This episode explores venture capital through the eyes of Albert Azout, Managing Partner at Level Ventures. We discuss thematic investing, the fund of funds model, and the significance of data analytics in selecting promising funds and sectors in today's market.
• Albert's journey from founder to venture partner
• Thematic investing as a strategy in venture capital
• The fund of funds model explained
• Importance of data science in identifying venture opportunities
• Sectors currently outperforming in venture investments
• The intersection of personal characteristics and investment success
• The future of crypto amidst regulatory challenges
• Balancing investment allocations between funds and direct co-investments
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
We believe that in venture, there's heterogeneity
in terms of where innovation isbeing created and where returns
will be created and where thesupply of later stage capital
will be.
We don't believe, generallyspeaking, in just being a
generalist, and so we take athematic bet in each fund cycle,
based on where we think themarket will go, and so we care
(00:20):
about which areas our fundmanagers are indexing against
and whether they're specialistsin those areas.
Speaker 2 (00:28):
Today I'm excited to welcome Albert Azut, venture
Partner at Level Ventures and aseasoned entrepreneur and
investor.
Albert's portfolio includesunicorns like Hippo Insurance
and Oak North, as well as exitslike Uber, quickie and DeepScale
.
He's also founded and soldsuccessful ventures like Velos
and Stylecaster.
In this conversation, we'regoing to talk more about
Albert's approach to buildingand backing businesses, his
(00:49):
journey as a founder and hisvision for the future of tech
innovation.
So with that, let's get intothe episode.
Speaker 3 (00:57):
You're listening to the Seed to Exit podcast with
your host, rhys Keck.
Here you'll learn from startupexecutives, founders, investors
and industry experts.
You'll learn from the bestabout building amazing products,
scaling companies, raisingcapital, hiring the right people
and more.
Subscribe and listen in for newepisodes and enjoy the show.
Speaker 2 (01:21):
Albert, welcome on, excited to have you.
Thank you for having me, albert,welcome on, excited to have you
.
Thank you for having me.
So the fund of funds approach.
I know, obviously it's, ofcourse, it's a less common
investment model that you dothan you know.
Call it your more traditionalVC fund and we haven't had
anybody on the show yet who runsa funds of funds.
So I'm excited to dive intothat today.
Learn a little bit more aboutwhat the model is like.
(01:41):
Before we get into that, I'dlove to learn a little bit more
about your background.
You'd, of course, founded yourfirst company, velos, back in I
believe it was 2010.
If you wouldn't mind justgiving us an overview of what
that looks like and how you gotto ultimately founding Level.
Speaker 1 (02:00):
It sounds good.
So actually that wasn't myfirst company, but I'll go back
and give you sort of a historyof me.
So, software engineer, I wentto school in Boston and then I
went to work on Wall Street twoand a half years as an
infrastructure engineer, gotmore into more quantitative
engineering and was starting toget really fascinated with data
science and data analytics.
And then I started a fewcompanies in New York.
(02:21):
Actually, my first company wasactually a digital ad agency
which scaled up.
We also had a bunch of mediaproperties and I did that for
about three years and then Iended up wanting to build
venture-backed companies.
So my first company was acompany called Stylecaster which
was a large-scale ad networkfocused on fashion, beauty and
(02:43):
media lifestyle.
We were one of the largestplatforms in the space.
We raised capital and then weended up selling to private
equity and then from thatbusiness I had been thinking a
lot about machine learning,because we were doing a lot of
recommendation and optimizationand whatnot and some of the
challenges I saw with largescale data and so at that point
(03:05):
in the market, you know,companies were starting to talk
about predictive analytics andusing machine learning at scale,
and so we started to explore inthat area.
I recruited some, some folksfrom MIT and other places, we
had a really good team and westarted to work on what became a
low code platform for machinelearning infrastructure.
And at that point in time thatwas not a thing, but essentially
(03:27):
what we did was we helpedcompanies get from raw data to
at-scale machine learning, whichhad two parts.
One was what they call featureengineering, etl and feature
engineering, which is hard to doat scale, and the other was
model serving, inference and sobeing able to to essentially,
you know, deliver predictions atlow latency.
(03:48):
And so, yeah, we ended upselling the company to AOL and
Verizon bought AOL and ended up,you know, going going over to
them.
And yeah, that's sort of thehistory.
Speaker 2 (04:00):
Cool, I love that.
So how did you get from sellingto AOL to starting level?
Speaker 1 (04:05):
So I was.
You know, when I moved actually, when I sold to AOL, I worked
for them for about a year and ahalf I ended up relocating to
the Bay Area.
So I lived in Palo Alto and hadan office in San Francisco and
about the time that I moved, Iwas already starting to meet
with entrepreneurs and startingto invest a lot and investing in
(04:28):
funds and other things.
And then one of the investorsin my company his name is Bobby
Asdani was a prolific angelinvestor in the Bay Area and he
was putting together a venturecapital firm called Koda Capital
and I ended up joining as apartner there in 2016.
And I was involved in sort ofhelping build the firm.
(04:49):
You know, the firm grew reallyquickly and had an interesting
strategy which focused on bothprivate investing and public
investing.
I was doing private investingmostly in this.
You know, seed, series A,series B stages.
Everything we did wasenterprise technology,
enterprise infrastructure, andwe did some everything from like
(05:11):
semiconductor to applicationlevel.
We did.
And, yeah, so that's where Iactually learned the business of
venture capital investing.
You know, over four years I wasthere, you know, sitting on
boards, leading investments.
You know managing portfoliocompanies, et cetera.
Speaker 2 (05:24):
What motivated you to start your own firm?
Speaker 1 (05:26):
Yeah.
So during the pandemic, I endedup wanting to move back closer
to family, and so we ended up onthe East Coast, back on the
East Coast, and so I wanted tobuild my own firm, and this is
like the last thing I want to do, I hope.
But basically one of thechallenges and I think it's just
a general challenge in theecosystem is just it's very
opaque and as someone, forexample, at an early stage firm,
(05:51):
when you're trying to source,it's very difficult to
understand what's going on.
What are the C firms?
What are they doing?
What are founders doing?
Where are they coming from?
Where are they going?
Who has access to them?
What are the trends, topicareas that are evolving?
It's actually a very difficultproblem and usually it's done
very, very manually, and so thatwas always something in the
(06:12):
back of my head is like how doyou actually scale that system?
But the other thing that Inoticed because we have a family
office we were investing prettyheavily in technology was
actually where we did the bestfrom a performance perspective
was when we invested in thisnotion of small VC funds or
emerging VC funds and when weco-invested with them, and that
strategy seemed to be veryapplicable and sort of very risk
(06:34):
diversified, but really it'snot done systematically, and so
I was toying around with thosetwo concepts when I started and
that's kind of where we ended up, which is, you know,
essentially reinventing theemerging VC platform and
thinking about building astrategy that we think is, you
know, really optimal in ventureinvesting from an LP perspective
(06:55):
.
