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Hello, and welcome, everybody. This is Joe from Startuprad.io, your

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Startupradio podcast, YouTube blog, and radio station from Germany,

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Austria, and Switzerland. Today, I would like to welcome

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Max again. Hey. How you doing? Hey, Joe. How are you doing?

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I'm doing great. Thank you. People

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may know you as the cofounder of

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Endozane that I interviewed recently, but we're

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here because you kind of shifted careers?

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I mean, I think that's a lot to say. Endosane is still very

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much up and running. And, as a matter of fact, in

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December, we closed our last financing round. We were kind of

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discreet about it because, we don't really profit from a lot of

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media attention at Endosane, and, that's why we're not

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searching for it. However, as you might remember,

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Endosane is focused on creating

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new drugs for the treatment of psychiatric and neuropsychiatric

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diseases. And as such, we conduct clinical

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trials in order to obtain FDA and EMA

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approval. And now these trials and why

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these studies are running, my personal involvement

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as nonscientific founder,

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cofounder of of Endosain, is somewhat limited.

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So, my my primary duty is always to

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create the infrastructure, the setting, and primarily the,

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the financing needed in order to conduct studies.

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Once that has happened, my involvement is is not needed

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to the same extent as before. So we're in this situation, which

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allows me to to, also follow,

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other avenues I'm very interested in and leave the day to day

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operations to the scientific, cofounders of mine, mainly

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Catherine or Leiter. I see. And

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we are talking because one of those venues you've been talking

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about is setting up a new VC fund in

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Berlin. You have announced this, I I do

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believe, a few weeks ago? Yes, indeed. Very excited about

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that. We founded Blue Forest Ventures. We're

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based in Berlin. However, we invest, across

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Europe and selectively in North America and Southeast

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Asia. Uh-huh. I see. And, of course, the first important

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question everybody has on on their minds, why Blue Forest?

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Now I could tell you, like, the the beautiful

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marketing story that's that's, I could make up around this, which

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is that, I like the

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metaphorical image of, individual trees

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representing our portfolio companies that do stand alone

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but are all interconnected with their roots, below the ground.

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However, the truth of the matter is that it's a combination

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of one of my favorite strategical frameworks, which is

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Blue Ocean, which is about, having a different

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perspective on your strategy and trying to think outside the

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box. And, the second part, so the

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blues from that, and the second part, the forest,

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is, from, my favorite

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algorithm for predicting future, for

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predicting basically future events, which is random forest.

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And since this venture capital fund, and we'll we'll talk about this

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in a couple of minutes, I'm sure, is, heavily data

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driven and heavily, driven by the attempt

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to use data to predict the future as much as possible.

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This felt very adequate and and fitting to to a name.

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For, the people now following us,

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we first may get out of the way that you're early stage

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investor. That means people can pitch you a pre seed seed. Are you

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also doing series a? Typically, we wouldn't. We

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might in a series a, because, obviously,

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we do save up a a certain amount of of

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our total funds in order to be able to

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do follow on investments. So, if we're already

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invested through precede and seed financing in a company

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and we feel for whatever reason we want to participate in the

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series a, we can. However, that's certainly not our

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sweet spot. What what was what was the core vision, the

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idea behind it, why you've set up

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this fund? Is there, was

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there, like, one event, one minute where you thought,

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oh, that struck me. That's why I decided to set it up or was

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it a gradual process? I mean, I think

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there's element of both in there. I think at the core,

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there's a fascination with 2 things. On the one

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hand, the fascination with science and with

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the scientific attempt to solve the problems that we

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face. So we haven't mentioned that so far, but, Blue

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Forest is focused on deep tech companies

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solely. Joe, companies that come from a

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scientific or engineering background. Now this fascination

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of the scientific approach is paired with the

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specific stage that these companies we're looking in,

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into are in, which is pre seed and seed,

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which means really the 1st institutional money they

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typically get. So you have a

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a university project, a PhD project,

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where a team comes together because they have an

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idea to commercialize a scientific solution that

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they've discovered. And now they

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might become part of an accelerator program or an incubator

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program. They might get a little bit of grant funding, but

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there comes the stage where they need to sort of leave the nest, so to

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speak, and receive the first external financing.

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And this is us. We want to help these companies

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with that leap. And in my experience, and I can tell that

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from my own experience having founded a biotech before,

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this first step outside of the protected

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environment of the of this academic background into

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the commercial world is one of the most difficult

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steps to achieve at a start up. And specifically, to

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obtain, the the amount between 250,000

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a1000000 is very difficult because there are not a lot

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of VCs that are ready to invest ticket sizes

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that small. And so there's there's the fascination of

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the complexities of the situation on the one hand. And on the

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other hand, maybe more towards the, oh my god.

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Now we can do it. The one thing that always

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bothered me in early stage venture capital is that

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the conflict you have between having to look at 100

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and 100 of companies in order to find the companies you're interested

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in, on the one hand. And your fund

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size is limited, so the budget you have

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through your management fee is limited. And so there's this

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conflict of you don't really have enough money to do a

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proper job in evaluating all the companies you look at.

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So you have these VCs that do what what's called spray and

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pray, and they just invest 50,000 in

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whatever company basically has half way of a PowerPoint

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presentation in a certain segment, and they just hope that one of them will make

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it. And there is the other sort of segment, and I

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I that that's not an attack on anybody, but that's like,

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okay. I've come from this world of venture capital. Now

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I am some sort of a guru and through sitting across from

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somebody, I can somehow feel whether they will have what it takes to

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be successful. I don't really think that that's interesting.

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Even if it was true, it wouldn't be scalable or repeatable.

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So the moment that really brought my partners

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and I to this moment of, yes, we can do it,

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is basically the possibilities that

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arose through robot process automation as

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in AI and the AI tools that are available now

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in the combination with the computation cost that

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basically went down dramatically over the last years, which means

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that the kind of mathematical modeling that

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was reserved to hedge funds and large funds in the

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past is now achievable for

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small funds as well. And that means that we can save

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so much time and so much effort, on the

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human resources side that we can do a proper job evaluating

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those companies. Right? And so, I'm sorry for the

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for the little bit long answer to that question, but the

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reality is it's a combination of all these factors that led us to be like,

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okay. Now is the right time. We're ahead of the curve with this approach,

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and and we want to do this. Let me quickly

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structure my thoughts here. So, at at first, I'm not

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trying to make light of the situation because an investment the first

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VC investment may have a life

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changing impact, hopefully, for

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the better. But it sounded kind of

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like you are the business body of a more

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scientific oriented founder team. Would that be appropriate to

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say? I think that's not making light of it at all. I think that's,

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yeah, I think that's appropriate to say. I I typically,

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say them a little bit different way, which is we are also translators.

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Right? We translate, because

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very heavily scientific founding

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teams with typical scientific

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backgrounds, on the one hand, don't really speak the

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same language, so to speak, as the investors world

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on the other. And, in a way,

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there is almost a translation needed from a complex

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solution to a compelling equity story. And we

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believe that there is lots of good projects and lots of good

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ideas and even good teams with high potential

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where the ideas never get funding just because they

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were uncapable of translating the idea

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into a shape and form that's digestible

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for most VC investors. And, the

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other question I had is where or did the

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statement or explanation called as you like? Because,

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you are aiming for around €60,000,000 in your

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fund. And, the people are calculating in their head, well, they can

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make up to 60 investments, but that's not necessarily

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true. As you said, you may participate in a series a round.

