Hello, and welcome, everybody. This is Joe from Startuprad.io, your
Startupradio podcast, YouTube blog, and radio station from Germany,
Austria, and Switzerland. Today, I would like to welcome
Max again. Hey. How you doing? Hey, Joe. How are you doing?
I'm doing great. Thank you. People
may know you as the cofounder of
Endozane that I interviewed recently, but we're
here because you kind of shifted careers?
I mean, I think that's a lot to say. Endosane is still very
much up and running. And, as a matter of fact, in
December, we closed our last financing round. We were kind of
discreet about it because, we don't really profit from a lot of
media attention at Endosane, and, that's why we're not
searching for it. However, as you might remember,
Endosane is focused on creating
new drugs for the treatment of psychiatric and neuropsychiatric
diseases. And as such, we conduct clinical
trials in order to obtain FDA and EMA
approval. And now these trials and why
these studies are running, my personal involvement
as nonscientific founder,
cofounder of of Endosain, is somewhat limited.
So, my my primary duty is always to
create the infrastructure, the setting, and primarily the,
the financing needed in order to conduct studies.
Once that has happened, my involvement is is not needed
to the same extent as before. So we're in this situation, which
allows me to to, also follow,
other avenues I'm very interested in and leave the day to day
operations to the scientific, cofounders of mine, mainly
Catherine or Leiter. I see. And
we are talking because one of those venues you've been talking
about is setting up a new VC fund in
Berlin. You have announced this, I I do
believe, a few weeks ago? Yes, indeed. Very excited about
that. We founded Blue Forest Ventures. We're
based in Berlin. However, we invest, across
Europe and selectively in North America and Southeast
Asia. Uh-huh. I see. And, of course, the first important
question everybody has on on their minds, why Blue Forest?
Now I could tell you, like, the the beautiful
marketing story that's that's, I could make up around this, which
is that, I like the
metaphorical image of, individual trees
representing our portfolio companies that do stand alone
but are all interconnected with their roots, below the ground.
However, the truth of the matter is that it's a combination
of one of my favorite strategical frameworks, which is
Blue Ocean, which is about, having a different
perspective on your strategy and trying to think outside the
box. And, the second part, so the
blues from that, and the second part, the forest,
is, from, my favorite
algorithm for predicting future, for
predicting basically future events, which is random forest.
And since this venture capital fund, and we'll we'll talk about this
in a couple of minutes, I'm sure, is, heavily data
driven and heavily, driven by the attempt
to use data to predict the future as much as possible.
This felt very adequate and and fitting to to a name.
For, the people now following us,
we first may get out of the way that you're early stage
investor. That means people can pitch you a pre seed seed. Are you
also doing series a? Typically, we wouldn't. We
might in a series a, because, obviously,
we do save up a a certain amount of of
our total funds in order to be able to
do follow on investments. So, if we're already
invested through precede and seed financing in a company
and we feel for whatever reason we want to participate in the
series a, we can. However, that's certainly not our
sweet spot. What what was what was the core vision, the
idea behind it, why you've set up
this fund? Is there, was
there, like, one event, one minute where you thought,
oh, that struck me. That's why I decided to set it up or was
it a gradual process? I mean, I think
there's element of both in there. I think at the core,
there's a fascination with 2 things. On the one
hand, the fascination with science and with
the scientific attempt to solve the problems that we
face. So we haven't mentioned that so far, but, Blue
Forest is focused on deep tech companies
solely. Joe, companies that come from a
scientific or engineering background. Now this fascination
of the scientific approach is paired with the
specific stage that these companies we're looking in,
into are in, which is pre seed and seed,
which means really the 1st institutional money they
typically get. So you have a
a university project, a PhD project,
where a team comes together because they have an
idea to commercialize a scientific solution that
they've discovered. And now they
might become part of an accelerator program or an incubator
program. They might get a little bit of grant funding, but
there comes the stage where they need to sort of leave the nest, so to
speak, and receive the first external financing.
And this is us. We want to help these companies
with that leap. And in my experience, and I can tell that
from my own experience having founded a biotech before,
this first step outside of the protected
environment of the of this academic background into
the commercial world is one of the most difficult
steps to achieve at a start up. And specifically, to
obtain, the the amount between 250,000
a1000000 is very difficult because there are not a lot
of VCs that are ready to invest ticket sizes
that small. And so there's there's the fascination of
the complexities of the situation on the one hand. And on the
other hand, maybe more towards the, oh my god.
