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Joined May 2011
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The typical unicorn founder starts the company at 34.
My team at the Stanford GSB Venture Capital Initiative and I estimated the age at founding of 3,586 founders of 1,540 US VC-backed unicorns.
Three out of every four founders started between 25 and 45. The median age at founding is 34, and the average is almost 36.
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I think this is really spot on and worth reading. Also very similar to how we approach AI Adoption at Won.
A few additional thoughts:
- The legacy 'technical' vs. 'non-technical' gap was never as large as people made it seem. In retrospect, more companies should probably have been investing more in engineering upskilling before AI. @Austen was early to this.
- With AI, the gap is less about technical proficiency and more about 'builders' vs. 'non-builders'. More technical folks are certainly more likely to be builders, but it's a not a direct correlation, and you likely have builders popping up in places you never expected.
- AI as 'The Great Bundler'. We've seen this already across tools, eg just using Claude/ChatGPT (often w/ connectors) instead of opening individual SaaS apps, but I expect we will see this more across human capital too. If you're a builder, past some level of domain proficiency, why would you just be a builder focused on Design, or Finance, or CX? Job titles and scopes are going to evolve rapidly and horizontally.
- Maybe <5% of the workforce dedicated to building (i.e. Engineers) was too low, but it was probably never going to be >10%. Automation tools (Zapier, Make, n8n, etc.) were on a path to bring that number closer to 15-20%, but AI has blown past that and is likely approaching 50%. However, 50% is still not 100%, hence we should still be realistic about what the KPIs for AI adoption might look like.
- From an ROI perspective, AI feels much more reasonable than teaching everyone at the company how to code, but actually once you knew how to write code the cost was basically just the upfront investment. AI is different, and the investment cost (token spend) is recurring. If you're not careful, AI adoption can become a runaway train.
- Just as an observation, defaults and herd behavior are extremely powerful. It feels like upskilling was generally default 'Off' for over a decade. It is now default 'On'. Defaults are also, as they are in this case, never precisely optimal, and they come with tradeoffs.
- Plus a few diagrams I'm riffing on while thinking through this.
Interesting how even Anthropic's personal agent is still extremely enterprise-coded.
