E67: Defense Tech’s Rise with 8VC’s Alex Kolicich

3 Dec 2024 · 54 min

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In short

Podcast Notes: Turpentine VC E67 - Defense Tech’s Rise with Alex Kolicich

Episode Overview

  • Host: Erik Torenberg
  • Guest: Alex Kolicich, Founding Partner at 8VC
  • Description: A discussion on Alex Kolicich's H1 2024 report on the venture landscape focusing on AI investment challenges, the resurgence of defense technology, and broader economic trends influencing markets.

Key Themes Discussed

  1. AI Investment Paradox
  2. Optimism vs. Uncertainty:
  3. Rapid expansion of AI capabilities contrasted with investment risks.
  4. Technological shifts can disrupt existing value capture structures.
  5. Investors' Dilemma:
  6. Investors face challenges in identifying where value will accrue amidst high market expectations.
  7. Companies in AI are often priced to perfection, leading to questions about sustainability and potential market corrections.
  1. Public vs. Private Market Dynamics
  2. Current Valuation Trends:
  3. Public software companies trading at lower multiples (around 6.5x revenue).
  4. AI startups experiencing high valuations (100-200x revenue) due to market hype, leading to potential volatility.
  1. Challenges and Opportunities in AI
  2. Layered Market Structure:
  3. AI technology is segmented into multiple layers: hardware, foundation models, applications, and services.
  4. Uncertainty exists around which layers will capture the most value.
  5. Investment Strategies:
  6. Need for flexibility in investment strategies due to changing business models and market conditions.
  7. Emphasis on the necessity for agile investors who can adapt to market changes.
  1. The Rise of Defense Technology
  2. Resurgence in Defense Ventures:
  3. Changes in warfare dynamics, especially observed during the Ukraine conflict, signaling a shift towards technology-driven defense solutions.
  4. Opportunities for Startups:
  5. Startups are well-positioned to fill gaps in the defense sector, especially in software and AI, where traditional defense contractors may lack expertise.
  6. Cultural Shift:
  7. The perception of defense investing has evolved, with more acceptance and interest in startups within this sector.
  1. Defense Procurement Reforms
  2. Contracting Changes:
  3. Shift from cost-plus contracts to fixed-price contracts, pushing risk onto companies rather than the government.
  4. Increased use of Other Transaction Authority (OTA) contracts, allowing for faster, less bureaucratic procurement processes.
  1. Advice for Aspiring Defense Startups
  2. Team Composition:
  3. Importance of having a skilled team with expertise in military needs and defense technology.
  4. Engaging with Government:
  5. Early engagement with government entities is crucial for success.
  6. Fundraising:
  7. Ability to secure funding in a landscape where immediate commercial progress may be limited is essential.

Key Takeaways

  • The venture landscape is in a state of flux, with AI and defense sectors poised for growth but filled with uncertainty.
  • Startups in defense must leverage unique insights and expertise to navigate a complex market.
  • The current enthusiasm around AI and defense presents significant opportunities, but investors and entrepreneurs must remain vigilant and adaptable.

Additional Resources

  • Alex Kolicich’s Substack: [Link](https://www.alexkolicich.com/)
  • 8VC Website: [Link](https://www.8vc.com/)
  • Full H1 2024 Report: [Link to report](https://www.sourcery.vc/p/alex-kolicich-8vc-ai-and-defense)

Conclusion This episode highlights the intricate balance between optimism and caution in the rapidly evolving venture landscape, particularly around AI and defense technology. Alex Kolicich provides valuable insights into the current market conditions, encouraging a thoughtful approach to investing and building in these dynamic sectors.

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Transcript

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0:04Welcome back to Turbentine VC, the podcast where we discuss the art and science of building successful venture firms, VC to VC. Today, we're airing a conversation between Sorcery's Molly O'Shea and Alex Kolesic, founding partner at 8VC, an early investor in Palantir, Andrel, SpaceX, and OpenAI. Up ahead, they dive into Alex's H1 2024 report on the venture landscape, exploring three key themes, the AI investment paradox, the renaissance in defense technology as a venture category, and the broader economic outlook shaping markets. If you enjoy this episode, You can find Alex's previous appearance, along with interviews with Delian Asbruhoff and Chris Power, over at the Sorcery podcast feed linked in the show notes below.

0:49Without further ado, here's Molly and Alex.

0:56Welcome to Sorcery. I'm your host, Molly O'Shea. Today, we have Alex Kolosich, founding partner at 8BC. Alex invested into IT and bio-IT. Prior to joining 8BC, Alex was a partner at Formation 8 and also worked with Peter Thiel at Mithril Capital. Before venture, he was an engineer and early product advisor at Clarium, Palantir, and Google. This is a very fun conversation on the state of AI and defense. I hope you enjoy. Hey, Alex, it's great to have you back on. Molly, thanks so much for having me. It was fun last time. Happy to be back. This is round two, so this will be a fun conversation. Back by popular demand, right?

1:37Back by popular demand, I was getting lots of DMs by everyone. You wouldn't believe it. It was insane. And here we are. So we'll just kind of go through this conversation based off your last H1 2024 report. Again, you put out these amazing reports every quarter. And this one really laid into AI and defense. So I want to start off with one of the quotes in your report. I've been contemplating how the unique optimism and rapid expansion of AI capabilities we've witnessed over the last year and a half are juxtaposed with danger and uncertainty in the investment world. Could you break this down a little bit more?

