E411: How AI Is Creating a New Generation of Venture Firms

23:39Andre Retterath:And suddenly there is tons of money available. So I think there is a lot also about timing. But what the founder of RFI has done really well is the sense of we are close to a breakthrough. I don't know if it will happen this year or if it will happen next year, but I'm sure it will happen in the next three years and not in 10 years. You mentioned today founder brands are much more important than they were five years ago. How do you explain that? And what are the second order effects of having a great founder brand? For most founders, it's not intuitive to show their face, to be online every single day, because most of them just want to be heads down building a company.

24:20Andre Retterath:But what most founders forget is that it's a very scalable way. And I mentioned earlier, most often the shareholders, the team, the customers, the early partners, they join a company because of the ability to sell a vision from the CEO, from the founders. that's in most cases and if you look into the scalability of this ability to sell this is what i see as a founder brand so a founder brand for me is a scalable way of packaging your skill to bring people behind your vision and scale it into the world make it accessible for everyone so that everyone hears about your brand everyone can see if they are a good fit and they buy into your story and want to join your journey in whatever capacity.

25:06Andre Retterath:So I think it's for many companies unavoidable. I think some other companies, if you build a very deep tech, deep in the stack and hard tech, you might have other problems for now. You might already have access to the talent. But for most companies that sit higher in the stack, I think it's unavoidable because also the higher you sit in the stack, the more difficult it is to differentiate. Today, when we can vibe code something over the weekend, there are a thousand companies that are lookalikes and they tackle the same problem. So how can you actually stand out? If you look into wide coding, I mentioned this example before, there were probably at the time 40 or 50 companies with similar approaches.

25:50Andre Retterath:But some of them have managed to attract the capital, but also the talent. And I think founder branding was a very strong instrument for that. Out of those two traits, attracting capital and attracting talent, which one's more important today? It depends which part of the stack you're building in. The deeper you sit in the stack, the more I lean towards the ability to attract capital. the higher you sit in the stack the less relevant the capital is and the more important the talent becomes relative i think always both is incredibly important but if you cannot have both i would say this is a framework to look at it because if you sit deep in the capital you can have it deep in the stack you can have the best talent in the world but if you do not have the capital, you will never succeed.

26:46Andre Retterath:And the other way around, if you sit very high in the stack and you have all of the capital in the world, but you don't have the right talent, you won't be successful. If you sit lower in the stack, you have all of the capital in the world, but you don't have the right talent. I think there's a good chance you can still outcompete some of the peers because access to capital is incredibly important. So the deeper in the stack, if you're a power, chips, large language models, the more capital you need. And the higher in the stack, in terms of the application layer, you need more talent. Why is that?

27:21Andre Retterath:Because the higher in the stack, the less relevant the capital becomes. Today, you can vibe code something over a weekend, and you can find value proposition market fit right away. So you can just send it to your ex-followers or something, and you will have the first testers. and you can even bootstrap such a company easily into a few millions of ARR profitable. But then later on only, it becomes important to raise more capital and also get the distribution right. So the higher you sit in a stack, the less capital you need to achieve specific milestones. The lower you sit in a stack, the more capital you need early on to even show a proof of concept and de-risk so that you can bring it into production.

28:03Andre Retterath:For example, if you build a rocket or something, you need to first build the engine. Then with the engine, you need to show a hot fire test. You need to burn for 30 seconds or something. Before you get this, you won't get access to more capital. So there are technical milestones which all require huge amounts of capital. And this is why I'm saying the deeper you sit in the stack, the more it matters to have skills to attract capital. When we last chatted, you mentioned that Early Bird as one of the first firms really building an AI native platform. What does that mean exactly? When I joined Early Bird in 2017, I had more than five years background in industry.

28:42Andre Retterath:I did process automation. So I was really trained for efficiency and then got into venture capital and saw the most brilliant people I have ever met doing literally monkey work every single day. So I remember to give you an anecdote. One of the founding partners asked me, look, I will attend this conference in three weeks. here's a list of all of the attendants 400 people let me know who to meet and I was like okay how should I do this yeah just google the people so I started going through crunch base back then I looked up there in Germany there was like a version of LinkedIn called Xing so I looked up different people I looked up people on LinkedIn and then I created a scoring I looked into our basic CRM system back then and said, have you been in touch?

29:30Andre Retterath:Yes, no. That was mostly the only information that was available. And then after about a week of work, I could tell him these are the most promising companies. And I was like, I cannot believe that such smart people around here spent a whole day doing stupid stuff like that. This can be easily automated. Even stuff like, can we do an introduction to one person? Suddenly everyone is chatting in the Slack or WhatsApp. do we know this person? So investors spent so much time with manual and inefficient work that I took this as a trigger and really think venture capital from first principles. And I presented that to the founders of early birth back then, and they got really excited.

30:12Andre Retterath:So they said, look, this is the future of venture capital. We need to invest in this direction. So I joined as an investment professional, which I think was also very important that I did not join as an engineer, for example, because you need to understand the processes end to end. You need to be in the full depth of the decision making to actually leverage technology to change the processes. It's very difficult outside in. If I would need to understand your processes and I would want to transform them, it's very difficult outside in. It is possible. I could shadow you for, say, two months and then start automating step by step.

