"There is a growing belief that AI will dramatically reduce the need for experienced software engineers. It won't. The demonstrations are compelling. We see AI connected to Figma for design context, Jira for tickets, source control for repository history, and CI/CD pipelines for deployment. A feature request goes in, code comes out, and a pull request appears. The workflow looks increasingly automated."
"In more than twenty years of building software across enterprise systems, I have never seen a ticket that meets that standard. Real tickets are approximations. They capture intent, not full reality. They rely on knowledge that exists in conversations, prior decisions, Slack threads, and architectural conventions that were never formally documented. They reflect trade-offs that were negotiated informally. They assume the context that experienced engineers carry implicitly."
AI removes much of the difficulty of writing code but does not remove the responsibility of designing systems and making architectural decisions. Integrated demonstrations show AI producing code from feature requests when inputs are complete and unambiguous. Real-world requirements are approximations that capture intent, not full reality, and depend on conversations, prior decisions, Slack threads, and undocumented conventions. Automation effectiveness is directly tied to the precision of problem definitions and acceptance criteria. Experienced engineers remain essential to interpret ambiguity, negotiate trade-offs, define boundaries, and ensure system integrity. Organizations must invest in clearer specifications, shared architectural knowledge, documentation, and feedback loops to maximize AI benefits.
Read at InfoWorld
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