This question comes up on almost every counselling call now, usually from someone who read a headline the week before. We do not have a hiring-volume dataset to hand you — nobody outside the hiring companies does — but we do sit across the table from our placement partners every week, and here is what that conversation has actually been about.
What has genuinely changed
AI coding assistants have changed what the first few months of a junior role look like. Boilerplate, scaffolding, and a lot of what used to be "learn the codebase by writing small tickets" now happens faster, with the tool doing more of the typing. That is real, and pretending otherwise does not help anyone preparing for interviews.
What has not changed is who gets hired to do the surrounding work: deciding what to build, reviewing whether the generated code is actually correct, debugging it when it is not, and owning the outcome when something breaks in production. That work has arguably grown, not shrunk.
What interviewers are asking differently
The technical round increasingly includes a "here is some code, what is wrong with it" segment, sometimes explicitly AI-generated. Interviewers are testing whether you can read and judge code, not just produce it. Candidates who can only write from a blank editor and cannot review someone else's (or something else's) work are being screened out at a stage that did not exist two years ago.
- Can you explain why a piece of generated code is wrong, not just that it fails a test?
- Can you debug something you did not write?
- Do you understand the fundamentals underneath the tool — data structures, how a request actually flows through a system — well enough to know when the assistant is confidently wrong?
Where the demand moved, not disappeared
Teams that shipped an AI feature last year are now hiring to maintain, evaluate and improve it — that is real, additive demand, not a replacement of the roles it sits next to. See our piece on the AI skills employers are hiring for if you want the specifics on RAG, agentic systems and evaluation, which are the fastest-growing lines in that direction.
The honest advice
Learn to use AI tools well — refusing to is its own competitive disadvantage now. But learn the fundamentals first, hard enough that you can catch the tool being wrong. That combination is what is actually being hired for, and it is also, not coincidentally, what makes someone good at the job once they have it.
Written by the Xypron Admissions Team. If anything here contradicts what an institute has told you — including us — ask them to explain the difference. That question is usually more informative than the answer.