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So that means you have assigned some of the money

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for the initial investments and then maybe

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half or even larger part for the so called follow-up investments

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that you still have, a relevant stake, a big stake

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in the Startupradio that are doing success for the

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fund raising instead of growing successfully. Right?

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Absolutely. So, we're aiming to have a portfolio

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size of around 40 companies, which means that we're,

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aiming to to, invest in 40 initial

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companies and have the rest as follow on. So now if I

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tell you that our average ticket size will be between 500

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and a1000000, right, you can do the math very quickly that

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we save up up to 40% to do follow

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on investments. And that's that's that's a

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fairly standard ratio, specifically at early stage

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to to have some dry powder to do follow ons. We've

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been talking about ticket size. We've been talking about the stage.

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But we have not talked about which industries, which

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sectors are you investing in. And little disclaimer, if

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there would be only AI, it would be too boring and we wouldn't have an

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interview here. You are talking about

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deep tech, but very, differentiated.

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What kind of investments you are looking for

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here? Look. I think let's start by talking about what deep tech

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really means because as AI and deep tech feels like

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this almost label that people slap

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on onto something, and nobody really knows what it means. So

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at the core of it, deep tech just refers to

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technological solutions for complex scientific

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or engineering problems. Right? So now I understand that that's

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very broad, and it is broad. Right? And and we're not

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we're not trying to say it's not, it's it's not a very broad

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field. Now there's we invest across a

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very large part of that field for two reasons.

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1st, the

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there's every venture capital fund, every investor

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has to ask themselves the question of what do they bring

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to the table for the companies. Right? And,

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a lot of of VCs specialize in certain fields and

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certain industries, and so they can bring a lot of industry and field

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specific knowledge to those companies. However,

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that's not what we primarily claim. Of course,

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we can bring that through our network of advisors and through our network

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in the industry. But our primary asset that

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we bring to the table is in the translation, is in

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everything that's not specific to the deep tech field

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we're talking about. Because these founding teams

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often struggle with most of the other

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issues that are not related to their specific technological

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invention, right? They struggle with creating a proper business

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plan, with assessing product market fit, with, with

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building the structures, with building the teams that are necessary

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to achieve their goals. And that's where we we come in

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and we can really help them. And so this is true for all of

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of of the subsegments of Deep Tech.

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Now the only reason that this is

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possible is because of the stage we're investing in.

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Right? If we talked about the series a company, that wouldn't be

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possible. We would need to specialize because the problem that

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a series a company faces has nothing to do

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anymore or very little to do anymore with creating a proper business

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plan. They're past that. Right? So in a way, the

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stage we have selected allows us to be very broad

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in our investment approach. And the second part is

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that the sectors we don't invest in. Right? And and,

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it pains me to say, but drug development, the the company

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I cofounded would not fall into our investment scheme.

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Right? And, this has something to do with

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drug development and all the the deep

249
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tech fields that are heavily regulated.

250
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One of the biggest risks they are facing is of regulatory

251
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nature. And the regulatory risk is very

252
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difficult to hedge outside of being very

253
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specialized in the field. Right? Because we have the scientific

254
00:15:50.660 --> 00:15:54.395
side, you have the, you know, what we bring to the table, the financial,

255
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the business side, we can handle these 2. Right? But if there is

256
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the complexity, the added complexity of a very, very

257
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complicated regulatory process that is,

258
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highly specific to the specific field we're looking into,

259
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that's something we cannot handle on top of it. Joe, unfortunately,

260
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drug development would fall into a category of company we cannot

261
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invest in. So my understanding is that drug development,

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especially if you want to do it on an impact

263
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on a medical scale, is very, very expensive. We do

264
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have this frequently in our news that goes up to the billions.

265
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That is so. And the the money is not our primary

266
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concern. Although, I mean, we we have we wouldn't participate

267
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significantly in the total needed funding. But our primary

268
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concern is that you need to be an expert in the regulatory

269
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environment in order to be able to handle these kind of investments.

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Talking about investments, what kind of

271
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criteria do you use to

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evaluate your investment? Because as we've been talking, it's

273
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very specific. It's very scientific.

274
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You you just cannot say, well, it's an ecommerce shop. They do it as

275
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much as this. This is the multiplier of public listed company.

276
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That is what I can use. That just doesn't work. It it's

277
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way more complex than that. Right? That is true, and that's a

278
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very, very good question. And I

279
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it's gonna be a little bit of a longer answer, but but bear with me.

280
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There's there's certainly and and that's something I hear a lot

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when talking about, this with other early

282
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stage venture capitalists that tell me, well, Max,

283
00:17:41.265 --> 00:17:44.865
it's it's nice that you say that you're data driven. It's nice that you say

284
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that you want to evaluate the companies more in a

285
00:17:48.625 --> 00:17:52.320
more in-depth way. But these companies are so

286
00:17:52.320 --> 00:17:56.080
young. There is no data on these companies that is available for

287
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you to evaluate. And I'm always saying, well,

288
00:18:00.165 --> 00:18:03.465
that's only true really in part. Right?

289
00:18:03.765 --> 00:18:07.605
Because, yes, there is no data about the companies or not much

290
00:18:07.605 --> 00:18:11.320
data about the companies in terms of financial data, that

291
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doesn't mean that there is not a lot of other

292
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alternative data that's available around the markets,

293
00:18:18.985 --> 00:18:22.684
around the scientific field, around the founders themselves.

294
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Right? And we're talking beyond the track record of the founders.

295
00:18:26.745 --> 00:18:30.450
We're talking about the background of the

296
00:18:30.450 --> 00:18:34.290
founders, even as far as the credit scores of the

297
00:18:34.290 --> 00:18:38.005
founders. Right? Those are all information

298
00:18:38.005 --> 00:18:41.525
that exists out there. And now before creating a

299
00:18:41.525 --> 00:18:45.370
discussions I don't want to create here, I'm not saying that the good or

300
00:18:45.370 --> 00:18:49.049
bad credit score is either good or bad. Right? But I'm saying it's

301
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a variable we're looking into to feed

302
00:18:52.525 --> 00:18:56.285
into our model that evaluates the companies we invest

303
00:18:56.285 --> 00:18:59.850
in. Now the the

304
00:18:59.850 --> 00:19:03.610
general criteria is still a

305
00:19:03.610 --> 00:19:07.290
lot of the times the same across VCs and it's the

306
00:19:07.290 --> 00:19:10.684
same for us. Obviously, we need to see a

307
00:19:10.684 --> 00:19:14.125
certain market potential. Right? We need to see a

308
00:19:14.125 --> 00:19:17.740
certain value that's created through the solution

309
00:19:17.880 --> 00:19:21.640
that the company presents. Because somebody needs to

310
00:19:21.640 --> 00:19:25.475
pay for it in future. Right? And people will be willing to

311
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pay for it in function of the economical

312
00:19:29.075 --> 00:19:32.720
benefits that the solution generates for them. Right? If I

313
00:19:32.720 --> 00:19:36.559
can solve a big scientific problem, but if it's if

314
00:19:36.559 --> 00:19:40.320
it's not reflected in a big economical problem for

315
00:19:40.320 --> 00:19:44.115
somebody, right, nobody will pay for it. Because

316
00:19:44.115 --> 00:19:47.895
the person still needs to make more money because

317
00:19:48.035 --> 00:19:51.495
he employs he or she employs the solution that's presented.

