158. The Deep Tech Debate: Why Invest in a Capital Intensive, Long Time-to-Exit Category? (James Hardiman)

james hardiman the full ratchet

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James Hardiman of Data Collective joins Nick to discuss The Deep Tech Debate: Why Invest in a Capital Intensive, Long Time-to-Exit Category?. In this episode, we cover:

  • James’ definition of deep tech
  • The segments within the category
  • How this area differs from others
  • Types of founders and founder profiles that he looks for
  • Why he’s willing to invest in a capital intensive, long-time horizon category
  • Why deep tech will drive the biggest outcomes
  • How founders are able to de-risk deep tech opportunities where many others don’t
  • The waves that are coming in deep tech
  • and finally how some consumer, social companies became deep tech companies by accident.

 

Guest Links:

Quick Takeaways:

  1. Deep Technology companies have a technological, engineering or scientific advancement– and that’s the core asset
  2. Investment areas within deep-tech include biotech, medtech, semiconductor, quantum computing, robotics, AI, blockchain and new materials
  3. In the past business founders were looking for technical co-founders; now it seems the technical founders are looking for business or domain experts
  4. A key question James asks w/r/t founding teams is: “Do the complementary skill sets need to manifest in one person or two different people?”
  5. The pivot-based approach of “move fast and break things” is not one that lends itself to deep tech– That’s one for software-based companies
  6. Technologists in deep tech should validate market questions early, then build the technology– Many entrepreneurs are taught the reverse
  7. In many deep tech areas high-level SMEs are required to validate the tech
  8. Deep tech innovation requires more time and more money– and that timeline seems to be increasing
  9. If you build core technology that provides massive economic value, it leads to monopolistic outcomes that are large and defensible
  10. High growth, low friction software businesses have lower barriers to entry– the same goes for investors targeting startups in these areas
  11. Hardware is finally at a stage where quantum computing may become a reality
  12. Many consumer social companies became deep tech by accident b/c they ran up against scale and speed issues that required novel engineering

Transcribed with AI:

0:03
welcome to the podcast about investing in startups, where existing investors can learn how to get the best deal possible. And those that have never before invested in startups can learn the keys to success from the venture experts. Your host is Nick Moran, and this is the full ratchet

0:22
welcome back to another episode of TFR today the analytical deep tech specialist James Harden joins us to discuss why this category of investing is so compelling, and yet so different than other areas. James is a partner at the iconic firm data collective, a VC that invests in novel technological, scientific and engineering advancements. In today’s episode, we discuss James’s definition of deep tech, the segments within the category, how this area differs from others types of founders and founder profiles that he looks for why he’s willing to invest in a capital intensive, long time horizon category. Why deep tech will drive the biggest outcomes in tech. How founders are able to de risk deep tech companies were founders and other categories don’t the future waves that are coming in deep tech. And finally we discuss how some consumer social companies became deep tech companies by accident. After discussing moats with blossom Berry, I was excited to feature data collective and dive into this topic. Here’s the interview with James Harden.

1:37
James Hardiman joins us today from Palo Alto. James is a partner at data collective, he has extensive experience advising healthcare and biotech companies on their sales and marketing strategy. Previously, James was a consultant at Zs associates and worked in the Blackstone group’s m&a advisory practice. James, welcome to the show. Thanks, Nick, can you start off with sort of your path to, to investing in venture capital? Sure.

2:04
I think like a lot of people, you know, there’s not really like a proven path into Vc. I did my undergrad over at Berkeley, where I studied physics. And so I think just kind of being in the Bay Area, you know, there’s a lot of people that are always talking about startups. So it was always kind of in the back of my mind, in terms of a career option that could be interesting. But, you know, wasn’t something that I really actively actively pursued. I ended up you know, after working at Zs for a number of years, going back to business school. And while I was there, I found through a friend, that data collector was actually looking for interns. So I didn’t really expect that to ever translate into a full time opportunity. But it ended up having a couple of conversations with them came out, you know, for the summer, basically work for free and closely with one of their portfolio companies for about eight weeks, and then went back to back to Chicago, kind of stayed in touch with the team, you know, continue to kind of look at some stuff for them. And then it ultimately just turned into a full time opportunity, which I’m really, really thankful for.