2:17Yeah, I think there's two elements here. I think there's an element that investors have to worry about and then an element that everybody else has to worry about. So if you think about technological change and there are phase shifts in the market, new capabilities, they usually upset the established structure of value capture. Not always often. And so Satya Nadella had a very good quote where he said, in the new technological wave or phase shift, all the winners are decided within the first two to three years. That's why he's being so aggressive on AI. He doesn't want to be left behind. And so if you take AI as a phase shift, You assume that value is going to accrue somewhere.

3:04There'll be winners, there'll be losers. And it's very important today to try and be a winner versus a loser. And so that matters for entrepreneurs and that matters, I think, for investors too. From entrepreneurs' perspective, there's a big question, where is value going to be captured in the market? If you're going to start a company, you're obviously very levered to one business, one company. And you should make sure it's the most successful one possible, obviously. And if we think about AI as having multiple layers of the stack, from hardware to the foundation model layer, there's infrastructure and tooling, and there's application layer on top of that.

3:39And there's actually probably other layers as well we could talk about. There's going to be a fight to see who gets the margin that gets developed, which of these layers is going to be the winner. And I think if you look at it as an entrepreneur, it's actually not 100 % clear today. So we have a few theses internally. we're very excited about AI-enabled services. And so can you pair the traditional private equity roll-up model with sort of the software development team by traditional services businesses, use an engineering team to automate a lot of the work, and turn what used to be services revenue into software revenue?

4:15That's one thesis people have. That's maybe like another layer on top. So more of the business layer. Is it going to be the case that there'll be new winners in application software? software? Will the existing incumbents be disrupted should you start at that layer? Is it going to be the case that foundation models will capture all the value? Is it going to be the case that the hardware layer is going to capture all the value? Is it the platform layer that will capture all the value? I don't think anyone has an answer to these questions. Is it going to be the customers, actually, that capture all the value?

4:44And maybe software players don't capture very much at all. And so I think we're at peak uncertainty today on where the value capture will be. And now if you transition that to being an investor, all these industries, especially, you know, in places where it's working, so to speak, there's revenue growth, there's traction, they're all priced to perfection. And so as an investor, now you have to look at it and say, every business when it starts working is priced perfectly. I think in my last update, I showed the multiples, but you know, it's not uncommon. Now we're seeing companies, you know, 5 million revenue in AI revenue, and they're raising at five, six, 700 million valuation.

5:24That's price to perfection, right? We're talking about 100X multiples sometimes in these cases. And so if you're going to make a bet today on where the value capture will be, you better be right. And it made me start thinking about early in my career, I used to be at Clarion Capital, which was a hedge fund. And there's a lot of things you worry about hedge funds, like style drift and things like that. But I thought one aspect that was always underappreciated in hedge funds generally was sort of overfitting, I thought, that existed with managers, where you'll often see this trend where managers will make money in one macro regime.

5:57You know, there's a bunch of funds just because, you know, there's proximity here. There's a bunch of funds that are tech hedge funds. They're very bullish tech and tech has done very well. And so the bullish tech hedge funds have done very well. If you go into a regime where tech underperforms, it's very rare for those managers to actually now be bearish on tech and, you know, maybe go into industrials or something like that. Most money managers are one-trick ponies. And so you have to think about that now as an investor in software or in software and venture today, that the way you made money in SaaS before, it almost became formulaic.

6:32You know exactly what to look for in terms of metrics, in terms of business models that worked. All those rules are changing. And so now that's another place where a peak uncertainty as an investor, where you don't know what business models will work. You don't know what tactics will work. You don't know where actually durable value or moats will form. And so you need to be very agile and change the way you invest. And there's very few investors that can do that. And so I think you'll see over the next 10 years, a lot of retirements, a lot of firings, perhaps, because people won't make that transition.

7:06And so I think it's a really unique time. And it was just funny that, you know, juxtaposing this big change, which we all know will be important and positive for the world, is also really dangerous for many people in the ecosystem. Right. And it's making for a really incredibly unstable environment for most VCs and founders. Exactly. That's quite interesting. What have you seen reflecting between startups and public companies? Software seems cheap. What are the public market multiples like? Are they still very volatile? We just saw an incredibly large funding round with OpenAI just so off the back of the heels of XAI, which raised$6 billion.

7:44OpenAI just raised$6.6 billion at$157 billion valuation. Are these startups anymore? What is this class of AI and where are we with VC funding? Yeah, I think in the market, we're still seeing a tale of haves and have nots, I'd say. If anything that, well, we could first start with the public markets. Software companies are cheap. I publish our internal software benchmark, and you see forward revenue on a software company six and a half times. That's about where it was for the last, I don't know, before five years ago or the last decade. So, you know, five years back, it's the 10-year average. So we're back to a normal market there, which I think is curious because my particular view is that incumbent software companies will capture a lot of the value of AI.

8:30You know, if we're understanding that my views change maybe every quarter, as people see in my updates, you know, my current thinking is you probably won't capture a lot of the value on the base model layer. That doesn't mean no value. Obviously, there's value to providing a commodity service, and most commodity businesses in the world are big businesses. You know, there are people who make memory and who make hard drives, and they're quite big businesses. There's some returns to scale. So all those caveats aside, I tend to think the base model layer will be quite competitive and probably not as profitable as people think.

9:07You see the dynamics today where, you know, there's huge convergence, I'd say, in the performance of these models. O1 just came out. We haven't fully benchmarked it on our side yet. It's definitely an improvement. And so OpenAI is ahead of everybody else. It's an improvement in reasoning, which is good. We can talk about that later on. but I expect people will catch up within six months. And so the dynamics in this business are gonna look a lot more like a memory manufacturer where you have long or sustaining investments every year to stay on the technological frontier. And then the sort of lifespan of your technology of your current model is on the order of months or years.

