Andrej Karpathy described the pattern: “I ‘Accept All’ always, I don’t read the diffs anymore.” When AI code is good enough most of the time, humans stop reviewing carefully. Nearly half of AI-generated code fails basic security tests, and newer, larger models do not generate significantly more secure code than their predecessors. The errors are there. The reviewers are not. Even Karpathy does not trust it: he later outlined a cautious workflow for “code [he] actually care[s] about,” and when he built his own serious project, he hand-coded it.
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Machine learning interatomic potentials like MACE (NeurIPS 2022) have reached a point where they rival density functional theory in accuracy while running orders of magnitude faster. But using them still requires Python scripting, command-line fluency, and environment setup that shuts out a large number of researchers, especially those with accessibility needs, those in under-resourced labs, or students encountering computational chemistry for the first time.
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Варвара Кошечкина (редактор отдела оперативной информации)