3:04
Can you talk about your focus area at data collective, and maybe highlight some of the portfolio companies that you’ve led investments in?

3:13
Yeah, certainly. So because of my background, having done data analytics for biotech, and pharma companies, I do spend a lot of time looking at things in that space, particularly things at this intersection of AI, I’d say biology and intelligence, I probably spend about half of my time doing that stuff. And then the other half of my time is more on the longer tail of things, I’d say, of the people on the team, I’m probably more more than generalist. And so if something comes in, that’s not going to be routed immediately to a specific partner, I usually take kind of a first pass, you know, in in terms of companies at that intersection of biology and intelligence. There’s a couple that that I’m pretty excited about. Recursion Pharmaceuticals is one where we we were early investors, and then recently led a big series B. They’re using computer vision, you know, to characterize cellular systems and understand what healthy cells look like, what disease cells look like, you know, plot those in this high dimensional space. And then to use that as a as a platform for doing drug screening. Another company where I’m on the board is a company called sequence bio. They’re a company up in Newfoundland, Canada, where there’s a founder population. That means it’s a population that has very low genetic diversity. And there’s this thing called founder founder population disease, where those populations tend to have higher prevalence of, of these rare genetic diseases. Sure, so that what that means is not only is there less genetic variants, and so less noise in your data set, but you have higher instances of this disease. So it’s a really nice data set on which you can kind of try to find and elucidate the genetic underpinnings of what’s causing the disease and use that for target discovery, or other drug discovery? Wow, I think those are two great, great examples of companies kind of that sit at this intersection between biology data and intelligence. And then there’s a couple of other companies that I’m involved with that are in are in other areas, but maybe I’ll say those a little bit later in the conversation.

5:17
Right? And can you talk about the entry point for you guys? You know, where do you like to invest? What stage and checks us?

5:25
Yeah, certainly. So in terms of the fund, we’re really focused on the seed and the Series A, so we want to get involved kind of at the earliest stages. Cheque size is very, you know, typically anywhere from 250k, up to 5 million, kind of depending on whether that’s, you know, at the seed stage or the Series A, we’re really looking for companies where there’s a deeply technical asset, that kind of creates, I’d say, differentiation, and then sustainable value over time. So really where, where we’re focused. We do have some later stage funds, so we have the capital for doing larger later stage deals, but those are really, really reserved for companies where we’ve been involved from the beginning.

6:07
So it was fortunate to have blossom berry on the program to talk about moats primarily. And we also touched on deep tech. But I would I would like to go deeper. Pardon the pun here, but I’d like to go deeper on this topic today. And, yeah, you know, I know you’ve got a specialty in this area, you’ve thought really deeply about it. But can you can you start off just kind of with an overview of how you think about deep technology. Sure.

6:34
So when I say deep technology, I usually mean a company with some kind of technological, scientific or engineering advancement. And that’s really the core asset of the company. And so when I think about this field, I tend to think about companies that either fall into biotech, you know, med tech, you know, semiconductors, quantum computing, you know, it’s usually easy to point at something and say, you know, that’s what’s novel, that’s why this company is special. And I would maybe contrast that with things that tend to be more business model innovation, or more, you know, what I think a lot of people have invented and been investing against for the last 10 years, which is kind of just transition from physical to digital, let’s take these business processes that used to pen and paper, let’s let’s put them on the cloud, or let’s take something that someone used to have to get on the phone and call a taxi and design an app, you know, and put it on someone’s phone, you can certainly make a lot of money doing those things, there’s a there’s a large number of vendors that have been extremely successful. But I think that’s, you know, that’s an area that as people have invested against the last 10 years and and maybe have started to saturate you know, the problems or tackle all the problems that that can be addressed this way, there’s been more of this pendulum swing back to, you know, deep tech, let’s invest in things that are really hard, you know, that are expensive. And through that nature, in part, you know, long term kind of competitive advantage. I think those are the things that we’re interested in, I think VC has a long history of investing in that journal, people credit VC, you know, the first instance of a VC investment all the way back, I think Fairchild Semiconductor back in, like the 1950s. That was clearly a deep tech investment. And I think as you know, as new platforms evolve, things will shift back towards, okay, let’s take processes that existed on something else and park them to this new platform. But as that opportunity kind of diminishes over time, then there’s this pendulum swing back to back to deep tech. And I think we’re really starting to see that see that now.