318
00:19:53.049 --> 00:19:56.509
So a quick example. Right?

319
00:19:56.809 --> 00:20:00.110
I was working with this company in the field of photonics.

320
00:20:00.655 --> 00:20:04.275
Photonics now, is a specific

321
00:20:05.215 --> 00:20:08.995
subsegment of electrical optical modulation,

322
00:20:09.970 --> 00:20:13.730
which means the translation of an optical signal

323
00:20:13.730 --> 00:20:17.170
into an electrical signal. Right? I mean, optical signal, glass

324
00:20:17.170 --> 00:20:20.265
fiber, you know, all kinds of information,

325
00:20:21.045 --> 00:20:24.885
but our computers or our devices still work with

326
00:20:24.885 --> 00:20:28.679
electrical signals. Electrical signals. Right? And so the modulator, which is the

327
00:20:28.679 --> 00:20:32.120
core of every computer chip really, translates the

328
00:20:32.120 --> 00:20:35.585
optical signal into an electrical signal. We've

329
00:20:36.284 --> 00:20:39.745
inadvertently a lot of people talk a lot about this because,

330
00:20:40.365 --> 00:20:44.100
this is really the field, where all the energy is

331
00:20:44.100 --> 00:20:47.400
lost and all the, heat is created, for instance,

332
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because of the energy that's lost in this translation. So

333
00:20:52.100 --> 00:20:55.395
now a big step would be to solve

334
00:20:55.934 --> 00:20:59.155
to to create a more efficient and more,

335
00:21:00.255 --> 00:21:04.060
capable modulator. Right? So if you

336
00:21:04.060 --> 00:21:07.820
were to solve this issue, the problem you would be

337
00:21:07.820 --> 00:21:11.420
solving is immense. And the

338
00:21:11.420 --> 00:21:15.105
companies, the intels of this world, and

339
00:21:15.805 --> 00:21:19.565
other chip manufacturers of this world would be paying would be willing to pay

340
00:21:19.565 --> 00:21:22.870
a lot of money for somebody to solve that issue.

341
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Now, you might solve some other issue right

342
00:21:27.090 --> 00:21:30.745
around that. It's a huge market. However,

343
00:21:31.284 --> 00:21:34.825
if the problem that you solve is not significant

344
00:21:35.044 --> 00:21:38.024
enough for the people to change their process,

345
00:21:38.780 --> 00:21:42.539
then you will never be successful with it. Right? If you if if the

346
00:21:42.539 --> 00:21:46.015
gain in efficiency would be, I don't know, 5 percent,

347
00:21:46.015 --> 00:21:48.915
then Intel is not going to rebuild its $1,000,000,000

348
00:21:49.535 --> 00:21:52.895
Gigafactory in order to adapt to it. It's just not good

349
00:21:52.895 --> 00:21:56.730
enough. Right? So you you always need to understand that

350
00:21:56.790 --> 00:22:00.230
there needs to be a market, that's ready to pay for the

351
00:22:00.230 --> 00:22:03.924
innovation you've created. That some people call

352
00:22:03.924 --> 00:22:07.765
that product market fit, however you wanna

353
00:22:07.765 --> 00:22:11.440
call it. Right? You need to be aware of the

354
00:22:11.440 --> 00:22:14.800
market and the that somebody needs to pay for the

355
00:22:14.800 --> 00:22:18.560
innovation. And, a lot of people underestimate that that's

356
00:22:18.560 --> 00:22:22.195
not a view of right now. That's

357
00:22:22.195 --> 00:22:26.035
a view of in the amount of time

358
00:22:26.035 --> 00:22:29.360
that you need to get your product ready. So if you're still in the r

359
00:22:29.360 --> 00:22:32.960
and d stage, it's irrelevant what the market looks like right

360
00:22:32.960 --> 00:22:36.735
now. What's relevant is what the market will look like in

361
00:22:36.735 --> 00:22:40.495
5 years when your product will be ready. Right? And so this

362
00:22:40.495 --> 00:22:44.100
is something where where I think it's possible to

363
00:22:44.100 --> 00:22:47.940
start to understand why it's so important to be data driven. Because the data

364
00:22:47.940 --> 00:22:51.720
driven allows us to make much better predictions

365
00:22:51.940 --> 00:22:55.505
of future market developments and future product developments

366
00:22:56.044 --> 00:22:59.725
in order to assess not what's the market right now, but what will the

367
00:22:59.725 --> 00:23:03.350
market be when the product's ready. And the second

368
00:23:03.350 --> 00:23:06.810
big aspect is, okay, this is the goal where you want to go.

369
00:23:07.350 --> 00:23:11.015
What are the resources and capabilities that you need to

370
00:23:11.015 --> 00:23:14.555
achieve that? Right and this is when it comes to the team

371
00:23:14.935 --> 00:23:18.295
and obviously the team and this is every VC is gonna sell that this is

372
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not the reinvention of the wheel and I'm not claiming it to be. The team

373
00:23:21.470 --> 00:23:25.090
is one of the core factors that we evaluate.

374
00:23:25.549 --> 00:23:29.155
Now the crux of the matter is what specifically,

375
00:23:29.215 --> 00:23:32.835
and and I'm I'm not going to go into the nitty gritty of it.

376
00:23:33.135 --> 00:23:36.880
But, it's it's suffices to say that we are

377
00:23:36.880 --> 00:23:40.340
very deliberate in the variables that we consider

378
00:23:40.799 --> 00:23:44.635
in terms of the team. And it's far beyond, does this

379
00:23:44.635 --> 00:23:48.395
person have a prior track record? Or do I like this person?

380
00:23:48.395 --> 00:23:51.695
Or do this person seem competent in the 1 on 1?

381
00:23:52.040 --> 00:23:55.740
It's a lot of variables that are related to the professional

382
00:23:56.040 --> 00:23:59.800
and personal, aspects, of the founding team

383
00:23:59.800 --> 00:24:03.235
and the founder we sit sit across. And that again

384
00:24:03.235 --> 00:24:06.295
feed into the model that predicts certain variables.

385
00:24:06.915 --> 00:24:10.510
An example for that would be the founder's ability

386
00:24:10.730 --> 00:24:14.570
to raise money in the future. Right? Because one of the the key

387
00:24:14.570 --> 00:24:18.275
aspects of the success of the company is will it

388
00:24:18.275 --> 00:24:21.875
have enough money to survive long enough in

389
00:24:21.875 --> 00:24:25.670
order to have success? Right? Will will the the team be

390
00:24:25.910 --> 00:24:29.350
able to raise the money that it needs to fully

391
00:24:29.350 --> 00:24:33.030
develop the product? And, of course, we help with

392
00:24:33.030 --> 00:24:36.075
that. And, of course, we have a network and we'll support the support the team.

