The gradual integration of generative AI into life sciences research is undergoing what researchers term a creeping normality, fundamentally altering scientific workflows before formal guidelines are established. A recent international study published in Frontiers in Ecology and the Environment warns that routine reliance on large language models risks reshaping the foundational practices and culture of biological research. The report highlights three primary areas of disruption. First, large language models are increasingly substituting traditional colleague interactions, potentially weakening cross-disciplinary collaboration and reducing exposure to diverse methodological perspectives. Second, the shift from traditional literature searches to AI-generated feedback threatens to reinforce confirmation bias, create intellectual echo chambers, and diminish originality in scientific communication and problem-solving. Third, while artificial intelligence lowers entry barriers for early-career researchers, it simultaneously risks deskilling core competencies such as statistical reasoning, coding, taxonomic judgment, and natural-history analysis. This outsourcing of foundational skills may inadvertently discourage academic hiring and exacerbate existing inequalities within research institutions. Rather than opposing technological adoption, the authors advocate for the immediate establishment of strict usage boundaries. The research team, led by Ivan Jarić of the Czech Academy of Sciences and including Susan Canavan from the University of Galway and Michael Bertram of the Swedish University of Agricultural Sciences, recommends deploying these tools for routine administrative and editorial tasks while maintaining rigorous human oversight for literature synthesis and data extraction. Crucially, the authors stress that artificial intelligence must be excluded from domains requiring independent scientific judgment, including research prioritization, peer review, funding allocation, and ethical decision-making. The study concludes that the question is no longer whether generative AI will permeate life sciences, but how the research community will define its operational limits. Establishing clear boundaries now is essential to preserve creativity, methodological diversity, accountability, and human expertise as the bedrock of scientific progress.