The Rows Everyone Deletes: What Accelerator Outcomes Taught Us

I lost close to a million dollars as an angel investor before I understood what I'm about to tell you, so read it as a confession rather than a lecture.

This week we finished building something I'd wanted for years: an outcome graph of thousands of accelerator companies — every Y Combinator batch since 2005, plus publicly announced cohorts from other programmes — with each company linked to what happened next. Raised. Acquired. Went public. Or died. The point of the exercise was never the directory. It was the outcomes — the column that turns a database into a teacher.

Three things fell out of it that are worth your time.

Failure is the training data everyone throws away

Among Y Combinator alumni — who make up the large majority of the known-status data in the graph — about one in six is listed inactive. That number should stop you, because these companies were selected — vetted by one of the most competitive early-stage programmes in the world, funded, mentored, paraded at a demo day. The yes did not protect them. Now hold that against how startup-scoring models are usually built: on decks that raised, founders who made it, outcomes with a headline. The failures are missing, because failures don't issue press releases.

A model trained only on survivors hasn't learned what good looks like. It has learned what lucky looks like, and it will call almost everything promising. The fix is boring and hard: go and label the dead ones, and give them full weight. A model that has studied the failures is simply harder to impress — and that scepticism is most of its value.

Does getting into a top accelerator predict success?

It's tempting to treat "got into a top accelerator" as a proxy for quality. The graph won't let you. Among Y Combinator alumni alone, about one in six of the accepted is listed inactive; meanwhile the archive is full of companies that got the no — or never applied — and outgrew the batch that got in. This isn't an argument that selection is noise. It's a sharper claim: a committee is pricing fit — this programme, this partner, this year's thesis, this narrow window — and fit is only loosely coupled to whether the market will eventually decide your company deserves to exist.

For a founder, the practical translation is unsentimental. An accelerator's rejection is information about a door, not a ruling on the building. For an investor, it's a warning against outsourcing conviction to someone else's admissions decision.

The outcome is the only signal that doesn't flatter you

A pitch is an opinion about what might happen. An outcome is a fact about what did. Tracking each company to its result — raised, acquired, public or closed — is the only part of the exercise that can't be talked around. It is also the part that is hardest to see from the outside: for most programmes, a company's later story is simply not written down anywhere public.

None of this is exotic. It's the unglamorous middle of the work — the part that doesn't fit in a launch tweet — and it's exactly the part that compounds.

A note on the data. "Dead" is a snapshot, not a verdict for all time; some of those companies will be quietly reborn. Deep-tech outcomes play out over a decade, so any exit rate computed today under-counts them. Survivorship bias runs through all of it: the companies we can see are the ones that left a trace, and exits and closures are the easiest outcomes to find. I'd rather tell you that than sell you a clean number.

The direction, though, is not in doubt. The teams building AI for venture are converging on the same commodity — an agent that reads a deck and returns a score. That harness will be free soon. The edge was never there. The edge is the slow, unfashionable ledger of what happened to companies — kept honest even when the honest record is embarrassing.

You cannot prompt your way to that ledger. It accrues by waiting, and by doing the part nobody photographs: labelling the failures, correcting the bad matches, keeping the record honest when the honest record is embarrassing.

Think in decades and it's not a close call. The team that logs outcomes for three years beats the team with the cleverest model, every time. One of those compounds. The other churns.

Frequently asked questions

What share of accelerator-backed startups fail?

Among Y Combinator alumni — who make up the large majority of the known-status data in our outcome graph — about one in six is listed inactive, despite having been selected and funded by one of the most competitive programmes in the world. We could find a status for fewer than one in three of the non-YC alumni in the graph, which is its own, separate finding. It's a reminder that acceptance was never a guarantee.

Why is survivorship bias a problem for startup scoring?

Because the clean, available data in venture is the winners — the decks that raised and the companies with a headline. Failures leave little structured trace, so models train mostly on survivors and learn to call almost everything promising. Labelling the failures and weighting them equally is what makes a score sceptical enough to be useful.

Does an accelerator rejection mean a startup is weak?

No. Selection prices fit — this programme, this partner, this year's thesis — far more than it rules on whether a company deserves to exist. The archive is full of companies that got the 'no', or never applied, and outgrew the batch that got in.

Correction (2026-10-07, updated 2026-10-19). This post originally described the "about one in six is dead" finding as applying to "accelerator companies with a known status" in general. A later data-quality pass found that pooled figure is, in substance, a Y Combinator figure: we have a status for every YC company, and about one in six is listed inactive. For non-YC alumni we could find a status for fewer than one in three, so there is no comparable non-YC rate to state, and the YC figure is itself subject to survivorship bias. Every instance of the claim above has been corrected to say "among Y Combinator alumni." Corrected 2026-10-19: an earlier update understated the share of non-YC alumni with a findable status and printed the underlying counts; the share is now stated as fewer than one in three, without raw counts. We also removed a paragraph about which investors recur next to the wins, because we cannot support an investor-level claim from this data; a size figure for the graph that had gone out of date; and a sentence describing how outcomes feed into our scoring. The visibility gap itself — why we can look up almost every YC company and so few of everyone else's — is the subject of a companion piece.