Y Combinator's Efficiency Obsession Exposes a Critical Gap in How We Scale AI Companies
The goal here isn't to slow down these hyper-efficient teams, but to demonstrate that robust legal design can be a competitive advantage in the AI economy, enabling sustainable growth rather than constraining it.
I keep reading Y Combinator's latest startup requests and thinking about what's missing from their vision of the future. Six AI-focused asks, all centered around radical efficiency—10-person teams building $100 billion companies, AI agents replacing entire departments, personalized vocational training that takes months instead of years. It's Silicon Valley's efficiency gospel pushed to its logical extreme.
The problem is YC is optimizing for speed and scale without addressing the governance frameworks that will make these ultra-lean operations actually sustainable. When Aaron Epstein talks about "revenue per employee" as the key metric for AI-native companies, he's describing a world where human oversight becomes vanishingly thin. When Harj Taggar envisions AI-powered vocational training that can "retrain workers in months rather than years," he's imagining systems that could reshape entire labor markets with minimal regulatory friction.
This reflects a broader pattern in how we're approaching AI company building. The same venture capital model that prioritized "move fast and break things" is now being applied to technologies that can automate decision-making, reshape employment, and process sensitive data at unprecedented scale. The legal and ethical infrastructure isn't keeping pace with the operational efficiency gains.
Consider what happens when a 10-person company processing sensitive government data through AI consultants scales to $100 billion in revenue. The traditional checkpoints that larger organizations typically provide—such as legal review, compliance oversight, and cross-functional risk assessment—don't exist. YC's vision assumes that efficiency gains will somehow solve the governance challenges that come with that level of impact.
Product counsel working with AI startups need to recognize this dynamic early. The companies YC is funding won't have large legal teams or established compliance functions. They'll need legal frameworks that are as elegant and scalable as their technical architecture. This means building governance into the product from day one, not retrofitting it when the company reaches enterprise scale.
The goal here isn't to slow down these hyper-efficient teams, but to demonstrate that robust legal design can be a competitive advantage in the AI economy, enabling sustainable growth rather than constraining it.