Benoit Schillings contrasts earlier limits imposed by hardware and human modular reasoning with an AI-driven environment where producing code becomes less expensive. He argues that deciding what a system should do and managing a large existing codebase remain distinct from generating a syntactically correct function.
Benoit Schillings discusses why code is an attractive learning domain: plentiful examples and executable checks support feedback. He presents self-play and generated challenges as ways to extend learning beyond human-written training material, while identifying planning and cross-domain reasoning as continuing research problems.
Benoit Schillings calls for evaluations that cover architecture and open-ended improvement, not only correct outputs. He also considers stronger type systems, correctness-oriented languages and active security guardrails. His predictions about future code-review practices and wider scientific applications are forward-looking views, not established outcomes.
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