30:48Andre Retterath:But if you build something for yourself and you have the skills to actually do this. I think that's the best setup. And then I started building tools for myself. So simple automations in the first beginning, then knowledge graphs to represent our network, to calculate the network density, like which people do best. Is there a new opportunity evolving in the network and so on and so forth. And we scaled it from there. So I built for myself between 2017 and 2020. Then we hired the first full-time engineer because the strategy was set. We knew the different components that were required. And then we ramped up the engineering team.

31:27Andre Retterath:So at the peak, we had eight full-time engineers at early bird. So that was about 20 % of our total team size, which was quite big to do the fundamental groundwork, the data plumbing, as I called it. And today we have three senior engineers who really spend most of their time sitting on top of this data infrastructure and really making sure that it is actionable in all of the functions across the investment firm. So we have rethought from first principles what the tech stack of an investment firm should look like. And then secondly, we have transformed and are still in the process of transforming, frankly speaking, all of the processes and workflows because investors are very reluctant to change the workflow.

32:14Andre Retterath:So a lot is also about change management that we are facing in the last, say, 12 months, as many more of these tools have become available. I want to get to change management in a bit. Today's Friday. Give me a couple examples of how you're using AI just this week. Look, everywhere. I can tell you, simply said, like our stack has changed a lot. So back then we built all of this data infrastructure and we built a dashboard on top, but a bit more than one and a half years ago, I questioned if a dashboard is actually the future way of interacting with data. So you could see that there were different LLMs and AI chatbots available.

32:58Andre Retterath:And I personally believed already back then and even more so today that the single interface to interact with different software tools and databases will be AI chatbots. So today we use a cloud code across the organization. Everyone uses it. And then it's important to connect all of the data, all of the knowledge that we have through different MCPs with our system. And with that, I do everything. Like, for example, earlier this week, one of my portfolio companies received a term sheet. So I just throw it in. It benchmarks against some of our existing term sheets in the database if I'm preparing for a meeting.

33:35Andre Retterath:So I have a sequence of different founder meetings every week. And I always have 15 minutes in between. and 15 minutes before the next founder meeting, I get a digest, which tells me this is the next company. Here are our interactions. It's a brief summary about what the business does, core questions, automated generation of competitive landscape. We can see all of the traction that is available, recent media, news mentions, and so on and so forth. So you get like a one-pager digest. And if I get into a founder meeting today, I'm probably in 95 % of the cases, better prepared than some of the other investors who are oftentimes not preparing for some of these first intro meetings.

34:19Another example is that if we create an investment memo, for example,

34:25Andre Retterath:I remember back then it was a working document that easily took us one or two weeks to prepare. Today, we take all of our existing knowledge and we just say, create an investment memo. We have trained that on thousand plus investment memos from the past, so I can easily generate it. Another example, we have built an internal skill. So like everyone in the organization builds skills. We have a process where we can submit them. They are reviewed internally, if they are secure, if they are compliant and so on. And then they get rolled out across the organization. We have one that has the early bird CI in a presentation.

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38:29Andre Retterath:Create in the early bird CI the presentation. It will cut it in the right kind of sizes for the slides and so on and so forth. And by click of a button, it's like 98 % there. I could give you so many more examples, but it really, it's part of every single workflow from Monday morning until Sunday night. What was the hardest part about integrating AI into a venture capital firm? Cultural change. Initially, if you would have asked me, I had assumed it was data availability and data quality. So lots of this data is qualitative by nature if you invest in pre-seed or seed. So lots of this data is not available.

39:10Andre Retterath:We learned over time that you can quantify this data. So for example, a university degree might be very qualitative and subjective. but if you look at it you can actually rank different universities and then more importantly you can also rank different degrees for example in Germany there's University of Bonn if you would just rank them they are tier two but if you study math at University of Bonn it's suddenly tier one the same is true say 10 years back you worked at Google you would assume this is a tier one but if you did say front office at Google not sure if this is still tier one as a founder But if you were like level five engineer at Google for four, five, six years, this is very, very strong.

39:55Andre Retterath:So you could see how you can suddenly classify some of the qualitative data. And this is what I assumed was a problem earlier. Then contradicting data. That was for along my assumption. So if you have two sources stating different information, how can you actually balance the data and what is the most credible one? So this is called entity matching and deduplication so that you can create a single source of proof. For long, I thought that's the problem. But now if I zoom out, I think all of these technical problems are solvable. If you bring the right people, if you have the right tool set, they are solvable.

40:31Andre Retterath:Something I completely underestimated is the change management and the ability to adapt workflows and make the data actionable. So specifically, to us as partners, it was very clear that this is the future of venture capital. But if you have an investment professional who had been with the firm for five years, and they had one specific way of adding a deal to the CRM system, doing follow-up, adding specific notes, it was very difficult to tell them, look, there is this new tool. You can just transcribe the call with Granola. It will push with the NCP into our system. You can just use different skills to build the investment memo.