8:52
Gotcha suit, are you primarily avoiding tech enabled businesses? And or, you know, more of these incremental changes? Is that is that for another firm? Or do you look at opportunities that don’t have this this sort of deep tech core science approach?

9:10
So I am very interested in the application of deep technology to specific problems. So I’m very, I’m usually not interested in investing in a company that’s working on, you know, a primary scientific endeavor. You know, I think there’s other funding sources for that. Mostly government, and that kind of work should be done at universities. However, I or commercial labs, however, I do think there there is an opportunity to invest in companies that are taking that kind of tech they’ve identified or paired it with a problem. One that’s very important to usually enterprise customers, and then driving a lot of economic value for those customers through the application of that technology. Those are the kinds of businesses that that I’m interested in. I think when you said you know technology enabled businesses, you know, maybe you weren’t necessarily talking about that specifically. But I just wanted to kind of draw that that distinction. That’s the area where I think we sit things that are much more, as I mentioned, you know, let’s take something a business process that existed, manifested in a SaaS business. You know, that’s that those those are things that are better fits for other firms. And I will, I will readily admit that those can be great and great investments. And you can build really big businesses doing that. It’s just things that we’re here at data collective, we’re not we’re not interested in. Can

10:34
you talk more about the categories and or sub sectors that you look at, within deep tech, you mentioned a few before. But can you talk more about those categories, and maybe give us some examples, or some applications or some, you know, real tangible instances of, of deep tech? Sure.

10:55
So I talked about a couple biology companies earlier. So I think, I think, you know, one big wave that’s really coming is, you know, the synthetic biology or computational biology waves. So, we’ve really improved our ability to characterize biology, and now increasing in the last few years with CRISPR technology, which a lot of people are talking about, now our ability to very precisely manipulate life. And I think there’s a lot of opportunity to take, you know, biological systems, you know, manipulate them either for the treatment of disease, or for the production of chemicals. And there’ll be a lot of, there’ll be a lot of value that’s created by by doing that. And I think that’s something that we just haven’t had the ability to do historically. And so there’s a very credible reason as to why these companies haven’t been built yet. And so I think that’s, I think that’s one massive wave that that we’ve been investing in, for at least the last several years. One of the companies in the portfolio is a company called zymogen, they tend to work with large chemical companies, like the Dow and DuPont of the world, you know, they take their, you know, the microbes that they they might, they might be using to produce some chemical, and then they can very rapidly drive them towards a theoretical yield. So you can take some input, and now get more output for it. And in those businesses, that savings that basically flows through the balance sheet all the way down to the bottom line. And this is something that, you know, we’ve only been able to do relatively recently in zymurgy. And in particular, I think he’s doing it on a scale that I haven’t seen any other company doing. I think that’s probably the reason why they, you know, they’ve scaled so rapidly, and have been been so successful in such a short amount of time. I think recursion, you know, as I mentioned earlier than other one, their ability to characterize these biological systems, you know, at least in my opinion, is somewhat unprecedented, you know, so for every cellular assay that they they run, they’ll extract 1000 features, and plot that in this high dimensional space. And those were things that have really only been recently possible, because a lot of the tools that have been developed in this big data revolution, that’s kind of happened over the last 10 years, you know, a lot of assays in biology have a single endpoint, you know, so you’re really only measuring one output, whereas recursion is measuring, you know, what 1000. So I think the synthetic biology wave, and this computational biology wave is something that, you know, was will probably last for the next 50 to 100 years, as we get better and better at characterizing and understanding and manipulating these systems. And I think we’re really at just kind of the front, the front end of that. The other one that I think is really big. And I’d be remiss not to talk about, I think you’ve even had some people in your program talking about is this kind of like aI revolution. So again, I think the algorithms that people are leveraging have been around for a long time. But because of this big data way that’s happened over the last kind of 10 years, we now have the technology and the datasets to actually start to apply them. And I think that’s something that is also going to be pretty transformative. I think, you know, one of the most transformative things about AI is the solving kind of this perception problem. And I’m really interested in the application of computer vision, to lots of different problems. So anywhere where I think you have a human, you know, looking at an image, and then classifying that or trying to make some decision is really ripe to be disrupted now, or to have this technology pointed at it, and drive this massive cost savings, you know, mostly mostly from labor, because people are expensive and our lives are cheap. And so I think that that’s also going to be a technological way that gets that’s like it’s written.

