What 30 one-person and tiny AI-era businesses
have in common — and where they split.
Patterns that show up only when you read every case at once. Hover any chart to see which cases the bar contains.
The shape of the fit-score distribution.
The highest-fit cases combine observable distribution, concrete monetization and a model a solo founder can actually study.
Which business models look most reusable.
Paid databases, SaaS and education products rank well because their offer, channel and operating model are easier to observe from public sources.
Where distribution is most visible.
X and SEO are the clearest public surfaces in this dataset. X exposes the founder's cadence; SEO exposes the product's demand capture.
The actual AI leverage pattern.
AI-native and AI-assisted are different cases: one sells AI output, the other uses AI to lower research, support or production cost.
Source depth vs OnePersonAI fit.
The best cases are not just famous. They have enough public evidence to make the operating pattern readable.
Which moat types show up most often.
Brand and channel moats are easier to see publicly. Data and tech moats require stronger source notes before we overclaim.
Who actually builds these.
The default story is 'an engineer who learned marketing,' but that's only ~40% of the dataset. Designers, marketers and operators each have a real lane.
How source confidence is distributed.
This launch version does not pretend every case has the same evidence quality. The useful view is confidence and depth, not fake geography.
Research depth across cases.
Median research depth = 8. Range: 8-10. Lower-depth cases stay useful for comparison, but should not be over-positioned as premium-grade teardowns yet.
Each insight links to the cases behind it.
Members can filter the database by any cell, inspect the cases behind each pattern, and follow revisions as source confidence improves.