9:47And if you miss one of those sustaining innovation cycles, then you're worth nothing. We saw that with inflection and in-depth, right? They missed one model cycle. They had to exit the market. And that's generally not a great setup for a profitable business. If you look at the 1990s, this is what a lot of those hardware companies looked like. Intel used to be a memory manufacturer and couldn't keep up, basically, when the similar dynamics were up late. So I think if that happens, and that's what the world looks like, then a LLM looks kind of like a query processor in a database. You give it context.

10:23It does its work. You get the answer out. But the point of leverage is the place at which the context lives, where the data gravity is, and how the answer is delivered. Sort of, I call it workflow gravity. And in both of those cases today, it's usually in the system of record where all the data sits and exists today. And the workflows that you will use to operate your business exist as well. So if I think what's the right insertion point for this like super intelligent base model layer, I would think it's the incumbent software systems. And so if they're very smart, you would expect them to have a lot of AI workflows in their software products themselves.

11:03And that should drive a huge amount of value for their customers. That should drive a lot of revenue growth. And so that's my view sort of on the public markets today. There's a lot of AI hype. But if you look at companies like NVIDIA, they're actually not that expensive on earnings basis. Like it's not we're not in bubble territory, you know, Cisco 2000 timeframe. Now, if we go to the private markets, you see parts of the market that are very connected to Publix and parts that are very disconnected and actually much more expensive than public markets. So if you are a non-AI first startup, you're still in the old world.

11:40You know, people are going to price you to converging to a 7x for revenue multiple if you're growing well efficiently. If you are an AI company, you get those 100, 200x multiples and you get a huge amount of rush for people to invest in you, even if you have like small incremental increases in performance. And I think that's going to stay the way it is. You know, I put at the beginning of or at the end of last year, rather, that I thought inflation was going to abate this year and the Fed would cut and it would be a good time for people to raise money. I think that's still true. I think even if you are one of these non-AI winner companies, it's still generally a good time to raise money.

12:19It's not going to get better. I mean, certainly, you know, we're not talking 2021. We're talking, say, 2016 timeframe type environment for fundraising. But I think it's a good time. If you are in AI or AI adjacent, you should be raising every three to four months, in my opinion. And you should be doing it aggressively. We have a saying we use at the firm that I tell all my companies. It's a quote by Eugene Kleiner, one of the founders of Kleiner Perkins. He said, the time to take a tart is when it is being passed. And the tarts are being passed. And so if you are an AI and you can raise money, raise it.

12:54At some point, I think the spigot will turn off, just meaning the multiples will normalize. And then it's going to be really hard to raise money. Hey, we'll continue our interview in a moment after a word from our sponsors. How deep do you go to seek out an answer to a question? Maybe you've spent hours clicking the source links on an obscure Wikipedia page. Or maybe you're even the type of person who checked out the entire shelf on the topic at your library. If you're nodding along, then check out GiveWell, an organization that researches questions about global health and philanthropy, even if a satisfying answer might require years of reviewing studies, talking to experts, and over 300 footnotes.

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14:00And GiveWell doesn't take a cut. If you've never used GiveWell to donate, you can have your donation matched up to$100 before the end of the year, or as long as matching funds last. To claim your match, go to GiveWell.org and pick podcast and enter Econ 102 with Noah Smith and Eric Torenberg at checkout. Make sure they know that you heard about GiveWell from Econ 102 with Noah Smith and Eric Torenberg to get your donation matched. Again, that's GiveWell.org to donate or find out more. Do you think the current multiples are reasonable? Yeah, I think that AI multiples are not, which is ironic because if you look at the open AI round that happened now, I think it was priced at something like a 15 or 20 times forward error multiple, which I don't know their margin structure, so it could be expensive, but that's not actually crazy.

14:50But there are rounds happening that are 100, 200x revenue in the early stage markets. And even there, you have to assume, you know, you're going to have 10x multiple compression in the later stage rounds. So I don't think those are sustainable for any period of time. I think that will live as long as there's an oversupply of capital in the venture capital ecosystem. I mean, I've heard about this before. I just think there's too many mega funds that need to deploy too much capital. And so in many ways, it's actually more appealing for some of these funds to write a$100 million check than it is a$10 million check.

15:24And so perversely, sometimes like the higher the value, because people are only going to dilute, say, 20 percent around. perversely, the higher valuation is, the better it is for the funds. They can deploy more capital. So there is an incentive there. And I think the multiples will last so long as those funds are outsized. It's quite the curious scenario, though, because funds are in a crunch with DPI and just distributions. And then also funding things where effectively you might lose, but we are in a risk game. So I'm just curious how you think about that. Yeah, so far. Is it worth it? Yeah.

16:07If you look at the general ecosystem of these mega checks, let's say a debt or inflection, I don't have any inside information. It seems like those investors were paid back. And so no money was lost there at least. And so I think so far, so good. If you're an AI investor doing very big checks and foundation model up, foundation model layer up, you haven't really had many write-offs. You've, in your losers, or I wouldn't say losers, in the companies that haven't broken out, you've made your money back in those cases. And so there's no signals, I think, telling people to stop. I think that will change.

16:44There'll be, you know, usually in these cases, there's some inflection point. There's some catalyst that grinds everything to a halt. but we haven't found that yet. And I think I put in my update that labs are starting faster than they're shutting down. So Ilya just raised a billion dollars for his new AI lab. And so I think the music keeps going. And I guess to push on that a little bit more, CRV just announced that they're returning some capital from their growth fund. So if that's the case and options are limited on the growth side, Is that going to be a trend or do you think that's an isolated scenario?

