[bonoboTP]

> finding your niche

Exactly. It used to be that way in AI a decade ago. Different subfields used bespoke methods you could specialize in and could take a fairly undisturbed 3-5 years to work on it without constant worries of being scooped and therefore having to rush to publish something half baked to plant flags. Nowadays methods are converging, it's comparatively less useful to be an expert in some narrow application area, since the standard ML methods work quite well for such a broad range of uses (see the bitter lesson). This also means that a broader range of publications are relevant to everyone, you're supposed to be aware of the NLP frontier even if you are a vision researcher etc., you should know about RL developments etc. Due to more streamlined github and huggingface releases, research results are also more available for others to build on, so publishing an incremental iteration on top of a popular method is much easier today than 15 years ago when you first had to implement the paper yourself and needed expertise to avoid traps not mentioned in any paper and is assumed common knowledge.

It may not be a big problem for overall progress, but it makes people much more anxious. I see it on PhD students, many are quite scared of opening arxiv and academic social media, fearing that someone was faster and scooped them.

Lots of labs are working on very similar things, and the labs are less focused on narrow areas, everyone tries to claim broad areas. Meanwhile people have less and less energy to peer review this flood of papers and there's less incentive to do a good job there instead of working on the next paper.

This definitely can't go on forever and there will be a massive reality check in academia (of AI/ML).