Nnamdi Okike of 645 Ventures joins Nick to discuss From Power Law to Proprietary Insight: Unlocking Early-Stage Alpha, Data-Driven VC, and Building a Next-Gen Firm. In this episode we cover:
- Frameworks and Signals for Early-Stage Investing
- Challenges and Opportunities in Non-Consensus Investing
- The Role of AI in Venture Capital
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The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area.
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0:17
Nnamdi Okike joins us today from New York City. He’s the Co-Founder and Managing Partner at 645 Ventures, an early-stage VC firm investing in software and software-enabled startups. 645 has invested in companies including Resident (acquired for $1B), Iterable, Overtime, LeagueApps, RentSpree, and Setpoint.
Prior to 645, Nnamdi spent eight years at Insight Partners and invested in standout companies like Privalia and Mimecast, which were acquired for $600M and ~$6B, respectively.
Nnamdi welcome to the show.
0:54
Thanks for having me. Really appreciate the opportunity. Great to be here. It’s
0:58
a pleasure to have you on, sir. I mean, you guys have accomplished a lot, you know, you’ve done a lot at 645, it’s a very well respected firm in the industry, so it’s a pleasure to have you. Good place to start. Maybe, maybe some quick backstory on, you know, your path to venture?
1:15
Yeah, yeah, no, no. Great question. Great way to start, you know. So I first got interested in technology when I was in college. I didn’t have any family background in it. My parents weren’t in tech really. All I knew about tech at the time was, you know, I like tech products, you know, I was kind of a early years over the internet and, you know, but I didn’t know anything about how tech companies worked or how they were financed. So when I was in college, it was kind of like the.com boom. So a long time ago, kind of like early days of the internet, and had some friends who were, like, doing startups and starting companies. I was like, Oh, that’s really cool, you know. Like, didn’t know that was something you could do, you know, and it kind of intrigued me. So I said, Wow, I want to learn more about, kind of how this all works. And so, you know, I was at Harvard College undergrad, and you couldn’t take any classes at their business school, that was one of the rules. But you could cross register it over at MIT, MIT B School, the Sloan School. And so I audited a couple classes in one class was called new enterprises. It’s a pretty legendary class now. It’s been taught maybe for 40 plus years, and it’s a class that’s basically spurred, or kind of catalyzed, the formation of companies like HubSpot and bunch of great tech companies in Boston. And so I took this class. It was taught by a couple of guys who were professors, adjunct professors, but their full time job was they were VC. Co founded battery ventures. His name was Howard Anderson, and then the other professor, Todd daggers, co founded spark later on. And so great guys to learn from. And at the time, I didn’t even know what venture capital was, honestly like I was taking this class. I was like, What do you guys do? What’s what’s your what’s your background? What do you what’s your job, exactly, and, and I was just really intrigued by it. I was like, Wow. I didn’t know this actually was a profession where you could invest in these companies, and, you know, play a role in in helping them to grow. And they’d tell stories about their experiences backing founders. And for me, it was just fascinating. I said, Wow, this is pretty interesting. This is really a whole new world. And so that’s kind of how I first learned about venture. And then, you know, I got a lucky break, going to work for insight partners, which at the time was a pretty young fund. Well, they were about 15 people. This is early 2000 2002 and I joined them as an analyst, so I kind of like lucked into it. I would say it was kind of serendipitous. But learning about VC through taking this class and then getting a job as an analyst coming out of school were two kind of lucky breaks. But I was able to get into the industry early and kind of build from there.
3:38
I saw that you have three degrees from Harvard, namdi, did you have any overlap with with Mark, or, you know, other founders that were there, and kind of, the odds, yes, well,
3:47
that’s, that’s an interesting story, and, and we could kind of spend time there, but long story short, so, so I didn’t know Mark when I was in school, but the year after I graduated, Facebook got started a year and a half ago, year and a half after, I should say, and in when I was at insight, I sourced that, that company we didn’t invest now, but, yeah, true, true story. So, you know, at the time, a bunch of my friends were joining the network, and I was tracking early stage companies, although Insight was a growth fund, you know, and that was the reason we didn’t end up investing So, long story short, I was tracking it. I joined the network in 2004 which was the first year it was called the Facebook. And I remember going to the website in in at the bottom, at the bottom of the website, it said a Mark Zuckerberg production. And that was pretty unique, because when I was sourcing deals and insight, most founders didn’t put didn’t do that. That was not on the website. You got to go find the founder, or maybe there was, like, a a team page. And so I clicked on this link. It said a Mark Zuckerberg production, and it basically email popped up Mark Zuckerberg, so I emailed him, and he replied, and he said, Hey, you know, thanks for the email. We’re not raising money yet. We’re going to be raising, I think he said, in the next six to 12 months. But appreciate you reaching out. And would be great to meet up in New York and then. Then the most interesting thing about it was, and this is a long story, but Eduardo Saverin applied to work at our firm as an analyst that same year, kidding. So long story short, we interviewed him. Didn’t he didn’t get the job and and in the deal was too early for insight. It was basically pre revenue, pre revenue consumer company, and that wasn’t our mandate, so we didn’t end up pursuing it, but, but short story was, it was something that, you know, taught me a lot, both in terms of kind of how different these companies might look at the early stage, and also, you know, I think being really young and being somebody that just graduated, I think I had a sense that there was potential there, but it’s really hard to articulate, because didn’t have any revenue. It was just a bunch of people using the site. But long story, yeah, so they were there when I was there. I think there were a couple years after me, a couple years younger, but yeah, there was, there were some interesting folks starting companies at that time, you know, like Parker Conrad was my same year, you know, he started, obviously rippling and Xanax. So it’s a bunch of, a bunch of interesting founders that were there at the time. Wish I had an Angel fund at the time to be able to be able to invest in those companies, but I know, right? You know, it was too young, if we’re doing for that didn’t, hadn’t made any money.