17:25Money managers returning fee-bearing assets will never be a trend, in my opinion. It takes someone who really believes in their craft to say, hey, I can't actually deploy this capital. Usually funds when their rates get deployed and people find ways to do it. I think that's more of a representation of the fact that, in my opinion, the venture market as a whole, like fundable companies, has shrunk since 2021. And so people kind of raised funds assuming the market would stay like 2021. And in fact, it's changed. My estimate was I think the amount of venture scale opportunities has gone down 60, 70 percent.

18:08And so, yes, it is the case that some companies were overfunded, have actually managed their capital well and are not willing to take a haircut on valuation to do that growth round. So that does exist in the market. But I also think there's just like less opportunity in there. And if you're only doing software, I should say, or AI, than there was in the past, even still today, even with the AI wave. And I think it's more a representation of that. So do I think it'll be a trend? No. But I do think fewer people will raise opportunity funds or growth funds. I think that is likely. So to go back to your report, you mentioned a couple of terms that I thought were great.

18:44So one of them was the AI capabilities gap, which you touched upon a little bit. But what are the effects of the AI capabilities gap? Yeah, we see this often. And I wouldn't say it's pervasive because we had a company, Case Text, that got purchased in 2023, I think it was. It feels like five years ago, but I think it was just last year where they deployed AI to lawyers. And I could tell you that product has great usage and it is commercial. I won't mention the numbers, but it's doing very well. So there are areas where LLMs are powerful enough and reliable enough to go into production. But I'd say broadly when we look at it, because you think of AI and LLMs today as like being this world changing technology where you can deploy it everywhere.

19:26And inevitably we see when people try to deploy in production, the models are good enough, are almost good enough. They're not quite accurate enough. They hallucinate a little too much. They don't make errors a little too much. A canonical example somebody recently brought up to me was text-to-SQL. I'm sure somebody out there has figured out how to do text-to-SQL reliably, but it's very hard. It's not like you can give an LLM, you know, the column structure of your database and try and do semantic mapping and then get it to create SQL queries for you. It just doesn't quite get it. You see that over and over again where the LLMs have an illusion of intelligence, but it's not like true base intelligence yet in some cases.

20:13And if you juxtapose that with sort of like the intense economy-wide optimism that AI is going to take over everything, I think it does fall short today. You don't see as many production use cases that can operate at high enough reliability as you would expect. Now, that doesn't mean it won't happen. In fact, I think for sure AI will be as revolutionary as we think. But oftentimes in these technological waves, we overshoot early on. And I think we're pretty likely at an overshooting early on time period or situation. If you look at scaling laws on the models, and again, a lot of this does change a little bit with O1.

20:55So you can take it all with a grain of salt. We saw a lot of convergence of providers where the models became close to as good as each other. And for a while, actually, Claude was better than OpenAI. I think you'll see that dynamic happen from time to time where it won't be OpenAI that's necessarily the leader. And you start asking yourself, you really understand these transformers, where will the next, you know, increases in performance come from? And the biggest drivers traditionally have been more data or more compute. If you look at it today, though, the exponential in LLM performance is so far working against us that you need order of magnitude increases in compute, 10x increases in compute, to have relatively sublinear increases in performance.

21:49So you're at decreasing returns to scale to compute. And it also seems to be similar for data as well. And so if you take those as true, which just it just comes out of the scaling laws that that is true today. We may not have with LLMs a true line of sight to them getting good enough to address most complex problems that we're trying to solve. Now, I'm making a lot of leaps here that says the scaling laws will look similar to the way we've benchmarked them in the past. I'm also making a leap saying that the way we measure LLM performance is accurate. It could not be. It could be that like intelligence as a whole, it's really hard to measure.

22:30And so as we do scale to multimodal data to, you know, people are building data centers that are 510x bigger than the ones today, that you could have orders of magnitude increase in knowledge and reasoning. Right. Not just MMLU benchmark. If that happens, then, you know, all bets are off. It's very possible that we get huge capability increases. But as it stands today, I think there is a risk until unless you have research advances, like fundamental research advances in the transformer infrastructure that we could be stuck in the, you know, it works most of the time, not all of the time. And it actually enters into our more Palantir land of the LRM is 80 percent correct.

23:11And so you need a human to oversee it to get to that 100 percent. And so that would mean the future of software looks more like man machine symbiosis. than it does AGI, you know, the computer does everything for you. So that's kind of the way we're thinking. That's what I think about. That's what I call the capabilities gap in that it's not quite good enough to address most of the use cases that we want AI to address today that humans can do with 100 % accuracy. And with current scaling laws, I think it's possible that we don't have near-term line of sight to it either. Are there any opportunities that you see within the gap?

23:48What would you need to see breakthrough in order to reach the next level? I think two things. The opportunities we see are the man-machine symbiosis. I think in many ways, LLMs are good enough to automate 80 % of the work. And that's a lot, actually. And so rather than trying to do moonshot systems where you have an agent that does everything, I think you could have application software that looks a little more like Devon-type systems, where you have the human supervising the agents and correcting them where they're wrong, helping them direct into the right direction if they're on the wrong path of a decision tree, helping correct errors directly.

24:31That's what software could look like. We're so biased towards this AI does everything. It's a totally autonomous system, but the opportunity could just be man-of-machine symbiosis. The AI does some, and the human does a lot too still. And so I think that's likely where we're going to go, at least in the near term. And what could change is you could just have changes in fundamental research.

25:0001, OpenAI, for people who don't know, GPT-401, they launched a new model, does seem different. And I don't know if everybody knows the techniques they're using. And it could be a step function increase, like a research level change. that it's not just like some base transformer model that they're throwing more compute at. Another term or I guess phrase that you used was the trough of AI disillusionment. Are we in the trough? Are we out of the trough? Are we coming up to it? Where are we in that respect? We are certainly not at the trough. So what I'm referring to is there's this idea of the Gartner hype cycle.

