort ofhypothesis there was that these
fund of funds run by, say, largebanks or the asset managers,
they tend to be in verycorrelated funds.
(26:39):
What I mean is they tend to bein very correlated funds.
What I mean is they tend to goafter the top funds and they
kind of end up investing in eachother's syndicates.
Someone led a series B, someonewill come after series C, and
that I'd call it sort of acorrelated sort of outcome.
So how do you actually modelsomething where you can start to
see uncorrelated funds?
And one thing that has what Ihaven't talked about is one data
(27:01):
structure, as we call it incomputer science or
computational algorithm that hasdriven everything I've done
across I would say the work atWall Street, in Salesforce, in
cancer research and AI has beengraph algorithm.
How do you think about theworld in a network graph?
So we so the proprietary thingI'm talking about is that we did
.
We looked at all the venturecapital funding in the last 10
(27:24):
years at the time in 2021 andgot our hands on some data and
we looked, we mapped it out andwe created a graph network of
that and applied a graphalgorithm and we start to see
where the uncorrelated nodes areand the node is represented by
a fund.
To see where the uncorrelatednodes are and the node is
represented by a fund.
And so that's where aninteresting way of actually
looking at okay, well, if a fundthat is clustered together with
(27:48):
other funds, we don't want togo and invest in more of those,
we can just invest on one ofthose and then go after an
uncorrelated node which is in aspatial distance away from that,
and so what that allows you todo is get uncorrelated sort of
basket of these funds.
So that's one example I cangive you on how the quantitative
analysis and modeling actuallycomes in handy.
(28:09):
And looking at even just thefunds world, which one, which
funds, we want to invest in?
Yeah, and what's the outcome ofthat?
I'll give you a more tangibleoutcome of that.
So, for example, we invested ina fund that purely only looks
at phase two plus assets.
We also have a fund that onlydoes sort of royalty investments
(28:32):
, meaning after phase three.
We have a fund that looks atcompletely probably the most
earliest investments that arespinning out of colleges and
universities and pre-IND, so tospeak.
We have a fund that is quitegeneralist but 28% of their
portfolio looks at digitalhealth and medtech and all of
that which is not governed bythat biospectrum, but it is in
(28:54):
the biospace.
We have a fund that 33% oftheir allocations are in
computational bio, the techbiospace.
We call it the AI, drugdiscovery, the computational
nature of those things.
So you can start to see how doyou find those funds that are
uncorrelated, and that one partis it allows us to look at where
they fit into the grand schemewhich I call a network.
Ben Comer (29:14):
So by cobbling together these uncorrelated
funds.
That's how you get to whatyou've described as bending the
curve, meaning that it's asimilar amount of risk.
However, you potentially have agreater endpoint.
You have a better result.
Is that what you're saying?
Sahir Ali (29:35):
Yes, partially correct.
However, the bending of thecurve happens by mixing lower
risk assets, which I will bundleas limited partnership
positions, with directinvestments, where we're also
making direct investments.
So think about it like a bondin a stock.
That's where the so modern dayportfolio, the wealth manager,
will say we'll do 60% in stocks,40% in bonds or 20% bond.
(29:57):
It allows you to modulate yourportfolio to say is it a low
risk?
It's a moderate risk, a highrisk, right?
So that's one framing, evenwithin the LP positions that I
described to you.
Not every fund carries the samelevel of risk.
For example, we are LPs inCoastal Ventures.
I would consider that as alower risk investment
established funds.
However, we do have a couple ofemerging managers.
(30:19):
I would consider them a littlebit higher risk.
However, if you compare theboth, one has a higher
propensity, if they'resuccessful, to deliver more
higher returns and outsizedreturns than, say, a
billion-dollar fund.
It's a reality and so it's arisk-return profile, even within
the limited-partisan position.
But we also take that as a fullportfolio approach of LP
(30:42):
positions and directs.
Typically, what you see in theindustry is a fund of funds that
might just offer co-investmentswith the funds they work with.