Now we can do it. The one thing that always
bothered me in early stage venture capital is that
the conflict you have between having to look at 100
and 100 of companies in order to find the companies you're interested
in, on the one hand. And your fund
size is limited, so the budget you have
through your management fee is limited. And so there's this
conflict of you don't really have enough money to do a
proper job in evaluating all the companies you look at.
So you have these VCs that do what what's called spray and
pray, and they just invest 50,000 in
whatever company basically has half way of a PowerPoint
presentation in a certain segment, and they just hope that one of them will make
it. And there is the other sort of segment, and I
I that that's not an attack on anybody, but that's like,
okay. I've come from this world of venture capital. Now
I am some sort of a guru and through sitting across from
somebody, I can somehow feel whether they will have what it takes to
be successful. I don't really think that that's interesting.
Even if it was true, it wouldn't be scalable or repeatable.
So the moment that really brought my partners
and I to this moment of, yes, we can do it,
is basically the possibilities that
arose through robot process automation as
in AI and the AI tools that are available now
in the combination with the computation cost that
basically went down dramatically over the last years, which means
that the kind of mathematical modeling that
was reserved to hedge funds and large funds in the
past is now achievable for
small funds as well. And that means that we can save
so much time and so much effort, on the
human resources side that we can do a proper job evaluating
those companies. Right? And so, I'm sorry for the
for the little bit long answer to that question, but the
reality is it's a combination of all these factors that led us to be like,
okay. Now is the right time. We're ahead of the curve with this approach,
and and we want to do this. Let me quickly
structure my thoughts here. So, at at first, I'm not
trying to make light of the situation because an investment the first
VC investment may have a life
changing impact, hopefully, for
the better. But it sounded kind of
like you are the business body of a more
scientific oriented founder team. Would that be appropriate to
say? I think that's not making light of it at all. I think that's,
yeah, I think that's appropriate to say. I I typically,
say them a little bit different way, which is we are also translators.
Right? We translate, because
very heavily scientific founding
teams with typical scientific
backgrounds, on the one hand, don't really speak the
same language, so to speak, as the investors world
on the other. And, in a way,
there is almost a translation needed from a complex
solution to a compelling equity story. And we
believe that there is lots of good projects and lots of good
ideas and even good teams with high potential
where the ideas never get funding just because they
were uncapable of translating the idea
into a shape and form that's digestible
for most VC investors. And, the
other question I had is where or did the
statement or explanation called as you like? Because,
you are aiming for around €60,000,000 in your
fund. And, the people are calculating in their head, well, they can
make up to 60 investments, but that's not necessarily
true. As you said, you may participate in a series a round.
So that means you have assigned some of the money
for the initial investments and then maybe
half or even larger part for the so called follow-up investments
that you still have, a relevant stake, a big stake
in the Startupradio that are doing success for the
fund raising instead of growing successfully. Right?
Absolutely. So, we're aiming to have a portfolio
size of around 40 companies, which means that we're,
aiming to to, invest in 40 initial
companies and have the rest as follow on. So now if I
tell you that our average ticket size will be between 500
and a1000000, right, you can do the math very quickly that
we save up up to 40% to do follow
on investments. And that's that's that's a
fairly standard ratio, specifically at early stage
to to have some dry powder to do follow ons. We've
been talking about ticket size. We've been talking about the stage.
But we have not talked about which industries, which
sectors are you investing in. And little disclaimer, if
there would be only AI, it would be too boring and we wouldn't have an
interview here. You are talking about
deep tech, but very, differentiated.
What kind of investments you are looking for
here? Look. I think let's start by talking about what deep tech
really means because as AI and deep tech feels like
this almost label that people slap
on onto something, and nobody really knows what it means. So
at the core of it, deep tech just refers to
technological solutions for complex scientific
or engineering problems. Right? So now I understand that that's
very broad, and it is broad. Right? And and we're not
we're not trying to say it's not, it's it's not a very broad
field. Now there's we invest across a
very large part of that field for two reasons.
1st, the
there's every venture capital fund, every investor
has to ask themselves the question of what do they bring
to the table for the companies. Right? And,
a lot of of VCs specialize in certain fields and
certain industries, and so they can bring a lot of industry and field
specific knowledge to those companies. However,
that's not what we primarily claim. Of course,
we can bring that through our network of advisors and through our network
in the industry. But our primary asset that
we bring to the table is in the translation, is in
everything that's not specific to the deep tech field
we're talking about. Because these founding teams
often struggle with most of the other
issues that are not related to their specific technological
invention, right? They struggle with creating a proper business
plan, with assessing product market fit, with, with
building the structures, with building the teams that are necessary
to achieve their goals. And that's where we we come in
and we can really help them. And so this is true for all of
of of the subsegments of Deep Tech.
