AI-Enabled Coding Interviews: What's Actually Changing in 2026

The coding interview you prepared for in 2022 still exists. It's just not the only format anymore, and the bar for passing it quietly moved.

AI didn't kill the coding interview. In some ways it made it worse. Companies are now trying to filter for the one thing AI can't fake: understanding what you built and why. That's pushed the format in two directions. AI-enabled coding interviews at some companies, tighter algorithmic scrutiny at others. Which direction you get depends a lot on where you're applying.

The Industry Split Nobody Warned You About

interviewing.io's 2025 survey of Big Tech interviewers is clear: big companies and startups are going in opposite directions.

Big Tech (Google, Meta, Amazon, Microsoft) isn't abandoning algorithmic interviews. If anything, they're reinforcing them with harder problem variants, more follow-up pressure, and in some cases a push back toward in-person. Google wrapped up a pilot of on-site SWE interviews at its biggest engineering hubs and is rolling it out aggressively. The logic: remote interviews are too noisy when candidates have AI assistance a keystroke away. Google's answer to "AI is everywhere" is apparently "come into the office and prove it."

Startups and mid-size companies are doing the opposite. Takehomes are largely dead (AI solves them with minimal effort). Algorithmic puzzles are on the way out. What's replacing them is a format where you're expected to use AI during the interview, on realistic tasks, while the interviewer watches how you work.

Two forks. You need to know which one you're walking into before you show up.

Meta's AI-Enabled Coding Interview

In October 2025, Meta added a new round to its onsite: the AI-enabled coding interview. It replaced one of the two traditional algorithmic sessions.

The format is deliberately different from anything you've practiced. You get 60 minutes in a specialized CoderPad environment, a multi-file codebase that's already partially built (models, classes, existing logic), and a choice of AI assistant: GPT-5, Claude Sonnet 4.5, Gemini 2.5 Pro, Llama 4 Maverick, and others. The AI can only respond in a chat panel. You write all code yourself. The problem has multiple checkpoints, and later stages require real engagement with AI to finish in time.

Think of it as: you have a very fast, very confident intern sitting next to you. They can answer questions but can't touch the keyboard. And you're being evaluated on whether you give them good directions.

Meta is evaluating three things: problem solving (clarifying requirements, generating solutions), code development (navigating an unfamiliar codebase, evaluating quality), and verification and debugging (finding errors, justifying fixes). The non-coding parts, like runtime analysis and trade-off reasoning, are deliberately areas where the AI can't carry you. That's by design.

If you show up having never used an AI model for real coding work, it shows.

Canva Went a Step Further

Canva made a different call. They don't allow AI use as an option. They require it.

Backend, ML, and frontend engineering candidates are expected to bring Copilot, Cursor, or Claude and use them actively during the interview. The problems are designed to be complex enough that AI alone can't solve them, and ambiguous enough that prompting skill matters less than judgment.

What Canva is actually measuring is whether you can think clearly alongside AI, not whether you can prompt it well. Their rubric checks: can you break down ambiguous requirements, catch what the AI got wrong, make sound architecture calls, and ensure the output meets a production standard?

Engineers who ace the interview, according to their interviewer notes, treat the AI like a junior engineer they're responsible for reviewing. Not a crutch. A collaborator with a non-trivial error rate. Their questions are harder than before specifically because AI handles the boilerplate, so the interesting parts are all that's left.

The Cheating Numbers Are Uncomfortable

A significant fraction of candidates are already using AI in interviews where it's explicitly banned. A 2026 survey found 81% of Big Tech interviewers have suspected AI cheating, and 31% have definitively caught it. Adoption doubled from 15% to 35% of candidates between June and December 2025.

This is partly why the format is changing. Companies aren't naive. Invisible overlay tools render AI responses on top of the screen-share, invisible to the interviewer. Interviewers have responded by making problems less solvable by AI: more unusual wording, more variant problems, more oral follow-ups that you can't outsource.

Google's move toward in-person is the logical endpoint. You can't use an invisible overlay when someone is sitting across the table.

Two stick figures - one labeled "Interviewer" confidently saying no AI, one labeled "Candidate" secretly using AI on a hidden screen

81% of Big Tech interviewers have suspected it. 31% have caught it. The industry isn't ignoring this.

If you're preparing honestly, none of this hurts you directly. It does mean the format of the interview, and what gets tested, is shifting faster than prep resources can track.

