Three unrelated posts made the rounds this week. One argued that reviewing AI-generated code requires a completely different mental model than reviewing human code. Another described engineering teams moving so fast on AI-assisted commits that design discussions and review quality are quietly collapsing. A third put a name to something a lot of engineers have felt but not said out loud: reviewer fatigue — the specific exhaustion of constantly having to judge whether a model's output can be trusted.
None of these were about interviews. All of them are about interviews.
The tension
For a decade, "can you write correct code" was the entire coding interview. AI agents now write correct code faster than almost any human can type it. If that were still the only thing being measured, the interview would already be pointless — the model in your IDE would ace it on your behalf.
It is not pointless, because the bar moved to the thing AI is worst at: deciding whether the output is actually right, for this system, under these constraints, and being able to say why. That is a review skill, not a writing skill. And most candidates have spent years practicing the wrong half.
The insight: writing got cheap, judgment got expensive
The engineering blog post making the rounds this week put it plainly — reviewing AI-generated code pulls attention away from syntax and toward validating requirements, architecture, security, and correctness. A human wrote most of what senior engineers used to review, so the reviewer could assume basic competence and focus on judgment calls. AI-generated code does not come with that assumption. It can be fluent and confidently wrong in the same sentence, so the reviewer has to independently reconstruct whether the approach is right at all — not just whether the syntax passes.
That is a heavier cognitive job than it sounds. It is also, per the reviewer-fatigue piece, a genuinely tiring one. Constant oversight of AI output produces its own kind of burnout — not because the work is hard exactly, but because it demands sustained, active skepticism instead of the more relaxed trust you extend to a colleague's PR. The skill did not disappear. It moved, and it got heavier.
When an interviewer hands you AI-generated or AI-assisted code and asks you to evaluate it, they are not testing whether you can spot a typo. They are testing whether you can independently verify a solution you did not derive yourself — under time pressure, out loud.
Evidence: the collaboration layer is where it breaks first
The third story this week was less about individual review and more about what happens at team scale: engineering teams shipping AI-assisted code so fast that technical design discussions get skipped, product requirements arrive fragmented, and code review degrades into a rubber stamp because there is too much surface area to interrogate properly.
That is the same failure mode as reviewer fatigue, just at the org level. And it points at exactly what a strong interview candidate needs to demonstrate: the ability to slow a fast-moving AI-assisted process down at precisely the point where slowing down matters, and to articulate why.
| Weak signal | Strong signal |
|---|---|
| Approves AI-generated code because it runs and passes tests | Traces the logic independently before trusting the tests |
| Reviews by reading the diff top to bottom | Reviews by asking what the code needed to do, then checking if it does that |
| Cannot explain why an AI-suggested approach was chosen | States the tradeoff the AI made and whether it is the right one here |
| Treats AI output as either fully trusted or fully suspect | Calibrates trust per-decision — syntax vs. architecture vs. edge cases |
What to do
The engineers who will do well in 2026 interviews are not the ones who can produce the fastest correct answer — the model already does that. They are the ones who can look at code they did not write, decide whether it deserves to ship, and explain that decision clearly enough that someone else can trust it too.
Rubduck's AI-assisted interview format hands you real code — some of it AI-generated — and grades how you review, verify, and explain it under a live voice interviewer, not just whether you can produce a solution. Start your free sessions →