Deep Tech Investing in the Age of AI: Jacob Jackson (Julian Capital) & Jordan Kretchmer (Outlander)

Hello everyone and welcome to the Founder Institute Venture Network event, Deep Tech Investing in the Age of AI. This is brought to you by Founder Capital, the official VC fund of Founder Institute. We had about 210 people register for the event today, investors and founders from all over the world. So if you're in the audience, maybe you could just let us know where you're tuning in from, say hello. and maybe even if you're working on a fund or some other project, feel free to share the details in the chat. My name is Ryan Michaeletti. I'm head of global operations for the Founder Institute and general partner at the Veteran Fund. I've spent the last decade leading the team here at Founder Institute, scaling the organization to 8,000 companies across 200 cities. I built a personal portfolio of over 700 startups. Today I run the FI Venture Network and the Veteran Fund, which invests in seed stage critical technology startups with elite veterans on the leadership team. I'm excited to host this session today. We're going to talk about two really interesting topics around deep tech investing in the age of AI. And before I begin, I'd like to just give a quick shout out for the Venture Network, which is Founder Institute's private investor network of early-stage VCs, angels, family offices from around the world. That also includes DESILE and VC Lab communities. I think we have a lot of VC Lab folks here on the call today. The investors in the audience can join the FI Venture Network for free. It's a way for us to share deal flow from Founder Institute and for Founder Capital, so you can co-invest with us alongside the official VC fund at Founder Institute, Founder Capital. So the application takes 5 minutes. It's completely free. You can learn more by going to fi.co/venture. All right, let's go ahead and get started. Jordan, Jacob, I appreciate you guys being here. I'm excited to introduce you. Jordan is the senior partner at Outlander VC, and Jacob is a principal at Julian Capital and the general manager of DeepCheck. So we'll start with some quick introductions. Jordan, thanks again for joining us. Do you want to do an introduction on your side? Share a little bit more about your background and what you're working on at Outlander.

Yeah, sure thing, man. So again, Jordan Kretschmer, senior partner at Outlander. Prior to being an investor, I was a founder for about 20 years. Um, and so bring a very unique perspective, uh, to investing across most sectors, uh, in that, uh, I built an enterprise software company. Um, and then I built a robotics company, uh, for manufacturing and supply chain. So for the last about 8 years, um, have worked, you know, more on the deep tech side. Um, the unique perspective is, uh, at Outlander being founders we're able to understand and appreciate what founders are going through. And so we pride ourselves on sort of that empathetic view. And so I, you know, it was angel investing for about 10 years prior to joining Outlander, you know, and Outlander for the last year, investing, writing anywhere from $1.5 to $3 million checks. Earliest stages, we will write checks on back of the napkin, all the way through, you know, strong product, product market fit, and all that stuff. So we're a $150 million fund, you know, again, pre-seed, seed, very early stage. And we're on fund 3 right now. And yeah, so that's a bit about Outlander. Oh, I'll give you a little bit more. So Historically a generalist fund, and so, you know, we'll invest across every sector. I focus almost entirely on deep tech.

Awesome, Jordan, thank you. And I love your background in robotics and, you know, coming from the founder side, so I think you're going to have a lot of good input for us today. Up next is Jacob Jackson. Jacob, welcome. Do you want to do an introduction on your side?

Yeah, of course. Nice to be back. Hey everybody. Yeah, so my background, I started my career in physics. That's what I studied in university. And then we built some new MRI imaging techniques. So started everything back in deep tech, but took about a 6 or 7 year hiatus for B2B SaaS and built a company there. And, you know, after I finished that, started angel investing, And, you know, was doing it in software, but then sort of got really excited about, you know, deep hardware and deep technologies. So I joined Julian Capital, and we're a pre-seed, seed fund. We invest in deep tech across the board, all kinds of different technologies from bio to aerospace to robotics, materials, nuclear, all kinds of things. And, you know, we have a tight portfolio, and we really roll up our sleeves and support the companies that we invest in. We're all ex-operators, similar to Outlander, and we help with growth and design. And, you know, that's sort of where our expertise lies. And then we're also the creators of DeepChecks. If you've heard of DeepChecks, it's the world's largest aggregator of deep tech deal flow for VCs. And it connects founders who are working in a specific deep tech niche with the investors that actually will invest in that niche, like saving them a ton of time finding the right fit. And because of that, we have the deepest network in deep tech through both VCs and founders. We've, you know, we're connected to over 7,000 founders and hundreds of VCs. And so when we invest, we open that network up to our portfolio companies and it can be a a huge accelerant.

