Eight Myths on Software Engineering and GenAI
This is a welcome reset for an argument too often conducted with demos and vibes. The authors separate code generation from software delivery, point out that developers spend only a fraction of their time typing code, and reject lines produced as a proxy for value. AI assistance can help, but its effect varies with the task, the developer, the codebase, and the surrounding organization. Buying licenses does not redesign review queues, clarify requirements, or make legacy and regulated systems less consequential.
The practical correction is to measure the whole system. Faster implementation can simply move the bottleneck into verification, coordination, security, or operations; more generated code may increase the amount that experienced engineers must understand. None of that makes the tools useless. It makes "productivity" a claim that needs an outcome, a baseline, and a time horizon. Teams should ask whether dependable changes reach users sooner without increasing defects or hidden maintenance work, not whether the editor produced more text.