41:13Andre Retterath:Lots of investment professionals actually struggled and some even avoided it. Like proactively said, look, I don't know if this is so useful. I think I'm faster the way I used to instead of just spending now the weekends learning how to do all of this stuff. And what I found is that lots of people are just afraid if they see a console, they are like, this is for the developers. This is not for me. Like I will use my software interface and do simple stuff as I used to do. And I completely underestimated this. So thankfully, we are now one step further. But I think that will be the biggest blogger for a majority of the investment firms.

41:51I think a lot about this. I have a second master's in psychology. I'm a big fan of BF Skinner who populized this concept of operant conditioning, which is basically human beings react to rewards and punishment, almost like Pavlov's dog, but for humans. And I had Ryan Hoover from Weekend Fund and founder of Product Hunt on the podcast. And he's really changed my thinking because he doesn't look at things as processes or as one-time things. He looks at things as products. So he creates products and processes in order to streamline a lot of things that he does over time. And I was really influenced on that.

42:27But then when I started trying to apply it to my life, there's a friction. In other words, in order to automate something, it might take two to four weeks of managing an engineer. So the easy thing is to go and do it. So go on LinkedIn and do this manual process. I get positively conditioned for it right away. I get a positive effect. The hard thing is to lengthen your reward cycle to four weeks and having to go through all these issues. And then after two weeks, you're almost like back to break even. So six weeks in, you're at break even. And then the rest of your life, you could use these new processes, these new technologies.

43:03But it is hard to get out of this mindset of just doing something today and just getting through the end of the day versus building in a tech-centric way.

43:13Andre Retterath:I couldn't agree more. And I think it's very important to start with the quick wins. So I'll start with a simple project where within 30, 60 minutes, you get the first win. For example, our founders, they are in the early 60s. And I remember after Christmas when I retested Claude Code and the new model came out that completely changed the game in early December last year. I told him, now is the time. You should just use it. You can describe whatever. And I remember a few weeks later, I sat in a board meeting and like a virtual one. And my partner came in, he knocked on the door and he was like, Andre, Andre, I have this Eureka moment.

43:52Andre Retterath:It's insane. I tested it to do X, Y and Z. that I've never been so productive, like everything will change now. And I think that's really the mindset to start with something very simple, to get these quick wins, reduce the time to value, because then you can start to build more complex projects and you can layer it on top. And that's exactly what you just mentioned, because it might take longer to build something to solve the task once. Like sometimes you are just like, I could just quickly do it, of course. But if you do this task 20 times a week, then after two, three weeks, you're already break even.

44:27Andre Retterath:And I think that's right, mental model. What you also said from Ryan, and I think that was also an insight that we had, I would call it, you need to measure what matters. And if you don't measure it, you cannot improve it. And to give you an example of what that means, we have built a sourcing engine. So first of all, we looked at the status quo. We believed we had comprehensive coverage all across Europe back then, as most other investment firms believed. Now, we built a system. First of all, we defined what is comprehensive coverage. So we said we want to see all opportunities where our competitors end up investing.

45:05Andre Retterath:So we defined a list of 200 investment funds. And then we looked at the legal entities and scraped all of the public registers across Europe. So suddenly we could see the full universe of opportunities where our competitors were investing in. And then we looked at our internal system and said, which companies had we been in touch with? Where did we upload some documents? So we call them the hits. And then we divided the companies that we have seen by the number of companies our competitors had invested in. And you get something that we call a hit rate. So we could see that all across Europe, we had seen about 70, 72 % back then.

45:45Andre Retterath:This was 2019, 2020. And we believed we would see everything, but we missed one in four opportunities. So we introduced the system and we built our platform Eagle Eye to a point where we have, depending on the quarter of the season and so on and so forth, about 95, 96 % of hit rate today. meaning we see 19 out of 20 opportunities and we introduce a time factor before the closing so we see 19 out of 20 opportunities across europe at least six weeks before they close the funding round and now we asked our investment professionals why are you not using the platform and they said look i just meet the other investment professionals my peers at other firms and i'm sure i will see them.

46:32So we said at some point, look, we need to introduce the right measures.

46:37Andre Retterath:And we agreed as the partnership that we measure our investment professionals who are split geographically on the more junior levels by hit rate. So suddenly you cover France and you have the hit rate where you measure your coverage. And I don't care if you use our platform Eagle Eye or not, but just as a hint, Eagle Eye has 96 % of the relevant opportunities in this geography. So it might be easier to just use the platform versus going for another coffee meeting with this other VC. And this is the way how we got our investment professionals to actually start using the Eagle Eye platform. And we made it part of their performance reviews.

47:12Andre Retterath:So we said, look, we don't care how you get there. We look at the outcome, but we will measure your performance among other dimensions by the geographic hit rate. And we have been doing the same across other dimensions. And where we see, once we measure it we know the status quo and we know if we are on track for something and we can actually measure the impact of any initiative that we have be that on the tech side also the human more traditional side of things going back to operate conditioning you had vcs in your organization analysts associates that have been trained that have been just conditioned to meet with other vcs and you weren't pushing a process to them you were pushing an outcome and output.

