AI Makes Code Easy to Produce, So Tech Interviews Are Shifting to Test Judgment

For roughly 15 years, coding interviews relied on take-home projects or whiteboard exercises as proof of ability, but AI tools have made producing plausible code trivially easy, undermining those artifacts as reliable signals. Hiring practices are now evolving toward conversation-based formats that assess a candidate's reasoning, such as diagnosing slow services, spotting flaws in code, or explaining past design decisions and their tradeoffs. These questions demand real-time judgment that cannot easily be borrowed from an AI, making them more resistant to shortcuts. Experts advise candidates to practice thinking aloud, preparing honest stories about past decisions, and pairing admissions of uncertainty with a clear plan to find answers. Professional reputation and personal references are also gaining weight, as vouching from colleagues is harder to fabricate than any submitted work.