No Country for Old Founders
Why domain expertise is becoming more important in Vertical AI, not less
Scene: YC Demo Day, Spring 2027.
Lights dim for startup presentation #612.
A founder in a quadruple-oversized parachute pants saunters to the stage. She hits the clicker, and on the main screen, “Healthcare is dead” appears in all caps. “I was the reigning world Yu-Gi-Oh card champion from ages 8-14. Now, I’m dropping out of college to build the AI-native operating system for Coordination of Benefits in health insurance, a $4B problem.” 3 minutes later, the company raises $15M at a $60M post.
What’s wrong with this picture? We would argue, just one thing.
YC’s Great Inversion
Y-Combinator’s founding ethos was built around a specific kind of founder. Paul Graham launched the program in 2005 to back “younger, more technically oriented founders.”1 In a 2018 TechCrunch interview, Graham placed the ideal YC founder age in the late twenties, and the average founder age held steady around 29 from 2015 through 2022. Graham explicitly warned against funding overly young founders, calling it “premature optimization”: he argued they should accumulate real-world context before committing to build against it. “In college,” the thinking went, “you should be figuring out what the options are, not picking one option and running with it.”2
In recent years, YC has bucked its own traditional wisdom. By the end of 2024, the median age of cohort members had dropped to 24, down from 30 in 2022. As of last summer, acceptances of 18-22 year olds were up 110% year-over-year. 2025 was the first year in YC’s history that the majority of founders were 25 or younger.
While YC CEO Garry Tan positions it as “refocusing YC on its original DNA”, it’s in reality a bit of a departure — one perhaps best explained by his second rationale: the AI revolution itself. As Jared Heyman of Rebel Fund posited: “Since generative AI is such a new technology, younger founders are at no disadvantage understanding and building around it.” One could take this a step further and imagine that younger founders might be less hampered by traditional conceptions of software success, unbound to approach problems in fresh ways. It seems YC is betting so, with an average of ~1.5 years of experience in its most recent YC cohorts.
A study conducted in late 2024 by Data-Driven VC found that the average unicorn founder started their first unicorn at age 35. The median age was 33. Younger founders are overrepresented in unicorns relative to the general startup population (with < 30-year-olds punching above their weight ~2-to-1), and unicorn founders overall averaged 8 years of work experience.
The picture gets more nuanced when you look at what else changed alongside age. The Rebel data shows these younger founders are more technically credentialed than their predecessors. The “technical” cohort has dominated since 2023 — the year following ChatGPT’s launch. While YC always leaned towards engineers, the historical average share was a relatively steady ~61%. So the YC founder profile is markedly different in the modern AI era: younger, with dramatically less work experience, and more technical.
It’s easy to see the rationale for younger, more technical founders in this era of application-layer AI. The argument is twofold:
Technical: When a technology platform shift is first underway, the stack is highly dynamic and volatile. Therefore, those with the technical chops to navigate it will have an advantage. A 40-year-old coder with a decade-plus from FAANG may outstrip everyone classically, but the 23-year-old who’s been building with LLMs since GPT-3.5 has intuition for what these models can and can’t do.
AI-Native: In the dynamic environment above, founders more “native” to the stack may be free of constraints in thinking from older paradigms. The modern version of an old quote attributed to Henry Ford: “If ever I wanted to kill opposition by unfair means, I would endow the opposition with experts.”
Let’s examine each of these arguments to see if YC’s implied bets make sense.
The Technical Argument
Over the last 18 months, the founder-facing AI stack has become drastically more legible and accessible. Previously — from the release of ChatGPT in 2022 to arguably early last year — the founder-facing AI stack was a relative mess, requiring meaningful ML, data engineering, and infrastructure experience to construct anything non-derivative. During that 2022-2024 period, the model layer was much more of a monopoly; data management and retrieval were significantly more manual; and AI “agents” didn’t exist conceptually, much less have vetted frameworks for orchestration.
