Every scientist a modeller? The promise and peril of AI ‐assisted mechanistic models

Mechanistic models, expressed as simulations that can be run and visualised, are a powerful way to understand biological and other systems. Building them, however, requires formulating mathematical or rule‐based descriptions and translating them into working code, skills that many ecologists and other disciplinary researchers do not have. Generative AI can now perform much of this translation, making mechanistic modelling accessible to researchers, educators and students previously excluded by its technical demands. But delegating this translation to AI also carries real risks: modelling assumptions can become hidden, plausible but incorrect code can pass unnoticed, and the skills and critical habits that model building once developed may never form. We examine the benefits and risks of using generative AI as a modelling companion at each stage of model development (conceptual specification, formalisation, implementation and exploration), drawing on recent experimental evidence on AI‐assisted learning and on a worked predator–prey example built with AI, with a rule‐based epidemic model as a parallel case. At each stage we identify where consequential decisions are silently delegated to the AI and set out practical mitigations that keep human judgement engaged without requiring the technical skills AI has made optional: specifying the conceptual model explicitly before engaging AI, having the AI translate its formalisation back into plain language, testing the implementation against outcomes known from domain knowledge and designing the interface to support interrogation. This new opportunity is substantial: researchers, educators and students who understand their systems but have never been able to build models of them are now able to explore these systems mechanistically, testing how components interact and how the system responds to conditions it has not yet experienced, questions that correlative approaches alone cannot answer. Used carelessly, AI risks eroding modelling skill for little lasting benefit, but used deliberately, in the ways set out here, it opens a new pathway to mechanistic understanding without removing the critical thinking the old technical barrier once enforced.