So that's kind of how it cametogether.
Speaker 2 (06:59):
So for those who are a little bit newer to the
venture world, could you explainwhat the fund of funds model is
and how it operates?
Speaker 1 (07:05):
Yeah, I mean generally speaking.
In private equity and in hedgefunds and whatnot, you usually
use a fund of funds as anintermediary to invest in funds.
So of course an LP can godirectly to funds.
But often it's very difficultto underwrite funds directly in
some cases and or to get accessto them.
And so traditionally when afund of funds existed is either
(07:27):
for like underwriting or youknow sort of underwriting alpha
and or access, and you know it'sa structure that exists in the
market and it services.
You know everything from hedgefunds to private equity to
venture capital, generallyspeaking.
And so that's the model and theidea is to deploy capital for
LPs into funds and to manage allof the investing activities
(07:50):
underneath that.
So that's sort of how it worksgenerally speaking.
And usually investors don'tnecessarily like fund-to-funds
because there's typically adouble layer of fees because
you're paying both theunderlying manager as well as
the fund-to-fund funds.
But in venture we believethat's kind of a different
problem, just because in ventureyou have the ability to have
(08:10):
outliers in performance and evenwith fees you can actually do
very well by diversification.
But we can talk about thatseparately.
Speaker 2 (08:17):
Per fees is 2 and 20 still the standard fund-to-fund
model fees, or what does thattypically look like?
Speaker 1 (08:23):
It's 1 in 10, usually as a market for fund of funds.
So that's, that's usually whatyou see.
Speaker 2 (08:28):
Gotcha, you mentioned earlier the difficult problem
that you're trying to solve interms of where the seed funds
are investing, where foundersare going to how.
How have you approached thatand how are you working to solve
it?
Speaker 1 (08:39):
Yeah, sounds good.
So we we have a sort of a dataangle.
We have a team of seven datascientists and engineers.
I think we built one of themore sophisticated intelligence
engines in the market.
We aggregate terabytes of dataacross many different kinds of
data sets, so basically anythingthat touches venture whether
it's private market transactions, people profiles, work
experiences or GitHub orscientific journals, business
(09:02):
filings, those kinds of thingsand what we try to really
understand is unpack andreconstruct are the networks
that are forming and evolving,you know, around the tech
ecosystem.
Speaker 2 (09:12):
Yeah, sorry, sorry, please continue.
Speaker 1 (09:14):
That was it, and so we do that by.
When it comes to networkstypically it's networks of
co-investors, networks of talent, founders, team members, you
know those kinds of things andso we have algorithms that we've
developed, as well astechniques to essentially give
us some knowledge on where weshould deploy capital.
When it comes to GPs, Forexample, we have a set of models
and sort of other supportingdata to give us an early
(09:36):
indicator as to this GP mightoutperform the rest of the
market, and so, anyways, that'sthe kind of stuff that we do on
the GP side.
We also use this sort ofintelligence to understand all
of the companies, especially thecompanies in our portfolio.
We use that to essentially giveus sort of preempt potential
co-investment opportunities, andthen we also give back
(09:58):
intelligence to the GPsthemselves, the fund managers
themselves, and that's usuallyin the form of intelligence,
whether it's for sourcingintelligence or market
intelligence or relationshipintelligence, and so you can
think of us like a technologyplatform focused on VC fund to
funds investing.
Speaker 2 (10:14):
Super interesting.
So I'd imagine you're probablyconnecting to the Crunchbase
Pitchforks API, like you said,github, linkedin, scraping all
these other sources, and so thenaggregating all of that
together and almost buildinglike a map of what's happening
where.
Speaker 1 (10:29):
Exactly, and we also have a lot of proprietary data
sources as well, both ones thatare unstructured, that we ingest
as well as just because we meetwith so many funds on a regular
basis.
We just have a lot of data.
But yeah, you're right, thehardest part is tying all of
this stuff together in a waythat's coherent, which involves
both resolving entities acrossdatasets, which is a very
difficult problem, but also justnormalizing time series,
(10:53):
returns data and a bunch ofother sort of noisy stuff that
needs to get resolved for us toactually have a dataset that we
can use for large-scale modeling, and the modeling we do is
actually based on networks.
So that's also another problemthat we've been solving, which
is how do you build theselarge-scale machine learning
systems on network-based data,because traditionally it's done
on language data or spatial orimages and things like that.
Speaker 2 (11:16):
Well, it's really interesting because
traditionally pre-seed and seedfunding is so almost feelings or
intuition or conviction basedright, because really what you
can evaluate at that level isfounder product market, but
you're taking what is inherentlykind of a low level data
product or investment vehicleand then getting data out of it.
(11:38):
So I'm just curious then whatare some of the leading
indicators that you have foundwhen it comes to saying, okay,
we're going to invest in this GPor we're not going to?
Speaker 1 (11:49):
Yeah, definitely.
So.
One thing to note about venturewhich is well understood, is
that the underlying returndistribution is power law right,
so you have a very long tail ofperformance and sort of
essentially, in the market, youknow, very few companies
actually return the whole market, and so it's important to be to
index yourself in an area whereyou'll get access to to those
(12:12):
companies Right, in order todrive returns, and if you do,
then the returns are meaningful.
That's sort of one thing.
The other thing is that whenyou you know, when you choose a
fund manager or a portfolio offunds, for example, each of
those funds are selecting fromthat underlying return
distribution, and so when youlook at the performance,
especially when it comes to likeseed managers, you end up with
(12:33):
a very similar long tail, and soit's essential, essentially,
that you are able to select fundmanagers that are at the sort
of the tail, you know, the righttail in order to have like
outperformance, because it'svery high volatility, right,
because when you have an earlymanager, you know a lot of them
really underperform, or most ofthem underperform, and then some
really outperform, and so it's,you know, the whole methodology
(12:55):
is to select a portfolio ofreally good small funds that
have the potential for outlierperformance, and if you do that
well, then you'll have a reallygood performance as an overall
basket.
So that's just something that'simportant to understand in our
time, and the point is that,first and foremost, fund
strategy really matters.
In venture, small fundsoutperform Historically they've
(13:17):
always outperformed large funds,and it just makes sense
statistically speaking, becauseif they have an outlier in their
portfolio, it usually returnsin multiples of the fund, and so
that doesn't happen.
As you scale AUM, you tend tohave to go to either later
stages or have multiple products, and so the small funds is
really where we play and we tryto get alpha there.
Now, in terms of, like, thefeatures that we use to select
(13:39):
managers, I guess there's likethree orthogonal vectors.
