The traditional coding interview was built to test whether a candidate can write correct code under observation. In 2026, that skill has partly moved to the machine. The Stack Overflow survey found that 84% of developers now use or plan to use AI tools, and 47.1% use them every single day (Stack Overflow 2025). If your interview tests the thing an AI does well, you are no longer measuring the candidate. You are measuring their tool. The fix is to change what you look for, not to ban the tools they use every day at work.
Key Takeaways
- 84% of developers use or plan to use AI tools, so testing raw code-writing measures the tool more than the person.
- Shift the interview toward judgment: is this AI output correct, and would you ship it?
- Watch how candidates review, debug, and reject AI suggestions, which is the new core skill.
- Banning AI in interviews tests a world your job does not operate in.
The Interview That Broke
For years, the standard screen was some version of "write this function while I watch." It worked because writing correct code was hard and revealed real ability. AI coding assistants changed the economics of that task. A candidate can now produce plausible, working code for most interview-sized problems in seconds, and a lot of it is genuinely fine. The problem is that the AI also confidently produces code that is subtly wrong, insecure, or inappropriate for the situation, and it does so in the same fluent tone as its good output.
That shifts the valuable human skill from writing code to judging it. The engineer worth hiring is the one who looks at an AI suggestion and knows whether it belongs in production, catches the edge case the model missed, and notices when the confident answer is quietly wrong. That judgment is exactly what a "write this function" interview no longer measures.
What to Test Instead
Design the interview around evaluation and correction rather than blank-page generation. Give the candidate AI-generated code and ask them to review it. Introduce a plausible-looking bug and see if they catch it. Ask them to take a working AI solution and explain what they would change before shipping it.
| Old interview tested | New interview should test |
|---|---|
| Can you write this function? | Is this AI-written function correct? |
| Do you know the syntax? | Would you ship this? Why or why not? |
| Speed of typing code | Speed of spotting the flaw |
| Memorized algorithms | Judgment under confident-wrong output |
A Concrete Screen
Hand the candidate a chunk of AI-generated code that works for the happy path but has a real flaw: it does not handle an empty input, or it builds a database query by string concatenation and is open to injection, or it silently loses precision on money. Do not tell them anything is wrong. Ask them to review it as if a teammate opened the pull request. A strong engineer reads critically, finds the problem, and explains the risk. A weaker one sees that it runs and approves it. This single exercise tells you whether they can be trusted with the AI-heavy workflow your team actually uses.
The Honest Counterpoint
There is a real case for still testing some fundamentals directly. An engineer who cannot reason about basic data structures or read code without AI assistance will struggle to review what the AI produces, so a little from-scratch problem-solving still has signal. The mistake is making that the whole interview. The balance most teams want in 2026 is a small check that the fundamentals are there, plus a larger focus on judgment, review, and debugging in an AI-assisted setting. Test the fundamentals lightly and the judgment heavily, because judgment is the scarce thing now.
Why This Matters for Hiring
Vetting for judgment is harder than running a candidate through a puzzle, which is one reason a lot of hiring processes have not caught up. It is also why a rigorous, human-reviewed process matters more than ever. Our five-stage vetting process leans on senior engineers evaluating how candidates reason and review, precisely because a scripted coding test is easy to pass with a good model and tells you little about whether someone can ship trustworthy work. For more on how AI is reshaping the pipeline, see AI is changing how we hire engineers. See available engineers.
Frequently Asked Questions
Should I ban AI tools during coding interviews?
Usually no. Your job uses them every day, so an AI-free interview tests a situation that does not match the work. Instead, test whether candidates can judge, review, and correct AI output.
What should I test now that AI writes code?
Judgment. Give candidates AI-generated code to review, plant a subtle flaw, and see whether they catch it and can explain the risk before shipping.
Does that mean fundamentals no longer matter?
They still matter, because you cannot review what you do not understand. Test them lightly to confirm they are present, then spend most of the interview on judgment and debugging.
How do I know if a candidate is just good at prompting?
Focus on review and debugging tasks with planted flaws. Prompting produces code; catching the confident-wrong output and explaining why it fails is the human skill that separates strong engineers.
The Bottom Line
When 84% of developers use or plan to use AI tools and nearly half use them daily, an interview that tests raw code-writing measures the model more than the person. Move the screen to judgment: can this candidate look at fluent, confident, AI-generated code and tell whether it is safe to ship. That skill is scarce, it is what your team actually needs, and it is invisible to the coding interview most companies are still running.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
