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Hiring teams built better cheating detectors. Candidates got better at beating them. The real fix is changing what a coding assignment is supposed to measure.
Cheating on take-home coding tests is near 48% for technical roles. The rush back to camera-monitored live coding solves the cheating problem while breaking a different one.
AI tools complete most take-home coding assignments in minutes. The more important question is what we were measuring in the first place, and whether the format ever measured it well.
When Anthropic ran their take-home assignment through Claude and it passed, the team kept making it harder — until the test no longer resembled real work. That conclusion reveals something that was always true.
General software engineering roles are down ~36-49% while AI/ML openings are up 59%. The result is two candidate pools with almost no overlap — and most job descriptions accidentally fish in both.
AI didn't kill the take-home coding assignment — it exposed what was always wrong with it. Five alternatives that actually predict whether someone can do the job.
Most engineering take-homes broke when AI tools arrived. But the format was already measuring the wrong thing. Here's how to redesign the rubric so the assessment holds up.
Most engineering ladders have a technical track on paper and a management track in practice. Here are the three structural reasons this happens, and what to change.
Take-home coding assignments were designed to measure unassisted engineering work. AI coding tools made that assumption obsolete. Here is what the replacement formats look like.
AI has broken the signal in most engineering take-home formats. The one that survives is code review — and the reason it survives reveals what you should be measuring in the first place.
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