I think one is network, so wehave network-based algorithms
that essentially what we do iswe're reconstructing the
networks of managers when itcomes to their co-investor base
or their talent base, et cetera,and we have essentially models,
predictive models that arecalibrated on historical
performance, which, you know,essentially say this sort of
(14:00):
structure, this sort ofstructure, this sort of
embedding of an individual, canlikely lead to outperformance
with some confidence.
That's sort of one piece ofwhat we do.
The other piece of what we do ismore sector-based and
thematic-based.
(14:23):
We believe that in venture,there's heterogeneity in terms
of where innovation is beingcreated and where returns will
be created and where the supplyof later stage capital will be,
and so we don't believe,generally speaking, in just
being a generalist, and so wetake a thematic bet in each fund
cycle based on where we thinkthe market will go, and so we
care about which areas our fundmanagers are indexing against
and whether they're specialistsin those areas.
To give you an example, like inour first fund in 2021, we
(14:45):
invested in, you know, many deeptech firms which were focused
on, you know, technologies,tackling industrials, software,
hardware enabled solutions, butalso defense, et cetera, and and
there was a lot of reasons whywe chose that but those ended up
being like really great areasto invest in, because a lot of
the capital now is going intothose areas, versus, let's say,
(15:05):
you know what is a traditionallyFinTech or or just traditional
vertical software, et cetera.
So innovation does matter, andso thematic areas another thing
we look at.
And then the other thing isjust is just the strategy firm
strategy, you know, cause as ascan the firm execute on what
it's intending to execute, andis it drifting out of an initial
strategy as they get larger,because the biggest danger for
(15:27):
funds is them getting too largeand the competitive dynamics
change, and so we have a lot ofwork that we've done in that
area as well.
Speaker 2 (15:33):
Hope that answers it does.
Is there a sweet spot?
So we know that smaller firmsoutperform larger firms.
Is there a sweet spot in termsof firm size, like, are you
looking as small as solo GPs?
Is it up to a certain amount ofAUM or a number of partners?
Speaker 1 (15:47):
Yeah, we tend to say less than 100 million, and
there's really within thatthere's like a bifurcation,
which are it really has to dowith the game theory.
You know the game mechanics,which is, you know the smaller
funds can collaboratively investin companies and they don't
squeeze others out, and you getto a point where you have to
lead or co-lead, and thatrequires more competition, more
(16:13):
winning, and so your convictionlevel needs to be higher.
So there's not an exact point,but it's somewhere between 40
and 50 or 55 where that happens.
If you maintain the sameportfolio construction strategy,
which we like.
Firms that have 25 to 35companies, you know, essentially
so that's kind of what happensin market, but there's no like
there's no.
You know you could have a firmthat's larger, do very, very
(16:34):
well, but if you want to seethese really long tail kind of
outcomes, then you need to havewe believe you need to have a
small fund.
Speaker 2 (16:41):
Are there particular sectors that you've seen
outperforming in recent years?
Speaker 1 (16:45):
For sure, for sure.
So what we look at isgraduation rates.
You know, it's sort of onething because it's hard to know
the outcomes, right, because thetime is so long between you
know, like the maturity of acompany, its outcome.
We can get some earlyindications from you.
Know where the supply ofcapital is going and also the
graduation rates of companies aswell.
Speaker 2 (17:05):
And just to pause you , sorry.
When you say graduation rates,are we referring to like?
What number graduate from likeC to series A to series B, et
cetera?
Speaker 1 (17:13):
Yeah, typically we're looking at the bridge between C
to A, which is a verymeaningful bridge.
Yep, there's a high failurerate especially, and also
graduation rates in the marketsalso fluctuate.
Of course, you know, in 2021period everything was graduating
and then right now it's verydifficult to graduate and so a
few companies do.
But we sort of look at that andwe index, you know, managers,
(17:34):
sectors, et cetera, against thatmetric on an annualized basis
to see how it's evolving.
We think, like, where there's alot of you know activity today
is in this intersection ofhardware, software-enabled
solutions, tackling industrialsand defense, which is these are
traditionally businesses that alot of VCs didn't want to touch
(17:56):
because of the R&D intensity andcapital intensity and whatnot,
but these days, because of justa convergence of technologies
that are maturing withdiminishing cost curves, now you
can tackle these problems.
You know sort of very well and,additionally, there's just a
lot of geopolitical and othersort of economic you know trends
(18:18):
that are forcing us to focus onthese things, whether it's like
onshoring, offshoring, orwhether it's the globalization,
or or just generally, like youknow, the political environment,
um, or just war, generallyspeaking, so, uh, so anyway.
So that's what happens is likeyou have these sort of macro, um
, you know needs that need toget filled and um, so that's
(18:40):
sort of one area.
The other area we're seeing alot is in in life sciences is
the intersection of computationand biology applied to like
therapeutics, um, orics ordiagnostics and whatnot.
That's an area where that'sevolving because biology is
becoming more of an engineeringproblem, right, and so that's
that's a very attractive area.
And then generally like datainfrastructure, whether it's
like AI data infrastructure, butjust like how you develop,
(19:01):
deploy software in like in thecloud and sort of multi-cloud
and all that is like it's.
It's always like evergreen,it's an evergreen thing.
So that's what we're seeing.
And then we're seeing somenewer things, like in consumer,
which is interesting, as well aslike, I think, crypto is having
like another, anotherresurgence, which is interesting
.
We're seeing a lot more now likekind of newer age crypto funds
(19:22):
getting started um but um.
Speaker 2 (19:25):
But generally speaking, we're you know there's
a lot happening is there stillappetite for crypto, given all
of the collapses of the lastcouple of years between Celsius,
ftx and gosh?
Who knows how many others?
Speaker 1 (19:35):
Yeah, I think the challenge in crypto is just the
regulatory environment and whatis the security and that.
But there is a lot of you knowwe're seeing a lot of interest
in, you know, sort of AI meetssort of crypto, like the
centralized data, you know sortof data management, data sharing
, those kinds of things, andthen just the sort of the
rethinking still the rethinkingof, like, the financial
(19:57):
infrastructure for enterprisesusing sort of distributed
ledgers.
We haven't really made a bet.
We made one bet there, but yeah, we haven't been incredibly
active there because we justdon't see a lot of adoption in
the areas that we think areimportant.
Speaker 2 (20:12):
What about on the data center side?
You know Sam Altman's talkingabout how he needs to raise $7
trillion to run all of OpenAI'sdata centers, and you know, of
course, that sounds like sellingthe shovels in the gold rush.
Have you looked into that atall?
