I’ve been observing the discourse around GitHub Copilot with considerable interest, particularly the dominant narrative that it universally accelerates development velocity. While I do not dispute that it can serve as a powerful autocomplete tool, my personal experience has trended in the opposite direction. I find myself spending more time, not less, when Copilot is active. The reason, I suspect, is rooted in a moderation and code review mindset: I feel compelled to scrutinize every single suggestion it offers.
The advertised workflow implies a seamless acceptance of intelligent completions. However, in practice, especially in a B2B SaaS context where code clarity, maintainability, and adherence to specific internal patterns are paramount, blind acceptance is untenable. Copilot will often suggest a plausible-looking block that, upon closer inspection, may introduce subtle inefficiencies, use a library pattern we've deprecated, or simply solve a problem in a way that is incongruent with the surrounding architecture. The cognitive load shifts from writing code to auditing it in real-time.
For example, when implementing a fairly standard API service method, Copilot might generate a complete function including error handling and logging. My instinct is not to accept it and move on, but to pause and evaluate: Does this error handling match our team's agreed-upon taxonomy? Is the logging statement at the correct severity level and does it include the necessary structured data? Could this loop be expressed more readably as a map function? This process of review and potential refactoring often takes longer than if I had simply written the conventional pattern from memory.
This leads me to a broader question about tool adoption and netiquette. Are we, as a community, perhaps over-indexing on raw speed as the primary metric of value? There is an argument to be made for deliberate, thoughtful coding, even if it is mechanically slower. The time "saved" in initial keystrokes might be later lost in code review cycles, debugging obscure suggested logic, or in onboarding new team members to codebases filled with heterogeneous, AI-generated patterns.
I am curious if others in the community, particularly those with responsibilities for code quality and long-term maintainability, have encountered a similar phenomenon. Have you developed specific workflows or Copilot configuration strategies to mitigate this review burden, or have you concluded that, for certain types of work, a more traditional autocomplete is preferable? I am not advocating for abandoning the tool, but rather for a more nuanced discussion of its impact on the entire software development lifecycle, not just the initial drafting phase.
— EthanP
Let's keep it constructive