The Problem
How do you think through AI business opportunities at scale without getting trapped in hype or execution fantasyland? I was starting a new VP role, had two toddlers, and needed to systematically figure out which AI businesses made sense for me to build — given my actual constraints, edges, and available time.
The Experiment
35 Claude agents, each given a single industry vertical and instructions to generate ideas across a spectrum from “obvious and safe” to “weird and ambitious.” They ran in parallel across 26 industries. 1,160 ideas in 6 hours.
What I Actually Learned
The scoring framework went through 4 rounds of recalibration. Nine dimensions: Autonomy, Revenue, Market, Speed, Buildability, Defensibility, Execution, Experience, Geography.
The biggest insight: autonomy matters more than revenue. When I deprioritized revenue-per-customer (from 20% to 12% of the formula), “boring autonomous” ideas — HVAC seasonal campaigns, internet outage refund bots, permit scrapers — surged past high-revenue plays that needed humans in the loop.
The decisive winner was a Property Tax Appeal Automator. It runs on public county data, triggers deterministically, and compounds via a proprietary outcome database. Not sexy. Just correct.
Live
Explore all 1,160 ideas at ai-ideas-explorer.vercel.app.
