Interviewing Developers in the AI Era: What to Test When Everyone Uses AI

Your technical interview probably still tests whether a candidate can reverse a binary tree from memory. In 2026, that tells you almost nothing — an AI assistant does it in two seconds, and so will the candidate the moment they start the job. The hard question for employers now is: if everyone codes with AI, what are you actually hiring for? Here's how to interview developers when AI is a given.

The problem, in one stat

71% of engineering leaders say AI is making technical skills harder to assess. Meanwhile, 46% of hiring teams now allow AI in technical interviews (another 20% decide case by case). The old signal — "can they produce working code" — has been commoditized. You need new signals.

What still matters (and what doesn't)

Stop over-weighting: algorithm trivia, syntax recall, "implement quicksort on a whiteboard." AI erased the gap between candidates on these.

Start weighting:

• Judgment — can they decide what to build and why, from an ambiguous problem?
• Verification — can they catch when the AI is confidently wrong? This is now one of the strongest hiring signals teams look for.
• Debugging — given a broken codebase, can they find and fix the real issue?
• System thinking — do they hold performance, security, cost and maintainability in mind at once?
• Communication — can they explain a tradeoff to a non-engineer?

Four interview formats that work in 2026

1. The "fix the AI's code" exercise. Hand them a plausible-looking function an AI generated that's subtly wrong (an off-by-one, a race condition, a security hole). Watching them find it separates engineers from prompt-typists.
2. AI-allowed, judgment-scored. Let them use AI on a realistic task — then score how they direct it: the prompts they write, the outputs they reject, the tests they add. Ban "vibe coding" where they paste output without understanding it.
3. Debug a real repo. Give them a small, broken project and a failing test. Real work is 80% reading and fixing existing code, not greenfield.
4. The "why" conversation. Walk through a past project: why that architecture, what they'd change, what broke. Depth here can't be faked or AI-generated.

Skip the take-home arms race

Long take-homes are now meaningless — a candidate can AI-generate a polished submission in an hour. If you use one, keep it short (60–90 min), then spend the interview asking them to extend or debug their own submission live. That's where the truth comes out.

Screen for this before you interview

Interviews are expensive — don't spend them on poor fits. On RightHiring AI you describe the role in plain language and get an AI-ranked shortlist of remote developers matched on real, specific skills across RightHiring talent, GitHub and licensed profiles — so the people who reach your interview are already worth the hour. No agency fee.