That legibility arrived in fits and starts. OpenAI’s Chat Completions API (early 2023) made models programmable rather than just promptable. Function calling (which followed shortly) in OpenAI was the first reliable primitive for connecting LLMs to external tools. Structured Outputs (August 2024) then pushed schema adherence from <40% on GPT-4 to 100%, compared to <40% with the prior GPT-4 model from OpenAI. It was a watershed moment for making the model layer a dependable building block for application-layer founders.
Another massive wave of interoperability and usability breakthroughs kicked off in 2024, with Anthropic’s launch of Model Context Protocol (MCP). Quickly adopted by OpenAI, Google DeepMind, and Microsoft, it was described by Bessemer as “the USB-C of AI.” It gave AI systems a universal spec for accessing external APIs, tools, and real-time data. Before MCP, every tool integration was custom plumbing that required a dedicated engineer. Now, AI agents — which in mid-2023 were still hallucinatory, token-burning science experiments — have production-grade orchestration frameworks built on top of a shared infrastructure with standard connectors. And the rest of the stack has followed, from RAG to vector databases, to deployment and visual AI pipeline editors.
Most important, of course, is the fact that LLMs themselves have made AI applications easier to write by leaps and bounds. By 2025, GitHub Copilot will have generated 46% of the code written by its 20M+ active users and made developers 55% faster in controlled studies. Cursor, its closest competitor, crossed $500M in ARR in 2025. A quarter of YC's W25 batch had 95% AI-generated codebases. And non-technical Claude Code and Codex vibe-coders have become so ubiquitous that it’s hard to keep up.
Our point is that over the last four years, the AI stack has become vastly more legible. Shawn Wang — aka swyx, former leader of developer tooling at AWS, Two Sigma, and three unicorns — was early to this thread of thinking in his now-storied essay, The Rise of the AI Engineer: “A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013 now just require API docs and a spare afternoon in 2023.” His implication was that the critical aspect of technical fluency in the applied AI stack is moving away from research and engineering, and toward product and design. Since 2023, our view is that this effect has only accelerated. Technical talent in application-layer AI remains important, and that will probably never change in early-stage startup builds. But hyper-focus on that one quality made more sense when the stack was significantly more nascent and less legible. The current leader of YC himself recognized this shift last year:
The ability to be successful is no longer limited by technical ability. The only thing that’s sort of the limit is can the founders get in the heads of customers. — Garry Tan
The Experience Argument
The other justification for a shift to younger founders is that they are less hampered by pre-existing norms related to either startup architecture or the industry status quo. Keith Rabois has been perhaps the strongest advocate of the anti-expert viewpoint over the last decade:
People with domain expertise learn what you can’t do, not what you could do. The most important companies are usually founded by people who don’t know much about what they’re getting themselves into. — Keith Rabois
The LLM era version of this argument goes further: AI itself has compressed the cost of iteration and the speed of learning to the point where deep industry experience is less important. As one commentator put it: “With ChatGPT or Claude, technical founders can now tap into domain expertise without needing any industry experience.” In a world where products can be developed at unprecedented speeds, perhaps velocity is becoming such a moat that it more than compensates for imperfect initial domain knowledge.
The challenge with this argument is that everyone is working with the same tools these days, and the markets one can identify as compelling from the outside are widely visible. What happens when every YC batch has three founders trying to build a copilot or voice AI intake solution for a particular industry? That’s approximately the situation we’re in today. We discussed the dilemma in a past essay, “The Dispatcher Problem”:
The result of cheaper AI is persistent deflationary pressure on Vertical AI offerings, which are predominantly attractive due to the consumer surplus enabled by LLMs. Pulling data out of documents? Answering inbound phone calls? Drafting perfunctory compliance reports? Products like these can be excellent wedges today when infrastructure and know-how are scarce, and adoption is low. Soon, they will be table stakes — as excess margin is competed away by several well-funded, credible, nicely growing startups in every category. Any startup that hasn’t developed a moat in the meantime will be a casualty.