In fact turns out our directinvestments are not correlated
with the funds we're in.
We don't go and double click ontheir companies as much we have
our own views of the world.
Yes, once in a while there isan overlap because that's the
relationships we have.
(31:02):
But it turns out in fund one weonly were correlated with the
funds we invested in.
The companies they have lessthan 12%.
Ben Comer (31:11):
Yeah, that's really interesting because, of course,
you do also have the directinvestment in exceedingly risky
assets in the you know meaningcompanies, early stage biotech
companies are.
Well, let me ask you this youobjected to the word biotech.
Do you prefer tech, bio, orwhat do you dislike about
biotech?
No objection, it's just that.
Sahir Ali (31:31):
You know.
I think it's just all aboutframing.
I'm starting to call itinvesting on the biostack rather
than biotech or tech bio, andI'll tell you why.
So the way I describe biostack,like anybody else would, is the
DNA, rna, protein, cells,tissues, organs and organism.
So that's your stack and I'mtaking a page sort of a tech
(31:58):
stack, right From electrons tosilicon wafers, to the
transistors, to the chip, to themotherboard, the operating
system, the computers and thecloud, and you know the whole
stack.
In fact, if you were a venturecapitalist, you invested in some
parts of that stack to get somediversification and all.
(32:18):
So you know you made someinvestments in the
infrastructure layer of thecloud.
You made some investments inthe software layer, you made
some investments.
So I think, although you couldmake that separate layers here,
I feel like because the space ofbio and medicine is having a
converging moment, the betterframework is the bio stack.
(32:39):
I'm saying this is not true foreverybody else the better
framework is the biostack.
This is I'm saying this is nottrue for everybody else, but at
least for us.
So if you looked at the stack,we have profound sort of
convergence and breakthroughs ateach of the stacks.
So, for example, at the DNAlevel, we call it genomics,
being able to read what's in theDNA.
That's a read breakthrough.
That happened, you know, withthe rise of DNA sequencing, ngs
(33:02):
and all of that.
But we can also write on thatstack now through CRISPR.
So we have a read and writebreakthrough.
Now we go up the stack on RNA.
We have RNA sequencing, quantumscience, a bunch of companies
being able to read, but we alsohave companies who have
foundational models that canpredict the structures of the
RNA.
But we can write at the RNAstack as well.
By the way, most people knowthat technology, that was mRNA.
So I can make a case that theentire stack has now read and
(33:26):
write breakthroughs.
We can actually create digitalbiology, use digital biology and
at each stack you have massiveamount of sequences of data
coming out.
This is where LLM technology isextremely exciting to say.
Well, we havesequence-to-sequence
breakthrough in computer science.
We have digital biologybreakthroughs.
We're understanding.
We're starting to understandthe cell.
You know there are a bunch ofcompanies pursuing virtual cell
(33:47):
concept.
We can start to think aboutbiology as an engineering
principle, systems biology.
The stack is the guidingframework.
Now I'll give you one lastexample, in the protein layer.
So some of the investmentswe've done so at the protein
layer, so some of theinvestments we've done so at the
protein stack.
We have a company calledgenerate biomedicine right, it's
pursuing the novel proteindesigns, generative, ai based,
um uh therapeutics that are atthe protein level, right.
(34:10):
So so that's, that's, that'syour traditional tech, biocon
kind of company.
But we also have an investmentin a company called unnatural
product that looks atmacrocyclic peptides.
These are peptides that arebetween small and large
molecules, but you can engineerthem to have a permeability in
the cell like a small molecule,or they can bind like a large
molecule.
(34:31):
That's a new sort of modality ofdrugs, as Merck is in there and
in fact the CEO of Merck abouta year and a half ago said this
will be the new class of drugs.
So you can see that they'reboth kind of uncorrelated, they
both kind of use AI, but verydifferent to each other.
The last example I'll say onthe protein is we have an
investment in a company calledAtherBio which looks at the
enzyme space and say how do wereduce it down using
(34:52):
computational techniques to dosomething in manufacturing space
?