47:56And then you show them essentially a new skill, Eagle Eye. And over time, as they start to outperform and get positively conditioned for this new process, now they started to just naturally adapt to this new process.

48:09Andre Retterath:Exactly. Perfectly summarized. Like in the beginning, I thought I had more of a product mindset. I said, if we build Eagle Eye into such a great platform, they will just intrinsically use it by themselves. And I said, look, we see 95, 96 % of the opportunities are in the system. So obviously they will use it because they will see that they have a competitive advantage. They did not use it. So we needed to create the right measures and incentives for them and then show them the tool available to get there. Because otherwise, if we look at France, back then we had whatever, 65 % coverage. So we missed literally one in three opportunities in France.

48:45Andre Retterath:The system had 19 out of 20 in there. Why not use the system? So you can change the behavior if you set the right metrics and also have the right incentives to achieve these metrics. Anthony Pompliano on the podcast a couple of weeks ago, he's one of these guys that could explain complex things very simply. And he talked about you could turn a loser into a winner, but you can't turn a winner into a loser. And I've been thinking a lot about that. And there's a paradigm in terms of there's a victim mindset and there's an agency mindset. That's certainly a thing. But there's also a skill gap. And sometimes, quote unquote, losers just don't have the skills needed to be a winner.

49:27Sometimes they're just using the wrong processes, the wrong technologies. And if you could just give them the right tool set, then they could become winners. 100 one of the most critical skills across industries is really to unlearn old behaviors and i think what brought us here might not bring us there and at times where the technology changes

49:51Andre Retterath:so fast like you cannot imagine how often i was between should i use codex or cloth or should i use different kind of llms i played around back then with open claw i hosted stuff locally so i retest it and there is news every week. And I think it's very important if you're deep enough, if you're a beginner, don't let yourself be confused by all of the news out there. Just start somewhere. Like no matter if it's cloud code or codex, they're mostly the same. They have some differences, but just get started. That's the most important thing. As we said before, try to reduce the time to value and get some quick wins.

50:26Andre Retterath:But if you're deeper into the trenches, I think it's very, very important to unlearn what you learned before if it is necessary and something better is available. And I think just by the nature of how most investment professionals are trained, most of them have a traditional business background. They have a banking background. They have traditional MBAs and so on and so forth by education. And I think most have not learned to unlearn some behaviors and relearn new workflows and skills. And that makes them very inefficient. And that also creates blind spots that oftentimes they are not even aware of.

51:07Keeping a beginner's mind as you get older becomes harder and harder every single year. Being bad at something too. When I started the podcast three years ago, just doing it publicly and just seeing yourself and just being so disappointed in everything and just wanting to kind of start as a beginner was just so mentally hard. And I just had to grind through it. But the older you get, the more your ego. gets bigger, the more you ossify in terms of like new learnings, it just becomes harder and harder. That's why these 22 year olds are adapting to AI so quickly.

51:37Andre Retterath:Yeah, it's a different form of innovators dilemma. Once you become too big, too established, and you feel you know it all, it's time for disruptive change. And there are oftentimes the new kids on the block. So you can look at the YC classes where you have 20 something year old starting companies and reinventing complete industries. And I think more often than not, they are better positioned because they do not have the existing kind of, it doesn't work. We tried this in the past. It just doesn't work kind of mindset. You mentioned win rate, clearly an important metric to track as a venture capitalist.

52:18What other metrics are you tracking via your AI driven platform?

52:23Andre Retterath:Look, we track everything, but eventually it comes down. Do we return the money that we want? And what's upstream of that? So you can really do the math. So early stage venture capital funds, typically 10 year fund duration, they can be extended by one or two years. But let's take the long term horizon. It is DPI, distribution to paid in. We want to return money, not just paper money. one step earlier you can look at tbpi so the total value to pay them this is paper money essentially now you can look at and that's an aggregate on the fund level but you can also look at individual assets if we look at the individual assets we can look at the last valuation we can look at fair money valuation whatever we want to apply as a fund and we can measure this against our initial investment.

53:15Andre Retterath:So we have a multiple on investment. So we can look at this metric. Now we can go shorter and shorter and shorter. And then we can look for example at follow on funding rounds. We can look at the time between follow on funding round. We can look at the step up in valuation. Do they just double the valuation or do they quadruple the valuation? So we can look at all of this. And if we look at an investment basis, we can also look at it from a cohort basis. So we can look at which partner did the investment. How was the investment sourced? Was it sourced through events? Was it sourced through our network?

53:51Was it sourced through the Eagle Eye platform?

53:54Andre Retterath:And then we can try to find patterns. So we might see that one partner is an amazing investor. As often, there are individuals who just have like a very strong gut feeling. So they have a good mix of biases that lead them to make very, very good decisions. and we need to understand this as early as possible, try to codify it and really translate that into the broader investment organization. We can look, for example, historically we had attended more events. So what we did is we attributed the source of an investment opportunity and tried to measure on a cohort basis if these companies are successful.

