University Spinouts and Deep Tech Investing | Marc Singer | Osage University Partners (OUP)

In this episode of Lab to Market Leadership, Marc Singer, Managing Partner at Osage University Partners, joins host Chris Reichhelm to discuss the transformative role of university spinouts in Deep Tech. With decades of venture capital experience, Marc offers valuable insights into what makes a successful Deep Tech entrepreneur and shares lessons learned from OUP’s portfolio of 150+ companies.   

Perfect for innovators, founders, and investors navigating the lab-to-market journey.  

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Podcast Production: Beauxhaus

Chapters

• 0:00 Introduction to the episode and Marc Singer
• 3:20 How the venture capital industry has evolved
• 7:40 The Origins of Osage University Partners
• 12:00 Identifying high-potential university spinouts
• 22:30 The traits of successful Deep Tech entrepreneurs
• 36:40 Lessons learned from 30+ exits and 150 investments
• 49:00 The importance of timing and cycles in Deep Tech
• 51:00 Conclusion and final thoughts

Transcript

Marc Singer: 0:00

To connect it to something I said before, what makes a good entrepreneur? The ones in our portfolio that have been extraordinarily successful have been deeply humble and deeply intense. Like those two things. So they have surrounded themselves with people who know their domain much better. They've hired really well. They have advisors. Um, but they're also, they work 24 seven. They're, they're not working at academic speed. They're working at entrepreneurial speed. They're determined to make what they're doing be successful. And, and product success and company success is more important than financial success for them. And they're so intense and driven. And it can be hard to see that combination of like deeply humble, deeply intense right away. It takes some time to figure that out.

Chris Reichhelm: 0:45

Welcome to the Lab to Marcet Leadership podcast. Too many advanced science and engineering companies fail to deliver their innovations from the lab to the Marcet. We're on a mission to change that. My name is Chris Reichhelm, and I'm the founder and CEO of Deep Tech Leaders. Each week we speak with some of the world's leading entrepreneurs, investors, corporates, and policy makers to what it takes to succeed on the Lab to Marcet Journey. Join Us. Deep Tech innovation emerges largely from the laboratories of universities or other research institutions, which makes the investors that are part of these ecosystems highly impactful when it comes to the development of young deep tech companies. Today, we are fortunate to be joined by one of the leading deep tech early stage spin out investors in the United States, Marc Singer. Marc is a managing partner of Osage University Partners. OUP have been investing in university spin outs for decades across the physical, the computing and the medical sciences. Today, with Marc, I want to understand basically the lessons. What are the lessons they have learned from investing in many different businesses across some of the world's most outstanding universities? What are the patterns they've learned to recognize for good and for ill? What can they share with us? This is going to be a dynamite episode. I hope you enjoy it. Let's get into it. Marc Singer, so much for joining me today.

Marc Singer: 2:26

Thanks, Chris. I'm pleased to be here.

Chris Reichhelm: 2:29

Marc, we're going to talk about OUP. But, uh, it would be great if you could give us a bit of a background on you to start off.

Marc Singer: 2:43

Yeah, sure. So, I've been in the venture capital industry for over 30 years at this point, and so it is my entire professional career, for better or for worse. And I actually started at a venture capital firm straight out of my undergrad studies. Um, and this was in the early 90s at a time where the venture industry was totally different than it is today. So, in about 3 months into my, my first job as an analyst at a venture capital firm, I went to a National Venture Capital Association meeting, which was an association of all VCs, um, VC firms in the U. S., and at the time, this was in 1993, Um, there was a raging debate about whether the 4 billion dollars, the venture industry had just raised from LPs in the funds was way too much capital and, you know, partners at VC firms were up in arms about the fact that there was too much capital in the industry and valuations were going to go crazy and there weren't enough, enough entrepreneurs. And within about seven years of that, the venture industry went from raising 4 billion dollars to 100 billion dollars. And in a sense, depending on what numbers you looked at, doubled or tripled since then. So it was a very different industry then than it is now. But, but as a result, I learned, I learned the industry in some sense, the old fashioned way.

Chris Reichhelm: 3:58

Yeah.

Marc Singer: 3:58

Um, you know, methodical, slow way of how do you build businesses and, and that was great for me all the way through.

Chris Reichhelm: 4:04

Yeah. And you had a pretty impactful hand in developing Osage University Partners as well. Didn't you?

Marc Singer: 4:13

That's right. That's right. So I had, I was at a, a partner at another venture capital firm that I had co founded prior to OUP. Um, that was in the New York area. And I'd actually moved to the Philadelphia area, which is where I am now. And I met my co founders, um, here at OUP. And we learned about, um, some of the dynamics that were happening at the University of Pennsylvania and some other universities around startup creation based on intellectual property where universities were equity holders in their startups, and they wound up with lots of equity positions, and to some degree, through a certain lens, started looking like seed stage venture funds. This is not what they were. They were licensing intellectual property in the startups and trying to push their research into the commercial world. But the reality is they would have, you know, 30, 40, 50, 60, you know, equity positions and alongside those equity positions, they would have the universities themselves would have follow on investment opportunities and follow on investment rights in their own spin outs that they were not exercising. And so a partner and I had been in the venture capital industry for a long time and heard about this dynamic of universities having all of these interesting investment opportunities and doing nothing with them. And so we spent a couple of years trying to figure out, is there a way to create a venture fund model that would partner with universities and create a win win around these investment rights?

