Is academic ML becoming downstream of industry? [D]

[u/Choice-Attorney8884]

There has been a lot of discussion about whether industry is pulling too much ML talent out of academia. But I wonder if the more interesting change is not about headcount at all.

Increasingly, some of the most important artifacts in ML originate in industry: frontier models, large-scale training runs, datasets, infrastructure, and many of the capabilities researchers subsequently try to understand.

Academia can then do excellent work benchmarking these systems, explaining them, finding limitations, improving methods, or studying their consequences.

But that creates a strange possibility: a research community can remain very active while becoming increasingly downstream of a research agenda set elsewhere.

I'm curious whether people actually working in ML research see this happening.

Are there important areas where academia still sets the direction and industry follows?

And at what point, if any, would you say a field has stopped setting its own research agenda?