6:07
It’s funny because I saw a pitch from Larry and Sergey when I was in college. Oh, wow. It was just Yeah, a little bit before your time. And so it was very early days of Google. But well, that must be interesting to see. We had no idea at the time how big it might become. Quick, quick lesson for the junior investors in the audience. Namdi, when a founder, when you reach out to a founder, you think it’s quite compelling, yeah. And they say, sorry, we’re not raising six to 12 months. What would your advice be to those, those young Oh,
6:38
man, that’s a great question. So there’s a few pieces of advice I would have. I think the first is, have coverage, coverage of your conviction. If you really believe something’s exciting, it might be the case that a deal that you’re advocating for is a little bit outside the strike zone of the firm, or, to your point, the founder may not be raising money. And I think, I think it’s a great time to build a relationship. So what I might have done that case would would have been a would have been to say, hey, that makes sense. But let’s like, how can we help you, you know, like, how can we provide help, like, resources to kind of get to know you, anything you need. And also, like, you know, really pin down the founder around, like, what deal they might do? Because usually, you know, like, there’s some flexibility there. Like, a founder might say, Hey, I’m not planning to raise for 612, months because of x and y. But then you might say, hey, well, you know, like our mandate is x, or we’re flexible, we might be able to put some money in ahead of that, or what have you so. So I think I would say, look, have the coverage of your conviction be really thoughtful. Try to engage the founder as best you can in in that is not like a hard No, oftentimes it’s myth, like, maybe a soft no and, and kind of, like, think through, okay, like, what’s driving that, you know. Like, because most founders, I find are pragmatic, right, especially in the early years of your company, somebody might have said, hey, you know, you should raise in 612, months, because you’ll have X revenue or retraction or what have you. But you know, if, if you believe in it, you know, like you might have flexibility. So those are my learnings.
8:05
So now, do you had a great run at insight? You’re investing in, in some, you know, notable companies, yeah, you have a great platform underneath you, yeah, and then you leave, I start six, four or five. Why did you? Why did you leave to start, yeah,
8:19
start your own firm. You know, looking back on it, it’s been over 10 years, almost 11 years now, I think a lot of the reasons remain pretty consistent. Which, which I’m happy with, right? Like, I like the fact that a lot of the insights that I had at the time, and Aaron, my co founder, had, have really remained true, and it really enabled our firm to grow. So I’ll kind of describe them. So Insight was one of the pioneers of outbound sourcing, which is basically going to companies rather than having them come to you and reaching out, like I described in the case of Facebook, reaching out to founders, you know, based upon certain signals. Right now, insight, which folks may know, is now a multi stage fund, but in their kind of first 10 years, they were really primarily a growth fund that looked for firms that were revenue generating, typically bootstrapped insights. History was in B to B software still the majority of what they do. Over time, they expanded into other areas, internet, consumer, internet, commerce, what have you. But their bread and butter is still software companies. And so the first decade of insight basically was focused on bootstrapped software companies, kind of off the beaten path, right? And the sourcing model was a way to find those companies typically way ahead of other firms and really lead rounds in those businesses. And the best exits that insight had. And really now, even now, like have, are deals that kind of fit that profile, right? So as I spent time in Insight, I started to say, Wow, this model is great. It’s worked really well. It’s enabled insight to have wonderful returns. But the early stage is changing, and I saw this interesting dichotomy between what I saw insight apply at the growth stage and what I saw at early stage. Investors doing, and it was almost like day and night. You know, early stage guys were kind of pure, gut feel pure, kind of network driven, very much, I would say top heavy in terms of how firms work. So a lot of firms at the time, and even now, at the early stage, are kind of partner only, or partly, partner primarily, primarily, didn’t really have junior teams, didn’t really have a really resource intensive model, and I’d seen an insight they built, they were one of the first firms to really build a platform, they called it on site, that really was kind of dedicated to helping companies and really serve founders and really help them grow their businesses. And I said, wow, you know, there’s this model that I’ve seen work really well at the growth stage that I think could be kind of tweaked, modified, but applied to the early stage in some interesting ways. And because I was seeing companies like Facebook and others that were growing dramatically fast early on, but didn’t yet have revenues, didn’t yet have a lot of things the Insight was looking for, I felt there was a bit of a opportunity to build a new kind of firm that leveraged software, advanced technology, that applied frameworks to early stage companies, and kind of found these diamonds in the rough, and really systematized how that worked. And at the time, and even now, I think that was kind of a contrarian, non consensus approach. When Aaron and I started working together, and we started pitching investors LPs, they said, hey, well, you know, what signal are you gonna go on if you’re doing early stage, you know, like, what are you tracking? Right? They would say, you know, early stage is typically a geographically constrained business where you do if you’re a seed fund in New York, you do see companies in New York, you don’t do deals outside of that. Or if you’re early stage in the Bay Area, you only invest there. You don’t invest outside of that geography. And we were talking about a multi geographic approach, using data and software, applying an outbound sourcing model, having a junior team, all those elements were pretty, pretty non consensus. And I think that actually gave us some confidence that, hey, we might, we might be onto something, because I had seen again, like this model work to source these early stage companies, but insight really wasn’t well suited in terms of its fund size, to do those deals. You know, when I departed insight, you know, they were raising funds that were, you know, $10 billion and so doing a small check in a company at seed or even series A the just possibly like, hey, not worth your time. Like, you know, like, that’s kind of rounding error on our phone. Like, why would we do that? Yeah, so I felt that it would be better to have a firm, a fund, and a firm, really, that were kind of dedicated toward that stage, and really kind of had a process and system to do this type of deals. And so that’s kind of where Aaron and I had a meeting in the minds, we said, look, you know, like we think this could work. Aaron was was at the early stage. Aaron is a computer scientist. Aaron was talking a lot about how to use software and systems in a firm, and I had seen insight be one of the first firms to have a database. And that was actually the bread and butter of what we used to source, was our database. And we said, hey, you know, how could we build a new type of software and database approach? But that was really well tuned toward the early stage, which is a different ball game, you know, in terms of what you look for. And I can kind of discuss that. So those are some of the reasons, you know, decided to depart. And, you know, I think a lot of the folks at inside said, wow, you know, like, Hmm, you know, we’re a big firm. We’re doing really well. It’s very lucrative. Like, do you really want to take that, that leap and and do this? And, you know, I felt, hey, it was, it was, it was a good time to do it. I felt there was an opportunity. We were willing to be scrappy. So that was kind of like the formational story.