Chris Reichhelm: 5:40

And when was that? When did you guys set it up?

Marc Singer: 5:42

So it started, it's. Yeah, we, well, we started working on it in late 2007 and started largely from a point of skepticism of, like, where, what was really going on here? Like, did, did, you know, at the, at the start, like, did universities really have all these equity positions? Did they really have all of these investment opportunities attached to those equity positions? Um, did they really not exercise them? Why? Was this a pool of companies you really wanted to be investing in? And so we actually spent about two years. methodically trying to kind of cross off questions and skepticism on our list regarding how this would work. And this was on like nights and weekends. So we had day jobs as we were doing this. And finally, we got to the point that we We thought there was really something special here, that universities did create really special companies, not all of them, a subset of them, and that you'd really want to invest in. And universities really did have rights to invest in those companies, and they really were not exercising those. And so we, we finally got to the point where we're like, this is something interesting, we should do it. But, but also recognize that the barriers to starting this were high, in particular, because working with universities can be challenging. And, and so what we decided to do was launch a test version, a beta form of, of our strategy, where we worked with five initial universities, and those universities took a big risk with us, and those universities included Penn, University of Florida, Duke, Caltech and Yale, and we put up our own capital, we didn't raise outside capital, and for a year we ran the strategy without a fund, without a team, just those five universities and a couple of us to figure out how would this work in the real world, and, and it was on the shields of that, and that, that was in two thousand, um, two thousand, really kind of midway through 2009 into 2010. And then after that, we concluded there was a very real opportunity here. And then we went and raised our first fund, which we finished raising in 2011.

Chris Reichhelm: 7:51

Okay. I want to go back to something you said earlier. You mentioned that it would, that whether this would work or there were certain universities you could do this with versus others. What did you mean by that? And what are those types of universities that this applies particularly well to?

Marc Singer: 8:11

Yeah, um, so it generally applies to universities with large research budgets. and large tech transfer operations where they are focused on patenting key discoveries, commercializing them. And, and when we started OUP, it was, um, there was in some sense more differentiation than there is today in the sense that back in say 2009, some institutions had invested much more heavily in their tech transfer operations than others. So some universities very much believed in two goals. One, they're, they're And the goal was to push their research discoveries out into the marketplace and to the second, the best way to do that was not through publishing papers, but through commercialization activities. So at the time when we started this, we were focused on finding universities that had big research budgets. So generally it meant the large, um, institution, research institutions in the US but also we, we did a lot of analytics around how many startups did they create. And then we did a lot of analytics on the quality of those startups. So how many of those startups are raising venture capital? What venture capital firms are investing in them? What sectors were they in? How many exits have they had? And so we were actually evaluating universities to some degree the way an investor would evaluate a venture capital fund. Looking at each university like a mini venture capital fund and say, would this be a portfolio if you could have picked the top investments out of you would have done well. And so that's how, that's how we started picking universities and deciding who we wanted to work with. Yeah. I would say that that's a little different today where today, most universities have now invested heavily in, um, in commercialization activities far beyond, Tech transfer. They invest in entrepreneurship and accelerators and other things. And so, um, so it's different today than it was then.

Chris Reichhelm: 10:03

And bring us up to speed now with where OUP is today. What does it represent? How, how broad is it? Is it spread?

Marc Singer: 10:12

Yeah, so, so our, our core strategy is to invest in, uh, university spinouts that are commercializing research at universities. We, we now have well over a hundred partner universities and those, and most of those partnerships are centered in the US although we also have partnerships in Canada, the UK, Israel, and Singapore, um, but, but it's mostly U. S. centric. And if you were to look at, if you were to rank order the top 30 research institutions in the United States by their research expenditures, 29 of those top 30 are partners of ours. So we really have partnered with most major universities. And we have, we have different levels of partnerships with the universities, but, but pretty much every major university you would think of is a partner of ours. And then our goal is to invest in their best spinouts, both in life science and in physical science. So we, we've now, we've raised four funds, we've invested in well over 150 companies. We've had over 30 exits, combination of IPOs and acquisitions of our companies. And you know, in each given fund, we look to invest in 40 to 50 companies. Some of which are early stage, some of which are mid stage, some of which are later stage across all of these different sectors from therapeutics, uh, devices and research, life science research tools to software, cyber security, semiconductor, it's quite broad.

Chris Reichhelm: 11:31

Yeah, yeah, yeah. What is, and the answer, you know, there are some obvious answers to this, but as you've, as you've spent, uh, a fair amount of time with this now, uh, as a firm, what does success look like? for you guys today. Where does, you know, and where does your activity kind of start? We start here at the very early stage with university spin outs. It will be at the early stage. And where does it end? So where does success look like for you guys? And, and, and what is, you know, what is the breadth of your activity?

Marc Singer: 12:06

Um, and I might answer the second question first, like where do things start and end for us and then, and then add on the success layer attached to it. So for us, The start is ideally at a point that somebody is thinking about starting a company. And, and somewhere between that point to when they've started a company, that's when we will all start engaging with a startup. And so our goal with any one of our partner universities is to grow.