54:33Andre Retterath:And what we found, at least for us, that we did source very few opportunities at some of the larger investment conferences. And those investments did not perform as well. So at some point, we just decided, look, we should attend less conferences and do more of, for example, vertical conferences. We are looking into some of the AI research conferences, ICML, iClear, and so on. And we are now looking into, are the companies that we source at these conferences more successful? So there are lots of metrics that we are tracking, but we need to define in the end, what are the key metrics and how do they relate to the input?

55:14Andre Retterath:Because otherwise, who cares if you like one specific investment has an amazing performance, nice, good for the LP, good for the person who did the initial investment. But if you do not extract learnings from that, if you don't do post-mortems, if you do not find patterns in this, it will be very difficult to institutionalize your knowledge. And I think that's a big difference. Lots of teams built an investment fund. It's a partnership, whatever, handful of partners, few partners, and they raise fund one, fund two, fund three, but they never institutionalize. And some others, and this is what we also try to do, is build an investment firm.

55:55Andre Retterath:So we try to institutionalize the knowledge so that it becomes more independent of individuals. And that's really our approach. And we leverage technology as one instrument to institutionalize this and make early bird less dependent on humans. Of course, in the end, we know it always comes down still to decision making, deal winning ability and so on and so forth. So the human factor still matters today. And I strongly believe it will still matter in the future. But the more we institutionalize, the better we can scale it up and give the best kind of characteristics and insights to the more junior investment professionals.

56:34What's compounded the most as you guys have grown to today, two and a half billion dollars under management?

56:39Andre Retterath:Look, if I think about what got easier over time and what started compounding, it's definitely the brand. So I think that was me being lucky. One of the right decisions I made was joining an established brand because the market has become very competitive and there are new funds evolving, new different models, micro, solo GPs, and so on and so forth every single day. And I think joining an established platform allowed me to, first of all, see the relevant opportunities. So back then, we already had about 10 ,000 investment opportunities per year that came inbound through our website. So building on top of a very strong network and inbound deal flow was very, very important.

57:25Andre Retterath:Secondly, it's not only about seeing the relevant opportunities. It's not only about making the right due diligence, but then also making the right decisions. So thankfully, I worked with very experienced partners who back then already did it for more than two decades. So I could really work very closely with them and understand the decision making part. So the decision making is the second part. And now the last part that is also very important is really the excess part. Nobody cares if you saw an investment opportunity, decided to invest, but the founder did not let you in. So you need to be in a position to really win the most competitive deals.

58:03Andre Retterath:And I think the firm brand is what opens the door. You as an individual partner need to walk through the door. So you need to convince the founder that you are the best partner from early bird in this case. And we all know, as much as the investment firms try to sell, if you work with Fund A, you work with the Champions League out there, and it does not matter which partner is doing the deal. But in reality, we all know it does. Lots of partners are employees. They are hired and they are jumping around firms. So every two years they have a new firm. But early stage investing is such a long-term business.

58:39Andre Retterath:So for us, it was also very important to incentivize the people long-term so that you also can build these long-lasting relationships, which I think is very important. So to your question, what does compound? I think the brand as the firm compounds, but also, and we mentioned that earlier on the founder level, we had founder branding. I think the personal brand of the investor also compounds over time. If I want to get in a very competitive AI deal here in Europe, it has become way easier for me after having invested in Aleph Alpha, Black Forest Labs, DeepCode, SNCC, and so on and so forth. Today versus just five years ago.

59:20Do you think the concept of the partner versus the firm is more appreciated by older or more serial founders? Or do you think that first-time founders now because of podcasts and media are starting to really appreciate that as well?

59:34Andre Retterath:I see a very clear picture that first-time founders tend to optimize a lot for the firm brand. It might be for many of them an ego thing. they might assume that it is kind of a good proxy. So they are associated with success. So if I work with them, it's like the holy grail, getting their money, it will help us to be better at talent attraction and so on and so forth. So I see first-time founders optimizing a lot for that. Second or third, fourth-time founders, I see optimizing a lot for the individual person. So of course, it needs to be a sufficient brand. They would not work with the tier three brand if they have an offer from a tier one brand.

1:00:14Andre Retterath:But if it is tier one brand against tier two brand, I have seen many founders who optimize in this example for the better personal fit. If the tier two brand had a better personal relationship, the more experienced and the more valuable partner on the individual level. So if I think about it, I think about it as a distribution. So you think about it as like a curve goes up and down. It has local maximas. It has local minima, but it always goes up and down. And I see every fund sitting somewhere different. So you can see many funds sitting next to each other. And then you have the maximum. On the maximum, you have some of these top brands out there.

1:00:58Andre Retterath:So Sequoias, Andreessen, Lightspeed, and so on of the world. And then you might have some other funds sitting down here or up here somewhere in between. Now, the interesting part becomes they all have like an amplitude around the curve. And this is where the fund is. And you can have a bad partner that really makes a difference on the negative. So it becomes locally lower in quality and you can have a great partner. Now, a bad partner at a tier one firm might lose a deal against a great partner at a tier two firm. And I've seen that too often in reality where they are like very sure they would win a competitive deal because they work for fund X, Y and Z.