— The Verticalist
How is one to develop a moat if you don’t have a unique, earned insight that allows you to see a problem and a consequent solution that others don’t? Of course, there will always be a portion of founders who have the drive, intelligence, and luck to navigate to the right outcome regardless. But generally, experience — if not true domain expertise — is the path to the “secrets” Thiel popularized in Zero to One. That is especially true in space, like Vertical AI, in which platforms embed within and even thrive on industry complexity, rather than abstracting it away as much as possible.
Perhaps more importantly, the data aren’t particularly supportive of the anti-experience camp. Returning to the case of YC: an analysis of their ~100 unicorn founders found an average of 8 years of work experience upon entry to the program. The founders who built Airbnb, Stripe, Coinbase, and DoorDash weren’t fresh out of school, as the majority of YC founders are today. Antler’s analysis of the wider AI unicorn founder pool found that, while they have gotten younger — the average age dropping from 40 in 2020 to 29 in 2024 — the mean work experience remained 8 years. Even Rabois himself acknowledges that, a preferable complement (at least in enterprise applications) would be pairing “a founder who’s very naive, very hungry” with “somebody who’s got a lot more experience.” Even if it’s just a few years at a high-growth startup, there is intuitive value in having at least enough experience to seed an earned insight.
When it comes to vertical platforms, the debate around the importance of founder domain expertise is particularly important. Not only for investors trying to understand what drives success and for future founders trying to plan their paths, but also as a lens into the core primitives of lasting success in business models. What backgrounds matter in vertical platforms? And how have LLMs changed that answer, as it pertains to the fast-growing world of Vertical AI?
What Backgrounds Drive Vertical Success?
The cleanest way to test whether domain expertise matters isn’t to ask VCs what they believe — it’s to look at where they actually deploy capital. Given the importance of this perennial question to what we do here at The Verticalist (and at Euclid), we decided it was time to rise above the anecdotes and generalist unicorn data points and run some serious numbers.
Over the last month, we conducted a comprehensive analysis of founder backgrounds to get to the heart of what drives success in the vertical. While we ran a similar analysis in 2024, looking at exited founders, this approach was a bit too retrospective, given the long timeframes for big outcomes in venture and the fast pace of AI. So this time, defined success a bit more broadly, setting our sights on high-velocity VC financings.
Our analysis contemplates founder backgrounds across 673 vertical software and vertical AI companies that raised rounds of $15M or more in 2025, classifying founding teams by whether they had prior experience in the vertical they’re now building for. And to speak to the Rabois case — pairing of “athletes” with “domain experts” — we consider all founders, not just CEOs. We traced every company that every founder had worked at prior to founding each startup.
Although there’s plenty to unpack, the findings aren’t ambiguous.
Overall, the two-thirds (66.1%) of leading vertical startups have at least one member of the founding team with vertical experience. That share is notably higher (~71%) in Vertical AI specifically. To further understand both the impact of vertical domain experience and its interplay with startup type (AI vs. SaaS), we can examine deal size, the quality of VCs leading the rounds, and “capital velocity” (defined as dollars per year raised since founding).
Across every metric, startup founders with prior vertical experience hold an edge. Most striking is the impact of founder experience on AI-forward vertical startups.3 Vertical AI founders with vertical backgrounds raised more than 2x the average deal size ($100M vs $45M) compared to non-vertical backgrounds. In Vertical SaaS, mean deal sizes for vertically experienced teams were only 21% higher. The tight banding of median deal sizes suggests a fatter right tail — vertical-background founders in AI are landing outsized rounds that pull the mean significantly above the median.
We also wanted to look at the “quality” of VC investors attracted to cap tables.4 While no perfect measure exists for this, we used subjective brand perception as a proxy to assign “VC Scores” to each startup in the dataset — and it was consistently higher for vertical background founders across every subset. While the delta was most modest in Vertical AI, we expect this is an artifact of the data.5 A dollar-weighted VC score heavily favors vertical experience.
Analysis by Stage
For rounds of $15-50M, vertical-background founders are a modest majority—61-63% of companies. At $50-100M, however, they account for over two-thirds. At $100M+, it’s nearly three in four. The bigger the check being written, the more likely the founding team has prior vertical domain expertise.