Yeah, that's great. I'm a big fan of Deep Checks. If you're a deep tech investor, definitely go— what's the website? Maybe you could put it in here. Lots of really good companies, good data coming from Deep Checks. So definitely a fan. Maybe let's go ahead and get started. Before we start the discussion, I do want to let the audience know that I want to make this as interactive as possible. So if you have questions, go ahead and put it in the chat. It could be about deep tech investing, could be about AI, could be about fundraising, whatever that looks like. But I want to make sure that we, we get some audience questions here, and I'll field them. But let's go ahead and get started. So I think like deep tech has evolved significantly, maybe, you know, over the last few decades, and it's certainly emerging again, as a really compelling investment area. How do you define deep tech? Maybe, maybe, Jordan, let's start with you. I mean, Outlander is a generalist, but how do you define deep tech in today's market? And how is it being reshaped by AI?

Yeah, so from my perspective, we're looking almost always at like the integration of two or more advanced technologies. And, you know, usually one of those is going to be rooted in the physical world, usually some very novel IP. And, you know, meaning that there's IP being created by the company. Even if one of the other technologies is not necessarily novel IP to the company. And so almost always means hardware from my view. And so I'm investing, there are a couple software companies in there, but they're physically impacting the physical world in some way or another. And it's in my book increasingly about infrastructure. So anything that's going to instantaneously step change how the world functions kind of at the most basic levels. So this could be everything ranging from compute power to supply chains to, you know, biotherapeutics, you know, and I would say another kind of defining feature is that the key risks are going to be technical. And they're going to be financial. And so can the team build it? Will they be able to raise enough money? That's a really big one. And, you know, it's one of the reasons we look at the founders very specifically. Are they, you know, fundraising founders? Because it is going to take an enormous amount of capital in a lot of these cases to build these companies. And so if those two things, you know, we believe they're going to be in place, there's typically like no question that the market demand is going to be there. The markets are kind of built in for these things. And a lot of times, you know, in terms of like what I'm investing in, it's because they are existing, you know, markets that just haven't been touched in 50, 75 years. And so for me, this is like different. Than frontier tech, which is usually about bringing kind of like sci-fi into reality. And those are much longer time horizons, a lot more capital is going to be required, much lower chances of success. And so I kind of split this and say like deep tech is imminent, frontier tech basically isn't imminent. And, you know, AI comes into this increasingly because we're seeing that like it's the second piece of the puzzle. So you have piece one, which is novel IP and technology that hasn't existed before, even if it's an existing market, and then intelligence contributing to that in, in some way. We're also seeing AI, you know, shorten R&D cycles, you know, speeding up the discovery processes. And so while deep tech is going to be mostly made up of those physical science and hardware practices, it's now supported by intelligence, both for discovery and for like integration into into products.

Yeah, I love the overview. And I remember, you know, we're co-investors in a company called Glide through the Veteran Fund and Outlander. Um, and, you know, we were discussing what, you know, the investment thesis was around Outlander and your experience in robotics and how, um, it's important that the company actually touches something and does something in the real world. Are you seeing that deep tech in this age of AI where, like, software, you know, is essentially becoming a commodity? It's really easy for anyone to produce software that deep tech is actually creating a little bit of a defensibility or a moat where it's, you know, hardware that typically hardware is hard. It takes a lot of capital and, you know, few people can do that because you need such specialization and networks. Do you think that's contributing to the resurgence of deep tech investing because all of this AI software is creating a lot of noise in the investment landscape?

Oh, yeah, I mean, completely. Enterprise software especially is becoming, you know, incredibly commoditized. When you look at, you know, any sort of like new enterprise software, it's, you know, add a prompt input box, right, to an existing, you know, kind of software like an existing CRM, add a prompt box, and all of a sudden you have, you know, a new application. So it's highly replicable. And this is one of the things I don't like about enterprise software anymore is, you know, it's very easy to copy. And so you have multiple, and you even see this in, you know, AI coding tools, right? It started off with Cursor, and now you've got, you know, 10 AI coding tools that are all sort of like competing for some sort of market share. And then when the new one comes out, there's no loyalty to an existing one. And so the barrier of entry is really low. And then you take deep tech where the barrier of entry is high. And so, you know, historically, investors have shied away from high barrier of entry because hardware is hard. But the cost of executing hardware is coming down, which means highly technical people can execute prototypes on the hardware side and build out capabilities that might have taken you know, $500,000 to get to an initial prototype, they can now do for $20,000, which means you can start running patents and create unique IP a lot earlier in those development cycles. And so, so yeah, I mean, hardware and deep tech generally is highly defensible. It takes highly specialized people to be able to execute. And so you find, you know, a lot more value being created in, in verticals You know, and, and so, so yeah, I mean, for, for me, the resurgence of deep tech is all about defensibility and creating immense amounts of value for the real world versus the digital world.

Yeah. Times that by non-dilutive funding, CBRS, STTRs, right? There's more capital going into that. I think, I think it's really interesting. Jacob, what about you? I mean, you guys are, you know, squarely in the deep tech space. How do you define deep tech and what are some of the things you're seeing in terms of AI reshaping it?