14:57
Awesome. Can you talk a little bit about The founder profiles that you see in these spaces, you know, I think a lot of us are kind of used to the traditional builder seller makeup, right? You’ve got, you’ve got the builder on a team, you’ve got sort of that marketing and sales focus person. It seems like with with some of the subject matter that’s being addressed here. It’s it’s going to require a deep level of, of scientific knowledge and awareness and expertise in certain systems. But that’s me speculating. Can you? Can you talk about the founder profiles that that you’re seeing across maybe your portfolio companies and in some of those, that you’re, you’re prospecting for investment? Yeah,

15:41
I think I think that’s I think that’s absolutely right. I’d say what what I’m looking for now in teams, is usually the marrying of these two skill sets. So in, you know, in computational biology, you know, there’s a lot of biologists out there, there’s a lot of people that understand computer science, but there’s actually not that many people that are that are, that are deep experts in both of those. And so I think you can either take two approaches, one, you can either try and find the people that have deep appreciation of both of those things, which I’d say at this stage in the field is probably still important. And this is going to be a broad overgeneralization. But, you know, the biological people that have been trained in biology oftentimes have a certain, they have certain mental models that they take to thinking about the world. You know, and those are different than the ones that people who’ve studied ces are trained in. And so finding someone that has been trained in kind of both of those fields, and so can understand where they can be combined. But where you know, someone who otherwise, you know, maybe dabbles in both tries to push things together. But that’s just the dead end and would ultimately fall apart. I think that’s why it’s important, at least at this stage to kind of find people that, that have a deep expertise in both of those. Sometimes you can find those into different people, you know, someone that has a deep biology background is paired with someone, you know, who maybe comes from, you know, the CF side of the world, and together they can, they can solve the problem. But again, sometimes there can be this mismatch in the, in the mental models that sometimes those people apply, apply to problems, I’d say on, you know, on the AI side, you know, I am looking for, you know, someone that is deeply technical, but then also has deep domain expertise. In this instantiation, I think I’m more open to those expertise and manifesting and to separate people. So I think you need someone you know, who understands the algorithms, their limitations, how to, you know, take a problem structured in a way that it’s amenable to, you know, the science that we’ve developed. But I also think you need someone who, who understands the domain, you know, what are the problems? What are the pain points, you know, what are people going to pay for, the joke that I, that I kind of make is it used to read all about the, you know, these business founders who like just trying to find that technical founder. And now I actually think the roles have been reversed, where, you know, you’ll have some AI expert, but he’s really trying to find some domain expert, you know, or business guy that he can pair with. Right. And I think I think both of those things are really important, which is, you know, similar to the to the builder seller model that I think you articulated in your question, I think that’s still you know, the the ideas that that advice is built on, I think is still very much true in this in this area. And the question is just does it does it need to be in the same person or cannot manifest into people?

