Funding agencies can't evaluate the research itself, so they look at numbers, metrics, impact factors, citations, h-index, publication count etc. They can't simply say "we pay this academic whether he publishes or not because we trust he is still deep in important work when he is not at a work stage to publish" because people will suspect fraud and nepotism and bias, and often the funding is taxpayer money. Not that the metrics prevent that of course. But it seems that way. So metrics it is, so gaming the metrics via Goodhart's law it is.

I don't think it's super bad, but it increases administrative work and busywork overhead on top of the actual research. The progress slows somewhat per person, as the same work has to be salami sliced and marketed in chunks, but there's also way more people in it, but of course most of them produce vary low quality stuff but it's not a big loss because these people would not even have published anything some decades ago, they would just have some teaching professorship and publish every few years perhaps just in their national language. It increases the noise but there are ways to find the signal among it, and academics figure out ways to cut through the noise. It's not great, not super easy, and it pushes a lot of people out who dislike the grind but there are plenty who see it as a relatively good deal to move to a richer country and do this.

A typical approach to science is finding your niche and becoming a person known for that thing. You pick something you are interested in, something you are good at, something underexplored, and something close enough to what other people are doing that they can appreciate your work. Then you work on that topic for a number of years and see where you end up in. But you can't do that in AI, because the field is overcrowded.

I agree that many fields essentially have papers as "proof of work", but not all fields are like that. When I worked as a mechanical engineer, publication was "the icing on the cake" and not "the cake itself". It was a nice capstone you do after you have have completed a project, interacted with your customers, built a prototype, filed a patent application, etc. The "proof of work" was the product, basically, and you can build your career by making good products.

Now that I am working as a scientist, I see that many scientists have a different view of what their "product" is. I have always focused on the product being the science itself --- the theories I develop, the experiments and simulations I conduct, etc. But for many scientists, the product is the papers, because that it what people use to evaluate your career. It does not have to be this way, but we would have to shift towards a better definition of what it means to be a productive scientist.

Perhaps one expects overgeneralization in consulting blogs though