Chris Reichhelm: 12:33

So they've incorporated something at that time though, has the, you know, has a company been incorporated at that point?

Marc Singer: 12:39

Uh, most likely yes, but, but 20 percent of the time, no, they're thinking about incorporating. So we want to engage early and, and the reason why is that because of our partner universities and our model, um, you know, we, we expect with each company, we will have multiple opportunities to invest in them at the very, at the seed stage, at the series A stage, the series B stage. So our goal is to get to know them as early as possible so we can start building relationships And we can evaluate them from an investment standpoint. It also helps us help that startup. So we can also connect them to other investors. We could connect them to other, um, uh, potential customers, partners. And so, um, so we like to engage, engage, I mean, in talking to them, building a relationship with them. As early as possible, right around the time they are starting the company, thinking about starting the company. It doesn't necessarily mean we're going to invest them, but we started building a relationship. And so, so our business model is centered around building relationships and tracking the progress of thousands of people. University startups. So we can first find the best investment opportunities and then also be helpful to them. And so, so that's why our engagement starts early in terms of actual investments. Um, about 20 percent of our investing winds up being at the seed stage. So the first round. of a company, about 40 percent winds up being at the series A stage, and then the other 40 percent is later, series B, C, D. So we, we have some companies, you know, we get to know and we invest in them three months later at the seed stage. We have some companies we've known for six years. and develop relationships and for one reason or another never invested in one of the early rounds and invested, um, at the later rounds. And the reason, the reason we do that is because our universities have companies at all of those different stages. So if our goal is to invest in the best spin outs from our partner universities, each university will have Companies that have spun out 10 years ago, five years ago, and are spinning out right now. And our goal is to invest in the best of all of those. Um, and that has us build a fund that is investor in lots of stages. So that's, that's the engagement side before I'm going to switch over to the success piece. Unless you have other questions about the, okay. Okay. So how do we define success? There, there are many different ways. So the most concrete, important one for us is delivering great returns to our investors. And so they're very happy, we're happy, and we can continue to raise capital in future funds. And so for us to stay in business for, you know, 40 or 50 years, which we hope to do. We need to continue to deliver great returns for our LPs. And then, um, and then continue to raise capital. And, you know, usually we define sets for LPs as delivering kind of tripling their money or more. Yep. So to, to do that, then within a portfolio, you need to have like a range of outcomes. And so, um, so first you need to have a certain percentage of your portfolio be successful and then you also need a range of kind of multiples of your invested capital, range of returns with those companies with the obvious goal being to have some of your biggest positions, where you have the most dollars invested in the biggest ownership, have big multiple outcomes, kind of 10 X or greater outcomes. And, um, and so, so the, and, and the, you know, the venture industry, there definitely is a power law where you want to have, you know, you need to have a few big wins to really drive the success of a fund. Um, you know, we have an interesting dynamic where we have some of those big wins, but also we have companies that perhaps weren't that successful at commercializing their technology. But you still, it still has acquisition value to other acquirers because the technology itself and the product is so valuable. So sometimes in our, on the negative side, when we're, when we have an unsuccessful company, maybe we get our money back or, and, and that actually can help at the same time. But the goal is to find winners.

Chris Reichhelm: 16:47

Yes. You've, you guys have been at this now for, for, I guess, 15 years. Is it? It's about 15 years. Um, and so you will, you know, and you've worked up a portfolio or worked up an investment portfolio of about 150 investments, 150 companies, I'm guessing. Um, and so I'm assuming you will have, um, you know, seen some successes. You will have seen a lot of frustration and challenge in there too. Um, you know, we're all about understanding the journey from Lab-to-Market and how to make that journey. If, you know, when you look back at, at the portfolio, you know, what percentage or, you know, even how many of those companies would you say, would you say have made that journey from those initial stages to full commercialization and market?

Marc Singer: 17:42

Um, you know, all. Um, I'll define it not as investment success, and the reason I say that is because sometimes you can have a company that has not made that journey yet that can be a great success because someone else wanted to buy it. And so I'll focus on

Chris Reichhelm: 17:57

Because of the asset value, because of the technology value or something in the IP, yeah, yeah, yeah, yeah, I get that.

Marc Singer: 18:03

And in particular, in life science, um, you know, you often can sell companies, let's say, after a Phase IIb clinical trial. So, it has not made it to the market yet, and it still is, I don't know, maybe five years from market, but you can monetize your investment.

Chris Reichhelm: 18:19

Sure.

Marc Singer: 18:19

And so, to answer the question of like, who has really made it from Lab-to-Market, um, I don't have perfect numbers, but I think of our portfolio, knowing that many of these companies are companies we invested in in the last five, six, or seven years, so they're still early in their journey. I think we have six or seven companies that have had drugs approved. on the life science side of our portfolio. So that's, that's what I define as like made it to market approved drugs. And so there are a number of approved drugs, um, in our portfolio that, um, that, that are helping patients every day. Um, and then on the life science side, but on the life science side, if you isolated that piece of our portfolio, which is a big part of our portfolio, Most of those companies have not made it to market, just because most of them are biotech companies. The drug development process takes a long time. Often, again, we've exited before they've gotten to market. Um, so, so that's a long journey. And, um, but, but, but having a number of drug approvals in and of itself is pretty remarkable and speaks to the strength of university spin outs. And, and, and maybe to deviate just for a second on that, um, you know, universities are extraordinary at creating biotech companies. And so, like, you know, we do a lot of analytics at OUP, and one analytic we've run multiple times is, of the biotech companies that have raised real capital, what percent of those are university spin outs to begin with, and it's well over half. So well over half of the ventures, significant venture backed biotech companies come out of the university.

