A benchmark score looks wonderfully solid until the model changes, the dataset changes, the deployment stack changes, the GPU behaves differently, the logging pipeline drops half the useful metadata, and someone asks whether the result still means anything for their actual application. At that point, the leaderboard number is not wrong. It is worse: it is under-described. ...
Procurement meetings have a habit of turning AI agents into theatre. A vendor shows a polished research assistant. It finds papers, writes a summary, cites sources, maybe generates a small experiment plan. Everyone nods. Someone says “agentic workflow.” Someone else says “autonomous discovery.” A budget appears. The machine is declared practically scientific, which is convenient, because the machine itself has not yet been asked to survive the boring parts of science: retrieval under controlled conditions, code execution, data analysis, experimental reproduction, hypothesis testing, and the small matter of completing all required steps without wandering into the digital bushes. ...
A research report lands on your desk. It has a neat abstract, respectable tables, clean code attached, and just enough methodological language to sound like someone suffered through the usual academic rituals. Except this time, no one did. An AI scientist system generated the idea, wrote the code, ran the experiments, selected the result, and drafted the paper. ...
Every R&D team has a shelf of papers that are theoretically useful and practically booby-trapped. The abstract is promising. The method is relevant. The results look transferable. Then reality arrives wearing a conda error message: the repository has three setup paths, two notebooks, one undocumented dependency, and a tutorial that assumes you already know the answer. The paper has been published. The method has not, in any serious operational sense, been delivered. ...