I’ve been building AI tooling and noticing a shift: shipping is cheap; discovering demand and capturing value is the hard part.

Hypothesis: as code/infra get commoditized, we may bifurcate into (1) commercial AIs that create and capture value (need-finding → offer design → pricing → distribution → support),

and (2) scientific AIs pushing the frontier. Humans concentrate where taste, narrative, and trust matter (IP, entertainment, relationships).

If agents can identify pain, design a product, source supply, spin up a storefront, set price, run ads, and close the loop, that starts to look like “quant for commerce”: models scanning micro‑signals, launching micro‑products, harvesting short‑lived edges. We already see early signs (e.g., tools that auto‑source SKUs from OEMs and generate Shopify pages, AutoDS‑style flows).

For founders/engineers/operators here:

Have you run (or seen) an end‑to‑end loop from need discovery → build → monetize with minimal human intervention? Where did it break (data access, distribution, compliance, returns, chargebacks)?

Which business functions automated cleanly vs stubbornly resisted it (BD/negotiation, procurement, sales ops, support, legal)?

Where do moats move in this world—distribution, proprietary data, regulatory position, brand?

Any concrete metrics you can share (time‑to‑MVP, CAC/LTV, return rates, fraud)?

Not looking for predictions—interested in real cases, trade‑offs, and failure stories. Happy to be wrong; keen to learn from people who’ve tried it.