Because enzymes are thebuilding blocks for us.
We can take that down inmanufacturing, say for removal
of PFS, low-grade brimes oflithium to high-grade, that sort
of speak.
So that allows you a frameworkto say I'm going to target a
layer and make some uncorrelatedbets.
So that's how we think about itand at the organism level, by
(35:16):
the way, we call it the health.
Ben Comer (35:18):
So some digital health and medtech fits there,
got it.
So, in thinking about thecompanies that you're making
direct investments into, are you, do you see those companies as
being, you know, at one layer oranother of the bio stack?
Is that you know how?
Yeah, and that's how you areyou trying to balance it
throughout the stack, or arethere, you know, certain parts
of the stack that you're moreinterested than others?
You know?
Sahir Ali (35:38):
that's a good question of the stack that
you're more interested thanothers.
You know that's a good question.
I don't think I was.
It's, ultimately what we haveto see is where the returns are
going to come from at the end ofthe day.
And so I didn't really, I don'treally think about what part of
the stack that I think it'sgoing to be.
It's not like the tech stackwhere the software just made a
lot of sense.
Now you know it had thedistribution, so what this
(35:59):
framework is.
In the hindsight it seemed likeour stack turned into more of a
protein sort of layer, becamelike the bell, if you were to
imagine the bell curve and theorganism level where we start to
make some deep tech investmentsinto, say, a dental robotics
company or a company that cantake digital auscultation and
turn it into ambient capabilityand things like that, right.
(36:22):
So I don't have to necessarilysee which stack becomes more
proliferating, but that stack iswhere we want to be able to
deploy, which also allows us tobe uncorrelated.
That's what I keep highlighting, that term which, by the way,
if you are, by virtue of that,you're also diversified.
Now one could also say that,hey, you're just diversified,
now it's the one.
One could also say that, hey,you're just too broad.
It's a fair thing, but our mathmodel is also very different
(36:45):
and so in that sense, it's okayfor us to be quite broad.
Uh, and you know, I'll also saythat we don't have to worry too
much about the market trends.
For example, you know funds whojust do a lot of therapeutics.
They tend to be very muchfocused on what a pharma is
going to acquire in next coupleof years, and so you get very
correlated to that.
So in and one bad phase threethat failed, and then the entire
(37:09):
field sort of takes a setback.
What we have here is it allowsus to be resilient, a portfolio,
to be resilient to to some ofthose forces, because you know,
as we both know and everybodyknows, that biology, investing
in bio world and biomedicines isnot just risky but also
uncertain.
Right Investors know how tomanage the risk.
(37:29):
It's not the risk part thatscares people away from this
space, it's the uncertainty part, because biology is uncertain.
And so this allows us, thisbiostack framework, allows us to
be a bit, build a resilientportfolio, other than you know
the mix, the quantitativeframework of LP positions and
directs, which takes the wholething and takes this risky space
(37:50):
and gives a basket of low-riskassets which are limited
partnership positions.
Ben Comer (37:55):
Right, so here is there anything else you could
say about the criteria that youuse to select the companies that
you're going to make directinvestments into?
Sahir Ali (38:05):
Yes.
So this is where I would liketo redefine what I call tech bio
a little bit.
What I say, that I like to seehow our companies are at the
intersection of these threefundamental technologies that
are converging.
So one is, as I mentioned, Icall it sequencing-based digital
(38:27):
biology.
Right, so we can now digitizethe biostack, as I said.
And so anyone that's buildingsomething deeper in that tech
for example, one of thecompanies we're invested in is
called Glyphic Bio.
That is built on top ofnanopores, that can do more
accurate protein sequencingworthwhile building that space,
because protein sequencing isgoing to have a huge sort of
market, um, and so that's that'ssort of uh, anything that is
(38:51):
touching the sequencing anddigital biology.
Uh, it could be a softwarecompany, it could be an
analytics company, it could besomeone's that doing something
in liquid biopsy and so thosesort of things.