18:33
Well, and it’s always a challenge, I was just talking with Ben Nelson from NEA about this. And it’s always a challenge, not just finding the right skill sets, but the right disposition overall, right, the people that have the right risk profile, sort of in their mental makeup, and have the tenacity and sort of the, the willingness to go the extra mile for many years to to kind of realize, you know, something like this outside of a research lab, but in a real commercial context.

19:01
Yeah, I think that’s right. When I think about deep tech investing, there’s not as much opportunity or room to pivot. Right. And so I do think it takes a certain amount of you know, it does, it does take a profile of someone that is confident in the technology and the application and has done the work to really validate that up front. I think deep tech investing does not lend itself as well to this, you know, move fast and break things where let’s build it and see if the market adopts, right, I think that kind of philosophy around company building, you know, he’s really built on the back of this, this business model innovation, where it’s, you know, it’s cheap to write code, you know, you can take a bunch of developers and have them you know, be working on one problem and then very rapidly point them in another direction. When your asset is, you know, is some fundamental technology, you can think about where you might apply that differently. But you know, the fundamental tax is not is not going to change. And so I think some of the models that people have applied, or maybe the conventional wisdom that has emerged in the last 10 years, really on the back of this software is cheap, your marginal cost is zero. So let’s just iterate until we find a niche against which we can scale this business, that model does not work very well in deep tech investing. Interesting

20:23
yet, personally, I don’t do a tremendous amount of deep tech, but I do some investing in that area, and a lot of investing in hard tech and IoT. And one of the, one of the fortunate things that I found in my founders is that a lot of the experiments and a lot of the business model and product model questions are front loaded. So before an investment is ever made, I feel like it’s a much more robust, thoughtful evolution of of the product itself, as opposed to dive in, take a bunch of Angel early stage funding, and, and figure it out as we go. So even though, you know, some investors give me a hard time, because they think I’m taking a lot more risk with more capital intensive businesses that I invest in, I feel like some of that risk is addressed, because the founders themselves have pressure tested things to a much higher level, before investment.

21:20
Yeah, I totally agree with that. I think this is a general observation, very technical founders tend to have tend to want to build technology, because that’s, that’s what they’re good at. And if you if you build technology, and then you try and validate that with the market, sometimes you build something that the market doesn’t want. And I think that’s that’s really where the pivot comes from. It’s, it’s really a symptom of Not, not having validated, you know, those assumptions early, early enough in the process, right? No, that’s, that’s fine. If you’re building software, and the way you test that hypothesis, you build it and see if people use it. But I think a much better way to build a very deeply technical business, is to try and reverse that, work closely with maybe a partner early on and validate all of those market questions, and then go and build the technology. Because oftentimes, the you know, the technology is not always an open question. We know we can build it, we know the science is there. But it’s really it’s really around, is the market there? And can we instantiate this technology in a product is interesting to them?

22:24
Yeah, yeah, I spent about three years at a large corporate taking sort of this concept called lab on a chip from the medical industry, and bring it to the water analytics industry. And people will ask me about my experience entrepreneurs and other investors. And it was a three year process, it was a three year, you know, product build from piece of paper to market. And, and most people get wide eyed and say that would never work in startups. But I don’t think that was just the byproduct of being in a large corporate, I think it was making sure that we knew everything we possibly could and had tested everything we possibly could before, you know, investing massive amounts of capital. And fortunately, it was a banner success for the organization. But you know, I feel like a lot of those things can be tested before, before dumping in, you know, the 10s of millions or hundreds of millions of dollars. Yeah,

23:22
I think I think that’s, that’s the smart way to go about about building building deep tech tech companies.

23:27
Cool. So, you know, you’ve talked about some other types of investing a few times now. And I think that’s kind of an interesting comparison here. So, you know, if you had to talk about how deep tech is different from, from other investing, you know, what are what are some of the key things that come to mind?

