I build an AI product for a living, and I'll tell you what the models are good at: they write my CRUD endpoints, my Terraform, my test scaffolding, and they'd clear most LeetCode mediums faster than I can read the problem statement. What they have never once done is defend a decision when I push back on it. Ask a model "why Kafka and not Postgres here," then attack its answer twice, and it folds into a hedged survey of options. That gap, the ability to hold an engineering position under interrogation, is the last part of the technical interview AI can't do for you. It's also exactly what a system design round tests.
So the hiring bar is moving. If your interview prep is still 80% algorithm grinding and 20% skimming a scalability blog the night before, you're preparing for the interview of five years ago.
The coding screen stopped measuring what it used to
A take-home or an unproctored coding screen used to answer a real question: can this person write working code under mild pressure? Now it mostly answers a different one: does this person have a Copilot subscription? Every engineer I know uses AI for boilerplate, and every hiring manager I've talked to knows it. Some companies respond by proctoring harder. The smarter ones respond by shifting weight to the rounds where assistance doesn't help, and the design round is the biggest one.