393
00:24:36.075 --> 00:24:39.835
But still, how likely is it that the team is able to do

394
00:24:39.835 --> 00:24:43.434
that? And there is now we we I'm sure that

395
00:24:43.434 --> 00:24:47.050
everybody here has ideas right away. If

396
00:24:47.050 --> 00:24:50.650
I ask you, what are the variables you think that I

397
00:24:50.650 --> 00:24:54.395
can look at in order to make a prediction on

398
00:24:54.395 --> 00:24:58.235
the likelihood of you being able to raise future money. Right? What are

399
00:24:58.235 --> 00:25:02.080
the personal and professional variables that

400
00:25:02.080 --> 00:25:05.920
I could look at? And this is something we spent a lot of

401
00:25:05.920 --> 00:25:09.365
time evaluating. We've looked at 100 and thousands

402
00:25:09.585 --> 00:25:13.365
of past companies and past, successful

403
00:25:13.505 --> 00:25:17.240
and unsuccessful StartupRadio order to figure out the

404
00:25:17.240 --> 00:25:20.779
key variables that we need to consider in order to make certain predictions

405
00:25:20.840 --> 00:25:24.460
about these factors. That's very interesting. So

406
00:25:25.465 --> 00:25:29.145
bottom line, I do believe a large impact is on the

407
00:25:29.145 --> 00:25:32.985
team, on what you like, think about the

408
00:25:32.985 --> 00:25:36.740
team. Before I get to to to to to to my

409
00:25:36.740 --> 00:25:39.720
next question or your question following that up,

410
00:25:40.585 --> 00:25:44.185
When you've been digging so much into past successes and

411
00:25:44.185 --> 00:25:48.025
failures, was there a variable that surprised you the

412
00:25:48.025 --> 00:25:51.360
most either predicting success or failure?

413
00:25:52.540 --> 00:25:56.215
That's a very good question. It

414
00:25:56.215 --> 00:25:59.975
was surprising to see that, I mean, that

415
00:25:59.975 --> 00:26:03.815
that I'm very careful with now,

416
00:26:04.054 --> 00:26:07.590
how the interpretation of the data and how I want to formulate this

417
00:26:07.590 --> 00:26:11.350
because, we are still in a stage where we try

418
00:26:11.350 --> 00:26:14.710
to really make sure that we narrow it down to the right

419
00:26:14.710 --> 00:26:18.375
variables because you have multiples Joe the multi

420
00:26:18.375 --> 00:26:21.735
correlated variables, right? Where where it's really difficult to say,

421
00:26:21.735 --> 00:26:24.610
okay, which of these variables are,

422
00:26:25.710 --> 00:26:29.250
in effect sort of predictive in it by itself

423
00:26:29.725 --> 00:26:33.105
and which of these variables are just correlated to the predictive

424
00:26:33.165 --> 00:26:36.125
variables. But, let me say it like this.

425
00:26:37.110 --> 00:26:40.250
According to the data, it looks like,

426
00:26:40.950 --> 00:26:44.250
a heterogeneous founders team, which means

427
00:26:44.790 --> 00:26:48.225
if you have a large diversity

428
00:26:48.685 --> 00:26:51.905
in the founders team, right, once you

429
00:26:52.845 --> 00:26:56.690
cross a certain threshold is very beneficial. So

430
00:26:56.690 --> 00:27:00.050
this is ironic. And the only way I can make sense of it is like

431
00:27:00.050 --> 00:27:03.775
this at the beginning. If you're very different, it

432
00:27:03.775 --> 00:27:07.375
makes it a bit harder. Right? At the very Menninger. Because it's

433
00:27:07.375 --> 00:27:10.275
just not as intuitive to to communicate.

434
00:27:11.350 --> 00:27:15.030
It's not as easy to find the consensus and so on and so

435
00:27:15.030 --> 00:27:18.490
forth. But once you

436
00:27:18.870 --> 00:27:22.375
found a working Joe to make that

437
00:27:22.375 --> 00:27:26.054
work, it becomes a huge asset because you're

438
00:27:26.054 --> 00:27:29.789
much more likely to have a larger spectrum of

439
00:27:29.789 --> 00:27:33.250
ideas at the beginning of a problem solving process.

440
00:27:33.630 --> 00:27:37.470
Right? And there is other research that backs that up

441
00:27:37.470 --> 00:27:41.265
to a certain extent, which shows that if you in

442
00:27:41.265 --> 00:27:45.025
an ideation process, if you wanna solve any kind of problem and and the

443
00:27:45.025 --> 00:27:48.860
ideation process, you have a very large spectrum of ideas and you

444
00:27:48.860 --> 00:27:52.380
start with a large funnel and you narrow it down as a

445
00:27:52.380 --> 00:27:56.115
team, that is beneficial to the solution and you'll actually have a

446
00:27:56.115 --> 00:27:59.554
better solution. So, we believe that

447
00:27:59.554 --> 00:28:03.380
founding teams, right, often you will see that all the time. It's also

448
00:28:03.380 --> 00:28:07.000
friends and, you know, you get along anyway, so you become a team.

449
00:28:07.460 --> 00:28:11.140
Yes. That makes it easy at the beginning, but don't fall into the

450
00:28:11.140 --> 00:28:14.685
trap that there is a risk if you always have the same

451
00:28:14.685 --> 00:28:18.365
opinion. Right? It's beneficial to have different opinions in the

452
00:28:18.365 --> 00:28:22.000
team. We talked a lot about team. About the

453
00:28:22.000 --> 00:28:24.500
ideas, I do believe they're very diverse,

454
00:28:26.800 --> 00:28:30.135
especially in this area. If you're talking about research,

455
00:28:30.835 --> 00:28:34.435
it's not only they have an idea, they evaluated something and they come

456
00:28:34.435 --> 00:28:37.980
around with a PowerPoint presentation. Sometimes it's even a complete

457
00:28:38.120 --> 00:28:41.559
life achievement of somebody who started out with a

458
00:28:41.559 --> 00:28:45.179
bachelor, did a PhD, and spent years years in a laboratory.

459
00:28:46.755 --> 00:28:49.655
But in general talking, the idea

460
00:28:51.315 --> 00:28:54.990
to look seriously at it from your perspective should have

461
00:28:54.990 --> 00:28:58.830
progressed beyond just the PowerPoint state. Right? Oh, yeah.

462
00:28:58.830 --> 00:29:02.525
Absolutely. I mean, in the field of deep tech or in the field of

463
00:29:02.525 --> 00:29:06.285
technological development, you talk about what you call

464
00:29:06.285 --> 00:29:10.045
TLR stands for technological readiness, not TRL.

465
00:29:10.045 --> 00:29:13.770
Sorry. Technological readiness level. Right? And it goes

466
00:29:13.770 --> 00:29:17.450
from 1 to 10 where 1 is really the ideation stage and

467
00:29:17.450 --> 00:29:18.190
10 is

468
00:29:25.445 --> 00:29:28.750
interesting for us because that means that you have done

469
00:29:29.529 --> 00:29:33.210
laboratory testing. You have built a certain lab scale

470
00:29:33.210 --> 00:29:36.875
prototype, and you have generated real data.