Now the only reason that this is
possible is because of the stage we're investing in.
Right? If we talked about the series a company, that wouldn't be
possible. We would need to specialize because the problem that
a series a company faces has nothing to do
anymore or very little to do anymore with creating a proper business
plan. They're past that. Right? So in a way, the
stage we have selected allows us to be very broad
in our investment approach. And the second part is
that the sectors we don't invest in. Right? And and,
it pains me to say, but drug development, the the company
I cofounded would not fall into our investment scheme.
Right? And, this has something to do with
drug development and all the the deep
tech fields that are heavily regulated.
One of the biggest risks they are facing is of regulatory
nature. And the regulatory risk is very
difficult to hedge outside of being very
specialized in the field. Right? Because we have the scientific
side, you have the, you know, what we bring to the table, the financial,
the business side, we can handle these 2. Right? But if there is
the complexity, the added complexity of a very, very
complicated regulatory process that is,
highly specific to the specific field we're looking into,
that's something we cannot handle on top of it. Joe, unfortunately,
drug development would fall into a category of company we cannot
invest in. So my understanding is that drug development,
especially if you want to do it on an impact
on a medical scale, is very, very expensive. We do
have this frequently in our news that goes up to the billions.
That is so. And the the money is not our primary
concern. Although, I mean, we we have we wouldn't participate
significantly in the total needed funding. But our primary
concern is that you need to be an expert in the regulatory
environment in order to be able to handle these kind of investments.
Talking about investments, what kind of
criteria do you use to
evaluate your investment? Because as we've been talking, it's
very specific. It's very scientific.
You you just cannot say, well, it's an ecommerce shop. They do it as
much as this. This is the multiplier of public listed company.
That is what I can use. That just doesn't work. It it's
way more complex than that. Right? That is true, and that's a
very, very good question. And I
it's gonna be a little bit of a longer answer, but but bear with me.
There's there's certainly and and that's something I hear a lot
when talking about, this with other early
stage venture capitalists that tell me, well, Max,
it's it's nice that you say that you're data driven. It's nice that you say
that you want to evaluate the companies more in a
more in-depth way. But these companies are so
young. There is no data on these companies that is available for
you to evaluate. And I'm always saying, well,
that's only true really in part. Right?
Because, yes, there is no data about the companies or not much
data about the companies in terms of financial data, that
doesn't mean that there is not a lot of other
alternative data that's available around the markets,
around the scientific field, around the founders themselves.
Right? And we're talking beyond the track record of the founders.
We're talking about the background of the
founders, even as far as the credit scores of the
founders. Right? Those are all information
that exists out there. And now before creating a
discussions I don't want to create here, I'm not saying that the good or
bad credit score is either good or bad. Right? But I'm saying it's
a variable we're looking into to feed
into our model that evaluates the companies we invest
in. Now the the
general criteria is still a
lot of the times the same across VCs and it's the
same for us. Obviously, we need to see a
certain market potential. Right? We need to see a
certain value that's created through the solution
that the company presents. Because somebody needs to
pay for it in future. Right? And people will be willing to
pay for it in function of the economical
benefits that the solution generates for them. Right? If I
can solve a big scientific problem, but if it's if
it's not reflected in a big economical problem for
somebody, right, nobody will pay for it. Because
the person still needs to make more money because
he employs he or she employs the solution that's presented.
So a quick example. Right?
I was working with this company in the field of photonics.
Photonics now, is a specific
subsegment of electrical optical modulation,
which means the translation of an optical signal
into an electrical signal. Right? I mean, optical signal, glass
fiber, you know, all kinds of information,
but our computers or our devices still work with
electrical signals. Electrical signals. Right? And so the modulator, which is the
core of every computer chip really, translates the
optical signal into an electrical signal. We've
inadvertently a lot of people talk a lot about this because,
this is really the field, where all the energy is
lost and all the, heat is created, for instance,
because of the energy that's lost in this translation. So
now a big step would be to solve
to to create a more efficient and more,
capable modulator. Right? So if you
were to solve this issue, the problem you would be
solving is immense. And the
companies, the intels of this world, and
other chip manufacturers of this world would be paying would be willing to pay
a lot of money for somebody to solve that issue.
