Ben Orthlieb of Blue Moon joins Nick to discuss AI Native VC, Achieving 50%+ Graduation from Seed to Series A, Why Access Is the Key to Success, and Why Network Driven Firms Can No Longer Compete. In this episode we cover:
- Challenges of the Traditional Venture Model
- Blue Moon’s AI-Assisted Human Judgment
- Evaluating Exceptional Founders
- Access vs. Picking in Venture Capital
- Blue Moon’s Sourcing and Screening Process
- Non-Obvious Data Sources and Market Dynamics
- Winning Deals and Founder Relationships
- Future of Blue Moon and AI in Venture Capital
- Importance of Price and Pre-Commitments
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0:18
Ben Orthlieb joins us today from San Francisco. He’s the Co-Founder and General Partner at Blue Moon, a seed-stage venture firm investing in B2B startups across North America. Before Blue Moon, Ben held senior leadership roles at LinkedIn and Oracle. He’s invested in unicorns, including Mercor and DevRev. And Blue Moon is outperforming the rest of the market by a significant margin in terms of graduation rates. I think it’s on the order of two or 3x the industry average. So he’s here to today to explain how he’s doing it with his AI native approach to firm building they’ve really been smart at seeing everything and investing in the best one. So Ben, welcome to the show and and then, what is the thesis at Blue Moon?
1:06
Blue Moon is what we would call, what we call an AI native fund at seed, we effectively back exceptional founders with smaller checks. And the way we’re different is through our operations. We effectively see everything, know everything, win everything, and obviously we have confidence in seeing that from the results from fun one. But that means for us that we’ve built effectively an intelligence platform that helps us take the best decision. So it’s not AI replacing humans, but it is a fun operating with a lot of products that we’ve developed in house. I see one of the pieces is, all of this is to help us found, find the best founders, and that’s, that’s what we look for. That’s why invest in we don’t have a space thesis. Our thesis is, if you back a lot of exceptional founders, the power load works very well for you.
2:06
Love it. And then I know you’ve said the traditional venture model is hitting a wall.
2:11
Why there’s three components? I think of traditional venture maybe four. One is, if you back up 10 years ago, the traditional story pitch from a VC, both to their LPs and to founders, is, I have highly differentiated networks. I have a brand. Then operationally, it meant people were still able to manage their time properly and Win Win deals when they wanted to. All of this has evolved. Networks have effectively became commodities. Everybody buys the same data sources. Everybody has the same LinkedIn. Borde is now amplifying that in the last few weeks even further. And so networks, the story of differentiated network, doesn’t really fly enough to build a fund anymore, especially scale fund at scale brand has become stronger and stronger, to be honest, as a component, both for LPs and for founders. So it is a very hard if you’re not a top brand, or if you’re an emerging manager, to compete with the traditional approach in that, in that field, and so and then finally, time is finite. There are potentially more and more startups to come to see and partners of the same amount of time. So this is hitting a wall. There’s more and more competition, and it’s been true and evolving over the last forever, but it is even more true now that founder choose who they want to work with, especially in an AI native world. And so how does that evolve? Well, our point of view is that you have to replace network by intelligence. You have to effectively algorithm the source and find the right signals to know what where to focus. You replace brand with insights, and some VCs are doing that right. You hear stories of people preempting deals on the basis of showing up with great research and market insight to top founders, and then finally, time is becoming critical, because how do you know what are the right signals to focus on, so that you know you’re spending your time wisely? So all of these together mean that it is very hard to start a new firm. If you’re not one of the top three to five brands in the market, you are suffering and a number of top seed series, a funds focused are suffering, and they’re telling us they’re looking for ways to compete with your Andreessen, your sequoias, your general catalyst, with mega funds, who actually can do seed at a high scale and velocity, because it’s mostly a feeder for them.