Read the full transcript

25:36When there's a new technological trigger, the general view of it is we will have huge gains from that technology. And it leads to what they say is the peak of inflated expectations, that optimism usually overshoots what's likely. And the technology inevitably disappoints because the human mind is extremely optimistic and we can imagine all sorts of possible futures. I think this is likely what we're doing with AI and AI safety and stuff like that, that we could foresee in 10, 20 years AGI where there's actual safety issues. But we are so far from that today. That's, I think, actually getting close to the peak of inflated expectations that people think of that as a front and center concern.

26:25You know, it's like I still talk to a person on the phone for customer support, but we're worried about AI is like hacking the nuclear weapons system. I'm like, let's take it one step at a time here. We're not really close to that. Once we have peak of inflated expectations, it usually leads to what you said is the trough of disillusionment, where actually you oversue it on the downside too, where all those positive expectations that we will have AGI often go to huge negativity that, oh, this whole technology wave is fake. And then after that, we have the slope of enlightenment and the plateau of productivity.

27:03And you saw this in the dot coms, actually, where you had the 2020 dot com run up. It burst and you had huge issues in Silicon Valley in general getting any funding because people who were very excited about Internet actually became very pessimistic about Internet. And then you saw a 20 year bull market where, you know, everything has become digital. You have the Internet on your phone now. You're connected constantly. Right. And so probably the world that exists today is at or maybe above the peak of inflated expectations people had in the 1990s. But it took, you know, 20, 30 years to get there.

27:40And so we're definitely not there with AI. You know, you have NVIDIA's at all time highs. You have OpenAI raised at 150 billion valuation. They're still growing extremely well. But if you look at aggregates in the space, you know, I think Sequoia had a good write-up on this where they took aggregate capex on AI hardware. It's like vastly exceeding revenue generated by AI companies in the actual deployment of AI. And that kind of starts to tell you that we make it close to the trough of disillusionment at some point where all this, you know, infrastructure that was built and the idea is build it and they will come.

28:20You know, the people actually don't come. The revenue doesn't manifest. And that's when people will be max pessimistic. And that's the time usually you go along. It will be fun. Certainly we're on a ride. So this would not be a VC conversation if we didn't talk about the exit environment. We are seeing more and more of what I call very large acqui-hires. And we can see this with the depth, with inflection. Is this a result of a diverse build innovation conundrum or incumbents just too large to build at this pace? Or is this a result of navigating anti-trust environment? You know, it's a great question.

29:00And I don't quite know the exact reason why we're seeing so few IPOs, as an example. I really thought that we would have more IPOs this year because the markets are actually quite good. There's a lot of liquidity. Anything AI adjacent is trading well. Multiples are not at a low. Even for software companies, they're at an average. And we just see, I think it's the lowest IPO count in over 10, 20 years. I think it's single digits, the number of companies that have IPO'd, especially in software. And it's the same in biotech, too. And I don't quite know why. There's maybe good capital availability for these businesses in the private markets.

29:46I know I'm on the board of a company that could go public. And the growth investors are saying, stay private, take my money. That's one driver it could be. On the M &A side, I do think it has a lot to do with regulation and Lina Khan, you know, threatening to block most mergers and acquisitions. And that's why you see these acqui-hire inflection character AI adept type acquisitions where it seems like they're doing a license agreement and then using that to dividend back to the investors. I'm only guessing at what they're doing because I don't have any inside information there. And then acqui-hiring the teams as a way around an outright purchase.

30:27So that does suggest that companies are afraid to do M &A. But it is actually, I also think that there's probably some part of the argument where companies are also at peak uncertainty, where you have this new trend. You're pretty sure foundation models will be important. And Microsoft doesn't care if the margins and foundation models are 80 % or 30%. They can't lose that layer, right? Even if it's a commodity, it needs to be a commodity on Azure. Same thing with Amazon. It needs to be a commodity on AWS. So those are like bread and butter acquisitions make a lot of sense. But if you start thinking about application software, is that going to get disrupted?

31:11Maybe you slow down your acquisitions there. So I think there's some probably cooling that's happening as a result of the new wave that companies don't want to make a bad acquisition. They don't know where value is going to accrue. And if you're going to make an acquisition, you care more that it's successful than that it is cheap. So you'd rather pay 2x three years from now and be sure that it's going to work than 1x now that you could lose all the money. That's my guess. What have you seen in terms of secondaries? You see more secondaries. I do think there's an opportunity out there for a sovereign wealth fund type entity to come and just do either fund-wide secondaries or fund position-wide secondaries.

31:50A lot of the secondary funds out there can't do very big size. But if you have funds that are 10 years old, like venture funds that have 10-year-old funds, there's no place really to exit a large position to get your investors liquidity. So there's some market for selling fund positions, but that's quite nascent, not that big. I think there's a big opportunity for someone to come in and say, hey, I'll take this position. I know it's good. I'll buy half a billion dollars worth of it at some slight discount. That's a gap that doesn't exist in the market today. but I think someone will do that. Hey, we'll continue our interview in a moment after a word from our sponsors.

32:29Shifting to the defense wave, you've mentioned that this is one of the most exciting and profitable areas of venture investment and now competing with primes on historic contracts. So I'd love for you to break this down. Why is this so exciting? Why is this most profitable and maybe overlooked? Yeah, we tend to think that the defense wave will be very important. And we're seeing, as we started the conversation, we said that there's a lot of change happening now, particularly due to AI, right? And driving a lot of change in industries and questions of where value capture is going to be. This is happening in defense, too.