The Follow-Up Is the New Filter

This matters regardless of format.

Traditional interviewers could evaluate you entirely on the code you wrote. That's changed. The follow-up question has become the primary signal, because it's where AI can't carry you.

"Walk me through why you chose this approach." "What happens if the input is empty?" "Where would this break at scale?" "Could you make this faster, and what would that cost?" These aren't new questions. But interviewers are leaning on them harder now, because a candidate who used AI to write the code often can't answer them without revealing that they don't actually understand what's in front of them.

Finishing the coding problem feels like the end. It isn't. It's more like the qualifying round.

Candidate completes coding problem looking triumphant, then interviewer says "great, now explain every line"

A strong follow-up answer does more work than the original solution ever did.

Practice this: solve a problem, then put yourself in the interviewer's seat and interrogate your own solution for five minutes. "Why a hashmap and not a sorted array?" "What's the worst-case trigger for this?" "How would this behave on a graph with cycles?" If you can answer those fluently, you're prepared for how interviews actually run right now.

Debugging Is Getting More Screen Time

Related shift: more interviews now include a debugging task rather than a blank-page problem.

You're given broken code and asked to fix it. Or you're given AI-generated code with subtle logic errors and asked to identify and correct them. This format maps directly to Canva's and Meta's reasoning: most engineering work is reading and understanding existing code, not writing new code from scratch.

The skills are distinct. Writing from scratch rewards pattern recognition. Debugging rewards methodical reasoning, hypothesis testing, and the ability to explain what you're looking for as you look for it. Good debuggers narrate. They say "I expect this variable to be X at this point, let me check" before they check. That narration is what interviewers are listening for.

If you've never practiced debugging under pressure while talking through your reasoning, that's a gap. The good news is you can practice deliberately: take a solved LeetCode problem, introduce a bug, then walk yourself through finding it out loud. It feels slightly ridiculous. It works.

See debugging in a coding interview for a systematic approach to this.

What Hasn't Changed (Despite Everything)

Two things haven't moved.

Communication is still the highest-leverage skill in the room. Whether you're in a traditional algorithmic interview or an AI-assisted one, interviewers are watching whether you narrate your thinking clearly, ask good questions before coding, and explain your choices as you make them. The technical interview communication fundamentals haven't moved. If anything they matter more now, because the code itself is less differentiating.

Problem-solving structure hasn't moved either. Understand the problem before touching the keyboard, trace through examples, articulate trade-offs. AI or no AI, the interviewer wants a structured thinker. The code is the output. The thinking is what they're evaluating.

What to Actually Prepare For

If you're targeting Big Tech (Google, Amazon, Microsoft), the core format is still algorithmic with a heavier emphasis on follow-up defense. Practice oral explanation after every problem you solve. Focus on pattern recognition and complexity analysis you can articulate cleanly, not just code you can produce. Keep an eye on whether the company has added an AI-enabled round (Meta has; others are piloting).

If you're targeting Canva, startups, or any company that's announced AI-assisted interviews, practice using your preferred AI assistant (Claude, Copilot, Cursor) on realistic engineering tasks. Build familiarity with debugging AI output. The goal is to demonstrate judgment, not prompting fluency.

For both: practice defending your solutions under live pressure. Finish a problem, then have someone or something ask you "why" on every decision you made. SpaceComplexity's voice-based mock interviews are built for exactly this: realistic interview dynamics with follow-up questions and rubric-based feedback on how clearly you reason through a problem, not just whether your code compiles.

And read why AI made the coding interview harder, not easier if you want the broader picture of how AI changed the dynamic between candidates and interviewers.

Key Takeaways

Big Tech is holding on to algorithmic interviews but adding more follow-up pressure and moving toward in-person for some roles.
Meta added an AI-enabled coding round in October 2025; Google is piloting a similar format. Canva requires AI use.
AI-enabled interviews are harder than traditional ones in scope (multi-file, multi-checkpoint) but evaluate different skills (AI fluency, code review, debugging, defensible reasoning).
The follow-up question is now the primary filter. If you can't explain what you wrote, it shows.
Debugging practice is no longer optional. More companies are using it as a standalone task.
Communication and problem-solving structure remain constant across every format shift.
Calibrate your prep to the specific format each company is using. They're diverging, not converging.