5:00
Yeah, I see. And you, you know, I want to unpack what some of these signals are, and I think we’re going to get into that a bit deeper in the discussion. But you talk about being an AI native, right as a venture firm, you know, how do you distinguish AI native between AI enabled in venture,
5:19
yep, at the core of the way we think about this is we’ve built a product. We are a product company more than we are a VC, and that actually founders oftentimes come back, come to that conclusion, why introduce what we do? What it means is a we’re amplifying human judgments. Not we’re not replacing it, but we’ve built what I call a proprietary intelligence platform that enables us, as I mentioned earlier, to see more, know more, and win more, and of course, compounding the learning at every cycle we can go into this but at this point, it means we have 15 proprietary, proprietary systems. We use 150 sources. We have more than 100,000 lines of codes in the back end, etc, etc. This is not your affinity, plus harmonics, plus, oh, I’ve got a competition deep research prompt that sort of hacked together. This is really a way to say there is a different way of competing across the value chains and for the things where I need to be differentiated or better, I’m building a product, a system, answer,
6:28
what are the parts of judgment and founder evaluation that AI can do, and what parts can it not? Do you said that it’s human assisted AI, right? Or AI assisted human judgment. But you know, how do you distinguish between, what can the AI help you with, and what can it not
6:52
for us? And you can take multiple paths. There are purely algorithmic funds. We have our view on that. One of the key question is, if you’re algorithmic, how do you show up and say, Hey, Nick, I love what you’re doing here is 500k and convince you to take it. But ultimately, for us, the core asset of seed is the founders. And the way you get alpha is you back in very exceptional, truly exceptional founders, and so you need to spend time to understand what that is and unpack, if anything, because that level of depth in conversation you can’t really have in one minute or outside in. Don’t get me wrong, we do use evaluation outside in of people’s personalities, etc, but it is not the depth of conversation, and frankly, the depth of options for what makes a truly exceptional founders that you can fully judge outside in. And there’s the other part of this, which is, I need to win those deals. I need to have full info, and it’s not just public info on those deals as I’m making my decision. And so I could never probably have all of that information, even though everything we do is to try to give us as much at this point, all of the public information, to be honest. And then we’re trying to, we’re doing some work, some private information zone.
8:21
So what are those factors that you found separate the truly exceptional from, you know, just the good or the great?
8:30
So the second part, so the first part of our process will, I’m sure we’ll go back through it, but sourcing and screening fully automated. We basically was looking at my number. We’ve sourced 11,817 teams so far this year. We screen it down to about 500 that we should talk to. We engage with them the first part of the process. So the screening is purely on the founders, not what they do. Then we spend time understanding what they do and what their space is, and the deal dynamics. If all of that shakes out, the real core of what we do is the second phase, which is deeply personal conversation. The frame I share with founders, in the way we think about it is it’s irrational to be a founders. Many of those founders could be having great careers at regular tech companies, they choose not to do this. Usually, there’s a reason, and that’s what we go deep into. And that conversation with all the co founders effectively helps us figure out the conviction for is this team truly exceptional? And why? Because it can be very different flavors, right? You’ve got people that have been obsessed about one thing forever, right? Data bento, the founder, Christina. She started a So Dana bento. Think about it as a Bloomberg competitor. Plg, modern era. Christina started a hedge fund when she was 19 in. It dorm. She’s been obsessed about this problem of financial data since forever. You’ve got other versions, which is the disruptors. An example of that is the team at Archie. They’ve been in some of the sort of exciting years leading payments at Uber. They have an eye for figuring out, how do I build a disruption playbook? And then they ended up applying it to something that had nothing to do with Uber, which is, effectively, how do I completely change how dental offices run themselves because of a personal story? And then you’ve got the then you peel that one layer further, so you try to figure out people experts or people in it for the disruption, but then you peel that further for what’s the true motivation? And this takes any flavor. Some people, they came out of poverty, raised their siblings, or helped fund their siblings, and they have this complete ability to go through walls, which is effectively a version of what we’re looking for. You’ve got people that quit Tresor in our portfolio. Quit my PhD before the end because I wanted to become an entrepreneur, even though PhD was what my dad wanted me to do since I was raised, this person has three exits, including two unicorns. Why are you doing it again? I’m still proving that I made the right choice. That’s the kind of depth of story that we need to access to figure out what drives people
11:32
so you talk about access, you know, there’s this age old debate in VC whether it’s an access game or a picking game. You know, many of the folks on this show that are sort of entrenched, established VCs and LPS argue that it’s an access game. Well, there’s a lot of up and comers that attend that it’s all about picking. You know, where do you land on this? Because clearly, you do have a picking lens, you do have a filter, but you also have kind of this differentiated way of seeing and observing all the opportunities.
