Is Domain Expertise the New Moat in Vertical AI? What the Data Actually Shows

In the, like, two things can be true at the same time category, just because OpenAI and Propet and Databricks and SpaceX are these far outliers and that the ceiling is higher, it doesn't mean that the average outcome is higher. And I think sometimes I hear people conflate these things. Yes, the top 1% of venture outcomes are bigger. And if you look at that vent cap chart, they get bigger every cycle. That doesn't mean that the average company also is going to have a proportionate increase in scale.

It's the week of April 13th. Welcome to The Learning Corner, a Precursor Ventures podcast. Welcome to The Learning Corner by Precursor. I'm Mia Farnam. I'm joined by RGP and managing partner Charles Hudson, where we will be talking through the articles and Goodreads that caught our team's attention over the past week. Our first piece comes from the newsletter The Verticalist from Euclid Ventures titled No Country for Old Founders. and it asks a question that is super timely in the current AI era. Does domain expertise still matter for founders, or has the rise of AI tools leveled the playing field enough that youth and technical chops are all you need? The piece pushes back on the trend YC has leaned into, dropping its median founder age from 30 years old in 2022 to 24 years old by the end of 2024, with the majority of founders now 25 years old or younger. Euclid team ran a comprehensive analysis of 673 vertical software and AI companies that raised $15 million or more in 2025. And the findings showed that two-thirds of leading vertical startups had at least one founder with prior domain experience. And that number jumps to 71%, specifically in vertical AI. Founders with domain backgrounds raise deals more than twice the size of those without. And what makes this particularly interesting is a counterintuitive conclusion of it all. As the AI stack becomes more accessible and more legible to everyone, domain judgment is not becoming less important. It's becoming more so because the product in vertical AI is the work itself, and that requires knowing what to build for whom and why they will trust you to build it. The data in the piece shows that the more regulated and credentialed the vertical is— legal, public sector, healthcare— the more domain experience dominates. Even with this data, deals are getting done at breakneck speed with founders who have little to no domain experience. Charles, what do you think investors are actually betting on and want your thoughts on this altogether?

I think there were a lot of really interesting elements to this article, one of which is I forgot how, quote unquote, old the average YC founder was back in the day. I had the mental model that they were younger, but if I think back to those batches, the data they cited is correct. I think the question is, does AI change this dynamic? We were talking about this in our team meeting. Whenever I look at our— the companies that are truly elite in our portfolio in terms of performance, it's a very striking bifurcation. Half of them are built by people who have deep domain expertise in the domain where they operate, and the other half do not. And I read the article and I was like, this is one of those kind of cheesy yes and VC posts. He's got a quote in there from Keith about, hey, like most of the people who've built these really amazing companies have no domain expertise. And I get it because sometimes domain expertise can help you see all the limitations and the instincts it gives you are, well, this is why this thing won't work, or these are the problems we'd have to solve. And if someone who doesn't have those constraints might just say, well, rather than try to solve that problem, how can I just avoid it altogether? There are other cases in our portfolio where that deep domain expertise gives you insight on the nature of a problem, but it It also gives you relationships. And I think the thing that when I read the post, I was like, the other superpower that domain experts have, it's not just domain knowledge, it's relationships. It's the ability to get in front of a customer as a design partner and be credible and to get them to tell you more about the problem they're facing than they would tell a stranger off the street. So I do think right now venture is maybe over-romanticizing naivete, whether that's like youth, whether that's inexperience in a domain. And I think a healthy counterweight is there will be great businesses built by people who are older and have domain expertise and know what they're doing. It's funny, I think of it as like a 2x2. Is it a founder who's like more experienced in life? And is it a founder who's experienced in the domain? And I think right now we have companies in all 4 quadrants. And I'll say being an inexperienced founder who's young with very little domain expertise is really hard. You have a lot to learn. I think it's a really interesting framing. I also, anybody who talks about consumer surplus and deflationary software economics will always have my attention, but I thought it was a really good piece.

There's some concern on younger founders being able to penetrate some of these more insurmountable sectors that have really dated ways of working, that having maybe a bit of gray hair is needed in order to gain credibility. You mentioned relationship experience. I think that's related. There's also this belief, however, that if the product works and potential customers need it, then they shouldn't necessarily care who is selling it to them. Like, how do we think about that? And I think that's the other tale of this.

