Ep. #1133 - Deep Tech Venture Capital
In today’s episode of Startup Hustle, deep tech venture capital is the center of attention. Matt DeCoursey discovers its ins and outs from David Van Wie, founder and CIO of Aventurine Capital Group, LLC. Their conversation also touches on the challenges of patenting software, foundational technology investment models, and more.
Covered In This Episode
What is deep tech, and how is it different from other companies? How difficult is it to patent tech like generative AI? Can you mitigate high-failure risks in the industry?
Matt and David discuss all the insights you need to answer these questions. What’s more? The session also covers intellectual property, patents, trade secrets, and early-stage investment tips.
Take your deep tech venture capital knowledge to the next level. Listen to this Startup Hustle episode now.
- David and his backstory (02:24)
- What is deep tech? (03:37)
- How to dive into deep tech venture capital (06:35)
- How long is the exit horizon for deep tech companies? (10:29)
- The Perpetual IP Investment (PIPI) Fund (16:15)
- Determining the possibilities (17:32)
- The challenge in large language models (18:46)
- Mitigating the high-failure rate in deep tech venture capitalists (24:43)
- How difficult is it to patent software (25:48)
- Talent versus genius: what’s the difference? (29:50)
- When is the trend going to hit the mainstream? (36:48)
- David’s advice for entrepreneurs (41:59)
When you work from the foundational point of view, you’re looking to option entire markets. Your best outcome is that everybody in a particular market space ends up adopting and integrating the technology.– David Van Wie
It isn’t impossible to patent software. It’s just difficult. The more foundational the system is, the closer you get towards the ability to patent. But you can’t rely on patents alone when thinking about IP protection for AI.– David Van Wie
Talent is being able to hit the target that everybody sees. And genius is being able to hit the target that no one even knew was there. And these are the things that change the shape of markets and products and the things we use.– Matt DeCoursey
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Following is an auto-generated text transcript of this episode. Apologies for any errors!
Matt DeCoursey 00:01
And we’re back! Back for another episode of Startup Hustle. Matt DeCoursey is here to have another conversation I’m hoping helps your business grow. Alright, look, if you’ve been in entrepreneurship or startups, any of that games, then you’re familiar with venture capital. But what’s deep tech venture capital? That’s what we’re gonna get into during today’s show. Before I introduce who my conversation is going to be with today, today’s episode of Startup Hustle is powered by FullScale.io. Hiring software developers is difficult. Full Scale can help you build a software team quickly and affordably and has the platform to help you manage that team. Go to FullScale.io to learn more. That’s my company if you’re not aware. And we love talking to Startup Hustle listeners. Once again, it takes two minutes to fill out a couple of questions at FullScale.io. And our platform will match you up with available testers, developers, leaders, and, who knows, maybe even a couple more people. With me today, I’ve got David Van Wie. David is the founder and CIO at Aventurine Capital Group, LLC, and they’re in Palo Alto, California. Ever heard of that place? I think that’s Silicon Valley. And am I right about that, David?
David Van Wie 01:09
You’re right, right in the belly of the beast. Yeah.
Matt DeCoursey 01:12
And that is also, I think, probably fair to say, the venture capital, you know, venture capital of the world.
David Van Wie 01:23
I think that’s probably right. There are other people who are trying to change that dynamic. But none of them have succeeded so far.
Matt DeCoursey 01:30
Yeah. Well, you know, there’s a lot of stuff going on there. And rightfully so, you know, it’s been out there for humans doing stuff out there for a long time. So I like to start all my conversations with a little bit about your backstory. So why don’t we jump in there?
David Van Wie 01:44
Well, great. I’ve been a lifelong entrepreneur. So this is my first foray into professional finance. Although I’ve been an angel investor since one of my companies went public about 20 years ago, I’m now professionalizing. That and taking the things I’ve learned from years of running companies. And one of those things is that deep tech companies are different. Deep tech companies have special considerations. They have all the normal startup stuff, but then there are some new things, some additional things, that are part of the mix in building that type of business. And I ran one of those companies, InterTrust Technologies Corporation, back in the 90s. And we had a real success story in that business. But it wasn’t because we were able to access the venture capital markets; we needed to use unconventional financing right up to the point where we were preparing to bring the company public. And so I learned a lesson there. And I’m the founder of seven different businesses, the one that I did just prior to this; I ran into the same kinds of financing issues that I’d experienced at InterTrust. And so I decided that I was going to build a company; that was the investor I wished I had met when I was running deep tech businesses.
