A coding interview used to test one thing: whether you can solve a problem from a blank page with nothing but a text editor and a clock. That premise is gone. At a growing list of companies — Meta, Shopify, LinkedIn, Canva, and Uber among them — the interview now hands you an AI assistant alongside the problem, and evaluates how well you work with it.
This isn't a rumor or a niche experiment. It's the direction the industry is moving, and it changes what you should be practicing.
What the format looks like
The common shape is one of two things. In a structured environment — often CoderPad or HackerRank, sometimes a company's own in-browser editor — you get a panel with a problem statement and a built-in AI chat. The interviewer walks you through an arc that typically runs bug-fix, then feature, then scale. In an open-ended format, you share your screen and use your own editor and your own AI tools, and the interview is a greenfield build or a deep design-and-implement exercise.
In both cases the AI is a first-class part of the environment. It's not an edge case you can avoid, and it's not a shortcut you're expected to abuse. It's the tool you're being evaluated on.
Why companies switched
The reason is practical, and it's not a gimmick. Engineers who ship in 2026 use AI assistants daily — for reading unfamiliar codebases, drafting implementations, writing tests, and catching mistakes. An interview that bans that tool measures a skill that's becoming less relevant by the quarter: solving problems in a world without AI. An interview that includes it measures the skill that actually determines on-the-job output: solving problems with an AI at your side, while staying in control.
There's a second reason. Companies want to see how you behave when the AI is wrong. In a normal interview you can't meaningfully test verification and debugging instincts because there isn't enough code to debug. Hand someone an AI that occasionally produces confidently wrong output and you instantly have a rich, realistic test of whether the candidate catches hallucinations, reads output critically, and keeps the build green.
What it's actually testing
Interviewers are watching for a specific set of behaviours rather than a final artifact:
- Whether you plan before you prompt. The AI answers instantly, which makes it seductive to skip straight to asking. Do you decompose the problem and decide the approach first, or do you let the AI's first answer become your plan?
- Whether you own the architectural decisions. You can accept the AI's abstractions and feel like you're making progress. Interviewers watch who makes the calls — the model or the engineer.
- Whether you verify what the AI produces. Do you run the code, read the output, and use tests as a contract? Or do you accept generation output because it looks plausible?
- Whether you keep the interviewer with you. You're managing two conversations — with the model and with the person grading you. Do you narrate intent and review out loud, or go quiet?
None of these are about the specific code. They're about the habits you'd want in a senior engineer on any team.
What this means for your preparation
If you're prepping for a traditional whiteboard interview, the core algorithms work still matters — you can't direct an AI toward BFS if you don't know when BFS is the right call. But the skill that decides the outcome has shifted from execution speed to judgment under an AI's influence: forming a plan, driving the tool precisely, and verifying that the output actually does what you asked.
That's a specific skill, and it's trainable. The most effective way to build it is to practice in the same environment you'll face — a constrained sandbox with an AI panel for structured interviews, and your real tools for open-ended ones — until the workflow of plan, prompt, verify feels normal.
The one-line summary
AI-enabled interviews don't ask "can you code." They ask "can you ship a correct solution when an AI does the typing — and can you stay in control when it's wrong?"
The short version
The interview changed because the job changed. Engineers now work alongside AI, so interviews test that partnership. If you understand what's being measured — planning, control, verification, communication — you can prepare for it directly, and the practice that used to build typing speed now builds judgment instead.