AI coding assistants like GitHub Copilot and Tabnine have become very popular. According to StackOverflow's 2024 developer survey, "72% of all respondents are favorable or very favorable of AI tools for development". While we also see their benefits, we're wary about the medium- to long-term impact this will have on code quality and caution developers about complacency with AI-generated code. It’s all too tempting to be less vigilant when reviewing AI suggestions after a few positive experiences with an assistant. Studies like this one by GitClear show a trend of faster growing codebases, which we suspect coincide with larger pull requests. And this study by GitHub has us wondering whether the mentioned 15% increase of the pull request merge rate is actually a good thing or whether people are merging larger pull requests faster because they trust the AI results too much. We're still using the basic "getting started" advice we gave over a year ago, which is to beware of automation bias, sunk cost fallacy, anchoring bias and review fatigue. We also recommend that programmers develop a good mental framework about where and when not to use and trust AI.