Matt DeCoursey 02:57
So how would you say deep tech? How do you define that?
David Van Wie 03:01
So deep tech, for me, really represents foundational technologies. So these are breakthroughs that can be applied in multiple markets. So when we’re doing our economic analysis, it’s at least three markets where the technology can apply to most of the people we’re talking with now. And the technologies we’re working with are applicable to six or seven markets. So that’s really when I say deep tech, that’s what I’m, that’s what I’m driving at.
Matt DeCoursey 03:25
Okay. All right. So, and then let’s delve a little further into that. Because, as I said, I think a lot of people are familiar with, you know, they, we often refer to riches in the niches and, and we’ve got industry-specific solutions. What’s a good example of some things that would be obvious for, you know, the last? I don’t know, however, whatever timeframe you want to go with, like, what’s it? What are a couple of examples of deep tech that most listeners would recognize?
David Van Wie 03:53
Well, right now, we’re working with a couple of companies in the AI field who are doing some technologies that are really quite different from generative AI. And that will move the whole field forward, particularly in the area of natural language processing, which has become a hot topic due to open AI and their chat GPT system. But there are some challenges within that technology that can be addressed by putting some new systems into play. And so we’ve been working for a number of years with people who have done some real breakthrough work in natural language. And that’s an example of a technology that applies to many, many different markets. Another example, we’re working with a generalized quantum computing technology right now that, again, has that same character, a foundational system that will apply in many, many different areas, everything from finance to Life Sciences.
Matt DeCoursey 04:42
Yeah, and you know, speaking of chat, GPT, while you were mentioning that, I asked it what it defined deep tech as, and it did mention AI and machine learning, quantum computing, robotics, biotech, nanotechnology, and these are all things that I think people also talked about emerging too. And, you know, one of the things that I’ve come across and realize you give you a little background about myself, David, is I employ a whole lot of software developers at Full Scale, and we help people staff their tech companies. And when it comes to deep tech or emerging technologies, we quite honestly have a very difficult time finding people to do work because there’s no experience with it. And so we have to take the approach of finding the smartest people that we can find in many cases and hoping that they are as smart as we thought they work as smart people figure things out. So when it comes to deep tech and venture capital, you know, is that a recognized thing? When you’re investing like, hey, there’s not a whole lot of domain knowledge or people that have experience in this space. So we’re hoping that the companies that we invest in or work with are going to find a way to figure it out?
David Van Wie 05:55
Yeah, we are; you’re right on the money map with the challenges in building these kinds of companies from the outset. And our approach to that has been to develop a new style of early-stage investing that’s complementary to traditional venture capital. So our approach is quite different from that of a traditional venture capitalist in that we work with these individuals, these scientists and engineers, from the very earliest stages of their development as they’re coming from the lab and into the market. And in order to do that, we have to give them more time than you would normally have in a venture capital-type environment where an exit and a three to five-year timeframe is really an essential part of their mandate as investors. And so we’ve built a different type of fund; it’s a perpetual fund. So our fund doesn’t last for five or seven or 10 years, like a traditional venture capital fund, but rather it’s an evergreen fund that runs continuously. And that’s because the core challenges that you face here in this area can’t be addressed readily through venture capital. And I’m gonna get to your point here in just Just a minute. In that, when you’re working with foundational technologies, you have a challenge that isn’t present in a traditional enterprise SAS business. And that you need to invest time and capital into getting the core technology to the point where you can use it for an application. And that takes more time and costs more money than normal venture capitalists will apply because they have alternatives. They have choices; they can work with technologies that don’t have those requirements. So what we did with our model was find a way to generate a return from that period of time. And that’s through the intellectual property is a core broadly defined that patents, trade secrets, product technology itself, the branding around that technology, as well as the data. And we’re increasing data now, especially in some of these fields is really essential. So we focus on investing in that element of it, in addition to the operating company that has been put together. But by doing that in the right way, we’re able to generate a return for investors from that investment early on. Now, the implications from that, from an operating point of view, are that you need to help the scientist and entrepreneur build the team and that people we run into are never the exit-worthy CEO that a venture capitalist is looking for. So we have to do two things, we have to be patient, we have to give them time. 123 years is really the window that we’re looking at when we engage before they’re really ready for traditional venture capital. And during that period of time is when we’re doing the recruiting and the incubation that you refer to getting the smartest people that you can find in an area that has the right kind of background. But when you’re doing this deep tech work and these highly inventive and innovative technologies, you don’t; you’re not going to find a ready pool of engineers. So you need to be prepared for that. And we do that through something we call our investment studio, which is taking the concepts of a venture studio, which you might have heard about and probably delve into where you really engage actively with the company to help them with their business plan with their staffing with all of the particulars that go into building a business. We do that in combination with the development of their intellectual property. So we incubate both parts of the business, and we take our time doing it. And that is really critical to being able to bring deep tech to market, and one of the challenges that we’ve seen is that deep tech isn’t getting funded. And it isn’t really being able to develop some of the amazing things that are in the lab into truly commercially viable systems. So our approach here is to invest differently and then incubate these businesses for a number of years to get them ready for the venture capital letter.