13:27
So Nabi, can you give us some examples of like, the frameworks or the signals that can reveal some of the diamonds in the rough that you
13:35
mentioned before? Yeah, and I’ll describe also how that evolved, right? Because there’s been a lot of evolution. That’s one of our core values at 645 is the idea of, like, you know, the firm is built to evolve. We talk a lot about kind of learning from from the greats, and then, you know, kind of evolving the firm as we go. So I’ll describe, kind of, like, our initial hypotheses and then specific case studies, and kind of how we learned to really apply this data driven approach. So as I described, coming out of insight, I was very traction driven right when I would source, I would look for things like, you know, growth in software downloads for for downloadable software companies, because some of the best wins we had at insight were kind of downloadable software companies. I would look for web traffic for consumer companies, because that was a signal for some of the best consumer companies. We had an insight like, you know, companies like jd.com or Trivago, this bunch of big wins we had that were basically driven off, you know, signals of web traffic and consumer traffic, you know, I would look for things like ARR growth and, you know, revenue retention and and some of those things that, you know, folks doing growth stage would look for in that data kind of became what we Call our analyst score, that basically, like, assesses a company’s traction. So you mentioned resident as an exit we had. That was a business that was growing dramatically fast at the early stage. You know, it went from a launch to 100 million revenues in like, two years, which is pretty crazy. We came into the series A of that business. One of our venture partners, Andy Berman, first told us about it, and Aaron kind of led. The due diligence and on the on the deal, and we were so impressed by the growth, and it was a company in a category that was kind of out of favor. So what resident basically provides today is different furniture, but primarily mattresses sold online. And at the time when we invested in resident, people had done Casper, they’d done purple. And I think people felt one that the market was already won primarily by Casper. And two, that was like, hey, is this really a good business model? Like, at the time, direct to consumer was going out of favor. And so when we do resident, like, it was kind of a non consensus bet, like they didn’t really have, I even, I mean, even when they exited at a billion dollars, like, they didn’t really have any big brand name VCs in that deal, I think because a lot of folks kind of looked past it, given the category, although it was growing again, if you actually looked under the hood dramatically, quickly and had really good marketing, return to marketing Great, just great data that kind of like validated that was a good company. So that was an example of a company that kind of came out of this data driven approach focused on traction. But what we also learned was that being able to assess founders and founder quality as well as market quality were really important. And I think that was one thing that I didn’t fully appreciate coming out of insight was look, if you’re doing a seed or even a Series A you’re not necessarily going to have, like, a lot of traction data points, especially for software companies in certain categories, you’re going to have to look at other signals. And so we started to develop ways to assess founding teams from the outside looking in. And this is really, for us, what we call a prioritization mechanism. We believe great founders can have all kinds of backgrounds, come from all kinds of places. Sometimes, you know, they’re first time founders, there’s no background or no historical data to go on. But we did find that there were certain clues or signals. So I would mention a few. You know, we invest in a company called Panther Labs, which is a security software company in our first fund started by a founder named Jack naglieri, our software flagged jack for, really, for two reasons. One was he was a security engineer at the time at Airbnb, which is think about to go public at the time, and was producing some really interesting founders. So Coinbase came out of air need be for example. But the second thing that was maybe more relevant was Jack had already started an open source project called stream alert, which basically was an early version of what Panther became. And so we had launched stream alert. It was starting to get adoption, and our software had flagged that. And so basically we had some info to say, hey, you know, Jack is a, maybe a high potential founder. Let’s get to know him. Reach out to him. Got to know Him. Invested in his first round. And you know, the deal did very well for our first fund. And so we started to learn about ways to look at the full picture of a business in that early phase. And, you know, we have the ability to invest at different we kind of describe them as different sub stages of early stage. So we primarily come in at seed. We can do pre seed. We can do, you know, growth seed, which is seed attraction. We can do series A and so we started to kind of create, almost frameworks and models that were kind of like tailored toward each of those kind of sub stages. And really kind of like our aim was to have a holistic view of a business. So founder, market, traction, competition, you know, all those different parameters. And we started to be able to say, look, there’s there’s data that we can kind of pull in using what we call our automated scoring, and then we have our animal scoring, and that gives us a complete picture of a business. And we found that there was a lot of companies that were under the radar that that model could kind of gin up. I’ll give you another example. So I’m on the board of a company called rent spree. A rent spree. A rent spree is a vertical SaaS company in the real estate market. They provide software for for for rentals. The companies in LA, when we first found rent spree, they were generating about $2 million of revenue. Hadn’t raised any venture money at the time, had really been bootstrapped, raised a little bit of angel money, growing at a really nice clip. And they were kind of in LA and very No, la funds wanted to invest in that company. I still don’t understand why. Looking back on it, I think it maybe it was the category. They were first time founders, but when we started looking at the business, we’re like, Wow, these guys are growing pretty quickly. They’ve got a really good software product. They’ve got a long term vision for the for the for the for the business. And then the thing that really intrigued us was the market. So in the case of rent spree, they’re providing software to, I describe it as the long tail of landlords, to basically streamline and improve the rental process, everything from screening tenants at the time, providing other things like renters insurance, now they have rent payments. So it was a part of the market that was really overlooked. Most folks focus on kind of large real estate, you know, kind of very big, large multi family, or these guys are going after the other side in a with a really convincing, compelling kind of go to market model that was very capital efficient. So that was another example of kind of an under the radar company that was really to, in our view, just overlooked, and I think it was primarily. Looking at the company’s early traction combined with market that got us to really prioritize it. So those are a few, a few case studies. Do you have
20:07
any advice for maybe founders in the audience or investors in the audience that are in a situation where they’re entering a category that’s been well covered? There are big players, and maybe they do have, like, an interesting, unique ICP, or maybe their go to market motion is unique, but they’re, they’re running up against that headwind of like, you know, there’s pretty well funded players, and this category is well covered.
