Researchers Are Caught in AI's Dependency Trap and They Know It

A developer on Bluesky put it plainly this week: trying to avoid AI for research has made research harder. Not because AI is good at it — they were emphatic it isn't — but because the information landscape itself has been reshaped around AI's presence. Avoiding it now means swimming upstream through an environment that was restructured to assume you wouldn't. That's not a complaint about AI being too useful. It's something stranger and more uncomfortable: a tool that people distrust has quietly become load-bearing infrastructure.

This tension keeps surfacing in the scientific and research community right now, and it doesn't fit the frame that normally gets applied to AI-in-science debates. The optimists point to genuine advances — CMU's new Center for AI-Driven Biomedical Research announced its first projects this week, targeting genomic complexity with automated laboratory platforms, and the Biomni framework out of Stanford is drawing attention for automating wet-lab workflows. The pessimists point to equally real failures: library AI tools surfacing book reviews as top scholarly sources, AI summaries that are wrong at some level often enough that one researcher said they'd never encountered one that wasn't. Both camps are correct, and the people stuck in the middle — the ones who use AI for research discovery while distrusting it for execution — are describing a situation that neither side's talking points account for.

What's clarifying is where the disagreement runs deepest. News coverage of AI in science remains strikingly credulous, treating each institutional announcement as confirmation of a trajectory. The research community on Bluesky reads those same announcements with a skepticism that borders on fatigue — not because they're opposed to AI in principle, but because they've used the tools and found the gap between the press release and the product wide enough to fall into. One researcher noted that AI is genuinely excellent for finding research and genuinely bad for doing it, which is a precise and useful distinction that institutional coverage almost never makes.

The dependency paradox matters because it changes what a solution would even look like. If AI had simply failed and been abandoned, the story would be straightforward. Instead, it has failed in specific ways while succeeding in others, and in doing so has restructured enough of the research environment that stepping away carries its own costs. The scientists most critical of AI's epistemic effects are not the ones who've avoided it — they're the ones who've used it enough to know exactly where it breaks. That's the position the research community is increasingly in: too embedded to exit cleanly, too experienced to be credulous. The institutions announcing the next breakthrough framework should probably take note.