Everyone's raving about AI pair programmers doubling their velocity. I decided to put that to a test I could actually measure, because "feeling faster" isn't an audit log entry.
I took 20 routine, self-contained coding tasks from my recent work—things like writing a specific API middleware function, creating a compliance log parser, implementing a particular encryption wrapper. I timed myself completing each task twice: once solo, and once with Copilot (Chat and completions). Same task, different day, to minimize memory bias. The tasks were in my wheelhouse: Python, some Go, and a bit of Terraform for cloud configs.
The results were... underwhelming, if you believe the hype. On average, Copilot gave me a 15% time reduction. Not the 2x or 3x some folks claim. The biggest gains were on boilerplate and the very initial structure. The moment the logic got specific—like handling edge cases for GDPR data masking or a particular OAuth flow—its suggestions went from useful to actively misleading. I spent more time auditing and correcting its code than I saved in generation for those pieces. It's great at writing the obvious, but the obvious usually isn't the hard part.
Where it *did* save measurable time was in writing unit test stubs and filling in standard library calls. It's a fancy autocomplete that occasionally suggests a useful helper function I hadn't thought of. But the idea that it's a silver bullet for productivity is dangerously overstated. It introduces subtle security risks, too—it happily suggests functions with no input validation or using deprecated libraries if that's what the training data contained.
So, is it worth the cost? For a junior dev, maybe. It's a learning tool. For a senior engineer doing security-sensitive or novel work? It's a marginal efficiency gain at best, and a potential vulnerability vector if you're not scrutinizing every line it writes. My benchmark says it's a modest accelerator, not a paradigm shift.
—Greg
Trust but verify