20:33
Yeah, yeah, that’s a really good question. And I do think it’s actually an area where VCs can do super well, because it’s almost like you’re, you’re a little bit contrarian. You’re looking at a place where other folks don’t believe, you know, will yield high returns. I think that’s actually a good place to play as a VC, but I’ll describe maybe a couple ways to kind of get comfortable, and also, as a founder, how you get VCs comfortable. So I think as you’re, a founder, you really have to describe why the world has changed or why the preconceived notions are incorrect. And I think using data is the most compelling way. Not everybody’s going to buy it, but I think it’s the most objective way to do that. So if you take brensperie, when we first looked at that company, Michael and Paul, the co founders, said, Hey, like, this is the data on rental properties in the US, and over 50% are what you might call, like, small landlord, you know, like, less than 10 units. We were like, wow, that’s kind of interesting. We didn’t know that, you know, we weren’t aware of that. We didn’t come in, per se, with a lot of preconceived notions about that market, because we really hadn’t invested in the rentals category, but that was kind of compelling, the fact that, like, hey, they’re going after a part of the market, that is the majority of the actual units. So the second question was, how do you get to them? Now, I think Michael and Paul were able to, in a compelling way, describe why some of the historical approaches hadn’t been good. A lot of folks had been focused on going to them directly, so trying to market to your small landlord or your small realtor. What Paul and Michael said is, hey, we’re going to go through a partner model. So instead of trying to go landlord by landlord, we’re going to pursue partnerships. You know, MLS systems, realtor organization, now they have partnerships with a bunch of companies like apartments.com and all these big aggregators. And we’re going to go partner driven, so we’re going to have a lot a lot more leverage in our go to market. And if we kind of play our cards right with these partnerships, they might bring us 1000s or 10s of 1000s of of landlords rather than, you know, hundreds or 10s. And so that was that was intriguing. It was kind of like a first principles approach. And so I find that as a founder, what you want to do is you can’t put your head in the sand and say, Hey, I’m just going to be oblivious to like, what people think about my category. I think that’s actually a failure mode. You have to speak to the concerns, regardless of whether they’re legitimate or not. Sometimes my people might have preconceived notions that are really the result of something that might have happened 10 years ago or 15 years ago. But I think as a founder, you want to be a student of the game. I think you want to understand what’s happened because it one informs what you might do differently, and it also enables you to understand or really articulate the why now. And we find this a lot like you find oftentimes that, like, there’ll be markets where there’s been a lot of failures, but the why now might be really compelling, right? It might be the case that the economics didn’t make sense five years ago, but now they make sense for different reasons. Or might be the case that, you know, the technology was kind of Crossing the Chasm, and you know, now it’s ready for prime time. I think there’s a lot of examples now of tech, where that’s the case, we could talk about it, but, you know, there are certain areas where, like, yeah, there’s been a lot of failures, but the economics are fundamentally different, you know, and this is the time to invest. So I think as a founder, you want to be able to articulate some of those elements, like the why now, what’s changed, what happened in the past? Why might it be different, and then be very objective. And then as a VC, I think you want to have that level of objectivity. And I think one thing I find that’s interesting is you might have naysayers within the firm who have some scar tissue, right, who, with good reason, say, hey, we invest in this category and we lost money, like, why is this going to be different? And I think like, you have to be able to, in a compelling way, explain why it might be different, and then just have conviction. I think that’s the last thing. Like, you’re not going to know everything. You’re gonna have to take some level of risk. But that’s what I’ve seen work and and I think it’s a tremendous opportunity to make money as an investor, because I’ve seen so many deals where where the consensus was wrong, you know, in terms of, like, what the category was perceived to
24:42
be. It’s funny, you mentioned that because I was, I was speaking with, I don’t want to name names, but I was speaking with one of the largest Angeles syndicate leaders recently, and we were talking through, and he said, you know, basically he gets an allocation and anything that’s led by, like, a top 10, top 20, tier one lead. Right? And he’s deploying, like, 100 million a year. Or I was like, wow. And I’m like, how has that been for LPS? And he said, not good. It’s a terrible investment model for the LPs, but it’s great for me. I was like, wow. That is, like, the hazard of the industry right there. It’s like, you’re just looking for signal from the lead investor. And, you know, the returns are not, not good,
25:24
yeah, so interesting. You know, it is, it is interesting. It’s interesting topic, right? Like, if you think about, like investing, where great funds are investing, there’s a few things that we think about. I think the first is, even the best firms, you know, they may have failure rates that are 40, 50% in their portfolio. So if you’re investing with a great investor, doesn’t mean you’re getting into one of the rest companies. The other thing is, like, Why do you have that opportunity? Right? So like, if you’re a great fund, you know, usually those funds are quite greedy, and if there’s a company they love, they’re gonna double down and do as much as they can, right? So if you’re getting allocation in a deal that’s led by a top firm, you gotta think about like, Why think about like, why is that? Sometimes it might be legitimate. Sometimes maybe the case that you’re bringing somebody to the table or the founder really wants you just have to kind of think about that a little bit. I think the other thing we’ve learned is, if you’re leading rounds, you really want to think in a non consensus way, because, you know, like, it’s it is the case that consensus companies can get big, right? There are some deals that are super hot from the get go, and they succeed. And, you know, people make tremendous amount of money. There’s a lot of examples of that, right? I would say Wiz is an example recently, right? Like, if you look at every round that wiz did, great investors in there from the get go, you know, in like, that’s a founding team that had built a company sold