AI Boosts Research Careers but Flattens Scientific Discovery

> 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.

We are headed towards the “trough of disillusion” of this particular cycle.

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.

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.

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

> AI is largely automating the most tractable parts of science rather than expanding its frontiers

By definition, creativity cannot be automated, and AI is a fantastic automation machine. It can explore thinking paths at a rate humans cannot match. But creativity is bringing the unthinkable into the thinkable, and as such there is still a large gap. This is not me, it is deepmind saying this [1]. Think of the knowledge volume as the axioms and inference procedures, and LLMs bringing closure to that. But what about new definitions and theories ? This requires sensory experience.

[1] https://philsci-archive.pitt.edu/28024/1/Scientific_Inventio...

I agree with some parts, but not all.

I see it as an overfitting problem. Fundamentally, the topic here seems to be that citation indices and similar metrics are actually flawed indicators, and obsessing over them is just Goodhart's law in action. Ultimately, the argument is that the entire design of those metrics is wrong. To be precise, it was a good metric at first, but now that the scale has changed, it's become bad. This is common in programming too—things that are correct in the beginning but become problematic as they grow larger.

From an individual researcher's perspective, it's rational. You get more citations, your career accelerates. Everyone knows this. Paper counts aren't everything. Citation counts aren't everything. Journal impact factors aren't everything. You shouldn't only play it safe. But everything is tied to those metrics anyway.

Most researchers who give me work are fully aware of these facts. But are they going to change anything? Funding is still distributed based on those metrics.

Max Planck said, 'Science advances one funeral at a time.' Science doesn't progress purely through reasoned argument. The authority of the older generation, research funding networks, journals, and school-specific evaluation criteria all move together.

And honestly, I think discoveries will keep happening—probably quite rapidly. Because AI doesn't have the factional conflicts or interpersonal issues that humans do. It's very good at connecting papers across schools of thought without bias. In other words, the current human system is flawed at consolidating research, but I think AI is actually strong in this area. I expect AI-driven discoveries will continue for some time. The people who ride this wave will clearly be the winners.

Everyone knows things are broken, but no one is trying to fix them. I always think human society is inefficient. I read this post, but I'm more curious about who will actually lead the improvement effort.

The entire article seems to rest on their use of an embedding model for clustering garbage science.

AI has been seriously around for how long? Two years? Isn't it a bit too early to say?

“Technology that is based on everything humanity has already done, fails to do things that humanity has not yet done”