AI can code, but it can't build software (bytesauna.com)

AI can already write code reliably for well-scoped tasks, but it still can't replace the human work of building production software. The author—fielding many requests from nontechnical founders seeking CTOs—argues that large models (GPT-5 and similar) excel at solving isolated, well-defined problems and producing demos, yet fall short when a prototype must be turned into a maintainable, scalable product. In practice “making an app production-ready” often means dealing with hundreds of routine but interacting concerns (integration, testing, deployment, security, maintainability) that require system-level judgment and end up forcing engineers to scrap demo code and start anew. For the AI/ML community this is a reality check: progress in code generation reduces routine implementation effort but doesn’t obviate software engineering. The gap points to where research and tooling matter most—long-horizon planning, compositional reasoning, robust integration, versioning, automated testing, observability, and socio-technical collaboration. Practically, demand for skilled engineers and technical cofounders will persist, and impact assessments of automation should shift from “can it code?” to “can it manage complexity over time?”