Matt DeCoursey 09:49
You mentioned the exit where the CEO and I want to talk about that because I think that’s an interesting comment. So With the deep tech, I’m assuming that at this point you’re talking about Alright, so I’m gonna make this as palatable as possible for anyone listening. Sometimes the mad scientists that invented these cutting-edge things aren’t great, aren’t the best people to go out and sell them? Is that what you mean by the exit? Where is the CEO? Or is that EMI off on that?
David Van Wie 10:26
No, you’re exactly right that you shouldn’t predict that you’re going to find those qualities in the same individual, the mad scientist is not going to be the sales-oriented, hard-charging CEO, and we anticipate that we’d give ourselves some time to do the recruiting around that role. It’s really essential to the success of the overall enterprise that the CEO is in place, as well as the classic VP of Engineering and marketing and the normal key roles in a startup. Normally, the people that we’re interacting with, if they are able to engage at all, operationally, there’ll be a CTO or a CSO Chief Science Officer, that’s the appropriate role for them. But in the venture capital world, that’s not what they’re looking for. Because you only have a three to the five-year timeline, you’re looking across the table; the very first thing that you’re assessing is whether or not this individual is likely to be able to build this business and get you the exit that your limited partners are expecting.
Matt DeCoursey 11:26
Yeah, we run into that a lot. We’ve had that conversation. Yeah, here we are in Startup Hustle. And this will be approximately between Episode 11 and 112 100. So there’s a pretty big sample space of conversation there. And I’ve had a lot of chats with, with people that were the mad scientist, as far as like many of us would look at it that someone that loved the product was obsessed with building it and then would say, and then there was a time when I just had to go kind of do something else. Because they weren’t very good. They weren’t very good at selling it or didn’t often want to. And that’s a very interesting personality dynamic. Now, you’ve mentioned the three to five-year exit horizon with deep tech and venture investment. I mean, is this more like a 15-year window? Because I feel like a lot of the stuff that, I don’t know, I’ve published some videos recently talking about inventors, and you know, most inventors that are working on big things are working, okay, so it’s 2023. They’re working on what they’re going to do, what’s coming out, and 2033 or 2043. So how long is that horizon stretched out to when a company like yours is gonna, or when a firm like yours is going to invest in a company into any deep tech company?
David Van Wie 12:46
Well, we look for a very long time horizon; as you just mentioned, if you look at the term of a US patent is 20 years. And that I just use that as a proxy; it isn’t a fast rule. But one of the interesting dynamics there is that if you’re 20, if you’ve licensed properly and genuinely found it, what we’re looking for is going to be your best year, followed by you’re 19, you’re 18, you’re 17. And so, as you’re building these technologies, you are very much looking at a longer-term time horizon. And because every time we invest, where we’re pushing that out 20 years time, that’s one of the reasons that drove us towards a perpetual fund rather than a fixed time, a so-called closed-end fund. So when we’re working now, because there are very few people who are able to really invest in this space, we are finding technologies that have licensing opportunities right away, not only an interesting go-to-market but have interesting opportunities to have the technology used and used in other markets. But we expect the real return window to be at least 20 years when we get it right. And if you look, if you imagine if you had the opportunity to invest in the transistor back in the 60s, and you keep the research rolling, you’d still be working on that technology today, you’d still be licensing that technology now. And I had some experience with that. And this inner Trust Company, which ended up creating the most valuable patent family in history. And we did that because the technology first started being licensed in the 90s and is still yielding royalties today.