471
00:29:37.255 --> 00:29:40.934
Right? So, everything before that is really

472
00:29:40.934 --> 00:29:44.440
purely theoretical and wouldn't be ready for us to

473
00:29:44.440 --> 00:29:47.960
invest in because we think that you need a certain proof of

474
00:29:47.960 --> 00:29:51.505
concept, right, in order to move forward. Now

475
00:29:51.505 --> 00:29:55.284
I'll give you an example again to to understand this. There was this,

476
00:29:55.985 --> 00:29:59.825
it's a company we're currently evaluating, so I'm not gonna go too deep into

477
00:29:59.825 --> 00:30:03.330
the details, but very exciting company that developed a

478
00:30:03.330 --> 00:30:06.310
material that if you mix it with water,

479
00:30:06.770 --> 00:30:10.545
will have a huge

480
00:30:10.545 --> 00:30:14.385
effect on the ability of soil to retain water. So

481
00:30:14.385 --> 00:30:17.900
I Menninger you're I mean, to to to make to dumb it down almost too

482
00:30:17.900 --> 00:30:21.740
much. Right? You're in the desert. You have no water. Right? Or

483
00:30:21.740 --> 00:30:24.799
you're in Southern California and you struggle with dry

484
00:30:26.375 --> 00:30:29.895
soils a lot. Right? And the problem with dry soil is

485
00:30:29.895 --> 00:30:33.600
water goes right through it and minerals go right through it.

486
00:30:33.600 --> 00:30:37.200
So you typically have the problem. You need to have a lot of irrigation water

487
00:30:37.200 --> 00:30:40.880
and you need to use a lot of fertilizers in order to get a proper

488
00:30:40.880 --> 00:30:44.664
yield. Now this product, you mix it as it's a it's a

489
00:30:44.664 --> 00:30:48.265
gel. Right? You mix it into the irrigation water, and it

490
00:30:48.265 --> 00:30:52.020
goes between the soil particles, and it helps the soil to

491
00:30:52.020 --> 00:30:55.640
retain water and minerals. And so now on the ideation

492
00:30:55.780 --> 00:30:59.485
stage, that wouldn't have been enough to for us to invest. But

493
00:30:59.485 --> 00:31:02.865
they now recently did large scale laboratory

494
00:31:03.005 --> 00:31:06.765
tests where they tested that with 7 different kinds

495
00:31:06.765 --> 00:31:10.060
of crops in the laboratory and

496
00:31:10.120 --> 00:31:13.800
literally observed soil with the product, soil without the

497
00:31:13.800 --> 00:31:17.325
product. And we look at the amount of irrigation I need, and we look at

498
00:31:17.404 --> 00:31:21.085
the yield I get. And this product, for instance, has

499
00:31:21.085 --> 00:31:24.924
on average require make made the the the

500
00:31:24.924 --> 00:31:28.760
crop require 80 percent less irrigation water

501
00:31:29.060 --> 00:31:32.760
and still gotten 30% more yield,

502
00:31:32.900 --> 00:31:36.475
which is absolutely incredible. If that would prove correct on a large

503
00:31:36.475 --> 00:31:39.914
scale, absolutely incredible. So but we

504
00:31:39.914 --> 00:31:43.674
needed this lab trial to be done before we can

505
00:31:43.674 --> 00:31:47.140
really evaluate it. So to come back to your question,

506
00:31:47.600 --> 00:31:51.360
as long as it was on the pure ideation stage or maybe, like, on the

507
00:31:51.360 --> 00:31:54.775
molecular design of the particle

508
00:31:54.915 --> 00:31:58.755
stage, too early for us. But as soon as it had

509
00:31:58.755 --> 00:32:02.190
these first lab trials and we come from a technological

510
00:32:02.570 --> 00:32:06.010
readiness 3, and and we transition into 4,

511
00:32:06.010 --> 00:32:08.669
right, that's when it becomes interesting to us.

512
00:32:10.125 --> 00:32:13.825
What my what I had in mind when I heard this was also

513
00:32:14.045 --> 00:32:17.885
well, there can be some areas that barely get rain. But

514
00:32:17.885 --> 00:32:21.650
when it rains, it starts flooding down the stream, and you can also

515
00:32:21.950 --> 00:32:25.790
work on those areas. Very interesting thing. Let

516
00:32:25.870 --> 00:32:29.225
let's go a little bit from, where you're

517
00:32:29.225 --> 00:32:33.005
investing, what do you how you're investing, a little bit into

518
00:32:33.865 --> 00:32:37.679
the fund itself, a little bit market trends and the outlook, and then we're

519
00:32:37.679 --> 00:32:41.440
good. We are already talking for more than 30 minutes. By the way,

520
00:32:41.440 --> 00:32:44.899
did you study philosophy? No. I didn't.

521
00:32:45.120 --> 00:32:48.265
I studied law, which, is a little bit counterintuitive,

522
00:32:48.725 --> 00:32:52.565
but, yeah. I've I've I haven't been

523
00:32:52.565 --> 00:32:56.370
working in law for the past 7 years, so so it's been

524
00:32:56.370 --> 00:33:00.050
a while. I see. I see. Well, nonetheless, that it plain

525
00:33:00.290 --> 00:33:03.995
explains long answers. Don't worry. Not going

526
00:33:03.995 --> 00:33:07.775
to this here. I I really like the answers. They're well thought through.

527
00:33:08.795 --> 00:33:11.695
Talking a bit little bit about Blue Force Ventures,

528
00:33:12.940 --> 00:33:16.720
the fund and the management. Can you describe a little bit,

529
00:33:17.500 --> 00:33:21.020
how you guys are set up? Because if you currently go on your

530
00:33:21.020 --> 00:33:24.445
website, there is a logo on there and 3

531
00:33:24.445 --> 00:33:27.498
locations, Berlin, London and Menninger.

532
00:33:28.044 --> 00:33:31.210
But not a lot more. Can you tell us a little bit about the team

533
00:33:31.210 --> 00:33:34.970
and how you guys are set up? And I would also

534
00:33:34.970 --> 00:33:38.410
be interested because before we talked, we talked a lot

535
00:33:38.410 --> 00:33:42.065
about important places where you could find

536
00:33:42.365 --> 00:33:46.205
deep tech investments because I do believe you invest all over Europe.

537
00:33:46.205 --> 00:33:49.420
Right? That's true. I mean,

538
00:33:50.120 --> 00:33:53.800
let let's unpack the the questions. First off, about us and the

539
00:33:53.800 --> 00:33:57.134
team. So, the the

540
00:33:57.595 --> 00:34:01.195
idea, to to create this fund, I

541
00:34:01.195 --> 00:34:04.580
think a lot of us had sort of simultaneously

542
00:34:05.120 --> 00:34:08.820
or ideas around that. And, I

543
00:34:08.960 --> 00:34:12.534
personally know my co founding partner,

544
00:34:12.915 --> 00:34:16.534
from business school because after studying law, I also did

545
00:34:19.059 --> 00:34:22.839
executive MBA at the business school in France. And I met,

546
00:34:23.299 --> 00:34:26.760
my co founder, Moises Janae. He's from Mexico.