23:45
So, I think, so I think there’s a couple things. So first, oftentimes, that diligence can be can be a lot harder. You know, I think the way you build expertise is as a as a VC, is you get to know a space really well, and you get really deep in that space. And I think I’m probably close to that in you know, in computational or synthetic biology, I can oftentimes make the call in terms of whether we shouldn’t do an investment or not. However, in other areas, you know, I’m not I’m not the expert. Now, for example, we’ve invested in a company called Brigante, quantum quantum computing, is based over in Berkeley, you know, I studied physics undergrad, you know, I’ve taken some graduate quantum courses, and I’m barely equipped to understand kind of what they’re doing. Wow. And so to really get to a level of conviction on those kinds of investments, you know, you need to have experts that you trust that you can you can bring to the table and really help you get comfortable, I think with the technology, particularly before it’s been validated by the market before you see customers. I think one thing that we’ve done here at data collective to enable that, is we have a net Work of, you know, 50 plus deeply technical equity partners. And so there are people that, you know, they’re either academics at Berkeley and Stanford, or sometimes they’re operators. But they have a depth of expertise in an area that’s interesting to us, whether that’s biology, computer vision, quantum, you know, databases. And so we can rapidly call on these people to, to go deep on a company. And then I’m also calibrated to, you know, what their feedback is like. So, you know, at this, at the early stage, when you’re building deep tech, there’s lots of things that aren’t worked out yet. So it can be easy not to like a company. And so understanding, you know, where the feedback is coming from, you know, what those people’s potential biases are, I think, allow us to take, you know, this kind of deep tech feedback, and make good decisions. Whereas if we were to hire, say, a consultant that maybe we never worked with before, it’s hard for me to know how to evaluate that opinion. Right. So I think the diligence can be harder, I think it can take more time and more money for a lot of the reasons that we’ve, we’ve already talked about. And we’ve done, we’ve done some studies, where we look at how much time does it take from investment to a company to go public, and, you know, over the last 1020 years, and I think there’s lots of material out here that supports that, you know, we’ve seen that timeline increase, I think some of that can be because, you know, VCs, and private investors are trying to capture more value. But I think a lot of tension on the technical side is just because, you know, it’s expensive, and it takes more time. Whereas, you know, investors who get used to, you know, software, only businesses where you’re building on AWS, you don’t need to stand up your own, your own servers necessarily, you know, are used to a very rapid pace of growth. And so I think that’s something that you know, you kind of need to get more comfortable with

26:55
is part of the thesis that the corresponding outcomes, the sizes of the outcomes are going to be bigger to compensate for sort of the longer duration.

27:06
I think that that can be true. I think the idea is that if we’re investing in technology that is fundamentally, you know, innovative, and drives massive economic outcome that’s difficult to replicate, you know, that drives businesses towards monopoly, like outcomes. And so I think the hope is, you know, if you’re a monopoly, then, you know, you can extract monopoly prices, you know, and and it kind of sustains that value over time. Where, yeah, in other areas where it’s harder to put your finger on, you know, well, this is the competitive advantage, that’s, you know, that’s going to differentiate this company, you know, competitors come in, they will down prices, you know, customers have a harder time differentiating between who they should buy from. And I think, if you are investing in unique and differentiated tech, it can solve a lot of those a lot of those downstream issues. And that’s drive larger, larger outcomes.

28:03
Sure. Other thoughts on, you know, how, how deep tech may may differ from from some other investing approaches? Well,

28:10
I think I think there’s fewer, there’s fewer VCs in the space, you know, that if you have the opportunity to invest in a, you know, high growth, low friction software business, or something that might take a little bit more time. And there’s some more technical risk around. I think we’ve seen the market kind of gravitate towards the former. So I think there’s just a smaller subset of potential co investors. And so, you know, so I think that, you know, that can have some challenges. I think it’s just yeah, it’s just a smaller, smaller space.

28:43
opportunities and advantages there, I guess. Yep. I

28:48
think I think that’s right.

28:49
So you talked about some of the categories and deep tech and some of the some of the areas that you’re focused on now? Are those some of the same areas that you’re looking at in the future for some of the waves that are coming in deep tech? Or are there some other areas that you think maybe nascent, but but are coming?