Matt DeCoursey 14:24
You know, I had an interesting podcast recording. It was, you know, a couple of 100 episodes ago at this point. But, you know, we’re here in Kansas City, and there’s a company called I verified that. You know, it was probably at this point 20 years ago, or close to 20 years ago, that had created some cutting-edge technology around retinal scanning, and they ended up selling it to Ali Baba, and Toby Rush is one of the founders here on the show was talking about you know, you ask, Well, why did you decide that or realized was the right time to exit. And he said, Well, I realized that what we had built was a feature; it wasn’t really a company; it was something that was going to be impactful for a lot of different things. And it was going to be a feature on a bunch of other people’s platforms and products. But it wasn’t necessarily a standalone thing itself; how often does something like that happen when you head down the road of determining the market viability or monitoring, you know, the ability to monetize any of these things that you’re building years in advance?
David Van Wie 15:35
We expect that to happen just about every time, okay, just the reason that you’re describing. So when we’ve been investing, I mentioned a couple of foundational AI technologies, one of which is able to understand the meaning of language rather than the statistical associations, and the other is really able to understand people’s state of mind; you can think of it as an empathetic AI, these types of technologies can be used as the foundation of a highly differentiated business. But the real opportunity is to see them licensed very, very broadly across the entire market. So when you have a foundational technology, it’s quite different in that rather than focusing on the capabilities of a particular business and finance speak, you option, the p&l of that company, right, you’re trying to, you’re hoping you’re buying an option that that company is going to grow and thrive and generate profits and ultimately exit. When you work. From the foundational point of view, you’re actually looking to option entire markets; your best outcome is that everybody in a particular market space ends up adopting and integrating the technology. And so that’s why it takes a little bit of a different model to be able to really develop those types of opportunities. And that’s why we created what we call the Pypi fund, the perpetual IP Income Fund, which takes some of the concepts of venture capital and applies them directly, and then adds this element of perpetual IP licensing and the royalties that can generate them.
Matt DeCoursey 16:57
How do you go about determining what that possibility might be? I know that in the world of traditional venture capital, there are a lot of formulas and approaches and expectations as far as a company’s growth or, you know, any of it. I’m just curious about what that conversation looks like for something that well, I mean, what did that look like for natural language processing 10 to 15 years ago.
David Van Wie 17:24
So our approach in that area is to target the known bottlenecks and roadblocks within the six different practice areas where we operate. And so we were always prepared to find a new one. But our approach is, rather, where it’s more of a spearfishing approach than a dragnet approach to investing. We’re not looking to bring in as many opportunities as we possibly can, and except for the best ones, we’re looking for solutions to particular problems that the market knows has, and for example, the challenge of hallucination in large language models, many people are just now becoming aware of that.
Matt DeCoursey 18:03
But that’s been an issue I’ve known about for many years. Can you explain that?