Yeah, for sure. So we define deep tech as a company where the business model becomes viable through a technology breakthrough. So for example, to put it to the brass tacks, you know, we have a company in our portfolio called Allo. They're building small modular reactors. They just raised— announced their Series B yesterday, $100 million. And for them, you know, if they can get the small modular reactor to work with the technology platform that they've built, every data center in the world is going to want to buy one because they can put it on site at the data center, run it as baseload power, and not have to connect to the grid, which is half of the cost of power and doubles the length of time to create a data center if you have to connect to the grid. So it's just a no-brainer if they get through this. It's a new business model that nobody's done before, and it's an extreme market pull. Now, that's how we think about deep tech. But on the side of AI, just as AI is making software less compelling, it's multiplying how compelling deep tech is, because it's creating, I guess, 3 factors that make deep tech, you know, really successful. One is the market tailwind. So in the example of Alo, You have this extreme data center buildout that is creating all this new demand for power, for servers, for chips, for networking equipment, materials to build all these things. That's this huge tailwind that's come because of AI. Secondly, you have, you know, how it changes the iteration of hardware teams. So, you know, something before where you have to try new things, maybe read a textbook, talk to an expert before you try to build something new. Now you can just open up your AI, your favorite LLM, and get the questions in a minute answered. So teams can iterate twice as fast on building their hardware. And probably the biggest thing that used to kill hardware companies was how long it took before you could get to that prototype, get it into someone's hands. And now if you cut that in half with access to AI, everything goes faster. And then finally, I'll just say that it changes investing as well. We're a team of 3. We have both running deep techs and running our venture fund. And we invest across every sector of deep tech. And it's only possible because of LLMs, I think. Because, you know, I can go on Wednesday, I can spend the day digging into a longevity company and get really, you know, deep into the nitty-gritty. I can spend 3 hours with Grok understanding, you know, the bio relevance of that tech. And then the next day, I can spend it on nuclear isotopes. And you only can do that today because of the LLMs that we have access to. And you can get 80% of the way there on your understanding for any topic in a day. And then, you know, you pull in experts for the rest to get the final 20% on diligence. So you can really evaluate whether you want to dig in on a company very quickly. And so, yeah, AI is changing kind of every aspect of deep tech.

Yeah, Jacob, I know you gather a lot of data around like deep tech investing. How do you think deep tech investing is now different than maybe other sectors?

Yeah, so we had a session through DeepChecks a little while back, and there's a lot of these— I wrote also a post about it on LinkedIn— but there's all these myths about deep tech, you know, takes way longer, it costs way more money, it's way less likely to succeed. You know, people have these ideas compared to software, but when you actually pulled out the data, Maybe it's because it's less crowded with bad companies, or I don't know, and all these other tailwind reasons that I've just sort of mentioned. But what we found in the data is that deep tech and life science companies actually exit 25% faster and exit, you know, have a higher likelihood of success. There's proportionally more unicorns in deep tech compared to the number of companies that get funded. And they're actually more capital efficient, surprisingly, because you're not spending, you know, in some cases half, 75% of your venture dollars on sales and marketing in software, whereas you can put all your venture dollars towards building the product, moving quickly, getting it to market, and then the market pull is there for your product if it works.

Yeah, I think that's really interesting. And when, when you're assessing— but let's go to Jordan really quick— when you're looking at deep tech companies or companies in general, Are you using any frameworks to assess them differently than maybe a more traditional company, or what are the checklist things that you're looking for when assessing a deep tech deal?