When I read the article, my immediate thought was like, if you'd written this article 5 years ago, what would have been different? And I was like, oh, AI, duh. The duh part is, I think previously if you were going to buy a system of record or a really expensive piece of technology, you probably actually wanted someone you thought was going to be around, someone who'd lived your problem for a long time. It was kind of harder to break through. And in some ways, the rate of AI change has inverted that paradigm. And now a lot of people are like, no, no, I want to buy from you. Upstart. I want to buy from OpenAI or Anthropic. I don't want to buy from some stodgy company we've been doing business with for 10 years that's going to like try to retrofit an AI solution. I want to buy from the up-and-comer. And I think that is creating space. We have a portfolio company, Interface. It's two young founders that are working in sort of oil and gas safety, not exactly an area where startups have thrived in the past. And they're getting a lot of entrée and attention. And I think it's because that buyer wants to buy from startup with people who feel AI native. And so in some ways it's creating new opportunities where maybe those would've been liabilities before.

That's a really cool point because I think we always talk about it from the perspective of larger foundational LLM companies eating up all the competition and kind of edging everyone out, but it might just prime everyone to be open to new things that they weren't ready to before because it just didn't seem as powerful or important. The last question here on this was the piece does make a really interesting distinction between domain experience and then just being an industry operator. And the highest performing cohort that they called out by capital velocity was actually in the ex bankers, ex-consultant background founders, not the ones who actually came from the industry itself. Does that match any pattern that you've seen in our portfolio or founders that you're drawn to?

We have one person in our portfolio who fits that pattern, and she's fantastic. But again, that's kind of an anti-pattern compared to the past. And so, I don't know, every time I read articles like this, I'm always like, it's really great that there are multiple ways to win because this business would be really boring if there were only one archetype that could succeed. I do think though My hunch is that the consultants and bankers are maybe winning because a lot of this sell the work is basically AI-powered business transformation at microscale. And I think those— and you know, you've, you've had that training. It does teach you how to break down a problem into processes and steps. And people whose minds work that way are pretty well primed to succeed in the era of AI.

Even like more broadly, just to me, like founder skills or selling skills, consulting and banking, if they teach you anything at all, it's essentially to like be client-facing and really push things as hard as possible in a way that you know it will land on the other side. Of the table, so that piece isn't lost. Our next piece is a LinkedIn post from Micah Rosenblum from Founder Collective, and it's another really great breakdown of how seed round inflation has fundamentally changed the return math for early stage investors. Micah in the post lays it out really plainly where a $500K check in an $8 million post-money round in 2012 bought you around 6.25% of the company, and a $500 million exit on that check returned over 30x. After dilution, that same $500K check today buys you roughly 2% at a $25 million post.

Yeah.

And the same $500 million exit now returns closer to 10x. The check size didn't change, the exit didn't change, but the return got cut by more than two-thirds. He's really careful to acknowledge that today's seed company is more mature product than 2012's, closer to what a Series A maybe looked like a decade ago. But his real question is whether the math still pencils out the same way and whether the biggest alpha is still hiding in the deals that are hardest to fund. I was thinking about this and I I think two things can be true at the same time. Valuations have gotten out of control and out of hand, and we can continue to do them because you believe that they are the strongest companies and they have the strongest outcomes. I know some of our best deals came at really modestly priced valuations, but how do you build the discipline and eye to know which ones are worth paying up for and which ones are worth staying uninvolved?