But once you have thosesequences one of the biggest
obviously not many people arenot convinced that there's a
role of artificial intelligenceon that biostack, a profound one
(39:12):
, right From not just drugdiscovery and design but being
able to understand causality ofdisease, for example, one thing
we've known is from Alzheimer's.
What are some of the variants atthe genetics level right.
Machine learning has played akey role to understand APOE
variants.
We have AI in medicine,radiology, pathology.
(39:33):
We're starting to use AI inphysical settings that we never
thought was possible or we havenever done but we're not
possible, but haven't doneambient AI between the doctor
and a patient, thoseconversations.
So AI has a profound role inthe entire biostack, from
organism down to DNA.
So that's the second sort of ifI was thinking about, if you
could visualize a Venn diagram,so sequencing artificial
(39:56):
intelligence.
And the third intersectingcircle is that, for the first
time you know since we haveunderstood in mid-1800s that
human disease effectively isabout cells being lost and
malfunctioning and theunderstanding that we need to
control the cell.
And if you can control the cell, we can control the system's
(40:16):
biology with it.
The cell programming space isvery, very interesting If it
combines with digital biology,ai, the idea that we can now
have tooling to go andmanipulate and control the cells
, such as the CRISPR, the geneediting, the viral vectors, gene
therapies, ips, stem cells.
So I'd like to invest in thatsort of intersection.
(40:37):
So the criteria could be thatyou could be creating new
therapeutics modality it's fine.
You could be a platform companythat brings a certain platform.
But again, you've got to builda platform, not for the sake of
building a platform, but thinkabout who those partnerships are
very early on.
Is there a partnership that youcan think about?
(40:58):
Who can help build you withthat?
One of the learnings forTechBio world has been is that
platform can be very exciting,hugely exciting, but if there
aren't any partners and at theend of the customers, it's a.
It's an interesting learning.
So you know we all adopt fromthat we can look at.
You know, how do we actuallyhave curative things, for
(41:18):
example?
You know how do we start tothink about cures?
How do we think about reversingdisease?
How do we think about diseasemodifying stuff?
So that is again part of thebiostack.
And lastly, I'll say that AI inmedicine and AI in healthcare is
(41:39):
very different than AI inmedicine.
In my opinion, healthcare iswhen you have to deliver care
and all.
It's a very difficult space.
We tend to stay away from that,not saying we don't do it.
But AI in medicine what can wedo from troves of data in
medical imaging and everythingelse?
What can we actually turn theminto digital biomarkers?
(42:00):
How do we enable oncologists tosay here's a risk score, here's
how you better risk stratifythese patients.
How do we identify forpharmaceuticals that companion
diagnostics, that works onrudimentary imaging HNE images,
radiological images right, theseare some real opportunities to
(42:21):
have and real marketopportunities as well.
So this is worth deploying for.
Yeah, so these are some of thecriteria.
I mean it's a very broad sport,but again, you need something
like a biostack to kind of helpyou frame that.
So it all comes down to thatsort of stack.
Ben Comer (42:36):
You mentioned Rebel a minute ago and I did want to
just ask a quick question aboutthat organization.
I saw the announcement.
Modi made a strategicinvestment in partnership with
Rice University.
What caught my eye was that thecompany is developing
intelligent bioelectronictherapeutics.
I wanted to get you to tell mewhat those might be.
Sahir Ali (42:59):
Yes, well, you know it's offensive word saying
combination therapies, and sohere's, you have a therapeutic
along with something that couldbe a device, a digital device,
and these are, this could be acombination of those.
So, drug delivery mechanisms.
And how do we, how do we embed,maybe, a device that can
control regulation of the blood?
(43:20):
Uh, sorry, a drug in turn?
Uh, I you know if we could, ifwe could have interesting
devices that control, uh, how dowe actually start creating new
proteins within in vivo?