David Van Wie 18:06
Sure. The challenge in large language models is that the entire system is based on statistical relationships between words and phrases. And, as you probably heard, the core of technology is predicting what the next word ought to be if you’re moving down a particular pathway. And those types of statistical relationships that the datasets are large enough can be really eerily similar to system understanding what you’re really driving at. But it doesn’t really have an understanding; it doesn’t really know what those words mean when you’re in that context. And so if you’re, if you’re able to add that dimension to it, you can take all of the different market spaces where generative APIs are applicable. And talk about integrating a new technology into the platform that does understand what words mean and is able to build these more concrete relationships. Because when generative AI is operating, it’s told to reach a particular outcome and has a goal that it’s trying to meet, namely, answer your question or your implied question in whatever it is you’ve typed into sage chat GPT. And then the process, it can’t really tell whether or not the answer it’s giving you has the appropriate meaning. It can’t really tell whether it’s accurate because it has no idea what the words mean. And so being able to address that has been a known problem. And so, some breakthrough technology is taking a completely different approach. The problem isn’t hard if you approach it from a totally different angle, which is what the researchers have done and the company that I’m referring to now. And so that the ability to recognize that this type of error can’t be factored out because there you can’t build a large enough data set that you’re going to completely eliminate this, you have to use a new approach. So that we know is a bottleneck or a roadblock. to the development of generative AI. And so by looking for natural language technologies that address that specific thing, we’re able to really zero in on large market opportunities because the market knows it has this problem. If you don’t have to teach the world that there’s this challenge within generative AI, we can work within that. And therefore, in our market analysis, we do some of the traditional things that venture capitalists do in analyzing markets and growth. But rather than identifying just a particular company, we’re looking across entire markets to see where that technology would be most applicable. And so in that way, it’s a, it’s a more of a broad-based survey of the different opportunities, although we do concentrate as well. And then there’s a go-to-market company; the strategy is not just on the licensing; in order to make the licensing viable, you also have to invest in a startup that’s going to use the technology for the first time that hardens up the platform improves the IP, and also serves as marketing for the other licensing areas.
Matt DeCoursey 21:05
Yeah, I want to talk a little bit more about that. And before we do, I want to remind everyone that finding expert software developers doesn’t have to be difficult, especially when you visit FullScale.io, where you can build a software team quickly and affordably. Use the Full Scale platform to define your technical needs, and then see what available developers, testers, and leaders are ready to join your team. Visit FullScale.io. To learn more, you know; thanks for explaining that. I just realized while you were explaining how something like chat GPT works that you know, I remember it was probably, I mean, man, probably like six weeks ago, that I actually read an article talking about the predictive relationship. It’s not as smart as we think it is, it is smart at knowing what word to say next, but it isn’t necessarily smart at knowing what words it has said. And that’s it. That’s kind of a dangerous thing in some regards because, at some point, you start to trust the things that spit out any kind of output. And I think that sometimes we don’t consider where it’s coming up with that output. And like David had explained, you know, GPT, by the way, if you ask chit chat GPT what chat GPT as it will tell you, there is no such thing; there’s only GPT. And I know that because we did an episode that was fueled only by chat GPT and those are the very first things, it corrected me one minute into the opposite. And I was like, okay, but yeah, it’s, you know, it’s scanning, you know, billions and billions of sentences, words, and whatever. And it will say, I’m gonna say whatever it wants to say, based on how somewhere along the line enough people said that in a phrase, a sentence, a Reddit post, a blog post, but it doesn’t necessarily know what it’s reading in a lot of regards. So you know, that’s an interesting thing. You know, I think the whole chat GPT thing and open AI is a very interesting example. You mentioned having this foundational technology and then needing to invest in a startup that is essentially the marketing tool or that helps prove it out. I mean, there has been ever since GPT came out, there’s been, like, there are 1000s of new startups that have popped out that is, Yeah, I was talking about this recently. I think many don’t think people realize how much stuff the open AI API is fueling. And that’s so that’s the kind of licensing that you have that’s got to be kind of like a holy grail kind of thing. If you’re, if you invested in that deep tech, am I correct?
David Van Wie 23:42
Yes, you’re correct.
Matt DeCoursey 23:45
That’s very much someone else’s car that doesn’t run without your fuel.
David Van Wie 23:49
Right, exactly. And that’s how you create this economic effect of optioning an entire market. So if you’re working with the gasoline metaphor or the battery metaphor, every manufacturer needs one.
Matt DeCoursey 24:03
Yeah. All right. So you know, I gotta feel like the failure rate. For deep tech venture capital, it has to be remarkably high.
David Van Wie 24:15
So that’s true. And so the way you mitigate those risks is by putting the intellectual property into its own entity, so that you’re able to take another shot, you get another bite at the apple by investing in the intellectual property, because if it works, if the technology really does address a particular problem, like the one we’ve been talking about now with generative AI, then you’re able to take another shot at it and another shot at it and try a different market. Because you have protected the body of IP, and in that way, you get multiple shots on goal with each investment in a foundational technology. So in that way, you can really reduce the risks because you’re not combating the risks of the technology and those technical risks with the operating risk of a startup.