1:01:41Andre Retterath:But they lose against a partner who's just giving their all to win this deal in a tier two firm. Going back to your research, we were talking about these factors that are evergreen that continue to persist. And then there's factors that change from generation to generation. with AI founders today, what has changed about what they're looking for from their venture firm? This comes back again, where they sit in the stack. For example, if you have a deep tech, hard tech company at the bottom of the stack, as mentioned before, they need lots of capital. They are looking for how big is your fund? Do you have connections to follow on investors?

1:02:27Andre Retterath:Is this a great signal for later stage investors? So we can attract capital. And I think we can show that within early bird, we have an early stage, we have growth funds, we have very deep pockets and can easily invest 50, 60, 70, 80 million over time. I think that's very important. Then if we look one level higher, for example, on the AI labs, on the software infrastructure, for them, it's actually very important that we have experience of commercializing research and technology. So for the AI labs, depending on which modality you work, if it's text, for example, coding, or if it is image, video versus different kind of world models that are in the 3D physical space, the go-to-market motions are the most complex part.

1:03:13Andre Retterath:So the teams themselves, they can pull it off. They have the credibility and with funding, they also get the computabilities models. But the biggest mistakes that I see being made for many of the AI labs is on the go-to-market side. Do you build a playground? Do you do direct sales? Do you do partner sales? Do you go for frontier? Do you go for forward deployed engineers and so on and so forth? So I think experience and that's lots of transfer learning. If you have an existing portfolio in this space already, and you can tell them this is how company ABC did it. Why don't you chat with the founder over here?

1:03:49Andre Retterath:Incredibly important. If you look at the application layer, I think it becomes a lot about specific metrics, like how do we reduce churn? What are the right metrics to think about lifetime value? What does CAC versus LTV look like? And then it becomes a lot, a lot at the application layer about distribution. So in the application layer, we have them with founder branding, for example. We give them experience with different kind of channels. Why don't you speak to this person? They have been doing amazing on a geo or search engine optimization or some of the other channels in the past. So I think the application layer is really a lot about distribution.

1:04:27Andre Retterath:So it depends. And I think that's also the nice part of the job that it's not one size fits all. And depending on which companies you invest in, they want to see something completely different. To give you another example, we have some companies in the portfolio that have a strong thesis on sovereignty. So for example, ISA Aerospace, they build a lower orbit rocket launcher, which is about independent access to space for Europe and for some of the other countries. Here, it's incredibly important to know the right politicians, to be close to policymakers and so on and so forth. So it's a very specific skill that these companies want to see in their investors.

1:05:09Early bird was really this early adopter in terms of going from a pre-AI world to now fully integrating into AI with your engineers and your processes. Today, arguably, we're in a post-AI world. Where does Alpha accrue to the venture capital firm in a post-AI world?

1:05:31Andre Retterath:This is an amazing question. I just published a new Data-Driven VC Landscape 2026. So I've been running the data driven VC community for many years and I've been writing a newsletter on this topic as well, which is really about the topics that I described earlier and I've been researching that are very close to my heart. Like how can investors leverage AI and automate their processes? What are the patterns and characteristics of successful companies and so on and so forth? And in the landscape report, we actually differentiate between why are investors leveraging technology? And we can see that about one third is leveraging technology for efficiency, but two thirds are leveraging it to generate alpha, meaning making a difference.

1:06:13Andre Retterath:And today, where majority of the public data is available, so you can buy Harmonic, you can buy PitchBook, Deer Room, whatever these providers are, but majority of the data is available and structured. I think it really becomes all about proprietary data. And proprietary data, I mean, transcripts of meetings. I mean investment memos. I mean decision-making. So for example, at Early Bird, we introduced a post-investment committee survey in 2018. Since then, I think we have captured more than 200 investment committees where every participant of the investment committee raised, how do I like the market, the product, the investment team?

1:06:53Andre Retterath:Would I do the investment? Yes, no. So we track individual decision-making and we can see, does that collectively lead to the right outcome? We can measure that against the performance of the companies over time and so on and so forth. And I think this is really the data that is the most unique. No firm will ever be able to sell this. This is unique to early bird. Now, the interesting part becomes if you productize all of that, if you make this available to replicate judgment, for example, in the upper part of the funnel. So you present investment opportunities to your investment professionals that have the highest likelihood of success based on success scoring model, but also represent the taste of early bird.

1:07:37Andre Retterath:Which opportunities have a higher likelihood of getting to a positive investment committee outcome at early bird? Every firm has a different taste. Every firm likes different kind of opportunities. And we try to codify that. And if we present new investment opportunities to our investment professionals and they don't like it, they might reject it. They say, I reject because I don't like the team. I don't like the market, the product, whatever. So they put in specific labels and we can continuously retrain our scoring algorithm, our recommendation engine, so that we understand what is the taste of early bird.

