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AI Software Development – What Does The Data Say?
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A lot of tokens tends to help move the needle, building in guardrails, sophisticated review of both in process work & final outputs, and consensus helps raise the bar considerably.
That and sane, universally consistent, well structured, low tech debt, organizationally elegant, systematically evolved codebases. Something the grand majority of teams don't have to begin with.
Nevermind being well documented, with excellent opinionated linting and static type enforcement configurations, with robust CI checks. More things many projects don't seem to have.
Turns out that giving AI poorly engineered and maintained codebases just amplifies the poor engineering and low rigor already present. Something that's endemic to our industry.