A "spend 100, save 10" promotion logic: the code AI produced also applied the 10% discount to a ¥99 product. The code runs and outputs numbers — but the result is wrong. This is the point a Juejin tech article this week hammers home: we've noticed that "runnable AI code" and "deliverable code" have never been the same thing.
What this is
This article, aimed at programming beginners, breaks AI code verification into five dimensions: syntax is correct (runs), logic is correct (meets requirements), exceptions are handled (edge cases), the code matches the project's tech stack — the combination of technologies in use, like frontend frameworks or backend languages — and follows code standards, and has been human-verified.
Using the promotion example, the author illustrates a common misjudgment: when developers see AI produce runnable code on the first try, they assume it understood their requirements. But for prompts like "write me a login feature" or "write me a promotion-discount logic" that omit the conditions, AI just guesses the most common case.
The author further flags the pitfalls beginners most often fall into: empty parameters, wrong types, missing fields, edge-value exceptions — plus a more insidious category: AI-recommended dependencies may not exist at all; the code may use deprecated APIs (application programming interfaces); and handling in sensitive scenarios may leave security holes.
Industry view
Supporters argue this article moves the "AI coding" discussion from "can it write" to "does it write well." When enterprises deploy AI coding tools, what stalls them again and again is rarely the generation step — it's verification. That's precisely why this piece deserves attention.
But there is dissent. Senior engineers argue that telling beginners "don't use AI code directly" is fine, but for experienced developers the framing is backwards. The point isn't "should we verify" — it's "how do we automate verification." In their view, treating AI tools as a "reference book" rather than a "collaborator" actually drags efficiency down.
Another overlooked risk is security. The article cites the example: directly concatenating user input into a webpage's innerHTML — a method that renders strings as HTML — will display content normally but leave an XSS (cross-site scripting) vulnerability. AI won't flag these on its own. Humans have to.
Impact on regular people
For enterprise IT: the share of working hours spent on manual code review will rise — especially for sensitive modules involving payments, permissions, and user information. AI has driven "write" costs down; "review" costs have been pushed up.
For individual careers: programmers' focus may shift from "lines of code shipped" to "defining requirements and verifying results." Put differently: people who can describe problems and spot errors are worth more than people who can simply write code.
For the consumer market: in the short term, users may see software updates slow down, because teams now add a verification step between AI generation and release. But once that verification pipeline is running smoothly, final product stability may actually improve.