1:08:12Andre Retterath:And if you map that, the likelihood of success of a company with a taste of early bird, then ideally you can have a fully automated pipeline, just scheduling meetings into my calendar. I wake up in the morning, I look into my calendar and I will meet the best founders at the intersection of likelihood of success and fit for early bird. And I will get the meeting prep, send 15 minutes ahead. I can look through that and I can do a sequence of meetings. they are transcribed, it is pushed into the system, and the system will relearn again to refine the scoring and recommendation for Andre. This is where I think alpha will be truly generated.

1:08:51Michael Gilroy from Marathon Venture Partners talked about this pre-portfolio concept that they have. Every venture capitalist, whether they want to admit it or not, are making bets on what they're likely to invest in and where they're making those bets with their time. If you could get better upstream in terms of what portfolio companies you're focusing on, then your investment decisions downstream will be better. Whether or not you could get access is a different thing, but at least you'll know exactly where you want to invest.

1:09:19Andre Retterath:100%. And that's something we try to codify and automate as much as possible. I want to leverage technology and specifically AI to free up time from our valuable investors that we can use to do the real value work, really spend time with the founders, spending time human to human, and not so much sitting there and crunching different sheets, and so on and so forth. I don't want to look at the input, move numbers around, look for different kind of research, and so on and so forth. I can look at the output right away and can leverage the output for my decision making. It's actually where I was going to go, which is in a post-AI world, do the characteristics of top quartile or top decile VCs change versus a pre-AI world?

1:10:08Andre Retterath:Some parts of it, yes. But I think the most critical part remains the same. And this is really decision-making. In the end, you need to be an excellent decision-maker. And we are not paid to make 10 ,000 decisions a year. We just need to make this one right decision. Because we all know, due to the power law, it is about these few outliers. And if we can ensure, as Early Bird has done in the past, we have raised 17 funds. And thankfully, we had one or more outliers in every single fund in the past 30 years. This is something that you need to repeatedly achieve. And I think this all comes down to great decision making.

1:10:49Andre Retterath:So I think great decision making is where we should spend our time. We should free up our capacity with redundant work that does not really create value, but really focus on the decision making. I don't want to reject 2000 opportunities a year because they are not in our geography. They are too late. We don't invest into this industry and so on and so forth. You cannot imagine how much time we have spent in the past because we also said, look, our brand stands for trust. So if we have 10 ,000 founders reaching out a year through our website, we got back to all of them, every single one of them.

1:11:27Andre Retterath:And the big question is, is this actually where we create the value? Or can we automate this part so that we see these are the 200 most promising opportunities where we know they fit all of the hard criteria. And suddenly, we can go way deeper on way fewer opportunities. In an ideal world, if you zoom out, we do in our early stage found a portfolio of about 35 companies. And we invest this across three to four years. So say this is 10 to 12 investments per year. If we do 10 to 12 investments in an ideal world, I only want to spend time for the full year with 10 to 12 companies. The problem is we don't know ahead which 10 to 12 companies these are, which is why we need to look upstream in the funnel.

1:12:13Andre Retterath:And the question is how far do you go up? And in the past it was we needed to go up to the highest point. And if there's 10 ,000 opportunities, potentially there are 500 that are from India, out of geography. There are some that have raised already 50 million. That was too late for us. So I needed to scratch them out one by one. And today I can look way deeper in the funnel because majority of this top of funnel screening work can be done by AI. So I can finally spend my time with way fewer opportunities, as close as possible to the 10 to 12 that I actually want to invest in per year, but can go way deeper.

1:12:49Andre Retterath:In the past, I spent like whatever I did four or five meetings with the founder, and then we came to a decision. Today, I can't afford to do 10 meetings with the founder, or I can have five way deeper conversations, way better prepared. So we are not scratching the surface, but I had so much time preparing for this meeting that we can go to the essence that we met up for the decision making in the end right away. You mentioned venture capital, really comes down to these couple decisions per year, or you even said one key decision. How do you get better at making that one key decision? I mentioned before we have this asymmetric cost basis and the false negatives are the expensive ones because as said, false positive, I say yes, but the company is not successful.

1:13:38Andre Retterath:I lose my money once. False negative, I say no, but the company could return 50 times the money. I lost 50 times the upside. So technically, yes, I just want to do this one decision per year, but I actually need to do the 10 ,000 decisions a year. Now, the question is how reliable is a no? And this is a big question where lots of people did not trust the algorithm to say no. I actually published a paper in 2020 where we benchmarked human investors against the machine learning model on predicting the confidence of saying no. And actually the machine learning model was as good as the best investor in our sample, I think from 120 investors.

1:14:23Andre Retterath:So we got the trust to let the machine say no at the top so that we can focus more at the bottom of the funnel where we can say yes. For this one decision where we need to say yes, I think lots of stars need to be aligned. So firstly, it needs to fit our hard criteria. It needs to fit into geography, stage, ticket size. I mentioned before that we are quite disciplined on shareholding. So it also needs to fit in there. Then it needs to fit into a thesis. So we are probably more on the thesis driven end than on the reactive end. Actually at early bird, if you say in a deal floor call or investment committee, oh, this is exciting because there is fund ABC looking at it, it's rather negative signal because we want independent thinkers.

