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How the 'creeping normality' of large language models is quietly reshaping the life sciences
Large language models (LLMs) are gradually transforming research in the life sciences in ways that extend far beyond improving productivity, and they are becoming a new normal before scientists have agreed on the limits of their use.
A new study in Frontiers in Ecology and the Environment by an international team of scientists describes this phenomenon as the "creeping normality" of generative artificial intelligence (AI), a process in which profound changes become accepted because they occur incrementally through small, subtle steps.
"While generative AI has rapidly become part of the everyday workflows of many researchers, its long-term effects on almost all aspects of science have received comparatively little attention," says Ivan Jarić, a researcher at the Biology Center of the Czech Academy of Sciences and lead author of the study. "Routine reliance on these tools could fundamentally reshape the foundations of scientific practice and culture," he adds.
When colleagues are replaced
The article identifies several domains where such changes are already emerging. One is scientific collaboration. Researchers increasingly use LLMs for support conventionally provided by colleagues and informal professional networks, like brainstorming ideas, troubleshooting analyses and seeking expert feedback.
Increased reliance on LLMs over human collaboration is expected to reduce motivation to seek external expertise from other research fields or regions, or expertise in particular taxonomic groups or methodologies. This may, in turn, weaken cross-disciplinary interactions, reduce exposure to diverse perspectives and encourage more siloed and less innovative science.
Ideas shaped by AI prompts
AI use may also fundamentally change how scientists seek information and generate ideas. "As LLMs increasingly replace search engines, encyclopedias, and even literature searches, researchers are going to become more dependent on AI-generated feedback, limited only to the questions one thinks of asking," explains Susan Canavan from the University of Galway, another author of the study.
"This could unintentionally reinforce confirmation bias, create intellectual echo chambers, and contribute to greater uniformity and lower originality in scientific language, approaches, and creative thinking."
Deskilling and new inequalities
Beyond cognition, the article explores the consequences of scientific training and career paths. While lowering barriers for researchers early in their careers or those who lack access to coding or statistical expertise, the use of LLMs can generate new types of inequalities and reinforce existing ones.
It will also intensify deskilling in core competencies, with some fundamental skills in the life sciences being outsourced to LLMs, such as literature research and synthesis, natural-history reading and knowledge acquisition, taxonomic judgment, coding and debugging, and statistical reasoning.
The authors suggest that it could even influence hiring decisions and career opportunities by reducing the need and motivation to open new Ph.D. and postdoctoral positions, as various types of work will increasingly be delegated to LLMs.
Drawing boundaries for use
"Rather than advocating for or against AI, we call for the life sciences community to establish clear boundaries for its appropriate use," suggests Michael Bertram from the Swedish University of Agricultural Sciences and Stockholm University, another author of the study.
"LLMs should, for example, be widely embraced for routine tasks such as proofreading, language editing, and workflow annotation, and, if underpinned by proper human control, also for advanced tasks like literature synthesis and data extraction. However, AI use should be undesirable in activities requiring independent scientific judgment, including research prioritization, peer review, funding decisions, and ethical considerations."
The authors conclude that the transformation of science by generative AI is already underway. The urgent question is not whether AI will become part of scientific research, but where the scientific community chooses to draw the line. Defining those boundaries now, they argue, will be essential for preserving creativity, diversity, accountability and human judgment as core scientific values in the life sciences.
Publication details
Ivan Jarić et al, The creeping normality of AI in the life sciences, Frontiers in Ecology and the Environment (2026). DOI: 10.1002/fee.70059
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Citation: How the 'creeping normality' of large language models is quietly reshaping the life sciences (2026, July 13) retrieved 13 July 2026 from https://phys.org/news/2026-07-large-language-quietly-reshaping-life.html
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