Now, you might solve some other issue right
around that. It's a huge market. However,
if the problem that you solve is not significant
enough for the people to change their process,
then you will never be successful with it. Right? If you if if the
gain in efficiency would be, I don't know, 5 percent,
then Intel is not going to rebuild its $1,000,000,000
Gigafactory in order to adapt to it. It's just not good
enough. Right? So you you always need to understand that
there needs to be a market, that's ready to pay for the
innovation you've created. That some people call
that product market fit, however you wanna
call it. Right? You need to be aware of the
market and the that somebody needs to pay for the
innovation. And, a lot of people underestimate that that's
not a view of right now. That's
a view of in the amount of time
that you need to get your product ready. So if you're still in the r
and d stage, it's irrelevant what the market looks like right
now. What's relevant is what the market will look like in
5 years when your product will be ready. Right? And so this
is something where where I think it's possible to
start to understand why it's so important to be data driven. Because the data
driven allows us to make much better predictions
of future market developments and future product developments
in order to assess not what's the market right now, but what will the
market be when the product's ready. And the second
big aspect is, okay, this is the goal where you want to go.
What are the resources and capabilities that you need to
achieve that? Right and this is when it comes to the team
and obviously the team and this is every VC is gonna sell that this is
not the reinvention of the wheel and I'm not claiming it to be. The team
is one of the core factors that we evaluate.
Now the crux of the matter is what specifically,
and and I'm I'm not going to go into the nitty gritty of it.
But, it's it's suffices to say that we are
very deliberate in the variables that we consider
in terms of the team. And it's far beyond, does this
person have a prior track record? Or do I like this person?
Or do this person seem competent in the 1 on 1?
It's a lot of variables that are related to the professional
and personal, aspects, of the founding team
and the founder we sit sit across. And that again
feed into the model that predicts certain variables.
An example for that would be the founder's ability
to raise money in the future. Right? Because one of the the key
aspects of the success of the company is will it
have enough money to survive long enough in
order to have success? Right? Will will the the team be
able to raise the money that it needs to fully
develop the product? And, of course, we help with
that. And, of course, we have a network and we'll support the support the team.
But still, how likely is it that the team is able to do
that? And there is now we we I'm sure that
everybody here has ideas right away. If
I ask you, what are the variables you think that I
can look at in order to make a prediction on
the likelihood of you being able to raise future money. Right? What are
the personal and professional variables that
I could look at? And this is something we spent a lot of
time evaluating. We've looked at 100 and thousands
of past companies and past, successful
and unsuccessful StartupRadio order to figure out the
key variables that we need to consider in order to make certain predictions
about these factors. That's very interesting. So
bottom line, I do believe a large impact is on the
team, on what you like, think about the
team. Before I get to to to to to to my
next question or your question following that up,
When you've been digging so much into past successes and
failures, was there a variable that surprised you the
most either predicting success or failure?
That's a very good question. It
was surprising to see that, I mean, that
that I'm very careful with now,
how the interpretation of the data and how I want to formulate this
because, we are still in a stage where we try
to really make sure that we narrow it down to the right
variables because you have multiples Joe the multi
correlated variables, right? Where where it's really difficult to say,
okay, which of these variables are,
in effect sort of predictive in it by itself
and which of these variables are just correlated to the predictive
variables. But, let me say it like this.
According to the data, it looks like,
a heterogeneous founders team, which means
if you have a large diversity
in the founders team, right, once you
cross a certain threshold is very beneficial. So
this is ironic. And the only way I can make sense of it is like
this at the beginning. If you're very different, it
makes it a bit harder. Right? At the very Menninger. Because it's
just not as intuitive to to communicate.
It's not as easy to find the consensus and so on and so
forth. But once you
found a working Joe to make that
work, it becomes a huge asset because you're
much more likely to have a larger spectrum of
ideas at the beginning of a problem solving process.
Right? And there is other research that backs that up
to a certain extent, which shows that if you in
an ideation process, if you wanna solve any kind of problem and and the
ideation process, you have a very large spectrum of ideas and you
start with a large funnel and you narrow it down as a
team, that is beneficial to the solution and you'll actually have a
better solution. So, we believe that
founding teams, right, often you will see that all the time. It's also
friends and, you know, you get along anyway, so you become a team.