Speaker 1 (20:24):
I mean we tend to stay like so one of our core
philosophies I think it's justgenerally a good rule in venture
is we like to be in businessesthat have, you know, sort of
increasing feedback loops andpositive, increasing positive
returns and where you have likewinner, take, go into like a
(20:53):
larger system.
You just get into this placewhere there's like diminishing
returns and you get swapped outand so we just we don't feel
like there's enough of aflywheel in some of these areas.
So we're not going to beinvesting like in fiber optic,
you know kind of equipment orthose kinds of things, because
you know we don't want tocompete there.
But but yeah, anyways, that'skind of.
So we haven't looked at thatarea as much.
(21:13):
I've looked at a lot of AI chipscompanies over time.
I think it's a very gruelingbusiness to invest in If you can
make it, especially when you'redealing with like the resources
of like larger groups.
So you know we we've stayedaway from that area.
We recently did do more of aquantum photonics company that
we think is very exciting, whichis more focused on quantum
(21:35):
computing at scale.
We did that recently.
Speaker 2 (21:39):
Interesting.
Okay, so when we're talkingabout investing in funds,
obviously we've talked about thetechnology approach.
But technology aside, are youthen also taking personal
characteristics into account tothese fund managers, or is it
purely quant-driven?
Or what does your overalldecision-making process look
like?
Speaker 1 (21:56):
Yeah, it's human.
I mean, of course there's a lotof human qualitative work that
goes into it.
We think of the technology asjust a guide.
We use it like a funnel forsure, like sour outbound kind of
activities and just initialqualification, but there's a lot
of work that gets donedownstream.
That's very human driven.
Uh, you know, I think withpersonality, nothing we do
(22:17):
quantitatively, of course, youknow we, we, we study the people
a lot, uh, and there has to bea sort of a good chemistry and
all that and we have to believein all that.
But but generally speaking,there's like a mixture of both
things that come into play.
And then there's a lot of likenetwork checks, like we do a lot
of what we call intelligentback channeling, like talking to
the right people about theperson to validate.
You know, to validate both likethe strategy as well as the
(22:41):
capabilities.
You know their ability to win,you know sort of their value add
, those kinds of things.
We just kind of we do thatduring the diligence process.
Speaker 2 (22:50):
On the, the size you mentioned I believe you said it
was 25 to 35 was was the sweetspot.
So does that imply then, asthat's more than likely going to
be an earlier stage fund like afund one or fund two, is that
is that?
Am I on the right track interms of what's appealing?
Speaker 1 (23:06):
yeah, we like young firms, so it's it typically
funds one, two, three is what wefocus on.
You know, firms that are, likeyou know, relatively new five
years, six years, something likethat at most.
There are situations where youknow firms stay purposely small
and they continue to deliver.
You know there's no reason whythat would not continue to be in
our portfolio.
But there are situations wherefirms get larger and more
(23:27):
institutionalized and then youknow, sort of our value add is
no longer, you know, as relevant.
Speaker 2 (23:32):
If you're looking at.
But if you're going so heavilyoff of data, wouldn't it make
more sense to look at moreexperienced fund managers where
you have more of a track recordand more data to analyze?
Speaker 1 (23:41):
Yeah, our data, like our whole systems, are geared to
finding early signal and seeingthings that others don't see,
and the nature of venture istwofold.
One is that you have a highvolatility of return dispersion,
right, so it's.
It's a hard problem, you haveto select really well.
But the other thing is isthere's low, like relatively low
, performance persistence, andso over time, sequentially,
people, you know the returnsaren't correlated and so it's
(24:03):
not the case that for that firmstay good for forever, and in
fact a lot of times fund two isvery different than fund one
performance for a lot of reasons, right.
So that's sort of like that'sour methodology, and it's true
that, yeah, you have moreinformation as the firms get
more mature, but then also, likeour alpha goes away and
typically like the, since priceand venture is not something
(24:25):
that you can move, right, causeit's two and 20, no matter what,
sure, and venture is notsomething that you can move
right Because it's two and 20,no matter what, sure, what ends
up happening is that what getsadjusted is fund size, and so a
lot of managers, just you know,they're attracted by fees and
they, you know, and believe inthe belief that they can execute
on that strategy, on a strategythat's larger, and we just
rather not take that kind of betsometimes.
Speaker 2 (24:46):
They're more focused on the two than they are on the
20.
Speaker 1 (24:49):
Exactly, exactly right.
Speaker 2 (24:53):
Exactly right, and you're also co-investing
directly in companies as well.
Along with the vesting andfunds, how are you deciding on
the allocation and capitalbetween the two things?
Speaker 1 (25:04):
We keep them separate .
In the next fund it'll bearound 80-20.
80 in the fund of funds and 20in the co-invest.
We think it's a good strategyfor LPs because, of course, the
assumption is you're selectingwell.
But if you're selecting well onboth, then you'll benefit from
some earlier liquidity.
On the co-invest side, becausewe're investing typically at
(25:24):
Series B plus companies, SeriesB-ish and so like the hybrid
strategy works really well.
But yeah, so it's about 820, Iwould say.
Speaker 2 (25:33):
When you raise your next fund.
I'm just curious, if you don'tmind me asking, how much are you
planning on that being?
What does that look like, andwhat is the plan for the next
fund look like?
Speaker 1 (25:40):
Yeah, it'll be around 230 across two vehicles, 230
million across two vehicles, andthe strategy is similar.
We don't think our strategyscales too much right.
So there's a point at which youcan't deploy that much capital
into a small fund, and so theflagship product will always be
sort of constrained.
We're very intellectuallyhonest about what we can deploy
(26:02):
and we care a lot about ourinvestor returns.
So, anyways, that's the nextvehicle that we're building, and
it'll probably be 20 or sofirms, 20 to 22, you know, funds
in the fund of funds and about10 to 12 companies in the
Co-Invest site.
Speaker 2 (26:18):
How do you square the fact that you're growing beyond
what your own models show thebest returns at in terms of that
25 to 35 or 50 level AUM?
Speaker 1 (26:27):
Well, we're a fund of funds, so it's a different
statistical model, right,because we're a portfolio of
potential outliers, and so, youknow, as long as we can get
allocation into those potentialoutliers, right.
So the constraint that we haveis how much capital can we
commit to a fund?
That's a certain size, right,and so that breaks down right,
(26:48):
like you can't put $15 million,and so that breaks down right,
like, if you can't, you can'tput $15 million into a $21 fund,
right, it's just, it doesn'twork.
And so that's that's where themath breaks down.
And so there's, there's a limitas to which, like, our strategy
will, will work, um, you know,without having to compensate by
investing in bigger funds.
Speaker 2 (27:10):
Gotcha Okay cool.
Well, this was superinteresting, much more, I would
say, probably math and financeheavy, than the normal
conversations we have, but Ilove this.
I love this component of it andreally grateful and excited for
you to come on the show.