Chris Reichhelm: 19:54

Is this global or is this just in, in, in the U. S.?

Marc Singer: 19:56

This was just in the U. S. This is just the U. S.

Chris Reichhelm: 19:59

Yeah.

Marc Singer: 19:59

Just there.. So, so you see lots of, lots of like activity now. Um, and then on the, to switch over to still like how many companies have made it from Lab-to-Market on the physical science side, which we use synonymously with the word tech. Um, in some sense, most of them. So almost all of our companies by the time we've exited are selling their product. They may not be selling a lot of it. So for a physical science company, by the time for you to create a successful investment and a successful company, not only do you have to develop that product, which can take many, many years, But you really have to get to the market and sell it, otherwise it will not be a successful investment. And so, in some sense, all of our companies, all of our physical science companies, at some point or another, make the journey from Lab-to-Market. The issue is when they get to the market, the market might not be that receptive.

Chris Reichhelm: 20:58

Yeah.

Marc Singer: 20:58

They, you know, maybe spent five years building a product, they sell, they sell some. So you could say, check, they've made it from Lab-to-Market, but they haven't really hit a market pain point with the right product, so they don't sell a lot.

Chris Reichhelm: 21:10

Yeah.

Marc Singer: 21:10

But, but, but all of them wind up making that journey, or just about all of them.

Chris Reichhelm: 21:14

Are there even a couple or a few that you can point to and say, actually they did wind up selling a lot

Marc Singer: 21:21

Yeah, sure.

Chris Reichhelm: 21:22

On the physical sciences.

Marc Singer: 21:23

Um, and, and still, still going. Yes. So, you know, we were, and um, an investor at a company called IonQ, which is a quantum computing company, which is now public now selling a lot. Yes. And, um, and, you know, an eyebrow raising amount, you could go look at their numbers of,

Chris Reichhelm: 21:38

yeah.

Marc Singer: 21:38

Of what's happening now we have a number of software companies, um, that are, you know, have very significant kind of called triple digit annual recurring revenue, having started at nothing and some of the, you know, some of those are in cybersecurity, some are in infrastructure, software or application software. So, um, so those are some of the, you know, and some of the cyber companies that have really scaled include Menlo Security, which is, um, uh, which is in, uh, cyberspace, also, um, Corelight which is an open source security company.

Chris Reichhelm: 22:14

Of those successful ones, both on the life sciences and on the on the tech side, on the physical sciences side, are there common features that you've been able to identify? I want to understand, is this just Are we just, you know, laying the chips where they fall and hoping we get our number come up? Or have you been able to identify key features at the outset that increase the probability of success within a particular investment?

Marc Singer: 22:45

Yeah, we, we are trying to answer that all the time. So again, our goal is to deliver great returns for our investors. It keeps us in business. So the better we can hone our investment judgment, and figure out what works and what doesn't and make good investments, the better we will be. Yes. If that were easy, you'd see even more, you know, venture capital firms. So it is, it is very hard. And of the many reasons why in a given success, there may be, there are many factors that go into a given success that are unique to that company, including luck and timing, which are really hard. So there's not one formula. And having been in the venture industry for 30 years myself, and being a very analytic person, I have tried to find formulas, in a sense, of like, or how do you reduce things to what matters and, and the industry defies it. So what are, to answer your question though, what are the commonalities? First, even though we're investing in Deep tech companies, um, that are based on tremendous research. At the end of the day, the team is still the number one important factor. And so we are, we as investors are extremely team focused. Who are we backing? What is their skillset? We're both from an experience base and also from like an innate personality standpoint is true. Are they, how good are they? What are they good at? Who have they hired around them? And, and so we're, we think number first and foremost, team matters. And we have had some of our companies that were commercializing great university research where that university, that research didn't wind up being the right path or they went down the wrong path and they pivoted to something else. And, and in some sense, most startups pivot. Either a big pivot or a small pivot. And that pivot, the quality of that pivot is all about the quality of the team. And so, so we, we do think first we are investing in a team and we spend a lot of time figuring out like what matters and are they a good team? Um, we also recognize that sometime that team changes over time. So you also need the ability to evolve the team. And so, so, but the team matters most. And if you, you know, in our successes, we think the team mattered and in our failures, the team mattered. And so that, that really is everything. After that, they're wind up. Um, we think about it a little differently between tech and life science of what really matters so on the tech side, I'm not, well, let me start on the life science side, you know what, um, there are a bunch of things that matter. First, like, is it feasible? Like, can you really, like, the number one reason our life science companies succeed or fail is because the, like, the, the, the drug they're developing, the target pathway they're targeting works. And, and, you know, so they wind up getting a drug that shows clinical effectiveness and when they fail, it's because they had toxicity along the way, or they couldn't even get a drug into the clinic because they couldn't find the right DC or things like that. And so, um, so there it's, it's all about, in some sense, technology development. And the management team still matters in that a lot. Drug development is a very experience based industry. And so, so you need a team that knows what they're doing. Um, and so, so, but it, but it's all about like lots of technical issues around drug development on the life science side. Also attached to the question of what is your lead drug? So most university spin outs wind up, start with some sense of a platform, a new way of doing things, a new way for you to target discovery or to create small molecules. And, and then they have the decision, do they commercialize that platform? Do they develop a lead, um, candidate out of that platform and commercialize that in most platforms not all wind up developing, you know, one or more drugs out of it. And that first, that most advanced drug is what really drives the value, at least for the venture investors during the life of our, our investment. So that decision of what is, what is kind of the lead drug and what, what do you want to develop in what market for what indication that matters a lot that gets back to team.