Yes. That makes it easy at the beginning, but don't fall into the
trap that there is a risk if you always have the same
opinion. Right? It's beneficial to have different opinions in the
team. We talked a lot about team. About the
ideas, I do believe they're very diverse,
especially in this area. If you're talking about research,
it's not only they have an idea, they evaluated something and they come
around with a PowerPoint presentation. Sometimes it's even a complete
life achievement of somebody who started out with a
bachelor, did a PhD, and spent years years in a laboratory.
But in general talking, the idea
to look seriously at it from your perspective should have
progressed beyond just the PowerPoint state. Right? Oh, yeah.
Absolutely. I mean, in the field of deep tech or in the field of
technological development, you talk about what you call
TLR stands for technological readiness, not TRL.
Sorry. Technological readiness level. Right? And it goes
from 1 to 10 where 1 is really the ideation stage and
10 is
interesting for us because that means that you have done
laboratory testing. You have built a certain lab scale
prototype, and you have generated real data.
Right? So, everything before that is really
purely theoretical and wouldn't be ready for us to
invest in because we think that you need a certain proof of
concept, right, in order to move forward. Now
I'll give you an example again to to understand this. There was this,
it's a company we're currently evaluating, so I'm not gonna go too deep into
the details, but very exciting company that developed a
material that if you mix it with water,
will have a huge
effect on the ability of soil to retain water. So
I Menninger you're I mean, to to to make to dumb it down almost too
much. Right? You're in the desert. You have no water. Right? Or
you're in Southern California and you struggle with dry
soils a lot. Right? And the problem with dry soil is
water goes right through it and minerals go right through it.
So you typically have the problem. You need to have a lot of irrigation water
and you need to use a lot of fertilizers in order to get a proper
yield. Now this product, you mix it as it's a it's a
gel. Right? You mix it into the irrigation water, and it
goes between the soil particles, and it helps the soil to
retain water and minerals. And so now on the ideation
stage, that wouldn't have been enough to for us to invest. But
they now recently did large scale laboratory
tests where they tested that with 7 different kinds
of crops in the laboratory and
literally observed soil with the product, soil without the
product. And we look at the amount of irrigation I need, and we look at
the yield I get. And this product, for instance, has
on average require make made the the the
crop require 80 percent less irrigation water
and still gotten 30% more yield,
which is absolutely incredible. If that would prove correct on a large
scale, absolutely incredible. So but we
needed this lab trial to be done before we can
really evaluate it. So to come back to your question,
as long as it was on the pure ideation stage or maybe, like, on the
molecular design of the particle
stage, too early for us. But as soon as it had
these first lab trials and we come from a technological
readiness 3, and and we transition into 4,
right, that's when it becomes interesting to us.
What my what I had in mind when I heard this was also
well, there can be some areas that barely get rain. But
when it rains, it starts flooding down the stream, and you can also
work on those areas. Very interesting thing. Let
let's go a little bit from, where you're
investing, what do you how you're investing, a little bit into
the fund itself, a little bit market trends and the outlook, and then we're
good. We are already talking for more than 30 minutes. By the way,
did you study philosophy? No. I didn't.
I studied law, which, is a little bit counterintuitive,
but, yeah. I've I've I haven't been
working in law for the past 7 years, so so it's been
a while. I see. I see. Well, nonetheless, that it plain
explains long answers. Don't worry. Not going
to this here. I I really like the answers. They're well thought through.
Talking a bit little bit about Blue Force Ventures,
the fund and the management. Can you describe a little bit,
how you guys are set up? Because if you currently go on your
website, there is a logo on there and 3
locations, Berlin, London and Menninger.
But not a lot more. Can you tell us a little bit about the team
and how you guys are set up? And I would also
be interested because before we talked, we talked a lot
about important places where you could find
deep tech investments because I do believe you invest all over Europe.
Right? That's true. I mean,
let let's unpack the the questions. First off, about us and the
team. So, the the
idea, to to create this fund, I
think a lot of us had sort of simultaneously
or ideas around that. And, I
personally know my co founding partner,
from business school because after studying law, I also did
executive MBA at the business school in France. And I met,
my co founder, Moises Janae. He's from Mexico.