Speaker 1 (27:18):
Thank you so much for your time, Rhys, and we'll be
in touch.
Speaker 3 (27:21):
Thanks for listening to See to Exit.
If you enjoyed the episode,don't forget to subscribe and
we'll see you next time.
We believe that in venture, there's heterogeneity
in terms of where innovation isbeing created and where returns
will be created and where thesupply of later stage capital
will be.
We don't believe, generallyspeaking, in just being a
generalist, and so we take athematic bet in each fund cycle,
based on where we think themarket will go, and so we care
(00:20):
about which areas our fundmanagers are indexing against
and whether they're specialistsin those areas.
Speaker 2 (00:28):
Today I'm excited to welcome Albert Azut, venture
Partner at Level Ventures and aseasoned entrepreneur and
investor.
Albert's portfolio includesunicorns like Hippo Insurance
and Oak North, as well as exitslike Uber, quickie and DeepScale
.
He's also founded and soldsuccessful ventures like Velos
and Stylecaster.
In this conversation, we'regoing to talk more about
Albert's approach to buildingand backing businesses, his
(00:49):
journey as a founder and hisvision for the future of tech
innovation.
So with that, let's get intothe episode.
Speaker 3 (00:57):
You're listening to the Seed to Exit podcast with
your host, rhys Keck.
Here you'll learn from startupexecutives, founders, investors
and industry experts.
You'll learn from the bestabout building amazing products,
scaling companies, raisingcapital, hiring the right people
and more.
Subscribe and listen in for newepisodes and enjoy the show.
Speaker 2 (01:21):
Albert, welcome on, excited to have you.
Thank you for having me, albert,welcome on, excited to have you
.
Thank you for having me.
So the fund of funds approach.
I know, obviously it's, ofcourse, it's a less common
investment model that you dothan you know.
Call it your more traditionalVC fund and we haven't had
anybody on the show yet who runsa funds of funds.
So I'm excited to dive intothat today.
Learn a little bit more aboutwhat the model is like.
(01:41):
Before we get into that, I'dlove to learn a little bit more
about your background.
You'd, of course, founded yourfirst company, velos, back in I
believe it was 2010.
If you wouldn't mind justgiving us an overview of what
that looks like and how you gotto ultimately founding Level.
Speaker 1 (02:00):
It sounds good.
So actually that wasn't myfirst company, but I'll go back
and give you sort of a historyof me.
So, software engineer, I wentto school in Boston and then I
went to work on Wall Street twoand a half years as an
infrastructure engineer, gotmore into more quantitative
engineering and was starting toget really fascinated with data
science and data analytics.
And then I started a fewcompanies in New York.
(02:21):
Actually, my first company wasactually a digital ad agency
which scaled up.
We also had a bunch of mediaproperties and I did that for
about three years and then Iended up wanting to build
venture-backed companies.
So my first company was acompany called Stylecaster which
was a large-scale ad networkfocused on fashion, beauty and
(02:43):
media lifestyle.
We were one of the largestplatforms in the space.
We raised capital and then weended up selling to private
equity and then from thatbusiness I had been thinking a
lot about machine learning,because we were doing a lot of
recommendation and optimizationand whatnot and some of the
challenges I saw with largescale data and so at that point
(03:05):
in the market, you know,companies were starting to talk
about predictive analytics andusing machine learning at scale,
and so we started to explore inthat area.
I recruited some, some folksfrom MIT and other places, we
had a really good team and westarted to work on what became a
low code platform for machinelearning infrastructure.
And at that point in time thatwas not a thing, but essentially
(03:27):
what we did was we helpedcompanies get from raw data to
at-scale machine learning, whichhad two parts.
One was what they call featureengineering, etl and feature
engineering, which is hard to doat scale, and the other was
model serving, inference and sobeing able to to essentially,
you know, deliver predictions atlow latency.
(03:48):
And so, yeah, we ended upselling the company to AOL and
Verizon bought AOL and ended up,you know, going going over to
them.
And yeah, that's sort of thehistory.
Speaker 2 (04:00):
Cool, I love that.
So how did you get from sellingto AOL to starting level?
Speaker 1 (04:05):
So I was.
You know, when I moved actually, when I sold to AOL, I worked
for them for about a year and ahalf I ended up relocating to
the Bay Area.
So I lived in Palo Alto and hadan office in San Francisco and
about the time that I moved, Iwas already starting to meet
with entrepreneurs and startingto invest a lot and investing in
(04:28):
funds and other things.
And then one of the investorsin my company his name is Bobby
Asdani was a prolific angelinvestor in the Bay Area and he
was putting together a venturecapital firm called Koda Capital
and I ended up joining as apartner there in 2016.
And I was involved in sort ofhelping build the firm.
(04:49):
You know, the firm grew reallyquickly and had an interesting
strategy which focused on bothprivate investing and public
investing.
I was doing private investingmostly in this.
You know, seed, series A,series B stages.
Everything we did wasenterprise technology,
enterprise infrastructure, andwe did some everything from like
(05:11):
semiconductor to applicationlevel.
We did.
And, yeah, so that's where Iactually learned the business of
venture capital investing.
You know, over four years I wasthere, you know, sitting on
boards, leading investments.
You know managing portfoliocompanies, et cetera.
Speaker 2 (05:24):
What motivated you to start your own firm?
Speaker 1 (05:26):
Yeah.
So during the pandemic, I endedup wanting to move back closer
to family, and so we ended up onthe East Coast, back on the
East Coast, and so I wanted tobuild my own firm, and this is
like the last thing I want to do, I hope.
But basically one of thechallenges and I think it's just
a general challenge in theecosystem is just it's very
opaque and as someone, forexample, at an early stage firm,
(05:51):
when you're trying to source,it's very difficult to
understand what's going on.
What are the C firms?
What are they doing?
What are founders doing?
Where are they coming from?
Where are they going?
Who has access to them?
What are the trends, topicareas that are evolving?
It's actually a very difficultproblem and usually it's done
very, very manually, and so thatwas always something in the
(06:12):
back of my head is like how doyou actually scale that system?
But the other thing that Inoticed because we have a family
office we were investing prettyheavily in technology was
actually where we did the bestfrom a performance perspective
was when we invested in thisnotion of small VC funds or
emerging VC funds and when weco-invested with them, and that
strategy seemed to be veryapplicable and sort of very risk
(06:34):
diversified, but really it'snot done systematically, and so
I was toying around with thosetwo concepts when I started and
that's kind of where we ended up, which is, you know,
essentially reinventing theemerging VC platform and
thinking about building astrategy that we think is, you
know, really optimal in ventureinvesting from an LP perspective
(06:55):
.
So that's kind of how it cametogether.