33:08And if you look at, we track, obviously, the war in Ukraine quite closely. If you look at the war in Ukraine, warfare has completely changed. I mean, it's not even World War II type blitzkrieg tactics. You're back to like small group infantry assaults, extremely high battlefield awareness with drones everywhere, ability to have highly precision strikes, literal drones that can fly and track cars or people and explode into them. That's just completely changing the way warfare works. And that usually means that there's going to be a change in the way defense procurement works. We find generally the defense primes are quite good at some things.

33:51It's not that everything is bad. Defense primes are good at aeronautics. They're good at explosives. I always say they're good at boom. Don't compete on boom. What they're bad at generally is software, is AI, is autonomy. And that's usually where we see a lot of the gaps. So I think those technological changes and the changes in warfare are driving new openings or in capabilities that we need to procure as a country to defend ourselves in the next generation of warfare that I think uniquely startups today can address because that talent, that expertise doesn't necessarily live in the primes. And the country is now recognizing this too, and so are many of the leaders.

34:30So that's what's changing. If I can make just a side comment on how amazed I am that defense has become a hot space. There's people raising funds around it now. There's online communities celebrating startup defense. There's a whole area in LA, the Gundo, which is about defense type companies. I think back to when we invested in Endural. We were the first outside investor with Founders Fund in the Series A. And we literally had conversations internal at AVC with our partners where we were worried about our offices getting protested. And we gave a small chance that actually maybe our reputation is completely ruined because at that time everyone was against defense.

35:18Google famously pulled away from working, I think it was with the Army. You know, we're like, maybe nobody is going to want to work with us again because this is an unpopular thing to invest in. But we don't care. Like, we're going to do what we think is right. We think this is an important company. We think it's important to the future of our country. And so we're going to do it. And we did it. That was a conversation then. And now it's like the crown jewel of the portfolio where everybody loves it. Everybody thinks it's cool. And everybody wants to start a defense startup today. And I think that change in sort of the zeitgeist is so funny to me.

35:55And, I mean, it's a positive transition. We had the courage to stand up early and do it, and so did Founders Fund. A lot of these other funds did not. But I think it's a general positive, but it is very funny and a little peculiar. So that's good. But those are generally why I think it's important that tech has changed and there are new openings today. And we can go through it. There have also been changes on the government side that have made it easier, I'd say, in general to sell into the government, to be successful, to get early market signals. That's helped. But I'd say like the number one thing that has helped has been these companies that have been successful.

36:35You had SpaceX, which was really the vanguard early on. You had Palantir, which is the vanguard early on. And now you have Endural. These are all three companies that have gone into the space people thought was impossible and have succeeded. And so in somewhat mimetic fashion, it's shown people the way. And so now there's a well-worn path to do it. And the government has created more opportunity to make it easier to get in. But that's not like sufficient. It's like necessary, but it's not sufficient for success. Yeah, it's honestly quite interesting because I remember maybe like even like six years ago, like investors I invested in to defense or anything like this were kind of seen as evil.

37:20But now because of and like that's kind of a weird thing to be evil for, considering we have military and large government spending on all of the defense in the US. But I just think it's been hugely eye-opening now because we have the performance and all of the great outcomes from Palantir, Anderol, and SpaceX, like you mentioned, that have helped with the communications around that and the glow and the aura. And now it's just like, yeah, that's a reasonable place to invest. And you can invest into that because it's a big part of our economy, too. So I guess to go into that a little bit further on more specifically 8BC's investments in the space, in your piece, you've mentioned a couple of them.

38:03We just talked about Anduril. You've also invested into Saronic, Epirus, Chaos Industries, and most of which were founded actually within 8BC. So I'm just curious on that standpoint, given your early inclination to go into defense, how did you then double down and start incubating these companies? Yeah, it's a great question. And people always ask me, given that it is now a hype area, more people want to start companies in defense. So they'll always ask, what is the right way to do it? And it's not really, you know, there's questions of analogies. There's many ways to start companies. There's many strategies that work.

38:42They're not all the same. Software has gone down the ethos of like the lean startup. A bunch of really smart people get together, bootstrap or raise a small amount of money, create an MVP, go to market, start selling it, get market signals, have good unit economics, keep raising over and over again. That's one way to do it. There's also the biotech way of doing things where it's usually you start with IP or you start with a team and a thesis. The team is usually very credentialed. It's not two people out of school. They've done it before. And they have to raise large amounts of capital and large amounts of dilution because it might be 10 years before revenue.

39:23And the first market signals come at clinical trials. So it's not a revenue signal. It's an efficacy signal. You have to fund to that. And sometimes it takes$100 million to get there. And so it's very hard to do that as two people in your garage. It just is. And I think defense is a little more like the latter than it is the former. It takes, even still, there's great programs now in the Defense Department on getting companies revenue early. But even with that, it takes a very long time to get anything you create deployed in the field. Many procurement cycles, those can take years to happen. And oftentimes, they can scale up immediately when there's an immediate need.

40:04It's not necessarily prospectively. And so, generally, you need to know what to build, right? It's unlikely you're graduating Stanford and you know what the Army needs. Usually you are involved in the Army or you are involved in warfare. You're very in the know on the future of warfare. You have some view of what threats will look like in the future. So you know what's needed. You need to be able to build it. Oftentimes these are very technical, hard tech or, you know, AI autonomy startups, which are notoriously hard to build to be reliable enough. And then you need to know how to engage with government.