12:04
It’s all about access, in our view. And by access, I mean two things in particular. I mean coverage, the ability or three things, but coverage and the ability to actually meet people as one and two win rate, meaning you need to see as many of the potentially exceptional founders and then be able to get into those deals the picking with our lens. When the picking is first and foremost about the people, it’s not hard you once you have that second phase with deeply personal conversations, and we share our answers, by the way, but you establish that deep connection and understanding. We know we we do get all the summaries. We have a we have an agent called Sigmund that gives us a card to make sure we haven’t missed something in the meet, in the meet, in the meetings, because there are hard meetings to sort of tracked. But at the end of the day, it’s not a hard decision. At that point, the question is, how do you get more and more into those conversations? And do you win? In our case, 97% of the deals once you want to win, that’s that’s the real trick. The picking once you see very good founders all day long. Is not hard.
13:23
So, you know, there’s like 3400 seed deals a year. There’s 9000 pre seed you mentioned, you know, you’re looking at, I think you mentioned 5000 earlier, right? And then you filter that down to maybe five to 800 you know, candidate companies. Are you meeting with each of those? Do you take meetings with that many companies every year?
13:44
So yes, we roughly, will be at 12,000 this year. We keep adding new ideas for how we source so 12,000 this year, bringing down to 500 completely, 500 based on unsupervised machine learning algo, and then from there, engage with them. The vast majority is outbound, and we get to convert about 75% of that into a meeting.
14:10
Is that automated as well? Ben the outbound
14:14
partially so when I see your company, my system will generate a number of questions about your company, so that I will send you a highly custom email, not only based on who you are, but also actually a few very punchy questions about your business, not generic questions, but like, what kind of regulation, if you’re in the Regular, regulated environment, etc, that is about Alpha far conversion, and the other half comes through, where the network is helpful is actually introductions. And you’ve been kind enough to make a few introductions this year for me as well. But that’s that’s as automated as possible, meaning I get a very personalized email with. Notes, I figure out very quickly who I know in common, so I can go through that waterfall of outreach.
15:07
And if you had to describe the agent that does the filtering process right from the many 1000s down to the 500 what are those signals that it’s looking for?
15:19
Think about it as so it’s unsupervised machine learning, but the way it’s working is it’s looking at the profiles of founders, education, professional experiences with a whole bunch of classifiers, personality outside in as much as we can find it based on what people say on LinkedIn, on Twitter, on wherever, and then from there, it’s effectively 1000s of bots who are answering the question, will I take, would I take this company into my portfolio solely based on the founders? And the independent function it’s solving for is basically, will this team go from C to Series B? So it is effectively trying to tell us who are the teams, because it rates the teams, not the individuals. Who are the teams that are most likely to succeed based on effectively, back, back training, on every seed deal since 2008 what’s interesting is it’s not your typical point system. You went to MIT or Stanford plus five. You went like you went to Google plus four, whatever you end up with some of those more traditional profiles, but you also end up with less traditional profiles. And so if you abstract it out, multi dimensions, etc, but the the algo finds seven or eight pockets of teams that tend to be highly successful, and so effectively, it’s telling us is Nick and team close to one of those pockets that you would from the get go. Say it’s worth going deeper. One thing that’s interesting is if you just invested in the companies that have a good score. So if you invested in the 500 that pass our score every year, you would already over perform the market on graduation rates in as a result, other measures but the graduation rate. If you take 2022 I think, as a as an example, year, as of last summer, 14% of companies have grown to a series a if you just filtered on the score, you would you would invest in a lot of companies, but you would end up with 24% graduation rate. So you would already be over performing with without any other signal,
17:35
and your graduation rates have been quite a bit higher.