One possible interpretation of this data supports the theory that vertical experience confers advantages in distribution and product expansion. While product velocity and vision dominate early on, later-stage fundraising rewards enterprise sales traction, regulatory navigation, and buyer trust — all of which are earned through true expertise and credibility. This, in other words, is what you’d predict if domain expertise were a durable advantage. Interestingly, it creates a through-line to our prior analysis of exited founder backgrounds, which found a >80% share of domain expertise by exit dollar.
Analysis by Vertical
As measured by VC quality, domain experience seems to have the biggest impact in sectors that are viewed as more clubby, credentialed, and opaque to laymen. Legal, for example, sees the greatest blended lift from industry backgrounds: 89% of funded legal AI companies have vertical-background founders, with a 100% median deal-size lift and a 22% VC-quality-score advantage. Harvey, EvenUp, Spellbook — a solid chunk of the companies defining the category post-LLM — were built by founders who practiced law or worked deeply in legal operations. Public Sector shows the most extreme VC-quality lift of any vertical (+75%). Like in Legal, it’s seen as a traditionally difficult procurement environment in which insider credibility and connections are critical.
Meanwhile, exposure to real estate and retail (which often blurs into commerce generally) has exposure across a broad array of companies, backgrounds, and life experiences. In those categories, branded funds tend to prefer outsiders.
Analysis by Age / Work Experience
The older a vertical founder, the more likely it is that they have domain experience. It’s not surprising we see this effect more-or-less monotonically, considering experience takes time to accumulate. Over two-thirds of vertical founders with 9+ years of work history have domain experience. Comparing Vertical AI vs. Vertical SaaS yields more interesting results. At every experience level, Vertical AI founders are more likely to have vertical backgrounds than their SaaS counterparts.
As covered above, domain experience is valued differently in Vertical AI vs. SaaS — that effect is amplified when we analyze by years of experience. Successful vertical AI founders with 0-5 years experience are 57% more likely to have domain experience than SaaS founders in the same cohort; amongst all with <9 years of experience, domain experience is 26% more likely. This suggests that the Vertical AI market is already self-selecting for domain expertise more aggressively than SaaS has been.
Another finding, runs against the narrative that domain expertise is the primary factor of success in Vertical AI: young founders attract more “brand” VCs. The 0-5 years-experience cohort achieves an average VC Score 20% higher than the 15+ years-cohort. Are we seeing quantitative evidence backing YC’s recent shift away from experience and towards youth in the AI era? That argument would hold that the proven lift afforded by vertical experience is merely correlation, and that youth — or rather, the free-thinking, fast-moving dispositions that often accompany it — is the rightful primary driver of top VC backing and ultimately success.
It’s worth remembering that validation of VC picking takes a long time, making the names on one’s cap table a very uncertain measure of ultimate performance — especially in a world where AI is theoretically changing the nature and profile of startup success. Perhaps brand-name venture firms have adopted the same mode of thinking as YC.
Here, all we’re interested in is the data — and that tells us that the youth-supremacy narrative falls apart on three counts:
If youth were the primary driver of success in Vertical AI, you’d expect young founders without vertical backgrounds to outperform old founders with vertical backgrounds. But they don’t. Those with 9+ years’ work experience have higher capital velocity across the board. Old vertical founders raised bigger rounds than young founders without domain experience. And although young founders with vertical backgrounds marginally underperformed peers without them on VC Score, they outperformed on all other measures. Youth may be preferred by brand-name VC, but it doesn’t substitute for domain experience.
The VC quality premium for youth is consistent across AI and SaaS, but the domain expertise premium is not. Young Vertical AI founders are 20 points more likely to have vertical backgrounds than young vSaaS founders (55% vs 35%). If youth alone were the success factor, you’d expect that gap not to exist — young founders would succeed regardless of domain background. Instead, the market is filtering young AI founders for domain expertise more aggressively. The ones who make it past the Series A threshold6 disproportionately have it.