And trust me when I say this is a good example for
having a fairly different approach to certain things
and, you know, Menninger it arguably a bit more difficult at
the Menninger, but now we really see how it how it benefits
us. But, that set aside, I had
another partner, in Germany that ironically,
I've made 2 deep tech investments with in the past. Well,
I advised him. He's an angel investor, and I basically
created the contact between him and the deep tech investments,
that, that he invested in. And Joe, we got
together and, and we we founded
Blue Forest Ventures, and, took on right
away a long term, a long time employee of mine
that I've, hired right out of university 4 years
ago, first at Sanity Group, then,
transitioned to Endosame, and now came
with me to Blue Forest. So he's basically employee number 1,
of our fund. So we are 2 full time
general partners, myself and Moises.
We have our partner, Julian, who is not a full
time partner and, hence, not a full time member and,
hence, is not a general partner, but is working with
us, very intensively. And we
have Nicholas, our associate and employee number
1, and now we are in the process of hiring 2 analysts.
Located in Berlin, what else is there? Yeah. Fund size,
we've talked about before. We are quite efficient in
our process as we've set it up in a way so that it can
be very largely automated. Right. Joe,
we we a lot of the things that typically take a lot of time away
from people, specifically analysts are fully automated in our process Joe
that that saves a lot of time. And the other
question that you had was the locations and, why we
chose certain locations, what we look at. So Berlin is
is purely optimistic choice. Right? And I I would love to
to tell you that there was some kind of elaborate thought behind it.
But the reality of things is Berlin is a good
location for a venture capital fund in general. There's a lot of
things going on in Berlin, and there's quite a big ecosystem. Now
Berlin is not the most relevant location for deep tech in Europe,
but it's the location where I had most of my personal
network, and we had the best chance to to get something off
the ground, on a short term basis.
But we are all quite flexible, and we all travel a
lot. So we can easily cover
Europe as a whole and even, selectively, as I said, the
Menninger, Southeast Asia, North America. And
the question of which locations are most relevant for us
in terms of finding suitable deep tech companies is
mostly related to the ecosystem
that research institutes and academic institutions
such as universities have created. So you really see a big
correlation between the amount of successful deep
tech companies, and the
support system and the the the information
system even that these institutions have created
around that. One of the primary examples of that and really
hats off to them is what the ETH Zurich has
created in Zurich around the ETH. There's a myriad
of accelerators, incubators,
support programs, people that will help you, people that
will give you advice on on how to set up a company,
on what you need to be to be cognizant of and so on and
so forth. And you really see that that creates a
lot of, it creates
a situation where StartupRadio just have a
better chance to take off. Right? And so,
BK Zurich is certainly or Zurich is is is a very
relevant location for us. Munich is a relevant location for
us. Eindhoven is location, and certainly
Cambridge, Oxford are relevant for us. And and I
could list more and more, but essentially, it
depends on the ecosystem that is created in and
around the research institutions and universities, in these
locations. Yeah. That's really
it. One of my questions
would have been how you're trying to support companies, but we've already
talked about you being a translator,
research into business. I do believe that was already a
very valid point.
I was curious how do you measure
success? On one hand side, there's the financial success. You
get a really big company. You you get really successful
in terms of finance because the valuation of the company goes
up. You list them. You exit it one way or another.
That is fine. But what else
would you consider success with the companies you invest in?
Look. We're very we're very fortunate in the
way because the field we're looking into makes it
very easy to combine
success on the financial level, which I would translate to return,
right and positive impact on the other side. Right?
And now everybody talks about impact and sustainability.
And oftentimes, this is a tough choice to make between
being return driven and being impact driven. In
deep tech, the compromise you need to make in
order to achieve both is not a lot.
Because almost in the definition of deep tech,
you have the impact that's imminent. Right? And
if you have a successful Deep Tech company, you will
have a large impact. Now is this a positive or
negative impact is something that that obviously
is a question that you need to ask. But generally
speaking in the overwhelming majority of
cases, we're talking about positive impact and a
clearly positive impact. Now there are certain exceptions if we
talk about certain defense application where really the only
thing you could think of is defense application. But this is
the the and and and, obviously, certain cases where I don't
know, I guess you could come up with
with innovation around, you know, gambling or something that
could qualify as deep tech. You know,
I'm I'm really shooting from the hip here. But you see, it's it's
almost hard to come up with something that that would have a
negative impact. So, this makes it
very easy for us to be able to say success
means it has a great return and has a positive impact.
The
measurement of sustainability. And, of the measurement of sustainability.