40:45And that's still a hard thing to do. Like we literally have people who work for us who help our companies engage with the government. And so we thought to ourselves, this is an area where we have unique expertise. We can have shared resources in engaging with government to understand what they need and help our companies get access to, you know, compete for contracts early on. And it requires huge amounts of capital up front where you need to really have conviction before to know this will work before you have any market signals at all. So we think that sets itself up really well for a build program, actually, where we can have these shared resources that all our companies use.

41:27We can partner with entrepreneurs who we do partner with, but we act like a co-founder. We're part of a builder because we bring a lot to the table and we support these companies with a lot of capital because it takes a lot of capital to get in. Right. And are there any other resources that you provide them, help with government contracts and that kind of thing? Yeah, exactly. It's a lot on the lobbying government contract side, engaging with government, understanding where the needs are, understanding what other primes or contractors are doing. We have like very good intelligence in general on like what the needs are in government.

42:03And I think this is a positive for the company because we're directly connecting innovators with the, you know, not necessarily policymakers, but people in the services via the correct channels, obviously, that can tell us where they think the puck is going and we can build for that. So you cited relevant changes and reforms to defense procurement for startups. What are each of these and what are their significance? I think there's a lot of things that have changed that have made it better. So I really want to highlight how important like SpaceX was in particular and getting these early contracts.

42:43And what they did initially actually was they got these firm fixed price contracts, FFPs, where the way defense used to mostly work, and I'll make this abridged if you're interested. You should just read my update. I go into more detail there. But you used to do cost plus contracting where the government would say, we will pay you X amount to do the work, to do the research, to build the hardware, plus a margin on top. And in that way, all the risk actually of development goes on the government, not on the company itself. And so that's why you have programs that go massively over budget and you have, you know, a lot of, they're not scandals, but you look at it, you're like, okay, there's a lot of waste going on there.

43:28Because the contractor in that case, they don't have any skin in the game. Actually, the more they spend to, you know, succeed in that contract, the more margin they make. You see this in healthcare too. The more the spend is, the more dollars in profit they make. So the incentives there are all for high cost. SpaceX at that time got firm fixed price contract. They said we will, where firm fixed price contract is like the way everybody else in the economy generally buys things, where it's, I will buy this at X price. It's firm fixed price. And there you push the risk of development to the company itself.

44:09And so the company has to have confidence that they can do what they say they do at the right price without overruns. And the defense primes generally don't like these contracts. and startups generally do like these contracts. Enduro, for example, would create and take the risk on products themselves. And when they sell to the government, the product is already done. They've taken the risk themselves on developing it and thinking the government wants such a product, and they'll sell it at a certain price, whereas the prime generally won't do that. So that's one. And then two is there's been an increase in this type of contracting called OTA, other transaction authority contracting rather than FAR contracting.

44:48FAR contracting is like a very bureaucratic way of contracting where you generally can't do one bidder or anything like that. The contracts are 900 pages long. The procurement cycles are incredibly long. They're actually generally biased towards the primes winning. And there's more use now of this new authority called other contracting transaction authority, OTA, where the branch in question can actually have a lot more autonomy on the decision gets made. how the decision gets made. The contracts are one page, like we'll buy X of this for Y price. It's a lot faster. It's much more the way I see the rest of the economy does business, where there is some procurement still, but there's a lot more flexibility to say like, we ran tests, this is the best provider, we're going to buy quickly, it's going to get delivered.

45:37Because time is the enemy of every startup. Time for defense crimes doesn't matter, but time Wasted is the enemy of every startup. And so that's just led to faster contracting with governments, with the DOD in particular, which means startups can grow faster, which means they get more funding. And there have been other programs like, I didn't talk about DIU, which is the Defense Innovation Unit that works with areas of government, of the branches of the military, tries to find areas of need, and then engages with investors and companies to try and fund at the earliest possible prototype stages solutions to those needs.

46:15And so when I talk about we engage a lot with government, it's like a lot is through DIU. They give real size contracts. You can get 10, 20,$30 million contracts from the DIU, very early stages to create prototypes to compete for some program, which is helpful funding. And then there's more programs, I won't mention all the acronyms because it's all acronyms, that come after that, where they'll try and match funding you got from the private sector in one way or another to help you build manufacturing capacity before you've gone through full procurement at the congressional level. Because you want to buy something, oftentimes it has to get into a budget.

46:52And if anybody's familiar with the way the U.S. government works, there's a lot of continuing resolutions. They don't want to deal with it until the very end. So it can take years to actually get to procurement, even when somebody wants to buy something. And so they have now funding that can help get you through what that used to be called the valley of death, but they help you fund the company, fund manufacturing until those big purchase orders come in. And so there's been a great infrastructure created to get ideas to the private sector in a more democratic way, where now everybody can talk to EIU and understand what the needs are.

47:26Get funding for prototyping to compete, right? There's no cronyism here. It's like the government will have a wreck. They'll have a bunch of companies compete. The best product is the one that gets bought. And then there is funding if that happens to be a startup. And that funding does exist in partnership, usually with the private sector, to bridge that value of death to when the real orders come in. And so that has really changed, I think, all of those things put together has changed the conversation where it's now an appealing investment. So this has really changed the playing field for defense startups.

48:02How would you advise startups to take advantage of this? I think it's still very important to realize, well, first you have a great team that can build complex hardware, software, or technology in need. And so you should really understand why what you're building is different, why Raytheon can't just do it, because Raytheon is good at a lot of things. We shouldn't be too pessimistic in general on the defense complex because American military equipment is better than any else, right? And so you should be technically excellent and know that you can out-execute these great firms. Two, you should have a great partner or great people on the team that can engage with government.

