> Scientists who adopt AI gain productivity and visibility: On average, they publish three times as many papers, receive nearly five times as many citations, and become team leaders a year or two earlier than those who do not.

To me this effect doesn’t seem to reflect on AI very much, it seems to reflect on humans. Like maybe this is more evidence of the Babble Hypothesis and the incentives in research than AI, no?

https://en.wikipedia.org/wiki/Babble_hypothesis

> “It’s not about the architecture per se,” Evans says. “It’s about the incentives.”

It would have been useful to check whether less original work was already getting more citations before AI adoption. That could reflect broader trends and network effects: heavily cited research areas attract more authors optimizing for citations, so high-productivity researchers end up clustering on the same topics.

As with other fields touched, AI is merely amplifying what was already there. The aim of many scientists isn't discovery in and of itself. Discovery is a side effect of their primary drive to publish and - hopefully - become well known. And establishments only make things worse, because it's the things that are most likely to produce tangible results (the papers, or economically valuable products) that get the most funding.

"boost research careers".. seems like a pretty drastic conclusion to draw based on a technology that has existed for like 3 years and only lately is any good..

A new breed of academics has appeared whose jobs is to put their names in every paper possible. Literally, their job is to work on frameworks to buy co-authorship.

They do this in various ways, like establishing paper pipelines, collecting rents on labs and committees, focusing on money layer, using their profiles and citation count to help with acceptance of papers of other people , etc. You talk to them and they can’t explain their papers beyond a superficial introduction.

They collect huge citations, travel and give talk on the winner horses, collect credit, which feeds back into this fraudulent scheme. A scientist used to be a scientist not long ago, not a credit collector.

I wonder if Google could invent a new metric to expose them (weak ratio of first authorship, etc).

I enjoy using AI loads. Yet I would be keen to see numbers on actual productivity increases. This reads as yet another datapoint similar to what I’ve experienced: maybe code was the bottleneck at some point, maybe now it isn’t but in my lived experience the bottleneck has simply shifted. Its easy to create “more” but to actually hit the business goals… I don’t see a 2x TRUE productivity boost in anyone in my company.

Please feel free to disagree with me! I am keen to hear more anecdotes to get more datapoints.

As a bioinformatics person that's spent time in and out of industry/academia, I agree with some of the article's thesis. While I don't think LLMs or AI are going away, I do think it will allow people in academia to pump out a bunch of inane papers and continue to prop up predatory scientific journal publishing via tenure and promotion. In fact outside of how utterly useless Fable 5 is via their aggressive guard rails for my work, I quite like using statically typed and/or functional languages with other LLMs since there are some baked in guardrails via compiler + type system.

I think the flattening of progress is the most interesting dimension to the article. For an example a useful biological product discovery with a nonlinear path to get to there, look at the Taq polymerase (https://en.wikipedia.org/wiki/Taq_polymerase). Without some NSF funded exploratory ecological research by Tom Brock in Yellowstone Hot Springs to test the theoretical limit of life at high temperatures (https://en.wikipedia.org/wiki/Thermus_aquaticus) we never get to the Taq polymerase, we never get reliable/robust PCR (https://en.wikipedia.org/wiki/Polymerase_chain_reaction), which is now a gold standard method in both clinical and environmental testing! It is rather improbable to think that large language models would associate those domain connections across the topic (molecular biotechnology + ecology + microbial physiology). I also did some exploratory work with text embedding models people might use for RAG and challenged them with an open source scientific MCA question dataset, generalist embedders performed worse vs. domain specific embedders trained on scientific corpora (doesn't surprise me at all). However, if everything regresses to the median of the universe of possible knowledge, it seems like scientific leaning frontier models would get locked into this asymptotic flattening before turning cashflow positive for model vendors OR they become so locked down that only big pharma, state actors, or big ag can afford the API rates and vetting process.

This isn't a real surprise to anyone who knows how "AI" works.

Ironically it's also written by AI :)

I like LLM's but this writing style is like eating the same dish 4 times a day.

Any flattening of discovery due to AI, but will be temporary.

We tend to think that obvious potential is the same as realized potential, for new technology.

For any specific context, there are generally innumerable smaller adaptations and capability thresholds that have to be crossed. And the price for that journey is often temporary loss off overt productivity.