29:06
So I talked about AI, and I think that is going to that’s going to be an enabling technology for lots of things, particularly better robotics and automation, but didn’t really specifically talk about that. I think that’s, you know, that is kind of fundamentally going to be a deep tech area, whenever you’re building hardware. You know, it’s, it’s usually expensive. It’s very difficult to do that well, in kind of a robust way that you can deploy a product to a customer site. I think that’s going to that’s going to be another another big wave that we’ve we’ve really already already seen. You know, the one that gets talked about most is autonomous driving. But I think that will I think robotics and automation will will be important. Agriculture, I think will be important in manufacturing. It could potentially be important in eldercare. You know, I think that’s that’s a very hard problem to solve, to build like a general robot for that. At but I think there’ll be, there’ll be pieces that get picked off. So I think I think that’s an area that that I’m really interested in new materials, I think is one. So we invested in a company called Citrine, that uses AI for materials discovery. And so really any any company that cares about the properties of the materials that they’re integrating in their product, you know, is a is a potential customer, and could use AI to make, you know, cheaper, higher performance, you know, better optimized materials for whatever their their application is, I think those are the kinds of like, massive problems and industries that, you know, artificial intelligence has the ability to start to address. And I think, you know, new materials might be, you know, might be like its own category. So whether that’s batteries, or whether that’s 3d printing, in kind of new materials that get integrated into that, because you need new, you know, new processes. I think those are all areas kind of of interest. I’d probably be remiss if I didn’t miss, or if I didn’t mention blockchain. I don’t, I want to be careful not to take our conversation down that road, because you probably probably could have five podcasts on that alone. But I do think that was that was a fundamental innovation. And I think there’ll be lots of lots of interesting technology that’s built on top of it. I think all those things are areas areas of interest for us, you know, quantum computing, I’d say is maybe the other one I’ll mention where people have been talking about it for a long time, I think we’re just starting to see the hardware get to a point where you can start to do interesting things with it. I think IBM just announced a 50 qubit system a couple of weeks ago, I think there’s going to be a whole ecosystem of companies around that. And I think talent is really scarce. So I think there’s going to be a need for a lot of a lot, a lot of new hardware to be developed, that can support and be used in these systems. I think there’s going to potentially be new software that’s built on that, well, there certainly will be new software, but on top of this, but it’s going to be really interesting, like what problems is this going to, you know, be the best at think people have theoretical ideas. But the hardware hasn’t been to a point where you can actually start to test those. But I think that companies that are going to are going to really ride that wave are kind of being started now. And they’re thinking about what are the customer problems that maybe I can solve with classical computing, that I can do better with quantum computing, and start to position myself to say, Okay, I’m going to, I’m going to gain the market and the domain knowledge now. So that when quantum computing arrives, I already have those relationships. And I can just drive that much more value. And kind of, you know, entrench myself as the player in this space.

32:54
Interesting, you know, I want to touch on consumer a little bit and some large consumer companies that have, you know, they’ve had to build certain novel technology as they’ve grown. And I’m curious to hear kind of how you how you see the interplay between consumer and deep tech, and, you know, if one helps helps drive the other?

33:18
Yeah, I think there are certainly examples of an interplay between consumer and deep tech companies, I think, you know, a lot of the a lot of the social companies, you know, the Twitter’s the Facebook’s, they were running up against scale issues. Sure. And kind of speed issues that require novel engineering to be done. And, and so, you know, for example, I know, you know, some of the team that was early at Airbnb, and then at Twitter, you know, they kind of spun out and started a company that we bet called mesosphere, that helps, you know, helps abstract companies from kind of their, their data center and treated as almost like a single single computer. And I think there’s actually a long, you know, there’s a, there’s a long history of entrepreneurs going to Google building kind of infrastructure there, which kind of a consumer centric company, and then going out and building that tech and making it available to to the long tail of companies and enterprises that you know, might want to leverage it, but no, can’t necessarily attract the talent to build that that technology themselves. So I do think there is there is an interplay, especially if, you know, the challenges that you’re facing as a consumer facing company, are ones that require novel scale, or novel speed, or novel novel kinds of compute, even.

