Expertise vs AI proficiency: a hiring dilemma

Bob, a senior leader at a fast-moving scale-up, faced a hiring dilemma that many engineering managers will soon confront. Two candidates completed the same take-home test: Candidate A delivered a solid solution without any AI help and could articulate trade-offs and alternative designs. Candidate B used AI to finish the optional parts faster but could not explain the underlying concepts or reason about the code.

The author argues that the default hiring rule should favor the expert who understands fundamentals over the AI-native who churns code quickly but lacks depth. AI-assisted coding tools are still unproven in terms of net impact on productivity; vendors are volatile, pricing is rising, and the long-term maintenance costs of AI-generated code remain unclear. Hiring someone who can reason about code reduces future technical debt and protects the team from the hidden liabilities of code that looks good on the surface.

Beyond the immediate hiring decision, the piece highlights how mandating tool usage-whether AI assistants or IDEs-can erode engineers' sense of autonomy and mastery, key drivers of intrinsic motivation. Over-reliance on AI may blunt cognitive skills, turning engineers into spec writers and reviewers rather than deep problem solvers. The upskilling cost to bring a junior AI-driven developer up to speed on fundamentals is far higher than teaching a seasoned engineer to use AI effectively.

While there are edge cases where an AI-savvy coder might be the right fit, the default stance remains: prioritize candidates with strong fundamentals and a growth mindset. This approach safeguards long-term productivity, code quality, and team morale in a landscape where AI tools are still evolving.

The author invites readers to discuss the dilemma in the community and offers professional services for fractional CTO roles, mentoring, and a paid engineering leadership community.