AI-Allowed vs AI-Banned: How to Prepare for Both Kinds of Coding Interview in 2026 (Meta, Canva, Google & More)

The biggest misconception is that an AI-allowed round is easier. It isn't. The problem is usually bigger and more realistic (often an existing codebase, not a blank LeetCode editor), and the interviewer has stopped grading whether you can type a solution. They are grading how you direct, check and correct the AI.

• Problem breakdown before prompting. Candidates who paste the whole problem into the assistant on minute one score poorly. Interviewers want to see you split the task and decide what to delegate.
• Verification. Do you read the generated code? Do you write or run tests? Do you catch the off-by-one or the missing edge case the model introduced?
• Debugging AI output. Many problems are designed so the first AI answer is subtly wrong. Spotting and fixing it is the signal.
• Explaining trade-offs out loud. Why this data structure? What happens at 10x the input size? The AI can write code; it can't defend your decisions for you.

Format 2: The AI-banned round — why it got harder, not easier

Because so many candidates now cheat with hidden AI tools, companies that ban AI have tightened their rounds. One analysis of more than 19,000 AI-led interviews flagged 38.5% of candidates for cheating behaviour. The result for honest candidates:

• More follow-up questions. Interviewers probe your reasoning mid-solution to check that the thinking is yours.
• Proctoring. Tab-switch tracking, paste restrictions and webcam checks are now common in online assessments.
• Talking matters as much as coding. Silent candidates look suspicious. Narrating your approach is now a defensive skill, not just a nice-to-have.

The skill both formats reward: thinking out loud

Notice the overlap. In the AI-allowed round you explain why you're accepting or rejecting the assistant's code. In the AI-banned round you explain your approach to prove the work is yours. Either way, the candidate who can clearly narrate their reasoning wins. It's also the skill people practise least, because LeetCode grinding is silent.

Worked example: the same problem in both formats

Take a common mid-level task: "Build a rate limiter that allows each user at most 100 requests per minute." Here is how a strong candidate handles it in each format.

In the AI-banned round

The strong candidate doesn't start typing. They spend the first few minutes clarifying: is the limit per user or per IP? Is it a fixed window or a sliding window? Does it run on one server or many? They then say out loud which approach they're choosing and why: "A fixed window is simplest, but it allows bursts at the window boundary, so I'll use a sliding window log and mention a token bucket as an alternative." They write the code while narrating, test it with a burst at the boundary, and finish by stating the time and memory cost per user.

When the interviewer changes the problem ("now it runs across 20 servers"), they don't freeze. They explain that local counters stop working and propose a shared store such as Redis, with the trade-off of an extra network call per request.

In the AI-allowed round

The strong candidate still clarifies the requirements first, because the AI can't do that for them. Then they give the assistant a narrow, specific request: "Write a sliding-window rate limiter class in Python with an allow(user_id) method, using a deque per user." They read the output line by line and notice that old timestamps are never cleaned up for inactive users, which is a memory leak. They point it out, ask the assistant to fix it, and write a test that sends 101 requests in a minute to confirm the 101st is rejected.

The weak candidate pastes the whole problem in, accepts the first answer, and can't explain why the deque is there. Same problem, same tool, very different score.

Common mistakes that cost offers in 2026

• Assuming the policy. Using AI in a round where it's banned is treated as cheating, even if another round that day allowed it.
• Over-trusting the assistant. Accepting generated code without running or reading it is the single most common failure in AI-allowed rounds.
• Going silent. In proctored rounds, long silences combined with sudden complete answers look like hidden assistance, even when they're not.
• Only practising on LeetCode. AI-allowed rounds often use an existing codebase. If you've never read someone else's code under time pressure, practise that specifically.
• Skipping the follow-up questions in practice. Most mock practice ends when the code passes. Real interviews start getting hard at exactly that point.

A 3-week plan to prepare for both

1. Week 1 — No-AI fundamentals, spoken. Solve 2 medium problems a day with no assistant, explaining every step out loud as if an interviewer were listening. Record yourself at least once and listen back for long silences.
2. Week 2 — AI as a junior pair. Take a small real project (or an open-source repo). Add a feature using Copilot, Cursor or Claude, but write the plan first, review every suggestion, and write tests for what it produces. Keep a list of the bugs the AI introduced: that list is your interview story.
3. Week 3 — Timed mock rounds. Alternate: one proctored no-AI mock, one AI-assisted session on a timed task. After each, review where you lost points: correctness, complexity, or explanation.

Before your interview: 5 questions to ask your recruiter

• Which rounds allow AI tools, and which don't?
• If AI is allowed, which tools: my own, or one provided in the environment?
• Will I work on a blank problem or an existing codebase?
• Is the round proctored (webcam, screen share, tab tracking)?
• How is AI usage itself evaluated?

Asking these isn't a red flag. Recruiters expect it in 2026, and it shows you take the process seriously.