Speaker 2 (06:59):
So for those who are a little bit newer to the
venture world, could you explainwhat the fund of funds model is
and how it operates?
Speaker 1 (07:05):
Yeah, I mean generally speaking.
In private equity and in hedgefunds and whatnot, you usually
use a fund of funds as anintermediary to invest in funds.
So of course an LP can godirectly to funds.
But often it's very difficultto underwrite funds directly in
some cases and or to get accessto them.
And so traditionally when afund of funds existed is either
(07:27):
for like underwriting or youknow sort of underwriting alpha
and or access, and you know it'sa structure that exists in the
market and it services.
You know everything from hedgefunds to private equity to
venture capital, generallyspeaking.
And so that's the model and theidea is to deploy capital for
LPs into funds and to manage allof the investing activities
(07:50):
underneath that.
So that's sort of how it worksgenerally speaking.
And usually investors don'tnecessarily like fund-to-funds
because there's typically adouble layer of fees because
you're paying both theunderlying manager as well as
the fund-to-fund funds.
But in venture we believethat's kind of a different
problem, just because in ventureyou have the ability to have
(08:10):
outliers in performance and evenwith fees you can actually do
very well by diversification.
But we can talk about thatseparately.
Speaker 2 (08:17):
Per fees is 2 and 20 still the standard fund-to-fund
model fees, or what does thattypically look like?
Speaker 1 (08:23):
It's 1 in 10, usually as a market for fund of funds.
So that's, that's usually whatyou see.
Speaker 2 (08:28):
Gotcha, you mentioned earlier the difficult problem
that you're trying to solve interms of where the seed funds
are investing, where foundersare going to how.
How have you approached thatand how are you working to solve
it?
Speaker 1 (08:39):
Yeah, sounds good.
So we we have a sort of a dataangle.
We have a team of seven datascientists and engineers.
I think we built one of themore sophisticated intelligence
engines in the market.
We aggregate terabytes of dataacross many different kinds of
data sets, so basically anythingthat touches venture whether
it's private market transactions, people profiles, work
experiences or GitHub orscientific journals, business
(09:02):
filings, those kinds of thingsand what we try to really
understand is unpack andreconstruct are the networks
that are forming and evolving,you know, around the tech
ecosystem.
Speaker 2 (09:12):
Yeah, sorry, sorry, please continue.
Speaker 1 (09:14):
That was it, and so we do that by.
When it comes to networkstypically it's networks of
co-investors, networks of talent, founders, team members, you
know those kinds of things andso we have algorithms that we've
developed, as well astechniques to essentially give
us some knowledge on where weshould deploy capital.
When it comes to GPs, Forexample, we have a set of models
and sort of other supportingdata to give us an early
(09:36):
indicator as to this GP mightoutperform the rest of the
market, and so, anyways, that'sthe kind of stuff that we do on
the GP side.
We also use this sort ofintelligence to understand all
of the companies, especially thecompanies in our portfolio.
We use that to essentially giveus sort of preempt potential
co-investment opportunities, andthen we also give back
(09:58):
intelligence to the GPsthemselves, the fund managers
themselves, and that's usuallyin the form of intelligence,
whether it's for sourcingintelligence or market
intelligence or relationshipintelligence, and so you can
think of us like a technologyplatform focused on VC fund to
funds investing.
Speaker 2 (10:14):
Super interesting.
So I'd imagine you're probablyconnecting to the Crunchbase
Pitchforks API, like you said,github, linkedin, scraping all
these other sources, and so thenaggregating all of that
together and almost buildinglike a map of what's happening
where.
Speaker 1 (10:29):
Exactly, and we also have a lot of proprietary data
sources as well, both ones thatare unstructured, that we ingest
as well as just because we meetwith so many funds on a regular
basis.
We just have a lot of data.
But yeah, you're right, thehardest part is tying all of
this stuff together in a waythat's coherent, which involves
both resolving entities acrossdatasets, which is a very
difficult problem, but also justnormalizing time series,
(10:53):
returns data and a bunch ofother sort of noisy stuff that
needs to get resolved for us toactually have a dataset that we
can use for large-scale modeling, and the modeling we do is
actually based on networks.
So that's also another problemthat we've been solving, which
is how do you build theselarge-scale machine learning
systems on network-based data,because traditionally it's done
on language data or spatial orimages and things like that.
Speaker 2 (11:16):
Well, it's really interesting because
traditionally pre-seed and seedfunding is so almost feelings or
intuition or conviction basedright, because really what you
can evaluate at that level isfounder product market, but
you're taking what is inherentlykind of a low level data
product or investment vehicleand then getting data out of it.
(11:38):
So I'm just curious then whatare some of the leading
indicators that you have foundwhen it comes to saying, okay,
we're going to invest in this GPor we're not going to?
Speaker 1 (11:49):
Yeah, definitely.
So.
One thing to note about venturewhich is well understood, is
that the underlying returndistribution is power law right,
so you have a very long tail ofperformance and sort of
essentially, in the market, youknow, very few companies
actually return the whole market, and so it's important to be to
index yourself in an area whereyou'll get access to to those
(12:12):
companies Right, in order todrive returns, and if you do,
then the returns are meaningful.
That's sort of one thing.
The other thing is that whenyou you know, when you choose a
fund manager or a portfolio offunds, for example, each of
those funds are selecting fromthat underlying return
distribution, and so when youlook at the performance,
especially when it comes to likeseed managers, you end up with
(12:33):
a very similar long tail, and soit's essential, essentially,
that you are able to select fundmanagers that are at the sort
of the tail, you know, the righttail in order to have like
outperformance, because it'svery high volatility, right,
because when you have an earlymanager, you know a lot of them
really underperform, or most ofthem underperform, and then some
really outperform, and so it's,you know, the whole methodology
(12:55):
is to select a portfolio ofreally good small funds that
have the potential for outlierperformance, and if you do that
well, then you'll have a reallygood performance as an overall
basket.
So that's just something that'simportant to understand in our
time, and the point is that,first and foremost, fund
strategy really matters.
In venture, small fundsoutperform Historically they've
(13:17):
always outperformed large funds,and it just makes sense
statistically speaking, becauseif they have an outlier in their
portfolio, it usually returnsin multiples of the fund, and so
that doesn't happen.
As you scale AUM, you tend tohave to go to either later
stages or have multiple products, and so the small funds is
really where we play and we tryto get alpha there.
Now, in terms of, like, thefeatures that we use to select
(13:39):
managers, I guess there's likethree orthogonal vectors.
I think one is network, so wehave network-based algorithms
that essentially what we do iswe're reconstructing the
networks of managers when itcomes to their co-investor base
or their talent base, et cetera,and we have essentially models,
predictive models that arecalibrated on historical
performance, which, you know,essentially say this sort of
(14:00):
structure, this sort ofstructure, this sort of
embedding of an individual, canlikely lead to outperformance
with some confidence.
