The cost of saying yes has changed

In the evolving landscape of software engineering, the cost dynamics of implementing small feature requests have shifted, with the decision-making process often being more expensive than the actual coding. Engineers must discern changes that genuinely require extensive scrutiny from those that can be quickly addressed by leveraging AI tools like GitHub's Copilot, which can generate initial code patches efficiently. However, while these tools reduce the cost of producing code, they do not diminish the cost of understanding, reviewing, and owning the changes. The new skill set involves rapidly pricing uncertainty, discerning when a task is genuinely small enough to attempt without significant deliberation. As AI can produce candidate solutions swiftly, the focus shifts to evaluating these outputs to make informed decisions, ensuring that the long-term ownership costs are considered rather than merely the initial generation cost. This approach allows engineers to move scope discipline closer to the review stage, emphasizing evidence-based decision-making over preemptive planning.

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