Venture Europe: Entrepreneurship | Technology | Venture Capital | Eu/Acc
00:10:49 · Calin Fabri
[00:10:49] S0: Got it. When you tackle an idea, I understand that maybe you and your brother kind of influence each other and you kind of help each other. How do you go about an idea? When does it form in your head? And then maybe you can take me to the moment where you're like, Hey, let's create Model ML.
00:11:14 · Chaz Englander
[00:11:14] S1: Arnd stayed on at Go Path in engineering for a few years. I ended up spending a lot of our time. We pulled a bunch of our money together and we were kind of investing ourselves and you can see where this goes as a model ML was kind of born out of this. But during that process, for me personally, I was working on my idea after idea. We'd hired a few people to work on the investing side, but I was just working on my ideas. And I'd constantly be pitching these things to Arles. Right? I was like, okay. This is it. Let's go. And he would be like, nah. And then that... Because, I mean, also, I should say, like, coming out of that sort of process, like, any any m and a process, frankly, regardless of size, you know, it's like grueling. You think it's like a three month period, it's more like a nine month period, and it's like every waking hour, you're sleeping next to your laptop, you know, particularly if there's like, you know, folks in different time zones, it's like it's grueling. We really thought hard and then eventually we were spending time building software to improve our own day to day, so basically to improve the way that we were investing ourselves. But we didn't really think. It was weird. I was brainstorming ideas, also working in software. Then suddenly, it kind of came to us where we'd met people through private investment that were like, hey, this is really good. We want this. And then it was so clear to Arne and I, this clearly should be a product. And had a couple of conversations, people wanted it. This was like 2024 and then ended up spending out and raising about $100,000,000 across a couple of rounds last year. But it was like instant pull, like it was clear the market wanted something that we'd ultimately built for ourselves. So in that respect, there was no need to debate it. It was so obvious, I think, at the time.
00:13:01 · Calin Fabri
[00:13:01] S0: And what were like the two, three features that you guys kind of start with? And also, I'm a banker and I work on a couple of transactions and like in my head there's just no way that an AI banker will not exist in two, three years. There's just like no way. Because if you think about like all the you just have to prepare all the documentations, you have to connect with several sources, you have to take the financial models, you have to talk about the market, you have to create a whole storyline and the narrative, then you have an investor list, you have to reach out to them. The whole like, you know, let's say the sales process is just like I remember like with one client, it's just like I asked Gemini to do a report about, I don't know, like an insurance brokerage market and it did in five minutes what an analyst did actually in three, four days. And this is like with updated numbers and everything. I was like, nah, like an AI banker in the next two, three years. And also I think like now you have like the biggest wealth transfer in history where most of the companies don't get sold because you don't have M and A advisors, right? So it's definitely a huge... But I kind of wonder what was the first feature where you kind of showed to someone and was like, yes, I definitely want that. Because I would assume that since then you kind of expanded horizontally to kind of...
00:14:22 · Chaz Englander
[00:14:22] S1: Yeah. So let me explain that chronologically. So first of all, you know, back in the day, you know, this was like GPT 3.5 time, and we were like, we were calling functions with large language models. Right? That's what we were doing. Obviously, now it's like, there's different terminology and like whole world's evolved, but we just thought that was really cool. Right? Like, could take something Like could in natural language, you could give it to a model, and you could put it front of the model, a bunch of functions, and you could say, call the one that's appropriate, etcetera. And that was really cool to us, right? Now what that kind of unlocks and where we started was we would receive something via email, you know, an opportunity, any opportunity, and we would wanna put together a one pager based on what we'd received. Right? But think it would be the work that you would, like, actually do as an investor. Right? So when you receive an opportunity, you know, even if we take a venture type opportunity, not not to invest as a fund, but direct. Right? One of the first things that you do is you'll go on LinkedIn. You'll be like, do I have any, connections in common with this person, etcetera? Real tasks that humans actually do. We did all this, and that was pretty much our first use case. What happened quickly was we were like, okay, well, there's so many applications of this. It's not just like, do I have connections in common? It's like, okay. Has there been any recent transactions, like, in this space? What do they look like? And and where do I get that data from? Is it from PitchBook? Is it from Crunchbase or S and P, Cap IQ, etcetera? And so we ended up building this tool that was like a workflow automation tool. So it'd be like, this would happen when this happens, you know, go and do this. Clearly, now these systems are like far more, you know, sophisticated. But back then, this was like insanely novel. We thought this was awesome and added, to be honest, was. And so so that's kind of where it started. When we actually looked at the market, we were really focused on, like, where the alpha was and, like, what... Where time was actually going. And if you look at the market, we kind of put it into three kind of core buckets, asset management, consulting and banking in general, so not just in investment banking, right?