to Microsoft. Yeah, that’s a consensus company in it was consensus for a reason, right? Great founders, big market, etc. But if you’re a newer fund that’s up and coming, it’s unlikely that you’re going to get into a deal like that unless you have a really compelling reason in, a really compelling positioning in and so I think it’s, it’s more likely that you’re going to find something that is non consensus, that other people are not pursuing, and where you have some kind of proprietary understanding, I think it’s more likely that’s where you’re going to really make your money. And so we think a lot about that. We think a lot about that as a firm like, how do we develop that understanding? How do we think differently. What are our advantages? We we love to have great investors come into our deals, but we’d rather have them come in at the growth stage. You know, when the company is 20 million, 30 million revenue is proven out cool, you know, they pay a great price, versus, like, you know, we’re coming in in a seat or a and, you know, it’s just, you know, it doesn’t necessarily. And we’ve looked at our returns as well. And, you know, like many deals where we came in alongside that big brand firm, you know, early on, didn’t end up that well, you know. So,
27:50
a really good example before with resident, right, well covered category, like contrarian, go to market approach, and there was money to be
27:59
made there clearly, yeah, and there’s, there’s a lot of examples. We have companies in our entire portfolio world say, man, like we were there early. We kind of, kind of, kind of saw something. We should have had some courage of our convictions. I think that those are, those are learnings. And I think now we, we very much believe that we do good work, we do good research. So we should be a prepared mind. We should know more than most folks and be able to make a smart decision. So we try to rely a lot more on our own intuition, our own work, rather than any third party validation whether or not better their own was, etc. A
28:33
few minutes ago, you mentioned the database, said insight that kind of inspired your own. You know what? What’s the overview, or what’s the difference between your early stage sourcing database and approach versus, you know, the the well documented databases, yeah, growth stage,
28:50
yes, yes. I’ll describe it. Also describe, like, how we use the database, and also how Junior folks use the database, because I think that’s the crux of it. Is like, how do you use it for sourcing? And also describe maybe the history of like databases within venture because I think it’s kind of interesting. So if you think about like using databases in venture firms have been using databases now for maybe 20 plus years. Like when I started insight, we were using a Lotus Notes database that kind of dates me a bit, but that’s what we were doing, is Lotus Notes, and then it became Salesforce, and it evolved and, you know, but it was basically like going back there, like, super dumb databases, right? Like, basically, you put the information in, there’s not a lot of intelligence. It’s solely a repository of information. It’s not going to really do, at the time, any analytics. It’s basically going to be a way for you to manually track so you can say, Okay, I talked to this company, and it’s 500k revenue. It’s got five customers, or whatever, 10 customers. You write that down, put in the database. These are the founders. You know, these their background, and then, you know, six months, 12 months later, you call them up and say, hey, you know, like, I remember we talked and you were doing X in revenue, and how big are you now? Or, you know, how many customers, or what have you? And you see those patterns, and you start to triangulate. Kind of in a manual way. Now, at that time, that was even more advanced than what a lot of firms were doing, which is basically not using a database, you know, like not tracking anything, you know, you’d be surprised, like, even now, like, a lot of firms don’t really systematically track their deal flow. And I think for that reason, you have a lot of companies that you just kind of meet once and you overlook and you know, never look back at again. And so what I saw at Insight was there was value in this kind of systematic tracking, although it was very manual, there was value in systematically tracking companies, writing down what you learned, using that to kind of start to see patterns, doing it across a lot of companies in as insight grew a very big sourcing team, and then being able to triangulate, right? Being able to say, wow, you know, we talked to 10 companies selling it to a vertical, and they’re all kind of growing like, maybe there’s some tailwinds. Or we talked to 25 or 50 companies. We talked to a lot of in certain areas, and this is clearly the best one, and this one we should invest in, right? So when we started 645, we said, Okay, we’ve seen databases work. Let’s build an intelligent database that could proactively surface insights rather than us having to manually extract them. So for example, could you have software that proactively showed you companies rather than just relying on you to put them in? That’s something we call candidates. We refine that over time. So our software basically will show us companies. And we have a system now where we kind of go through those every month, and we have quotas for each team member, and I go through it. Aaron goes through everybody goes through it, and we review those companies. And sometimes our software will show something great, because we provide it with effectively algorithms that will kind of uses to to fly companies. It might be a signal based upon the market. Might be single based upon the founder background. Might be a signal based upon traction. What have you. So we said, Okay, how do you bring intelligence into a database? How do you help it? How do you help the or enable the software to sort, enable you to source better, also to prioritize, to track businesses, also, how can it inform you value add so one thing that we have in our firm is what we call our success team. It’s basically our platform, and we track all of our value add activities in our in our software. It kind of informs like it’s a really objective way for us to see how we’re helping our companies, what areas we can be improving in, you know, where we’ve led to customers that have joined, or customer intros or hires, or what have you. So we kind of systematize that, and we try to use software to keep us honest. The other thing that I would describe is, how do you train junior people to learn what’s a great business? So one of the biggest challenges with a sourcing model, leveraging engineer team, especially at the early stage, is, how do you teach people like, what excellence looks like, right? Easier, it’s easier to do that at the growth stage, where you can say, you know, look for companies that are 5 million revenues and growing 100% and, you know, have this rule of 40 or whatever. But you can kind of like, teach that somewhat reasonably in how people learn. But if you’re trying to teach somebody like this is what an exceptional company looks like at the seed or the series A it’s, it’s harder, right? Because it may not have a lot to go on in terms of traction. In in founder backgrounds, can be kind of subjective, right? Or well
