alt Hacker NewsI have been in both academia and industry for years, and I don't think the model you describe is true anymore. It was definitely true 10 years ago, but the situation has flipped. Now, I see really ambitious and impactful research coming out of industry labs. Academia is often lagging behind the state of the art because they lack the resources (data, compute, and skills) to compete.
Academia is also incentivized such that everyone works on the same popular topics to secure grants and citations. This is currently LLMs, where academia needs to compete with multi-billion corporations on a technology that is notoriously expensive. In effect, many researchers work on topics that are pretty non-consequential from the get go (such as N+1th evaluation dataset), but it's the only way for them to stay relevant.
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A lot of those industry papers are in collab with an academic lab or even often first authored by a PhD student who interns in a big tech lab.
I recently talked with a PI from a well-known university lab, and asked why they were doing a startup, given the ML research problems they were working on.
They said a company was the only way to get access to the compute power they needed for that research.
A startup sounds like probably a good solution, if they get paired with the right product- and business-minded people, and together they find a winning collaboration. (Edit: Or if they get acquired rapidly in the AI boom, and negotiate the right deal to enable their research longer-term.)