17:38
Yes. Oh yeah. 48 so there’s other things that we do that seem to be working pretty well. So first, the filtering so effectively, and you have to be comfortable with type one, type two error, especially with a type two error, but effectively, you know, you’re concentrating your conversations in a pool of people that is more likely to be successful. You see, we care about who are the investors in round dynamics, and we score every other VC to evaluate who’s good, who’s a good picker, and then ultimately the rest of our decision process. And so the way it works is the market is 14% graduation rate. Our filtering gets you to 24 add that. Add to that the list of VCs, the top 40 to 50 VCs, who we want to co invest with. It is part of our strategy. We’re a small check so we care about who we co invest with. That gives us. That gives you 35 and then the rest of our decision process gives you 48 so in some ways, you’ve got three legs of this tool in terms of the strategy and our ability to decide is still the best of the three, but they’re pretty well balanced, which is interesting, when you decompose sort of what could happen in the future.
18:54
You know, some of the data sets that you mentioned are obvious, like where you went to school or where you’ve worked. Are there non obvious data sources that you’re pulling as well
19:04
on founders,
19:05
founders, teams, businesses for that
19:08
dynamic, I mean, the less markets, yeah, also very different. So for the for the founders, the less obvious piece is sort of elements of psychology that you can, you can you can glean outside in and we use that both for evaluating them, and we use that also to for engaging with them if they have strong sort of personal preferences. But overall, that’s pretty the algo is not straightforward. The data sets are pretty straightforward for our engine that helps us know everything no more is, is all we basically be able to fine tune rag that is proprietary, that’s been running for two years, and that basically takes close to 120 sources across the web, including podcast, including sub stacks, which are hard and are not in chat. GPT. Brenna to help us, before we meet a company, have a full on description of what they’re doing, description of their market, description of their segments. Who are the VCs that have invested in this space, who are the list of competitors, and all of these things are proprietary engines like competitors, is not your taxonomy from trench base or pitch book. It’s a dynamic taxonomy that is refreshed every week based on what we gleaned from 30,000 companies. So pretty comprehensive, and in this one pretty complex, we share that output with founders, to the point I made earlier, with sort of insights to win against against Brenna before I meet you, but if we have a meeting in two days, I will send you a link to, effectively, our card about your company, company description, which people find cool, but obviously they shouldn’t learn something new. Usually, they say it’s the best analysis you could do outside in maybe something has evolved in the last month. Let me tell you what’s going on. There’s an analysis of the structure of their website, which helps us effectively figure out signals about maturity. They tend to like that. And then finally, the analysis about their space in depth and their competition is very helpful, because they can use some of that for their own storytelling. They can use sources that they may have not sort of seen before. And all of this creates with the founders, which is true, this idea that we’re a product company
21:26
does the application of AI to the messaging, the website, the structure of of some of these businesses as they exist online, does that distort your analysis in any way, because, you know, they may look a little more polished than they otherwise would be. It’s
21:49
interesting, because I think the outcome is actually the opposite. We’ve seen non obvious founders more than legacy VCs. In fact, we feed a lot of deal flow to legacy VCs, we’ve made 278 intros last year. I think in the last 12 months, four top funds have led deals that we’ve made intros to. And so it’s either we meet different profiles and or we meet them earlier. But we do find through the application of AI at the sourcing and screening non traditional profile. In some ways, the biggest company in the portfolio have mentioned it at the beginning is mercore Merck or was not necessarily obvious until you met them. Once you meet them, my opinion, obvious, but their dropouts from Georgetown, their 20 year old at the time building a Turing like competitor. A lot of VCs passed on this. Dropouts is cool in B to C. They’re not really cool in B to B, 20 year old in B to B. Okay. Do you have the experience in selling to enterprise, etc? So a lot of people passed, and it’s the biggest success, I think, of the vintage or close to it.