We talked about this in our LPAC meeting earlier this week when I got together with our LPs and I think there's a bunch of things. Micah's been on a roll lately with some good posts. He really has. One thing I think everyone says, well, seed is the new Series A. I'm like, except we're not buying 20%. If seed really is the new Series A, we're applying a seed ownership strategy to Series A prices. And I think in most things in venture, when you have a price strategy mismatch, you should always be concerned that maybe you've like unintentionally or intentionally broken a rule. I do think that in some ways these more mature companies and they're more certain. Historically, that's been a recipe for buying more. And maybe one answer is seed investors are actually not buying enough of these companies to make the math work. So maybe the right answer is you should still buy 10% at seed. It's just going to cost you $2.5 million instead of $500,000. If you're going to do that, you're going to need a much bigger fund. The other thing in the like two things can be true at the same time category is just because OpenAI and Databricks and SpaceX are these far outliers and that the ceiling is higher. That doesn't mean that the average outcome is higher. And I think sometimes I hear people conflate these things. They're like, well, like outcomes are bigger. Yes, the top 1% of venture outcomes are bigger. And if you look at that vent cap chart, they get bigger every cycle. That doesn't mean that the average company also is going to have a proportionate increase in scale. So I think the nightmare case is sort of what Micah outlines, which is that valuations grow faster than exits on average. And so the tricky thing is how do you know when to pay up? And I talked to one of our LPs who was a GP at a Series A fund. And he told me some interesting data. He's like, you know, there's like a weak correlation between entry price and outcome at seed. But he said at Series A, many of the best companies became expensive and remained expensive. The TL;DR after trying to parse it with him for a while was there are good deals at seed. There is some mispricing of assets at seed, and you will find good companies that are cheap. And his argument was by the time they get to the A, there are so many I's and there's so much money that there aren't that many Series A bargains out there in the market, at least right now.

So it gets corrected in some sort of fashion, which kind of makes sense.

You would think with more data and time, the market would have a better sense of depth.

One thing I wanted to peel back is when Micah was mentioning companies today are a little bit more mature in product than they, than they looked a decade ago. My statement to that is like, of course they are. Now we have more tools available for people to build much quicker and bring something to life with fewer hands around the table. So my question is like, should we be rewarding people for that? You get brownie points for that, or should we be collectively not moving the starting line so far ahead and rewarding that when that only almost as table stakes now. It's a great—

and there's two other things. Also, these companies have more time and more money. So you would think if you gave a company better tools, more time, and more money— and this is like one of my long-held beliefs— is that venture is always about relative performance, not about absolute performance. And I think founders are always thinking like, oh, well, all of these companies that raised, they were at a million ARR, so that must be some absolute bar. I'm like, no, in the era that those companies raised, that level of financial performance was considered top tier. And I think that's the game with investors to figure out what does elite performance look like in terms of either metrics or team composition and invest in the teams that you think are elite. And I think what elite looks like changes every 6 months, maybe in this, in this era, maybe it changes every 3 months in terms of growth rate, in terms of total level of revenue attainment, nothing is static. And so I'm always asking myself, If we're going to pay 50 posts for a company, it better be amazing. And the only rule I can use is I got to believe we're going to see 200x price appreciation from this point. And sometimes I'm like, wow, this is a great team and a big market. I just closed something at 40 posts because I think there's a reasonable chance it becomes a $10 billion company.

Two things can be true. I can believe that maybe it's a little bit over what the price that I would feel comfortable with 10 years ago. I can still believe that it's a good deal to come into relative to the outcome that I think it's headed to.

I remember LP Solari, he's like, there was really no functional difference losing money and the company at 5 50, you're still going to lose your money.

Well, our last piece is from Kyla Scanlon from the Substack The Argument titled A Generation of Cheaters, making the argument that AI has dropped the cost of cheating to essentially zero. And when the cost of a behavior hits zero, it stops being a deviation from the norm and becomes the norm. Over half of American teenagers have already used AI for schoolwork, and the police draws a straight line from that reality to a much bigger legitimacy crisis. The social contract that once made honesty worth the effort— work hard, get a degree, get a good job. Is fracturing. Students, and honestly a lot of parents, are doing the math and concluding that the rules of a game they no longer believe in are not worth playing by. And Tyler closes with the real question underneath all of this, which is not how do we catch the cheaters, but whether we can rebuild something worth being honest for. Charles, you're at a Starbucks right now on Stanford's campus. You're also not just a parent, but you're a parent living in SF proper. So I'm curious to know how your perspective has maybe evolved, if at all, about this concept of what is considered cheating What's a good use of your time as a young person attempting to be successful and strive towards a great career while the ground constantly moves beneath you?

