Data source: I scraped it directly from YC's public API. If you go to the YC company directory (ycombinator.com/companies) and inspect the network tab, you'll see it hits an Algolia search endpoint. That gives you structured JSON for every company: name, batch, one-liner, tags, industry, team size, location, etc. I pulled all companies from the last 5 batches (W25 through W26), which gave me 793 companies.

For founder bios, I scraped the individual company pages on YC's site, each one lists the founders with short bios. That gave me 1,625 founder profiles to work with.

Analysis: A mix of things, all in Python:

-> Basic aggregations (counts by industry, tag, batch, geography)

-> Trend analysis across batches (what's rising/falling)

-> NLP clustering (TF-IDF + KMeans on company descriptions to find hidden themes) Cosine similarity between company descriptions to find competitive overlap ("crowding")

-> Cross-correlations between features (is_ai × is_b2b, founder count × hiring, etc.)

-> Founder bio keyword extraction to map backgrounds (ex-FAANG, PhD, repeat YC, etc.)

-> A simple heuristic classifier for the AI wrapper vs deep tech breakdown

Nothing fancy ML-wise — mostly pandas, scikit-learn, and some regex.

Built in less than 30 mins using Claude.

The Formula

Pick a boring, high-value industry. Build AI agents that replace manual workflows. Make it deep enough that it's not a wrapper. Have 2 founders - one technical, one with domain expertise.

Thanks

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