Matt DeCoursey 25:08
So you know that it’s difficult to patent software. How difficult is it to patent something like generative AI? Is it? Are you able to patent generative AI as that broadly? Or do you have to be hyper-specific with it? I feel like that would be almost impossible to do without understanding where your users or licensees we’re going to actually utilize it later?
David Van Wie 25:38
So you’re correct to say that patenting software is very challenging right now, particularly in the US. So the pendulum may be swinging a little bit; it’s gone back and forth on this particular subject. But in most cases, patents are not your first line of defense. When you’re building the IP, we call them IP clusters, which combine patents with trade secrets, product technology, branding, and data. And it’s really that whole cluster that you’re looking to license. And in the case of generative AI, the moral, the most interesting parts of it are likely in the trade secrets around how some of the algorithms have been fine-tuned for a particular purpose, as well as in the data that are being used, in particular training datasets that are being applied. And then, ultimately, the product technology, you have something that works and lives, and therefore, it’s easy to license. But it isn’t impossible to patent software; it’s just difficult. The more foundational the system is, the closer you get to the ability to patent. But you really can’t rely on patents alone when you’re thinking about IP protection for AI.
Matt DeCoursey 26:51
Yeah, according to my notes, and maybe this number is resin, it says that you have received 45 patents or you’re associated with them. So you definitely have some experience in that field.
David Van Wie 27:03
Indeed, I do that number that’s on the low end in the US, either a named veteran with about 200 US patents or about 650 worldwide.
Matt DeCoursey 27:18
That’s what we have; there’s a bunch of people that work at our company that I won’t name the company used to work out that they had been. So they take a lot of pride in that. And I think that they should; I think it’s a very interesting thing too. You know, I often refer to starting a business, it’s like, it’s, it’s like having a child, you know, you got to raise it, you got to give you got, you have to conceive it, you have to birth it, you have to deal with the really messy years, and you’re hoping that it grows up to be a productive adult. So yeah, it’s a lot of work. And there’s a lot to go through with that. It’s a labor of love on Sundays.
David Van Wie 27:55
Indeed, right it is. And that’s been my experience, which is crap; you have these eureka moments. But that is just the very beginning of the process of creating a valuable intellectual property by having the insights and then writing them down and getting a team of people excited about them. There’s a lot that goes into building defensible IP and then going out and licensing it effectively. And that’s really where that monetization dimension comes in. And that’s why it takes a team, you know, I’ve worked with a number of different people, I have one aspect of business, but I also work with monetization experts who really appreciate the nuances of creating defensible licensable IP, which, on the one hand, is, is highly technical, but in its own right, in a legal sense. But it’s essential to really drive the widest adoption of technology; those commercial incentives are the most effective tool that has been devised for getting interesting technology into as many hands as possible into the hands of everybody who will really value it. And that’s why it’s worth it. You put all that energy into creating the IP, and it’s ultimately to see the technology used as widely as possible.
Matt DeCoursey 29:10
Yeah, and you know, I think for those entrepreneurs that are listening, many of the people that hold the patents and have benefited from these licensing agreements have become quite wealthy, and they are often people you’ve never heard of; they aren’t. They aren’t Elon; they are people that are high profile. And I remember when I lived in Indianapolis. There was this giant house that we would always drive by. And eventually, one of my friends we were driving by said, you know, that’s one of the biggest houses in the city. Do you know how that guy earned it? And I said, No, tell me, man, and he goes, he invented voicemail. And you just talk about like, you know, there are just these weird little things like, you know, that you use that are an everyday part of all of our lives, and there was someone somewhere that said, Man, your cell phone should just answer itself, you know, or something like that. And that, you know, that kind of curiosity as I’m now approaching my 50th year on the planet has led to I have a hobby where I’ve I went, you know, when I find time, I like to study what makes people do genius things. And I think talent and genius are very often misunderstood. And let me explain; you may have heard this before, David; I have a feeling you have that. But you know, talent is being able to hit the target that everybody sees. And genius is being able to hit the target that no one even knew was there. And these are the things that change the shape of markets and products and the things that we use. And you know, somewhere, it’s like, you know, there’s a lot of a lot of famous people out there. And there are a lot of people that you also have never heard of that have done genius things. David, when you think about someone doing genius things, what traits or qualities do they usually exhibit? Or what do you see in the people that are really innovating the future of our planet?