And we we are attempting to create
something that that we refer to as sustainability by
design and, a positive impact by
design, which means one of the big things a lot of
investment funds struggle with is that they have
structures, They have that process. And now there is these regulations
that come on top of that and the the regulations around impact. And if we
talk about Europe, for instance, you have these, article
6 to 9 in terms of how the degree
of sustainability you really want to achieve.
And this is quite difficult to implement for a lot of funds. Right?
Because they need to basically retroactively
take this this set of regulation and apply it to
their existing process. Right? And that becomes quite tedious in terms
of how do you report that, how do you track the KPIs, and so on
and so forth. What we're able to do, and this is really nothing
that I want to take too much credit for because it's just because we're new
and we can set up structures new, is we
integrate the measurement of impact and the measurement
of of, you know, the effect of the companies in future,
the likely effect. We integrate that into our evaluation
system. They're an integral part of it, which
means that, that this is not something that
we that we that we take into account separately in the
aftermath or something like that. But that's an integral part
of the entirety of our evaluation system. And this makes it much
easier for us to be able to say, our
the companies that we invest in, if they're successful, they will
also have a clearly positive impact
on society, environment,
and the world we live in, basically. We are
now talking for 45 minutes, mostly you, which is totally
fine. The people out there already know my opinion and my voice,
enough. And I would only have 2 closing
questions. First one, what
are the market trends, any any merchant
technologies that excite you right now besides from
AI? And then I have a second
question, but this time, I'll only bother you with 1. Okay. I'm going to
try to keep it much shorter now. I don't
know if you heard about the Gartner hype cycle, and
basically the measurement of the hype. And I
I think that we're all subject to that in one way, shape, or form.
What excites me really is data transmission and data storing
technology. And I keep it that far because I think that whatever
innovation we talk about, whether it is quantum computing,
whether it's AI, whether it's the whole term term of, like, more
specifically language, speech recognition, and so on and so forth.
All relies or, like, all will add to the quantity
of data that we, the world, needs to handle,
right, and store and transmit and so on. So I
think that the the basic data infrastructure that we're
talking about on the hardware and software basis is what
fascinates me a lot. That was much shorter. Right? That
it actually surprised me. Yeah. And and and the
last question I would have because it is important
for a lot of StartupRadio, entrepreneurs, founders
seeking VC investments. How do you
stay informed? What kind of media? What kind
of outlet? Of obviously, besides StartupRadio,
need founders to get into in
order to grab your attention. I
mean, we try to not be too
much attached to the these hype topics because
that would be such a selection bias already at the
get go that would skew our entire model. Right? Our
model is based on a fairly unbiased
gathering of information. So what we do typically is actually
go to the universities into the accelerators and try
to have a fairly objective look at the entire cohort
of StartupRadio is there. We also use a lot of
scraping technology in terms of extracting
information from certain databases and websites
and the Internet in general in order to to fill
up our funnel with leads. Now,
of course, we follow certain,
large scientific publications and, and and
certain typical,
media outlets, in terms of I mean,
I'm not saying too much if I say that we do
Canvas for instance, LinkedIn quite regularly, although LinkedIn is tricky. I don't
wanna go too deep into that because they're very, very difficult to scrape.
Obviously, we're following whatever compliance there is
out there, But, there is certain sources,
that go beyond publication, which we talk more
about registration of patents, for
instance, that we look into in order to get an idea of what's
happening. We also follow a lot of the
actual scientific publications coming out of certain universities
in order to know what are the topics that they really focus on.
I see. So the the the the only question left
for me right now, everybody who has listened
through this is obviously interested in either
becoming an LP or pitching you for money.
What is the best way to reach out to you guys to
pitch you? Is it LinkedIn? Is it the website? Both is
fine. The website has an email address that's there.
And LinkedIn is also fine. We follow both. We're we're we're
very receptive for both. I'm also happy,
to to be hit up directly on LinkedIn. I'm not very
complicated with that, at all. You know it. We communicate through
LinkedIn a lot. So I'm I'm somebody that that does
try to answer most messages I get, on LinkedIn,
for instance. But, yeah, email is also perfect. You'll
find an email address on the website. Right. So I do
believe that was everything from my side.
Thank you very much for this very extensive interview. I I really,
really liked it, especially since I don't have to talk a
lot. And I do believe that gives a very
interesting behind the scenes insight of a early stage
investor that is usually not available for most
entrepreneurs here. Thank you very much, Max. It was a pleasure having you as
guest. It was a pleasure again. Have a
good day. Bye bye. You too. Thanks. Bye bye.