1:15:09Andre Retterath:I don't care what other funds say. It's nice in the sense of what we said at the very beginning, great founders who can raise will likely keep this capability later on. So it's great to know this founder can create momentum, but it should not impact our decision in a way of we should invest only because of this reason. So we should think through it from first principles. And then it depends a lot on the opportunity. It is very different for an AI lab than it is for a rocket company or a fintech company, the application they are. To me, it's kind of a paradox of making this great couple decisions per year, because I think there's actually two almost opposite ways to think about it.

1:15:51One is I become religious about this idea as quality is downstream of quantity. I went for nine months, I did five podcasts a week because I just wanted to get bigger, faster. I wanted this feedback cycle, watching myself, seeing the metrics and just constantly getting better. So at some level, quantity is upstream of quality. On the other side is, of course, it's about these couple of key decisions. And I think the other side is actually a capacity, keeping this almost empty mind. It's almost like a philosophical or a Buddhist concept. And the way to actually operationalize that is through different practices.

1:16:27For example, I go to sauna and cold plunge. It's basically bringing your mind to a level where it's not overworked with the constant kind of minutiae of the day-to-day. One of the things that I've learned is that doing mind competes with the thinking mind. Doing tasks, doing day-to-day tasks competes with thinking about the business. The way that I figured this out is on the weekend, I would go biking and I would always come up with great ideas. And I try to isolate all the variables. Maybe it's the biking, maybe it's the weather, maybe it's being outside, maybe it's... And then I realized, no, it's the weekend because I don't have all these doing things.

1:17:02So that wasn't competing with kind of thinking from first principles. So in some ways you want to do more quantity in other ways, you want to do the exact opposite, which is less quantity and kind of leave room to think.

1:17:13Andre Retterath:This is such an important topic. And I think wherever you start in life with a new topic, be that a new hobby, be that a new discipline you want to understand. In the very beginning, it's all about getting a rep. So there's this 10 ,000 hour rule. You need to spend 10 ,000 hours to get to like an expert level and something via the new language, via the new coding, new research field whatsoever. And I think in the beginning, also decision-making, I was very happy that as a junior investment professional, I got the reps. So I have seen thousands of opportunities and rejected them. And I had a more senior investor partner sitting on my side and we talked through these opportunities in our deal flow call.

1:17:58Andre Retterath:So I could really see what good looks like. But once you're at this point where you know what good looks like, you should not distract yourself with too many things because it will just slow you down and you have a higher chance of missing the good opportunity. So once you have all of the reps, I think it becomes about freeing up capacity, getting into the state of a prepared mind so that whenever you see something great you're ready to shoot and this prepared mind also a great concept it's not just having capacity it's not just having a free mind it's also your information diet what are you learning who are you talking to in order to have the mental scaffolding to be prepared for these opportunities that come yeah and as you said for the weekend i have the same for my week i also back then because i completed my phd in parallel to my investment work.

1:18:54Andre Retterath:Back then I had Fridays blocked to do some research work. And even until today, I have parts of my Fridays still blocked for reading, reading new papers, reading newsletters, just consuming different information because that's the input that I need towards the end of the week to process over the weekend when I have full freedom, where I just spend time with my family and I can really think through and process where I'm not running from meeting to meeting. And I found this to be a great setup for the week where I do all of the meetings from Monday to Thursday, Friday only selective meetings. And then Friday I can consume, prepare everything on input that I need for the weekend to process, come up with great idea and then put them down into a structured kind of format and really get into the week for executing again.

1:19:43If you could go back to when you were just in the final stages or your PhD and you just entered venture capital, what is one piece of timeless advice you'd give a younger Andre that would have either accelerated your career or helped you avoid costly mistakes?

1:19:58Andre Retterath:I have always done things in parallel. So for example, I started working as an engineer in parallel to my mechatronics engineering studies when I was younger. I have also worked full-time in parallel to my master's degree. I have worked end-to-end full-time alongside my PhD. So throughout my whole PhD, which was a full-time PhD at TU Munich, which frankly speaking is also not something that you just get gifted. So you really need to work hard for that. I worked full-time as an investor at Early Bird and Parallel. So I had probably two years of overlap where I did a full-time PhD and full-time work at Early Bird.

1:20:35Andre Retterath:And I think doing that for more than a decade, these two streams and Parallel, I always invested more and more and more. I had years of 100-hour work weeks plus that really consumed everything of me. And I think later on in life, I got to a point where I just saw, look, if you are just grinding the whole time, you miss the opportunity to zoom out and really see the bigger picture. So thankfully, I also had strong mentors who advised me, look, Andre, slow down a bit, zoom out, think about what really matters, about personal life, about health, about business. because then you will be in a position to make better quality decisions.

1:21:17Andre Retterath:And I think that's something I would give as an advice to my younger self to zoom out more often and that just working more and harder does not always lead to a better outcome. People ask me, are startups a marathon or a sprint? I always say both. You need to be good at both skills. Well, Andre, I think I heard about Early Bird back in 2009 when I first got into the startup world. It's one of the most iconic firms in Europe. So it's a pleasure to have you on the podcast and thanks so much for sharing your story. Thanks a lot, David. Thanks for the great conversation.