547
00:34:27.135 --> 00:34:30.494
And trust me when I say this is a good example for

548
00:34:30.494 --> 00:34:34.175
having a fairly different approach to certain things

549
00:34:34.175 --> 00:34:38.000
and, you know, Menninger it arguably a bit more difficult at

550
00:34:38.000 --> 00:34:41.520
the Menninger, but now we really see how it how it benefits

551
00:34:41.520 --> 00:34:45.034
us. But, that set aside, I had

552
00:34:45.175 --> 00:34:48.395
another partner, in Germany that ironically,

553
00:34:49.415 --> 00:34:53.259
I've made 2 deep tech investments with in the past. Well,

554
00:34:53.259 --> 00:34:56.639
I advised him. He's an angel investor, and I basically

555
00:34:56.699 --> 00:35:00.000
created the contact between him and the deep tech investments,

556
00:35:00.935 --> 00:35:04.695
that, that he invested in. And Joe, we got

557
00:35:04.695 --> 00:35:08.340
together and, and we we founded

558
00:35:08.340 --> 00:35:11.940
Blue Forest Ventures, and, took on right

559
00:35:11.940 --> 00:35:15.720
away a long term, a long time employee of mine

560
00:35:16.275 --> 00:35:20.115
that I've, hired right out of university 4 years

561
00:35:20.115 --> 00:35:23.315
ago, first at Sanity Group, then,

562
00:35:23.715 --> 00:35:27.450
transitioned to Endosame, and now came

563
00:35:27.450 --> 00:35:30.830
with me to Blue Forest. So he's basically employee number 1,

564
00:35:31.930 --> 00:35:35.615
of our fund. So we are 2 full time

565
00:35:35.915 --> 00:35:38.655
general partners, myself and Moises.

566
00:35:39.515 --> 00:35:43.340
We have our partner, Julian, who is not a full

567
00:35:43.340 --> 00:35:47.180
time partner and, hence, not a full time member and,

568
00:35:47.180 --> 00:35:50.875
hence, is not a general partner, but is working with

569
00:35:50.875 --> 00:35:54.715
us, very intensively. And we

570
00:35:54.715 --> 00:35:58.530
have Nicholas, our associate and employee number

571
00:35:58.530 --> 00:36:01.430
1, and now we are in the process of hiring 2 analysts.

572
00:36:03.410 --> 00:36:07.010
Located in Berlin, what else is there? Yeah. Fund size,

573
00:36:07.010 --> 00:36:10.724
we've talked about before. We are quite efficient in

574
00:36:10.724 --> 00:36:14.345
our process as we've set it up in a way so that it can

575
00:36:14.405 --> 00:36:17.900
be very largely automated. Right. Joe,

576
00:36:18.460 --> 00:36:22.300
we we a lot of the things that typically take a lot of time away

577
00:36:22.300 --> 00:36:25.980
from people, specifically analysts are fully automated in our process Joe

578
00:36:25.980 --> 00:36:29.555
that that saves a lot of time. And the other

579
00:36:29.555 --> 00:36:33.395
question that you had was the locations and, why we

580
00:36:33.395 --> 00:36:37.030
chose certain locations, what we look at. So Berlin is

581
00:36:37.030 --> 00:36:40.870
is purely optimistic choice. Right? And I I would love to

582
00:36:40.870 --> 00:36:44.490
to tell you that there was some kind of elaborate thought behind it.

583
00:36:44.684 --> 00:36:47.744
But the reality of things is Berlin is a good

584
00:36:48.045 --> 00:36:51.724
location for a venture capital fund in general. There's a lot of

585
00:36:51.724 --> 00:36:55.280
things going on in Berlin, and there's quite a big ecosystem. Now

586
00:36:55.280 --> 00:36:58.820
Berlin is not the most relevant location for deep tech in Europe,

587
00:36:59.440 --> 00:37:03.275
but it's the location where I had most of my personal

588
00:37:03.275 --> 00:37:07.115
network, and we had the best chance to to get something off

589
00:37:07.115 --> 00:37:09.695
the ground, on a short term basis.

590
00:37:11.030 --> 00:37:14.870
But we are all quite flexible, and we all travel a

591
00:37:14.870 --> 00:37:17.450
lot. So we can easily cover

592
00:37:19.265 --> 00:37:22.945
Europe as a whole and even, selectively, as I said, the

593
00:37:23.085 --> 00:37:26.165
Menninger, Southeast Asia, North America. And

594
00:37:28.040 --> 00:37:31.799
the question of which locations are most relevant for us

595
00:37:31.799 --> 00:37:35.494
in terms of finding suitable deep tech companies is

596
00:37:35.494 --> 00:37:38.954
mostly related to the ecosystem

597
00:37:39.335 --> 00:37:43.115
that research institutes and academic institutions

598
00:37:43.174 --> 00:37:47.000
such as universities have created. So you really see a big

599
00:37:47.000 --> 00:37:50.760
correlation between the amount of successful deep

600
00:37:50.760 --> 00:37:54.185
tech companies, and the

601
00:37:54.325 --> 00:37:57.785
support system and the the the information

602
00:37:58.165 --> 00:38:01.900
system even that these institutions have created

603
00:38:01.900 --> 00:38:05.740
around that. One of the primary examples of that and really

604
00:38:05.740 --> 00:38:09.164
hats off to them is what the ETH Zurich has

605
00:38:09.164 --> 00:38:12.865
created in Zurich around the ETH. There's a myriad

606
00:38:13.005 --> 00:38:16.760
of accelerators, incubators,

607
00:38:16.980 --> 00:38:20.820
support programs, people that will help you, people that

608
00:38:20.820 --> 00:38:24.040
will give you advice on on how to set up a company,

609
00:38:24.905 --> 00:38:28.665
on what you need to be to be cognizant of and so on and

610
00:38:28.665 --> 00:38:32.425
so forth. And you really see that that creates a

611
00:38:32.425 --> 00:38:36.050
lot of, it creates

612
00:38:36.430 --> 00:38:40.190
a situation where StartupRadio just have a

613
00:38:40.190 --> 00:38:43.944
better chance to take off. Right? And so,

614
00:38:44.424 --> 00:38:47.724
BK Zurich is certainly or Zurich is is is a very

615
00:38:47.865 --> 00:38:51.380
relevant location for us. Munich is a relevant location for

616
00:38:51.380 --> 00:38:54.900
us. Eindhoven is location, and certainly

617
00:38:54.900 --> 00:38:58.705
Cambridge, Oxford are relevant for us. And and I

618
00:38:58.705 --> 00:39:02.145
could list more and more, but essentially, it

619
00:39:02.145 --> 00:39:05.640
depends on the ecosystem that is created in and

620
00:39:05.640 --> 00:39:09.400
around the research institutions and universities, in these

621
00:39:09.400 --> 00:39:13.160
locations. Yeah. That's really

622
00:39:13.160 --> 00:39:16.835
it. One of my questions

623
00:39:17.295 --> 00:39:21.135
would have been how you're trying to support companies, but we've already

624
00:39:21.135 --> 00:39:23.970
talked about you being a translator,

625
00:39:25.230 --> 00:39:29.070
research into business. I do believe that was already a

626
00:39:29.070 --> 00:39:30.450
very valid point.