Over 90% of top vertical rounds were raised by founders with >5 years of experience. We’ve spent so much time in comparative, cohort base numbers that it’s important to remember: by volume, super young founders represent a small minority of the overall dataset. The majority of founders with successful Series A+ raises in 2025 had 15+ years experience. Examining the trend by sector, it’s clear that the more regulated and “credentialed” a vertical is, the more seasoned the founder is likely to be; as much as fresh founders transforming the stodgiest industries is in vogue right now, the actual founder splits suggests that winning founders’ experience levels align with the average ages in their respective industry. Not a single A&D founding CEO with less than 6 years’ experience raised a Series A+ in 2025.
So why would young, successful Vertical AI founders be so much more likely to have domain experience than their peers in Vertical SaaS? We feel the most likely explanation is the same we shared above: building AI products that automate complex industry-specific judgment requires deeper domain understanding than building software that mirrors and digitizes processes. Velocity, free-thinking, and AI-nativity matter greatly — but in Vertical AI, a founder’s knowledge advantage in their domain is increasingly important.
Analysis by Prior Function
Most successful vertical startup founders aren’t technical. Over a third (35.1%) of CEOs who raised $15M+ vertical rounds were previously executives (prior CEOs, presidents, or founders of other companies). When you add in Ops/Strategy/Finance (14.6%), Product (15.0%), and GTM (6.1%), non-technical business leaders account for more than seven of ten successful vertical startup CEOs. Although non-CEO engineers (including CTOs) represent just 8.9%, they achieved the highest median capital velocity ($7.8M/yr) among non-financial backgrounds. This runs directly counter to the prevailing narrative that technical founders dominate AI-era startups. In vertical markets, the CEO who understands the customer’s workflow, regulatory environment, and buying process has a structural advantage over the one who understands the model architecture.
The most intriguing finding may be the IB / Consulting cohort. Despite being a small group (~4% of successful vertical CEOs), they lead on every key metric: the highest capital velocity ($9.8M/yr), the highest VC quality score (1.69), and the highest median deal size ($50M). These are founders who combine analytical rigor with the pattern-matching and relationships that come from advising industries before building in them — the ex-McKinsey partner who spent three years studying healthcare workflows before founding a clinical AI company. The VC / Investor cohort (8.7%) is also notable as a growing category, with similar dynamics to IB / Consulting: former VCs raise at the second-highest velocity ($8.9M/yr) and command the second-highest median deal ($40M), leveraging their connectivity, if not domain expertise from deal experience.
The “From Industry” cohort represents founding CEOs who launched their startup immediately after an operating role at a strategic in their vertical. Luke Hansen, the founder of CompanyCam (which became a unicorn with the 2025 deal that landed in our dataset) is a perfect example — he was a 13-year exec at White Castle Roofing in Omaha, Nebraska, before going the founder route. These pure domain experts, however, had the lowest VC scores (1.38) and slowest capital velocity ($5.5M/yr). Does this run counter to our argument that vertical experience is critical to success?
At Euclid, we prioritize two founder characteristics in Vertical AI: (1) earned insight into their problem and vertical (most analogous to “domain expertise”), and (2) an innate understanding of what success looks like for a venture-scale business. The latter is difficult to derive without exposure to either high-quality startups or comparable high-stakes environments. Thus, we believe that optimal domain expertise tends to be either: (1) a background with a combination of software / AI plus industry, or (2) industry insight gleaned through prior vertical startup / AI success. That said, we have backed and will back backgrounds outside of any one mold, because no such rules are universal — a healthy chunk of the best founders break all the pattern-matching, and are better for it.
The Democratization Paradox in AI
Per the anti-expert argument, if AI truly democratizes domain knowledge — enabling technical founders to iterate and learn industries in weeks rather than years — then the vertical-founder advantage should shrink in vertical AI relative to vertical SaaS. AI-native, low-experience founders should be pulling ahead on attracting dollars and VCs, as the outsider disadvantage compresses.