Chris Reichhelm: 26:56

Yeah.

Marc Singer: 26:57

So that's, that's on the life science side on the tech side. It's a little different. So with tech companies that are university spinouts, I would say on average, they go about the entrepreneurial process a little bit backwards compared to a non university spinout. And what I mean is if there are many, many, um, uh, great stories of non, some of the most successful non university spinouts that developed a product where the founder or founders were solving a problem they were personally experiencing where they knew that market or they had experience in that market. So they, they identified the problem first, the market problem first, and then they went and built technology to solve that problem. With university spin outs, they go about it generally the other way. They develop some great technology. a breakthrough, and then they figure out what problem do they want to go solve, and how do you build a product to go solve that problem. So the journey happens backwards. And so what you need to really be really good about is mapping what can this technology do to what problem to go solve. And that is, that is very hard. The ones that are successful, figure that out. Well, usually that journey from technology to product, um, is led by someone, not from, not an academic, you know, by, by, um, just by training academics, that's a new skillset for them. A small number of them are extraordinary at figuring that out, but many it's new. And so usually there are outside entrepreneurs involved in doing that, but figuring it out. How do you translate technology to product is the hardest challenge for tech companies.

Chris Reichhelm: 28:35

And that comes back to the team again, being able to, because backing an existing platform that perhaps you've spent your PhD or postdoc working on, and that, you know, happens quite a lot. Here's a platform we think it has applicability there, there, and there. Well, let's go see if we can build something or get someone interested in what we've got and apply it towards that problem over there. And, and you're right, as you say, it's kind of a backwards way of doing it. It should still happen because there's still innovation and it could wind up, you know, being the answer to lots of big problems later on down the road. But if you're trying to build a company within a confined, you know, space of time, it can be quite problematic because you're constantly trying to back what you've got into a problem area as opposed to maybe looking at the problem area with fresh eyes and saying, how would we do this knowing what we know now?

Marc Singer: 29:23

That's exactly right. And I would say there's often a higher, another challenge on top, which is some of these technology platforms, these research discoveries can do many things.

Chris Reichhelm: 29:33

Yeah.

Marc Singer: 29:33

And

Chris Reichhelm: 29:34

yeah,

Marc Singer: 29:34

kind of the academic orientation is if you turn this into a company, let's try to go do all of this.

Chris Reichhelm: 29:39

Yeah.

Marc Singer: 29:39

You know, let's do three or four things at once. And, and we've invested early in the days of OUP. We invested in companies that did that, that were tech platforms that could have multiple products in multiple markets, and they pretty much always failed.

Chris Reichhelm: 29:51

Yeah.

Marc Singer: 29:51

And, you know, we learned you're much better off. You need to pick early.

Chris Reichhelm: 29:54

Yeah.

Marc Singer: 29:55

You can't do everything. Um, for many reasons, you're more likely to fail. You're better off picking one thing. And figuring out, get all the right team expertise, domain expertise, and doing that one thing.

Chris Reichhelm: 30:05

Yeah. You said earlier, you're still number one for both life science and the physical science is still tech. Sorry, it's still team. Still looking at the team. So let me go a little deeper there. When you're evaluating the team, when you and your team are evaluating the teams of these companies, what are you looking for? What features, what qualities, what dynamics? Do you really want to see that signal? Ooh, you know, we could have one here or we might not have one at all.

