Pretty sure this is about AI projects, i.e. potentially career-ending failures, not failed AI companies which in a VC context have a high expected rate of failure.
Anecdotally, I've never seen a project be a career ender. Most individuals have many projects under their belt, it's rare that an individual has bet the farm on an effort in such a way that others would not employ them.
When a project fails, the lessons are often valuable for the next project - when a project succeeds it can often just be do to market position.
That's bad management if it's seen as career ending failure. Proper management just does the ROI on the successful ones versus overall cost. And doesn't risk everything on them. Large tech companies test hundreds of model changes and rollouts per year in AB tests for that reason.