48:42I think the one thing we learned early on at Palantir was you needed to be engaging with government much earlier than we thought. Eventually, the best product will usually win, but not always. And eventually it can be very long. And so it's really important to have either lobbyists or government relations people that can have your voice heard, give you a seat at the table when these decisions are made, because it's not just like starting a normal startup, I would say. I won't go much more into depth of that. But there's a lot that goes into the early phases to reduce your time to market if you know what you're doing.

49:22and three is you better be very good at fundraising because you're going to be raising money with very little commercial progress or traction and that is a hard thing to do and so it's not an area I think most people should go into to be to be frank most of these companies I mentioned that we invested in are started by people who were actually at one of these successful companies Endural, Palantir, or SpaceX before, or who were in military service themselves, and they know sort of the landscape. It's very rare that someone's a complete outsider and comes in and succeeds here. And I'd also say that if there's one thing we're pessimistic on is that I think we probably think it's a little overhyped in general, where we think it's real, for sure, but it's not going to be an opportunity the size of software or AI or something like that.

50:14The defense budget is naturally limited as a percentage of the economy. And so we think there'll be many new primes created, but it's going to be on the order of tens, not hundreds or thousands. And so in terms of opportunity, unless you have some real competitive advantages, like, and you can articulate, this is why I'm the person to do this for this gap that exists in the defense space, it's probably not the right opportunity. Super insightful. So as we wrap up, one last question. But as you reflect on this moment in time versus even our conversation in Q1, there has been a lot of change and a lot of exciting moments, a lot of surprising moments.

50:54uh the devin devin launched ilia left open ai there's very large aqua hires there's shutdowns what has most surprised you over the course of h1 2024 and maybe even was there anything you got wrong my views on ai keep changing so um because everything's so turbulent you know i initially thought that application layer companies wouldn't do well i think incumbents actually well i initially thought incumbents would capture all the value. And then when I saw Devin, actually, my mind changed where I thought there is room. Like if you take the thought experiment of saying, now we have this super intelligent layer that we can enter into any software system that exists in the world.

51:41If that existed 20 years ago, would software look different than it does today? Like the answer is probably yes. So it's very possible then that we will create new application layer software companies today that could disrupt the incumbents. And I think Devin really opened my eyes to that, where I said, okay, this could be actually just what software looks like going forward. This sort of like supervisor worker type paradigm, where it's not workflow software, it's supervisor software, where you supervise the agents. So that was probably the biggest mindset shift that I've had. I still don't think it's going to be completely monolithic where all application software would die because I spent the first 10 minutes of this call saying I was bullish on application software.

52:27I think in most cases incumbents will stay, but there will be areas like software development, maybe law, things like that, where you can have a new workflow system or system of record. But perhaps the most surprising, and I keep tweeting about this and it's so perplexing to me, is I tend to really admire open AI AI as a company because they do the best work. They were the vanguard in the space. Sam took a bet very early that we would hit that inflection point in AI, and it was not obvious when he did it. And it took courage and it took a lot of foresight. I always think that should be rewarded when people make contrarian predictions and they're right, it should be rewarded.

53:09So I admire that company a lot, but it's very surprising to me how many people leave and some of their best people leave. It makes it a little worrying. And every time I see it, you know, we've three new leaders left last week and that goes to a litany of others who left beforehand. That's really surprising and shocking to me. And it makes me question, like, will they be the leaders going forward? I think, I don't know, like your guess is as good as mine, but I don't think we've ever seen pre-IPO so many leaders leave the best like crown jewel company in Silicon Valley. It's common like you IPO, everyone gets rich and they leave.

53:51Okay, that's normal. But before IPO, right in its like heyday, you don't see the best people leaving the best company. So it's just highly irregular. That's a really good point. It's a very good point. Well, Alex, back by popular demand. Thank you so much. This was so fun. And again, appreciate your insights and all your quarterly reports. Cool. Thanks so much for taking the time. Turpentine VC is a podcast from Turpentine, the network behind Moment of Zen and Econ 102. If you liked the episode, please leave a review in the Apple Store or rate us on Spotify.

From the publisher

This week on Turpentine VC, we’re releasing Sourcery’s Molly O'Shea’s interview with Alex Kolicich, founding partner at 8VC to discuss Alex's H1 2024 report on the venture landscape, covering AI investment challenges, the resurgence of defense technology, and broader economic trends impacting markets. For full show notes, visit: https://highlightai.com/share/3f9d98f9-c3d6-42c9-b2cc-e6ba32c668e4 


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LINKS:


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Alex’s first appearance on Sourcery: https://www.sourcery.vc/p/exclusive-alex-kolicich-8vc-exodus 

Alex’s latest H1 2024 report: https://www.sourcery.vc/p/alex-kolicich-8vc-ai-and-defense 


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TIMESTAMPS:


(00:00) Intro

(00:59) Meet Alex Kolicich

(01:50) Diving into the H1 2024 venture landscape report

(02:03) The AI investment paradox

(03:31) Challenges and opportunities in AI

(07:25) Public vs. private market dynamics

(08:24) The role of AI in software and market valuations

(12:32) Fundraising strategies in the AI era

(13:01) Sponsors: Carta | Oracle

(19:12) AI capabilities and the future of technology

(25:58) The Gartner hype cycle and AI's future

(28:08) AI market trends and valuations

(29:00) The exit environment and acquihires

(29:28) Challenges in IPOs and M&A

(32:03) Opportunities in secondaries

(32:52) Sponsor: Squad

(33:58) Defense wave: A new frontier

(43:52) Changes in defense procurement

(49:31) Advice for defense startups

(52:09) Reflections on AI and market surprises

(55:32) Wrap

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E67: Defense Tech’s Rise with 8VC’s Alex Kolicich"Turpentine VC" | Venture Capital and Investing · 54 min
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