Marc Singer: 30:34

Right, right. Yeah, we, we, we, we look at many things and I would say first, like in my own career, I would say that I would say I've, from an investment standpoint, I've had too high a false positive rate of thinking someone is great investing behind them and then you spend the next two years working with them and you realize all the things you've missed along the way. And, and I think it's true of a lot of other VCs also, whether they'll admit it or not. And, and so like, we think a lot about that question, like, what are the attributes that really matter? What are, and, and similar to something I said earlier, we would like to reduce that to a formula and, and it defies it. You can't. And, and, and, and I, it's having tried it enough times. We have not been able to figure out the, the simple answer to the question of what are the three things that really matter universally. Across all companies, all stages, it changes. And, and you can have the same person in three or four companies and maybe they succeed once or twice and fail two others. And, and so it's, it's very, very hard. So I'll talk about the things that we think about. Um, you know, the first thing we think about is, um, a number of experience based things of have, have they shown in their, whatever their domain is, that they're a high performer. So whether if they're, if they've been in the academic world, have they been a high performer in the academic world? And what are markers you can look at there? If they've been in a corporation, have they been in a high performer? There we look at rapidity of promotions. And with a, with a migrated to, we also look at how they bristled in a corporate environment because generally people in a corporate environment don't always make great startup entrepreneurs. Um, and if they did a startup, we look at track record and, um, and what they did. So, so we do spend a reasonable amount of time on track record to understand what it means about that person. Um, second, we spend a bunch of time on their judgment. Uh, how do they think about what they're doing, their problems, do they really deeply understand all of the choices they have to make and the inputs they need to make them, and so we spent a lot of time on strategy and strategic thinking, how do they think about products, how do they think about market, um, And so that's the second area. Third is how do they think about building their team? So sometimes we will, I'll simplify this more so than the reality. Sometimes we'll evaluate a CEO not by what we see in the CEO, but entirely by what they see and who they've hired around them. And so have they hired a great team who are better than the CEO? And, and then also, do they have advisors who are better than the CEO? And, and when they can recruit really, really well, that is, I think a great sign of many things around a CEO. So sometimes we'll think we'd like this person, and then we look around them and they've hired a bunch of people who we think are not the right people, and we We'll conclude just through that dynamic that the CEO, um, isn't great because they don't know how to build a great team, which involves how do you find people? How do you attract them? How do you close them? Um, how do you listen to them? And so, um, we think about team building quite a bit. And then I would say the last thing we think about is humility and listening skills. There are many more. I'm just simplifying to some of the interesting ones. So, We find this is not universally true. So there are some very, uh, not humble, overly confident founders who have been extraordinarily successful over time. Um, we find that that's, that's like a low probability. And the, the problem is with that phenotype of a CEO, if they're, it's their way or the highway. So they tend not to listen to signals from other people, signals from their team, input from their team, input from customers, input from investors. And they, they, they believe their, their way is the best way. And if they're right, they will hit it big. If they're wrong. There's no way to correct it, and we prefer, given that university startups, they pivot, they take time, they're going this journey from Lab-to-Market, um, they need to be more humble about learning, listening to signals from the market, from customers, from their team. So we've strongly prefer, you know, Entrepreneurs who have a sense of humility and intensity, like deeply intense and deep down inside, deeply confident, deeply passionate, but are also recognizing they don't know everything and they need help. And we find they wind up being more successful.

Chris Reichhelm: 35:07

How fair is it? So how fair is it to expect students or researchers, those with minimal executive experience to rise to the challenge, to be able to serve in these roles, especially within these very complex businesses, because deep tech companies are far more complex than just software companies, for example. So, you know, there, you know, there's just so many other things they've got to consider as part of the development of the company.

Marc Singer: 35:41

It's such a relevant question in this category because these are companies that are, you know, And are often started by students.

Chris Reichhelm: 35:47

Yeah.

Marc Singer: 35:47

And in particular in our world, which is, you know, we tend not to invest in companies started by undergrads because the companies we're investing in are research based. So usually it's in a lab where you have grad students or postdocs working on the technology overseen by a professor whose lab it is. And so we spent a lot of time, thinking about that question, learning the hard way. And so what we have generally concluded is that, on average, professors themselves, people who have been in the academic world for 20 years, tend to make bad CEOs. Um, and because they're, they're so ingrained in the academic world, and like their thinking patterns are so centered that way, they have not really learned, like it's, it's, it's much harder for them to learn a new way of thinking and being an entrepreneur is a new way of thinking. First about speed. How fast do you go? How quickly do you make decisions? Um, and, and how do you build a team and other things? So there are some very notable exceptions to that where professors have made great CEOs, but for the most part, we think that that's a struggle and an awful, a professor whose lab it is, is better off being an advisor or CTO than being full time at the company. But the right, the right grad students or the right postdocs can be extraordinary CEOs. And actually within our portfolio, uh, this is more true on the tech side than the life science side. If you isolate just our, our physical science investments, and you look at the grad students or postdocs who were CEOs in those companies, and you compare them to every other phenotype of CEO. So compared to serial entrepreneurs or compared to people who have startup experience, but we're CEOs or people from industry, the grad students and postdocs have outperformed in our portfolio. They have performed the best. They have outperformed serial entrepreneurs... And there is a selection bias in that data. So it is the grad students and postdocs that we have spent a lot of time with, gotten to know over time and we chose to invest in because we saw something special in them. And those special grad students slash postdocs, we combine them together. They, they can be high performing in anything they do. To some degree, they chose to go into research and academia for a while. They were very high performing there. Had they chosen to be in hedge funds or the non profit world, they would have been successful. They're also. And so if you could find, identify them and find them, they have tremendous upside. And so we spend a lot, having now seen this data ourselves within our own investment experience, we're, we're super interested. And, and how do you unpack that and, um, and how do we find more of them?

Chris Reichhelm: 38:29

Let me ask a quick question there. The amount of time you spent developing the relationship with these individuals, with these grad students, postdocs and so on, um, you know, did you get to know them for a while and then invest and then continue to build on that? Or did you invest up front and then, I mean, but then keep cultivating the relationship?

Marc Singer: 38:48

Right. It's generally the former. We got to know them for a while, and we saw them. And, and while being a year or two or three.

Chris Reichhelm: 38:57

Yeah.

