Five reasons AI startups fail to find product-market fit (www.doctormarket.fit)

An experienced founder lays out five recurring reasons AI startups struggle to find product‑market fit: easy prototyping but weak MVPs, scaling barriers, overestimating model capabilities, lack of a real moat, and competition from clients’ in‑house dev teams. The piece stresses a strict MVP definition — the minimal thing that actually delivers deployable, sellable value in the customer’s context — and warns that many demos never become implemented products. Scaling is hard for three specific reasons: agents that suit individual users don’t translate to multi‑person workflows, undefined org processes require consulting to map, and legacy, API‑poor systems make integrations slow and bespoke. Technically and strategically, the article argues startups should focus narrowly on 1–2 verticals, avoid pretending generic prompt stacks (OpenAI/Claude/Whisper) are a unique moat, and consider building specialized models tuned to domain specifics (e.g., localized medical voice models integrated with EHRs) as a defensible advantage. Finally, it reframes enterprise in‑house teams from pure competition into potential collaborators: design products devs can co‑own or extend to win adoption. The practical implication for AI/ML teams is clear: prioritize deployability, vertical specificity, integration engineering, and proprietary models or partnerships over flashy demos.