23:08
I guess that disputes the access point a little bit right? Like you can have access, but you still have to select the winners.
23:14
Yes, yes. What I’m saying is the problem for me was the access once I got to know they existed and meet them. I mean, you talk to Brenna, take my money like there’s no there’s no discussion
23:30
that could awesome. What do you say to those like the pundits out there that say that AI because it’s based on historical data. Is really good at analyzing past trends, but it’s really not designed to forecast for the future. You know, venture is very much a future game. So how do you respond to those folks?
23:49
It’s a very good point. And I think if you unclutch, or if you double click, sorry, or if you unpack, it depends what changes. In our view, the profile of founders doesn’t particularly change. And in fact, we’ve been running like the algo runs on data since 2008 the pockets evolve a little bit, right? It’s constantly feeding new new round outcomes. So you see the evolution. It’s not changing all that much. What’s changing, of course, is what they do, and that is where our other systems are very deductible as to what’s going on, right? I mentioned we we scrape 120 130 sources of information about industry, about market dynamics. This is constantly refreshed and up to date. In fact, I have a dashboard that can tell you what are the top 10 topics being discussed today by VCs and how that’s trending compared to three and six months ago. So that very much the focus. In fact, we made a recent investment in a company, channel 99 with a view on this, which is maybe the flip side of what people usually think about. Brenna. Chris is the founder of DemandBase, if you’re going to do B to B advertising, he’s probably one of the five to 10 people you want to talk to. He’s literally created the space. Now, the problem for us we’re a small check, is somebody else going to pick up that company in Series A, because we’re not going to be the ones leading them, helping them through, through small checks the whole way. And the talk track in venture for 10 years has been ad tech. Martech is horrible. It’s even worse in B to B, you shouldn’t be doing this, but that’s where, what we call blue our analysts effectively showed us what’s going on in the market. Market dynamics were positive, but the key here was investments in competitors by other VCs. Then all of a sudden, you’re like, huh, surprising that in last six to 12 months, a lot of the top 10 VCs have quietly made one or two bets in that space. And so in some ways, you get the information that tells you that space is reopening up in a way that the sort of talk track hasn’t caught up with. And so that’s an example of like, Chris’s profile. I would bet on Chris amongst multiple generations of types of companies. But the insight on is this market, in this very specific case, is this market open for VC land was a non obvious sensor that required up to date information.
26:34
I see, I see, you know, Ben, you’ve talked a lot about win rate, right? Why do you think it’s the most underrated metric in venture and how does Blue Moon win these deals?
26:51
So win rate is, is, is an interesting metric, if you ask people, because I think it encapsulates or you can unpack from that question what their strategy is and how differentiated, how good it is. Obviously, it’s a very highly guarded metric, and what people like to talk about forever to your point around picking. I’m highly selective. I only do five deals a year. Now, the dirty secret is, if you’re not effectively a top five or 10 company, your win rate as a generalist, let’s put it this way, is probably 15 to 20% and that’s what you know. Legacy funds that we know well have told us. And so, okay, so you wanted to make 25 investments, all of a sudden, it’s a very different story. And then you can unpack like, why or why do you only win 15 to 20% does that matter? How bad if that’s the case, is your entire portfolio worse than your portfolio? So you can really, it’s the one place where you can unpack a strategy for us, partially because our win rate is one of our top metrics. We don’t want to compete to lead. We compete with a 250k check in the allocation in a realm which make it easier to win for many different reasons. Still have to win it, but for us, it’s more important to back a lot of the exceptional founders than to have a huge anti portfolio.
28:26
And why are they selecting you? Right? Your win rate is very high. Ours is quite high too. We have like, a 95% win rate. And it’s easy for people to dispute it and say, well, you’re investing in pre revenue companies, so you don’t compete as much, or it’s adverse selection. And so, you know, I’ve heard a lot of the
28:44
objections your strategy, your strategy is aligned with what you’re doing. That’s right, that’s right. You get to a place where you can get conviction and could get to win those deals that you want to get to be into, like, that’s the best place.