33:14
into your point before, sometimes the non consensus things, yeah, the good ones. And how do you teach somebody to look for things that don’t match the pattern. So so
33:22
that’s one of the biggest challenges, is to to kind of blend this idea of like using data to kind of narrow down the field with having an open mind. So what you find oftentimes is it’s this interesting blend where there may be one area where there’s a strong signal. So it might be like, Hey, first time, founding team, no no real data to go on, but like, hey, the product launched is growing dramatically fast. Or it might be like, hey, product not launched yet, but founding team that looks pretty exceptional based upon these qualities. Or it might be like, Hey, I just think this market’s great because I’ve spent a lot of time and analyzing and I think the market is actually really exciting, and I’m just going to manually talk to a lot of founders, try to figure out, like, Who do I think is best positioned to to pursue this, right? And that’s a much more subjective analysis. You’re kind of looking at, like, the insights of the founder and like, you know, maybe their first principles thinking. And, you know, like, That’s a harder, a harder, a harder approach. We have created something we call founder paradigms. We’re going to release a blog post soon around this. It’s basically some of our learnings around founder quality. So we have things we call one, we call purity motivation, which is basically like the why, behind, behind, like why a founder is building a business. And it’s really like the deep kind of stimulus for the for the founder starting something. And that could be a personal purity motivation, that could be a professional one. That could be like caring about a prom deeply. We think a lot about that, and we try to, like, assess, like, do we think that’s there, right? We have other paradigms. There’s one we call it’s not really proprietary to us. I think, I think Andreessen might have, might have created this first, but it’s like this idea of like, an earned secret. I. Where a founder or founding team has spent a lot of time understanding a problem, and they’ve come up with some proprietary kind of earned learnings that most people wouldn’t understand and that are oftentimes non consensus. So we look for that. We try to define like, Okay, is there an earned secret? What is it? You know, like, do we believe in that? Are we like, do we think they’re right, you know, especially if it’s not consensus. So we have some of those paradigms that we use, and you know what kind of tag founders based upon that, we’ll say, hey, this, this founder scores really high. I’m pretty motivation. And this is why, you know, and so I think that’s another way. So, like long story short, we use our database for a lot of reasons, in a lot of lot of use cases. We’re always learning. You know, we try to make sure that it’s a it’s a way for us to get better and for us to learn. And so, you know, we’ll do things. We have something called After Action Review AAR, which is basically a process that we can run. So say we miss a deal that gets really big, and that’s, you know, exits for a lot of money, or what have you we could say, we’re gonna do an after action review. We’re going to basically pull the snapshot from Voyager. We have all the information. We’re going to go through and say, what did we miss, right? What was the thing that we should have looked at and understood about this business? And then how can we improve in the future? You know? And that might inform all kinds of stuff. It might inform like, a data point we track that we didn’t track in the past. It might inform a new founder paradigm. It might inform just, hey, we got to be more of a prepared mind and in a certain category. So that’s kind of how we use it in you know, it’s a different approach. It does require, like, it requires you to think a bit differently. And I think we have to, when we’re hiring people, we got to figure out, like, who wants to think this way? Because certain people don’t. Certain people are like, No, I just want to think about investing traditional way, and it’s kind of gut feel, and you know, it’s, it’s, it’s, it’s my network, and that model works, but you know, it’s different from our models
36:49
so well, or the growth model, where, you know, people might be better at numbers and spreadsheets and metrics and mental math and yeah, and now you’re optimizing more for subjective insight, yeah, yeah. You know, it’s funny, because we have a we have a very similar framework, where we we rank six different characteristics on a 50th to 99th percentile scale, and we’ve very specifically defined the differences. So what are, what are some of the characteristics you guys? Yes, their tenacity, resourcefulness, obsession, learning, appetite, magnetism, and I’m forgetting one. I
37:24
like this. I like learning appetite especially, I think that’s a great one, magnetism, that’s kind of interesting. Yeah, that’s, those are, those are great,
37:33
yeah, and some have some and, and not others. But, yeah, yeah, yeah. Not every founder is the same. You know, they’re all, you know, snowflakes,
37:42
yeah, yeah, you’re right. And it’s also, you know, like, you learn a lot, you know, like, you can try to, like, assess the founder and their their mental makeup. I think it’s easier for like, repeat founders, where there’s a lot of data, but I have found that, like, especially for first time founders, there’s just certain things you’re not going to know. You’re not going to know how somebody performs. You know, in situations you’re not going to know if this person wants to sell the business when they get an offer for 50 million, versus building it for another 10 years and trying to get it to a billion. Is this certain things you’re not going to know? So you just have to, like, make the best assessment you can learn as you’re going, you know, like, and I think that will inform the follow on. And you know, all the decisions you make subsequent to that, but there’s just certain things that are just unknowns. And, you know, I’m sure a founder would say the same thing about, you know, us as investors, or any investor, you know, like, you know, they don’t know how you’re going to react, you know, to certain elements. So there’s, there’s an element of, like, of uncertainty. And I think that’s maybe part of the magic of investing, is you just, yeah, you know, certain things you’re just not going to know. And that’s part of the fun of the game. And, you know, gives you the surprises positive and negative, you know.