I think these are, these areall the sort of purview of of
that incubation in the venturestudio, and I think these are
all the sort of purview of thatincubation in the venture studio
, and I think these are thingsthat are worth building for,
(43:42):
because this could have a huge,tremendous impact on some of the
very aggressive diseases andareas today that we just perhaps
don't even have righttreatments.
And it's a bold one.
It comes from Rice.
Omid is a fantastic scientistand a PI at RISE.
Some of the technologies thatthey've developed, you know 100
(44:06):
plus patents.
How do we turn those intocompanies?
And that's going back to what Iwas saying the opportunity
space in Houston.
There's fantastic scientistshere.
In fact, there are institutionswho have done over the lifetime
100 plus INDs.
It's just that I think we'rejust trying to put together an
innovative sort of factory orlifecycle or a flywheel, and so
(44:27):
I'm just playing my small partin this and I think you'll see
some exciting things come out ofRebel.
So very excited about that andexcited about our investment
there as well.
And plus I'm very committed tobuilding the ecosystem in
Houston in ways we can, and Ithink Rebel in many ways is
thinking about very similar tohow Boston biotechs have in the
(44:49):
last couple of decades right theflagships and the third rock of
the world.
I think there's opportunity todo something here, but also take
some learnings along the way todo something here, but also
take some learnings along theway, I wanted to ask you about
one of the other companies thatyou've invested in Lila Sciences
.
Ben Comer (45:04):
They're working to develop scientific super
intelligence.
What would you say about that?
Sahir Ali (45:14):
Yeah, that's probably the most interesting and
exciting investment, just givenwhere the markets, where the I
guess the general exuberance is.
I mean, if you think about thecurrent state of AI, right since
2020, when the chat GPT sort oftook everybody's imagination,
what we have done very well isin what I call in the in silico
(45:35):
world, in the software softwareworld, if someone was to prompt
something magically, you know, aset of sequences come out that
appear like an essay, it appearlike an analysis of something,
whatever you want to frame it,as it can generate a movie, it
can generate images, it canoperate on images and
understands again, from pixel toto text, to everything,
(45:59):
anything that can turn into abunch of sequences.
Great, either llms or thesediffusion models just tend to do
very well science.
So, if you want to make a superintelligence, on the science
side of things, though, sciencehas a different sort of a
flywheel or a path where someonehas to create a hypothesis,
that has to go and experimentphysical experimentation,
(46:20):
physical access to the world tobe able to do that, and then you
collect data, you analyze itand then you repeat that loop.
So if we were to think aboutwhat is the role of agentic AI
or any sort of AI framework increating science.
So you have to create somethingcalled a super intelligence,
and that super intelligence hasto have access to the physical
world.
And that's what Laila's sort ofcore thesis is that if we have a
(46:45):
super intelligence layer or AIlayer, which is a software layer
, but what if it had access tosomething called AI factories
where it can actually conductexperimentations on that stack
from maybe DNA RNA materialsciences, on that stack from
maybe DNA RNA material sciences,how do we turn that into sort
of a full, sort of an AI factory?
(47:07):
And so what could be thebusiness models of that?
I mean, I don't want to go intodetails of that, but one couple
of things that could happen iswe could improve therapeutic
indexes of existing therapies.
We could find new materialsthat could be better soluble, or
it could have different sort ofZ index, as we call it in
(47:28):
chemistry, which would require,say, 300 scientists or 100
scientists.
Here you could just have twoscientists who can do proper
prompt and let theexperimentation loop happen.
Right?
This is a fundamental sort ofshift in thinking about how we
do AI science, so AI-basedscience.
So that's one, and the secondpart I'll say is that I think
(47:53):
it's a bigger, bolder sort ofplan to what I think seven or
eight years ago, openai andDeepMind were saying is that if
we could tie reinforcementlearning with sort of a model
that can just generalize, well,on multimodality, what can we do
and I think it's a similar sortof question here is that if we
(48:13):
were to build this sort ofplatform and sort of create that
loop, what could happen?
So it's a very exciting thing.
