I formalised something most of us already feel but rarely say out loud: making things easier to build doesn't make things easier to succeed with.
Personal version: I've vibe-coded maybe 15 projects since the beginning of this year. Two are still alive. At work, our teams built hundreds of custom GPTs and dashboards. Handful survived. The failure mode was never "couldn't build it" - it was "nobody had the bandwidth to care."
So I wrote a paper about it. It combines Herbert Simon's attention scarcity, free-entry IO models, superstar economics, and preferential attachment into one framework. The central result: equilibrium attention per builder = k/p (entry cost over monetisation rate), independent of market size. As AI drives k towards 0, that ratio vanishes regardless of how much the market grows. Free entry absorbs everything.
Calibrated to the App Store (800K publishers, 38B downloads) the model matches observed concentration pretty well - top 1% get ~70% of downloads, quarter of apps under 100 downloads, Gini above 0.9.
The same mechanism works inside organisations (dashboard sprawl, GPT graveyards, tool fatigue) and across markets. It's the same math: finite attention, elastic production, winner-take-most.
I just got a bit fed up with the narrative I am constantly seeing online: that everyone will be a successful builder with AI; just build; forget about "everything else" that you do - if you aren't vibe coding, you aren't doing anything. And I'm saying this as an AI engineer who has built dozens of models and in the recent times many apps with Claude Code and Codex. I thought it's time to shine some mathematics on this.
Personal version: I've vibe-coded maybe 15 projects since the beginning of this year. Two are still alive. At work, our teams built hundreds of custom GPTs and dashboards. Handful survived. The failure mode was never "couldn't build it" - it was "nobody had the bandwidth to care."
So I wrote a paper about it. It combines Herbert Simon's attention scarcity, free-entry IO models, superstar economics, and preferential attachment into one framework. The central result: equilibrium attention per builder = k/p (entry cost over monetisation rate), independent of market size. As AI drives k towards 0, that ratio vanishes regardless of how much the market grows. Free entry absorbs everything.
Calibrated to the App Store (800K publishers, 38B downloads) the model matches observed concentration pretty well - top 1% get ~70% of downloads, quarter of apps under 100 downloads, Gini above 0.9.
The same mechanism works inside organisations (dashboard sprawl, GPT graveyards, tool fatigue) and across markets. It's the same math: finite attention, elastic production, winner-take-most.
I just got a bit fed up with the narrative I am constantly seeing online: that everyone will be a successful builder with AI; just build; forget about "everything else" that you do - if you aren't vibe coding, you aren't doing anything. And I'm saying this as an AI engineer who has built dozens of models and in the recent times many apps with Claude Code and Codex. I thought it's time to shine some mathematics on this.
Paper: https://arxiv.org/abs/2603.23685
PS. I was very inspired by Herbert Simon (Nobel Prize in Economics) and ironically enough, he is also considered one of the "founders of AI".