28:56
And how are you winning? You know, you have an exceptional win rate. Like, it’s a smaller check. So you mentioned that it’s a little easier to fit that in, but you still likely have to beat out other VCs if there’s a prominent lead in place, right? Yes. Why are they choosing you?
29:12
So the founders, it’s interesting, because it’s evolved over time, but the learning is, is things I mentioned earlier, it’s the the insights over brand. We don’t have a brand, so we have to fight a different way. Insight is one and two, the deeply personal connection. So effectively the first piece of the conversation, why do we get in? Why do we get into a conversation with founders? Right? We’re mostly 95% outbound or something like that. We get into the conversation by showing we tell them we found them with an AI, which they absolutely love, and we already, from the get go, a very personalized convert, personalized emails. Most founders understand it’s a system in the background, but that actually. He impresses them or intrigues them. And so a month ago, I get in touch with a founder, and he said, best email of the day. Obviously, it’s automated, but the fact that you’ve been able to pull this off, I want to talk to you. I talked to him. It’s his sixth company. He’s got three good exits. And he starts the meeting by saying, Look, I already have enough VC conversations on my calendar, because people have been backing me for years, so I want to talk to them. You’re the only one I took inbound because I’m building an AI native company. Clearly, you’re also I want more people like you to talk to. So that’s how we get through the door, almost how we win. We’ve done and we still do the traditional in fact, more than we will help you. We pay for services to help your team. So you know you’re getting some data serious or some value beyond the check. Because actually, we pay providers, so if they don’t deliver, we’re going to have a chat with them. Exec coach was three exit engineering mentorship program, sales coaching. People get that, and that used to be what we lead with. My expertise is M and A. It’s not a traditional expertise in venture. And at the same time, it turns out 92% of exits are m&a. It’s pretty important, it turns out, but it’s the second phase where you talk to them, about them as people. And that was a bit of a shock. That’s not why we were doing it, and it was a bit of a learning. People absolutely love this conversation, because intuitively, most founders, I mean, you do pre revenue, like most founders, know that at those stages, it’s about them. You’re going to pivot maybe like the space matters, but to your degree, but it’s about them. Very few people ask them about them, and if you dive deep enough, and we share our answers, you create that degree of not friendship but relationship within a very short cycle that when a couple days later we say, hey, we’d love to be in 250 and remember all those things that we’re going to do for you, it’s a no brainer. In fact, founders usually tell us at the end of the meeting or in an email shortly thereafter, like, let us know, we’d love to have you in to this point, right? We, for example, we were investors in bordy. Bordy is the ex clear co company founders, some of them, they raised billions with with clear Co. At the time, Matt, who was also in charge of investor relationships at Clear co now in bordy, at the end of our call, he says, Look, I’ve literally done 1000s of investor conversations. This was, by far and away the best we didn’t talk about what they were doing. We talked about what’s driving them. Why are they doing this? They’ve already had successes in their life. What keeps on motivating them founders understand that.
33:04
You know, Ben, I feel like we’ve talked a lot about the what you invest in, the who, the how you’re deploying, you know your model. What we haven’t talked a lot about is the when right so like, when is the right time for you to engage with a company. Do they have a lead in place? You know, is this after a pre seed? Is it, you know, during the raise itself? Is it somewhere in the middle? Talk to us about when you’re engaging with these founders in order to secure that allocation,
33:39
anytime, but as early as possible. So one of the things you won’t be surprised by this, we’re pretty obsessed with our entire portfolio, so we have an ongoing sort of anti portfolio going on in the background. Two years in, when we reviewed it, the main source of inside portfolio was not our picking or not our decision. It was actually not seeing people early enough. We built off a whole bunch of new sources mechanisms. At this point, our sourcing is effectively 20 different strategies coming to the same place. Now that’s not when we invest. We invest, meaning the as early as possible we invest at seed, broad definition, but the rounds we join are usually a three to four, $5 million round at a 20 something valuation. There’s, of course, lots of bars around that, but that usually means the company has a product five to 10 customers, POCs, that’s going on. We’ve had to be clear. We’ve had deals where they already have a lead in place, and we join in on the last minute, and we’re able to if we need to make decisions in a couple of days. We made an investment into ever current out of a 16 speedrun. My first meeting with her was on Monday, the second meeting about her life fascinating was on the Wednesday, and that was it. So we can move quickly. And sometimes we’ve got rounds reopen for us, but the real place right now is we go try to find people as early as possible, and if it is too early, we have all the systems to nurture and monitor them so that we know when to re engage. One of our value props is actually because we’re small, but we have a lot of deal flow. We share deal flow with others and make a lot of intros. I think I’ve mentioned 278, intros last year, and so if you’re doing a pre seed, it’s not for me, but I’ll still ping a few folks. Or if you’re doing a seed and you’re just starting, I’ll share with you the list of people I know. Tell me who you want to intro. Of course, if that’s like, what you’re doing
35:49
is this after you’re taking a meeting with somebody or Okay, so you do take meetings very early, even if they’re not ready for capital, and then you nurture from there. Yeah.
