What This Is

In real interview logs circulating on developer communities Niuke, Juejin, and SegmentFault, traditional coding questions are being swapped out: "implement quicksort by hand" and "explain the event loop" are giving way to prompts like "AI-generated code ships and triggers a P0-level (P0 = highest severity) incident—how do you split liability?" "A reviewer marked 'verified' but missed a bug—who's on the hook?" "Do you treat AI as a typewriter or as a junior intern?" The reason for the shift is straightforward. Once "shipping code" stops being scarce and every resume lists a few AI-built projects, interviewers face a new problem: "Everyone looks stronger on paper, but the difficulty of actually filtering out signal has gone up." AI has leveled implementation ability; judgment, verification, and accountability are three things it cannot replace. What we should care about: enterprise AI is moving from the "can we use it" stage to the "who's responsible when it breaks" stage. Coding is just the first profession to hit this wall.

Industry View

The supportive camp says this signals enterprise AI governance is starting to land—a mark of maturity. The "typewriter vs. junior intern" framework at least offers a layered approach: the submitter carries baseline responsibility, the reviewer verifies, and the process retains an evidence chain (complete, traceable decision records). The fact that interview questions can pose this problem means organizations are already thinking about it. But the objections and risks are equally clear. First, these 12 questions themselves prove that even the most AI-savvy tech companies haven't figured out the answer, let alone other enterprises. Liability allocation is still one standard answer short of the casual summaries floating around. Second, "typewriter vs. intern" is a metaphor, not a policy. Apply it to non-code AI applications—contracts drafted by AI, financial analyses run by AI—and no one can articulate where the breach point sits or how to slice responsibility. Third, this rise in interview screening difficulty could end up slowing down enterprise AI adoption: too few teams can handle the accountability question, so most companies will prefer to wait and see.

Impact on Regular People

For enterprise IT: AI governance will land on the compliance checklist. Practices like "specialized audits of AI-generated content" and "evidence chain logging" will migrate from technical documentation into management policy. For individual careers: even if you don't write code, anyone using AI to produce slide decks, contracts, or financial analyses faces the same question—when AI breaks something, can you personally bear the responsibility? The underlying logic is identical. For the consumer market: AI tools' disclaimers will increasingly resemble software license agreements. Ordinary users will need to start reading the fine print and understand what AI cannot underwrite on their behalf.