00:16:33 · Chaz Englander, Calin Fabri
So and we really quickly saw that a lot of time is going towards one effect as material creation, call it, Word, PowerPoint and Excel, really repetitive stuff. So at the beginning, we focused on the more sort of what we call deterministic type workflows, whereby you know, you would have a five page PowerPoint output, and you would be producing that output on the companies in your coverage universe, like every, like, quarter, for example. But quite quickly, as these models have evolved, they they can be sort of less and less deterministic, right? So they can actually go away and do the numbers on something, etcetera. And I can list some pretty incredible use cases, but that's kind of where it started. And now, obviously, it's like a different world that we're seeing. [00:17:16] S0: Super cool. So I have a client, work with this private equity firm, and they told me that what they're trying to do right now, because they have like a €250,000,000 fund, they're a little bit late stage so they're going to write tickets, 30,000,000 Euro, 50,000,000 Euro. So they're like, look, there are only seven companies that we're going to invest in. And the universe in our geography, it's actually not that big. There are actually just 100, 200 companies in which we're going to invest. We would like to create, we would like to ideally have a platform that kind of follows all these companies, it's connected with all the sources, and in my head, that's absolutely doable today. You know, that's just absolutely doable today.
00:18:02 · Chaz Englander
[00:18:02] S1: Let me let me, like, touch on, right, like, the... Where the world has come, know, in our space over the last six months. Right? Six months or so, it was great that you could come into a product like ours or similar, and you could build the first draft of a 50 page PowerPoint output model, IC memo in a word, whatever. That was great, right? But now what's happened is, I'll try and tie one very similar to the use case that you're describing is like, what is the work that actually gets done? If we take an investment banking type use case, everyone has their spreadsheets of companies that they cover in one way or another. Right? And then monitoring those companies across a bunch of sort of qualitative and quantitative metrics, right? The quantitative stuff, let's just say we are looking to take private opportunities quite easy. You've got like an Excel extension that's pulling them FactSet or Cap IQ. That's great. Then you have the qualitative stuff and you've got an analyst or associate coming in and updating those things. Role X has changed or this person has left, that sort of stuff. Then there'll be these signals and these triggers where an analyst associate will bring this to the desk of an MD and they'll say, Yeah, that actually is interesting. I've seen this before in my head. Based on these signals, go off and do the numbers, go off and do an LBO, whatever it is, go off and do the downstream modeling work. Come back to me with the work and then, okay, is the model in the numbers? Great. What's the next phase? You're nodding your head, right? You put together the marketing materials, right? You've got the marketing materials and boom, that's it. You're going down to sponsor X and you're pitching, right? Now, you know, in our product, that whole thing is automated. Right? It's called the list, the pulling of the quality and concept metrics, triggering the LBO. Is the LBO in the money? It is. Great. I'm gonna put together the 20, 30, 40 page, take private deck. Boom. I'm sending that whole thing, trigger all of the modeling work that's done and the deck to the MD in its inbox... His or her inbox at 9AM in the morning. That's it. They're ready to go pitch. That is the world that we're in now. This concept of all of that underlying work is a thing of the past. Now to your point, that means that we theoretically can get a lot more work done.