I think you'll hear some reallywonderful things coming out of
this one, but I'm very superexcited about Lila.
Ben Comer (48:29):
Well, I'm not going to ask you to put a specific
timeline on when that superintelligence might emerge here,
but I do want to find out whatyou think AI-driven science, or
just new applications of AI,might be capable of in, say, the
next five years.
Sahir Ali (48:48):
Yeah, so in science, one of the things that, if you
think about the past, scientistsused to use rulers and then the
microscope, then increasinglyelectronic sort of tooling,
right the sequencing and all ofthat what is the scientists of
(49:09):
today and future is going to beable to do?
They will be able to use thesesort of platforms where they can
quickly iterate on an idea,quickly experiment.
They don't have to keep havingto pipe it, you know that sort
of the boreas sort of things.
So I think we're sort of at anearly stage of can we, if it has
(49:32):
taken humanity to go fromdiscovery of electrons to
electricity, to say transistors,60 years, can we sort of use
our creativity in the same waybut reduce that timeline by, say
, 10 years?
And that's what scientificsuperintelligence allowed us to
do.
That's my hope, that's the,that's the opportunity.
(49:55):
Space is that, yes, we, we havedone phenomenal progress, but it
comes with the time and lots ofsort of human skills and all of
that.
Scientific superintelligencejust really accelerates all of
that.
I think it could perhaps put usin a whole different path that
we just haven't.
And also remember, science is aunique field where it's not
(50:15):
about what the public data isright.
It's also about proprietary anddata that doesn't exist today
perhaps, and that's where Ithink that intersection of
automated labs and being able tohave an LLM capability, the
prompting to be able to do that,create that flywheel.
I think there's a lot there,but I think you know the market
Synthetic data will be importantyou think, yeah, synthetic data
(50:37):
will be important.
Yeah, synthetic data is, is, isextremely important.
I think you know you can seesome examples in pathology and
and and especially what we callimbalanced, uh areas of of data,
where you know there's only one, uh one case that is positive
out of you know, say, a hundredthousand, you know,
glioblastomas of the world.
Right, synthetic data could bevery supremely useful in
(50:58):
medicine, but in many other ways.
So I think, uh, that's that'skind of my hope, is that, um, I
think, and also there's a clearspace to build.
Right, while everybody'sexcited about generative ai in
general sense, and enterpriseand consumer and all, I think
this is a space ripe forinnovation.
Uh, capital markets.
I think, uh, what I callengineering of life has always
(51:19):
been the most important part tous.
Ben Comer (51:22):
So super intelligence in five years.
Sahir Ali (51:26):
Yeah, I mean, I don't know what to say about
predictions.
All predictions are bad.
So I will say that I thinkmaybe in five years we might
hopefully have a moment where wecan start to capture people's
imaginations and say that thisis something that ultimately
will make a material differenceto people.
(51:48):
Ultimately, this space thatmost of us are excited about,
investment, is because we haveto remember the direct
implications are that people'slives.
We have to remember the directimplications are the people's
lives.
We can save lives.
We can alleviate pain andsuffering that comes about with
these aggressive diseases, andit's not the person with the
disease but there's an entiresort of society, family that
(52:11):
suffers with it.
If we could create preventativeand curative medicines, it can
also lessen the burden onsociety, the financial toxicity,
and there's lots at play.
And so, again, we don't want tocreate technology for the sake
of technology, but what is itultimately going to help?
So I don't have a five-yearprediction or 10-year.
I'm hoping that in five to 10years we are going to start to
(52:34):
shift towards precision medicine.
Again, precision medicine isabout right data, right target,
right patient, and all of thathas to do with being very
precise.
Personalized, generative is theterm in there as well.
So platforms like Lila can gobeyond just medicine, but also
(52:55):
in material sciences.
But I'm more excited about Liladoing something in the space
that I'm more excited about andthen other companies that are
building not just ours but thewhole ecosystem.
I think we're in an inflectionpoint of what I think truly the
intersection of tech, bio andmedicine can do for us.
