"And then AI detonated the whole thing. Sure, people could technically cheat before. You could have friends feeding you hints or solving the problem with you but even that was limited. You needed friends who could code. And if your friend was good enough to help you, chances are you weren't completely incompetent yourself. Cheating still filtered for a certain baseline. AI destroyed that filtration layer."
"You just need a second monitor. Lying. You don't even need that. Check this out. And we've seen the impact firsthand. Candidates read picture-perfect solutions out loud without understanding a line. Others paste flawless code but forget a comma that the model inserted. Some tilt their head and silently read behavioral answers word-for-word. One person froze when asked something AI didn't have an answer: So... what makes it interesting?"
Traditional software interviewing relied on algorithmic problems, behavioural rounds, and system design to assess candidates' reasoning and baseline skills. AI now supplies perfect code, explanations, and design plans, allowing candidates to reproduce answers without comprehension and bypass social networks or experience. Observed behaviors include reciting picture-perfect solutions, pasting flawless code with minor AI-inserted errors, reading behavioral responses verbatim, and freezing when AI cannot provide an answer. Hiring teams face rapid erosion of prior filtration methods and increasing difficulty distinguishing genuine skill from AI-assisted performance. Organizations must reconsider interview formats and evaluation criteria to reliably measure authentic understanding and problem-solving.
Read at Yusuf Aytas
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