That's sort of one piece ofwhat we do.
The other piece of what we do ismore sector-based and
thematic-based.
(14:23):
We believe that in venture,there's heterogeneity in terms
of where innovation is beingcreated and where returns will
be created and where the supplyof later stage capital will be,
and so we don't believe,generally speaking, in just
being a generalist, and so wetake a thematic bet in each fund
cycle based on where we thinkthe market will go, and so we
care about which areas our fundmanagers are indexing against
and whether they're specialistsin those areas.
To give you an example, like inour first fund in 2021, we
(14:45):
invested in, you know, many deeptech firms which were focused
on, you know, technologies,tackling industrials, software,
hardware enabled solutions, butalso defense, et cetera, and and
there was a lot of reasons whywe chose that but those ended up
being like really great areasto invest in, because a lot of
the capital now is going intothose areas, versus, let's say,
(15:05):
you know what is a traditionallyFinTech or or just traditional
vertical software, et cetera.
So innovation does matter, andso thematic areas another thing
we look at.
And then the other thing isjust is just the strategy firm
strategy, you know, cause as ascan the firm execute on what
it's intending to execute, andis it drifting out of an initial
strategy as they get larger,because the biggest danger for
(15:27):
funds is them getting too largeand the competitive dynamics
change, and so we have a lot ofwork that we've done in that
area as well.
Speaker 2 (15:33):
Hope that answers it does.
Is there a sweet spot?
So we know that smaller firmsoutperform larger firms.
Is there a sweet spot in termsof firm size, like, are you
looking as small as solo GPs?
Is it up to a certain amount ofAUM or a number of partners?
Speaker 1 (15:47):
Yeah, we tend to say less than 100 million, and
there's really within thatthere's like a bifurcation,
which are it really has to dowith the game theory.
You know the game mechanics,which is, you know the smaller
funds can collaboratively investin companies and they don't
squeeze others out, and you getto a point where you have to
lead or co-lead, and thatrequires more competition, more
(16:13):
winning, and so your convictionlevel needs to be higher.
So there's not an exact point,but it's somewhere between 40
and 50 or 55 where that happens.
If you maintain the sameportfolio construction strategy,
which we like.
Firms that have 25 to 35companies, you know, essentially
so that's kind of what happensin market, but there's no like
there's no.
You know you could have a firmthat's larger, do very, very
(16:34):
well, but if you want to seethese really long tail kind of
outcomes, then you need to havewe believe you need to have a
small fund.
Speaker 2 (16:41):
Are there particular sectors that you've seen
outperforming in recent years?
Speaker 1 (16:45):
For sure, for sure.
So what we look at isgraduation rates.
You know, it's sort of onething because it's hard to know
the outcomes, right, because thetime is so long between you
know, like the maturity of acompany, its outcome.
We can get some earlyindications from you.
Know where the supply ofcapital is going and also the
graduation rates of companies aswell.
Speaker 2 (17:05):
And just to pause you , sorry.
When you say graduation rates,are we referring to like?
What number graduate from likeC to series A to series B, et
cetera?
Speaker 1 (17:13):
Yeah, typically we're looking at the bridge between C
to A, which is a verymeaningful bridge.
Yep, there's a high failurerate especially, and also
graduation rates in the marketsalso fluctuate.
Of course, you know, in 2021period everything was graduating
and then right now it's verydifficult to graduate and so a
few companies do.
But we sort of look at that andwe index, you know, managers,
(17:34):
sectors, et cetera, against thatmetric on an annualized basis
to see how it's evolving.
We think, like, where there's alot of you know activity today
is in this intersection ofhardware, software-enabled
solutions, tackling industrialsand defense, which is these are
traditionally businesses that alot of VCs didn't want to touch
(17:56):
because of the R&D intensity andcapital intensity and whatnot,
but these days, because of justa convergence of technologies
that are maturing withdiminishing cost curves, now you
can tackle these problems.
You know sort of very well and,additionally, there's just a
lot of geopolitical and othersort of economic you know trends
(18:18):
that are forcing us to focus onthese things, whether it's like
onshoring, offshoring, orwhether it's the globalization,
or or just generally, like youknow, the political environment,
um, or just war, generallyspeaking, so, uh, so anyway.
So that's what happens is likeyou have these sort of macro, um
, you know needs that need toget filled and um, so that's
(18:40):
sort of one area.
The other area we're seeing alot is in in life sciences is
the intersection of computationand biology applied to like
therapeutics, um, orics ordiagnostics and whatnot.
That's an area where that'sevolving because biology is
becoming more of an engineeringproblem, right, and so that's
that's a very attractive area.
And then generally like datainfrastructure, whether it's
like AI data infrastructure, butjust like how you develop,
(19:01):
deploy software in like in thecloud and sort of multi-cloud
and all that is like it's.
It's always like evergreen,it's an evergreen thing.
So that's what we're seeing.
And then we're seeing somenewer things, like in consumer,
which is interesting, as well aslike, I think, crypto is having
like another, anotherresurgence, which is interesting
.
We're seeing a lot more now likekind of newer age crypto funds
(19:22):
getting started um but um.
Speaker 2 (19:25):
But generally speaking, we're you know there's
a lot happening is there stillappetite for crypto, given all
of the collapses of the lastcouple of years between Celsius,
ftx and gosh?
Who knows how many others?
Speaker 1 (19:35):
Yeah, I think the challenge in crypto is just the
regulatory environment and whatis the security and that.
But there is a lot of you knowwe're seeing a lot of interest
in, you know, sort of AI meetssort of crypto, like the
centralized data, you know sortof data management, data sharing
, those kinds of things, andthen just the sort of the
rethinking still the rethinkingof, like, the financial
(19:57):
infrastructure for enterprisesusing sort of distributed
ledgers.
We haven't really made a bet.
We made one bet there, but yeah, we haven't been incredibly
active there because we justdon't see a lot of adoption in
the areas that we think areimportant.
Speaker 2 (20:12):
What about on the data center side?
You know Sam Altman's talkingabout how he needs to raise $7
trillion to run all of OpenAI'sdata centers, and you know, of
course, that sounds like sellingthe shovels in the gold rush.
Have you looked into that atall?
Speaker 1 (20:24):
I mean we tend to stay like so one of our core
philosophies I think it's justgenerally a good rule in venture
is we like to be in businessesthat have, you know, sort of
increasing feedback loops andpositive, increasing positive
returns and where you have likewinner, take, go into like a
(20:53):
larger system.
You just get into this placewhere there's like diminishing
returns and you get swapped outand so we just we don't feel
like there's enough of aflywheel in some of these areas.