36:00
In fact, one of our latest commitments, the round hasn’t closed yet. I’ve nurtured for Oreo.
36:09
And what goes into the nurturing, aside from, you know, making sort of proactive introductions to VCs,
36:17
so a first conversation, get to know you what you’re building, classic, there’s introductions if and when I do share the list of people I know to make intros so it establishes that credibility. So, you know, it’s not just like again, if I like what you’re up to, simple,
36:41
basic stuff. Like, I actually follow up when I say I do, because I have systems, and so oftentimes I will sort of have two or three emails before it’s the time to reengage. So there’s a bit of a level of comfort with just showing up that is there. It sounds small, but it actually is important in founder’s mind. And then I mentioned earlier, this analysis of websites that we do, we basically deconstruct. So every week, we scrape 30,000 sites, including companies we track for good reasons others, and it gives me alerts when something is changing, so that if I said, Hey, Nick, let’s talk in six months, or you tell me, Ben, I’m building, let’s talk in six months. But three months in, I see that all of a sudden your website is changing to show that you’ve really owned your CTA, for example, I will re engage, because I get those signals that tell me Yes, Nick told you, in six months, but it seems things are moving fast, interesting.
37:43
So, I mean, we talked a bit about the model today. You know what you’re doing. Where do you think you go next? You know? What does blue moon and your AI native venture firm look like in three to five years?
37:59
I I think there’s two dimensions. There is the we keep building again. We’re a product company first, and so keep building. In some ways, we now have enough bricks of systems that combining them is very powerful. I’ve mentioned blue, our AI analyst. It’s effectively four bricks that we’ve been building over the years that we’ve rolled up into a product. So we continue building there’s a number of things we have in mind that we need to get to. So it keeps going. We keep it trading in some ways. Others are obviously not stupid and starting to do things. So we keep one or two generations ahead as a firm our model today, we apply it to seed, North America, B to B. Seed has to stay that’s that’s where our secret sauce is for me, or seed and series A with signals, but start at seed is ours. That’s where we win, because that’s where our systems are optimized for. But there’s nothing in our training set or what we do that limits us to North America or B to B. So as we grow, I think you would have whether it’s all the same fund or separate funds, too early to tell. But could you have a FinTech North America, B to B Europe totally. And I think that’s how we grow because we’re not going to grow assets by destroying the model, right? It’s a very specific choice of where we are and where we compete. If we became a 300 million B to B seed North America Fund, it would destroy part of why we have great returns, and so we want to focus on being a great firm, and great, yeah, we want to optimize, not for assets, even though you can be a small fund with two people in an army of. Of agents and do very well, but we’re not optimizing for we’re the largest fun around we want to be the best one around. Ben.
40:07
Something we didn’t hit on earlier in the discussion is the importance of price, right? So we’ve talked about markets, we’ve talked about the importance of the founding team. You invest at seed. You know, I can imagine what the guardrails are on valuation. But how much of a role does that play? Does price matter?
