Which software engineering skills matter in the age of AI?

Kunal Kushwaha argues that as AI increasingly automates software implementation, engineers will remain responsible for understanding problems, selecting approaches, evaluating outcomes, and standing behind what reaches production. He emphasizes that technical fundamentals help developers assess AI-generated code and avoid hype, while product judgment, empathy, collaboration, and the ability to identify valuable user problems become more important as building software becomes easier. Drawing on hackathons and a young participant’s rapid AI-assisted project, he notes that strong execution still matters, with small usable products often outperforming ambitious but incomplete ideas. Kushwaha expects automation to take on more code review, though high-stakes changes may still warrant human oversight because agents do not assume responsibility for failures or account fully for a change’s broader effects. He recommends running LLMs locally to understand the infrastructure, deployment tradeoffs, and systems behind AI services, and encourages developers to build experience through projects, open-source contributions, hackathons, and public sharing that invites feedback and demonstrates genuine understanding.

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