Why Technical Interviews Have Become AI Cheating's Biggest Target

A 2026 study of nearly 20,000 AI-powered interviews, reported by Forbes, found something specific enough to act on: AI-assisted cheating showed up in technical interviews at 48%, compared with 12% in sales roles. That's not a small gap — it's four times the rate, in the same dataset, over the same months. The overall numbers are worth knowing too, but the technical-specific split is the part hiring managers in engineering, data, and other technical functions should actually be planning around.

Why technical interviews are the easier target

A sales role interview lives or dies on delivery — tone, timing, the ability to improvise around an objection nobody prepared for. That's genuinely hard for a live AI copilot to fake convincingly, because so much of what's being evaluated is happening in real time, in response to something unpredictable.

A technical interview is a different shape of problem. A lot of what gets asked — explain how you'd design this system, walk me through this algorithm's complexity, what would you check first — has a "correct-shaped" answer that a language model is very good at producing instantly, and a candidate reading or paraphrasing that answer in real time can sound a lot like someone reasoning through it out loud. The signal that would normally separate "understands this" from "is reciting this" — hesitation, false starts, wrong turns before landing on the right one — is exactly what a fed-in answer skips.

Junior candidates are the most exposed group

The same study found candidates with 0–5 years of experience were flagged at nearly double the rate of senior professionals. That tracks with the technical-interview finding rather than contradicting it: junior technical candidates are the group most likely to be asked exactly the kind of "explain the concept" or "walk through the standard approach" question that a language model answers well and a live copilot can feed convincingly, while having the least interview experience of their own to fall back on if the tooling fails mid-answer.

What actually helps in a technical screen

A few adjustments follow directly from why technical interviews are exploitable in the first place, rather than being generic anti-cheating advice:

• Ask for the reasoning, not just the answer. "What would you check first, and why" surfaces genuine understanding in a way a memorized or fed answer often can't extend into.
• Push follow-ups that build on their specific answer, not a fresh question from a list. A recited response frequently can't be extended the way real understanding can — ask what changes if the input were already sorted after a sorting-algorithm answer, and watch what happens.
• Treat live coding as a signal generator, not just a pass/fail gate. How someone talks through a bug, not just whether they fix it, is exactly the kind of moment-to-moment texture that's hardest for a copilot to fake convincingly.
• Don't rely on one interviewer's ear alone. With cheating tripling in months and concentrated this heavily in technical rounds, a consistent, automated second read on the transcript catches what a tired interviewer on their fifth technical screen of the day might miss.

Where we fit

That last point is why every engineer we put forward for clients does a live technical interview on a real problem, checked with our anti-cheat. It reads the conversation in the background and notes the moments where an answer sounds recited rather than reasoned through, so the interviewer can ask a follow-up there and then. It's not a replacement for asking better questions; it's the layer underneath them, on every call rather than only the ones where something already felt off. A flag is a prompt for a follow-up question, never a verdict.

We vet every engineer down to the interview before you meet them. Book a call, or if you already have candidates, we'll check them.