Marc Singer: 38:57

And so it is hard. We have found so far, it's hard but not impossible to identify upfront. Are they that phenotype? That truly special phenotype? You could try, and we will try sometimes, and we'll invest in some of them early on, but it's much easier. But if you watch them over time, and part of the reason why is if you look at that phenotype of, you know, what a successful grad students and postdocs as CEO, as company founders, um, to connect it to something I said before, what makes a good entrepreneur? The way in the ones in our portfolio that have been extraordinarily successful, had been deeply humble and deeply intense. Like those two things. So they have surrounded themselves with people who know their domain much better. They've hired really well. They have advisors. Um, but they're also, they work 24 7. They're, they're not working at academic speed. They're working at entrepreneurial speed. They're determined to make what they're doing be successful. And, and, you know, Product success and company success is more important than financial success for them. And they're so intense and driven. And it can be hard to see that combination of like deeply humble, deeply intense right away. It takes some time to figure that out.

Chris Reichhelm: 40:10

Yeah. And then that, and given that success then, that almost reinforces your Or the company or the funds, you know, policy of waiting. Let's wait and see how these people do. Let's build relationships. Let's observe them over time. And then we can take a view, a more considered view and see how they match up against. And then we have a lot of other, other leaders in our portfolio.

Marc Singer: 40:32

That's generally right. And let's help them along the way. Um, and stay, you know, connect them to other people that might be helpful, stay in touch. Um, we also do a lot of like some training programs for entrepreneurs and things like that to try to be helpful. And so it is, it is our preference and usually it takes a company a little while to get going. So the natural course of it is that you can do that. I would say right now. In the AI world and in university spin outs that are focused on AI technologies. Unfortunately, you don't have that luxury. Like, there's, there's such an explosion of investment in AI companies, including in kind of academically founded AI companies that, that in that world right now, you know, we've been making some bets without that, that dwell time. And, and we'll see, like, it's, we, we have less confidence of, you know, Like, maybe we've identified great teams. We hope so. We've tried our best to, but you don't, when you don't have the benefit of time, there's just more uncertainty.

Chris Reichhelm: 41:29

Yeah. When you, when you look at your, you know, when you look at the underperforming part of the portfolio, what are the. Are there clear patterns there? You know, looking back, things you look at and see, you know, we have to be aware of that. Either, you know, certain type of behavior, it could be the inverse of what we've just been discussing, but are there, are there features, are there patterns that you look back in and, you know, that serve as lessons now for you guys?

Marc Singer: 41:59

Yeah, it's, it's a little bit, it's hard to be, there are, and we learn lessons all the time. I would say that what's tricky about it is sometimes the lessons of, if you, if you took every individual lesson you learned the hard way and said, I'm never going to repeat that, you probably would make no investments if you've been in the industry for 20 or 30 years, because you've learned every lesson the wrong way. And, and so it can often be situation specific. So I'll give an example of that in the, when we started OUP. That was at the tail end of the first explosion of what were then called clean tech companies, now called climate tech companies. Lots of them in solar, batteries, wind, all kinds of things. And, um, and just about all of them failed. Oh, yeah, almost all of them. And, and now we're in another explosion of that now. And where in some sense, they have the same exact challenges. Around how do you scale these technologies? They're very capital intensive. It takes a lot of time to get to commercial scale. Do you really know if you have an economic model attached to that? And so there, there's nothing that really changes the dynamics that much, except the fact that maybe the market is more interested in them today. Maybe there's a timing difference, um, and more customer interest today than that. But, but that's hard. So you could say, do you not invest in any climate technology companies? Universities create a lot of that, or how do you do it? And, um, So, so it can be, it can be very hard for us, you know, really the, the commonalities that things do that, that we think about a lot are one, does it take forever and a day and hundreds of millions of dollars before you know, can get a product to market and know if it works? And so we think a lot about signals early on and how can you get validation much earlier in the process, um, rather than waiting 10 years and through hundreds of billions of dollars and know, um, if there's really a market for it. So that's, that's really one we think a lot about.

Chris Reichhelm: 44:02

And I guess to a certain extent, and I guess to a certain extent, the strength of that validation, because you could have someone say, Oh God, if you built that, yeah, we'd love that. Doesn't mean they're going to buy it, doesn't mean they're going to buy it again. So, so it's, so it's thinking about, well, what does validation look like for something that's on that journey? Think of nuclear fusion. That's right. Sounds great.

Marc Singer: 44:22

Yeah.

Chris Reichhelm: 44:23

Sounds great. Could be great. Could be the answers, but someone's got to pay for it. And we've got to have other materials that supply these things and, and, and all of this other stuff. So there are all these other questions to answer as well. And so I guess the validation piece is quite a, an important one there.

Marc Singer: 44:40

It is, and there are, there are many signals along the way in validation, that either could be positive signals or false signals, depending on how you looked at it. So like, like we'll even see like early customers, so corporate customers today are willing to pay for things early and it's, it's no longer validation. It used to be, and now it's like engagement, but you can treat it as validation, um, and because corporations have moved into the startup world, they're interested in it and they're willing to experiment a lot more. So how do you listen to those signals?

Chris Reichhelm: 45:12

That's so interesting that you feel that way, that you guys have, have, have come upon that view. That just because a corporate buys something doesn't mean that's validation because they've moved into that. That's interesting. Do you think that view is shared across the US?

