AI Coding Interviews in 2026: What’s Actually Different and How to Prepare

In June 2025, Canva quietly updated its engineering hiring page to say candidates may use AI assistants during technical rounds. Meta followed a few weeks later. By late 2025, companies including Rippling, Red Hat, and Shopify had done the same. That’s a fast shift, and it’s still accelerating.

The coding interview you prepped for in 2023 no longer quite exists. That isn’t necessarily good news for candidates, and it isn’t necessarily bad. It’s complicated.

What’s actually changing on the company side

A 2026 IEEE-USA survey of 400 engineering leaders found that 71% say AI makes it harder to assess candidates’ technical skills – up from around 20-30% two years earlier. Interviewers aren’t wrong to worry. The old format assumed you were writing code from memory in real time. That assumption broke somewhere around the GPT-4 release, and the industry is still figuring out what comes next.

There are three things happening simultaneously, and they pull in different directions.

First, automated take-home tests are losing credibility. If a candidate can pipe a LeetCode problem through Claude in 4 seconds, a timed online assessment doesn’t tell you much. Many companies – particularly in New York – are shifting back to live interviews with actual engineers in the room, precisely because it’s harder to invisibly route questions to a second screen.

Second, some companies are running what you could call AI-authorized rounds. They hand you a laptop with Copilot or a chatbot available and watch what you do with it. The question isn’t “can you write a binary search from memory.” It’s “can you decompose a problem, spot when the AI’s suggestion is wrong, and explain your reasoning out loud.” Interviewing.io’s survey of 67 FAANG and startup interviewers found that 58% have already retooled their algorithmic questions in some form.

Third, a subset of companies is moving the other direction – toward deliberately AI-resistant formats. Whiteboard sessions with no internet. Verbal walkthroughs of architecture tradeoffs. Pair-programming exercises where the interviewer is looking at your thought process, not your output.

All three formats exist right now, often at different stages of the same pipeline at the same company. That’s the part nobody seems to acknowledge: you might get an AI-authorized take-home followed by a whiteboard round where AI is explicitly off the table. The goalposts shift between rounds.

The proctoring arms race

Fabric, an AI interview platform, analyzed 19,368 interviews conducted between July 2025 and January 2026. Their findings are striking: 38.5% of candidates triggered cheating signals. The rate climbed from 9% in July 2025 to 45% by September – a 3x jump in two months.

Tools like Cluely and Interview Coder use OCR to scan a problem statement on screen and return working code in 3-4 seconds. Detection systems now analyze keystroke rhythm (AI tools show uniform response latency; humans don’t), eye movement, and voice modulation. It’s a real arms race, and neither side is winning cleanly.

The uncomfortable stat from Fabric’s data: 61% of cheaters score above pass thresholds anyway and would advance without detection. Which means proctoring alone isn’t a solution. Companies that rely entirely on automated technical screening are probably already interviewing a meaningful share of candidates who aren’t showing their real abilities.

I’m genuinely uncertain whether stricter proctoring actually selects better engineers long-term, or whether it mostly filters out the less technically sophisticated cheaters while the sophisticated ones pass through. That’s worth sitting with before treating detection as the answer.

What developers actually think about AI at work

Here’s the tension the interview shift reflects. The 2025 Stack Overflow Developer Survey found that 84% of developers are using or planning to use AI tools, and 51% use them daily. But fewer than one in three say they trust the accuracy of AI outputs. The biggest single frustration, cited by 66% of AI-tool users, is “solutions that are almost right, but not quite.”

That gap – heavy use, low trust – is exactly the skill set companies with AI-authorized rounds are trying to measure. They’re not looking for blind faith in Copilot. They want candidates who use the tool, catch the errors, and fix them faster than they could write the code from scratch. Whether a one-hour interview can actually assess that judgment is a fair question. Most companies haven’t cracked it.

What this means if you’re interviewing in 2026

A few things that seem true based on where the market is right now:

• Research the format before you prep. Ask your recruiter directly: “Will AI tools be available during any rounds?” A company that says yes needs different prep than one that says no. Don’t assume.
• Algorithms still matter, but the bar has shifted. You probably won’t be asked to implement Dijkstra from scratch with no resources. You might be asked to debug an AI-generated implementation of it and explain what’s wrong. That requires understanding the algorithm, not memorizing the code.
• Practice explaining your reasoning out loud. AI-authorized rounds and AI-resistant rounds share one requirement: the interviewer wants to hear your thinking. If you can’t narrate a tradeoff decision while writing code, the format doesn’t matter much – you’ll struggle in both.
• Know which tools you actually use. If an interviewer asks “how do you use AI in your day-to-day development workflow,” having a specific honest answer beats a polished generic one. Saying “I mostly use Copilot for boilerplate, but I always review the output before committing” is better than “I use AI to enhance my productivity.”

The question companies haven’t answered yet

There’s a structural problem that neither AI-authorized nor AI-resistant formats fully solve. Interviews measure interview performance. The correlation between interview performance and actual on-the-job output has always been imperfect, and that imperfection is getting harder to ignore.

If a candidate passes an AI-assisted round by demonstrating strong judgment about AI-generated code, that’s probably a better signal than memorized algorithms. But the sample size is one hour and the environment is artificial. I’ve seen strong engineers have terrible technical interviews and mediocre engineers perform beautifully under pressure. AI hasn’t changed that dynamic – if anything, it’s amplified it.

Companies like Canva and Meta are running genuine experiments right now. We’ll probably have better data on what formats predict performance in 12-18 months. Until then, the most honest advice is: learn the AI tools you’d actually use on the job, and practice explaining what you’re doing while you do it. The format will keep changing. That combination of skills probably won’t become less useful.

For more on preparing for specific coding rounds, the coding interview prep guide covers the structural patterns that still show up regardless of whether AI is in the room. And if you’re looking at what FAANG-adjacent companies specifically are asking, the 2026 FAANG questions breakdown reflects the current landscape.