David Van Wie 31:13
Well, they tend to be iconoclasts; they tend to be people who have come into the work that they’re doing with an attitude that they’ll try to solve a problem that nobody else has, has solved. And the reason they’re going to be successful is because everybody else is doing it wrong. And that’s one of the traits I see just again and again. So sometimes this can make them a little bit difficult to work with.
Matt DeCoursey 31:41
I’m smiling and laughing there because you’re 100% on it. There’s a level of stubbornness and perceived arrogance that comes with that. Right?
David Van Wie 31:53
That’s right. That’s it. Maybe it’s a twist on an old phrase; it’s not really arrogant if you can do it. Right, right.
Matt DeCoursey 32:00
Well, no, you’re arrogant. And you’re crazy. Until you get it right, then you’re a genius.
David Van Wie 32:05
Exactly. And that’s been the case with essentially all of the real investors that we work with that kind of venturing as they all have that same character to it.
Matt DeCoursey 32:15
This is probably why they don’t make the best exit where the CEO is later on Sunday.
David Van Wie 32:21
But they don’t. Because, you know, if you, as a venture capitalist, are too dumb not to invest.
Matt DeCoursey 32:31
I’m not going to fight you on that one. You know, one of the things, so there is a list of traits that are very common amongst people that say, Well, I’ve learned a couple of things. There’s one thing that anybody that calls himself a genius usually isn’t. The same thing goes for a guru; I think that that’s an external point of view that someone else has with you. And then, there is one particular trader quality. And this is, basically, it’s not as sophisticated as being an iconoclast. But if you don’t possess, if you aren’t a highly curious person, you are almost guaranteed not to do things that people will judge later as a genius; you have to have that innate ability just always to want to try new stuff like and, and I’ve had this conversation with everyone from tech people to rock stars. And you know, and I have a friend who is a world-class guitarist too; when practicing, he intentionally plays wrong notes in order to see what naturally occurs with him next. And I think it’s a very interesting approach because, you know, there’s this, Yeah, he’s forcing himself to make errors which actually solves like a whole bunch of other problems down the road because eventually, you will make an error on stage. How do you react to it but it’s just like it’s your kind of having to force that that changed a little bit. It’s a disruption with, you know, what you normally wouldn’t want to do, like I don’t. I’ve never run into anybody that intentionally made mistakes during practice, but he always finds new stuff and different things that occur afterward, and so many of our best inventions and life-changing things have occurred, well we’ll say by accident, I don’t think they were truly an accident when you’re in the lab working on something and something else occurs and you figure it out. I don’t know if that’s an accident. Yeah, and then in regards to the iconic class and solving problems for you, everyone else is wrong, and I’m right. That’s still a level of curiosity. That means you’re you are at some point that Sam the first salmon swim upstream and all the other ones were like, you’re crazy. It’s so much easier to go the other way, but yeah, there’s a better life up there, I promise, and everyone kind of follows along, so now in the, in the spirit and the sake of following along, I’ve noticed as have many others that investment in deep tech, as we will say often does follow that first salmon that swim upstream. Now a couple more years ago, there was a ton of money pouring into AI there was before. How much of your companies or the companies you’ve invested in, or their success? I don’t want to say it is dependent, but it is sometimes driven by market trends or interest or whatever You mentioned as venture capitalists, and lets I’ll call myself one, just because I’m going to say this, sometimes we’re not smart enough to figure out the rest of it, and you follow the trends. I mean, how much of that is like, okay, like, if you were in generative AI? Well, I guess Open AI just got a $20 billion investment from Microsoft. That’s it. That’s not small. So how much of this is sitting around waiting for that moment or driven by trends? Or, like, Finally, there’s that awakening moment? I mean, is that something that you kind of depend on and hope for because that’s a marketing component, not necessarily a research component?