It's so hard because I think if I think about what cheating looked like when I was growing up, it was buying a little yellow and black book that summarized the novel you were supposed to read for a class, or it was a calculator that could do graphing and algebra for you. You still kind of had to know how to manipulate these things and you still had to perform. And I think what's so seductive about the LLMs is you don't have to do any of that. The sort of the returns to cheating are so high. And on the On the one hand, we talk about it as cheating, but we're giving people this very mixed message. We tell them, Mia, you must become very good at these tools if you ever want to have any hope of getting a job, and you absolutely must not use them in education. Which begs the question, well then, when are you supposed to learn how to use these tools? And I remember when the internet first came, I was like, oh my God, people are just going to look up answers and they're going to copy those answers. And I was like, yeah, that's— we figured out how to deal with that. And I just worry that with AI, the returns are so high and the skill level required is so much lower. And there's one other thing that I, I've become particularly attuned to. I teach at the, at the business school and I have a lot of friends who teach. In many cases, different classes have different standards for acceptable use. I don't envy the students who are like, well, in this class, I'm not allowed to use AI at all. It's considered cheating. In this class, I'm allowed to use it, but I have to disclose how I use it. And in this class, it's required. So how do you cognitively keep different standards for different use cases and different classes in your own head? That seems like an impossible challenge. Also, I think the temptation of having a chatbot where you can type something in, it can give you an answer so fast that you could get cold-called in class. And maybe if you're good at typing under the table, could maybe even get an answer while you stall or vamp for time. And this is just the new paradigm. And it feels like cheating today right now because it's a shortcut. I don't know if we'll still think of this as cheating in 12 months.

And I do go back to even my own experience. I think of my AP US History class. This in high school, and my teacher was one of the best ones in our school, and he did this exercise called the gauntlet. It was just 4 desks in the center of the room, and you had all week to prepare for basically a group debate, and then he would call on whoever, and you had to do it in front of everyone in the center of the room. You could bring your notes if you would like, but obviously no one's bringing laptops. You can't really fake that. I actually think like that would have been a really good use of melding together foundational LLMs to prep my work and prep my thought process. I don't have that laptop in front of me when somebody across the table from me brings together an amazing point and I have to rebut that in the moment, that's a test of my critical thinking and my delivery, which are skills. Like, I think Yoni from Slow released that whole piece that was like, hot people are always going to be in the market. Hot can mean a bunch of different things. Hot meaning you're able to corral a lot of attention and excitement and you can orient things in a really digestible way. And I remember like that AP US history teacher, he would run multiple gauntlets over the course of one day and people would try to cheat the system by asking like, my gauntlet's after yours, what were the most insightful things that your gauntlet brought up? I can repeat them, but like, he knew people were talking and cheating and they just don't land better the second time. So that piece I think can still live on. One thing did feel like was relevant for where we sit today. How do we still instill a sense of grit and critical thinking and how to make sure that founders can also screen for these things when they're hiring, because they're gonna continue to hire younger talent and how can you actually test if foundational work ethic gets set into them before they join your team if the rules are being shifted?

In some ways, I think younger people have it easier because they're going to have to navigate all of this stuff, having it present from the beginning and figure out like, what does it mean? Versus I think people who are maybe older in life or further along in their career are going to have to figure out how do I retrofit the existence of AI to my existing paradigms of what fairness is. Mostly the adults that are doing the embarrassing things. Like I look at these lawyers who go to court and cite court cases that never happened. Academics citing papers that were never written. People citing arguments that were never made. I do think at some point it's going to become professionally embarrassing to simply throw something into an LLM and just ship whatever comes out without any review. I kind of feel like we're already there. The question is like, where will the bar be for laziness? When I read the post, I also thought there's a difference between sort of like cheating and laziness. Yeah. And I do think to some extent, part of the appeal of LLMs is the ability to be lazy, is the ability to just say, I don't want to think about this. I don't want to read this article. You read it and tell me about. We just have what I need to know. I don't want to do this problem. You solve it. You think about it. And I don't, I don't think that's the right way to utilize these tools.

Well, we are all out of time. Thank you, Charles, for jumping on a train early to make the recording session from Palo Alto. Thank you to our listeners and subscribers for jumping in. Please like and subscribe and share with friends, and we will talk with you all next week. Thank you. Bye.