40:29
Price matters to an extent, and that’s a learned lesson. We used to effectively have hard caps in place. We still have those caps that require conversation for an exception with my co founder, but we have learned through anti portfolio and passing on perplexity, that sometimes things that go to zero, you don’t care what price you got in, but if they can go to a crazy outcome, then the price matters less interesting.
41:01
So make exceptions, just not too many of them.
41:04
Not too many of them. Right? Our average valuation is still our median valuation is still 22 or 23 but we’ve had a couple that were much higher than that, even Merck or publicly was higher than that at seed. Certainly don’t regret that decision, and in some ways it was a big reason of passing in perplexity of 90 million and that would have been a different story.
41:29
And will you pre commit to a deal before it’s priced? You know, if you meet a founder, they’re in the seed process. You want an allocation, but maybe a lead hasn’t come in yet, and priced it usually no
41:42
because we need a lead as part of our it’s one of our gates, if you will. We’re going to help you find a lead. We have some very defined exceptions as to where we could do that, but that ends up being one or two investment out of 50
41:56
in any situations where you introduce a lead investor, and they come in and they take the whole round, and they’re like, Sorry, no room.
42:04
Okay. In fact, what I see is the opposite, which is not even with leading deals, but once you are an active deal flow, sharer, people will always make room for
42:18
you. Let’s hope that’s the case. Ben, if we could feature anyone here on the show, who do you think we should interview and what topic would you like to hear them speak about?
42:27
I think Thomas from theory venture. He’s great about having a point of view about how venture is changing at a different stage. He does more A’s and B’s, so different stage. But I think he’s been a very publicly since he publishes pretty much every day, deep thinker about the venture space and how it’s evolving, and we know him and his tech team, and they’re building some very interesting systems in the background worth knowing more about. Interesting.
42:58
Ben, what book, article or video. Would you recommend to listeners?
43:03
One book that shaped our approach with my co founder is a ban for all market, which is the theory of it was used the story of Jim Simmons. It is a great example. So Jim Simmons sort of famous hedge fund investor, but it’s not the story about quant it’s actually the story about approaching a system as a problem to be solved or to be understood, and solving it. Jim is famous for extremely bright and he’s famous for always wanting to be the least smart person in the room, because he could always learn. And that’s effectively how Renaissance technologies operate. They go, take the best and brightest in every space they can think of, and bring them onto this problem of, how does the market as a sort of system work so that we can be better investors? Yes, it’s with tech. But for me, the learning was way more how do you approach the problem and try to solve it. Productize it. Productize different strategies.
44:05
Love it. Great book. Ben, do you have any habits or behaviors that are a secret weapon?
44:12
I have one that is now generally known. I’ve been meditating since 2007 and when I don’t do it too much, I feel it. The other one that’s a little bit more quirky, and he’s a boost or replacement to my meditation. He’s listening to reggae, obviously Bill Marley in the background. I just love that backbeat. It puts me into a zone where I can just be more focused.
44:37
Love it. Perfect. And then finally, here Ben, what’s the best way for listeners to connect with you and follow along with Blue Moon,
44:45
our website, blue dash moon.vc is easy, LinkedIn, Blue Moon, and my email is Ben at Blue moon.vc okay.
44:53
He is Ben Orth Lieb, and the firm is Blue Moon, Ben, it’s been a pleasure getting to know you and just you know. Very excited for all your success and growth of the firm and everything you’re doing to be on the cutting edge of VC. You know, we don’t see a ton of that within our category. We see it, you know, within the startups. And so I applaud you, sir, and congrats on the success. Thanks, Nick.
45:21
All right, that’ll wrap up today’s interview. If you enjoyed the episode or a previous one, let the guests know about it. Share your thoughts on social or shoot them an email. Let them know what particularly resonated with you. I can’t tell you how much I appreciate that some of the smartest folks in venture are willing to take the time and share their insights with us. If you feel the same, a compliment goes a long way. Okay, that’s a wrap for today. Until next time, remember to over, prepare, choose carefully and invest confidently. Thanks so much for listening.