David Van Wie 36:08
It is a marketing component; you’re right. And the way we approach that is through these roadblocks and bottlenecks, ology meaning we really concentrate our efforts on problems that the market knows it has. And the generative AI example that you just gave is a great example; there’s a ton of investment flowing into it. And the more people become invested in the success. The more people are aware of the limitations and the areas where a new technology added to the platform could really address some of the growth challenges. And so our strategy is very much to concentrate on those particular elements. Because if you want to de-risk, the prediction that you just described a moment ago, when is the trend eventually going to hit this new category that I have invented, or I’ve invested in the inventors that have come up with that is to recognize that when markets evolve, and when markets developed, they do naturally understand and appreciate when these new technologies could be applied, you can’t predict exactly that last November, generative AI was going to hit the mainstream. But you can’t say eventually, this technology is going to hit the mainstream; we have a perpetual fund. And the term that we’re working with, we’re just not being constrained by the same timelines as other investors. And that’s one of the reasons you need to do that. Because you’re by yourself. Because I’ll just use Pat’s, again, you buy yourself that 20-year period of time. And it doesn’t really matter when it’s year three or year five, or year seven when the technology becomes most relevant. If you’ve given yourself an option on that market, you’re in a good position whenever that day does come. And the power of some of the applications, though, that can come out of something like generalized quantum computing. These are well-known people who understand how they can be used in drug discovery and how they can be used in security, and how they can be used in finance; these types of applications are well understood, and the challenge has been trying to create an environment where programmers can work with quantum computers in the way they work with a traditional digital computer. So in that way, it’s not as risky as it sounds. Because if you are solving a problem that the market knows it has, but it’s continuing to function, the markets continue to operate without that technology. You’re actually not taking as big of a risk as it first seems, it seems like an overnight success, but it’s not really.
Matt DeCoursey 38:44
Yeah, I’m not a big believer in overnight success. I think most of the ones that people think are like generative AI have been out there for a while. I mean, there’s been a lot of tools and things that will write a post for you or do a lot of different things, and most overnight successes or like nine years in the making. I’ve actually run into that on the podcast; people are like man, that got pretty big. I’m like, they’re pretty big fast. I’m like, No, it didn’t like we’ve been doing this every day for a long time. There was a time when 50 people would listen to that episode, you know, and that’s it. Yeah, I think the bottom line is to keep doing it, people. You never know when your time is going to arrive. It’s you talking about but going back to the people doing genius things, all of them. I have yet to find anyone who wasn’t who didn’t put in the work, and in my first book, Balanced Me, the thesis of it was that success demands payment in advance, and I have yet to disprove that. So, you know, even the smartest, most talented people still have to put it under wraps, and they still have to figure it out, and there’s nothing. Yes, some people are better at doing mean certain things and others, but those people usually find ways to polish it. And you know so much about this podcast is not just about success; it’s also about failure. And that’s a normal thing. So you know, if you’re out there listening, don’t get down. When you see, it’s easy to look around and beyond whatever social media platform, and you see all these people flexing their success? Well, first off, a lot of them aren’t as successful as you think they are. And a lot of them worked really hard to get there. So just keep, keep doing that. All right. Well, David, we are at the end of our show here. And I want to remind everyone if you need to hire software engineers, testers, and leaders, Full Scale can help. We have the people, the platform, and the processes to help you build and manage a team of experts. All you need to do is go to FullScale.io. Answer a few questions, and let our platform match you up with a fully vetted, highly experienced team of software engineers, testers, and leaders at Full Scale. We specialize in long-term teams that work only for you, helping you get a team of people that understand things that aren’t always easy to understand. So David, on our way up, I mean, what would you like to say to the entrepreneurs, technologists, and deep tech venture capitalists that would like to follow in your footsteps?
David Van Wie 41:19
Don’t give up. You’re saving the world.
Matt DeCoursey 41:23
Well, well, well said, well said, and, you know, it’s kind of a, you know, carry on what I just said; they’re, like, look at it. I don’t think you ever know when success is gonna knock on your door. I think that what is important is that you’re there to answer it when it does, you know, and that’s, that’s a challenge. So, David, thank you so much. I appreciate the stimulating conversation. To begin my week. I will. I will carry this through the rest of my day and try to do smart stuff.
David Van Wie 41:51
It’s been a real pleasure being here, man.
Matt DeCoursey 41:53