AI Coding One Year Later: A Product Manager's Lessons

AI coding tools have rapidly become common in software development, with the author describing their growing use at PubNub for prototyping, feasibility exploration, demos, and product development within appropriate guardrails. They are especially effective for greenfield projects and well-defined tasks, enabling teams to build and test tangible prototypes quickly, gather feedback, and iterate, though accelerated cycles can become exhausting or lead to premature changes without sufficient evidence. The author argues that human product judgment remains essential to define target users, prioritize problems, limit scope, interpret customer feedback, and assess business outcomes, since AI often proposes overly complete solutions before a core experience has been validated. Requirements increasingly need to serve both human collaborators and AI agents, requiring clear context, expected behavior, and outcomes while leaving technical implementation open to engineering exploration. Although AI can create interfaces quickly, it still produces inconsistent user experiences and cannot replace UX expertise, user feedback, or iterative design. As tools and practices continue to evolve, the author concludes that established principles such as focus, clarity, iteration, and documentation remain relevant, with judgment becoming an especially important additional skill.

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