nature in an experiment i don't know yet why is it like so far like it sounds so simple they had no idea

but now the data's i find this not only refreshing but but at some level astounding nature

welcome back to the nature podcast.

This week, how AI might increase researchers' productivity, but narrow their focus. And the mystery of the universe's little red dots.

I'm Benjamin Thompson, and I'm Nick Petit Chow.

Artificial intelligence tools are part and parcel of many areas of science, and I don't just

mean generative AI assistance like chat

GPT. Deep learning has underpinned tools like alphafold and machine learning has been used to

find associations in data for decades. And while a lot can and has been said about the effects of these tools on research,

not much is actually known about their impact on science as a whole.

Well, we were aware of the hype and the associated kind of natural motivation for scientists to use these tools.

But we just were wondering if there was a flip side to that.

What is that doing to science as a whole? This is James Evans, a data scientist who's been looking at this question

for a paper that was published in nature this week. And his results imply that these tools

may well make scientists more productive, but at a cost.

James and a team of researchers looked at more than 41 million papers published between 1980 to 2025,

that were, in some way, assisted by AI tools, which they refer to as AI augmented research.

They also chose to look at the natural sciences, ignoring computer science and mathematics.

We were interested in how AI is being applied to the sciences, rather than just the core development of AI itself.

And the natural science research they were looking at wasn't the only thing that was

AI augmented.

To read through these 41 million papers, the team used an AI language model called

...