So we're not going to beinvesting like in fiber optic,
you know kind of equipment orthose kinds of things, because
you know we don't want tocompete there.
But but yeah, anyways, that'skind of.
So we haven't looked at thatarea as much.
(21:13):
I've looked at a lot of AI chipscompanies over time.
I think it's a very gruelingbusiness to invest in If you can
make it, especially when you'redealing with like the resources
of like larger groups.
So you know we we've stayedaway from that area.
We recently did do more of aquantum photonics company that
we think is very exciting, whichis more focused on quantum
(21:35):
computing at scale.
We did that recently.
Speaker 2 (21:39):
Interesting.
Okay, so when we're talkingabout investing in funds,
obviously we've talked about thetechnology approach.
But technology aside, are youthen also taking personal
characteristics into account tothese fund managers, or is it
purely quant-driven?
Or what does your overalldecision-making process look
like?
Speaker 1 (21:56):
Yeah, it's human.
I mean, of course there's a lotof human qualitative work that
goes into it.
We think of the technology asjust a guide.
We use it like a funnel forsure, like sour outbound kind of
activities and just initialqualification, but there's a lot
of work that gets donedownstream.
That's very human driven.
Uh, you know, I think withpersonality, nothing we do
(22:17):
quantitatively, of course, youknow we, we, we study the people
a lot, uh, and there has to bea sort of a good chemistry and
all that and we have to believein all that.
But but generally speaking,there's like a mixture of both
things that come into play.
And then there's a lot of likenetwork checks, like we do a lot
of what we call intelligentback channeling, like talking to
the right people about theperson to validate.
You know, to validate both likethe strategy as well as the
(22:41):
capabilities.
You know their ability to win,you know sort of their value add
, those kinds of things.
We just kind of we do thatduring the diligence process.
Speaker 2 (22:50):
On the, the size you mentioned I believe you said it
was 25 to 35 was was the sweetspot.
So does that imply then, asthat's more than likely going to
be an earlier stage fund like afund one or fund two, is that
is that?
Am I on the right track interms of what's appealing?
Speaker 1 (23:06):
yeah, we like young firms, so it's it typically
funds one, two, three is what wefocus on.
You know, firms that are, likeyou know, relatively new five
years, six years, something likethat at most.
There are situations where youknow firms stay purposely small
and they continue to deliver.
You know there's no reason whythat would not continue to be in
our portfolio.
But there are situations wherefirms get larger and more
(23:27):
institutionalized and then youknow, sort of our value add is
no longer, you know, as relevant.
Speaker 2 (23:32):
If you're looking at.
But if you're going so heavilyoff of data, wouldn't it make
more sense to look at moreexperienced fund managers where
you have more of a track recordand more data to analyze?
Speaker 1 (23:41):
Yeah, our data, like our whole systems, are geared to
finding early signal and seeingthings that others don't see,
and the nature of venture istwofold.
One is that you have a highvolatility of return dispersion,
right, so it's.
It's a hard problem, you haveto select really well.
But the other thing is isthere's low, like relatively low
, performance persistence, andso over time, sequentially,
people, you know the returnsaren't correlated and so it's
(24:03):
not the case that for that firmstay good for forever, and in
fact a lot of times fund two isvery different than fund one
performance for a lot of reasons, right.
So that's sort of like that'sour methodology, and it's true
that, yeah, you have moreinformation as the firms get
more mature, but then also, likeour alpha goes away and
typically like the, since priceand venture is not something
(24:25):
that you can move, right, causeit's two and 20, no matter what,
sure, and venture is notsomething that you can move
right Because it's two and 20,no matter what, sure, what ends
up happening is that what getsadjusted is fund size, and so a
lot of managers, just you know,they're attracted by fees and
they, you know, and believe inthe belief that they can execute
on that strategy, on a strategythat's larger, and we just
rather not take that kind of betsometimes.
Speaker 2 (24:46):
They're more focused on the two than they are on the
20.
Speaker 1 (24:49):
Exactly, exactly right.
Speaker 2 (24:53):
Exactly right, and you're also co-investing
directly in companies as well.
Along with the vesting andfunds, how are you deciding on
the allocation and capitalbetween the two things?
Speaker 1 (25:04):
We keep them separate .
In the next fund it'll bearound 80-20.
80 in the fund of funds and 20in the co-invest.
We think it's a good strategyfor LPs because, of course, the
assumption is you're selectingwell.
But if you're selecting well onboth, then you'll benefit from
some earlier liquidity.
On the co-invest side, becausewe're investing typically at
(25:24):
Series B plus companies, SeriesB-ish and so like the hybrid
strategy works really well.
But yeah, so it's about 820, Iwould say.
Speaker 2 (25:33):
When you raise your next fund.
I'm just curious, if you don'tmind me asking, how much are you
planning on that being?
What does that look like, andwhat is the plan for the next
fund look like?
Speaker 1 (25:40):
Yeah, it'll be around 230 across two vehicles, 230
million across two vehicles, andthe strategy is similar.
We don't think our strategyscales too much right.
So there's a point at which youcan't deploy that much capital
into a small fund, and so theflagship product will always be
sort of constrained.
We're very intellectuallyhonest about what we can deploy
(26:02):
and we care a lot about ourinvestor returns.
So, anyways, that's the nextvehicle that we're building, and
it'll probably be 20 or sofirms, 20 to 22, you know, funds
in the fund of funds and about10 to 12 companies in the
Co-Invest site.
Speaker 2 (26:18):
How do you square the fact that you're growing beyond
what your own models show thebest returns at in terms of that
25 to 35 or 50 level AUM?
Speaker 1 (26:27):
Well, we're a fund of funds, so it's a different
statistical model, right,because we're a portfolio of
potential outliers, and so, youknow, as long as we can get
allocation into those potentialoutliers, right.
So the constraint that we haveis how much capital can we
commit to a fund?
That's a certain size, right,and so that breaks down right,
(26:48):
like you can't put $15 million,and so that breaks down right,
like, if you can't, you can'tput $15 million into a $21 fund,
right, it's just, it doesn'twork.
And so that's that's where themath breaks down.
And so there's, there's a limitas to which, like, our strategy
will, will work, um, you know,without having to compensate by
investing in bigger funds.
Speaker 2 (27:10):
Gotcha Okay cool.
Well, this was superinteresting, much more, I would
say, probably math and financeheavy, than the normal
conversations we have, but Ilove this.
I love this component of it andreally grateful and excited for
you to come on the show.
Speaker 1 (27:18):
Thank you so much for your time, Rhys, and we'll be
in touch.
Speaker 3 (27:21):
Thanks for listening to See to Exit.
If you enjoyed the episode,don't forget to subscribe and
we'll see you next time.
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