Our analysis, however, shows the opposite. In Vertical AI, >70% of leading 2025-funded startups have founders with prior experience in or around their industry — and those founders raised deals 2.25x larger, with greater overall capital velocities, and higher VC scores. Every metric points in the same direction: the more AI-native the company, the more domain expertise matters, not less.
If that finding is indeed structural, we believe it relates to the core function of AI-first solutions compared to traditional software. Vertical SaaS digitizes processes — it moves a workflow from paper to screen. Vertical AI enables something fundamentally different: automating context and judgment. It doesn’t just present information or offer a UI for abstracting knowledge into a database. Vertical AI, writ large, aims to do the work: generate documents, triage decisions, and even act autonomously.
When the product is the work itself, vertical nuance — industry buyer trust, regulatory nuance, in vivo edge-cases — becomes more important. Those are precisely the things that domain experience confers that iteration and obsession alone would take longer to unearth.
The argument against domain expertise predicts convergence between insider and outsider founder outcomes as AI capabilities improve. The data shows divergence — and deltas between Vertical SaaS and Vertical AI suggest the gap may be widening, not narrowing. As the AI stack becomes more legible, the greater rate-limiting factor to scale — to distribution and defensibility — becomes knowing what to build, for whom, and why they’ll trust you to build it. As the ceiling of Vertical AI rises with technical possibility and accessibility, the floor of domain expertise rises with it.
So What's Wrong With the Picture?
Certainly not the parachute pants or the Yu-Gi-Oh résumé — between your two authors, Euclid is rich with MC Hammer and Magic the Gathering phases that may or may not still be active. Coordination of Benefits is indeed a $4B problem, and one worthy of solving. Eye-catching proclamations like “Healthcare is dead” will be effective as long as we have human brains. $15M at a $60M post is clearly rich for a seed-stage company but, for better or worse, not wildly rare in the current YC milieu. Our issue with the picture is subtler: the implication that youth, AI-nativity, and audacity compensate for a complete lack of earned insight into a vertical as complex as health insurance. And while our example is tongue-in-cheek, there are examples at least as striking in every batch these days.
YC’s shift toward younger, more technical founders made sense in 2023, when the AI stack was volatile and the builders who could wrangle it had a genuine edge. But the stack has matured — dramatically. Structured Outputs, MCP, production-grade agent frameworks, and AI-assisted coding have eroded the technical barriers that once justified prioritizing engineering fluency above all else. In a world where every vertical is getting pounded by a dozen low-hanging-fruit AI ideas, we’re not even sure pure velocity — the wall-spaghetti approach of move fast and sling AI — has much life left in it. Garry Tan himself seems to agree with those points in some of his more recent interviews:
The ability to be successful is no longer limited by technical ability. The only thing that’s sort of the limit is can the founders get in the heads of customers.
Our data suggests the market has already internalized it. In Vertical AI specifically, 71% of founders raising $15M+ rounds have prior domain experience — a share that increases at later stages and in more regulated verticals. Young founders are not shut out; they’re just filtered more aggressively for domain knowledge in AI than in SaaS. Youth isn’t a disqualifier; in fact, it’s absolutely preferred. But because the product is the judgment in Vertical AI, and judgment has to come from somewhere — and that’s why experience matters more.
Some GPs have publicly shared their own philosophies in support of founder expertise in Vertical AI. a16z’s David Haber, in Context is King piece, argued that “AI dramatically expands what’s technically possible, but it doesn’t tell you what’s actually useful… [industry judgment] can’t be automated like code and is earned only through experience.” Greylock’s Christine Kim put it more bluntly: “pure technologists attempting vertical AI are at a disadvantage to founding teams who have both domain experience and a technology background.” Brian Feinstein, Kent Bennett, and Sameer Dholakia at BVP concluded in their Vertical AI Roadmap: “outsiders face steep learning curves that burn months and capital understanding nuances insiders grasp intuitively.”