38:46
Well, I apologize for the tangents. Yeah, that’s probably not going to get to all the meaty business stuff. But, you know, maybe I’ll throw it to you like, I know you invest around themes. Yes, I know there’s a lot of emphasis. And, you know, various vertical, SAS, FinTech, yes. You’ve written about AI, yes. And I’ve enjoyed kind of reading, you know, all the content coming out, but, but give us kind of some thoughts on, like, thematic areas of interest, and how you see AI playing a role, kind of in, in the next, you know, three to five years of of investing,
39:22
definitely, definitely. So I’ll maybe describe like, how we come up with our themes, and then how we apply them over time. And then, to your point, like, how AI plays, plays a role. So in this has been an aspect of the firm’s evolution as we’ve grown, you know. So we started off as Aaron and I kind of as investors. We were investing across a broad, broad set of areas within software we’ve always been software investors. You know, we love software companies. Obvious reasons, high gross margins, you know, capital efficient. You can invest relatively small amount of money in the early days and kind of prove it out. And so we like those elements of software companies. Yeah. Most people, many people do. But as we, as we grew, we really kind of built out a set of thematic areas. We do a lot of vertical SaaS. We love vertical SaaS for a lot of reasons. Some of those markets can kind of fit in that kind of, like overlooked, you know, kind of like sneaky, big there’s a lot of stickiness in vertical SaaS. Usually there’s this kind of, like, evolution of vertical SaaS companies. And I think now you’re seeing, and we’ll talk about this with AI, like, this idea of, like, you had SAS 1.0 you know, which is kind of like old school, you know, like, actually, maybe 1.0 was basically client server, way back when. And then, you know, 2.0 was, was SaaS, and now you got 3.0 with, with, with AI, we find within vertical SaaS is, like, some of these markets. One, they can be a lot larger than you think. And two, if you’re the winner, it can be quite lucrative, but you kind of have to find the number one or maybe the number two. There’s not, like, five winners. We do a lot of vertical SaaS Fintech is an area we’ve been being increasingly active in. We’re believers in FinTech for a lot of reasons, and we can kind of talk about this. But combination of, like, the pure sheer size of the Tams, the fact that there’s still a lot of inefficiency in terms of financial services, a lot of technology waves that are kind of like bearing fruit now. So we do a lot of FinTech, both consumer as well as B to B fintech. We will do some consumer Aaron really leads our consumer investing. Interestingly, although a smaller number of our deals have been consumer, some of our biggest wins have been consumer. So I mentioned I mentioned resident as an example. We have other companies like gold belly overtime that have gotten to quite large revenue size. So we’ll do consumer. We typically look for a lot of capital efficiency, usually kind of post revenue, where we have early evidence of consumer demand and adoption. We’ll sometimes do pre launch consumer, but it’s pretty, pretty selective. You know, they’re a couple other areas we do. We’ll do dev tools and infrastructure software, you know, that’s an area that requires more technical depth, but we will, we will invest there. We’re kind of growing out that practice. And we do a smaller amount of deep tech in and that’s that area is really led by fardan gatani, my my colleague, we’ll do, we’ll do some deep tech, deep tech as well. So AI, okay, so that’s a really in depth, and we can kind of go very depth if we want, but so we think about AI in a couple different ways. The first is, we think a lot about business model changes with AI. And I think this is actually an area that’s maybe been, been under studied by VCs. So I think if you think about AI today, there’s been pretty dramatic adoption of consumer AI as well as AI within businesses, right? You have companies that will go from zero to 20 million revenues, or what have you, or even more, some companies grow even faster. So there’s this really rapid early adoption cycle right now. At the same time, you have, oftentimes, in some of these categories, a lot of competition. And I think the jury is sometimes out around things like revenue retention, defensibility of the product. You know, whether the long term economics are really attractive, we do know that, you know, AI can potentially drive down costs in a lot of ways. But if you think about, like, the cost to train a model, the cost is deployed, and then, like, the staying power of that, I think the jury is still out on some of those elements. And so for AI, like, we think about a couple areas. The first is like, what’s up? What are areas where we think there’s, like, staying power, defensibility, and in some, some moat in terms of the business. So we’ve done a lot of, for example, vertical SaaS, areas that are a little bit off the beaten path, where we feel like, hey, there’s not going to be 50 founders going after this. And also the founders that do pursue it, they have to have some some level of domain expertise and understanding. So I’ll mention one. We did a deal, my colleague John, a deal in a company called infarmed. Infarmed is software for clinical pharmacy. The founder of that business has a PhD in pharmacy. He spent a long time thinking about how to provide a data service to pharmacists that was better than what existed, and that process, that kind of earned secret, led him to AI as kind of a deployment mechanism for that. But he wasn’t starting the business saying, I want to build a to build an AI company. He was basically saying, Let me solve this problem within pharmacy. And he came upon AI as the best solution. We like that actually a bit better than saying, hey, I need to, you know, apply AI to a certain area, right? I think it’s better to say, I deeply understand a problem and I’m going to use AI to solve it, because I think it’s the best way. It’s the best way.