Ben Comer (53:14):
Yeah, and we're running short on time here, but
maybe my final question for you,sahir, is just about that the
future of tech and bio and maybe, getting back to Modi, why you
think new financial engineeringwill different approaches to
(53:37):
strategic investing as tech bio,you know as a kind of
individual industry, you knowcontinues to progress.
Sahir Ali (53:47):
Yeah, well, you know, as I said, the space has become
mature.
So now you can actually havesome interesting financial
engineering.
For example, private credits,royalty plays are sort of 10,
15-year concepts.
There are funds who are justlooking to underwrite the entire
phase three with differentfinancial stacks offering mix of
private credits, mix ofroyalties.
(54:11):
You come into early stage.
States like Texas, maybe someNorth Carolina, california, led
the way with stem cells back inthe day.
What roles do state play infunding while the national
funding is in some kind of adisarray?
So that has changed.
In fact, we are forced to thinkabout what does curative
(54:36):
medicine look like and how do weprice those right?
So the crispers of the you knowthe sickle cell and crisper,
one, treatment that can justreverse the disease, our, our
financial models forreimbursements and everything
else is focused on not curingbut managing the disease.
So so new ways of new.
(54:57):
We have to think about new ways.
Even in the venture capitalworld we have not had technology
that was moving at pace that is, in this space.
We've had the breakthroughs ofAlphaFold and we're building on
top of that.
We have artificial intelligencethat's just pretty much applied
across the board, everywhere.
So that is going to have a bigcompounding effect.
(55:19):
We're going to need to figureout how do we think about the
value of some of these assets ininteresting ways.
I mean, these are platformsthat we've never seen before.
Traditionally, we think ofbiotech as a certain molecule
and what kind of peak sales it'sgoing to have and a few other
things, but beyond that we'regoing to have we have new
(55:42):
technologies and new sort ofplatforms.
So there's an exciting,exciting space.
Also, I don't want to underscore, I want to underscore the
consumerization of health.
It is, it is absolutely here.
There is a generational shiftbetween Gen Zs and millennials.
You know, as myself being amillennial, we were considered
(56:02):
the online first.
Gen Zs are digital, everything.
They may be okay with the ideaof an AI doctor, than say you
and I, you know that's not theera, that's not what we've sort
of mentally tuned ourself.
But you think about the alpha.
I don't know what thegeneration is called before, but
after.
Gen Zs yeah, gen Alpha, yeah,gen Alpha.
(56:23):
So Gen Zs are now enteringworkspace and in the next five,
six years they will be inpositions where they're going to
start to make a decent amountof money.
There will be a big consumermarket.
Alpha Gen similarly.
So I think, if you think aboutthe social media's rise, it was
(56:44):
millennials and then taken overby Gen Zs.
So what is?
And I think in this space Imean I was reading a report just
two weeks ago that in Gen Zsand some later millennials
they're not drinking alcohol asmuch and this is the first time
in 90 years we've seen that sortof data- so, there's
consciousness of being healthy.
There is a digital first rise offunction health of the world.
I think consumerization ofhealth is a big part of my
prediction of the next five to10 years, and I'm taking some
(57:07):
bets there as well.
So let's not forget aboutconsumerization of health.
Ben Comer (57:10):
Well, I really enjoyed speaking with you.
Sahir, Thanks for being here.
Sahir Ali (57:15):
No, thanks for having me.
This was a great conversation,really enjoyed it, thanks for
being here.
Ben Comer (57:18):
No thanks for having me.
This was a great conversation.
Really enjoyed it.
We've been speaking with SahirAli, phd founder and general
partner at Modi Ventures.
I'm Ben Comer and you've justlistened to the Business of
Biotech.
Find us and subscribe anywhereyou listen to podcasts, and be
sure to check out our new weeklyvideocast of these
conversations every Monday underthe Business of Biotech tab at
Life Science Leader.
We'll see you next week andthanks, as always, for listening
(57:41):
.
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