627
00:39:33.085 --> 00:39:36.765
I was curious how do you measure

628
00:39:36.765 --> 00:39:40.319
success? On one hand side, there's the financial success. You

629
00:39:40.319 --> 00:39:44.099
get a really big company. You you get really successful

630
00:39:44.240 --> 00:39:47.920
in terms of finance because the valuation of the company goes

631
00:39:47.920 --> 00:39:51.615
up. You list them. You exit it one way or another.

632
00:39:51.835 --> 00:39:55.055
That is fine. But what else

633
00:39:55.570 --> 00:39:58.950
would you consider success with the companies you invest in?

634
00:40:00.850 --> 00:40:04.485
Look. We're very we're very fortunate in the

635
00:40:04.485 --> 00:40:08.085
way because the field we're looking into makes it

636
00:40:08.085 --> 00:40:10.025
very easy to combine

637
00:40:11.589 --> 00:40:15.130
success on the financial level, which I would translate to return,

638
00:40:15.589 --> 00:40:19.315
right and positive impact on the other side. Right?

639
00:40:19.315 --> 00:40:22.214
And now everybody talks about impact and sustainability.

640
00:40:23.234 --> 00:40:27.015
And oftentimes, this is a tough choice to make between

641
00:40:27.700 --> 00:40:31.460
being return driven and being impact driven. In

642
00:40:31.460 --> 00:40:35.059
deep tech, the compromise you need to make in

643
00:40:35.059 --> 00:40:38.494
order to achieve both is not a lot.

644
00:40:38.714 --> 00:40:42.335
Because almost in the definition of deep tech,

645
00:40:42.795 --> 00:40:46.590
you have the impact that's imminent. Right? And

646
00:40:46.590 --> 00:40:50.350
if you have a successful Deep Tech company, you will

647
00:40:50.350 --> 00:40:53.790
have a large impact. Now is this a positive or

648
00:40:53.790 --> 00:40:57.245
negative impact is something that that obviously

649
00:40:58.665 --> 00:41:02.445
is a question that you need to ask. But generally

650
00:41:02.665 --> 00:41:06.080
speaking in the overwhelming majority of

651
00:41:06.080 --> 00:41:09.680
cases, we're talking about positive impact and a

652
00:41:09.680 --> 00:41:13.484
clearly positive impact. Now there are certain exceptions if we

653
00:41:13.484 --> 00:41:17.164
talk about certain defense application where really the only

654
00:41:17.164 --> 00:41:20.830
thing you could think of is defense application. But this is

655
00:41:20.830 --> 00:41:24.430
the the and and and, obviously, certain cases where I don't

656
00:41:24.430 --> 00:41:28.035
know, I guess you could come up with

657
00:41:28.035 --> 00:41:31.395
with innovation around, you know, gambling or something that

658
00:41:31.395 --> 00:41:35.130
could qualify as deep tech. You know,

659
00:41:35.130 --> 00:41:38.490
I'm I'm really shooting from the hip here. But you see, it's it's

660
00:41:38.490 --> 00:41:42.250
almost hard to come up with something that that would have a

661
00:41:42.250 --> 00:41:45.775
negative impact. So, this makes it

662
00:41:45.775 --> 00:41:48.595
very easy for us to be able to say success

663
00:41:49.214 --> 00:41:53.040
means it has a great return and has a positive impact.

664
00:41:53.040 --> 00:41:53.540
The

665
00:42:01.760 --> 00:42:04.694
measurement of sustainability. And, of the measurement of sustainability.

666
00:42:06.595 --> 00:42:09.654
And we we are attempting to create

667
00:42:10.090 --> 00:42:13.770
something that that we refer to as sustainability by

668
00:42:13.770 --> 00:42:17.210
design and, a positive impact by

669
00:42:17.210 --> 00:42:20.744
design, which means one of the big things a lot of

670
00:42:20.744 --> 00:42:24.265
investment funds struggle with is that they have

671
00:42:24.265 --> 00:42:28.019
structures, They have that process. And now there is these regulations

672
00:42:28.319 --> 00:42:32.160
that come on top of that and the the regulations around impact. And if we

673
00:42:32.160 --> 00:42:35.895
talk about Europe, for instance, you have these, article

674
00:42:36.675 --> 00:42:40.455
6 to 9 in terms of how the degree

675
00:42:40.515 --> 00:42:43.050
of sustainability you really want to achieve.

676
00:42:44.170 --> 00:42:47.770
And this is quite difficult to implement for a lot of funds. Right?

677
00:42:47.770 --> 00:42:50.510
Because they need to basically retroactively

678
00:42:52.335 --> 00:42:56.015
take this this set of regulation and apply it to

679
00:42:56.015 --> 00:42:59.775
their existing process. Right? And that becomes quite tedious in terms

680
00:42:59.775 --> 00:43:03.049
of how do you report that, how do you track the KPIs, and so on

681
00:43:03.049 --> 00:43:06.890
and so forth. What we're able to do, and this is really nothing

682
00:43:06.890 --> 00:43:10.250
that I want to take too much credit for because it's just because we're new

683
00:43:10.250 --> 00:43:13.925
and we can set up structures new, is we

684
00:43:13.925 --> 00:43:17.225
integrate the measurement of impact and the measurement

685
00:43:17.365 --> 00:43:21.200
of of, you know, the effect of the companies in future,

686
00:43:21.200 --> 00:43:24.900
the likely effect. We integrate that into our evaluation

687
00:43:25.040 --> 00:43:28.625
system. They're an integral part of it, which

688
00:43:28.625 --> 00:43:32.305
means that, that this is not something that

689
00:43:32.305 --> 00:43:36.079
we that we that we take into account separately in the

690
00:43:36.079 --> 00:43:39.700
aftermath or something like that. But that's an integral part

691
00:43:39.920 --> 00:43:43.704
of the entirety of our evaluation system. And this makes it much

692
00:43:43.704 --> 00:43:47.144
easier for us to be able to say, our

693
00:43:47.625 --> 00:43:51.224
the companies that we invest in, if they're successful, they will

694
00:43:51.224 --> 00:43:54.430
also have a clearly positive impact

695
00:43:55.050 --> 00:43:57.710
on society, environment,

696
00:43:58.730 --> 00:44:02.145
and the world we live in, basically. We are

697
00:44:02.145 --> 00:44:05.825
now talking for 45 minutes, mostly you, which is totally

698
00:44:05.825 --> 00:44:08.920
fine. The people out there already know my opinion and my voice,

699
00:44:09.960 --> 00:44:13.420
enough. And I would only have 2 closing

700
00:44:13.720 --> 00:44:17.420
questions. First one, what

701
00:44:17.559 --> 00:44:21.345
are the market trends, any any merchant

702
00:44:21.345 --> 00:44:24.785
technologies that excite you right now besides from

703
00:44:24.785 --> 00:44:28.600
AI? And then I have a second

704
00:44:28.600 --> 00:44:32.280
question, but this time, I'll only bother you with 1. Okay. I'm going to

705
00:44:32.280 --> 00:44:35.994
try to keep it much shorter now. I don't

706
00:44:35.994 --> 00:44:39.835
know if you heard about the Gartner hype cycle, and

707
00:44:39.835 --> 00:44:43.530
basically the measurement of the hype. And I

708
00:44:43.530 --> 00:44:47.030
I think that we're all subject to that in one way, shape, or form.