44:24
So what was the data issue for pharmacists? Why do they need access to
44:27
Long story short, in that area, you have a lot of kind of old databases where the information is not really surfaced in a way that can be actionable for the pharmacist. He also wanted to enable the pharmacist to ask questions, kind of the natural language. So if you’re a pharmacist that’s trying to figure out, like, you know, what your product, should I add to my formulary, or in a new area, say, of medicine, like, how advanced is it? And are these drugs ready for prime time? What have you like? And you want to be able to do research there, but you also want to, like, use natural language to query. You want to be able to customize your queries, have those queries you be responded to you in a fast period of time and and that didn’t really exist. And so that’s what Ashish, the founder of in farm, started to kind of think a lot about, and that kind of led him on his process of starting and really building that business. So there’s examples like, like that. You know, I’ll mention one example. So I’m on the board of a company called reflex AI. Reflex AI basically provides software for training and for quality assurance within call centers. The founders of reflex AI, Sam and John, had been actually working at a non profit that basically, effectively, was a nonprofit focused on mental health, and basically, they ran a call center responding to individuals who were having challenges. And within that environment, they started saying, hey, how do we train our call center operators better? How do we make sure they’re prepared for these really high stakes conversations. And so they said, Look, you know, let’s think about how to simulate conversations, and also how to track the quality of conversations that folks are having. And from that became the early kind of germ of an idea that became reflex AI, which basically today is AI software to enable training and in simulations and then quality assurance within call centers. And they started off focusing on high stakes call centers, so similar to the company they had worked at, but now they’re seeing opportunities in a pretty broad range of verticals. And what’s interesting about that one is the insights that they had for these very high stakes, kind of like really life or death conversations can be applicable to other areas. And if you can kind of get it right in those high stakes conversations, you could apply it to a lot of different engagements, right, a lot of different areas of call centers where the stakes aren’t nearly as high, I guess I would say, right. So that’s an example of a founding team that, again, was addressing a problem they had using AI. Then said, Look, we can build a standalone company based on these learnings, but it was kind of an iterative process. So we like that a lot. We really like this idea of founders kind of almost on a quest to solve a problem, rather than a founding team saying, hey, like, I’m trying to find a problem
47:20
to solve. Yep, it’s like, when you’re when you’re a hammer, everything looks like a nail, like, here’s the tool, I know. Yeah, now what should I go do with it? Right?
47:28
And the other thing is, this one I’ve learned. It’s, it’s a little bit of a test around how much you care about it, because, yeah, you know, if you’re looking for a problem to solve, if everything goes well at the beginning, you might stick with it, right? But that’s more of a mercenary approach. The missionary approach is like, I care about this problem, and so even if it doesn’t go out to begin, even if it takes me years to figure this thing out, even if it’s, you know, like I’m slogging along, I’m gonna, I’m gonna do this because I care about it, right? That’s, right. It’s, it’s, it’s a signal that they’re gonna see that, see the, see the course, and in in that super important, you know, like, one thing we found in our portfolio is, like many of the best companies, like they don’t often look great in the beginning. There’s, there’s a lot that’s kind of moving, moving and, like, you’re not really sure of it, but if they’re willing to kind of slog it out and go through those tough times, because they care about solving a problem, like that is really valuable. Like that can be that can really enable a founding team to figure something out that other folks cannot figure out just because they spent the time, because they cared enough to really, really solve that problem 100%
48:32
I mean, the only guarantee is there’s going to be high highs and low lows, and you’ve got to really care. Yeah, survive, yep, the roller coaster. That’s very true. All right, just a few wrap up questions here. Namby, you know, I noticed you recently opened an office in the Bay Area. Yeah, would love to hear your thoughts on the future of, you know, six, four or five, and what it looks like in five years.
48:56
Yes, so we did open up that office. We’ve been growing it. You know, we opened it up for a couple of reasons. One, because many of our best early wins were Bay Area companies, companies like resident or iterable, which is now over 200 million revenues. We felt as we were stepping up from CO investing to leading rounds, it’d be really important for us to have a presence there. So we opened up the office. We’re growing it. We’re actually looking for a senior investor, a partner investor, in that office. So if folks are interested, they should definitely drop me a note. But it’s, it’s, it’s part of our strategy. We do invest across geographies. That’s important to note, but we have found that a lot of our best businesses are in that general area, especially given now the growth of AI. So it is, it is, you know, part of our strategy, and that’s why we expanded there.
49:40
Now me, 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?
49:46
Yeah, yeah, there’s a couple folks that I think it’d be great to profile. I’m not sure if you’ve had these folks yet, but you know Scott burnbound, who started a firm called resi ventures here in New York, he’s got a great story, really compelling, really interesting approach to investing in. Consumer, he’d be somebody I highly recommend. Anu juga from female founders farms. I’m not sure if she’s joined you in the past, she’s built a great firm. She’s had some great wins. She’s built a pretty amazing community. I think she’d also be a great have a great perspective on how to take a pain point that she identified in terms of a lack of capital going to great female founders and how she built a firm around solving that problem, and she’s done that really
50:24
effectively. If you could recommend a book, article or video to listeners, what would you recommend
50:29
pattern breakers by Mike Maples, you know, I think it’s one of the best books about investing in exceptional companies, but also, like what it takes to build an exceptional company. I think Mike’s amazing, but I think the book just has some great insights that I think are super valuable.
50:43
Absolute gem. We just had Mike on the show a few weeks. Yeah, yeah. It’s great. And then finally, here Nami, what’s the best way for listeners to connect with you and follow along with firm?
50:51
Yeah, yeah, definitely. Well, you know, a bunch of ways you can definitely connect with me. On LinkedIn. You can send me an email. It’s just my first name, namdi, at 645 ventures.com you know, I try to be pretty responsive to emails, but I’d say LinkedIn or email is probably the best.
51:07
Well, huge congrats to you sir for the success. You know, there’s many firms that are started, but then not many that make it to fund four and have the winners that 645 does. So thanks so much for joining us and appreciate all the insights
51:19
today. It’s a pleasure. Nick, thanks for having me. I really enjoyed it. Thank you. Thanks for having
51:28
me. 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.