34:40
James, if we could address any topic here on the program, What topic do you think should be addressed? And who would you like to hear speak about it?

34:48
Um, that’s a good question. I think on this program, you’ve invited a lot of VCs. You know, I have to admit, I’ve listened to some of your podcast but I haven’t been a longtime longtime listener. are. So I really appreciate that you have these kinds of ketchup podcasts where you go back and you kind of summarize the podcasts that you’ve covered. So I don’t know how many operators you’ve had, you’ve had on the on the program. And one thing that, you know, that I always find interesting is hearing, you know, hearing more stories from from, from founders directly about their particular narrative. I learned a lot when you know, when a founder takes the time to maybe write a Medium post about, you know, why a company didn’t work out? Certainly, I’ve learned the most from the companies that I’ve invested in that haven’t gone that well. You know, those can be you pay a lot of you pay a lot of attention to those to those situations and thinking about why didn’t get good go? Well, did I miss time the market? You know, did I think there was product market fit, and then later, I realized that wasn’t. And I think in general, that’s true in life, you learn a lot more about your failures than you do about, about your successes, or from your successes, rather. But I don’t know if there’s a I don’t know if there’s a central forum where founders can kind of come and talk about the challenges that they’ve had maybe going back to, like we talked about wanting to celebrate failure, but we often we often actually don’t. And that might be that might be interesting to hear from if you can kind of get people to talk about those things. I bet there would be a large, you know, I certainly would be interested in hearing those stories.

36:26
That’s great. What investor has influenced you most and why?

36:32
I think that what I’ve come to what I’ve come to appreciate, is VCs are incentivized to, you know, appear kind of like all knowing, like, oh, we knew this outcome was going to be huge for these reasons. And these are my frameworks. I don’t know if there’s a there’s one investor that I can point out that, that I’ve said, I’ve learned the most from that person, I think everyone has their own way of practicing VC. And one thing that I’ve thought about over the last four years is, you know, what is my style? How, what kind of VC do I want to be? What things work for me? And so there’s a lot of investors where I’ve, you know, I’ve learned, you know, kind of, like bits and pieces from, I don’t think there’s, there’s, there’s not a VC out there, where I, I wish I was that person. But I think I have, I have a lot to learn from people who’ve been around in the business for a long time. You know, certainly at my firm, man, ACO I’ve learned a ton from, you know, great, deep tech investor has been investing for 20 years, you know, with with a ton of successes. You know, I’d say also in my team, Scott Barkley deep expertise in healthcare, I really learned a lot from him in terms of, you know, what it means to be a successful investor in that space. And then I tried to read a lot when other investors kind of write and put that out there. So I think, you know, Fred Wilson, you know, I tried to read a lot of his blog posts. And I certainly learned a lot, a lot from him. Brad Feld, people like that.

38:07
Awesome. And finally, just wrap up here, what’s the best way for listeners to connect with you?

38:14
Yeah, I’d say, you know, try and try to reach out to me through through my network. One thing that one thing that that I’ve come to appreciate at the seed stage is there’s a lot of companies and people that reach out to me directly. You know, I can, I’ll take a look at materials. And you know, sometimes have a quick, have a quick phone conversation. But if you can get someone that, you know, I know and trust to advocate for you, that’s a high signal to me. And I’m really, I’m really interested in engaging when you know, when you can kind of find a validated channel to come through. And I also think that, that signals to me that you have the ability to kind of leverage your network and to get introductions, which I think is really important at the earliest stages. Whereas direct outreach, yeah, I look at those things. But I’m much, much less likely to engage because it hasn’t been validated. And I have limited, I just have a limited amount of time to spend on those things.

39:10
Well, James has been a huge pleasure getting to know you, and that the short time that we’ve known each other, big thanks to John Brennan for making the intro, and I can’t wait to chat again soon. Yeah, thanks, Nick.

39:20
I really appreciate it.

39:27
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. You