Article URL: https://mentalium.me/en/research/mental-health-startup-graveyard-dataset/ Comments URL: https://news.ycombinator.com/item?id=49172079 Points: 24 # Comments: 8

542 digital mental health organizations that left the market between 2000 and 2026 — shutdowns, bankruptcies, acquisitions, pivots and consolidations. Each coded on up to 18 fields: business model, who actually pays, funding raised, reason for leaving, clinical evidence, medical co-founder, revenue model, exit size, country, years of operation. On top of that, four independent classification axes: what the product is (product_type), what kind of organization it was (entity_type), whether it replaced a clinician or wrapped around one (care_mode), and whether a human clinician was in the loop. And one field that is not a category at all — key_mistake, a paragraph on what actually killed each company. Who pays predicts survival far better than anything on the founding team. A clinician co-founder moves the exit rate by nothing at all: 47% against 47%. The four axes were not coded by hand. LLM agents read each company’s full description and assigned labels against a fixed taxonomy. This is the weakest part of the method, which is exactly why the written reasoning for every single label ships in mh-graveyard-labels-rationale.csv — so the coding can be checked instead of trusted. The factual fields — funding, dates, country, outcome — come from Crunchbase, CB Insights, Tracxn, public deadpool databases, app store removals, Ahrefs domain data and trade press, not from the classification pass. If you find labels you disagree with, that is the most useful thing you can do with this data. This is a graveyard, not a random sample. Every company in it already left the market, so the shares compare groups against each other — they are not probabilities of failure. Funding is disclosed for 59% of companies, and money cuts run on those only. About 67% of the sample is US and UK. Groups under 25 observations show a direction, not a precise value.