Yeah, so I think number one, technical depth, right? And this kind of all talks about the founders. Technical depth of the founders. What is the relevance of the founders to the actual problem they're solving. Right? So if we see a company where the founders are building in an area where they haven't, you know, don't have a ton of experience, we won't invest. I'll give you an example of this, because it actually happened yesterday. I was talking to a company, all 3 founders come from quantum. And so they're all working on quantum computing problems and decide that that's too far afield, right? It's what I was talking about, the frontier tech, my definition of that, which is it's not imminent. And so they decided they wanted to apply sort of some of those theories and concepts that quantum will be able to do to material sciences. And so all three of these quantum founders are now building a company doing material sciences and discovery of new materials and all of these. And And, you know, my feedback, I passed on the spot because I'm like, you know, none of you have ever worked in material sciences before. And so there's no technical depth. Well, they're highly intelligent individuals, right? Clearly they have an enormous amount of experience in quantum. They have no experience at all in material sciences or discoverability of new materials. And so that technical depth The ability to translate into product, I think, is huge, right? You get a lot of scientists, and a lot of scientists are going to be the ones who have the technical depth to start these companies. But they've never built an actual product before. And so product development is obviously a huge component of building a company. And so if they've never productized, right, and they've never built anything that actually had to be delivered into the real world, that's kind of an orange to red flag for me. You know, and because if they don't have that experience, then how are they going to do go-to-market? How are they going to understand how to price? How are they going to understand, you know, customer stakeholders and how to, develop a product that's right for the customers. And so, the ability to build a company, all functions of building a company. And we have, and this is kind of public out there, we have a 38-point founder framework that we use to assess a founder's traits and whether or not they're going to make exceptional founders. And so, by virtue of that, we're also able to assess like, will these highly technical, science-oriented, historically R&D-focused founders, do they have the traits and the capabilities in their histories to actually be good founders beyond do they have technical depth in the sector? And so have they turned that IP into value before, not just built a company or built a product, but that IP How valuable actually is it in the real world? And, you know, Jacob was talking about research. I go through the exact same process, right? I know nothing, you know, about, you know, a highly deep tech, you know, piece of technology going into it. Right. I may know a lot about the general sector, like energy or infrastructure. But when you get down into the very atoms of what some of these companies are doing, you have to do research and understand what the IP is that they're actually building. If they have patents, digging deep into what those patents are, are they process patents? Are they non-core patents on features? Because all of those things matter when you're actually trying to determine, is there actual value here? And then that also goes into claim verification, which is again, straight up research. I was talking to a company that said that they can do 4D printing, 4D additive printing. If they can do this, it is a massive step change in, for example, chip manufacturing. The ability to have chips change their values based on different kinds of electric signals within a chip is huge. I did a lot of claim verification. It turns out there are scientists at MIT and Stanford that have been working on this problem for the last 10 years and haven't solved it. So how is this guy who doesn't have that background going to solve this problem? So claim verification, it's not true. And then I mentioned this, the strength of the patents, process versus non-core. I mentioned that earlier. Non-core patents, patents on features. Not defensible. Process patents that cover all of the different use cases and even the marketability of a technology, those are huge. And almost done here because I'll just use another example. So Ryan, you brought up Glide earlier. And so Glide, you know, one, they deliver on all of those things. The founder has enormous founder market fit from his history. He'd been in logistics for the last 15, 20 years. And so he has a lot of credibility there. But when I read that patent, it was one of the strongest patents I've ever seen because it literally covers every single solitary aspect of what they're building and what the company does. And so those are the things, you know, I think the framework that I use when assessing these companies.

Yeah, there's so much to unpack there. I have like 20 questions and Jacob, it looks like you want to add something to it. Let me, let me ask you and then I can dive in.

Well, first of all, I'd love to see your 38-point founder evaluation. That's like something we're always working on.

That's, that's our idea. Oh, wow.

Okay. I see how it is now. I'm just kidding. No, that's crucial. And something that I think a lot of people forget is that it doesn't matter if you're investing in deep tech software or anything. It's probably about— we think about it as it's a 50/50. You're investing in a business that you have to love everything about it, and a founding team is the other half. And so you can spend all this time trying to get obsessed with the business, but if the founding team isn't, you know, top 0.1%, then there's no point in doing the investment. And so we try to spend just as much of our decision-making on both. And then the other thing I just wanted to add while you're chatting Jordan is, you know, it's still venture and venture has a roughly a 10-year horizon and you can't invest in frontier technology that's going to take 20 years because you just cannot produce the returns. Venture needs to be 25% or that's what you should be targeting, you know, 25% would be really good fund, it'd be a 7x fund in 10 years. But if you take out 25% over 20 years, it's 70x. There's been like 2 funds in history or something that have hit that out of tens of thousands of funds. So it's just not possible to build a fund about 20 years in the venture asset class.

Yeah. And I think that, that's why, you know, sort of like in my definition, it's like imminent versus not imminent. Right. And imminent doesn't mean like in 1 year or 2 years, but, you know, yeah, 7 or 10 years. But on that same note, if I see a company, and it happens all the time, a lot of this happens in space. I see a lot of space companies that have, hey, we're going to raise $3 million or $5 million now. And then our next milestone, which is building a small-scale model of what the space program is going to be that they're launching, is going to be in 3 years, and then we're going to raise $50 million, right, to get to the next milestone, which is, you know, getting some flight heritage and actually getting space on a rocket and getting out into, you know, into, you know, whatever, you know, layer of space they want to get to. And you look at that and you're just like, okay, this is, you know, going to take, you know, hundreds of millions of dollars just to get to some sort of flight heritage. And we're talking 7 years before there's value. And you look at these timelines that they put out, and you just can't wrap your head in most cases around that 15 or 20-year time horizon. And I always wonder when those companies come around, Jacob, who is actually making those investments? Because those are truly moonshots. And there are moonshots in almost every sector. But when you look at that stuff, you just wonder who is making those very early-stage investments and okay with owning, you know, 0.05% of this company by the time they're done, because you can't follow on at a certain point, right? As an early-stage fund, you can't deploy more capital at some point. You're done. And usually, it's that second or third round if these companies are raising that much capital. And so, yeah, I mean, I totally agree with you. Looking at these insane time horizons for some of these very frontier companies just make them, for funds like us at least, uninvestable.