None of this means YC is wrong to back young founders per se. They may have simply leaned into the profile because it’s their historical domain. If they are better sourcers and pickers amongst young, technical founders — and if they drive better returns for the program — then that’s where they should play. They are now so ubiquitous, however, that some in the VC and startup ecosystem interpret the latest Y-Combinator filter as a proxy for “founder profile of the future.”
That model is incomplete if youth and technical skill are treated as substitutes for domain insight rather than complements to it. The best version of our young, pantalooned founder — the one who raises $15M and then actually builds a lasting company — pairs AI-native intuition with hard-won knowledge of how benefits actually adjudicate, where the edge cases live, and why the buyer across the table should trust her to automate their highest-liability workflow.
So to the investors seeing YC’s recent shift toward youth and technical talent, and interpreting them as the primary founder rate-limiters in the AI era: caveat emptor. Our data says distribution, trust, and domain judgment are catching up fast — and in Vertical AI, the market may already be placing them first.
And to founders wondering what to make of all this, perhaps Paul Graham himself said it best over a decade ago:
What you need to succeed in a startup is not expertise in startups. What you need is expertise in your own users…. here is the ultimate advice for young would-be startup founders, reduced to two words: just learn.
We built Euclid from the ground up to serve category-defining Vertical AI founders. By and large, those are builders who pair AI-native vision and ability, with earned domain insights, and first-hand knowledge of what great looks like. The data says they win more often, raise more capital, and attract better partners. We think that in Vertical AI, their advantage will only widen.
Everything’s getting cheaper, easier, and more accessible — except judgment.
Thanks for reading The Verticalist!
Euclid is an inception-stage VC built for Vertical AI founders. If anyone in your network is thinking about leveraging their domain experience to build in AI, we’d love to help. Just drop us a line via the comments below or on LinkedIn.
Wikipedia contributors (n.d.). Paul Graham (programmer). Wikipedia.
Loizos. (2018). Paul Graham on Why He Doesn't Like Seeing College-Age and Younger Founders. TechCrunch.
Admittedly, Vertical AI vs. SaaS is hard to disambiguate in many cases. Euclid subjectively evaluated detailed company descriptions (from Pitchbook and website) to understand how it positioned itself, noting specific mentions of AI, LLMs, and related terms vs. explicit mention of SaaS. Many middle-ground startups were evaluated manually.
VC quality is, of course, subjective, and should be considered to be mostly reflective of brand perception. To compose the “VC Score” used here, the Euclid team scored the top several hundred venture firms according to our own perspective, assigning each either 1 (the baseline which all funds receive), 1.25 or 1.50. For each additional VC above baseline on a cap table, the startup received an additional bonus, resulting in scores ranging 1-4. The highest score was Periodic Labs, which brought together Accel, BCV, a16z, Elad Gil, Felicis, DST, Eric Schmidt, and Jeff Bezos into a single $300M round. This does inherently bias toward larger / later-stage rounds, which have room for more large-check syndicate members generally. So take it with a grain of salt. Investor brand is not synonymous with investor quality but there is directional value in the data.
We cannot use median VC scores due to the prevalence of “baseline” VC Scores (only a few hundred top VCs yield score bonuses >1). The mean, therefore, understates true advantage.
More specifically, those who landed $15M+ raises in 2025.
This was a refreshing read.
What we must realize is human insights, based on lived experiences, coupled with AI, is the moat.
AI does not (as yet) have our ability of using our 6 senses (6th being intuition, or insight)
Without our sense elements, intelligence becomes a commodity as we are seeing now.
As a 50-year-old who worked in Oil and Gas, and made a mistake that could have been disastrous, AI by itself would not have been able to solve that problem because, it never lived it.
"Everything's getting cheaper, easier, and more accessible, except judgment" is the line that sticks.
I align to this general thesis and am placing my bets on a couple projects. One is an MCP-first collaboration platform. The thesis is that AI-exchanged domain knowledge raises the bar on judgment rather than lowers it. Curious if you're seeing a similar approach with your founders? I also have a side media project with Guidesof.ai pushing to enable the people who aren't the most technical but they're the ones who know where their judgment lives.