Marc Singer: 45:34

Probably. Uh, I don't know if it's universally shared and to some degree, you know, it comes from bad lessons where we invested in a company that had one or two great corporate champions who, and, and a year or two later, those champions like, they're like, ah, it just didn't work for us. Whatever it is, they walked away or that validation from those one or two corporate champions didn't translate to anybody else. It was unique to the people in that company. And, and so I would say we had battle scars from that. And I would assume other investors do also. The hard part is that still you need that. It's, it's kind of like the path to market goes through, like having some corporate champions. It's, it's necessary, but not sufficient.

Chris Reichhelm: 46:18

Yeah.

Marc Singer: 46:18

Like you have to get deeper with those customers.

Chris Reichhelm: 46:20

Yeah.

Marc Singer: 46:21

It has to not be because of one or two, Big champions within that company, that broader buy in. And so, um,

Chris Reichhelm: 46:27

There needs to be more going on in the industry to accept that proposition than just the, the willingness of a couple of people within a company.

Marc Singer: 46:39

That's right.

Chris Reichhelm: 46:40

Yeah. You need other signals.

Marc Singer: 46:42

Yep. Yep.

Chris Reichhelm: 46:44

What are the lessons? What are some of the key lessons that you've learned along the way about investing in these young companies?

Marc Singer: 46:53

Yeah, um, too many, um, you know, the key is what are the ones that are universal? Uh, and I'll maybe just focus on a couple. One, the obvious one that we had a lot, which is like team matters and do yourselves as investing in people first. And, but the, the team you're investing in, but the future team you will be investing in and how do you recruit and build the right team. And so team is everything. Um, that's one. Number two, um, I would say things cycle in and out. And you see these massive gyrations and investor interest, exit opportunities, customer interest. And so when you've been in the industry for as long as I have, you've seen lots and lots of those cycles. And you know, what you, what I conclude from that is a few things. Number one, you, and these, these will be contradictory thoughts. Number one, ignore the cycles. Be head down, heads down and, and just build your best product and your best team because you can't predict what the, what really, part of what will matter for company success is what's the cycle like five years from now when you're looking to exit. You cannot predict that, and so do the best you can, um, but then second, and so don't overreact to those cycles and in some sense have blinders on about the environment that you're in. But the second is you, you know, you take advantage of like a good environment when it's there. Like if there's a great environment for raising capital, you do it. If there's a great environment for selling a company and it maybe is premature, you take advantage of it. And so when, in particular on the good side. Like when, when that, when, because the venture industry cycles wildly between really good exit times, let's say in capital raising times and really bad ones. And when you're in those good times, take advantage of it because timing matters in terms of company success, investor success. So you want to do that.

Chris Reichhelm: 48:48

Yeah. That's a great, yeah. Those are some great lessons. Um, Last question, because I know we're nearly out of time, believe it or not. Um, I hate to get political on this, but let me ask. Trump, is the new administration going to be good for technology and the broader industry? Is this going to help us? Is it going to hinder us? How do you see it?

Marc Singer: 49:13

Yeah, we don't know, is the truth of it, there's just so much that's unpredictable, and it's hard to know what goes into policy, and so, um, you, you could, you could craft a very coherent argument that it will be better for startups, you could craft it will be worse. Yeah, I would say one of the things we don't know is, Let's say in the climate tech industry, where there's been lots of, in the U. S., lots of government support, um, also, you know, uh, for climate tech companies, for semiconductor companies, does all of that continue? We don't know. Does it accelerate? We don't know. Does it contract? We don't know. What happens with Um, government research funding, NIH funding.

Chris Reichhelm: 49:51

I mean, the Inflation Reduction Act

Marc Singer: 49:53

and all of the support.

Chris Reichhelm: 49:54

Yeah,

Marc Singer: 49:54

the IRA and all of that. We just, and it's all just, um, it's less predictable than ever. And it feels like there's more uncertainty. And so right now we're not really trying to predict it because it just feels not predictable and we have to wait and see what happens.

Chris Reichhelm: 50:11

But by focusing on the, on the fundamentals, which is really what your key lessons are about, what you just highlighted there, building the team, focusing on building a good business, regardless of the cycles, don't try to predict them, take advantage of, you know, when it's a good time to sell or a good time to buy or a good time to raise, take advantage of it, but focus on the fundamentals of building a great business and, uh, and focusing on the team and, you know, and the rest. You'll have to adapt to. And, and, you know, and those are some of the, and those are the features of a good team anyway, in a good business.

Marc Singer: 50:45

That's right. Yeah. Some of the best companies ever were founded in terrible times where it was hard. And, and so like the, the founding side is not cyclical. Like, like it doesn't mean you have to start a company in the best time or the worst time. You want to exit the company in the best time.

Chris Reichhelm: 51:02

Especially in these areas. Especially in these areas that take so long in terms of research and technology development and the Lab-to-Market journey and all of that.

Marc Singer: 51:13

That's right.

Chris Reichhelm: 51:13

Great. Marc, I've loved this. Thank you so much for joining me.

Marc Singer: 51:19

Chris, it's been a pleasure to be here. Thank you.

Chris Reichhelm: 51:21

Good to see you. You've been listening to the Lab to Market Leadership podcast, brought to you by Deep Tech Leaders. This podcast has been produced by Beauxhaus. You can find out more about us on LinkedIn, Spotify, Apple, or wherever you get your podcasts.