Yeah. I do think that there's a bifurcation of the venture market where it's two different asset classes. There's the kinds of funds like us that have a seed fund, and, you know, the kinds of companies that we want to invest in where we want to minimize dilution over time. And then there's the Andreessen Horowitz and Founders Fund and those that actually are fine to put $5 million in right now. It's literally negligible from their $2 billion fund so that they have the right to keep piling in cash at the $50 million fund and the $200 million fund because they have to deploy all that capital. And so You know, sometimes it's just not— we're not investing in the same kinds of companies. And it's also what pushes seed valuations up because they don't care if it's $25 million or $30 or $40 million seed post-money because they're going to pile in their capital later and get to their 15% ownership or whatever they're targeting. Whereas we just are playing a different game.

Yeah, they're buying that option.

Yeah, exactly.

Such a good discussion here. There's a question in the chat from Nadia, and this is when you were going back to the deep tech founders that you were talking about, Jordan. She's saying most deep tech founders come from research who have not built products to date. So are you always discounting those founders? Or how do you like, is there a way for those teams to like compensate? Are you looking for maybe like, you know, two scientists that are like deep experts in that area? And then a product guy? Like, how do you think about the product development piece of that team?

Yeah, so from my perspective, like, if there is a deep research R&D scientist, and we see a lot of this, like so as like MIT, Stanford, Berkeley, um, tend to be where we see these companies. And, um, and, and so there can be one deeply scientific, uh, researcher who's, you know, been researching in this area for the last 15, 10, 15 years. Um, that can be obviously a huge asset. Um, but if there's not a real, like, maybe that person is the CEO But we'd like to see at least one of the co-founders in either a CEO or chief product officer or, you know, chief revenue officer, somebody in a business seat who understands that market, right? Who, you know, maybe, you know, did their PhD in it, but then went and, you know, worked at a larger company or ideally started something or joined an early-stage company before. Somebody that has that experience of company building. If it's two scientists, you know, I keep talking about companies that I've passed on because I think they're relevant to this discussion. Two founders, deeply R&D focused, highly, highly intelligent, building a robotics company, teleoperated humanoids. It's a really unique approach to this problem. They've been working on this problem for 10 years. And loved the solution, loved the founders, but couldn't wrap my head around who was actually going to run the company. And so the quick— the short answer to that question is, is no. Um, you know, I, I won't invest in a company where there's 2 scientists or 3 scientists who have no company building experience, no productization experience, because those founders right? Like, how do you learn how to do go-to-market on the spot? How do you learn how to productize a solution? How do you learn— this is a big one— how to integrate customers, customer feedback, and stakeholders along the development process into what you're doing? These are not people that are used to taking other people's perspectives into the work that they're doing. And so the ability to listen, knowing what questions to ask, I've just found that at least for me, and time will tell with some of these, I've probably passed on some amazing companies that the founders learn on the job and they gather that experience and end up being phenomenal business leaders. But again, for a fund like us being so early stage, it's too much of a risk for me to take investing in two or more founders that come from that background.

Yeah, I think there's something to be said too, you know, when you're talking about like the experience level of the founders in that, in the particular industry that they're building in. So quantum versus, you know, material science, you know, one is like there's an expertise and knowledge level, but then two, there's also like a network level as well. Like, who are you going to go to market with? Like, do you have a network of people that you can go and maybe get some LOIs or early revenue from? Like, how do you build How do you expand on the founders network itself? You know, Nadia came in here and she's— I'm going to kind of consolidate a few things here. But, you know, she's saying that, you know, with your VC and founder network, surely you can suggest how to supplement those teams with a product or market expert. And my comment to that is like, yes, it's possible, but that's also the job of the founders, right? So like step one for any founder here, it's like you have to build the right team. The right makeup and archetype of a team. And then once you have that, that's when you would go out and build your product, get a little traction, go get some funding. Jacob, I know I want to dive into IP, but before we do, is there anything you want to add to that?

Yeah, I think it's really important to get in the head of a VC when hearing this. And venture is a game of mental models because we're going to see 10,000 decks this year. You have to filter. With mental models, but it's also super important to remember, or we always try to remind ourselves that we have to have high conviction ideas loosely held, right? So, you know, there's always an exception to every mental model. There's always an exception to every rule. So, you know, maybe we don't invest in nuclear fusion companies because we have this idea that it's 20 years away, but we invested in a nuclear fusion supply company because there's tens of billions of dollars going into funding nuclear fusion, someone has to give them the isotopes that they need to test with. And that company is Hexium and they're breaking out. So you have to, you have to have a loose hold on that. And I think on this founder discussion, there are definitely teams of scientists that are so incredible that they can pick up anything very quickly. And we have a few in our portfolio where they they have a knack for marketing, they just never tried it before. And so as soon as they try it, they pick it up, and they have a knack for hiring really well. And so, yes, most scientists have spent 20 years in academia, may struggle with these things. But if you can learn really quickly, if you're really a truly exceptional founder, and you find the right people to learn from or hire, then anyone can build a great company.