709
00:44:48.290 --> 00:44:51.795
What excites me really is data transmission and data storing

710
00:44:51.795 --> 00:44:55.555
technology. And I keep it that far because I think that whatever

711
00:44:55.555 --> 00:44:59.335
innovation we talk about, whether it is quantum computing,

712
00:44:59.395 --> 00:45:03.140
whether it's AI, whether it's the whole term term of, like, more

713
00:45:03.140 --> 00:45:06.440
specifically language, speech recognition, and so on and so forth.

714
00:45:06.980 --> 00:45:10.714
All relies or, like, all will add to the quantity

715
00:45:10.714 --> 00:45:14.395
of data that we, the world, needs to handle,

716
00:45:14.395 --> 00:45:17.910
right, and store and transmit and so on. So I

717
00:45:17.910 --> 00:45:21.750
think that the the basic data infrastructure that we're

718
00:45:21.750 --> 00:45:25.270
talking about on the hardware and software basis is what

719
00:45:25.270 --> 00:45:28.935
fascinates me a lot. That was much shorter. Right? That

720
00:45:29.675 --> 00:45:33.435
it actually surprised me. Yeah. And and and the

721
00:45:33.435 --> 00:45:36.960
last question I would have because it is important

722
00:45:37.020 --> 00:45:40.640
for a lot of StartupRadio, entrepreneurs, founders

723
00:45:41.020 --> 00:45:44.575
seeking VC investments. How do you

724
00:45:44.575 --> 00:45:48.175
stay informed? What kind of media? What kind

725
00:45:48.175 --> 00:45:52.015
of outlet? Of obviously, besides StartupRadio,

726
00:45:52.255 --> 00:45:56.070
need founders to get into in

727
00:45:56.070 --> 00:45:59.670
order to grab your attention. I

728
00:45:59.670 --> 00:46:03.125
mean, we try to not be too

729
00:46:03.125 --> 00:46:06.505
much attached to the these hype topics because

730
00:46:06.805 --> 00:46:10.645
that would be such a selection bias already at the

731
00:46:10.645 --> 00:46:14.220
get go that would skew our entire model. Right? Our

732
00:46:14.220 --> 00:46:16.720
model is based on a fairly unbiased

733
00:46:17.820 --> 00:46:21.485
gathering of information. So what we do typically is actually

734
00:46:21.485 --> 00:46:25.245
go to the universities into the accelerators and try

735
00:46:25.245 --> 00:46:28.785
to have a fairly objective look at the entire cohort

736
00:46:29.150 --> 00:46:32.510
of StartupRadio is there. We also use a lot of

737
00:46:32.510 --> 00:46:35.809
scraping technology in terms of extracting

738
00:46:36.030 --> 00:46:39.715
information from certain databases and websites

739
00:46:39.855 --> 00:46:43.535
and the Internet in general in order to to fill

740
00:46:43.535 --> 00:46:46.250
up our funnel with leads. Now,

741
00:46:47.349 --> 00:46:50.010
of course, we follow certain,

742
00:46:50.869 --> 00:46:54.650
large scientific publications and, and and

743
00:46:55.435 --> 00:46:56.655
certain typical,

744
00:46:59.595 --> 00:47:02.820
media outlets, in terms of I mean,

745
00:47:03.300 --> 00:47:06.760
I'm not saying too much if I say that we do

746
00:47:07.380 --> 00:47:11.185
Canvas for instance, LinkedIn quite regularly, although LinkedIn is tricky. I don't

747
00:47:11.185 --> 00:47:14.725
wanna go too deep into that because they're very, very difficult to scrape.

748
00:47:15.105 --> 00:47:18.785
Obviously, we're following whatever compliance there is

749
00:47:18.785 --> 00:47:22.270
out there, But, there is certain sources,

750
00:47:23.450 --> 00:47:27.050
that go beyond publication, which we talk more

751
00:47:27.050 --> 00:47:30.635
about registration of patents, for

752
00:47:30.635 --> 00:47:34.155
instance, that we look into in order to get an idea of what's

753
00:47:34.155 --> 00:47:36.895
happening. We also follow a lot of the

754
00:47:37.680 --> 00:47:41.380
actual scientific publications coming out of certain universities

755
00:47:41.440 --> 00:47:45.060
in order to know what are the topics that they really focus on.

756
00:47:45.565 --> 00:47:49.405
I see. So the the the the only question left

757
00:47:49.405 --> 00:47:53.100
for me right now, everybody who has listened

758
00:47:53.100 --> 00:47:56.620
through this is obviously interested in either

759
00:47:56.620 --> 00:48:00.320
becoming an LP or pitching you for money.

760
00:48:00.940 --> 00:48:04.555
What is the best way to reach out to you guys to

761
00:48:04.555 --> 00:48:08.075
pitch you? Is it LinkedIn? Is it the website? Both is

762
00:48:08.075 --> 00:48:11.615
fine. The website has an email address that's there.

763
00:48:13.259 --> 00:48:17.019
And LinkedIn is also fine. We follow both. We're we're we're

764
00:48:17.019 --> 00:48:20.079
very receptive for both. I'm also happy,

765
00:48:21.245 --> 00:48:25.085
to to be hit up directly on LinkedIn. I'm not very

766
00:48:25.085 --> 00:48:28.685
complicated with that, at all. You know it. We communicate through

767
00:48:28.685 --> 00:48:32.520
LinkedIn a lot. So I'm I'm somebody that that does

768
00:48:32.520 --> 00:48:36.360
try to answer most messages I get, on LinkedIn,

769
00:48:36.360 --> 00:48:40.055
for instance. But, yeah, email is also perfect. You'll

770
00:48:40.055 --> 00:48:43.655
find an email address on the website. Right. So I do

771
00:48:43.655 --> 00:48:47.020
believe that was everything from my side.

772
00:48:47.320 --> 00:48:51.080
Thank you very much for this very extensive interview. I I really,

773
00:48:51.080 --> 00:48:54.444
really liked it, especially since I don't have to talk a

774
00:48:54.444 --> 00:48:57.964
lot. And I do believe that gives a very

775
00:48:57.964 --> 00:49:01.780
interesting behind the scenes insight of a early stage

776
00:49:01.780 --> 00:49:05.460
investor that is usually not available for most

777
00:49:05.460 --> 00:49:09.220
entrepreneurs here. Thank you very much, Max. It was a pleasure having you as

778
00:49:09.220 --> 00:49:13.005
guest. It was a pleasure again. Have a

779
00:49:13.005 --> 00:49:16.065
good day. Bye bye. You too. Thanks. Bye bye.