opinion tech single source: Undark

The Slow Rebuilding: How AI is Remaking the Ship of Scientific Inquiry

AI is changing how science works by integrating into research and publishing processes.

Two thousand years ago, Plutarch chronicled a story of preservation: the 30-oared ship carrying Theseus. To keep it afloat, its caretakers methodically stripped away rotting planks and replaced them with fresh timber, piece by painstaking piece. Eventually, the repair led to a strange philosophical knot: if absolutely none of the original wood remained, was it still the Ship of Theseus?

We tend to imagine the rise of Artificial Intelligence as some cinematic event—a singular machine suddenly leaping ahead of every human expert to deliver a Nobel-winning flash. But that drama misses the quieter danger. The real challenge isn't a sudden takeover; it’s the incremental displacement of science itself.

There is no big vote handing over our methods to robots; it happens through silent software updates, each seemingly efficient improvement slipping into place unnoticed.

This creeping transformation starts in education. Consider high school math students given access to standard generative AI interfaces like GPT-4. They perform better on drills, yet score seventeen percent lower on exams afterward.

While specialized tutoring helped cushion this drop, it signals a subtle warping of how knowledge is absorbed.

Then there is peer review—science's bedrock activity, often fueled by overworked academics operating under pressure and guilt. Researchers submitting to top AI conferences found that between sixty-five percent and sixteen point nine percent of their text could have been significantly altered by Large Language Models (LLMs).

The involvement deepens further into documentation; an audit tracking language favored by LLMs discovered that at least thirteen point five percent of PubMed abstracts in 2024 had been touched by AI processing.

But the issue transcends simple textual editing. A warning from working scientists in 2025 highlighted how code generated by AI can subtly tweak statistical parameters in ways users won't detect—potentially yielding authoritative-sounding results that ultimately answer the wrong question while passing all standard checks for validity.

Even more alarming is the emergence of what one report termed an "AI Scientist," an algorithm capable of everything from conceiving ideas and running simulations to drafting manuscripts that successfully navigate initial peer review workshops alone.

Why treat this as more than just clever gadgetry? Because incentives drive behavior within professions built on collaboration. Scientists embrace these tools because they boost metrics upon which they are judged; studies confirm authors using AI achieve higher citation rates and advance faster professionally.

Yet, looking at this trend collectively reveals a serious problem: despite individual career boosts, wide-scale adoption correlates with a four point six three percent shrinking of research topics covered and a twenty-two percent slump in community engagement across science globally. An incentive structure rewarding isolated brilliance risks creating a narrower, less communal enterprise overall.