Yeah, remind me to send you one of our companies, Aerofuse, that follows that same model. I don't know if you've come across them, but they're doing— that originally was starting out with like fusion for aircraft, and obviously like that's, you know, 20 years away. But then they pivoted into the supply side of fusion. So there's some interesting revenue opportunities there. Let's dive into IP here because I think, you know, you kind of jumped into this, Jordan. I think it's super important. Deep tech is really about the technology, and you have to protect the the technology through patents. At the same time, you see companies like SpaceX who are, you know, operating more on the trade secret side. What, how do you guys think about patents these days? And, you know, there's even some, some people out there that think like, you know, the value of patents are going down, especially with like AI, AI could probably crank out and create, you know, a bunch of different new technology and patents. Like, where do you think it's going? And like, how relevant are patents now versus where they might be in like, let's say 5 or 10 years?

Yeah, well, I think, you know, patents have always been a really important part of technology and innovation, and they've kind of— were forgotten for 20 years there where everyone was building software and everything was moving so quickly that, you know, well, you don't need patents because, you know, it's all in the knowledge or whatever that people were saying. But it turned out that you actually did need the patents because everyone's product, unless you have a network effect in software, are commoditizable. So patents are really important, but it's not enough. It's a piece of it. So that's how we think about it. We think, you know, patent is a great thing. You have to have it. It gives you a little bit of an advantage over other new entrants, but it can't be the only defense that you have. And so we really do think about it as a single component and not the full defensibility story. For defensibility, we look for network effects, compounding data moats, you know, if it's a market where there are only so many customers, if you can capture the customers before everyone else and build some kind of lock-in. So we do need to see something else on top of patents when we're making an investment.

I'm going to consolidate a question here from Camilo. His question is like, when should startups apply for IP or patents. Do you, would you invest in a company that has technology that's potentially patentable, but they don't actually have the patent yet, or maybe it's patent pending? Like, how do you think about that in terms of it being a requirement for investment?

I think if they have something that's defensible, then they should submit the provisional right away. High agency is really important. And if you're not ready, if you haven't raised capital within 2 years of doing that in order to sort of fund the rest of the patent, then you're not moving fast enough anyway. So I think get your provisional in right away and then try to raise capital in time to fund the rest of it. That's my opinion. I'm curious if Jordan has a different idea.

Yeah, no, no, I agree completely. And it goes back like towards the beginning when I was talking about the ability to get to value faster in deep tech and primarily, you know, with the creation of novel hardware. I have a company called Nomadic Drones where for $10,000, using 3D printers and off-the-shelf circuit boards and then circuit boards that they actually made themselves, they created a drone that can land on a power line to recharge itself using induction. They did this by connecting— they tested it by building, taking a microwave and running sort of a power line material across their room and then just placing the drone on top of this thing that was connected to a microwave. They did this entire thing for $20,000. They submitted patents, and by the time we invested $1 million in their pre-seed, they already had patents in both Europe and the US. And so that early, like, very creative product development that is now possible with much less capital means, you know, if a company says, oh, we need to raise this round so that we can finish product development so that we can go start to, you know, you know, get patents processed. I just look a little bit more skeptically at those companies. And, you know, For us, you have to do a lot more research in those cases. You have to really understand the patent environment around what they are building, because until that patent is pending and you start to see some uniqueness and strong indication that that patent is going to be granted, how do you as an investor know if that is a patentable process or development? And so, yeah, for me, it's— they have to have that process started. I will ask questions if they don't have that patent submitted and in that process, I will have the company do the research, show me all of the research and patents sort of surrounding what you guys are building and kind of prove to me that there's uniqueness here and that with this pre-seed round, you actually can get to a patentable place. And so, yeah, I mean, kind of agree with, with Jacob there. I'd like to see, I think, a little bit more progress when we're investing in pre-seed, especially in, especially in seed, that there is patentability there.

I think one of the big takeaways from this conversation that's validated something for me is, you know, let's like rewind like 10 years where, you know, maybe someone's building like a consumer software startup. And if they didn't build a product like that, might be a little bit of a red flag. Like maybe there's a barrier to entry. Maybe they have a technical person on their team who built it. You know, fast forward today, it's like absolutely imperative that you have a product built, especially if it's software before you invest. And in this age of AI, you're seeing that with deep tech now, right? To your point, it's much cheaper to do deep tech, whereas before it'd be like, hey, we need $1 million just to build the thing. Um, and so I think you're going to see maybe an increased number of deep tech companies arise because the barrier to building deep tech companies is being lowered through artificial intelligence.

100%. And I mean, software—

is software even patentable anymore?

Like, good question. And even if it is, is that— is there value in that, right?

Like, how defensible is it? And it was like I said before, right? Like, you know, all the new software that I see is, you know, AI for this, AI for that. And they built this software in like 3 months or 4 months. And, and so I've been questioning that. I don't know the answer, but like, is software actually patentable anymore? But anyway, the question for another question.

