AI as an intern, the death of "Scale," and choosing clarity over cleverness.

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Engineering leadership becomes more critical as AI tools commoditize code generation, requiring strategic oversight and judgment rather than technical execution. The shift from "scale" to practical AI application, federal AI regulation consolidation, and the emergence of context engineering over prompt engineering signal fundamental changes in how engineering teams operate. Leaders must focus on customer problems first, measure AI tool impact through adoption and delivery metrics, and prioritize clarity in naming and architecture decisions to support both human and AI comprehension.

Why engineering leadership matters more than ever | Manoj Mohan

5. Your project name should explain its function, not your sense of humor