What would the last software engineer still need to do?

As AI coding agents increasingly handle implementation tasks such as writing code, opening pull requests, running tests, and incorporating review feedback, Kent C. Dodds argues that software engineers’ greatest value will shift toward deciding what should be built. He describes this as product engineering: combining deep understanding of users, business needs, architecture, data, infrastructure, and long-term maintenance to distinguish what a system can do from what it should do. Engineers should investigate underlying problems rather than immediately implement proposed solutions, gain direct exposure to customer and support experiences, and retain ownership of decisions before and after release. In this model, agents can perform much of the coding and iterative review work, while engineers assess broader system effects, including new abstractions, migrations, operational costs, and user outcomes. Dodds recommends that developers learn one AI-agent workflow well enough to be productive, build deep knowledge of real technical systems, avoid overinvesting in short-lived tool techniques, practice problem discovery, review decisions rather than only code diffs, and use observability and feedback to remain accountable for software after it ships.

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