I think, you know, what, what's interesting is like you're seeing venture kind of make this full circle where the first venture-backed companies were all deep tech. I mean, that's what venture was. It was like high-risk dollars to invest in deep technology. And then you kind of in the 2000s and beyond, you kind of had this like surge of like software and consumer-based startups. And then now that that's being commoditized down, you're seeing now the resurgence of deep tech as like a really good category to invest in. I'm curious, Jacob, do you guys have data around like the performance of deep tech companies, maybe from a return profile? I know you mentioned that there's more unicorns in that space, but is there any other data you can share for the investors on the call today around results of investing in deep tech?

Yeah, I think it's hard to say because the resurgence is pretty recent for deep tech. So you haven't seen full fund cycles. But I totally agree with you that the early, you know, and the true meaning of venture is to back really hard technology problems. And not just to fund Facebook ads or sales teams. So I think we're back to where venture is supposed to be. And I, I do believe that there will be better returns because you actually can build defensible businesses. And I was going to— yeah, you were— what were you saying before? Oh, yeah. So I think, yeah, on the, the founder piece, getting to a product quickly that you can patent is super important. Because the best founders in the world, you know, they don't want to waste any of their time in their life on something that isn't going to work. And so they will, on their own, order all of the risks associated with the business and tackle the first risk first to prove to themselves that this business is worth spending time on. And so they don't have to build the full product, but they'll figure out what that that most important question is to see if this company is going to work. And they'll, they'll go and answer that with the quickest experiment possible. And if you see a founder that's doing that, laid it all out and is tackling the questions one by one in a really ordered fashion, you know that they're trying to build something really great and they're not going to waste your time or your capital on it. So I think seeing that, that momentum, solving problems as quickly as possible, don't have to build the first market-ready product immediately. But to your point, getting something built very quickly or some questions answered very quickly is extremely important.

There's a question here from Andy Wells. You know, are there any common weaknesses that you see that many deep tech startups share, like an area that they need to strengthen before you invest in? Is there any common themes that pop up in this area?

I think one that we pay really strong attention to is how well they hire. And that doesn't— you may not have to have hired an employee yet. Hiring a co-founder is the same thing. The best founders only want to work with the best people in the world, and they really learned that early. You cannot compromise at all on the people that you hire because A players will only want to work with A players. As soon as you hire a B player, they don't even know what an A player is, and they'll hire other B players, C players, and your company gets diluted quickly. So founders that really take that to heart and have proven it, that they went, scoured the earth for the best person in the world to be their co-founder in building this company, is a huge signal for us that this is going to be a great company. That's one that we look for.

Yeah, since I didn't have an immediate answer to that, I'm going to latch on to Jacob's because I completely agree with it. And, you know, so early hiring, vital, hire the A players, but also hire against your weaknesses. And so a lot of the times you see investors, you see founders, especially in deep tech, highly, highly technical founders, not fully understanding where their gaps are and who they need to hire, right? They've never hired a head of business development, head of partnerships, head of sales. They've never hired those people before. And so them understanding, and this is where investors, I think, can play a huge role in supporting, educating, and working with deep tech founders, is helping them understand, you know, and being honest with them, these are your weaknesses. Right, and making sure and watching those founders hire. And especially like if it's before an investment, have they hired, whether it's a co-founder like we talked about earlier, or a founding engineer or a founding business person where they are filling the gaps in their own capabilities and skill sets with people who, you know, can shore up those areas of the business.

Yeah, great, great feedback there. No, we talked a little bit about the role that AI is playing now. Let's talk about a little bit like what it might be doing in the next 5 years and how it might affect deep tech. So do you see AI playing a role in maybe accelerating deep tech commercialization? You know, what are your thoughts on how AI might affect this sector, let's say over the next 5 years?

Yeah, well, I think the first thing is that everyone's going to bail on building software companies and AI wrappers and stuff. And so you're going to have this huge influx of founders into deep tech, trying to build something that can actually build a big company with. So I think that'll be the first result. I think we still have this huge build-out to do, like invest, like the investment in AI infrastructure. While most of those, the value is going to accrue to the hyperscalers, probably, um, the, the buildout itself is creating a tremendous opportunity. So that has about 5 years to play out at least. We'll probably have a bubble in there and it'll crash like every other big technology buildout in history, but then it goes and continues like the internet crash, and then it continues. The internet, the next 20 years were huge. So I think that there's a tremendous amount of opportunity in building out the energy infrastructure, the transmission lines, the mining technologies, um, the robotics, everything to supply this, this AI push. And so that's how we're thinking about it. But it's not necessarily, you know, the next LLM that is going to be the, the big company.

Jordan, what about you? Like, any trends you're seeing or, or interesting things you're looking for over the next 5 years?

Yeah, infrastructure, infrastructure, infrastructure. You know, to me, you know, that's the most exciting area, you know, of deep tech right now. And, you know, Jacob mentioned energy, right? Like infrastructure, energy, supply chain, manufacturing. There is a lot of deep tech that's happening there using like the application of AI to some of these very legacy businesses. That I think are going to accelerate innovation in places where it's like it's been 50 years, right, since we've thought about how products move across the country. It's been 50 years since we've thought about how to inspect our energy grid, which is coming back to bite us in the ass hard right now with wildfires raging all over the country, all over the world, caused by sparks from old energy infrastructure. AI is playing a huge role now in identification and how we are— the company I mentioned before, Nomadic Drones, they're using that technology, their first use case of landing on power lines to recharge. It's giving a 24/7 analysis of exactly what's happening on every power line and every power station in the country and eventually the world. Which means, you know, AI is now load balancing energy, telling power companies and utilities exactly how much energy they can put down lines to maximize and do grid optimization while simultaneously using AI to identify potential issues coming from that same data they're processing. And they're doing all of this on the edge, right? Every single drone that they land on is processing all of this data on the edge. Only possible with, you know, utilizing AI and utilizing the hardware, the GPUs that come along with AI enabling that sort of edge processing. So yeah, I mean, AI and I look almost primarily at infrastructure now. So very rarely am I looking outside of infrastructure because I think that that is where a vast majority of the innovation can happen. And like, it's easy for me to wrap my head around energy and manufacturing and supply chain and all of these categories because, you know, generally I'm familiar with how those, how those categories and how those technologies have historically worked. So yeah, AI application, legacy industries accelerating that in a big way.

Yeah, this has been a great discussion. Let me ask you one final question here each. Consolidate kind of two into one. Are there any mistakes that you see new investors make who are maybe new to deep tech investing, or do you have one piece of advice to give angels or VCs who are maybe looking to get into deep tech investing?

Yeah, 100%. There's one— there's only one thing that matters in my mind, and it's getting reps. And I didn't fully understand this until I, you know, until the last few years. But if you're looking at 20 or 40 deals a year and you pick your favorite 3, the likelihood that they're any good is quite low. And so until you've seen 10,000 startups and talked to 500, 1,000 founders, you don't know what good looks like. So I'd save your money investing in the first SPV that comes your way and instead Try to get reps meeting founders, seeing decks, meeting great founders, because that's another data point that a lot of people don't have. So unless you've worked, you know, alongside Travis Kalanick at Uber and know what it looks like, then get out there and get your reps into seeing what great founders, great companies, and volume so that you can actually know what a good deal looks like when it comes your way.

Yeah, man, you took mine. Investing in areas too early. You have to go through that discovery cycle. I looked at 20 space companies before Outlander made our first space investment. It was an area that, you know, obviously was very excited about, you know, and it was so hard for me to wrap my head around the timescales of these companies. And, you know, I probably missed— I know for a fact there was one that I definitely missed that, you know, hurts, you know, because they were a little closer than I thought they were. But I wasn't going to deploy some of our capital to something I didn't fully understand, you know. And so you need to see a bunch of companies in unfamiliar sectors. You need to research them heavily before investing. Jacob's 80%, I think you said 80% of the time on ChatGPT, getting a really good starting point on, you know, any sort of category of deep tech. I found it to be highly productive. And then that can launch you into research papers and, you know, things that have come out of the universities where you can start to wrap your head around the feasibility of these things, you know, and So I'll add to that, getting excited about things that sound cool. In deep tech, there's a lot of stuff that on the surface just sounds, sounds like sci-fi, very easy to get excited about these companies. And you can overlook key issues, right, with the technology or with the business if you don't understand it completely. So don't get sort of like Happy Feet syndrome when you see something that sounds like it's going to change the world. Make sure that it is and try to look at as many other things as you can in that space. In terms of advice, I kind of said this earlier, but start with areas that are already easy to understand the concepts, right? So, you know, don't start with quantum computing if you've never invested in deep tech before. Don't start with investing in nuclear fusion. Don't start with investing in pharma. Start investing, right, which is what I did, in things that are very accessible that you already understand. You understand the general concepts, and now you just have to go deep understanding the details.

That's awesome. Jordan, Jacob, this was really fun. We should do this again soon. Maybe we'll do a pitch event or something with some deep tech founders. But thank you for joining us. That's all the time we have. So, you know, feel free to put contact info or LinkedIn in the chat. I did it earlier and we'll make sure to send out the recording to everyone here so you can rewatch this. A lot of really good pearls of wisdom here at the event today. So just to wrap us up, if you're not already a member of the FI Venture Network, feel free to join by going to fi.co/venture. You must be an accredited investor. You've got to, you know, be an angel or VC. Because it's really a deal flow sharing network. So for those of you who have questions, you can always drop us a line and reply to any of our emails. And I look forward to the next event, gents. Have a great day.

Thanks, Ryan.

Thanks, everyone.