The Future of Frontend Interviews in an AI World

Let me describe a scenario that's already happening: a candidate walks into a frontend interview, is asked to build a component from scratch, and produces beautiful, well-structured code in record time. The interviewers are impressed. The candidate gets hired.

Three weeks later, it becomes clear the candidate can't debug a production issue, can't explain why the code is structured the way it is, and freezes when asked to modify something that doesn't fit a standard pattern.

What happened? The candidate had practiced every common interview question with AI. They could reproduce the outputs but didn't understand the reasoning. And our interview process couldn't tell the difference.

Frontend interviews are broken. AI didn't break them — they were always fragile — but AI has made the cracks impossible to ignore. It's time to rebuild them from the ground up.

Why Traditional Frontend Interviews Don't Work Anymore

The Take-Home Is Dead

Take-home assignments used to be a reliable signal. You give a candidate 4-6 hours to build something, and the result tells you about their coding style, decision-making, and attention to detail.

In 2026, a take-home tells you about their prompting skills. Any candidate can use AI to produce a polished take-home project in a fraction of the time, with code quality that looks impressive on the surface. The signal-to-noise ratio has plummeted.

Some companies try to ban AI from take-homes. This is unenforceable and arguably counterproductive — in real work, we WANT developers to use AI effectively. Banning AI in interviews to test skills that won't be used in isolation on the job is testing the wrong thing.

The Whiteboard Algorithm Is Irrelevant

Whiteboard algorithm questions were already a poor predictor of frontend development ability. With AI, they're actively harmful. Any developer can solve a medium LeetCode problem by asking AI. Testing algorithmic problem-solving in a world where AI solves algorithms trivially is like testing handwriting speed in a world with keyboards.

The "Build This Component" Live Coding Is Gameable

Live coding sessions where candidates build a specific component (a todo list, a search dropdown, an image carousel) are now deeply gameable. Candidates prepare by having AI generate solutions for every common interview component, memorize the patterns, and reproduce them. The candidate looks competent, but they're performing rote memorization, not demonstrating engineering ability.

What Should We Interview For Instead?

Here's my framework for frontend interviews in the AI age. I've been refining this across multiple hiring processes, and it consistently identifies the candidates who actually perform well on the job.

1. Architecture and Design Discussions (No Code)

Give candidates a realistic scenario and discuss how they'd approach it. Not "build this component" but "our application needs to support offline mode — how would you architect the data layer?"

This tests:

• Systems thinking
• Understanding of tradeoffs
• Ability to consider constraints
• Communication of technical ideas

AI can't fake this because there's no single right answer. The value is in the reasoning, the questions the candidate asks, and how they handle "what if" scenarios.

2. Code Review Sessions

Show candidates real code (not yours — use open-source or purpose-built examples) and ask them to review it. Include subtle bugs, performance issues, accessibility problems, and architectural inconsistencies.

This tests:

• Depth of understanding
• Attention to detail
• Ability to identify non-obvious issues
• Knowledge of best practices
• Communication about code quality

This is brutally effective at separating candidates who understand code from those who can only generate it. You can't review code well if you don't understand the underlying concepts.

3. Debugging Live Issues

Present candidates with a running application that has bugs. Not simple "typo in the code" bugs, but realistic issues: a race condition in data fetching, a subtle CSS layout issue that only appears at certain viewport sizes, a performance problem caused by unnecessary re-renders.

This tests:

• Debugging methodology
• Understanding of browser DevTools
• Knowledge of how frameworks actually work under the hood
• Problem-solving under realistic conditions

This is where AI-dependent candidates fall apart. Debugging requires understanding, not generation. You need to form hypotheses, test them, narrow down the cause, and fix it — skills that require deep understanding of how the technology stack works.

4. Refactoring Exercises

Give candidates a working but poorly structured codebase and ask them to improve it. Not rewrite it — improve it. This mirrors real work much more closely than greenfield development.

This tests:

• Ability to understand existing code
• Judgment about what to change and what to leave
• Understanding of code quality principles
• Practical refactoring skills

5. AI-Assisted Problem Solving (Yes, Let Them Use AI)

Here's my most controversial suggestion: include a session where candidates CAN use AI. Give them a complex, non-standard problem and watch HOW they use AI.

Do they:

• Craft thoughtful prompts with context?
• Critically evaluate AI output?
• Iterate on solutions rather than accepting the first one?
• Combine AI output with their own knowledge?
• Understand the limitations of what AI generates?

How someone uses AI is a better predictor of their real-world effectiveness than whether they can code without it.

The Evaluation Framework

I evaluate candidates across five dimensions:

Understanding (40%): Do they understand WHY things work, not just WHAT to type? Can they explain the React rendering lifecycle? Do they know why we separate concerns? Can they articulate the tradeoffs of different state management approaches?

Judgment (25%): Do they make good decisions about architecture, complexity, and tradeoffs? Can they decide when a simple solution is better than a clever one? Do they ask the right questions?

Communication (15%): Can they explain technical concepts clearly? Do they facilitate productive technical discussions? Can they document decisions for future team members?

Practical Skills (10%): Can they use their tools effectively? This includes AI tools. Can they debug efficiently? Do they know their way around browser DevTools?

Cultural Fit (10%): Do they approach problems collaboratively? Are they open to feedback? Do they balance pragmatism with quality?

Notice that "can write code from scratch without any assistance" is not a dimension. Because that's not what the job requires.

The Hiring Manager's Dilemma

I know what hiring managers are thinking: "This sounds great in theory, but it takes more time per candidate." You're right. Architecture discussions, code review sessions, and debugging exercises take more preparation and more evaluation time than "solve this LeetCode problem."

But consider the cost of a bad hire. A developer who can reproduce AI-generated solutions but can't think independently costs you months of wasted salary, plus the opportunity cost of the right hire, plus the cleanup cost when they leave. Investing more time in evaluation saves money in the long run.

Advice for Candidates

If you're on the other side of this equation, here's what to focus on:

Stop memorizing solutions. Start understanding principles. When you learn a pattern, understand WHY it works, not just HOW to implement it.

Practice explaining your reasoning. The ability to articulate why you'd choose approach A over approach B is more valuable than the ability to implement either one.

Get good at code review. Review open-source PRs. Review your own AI-generated code critically. This builds the evaluation skills that interviewers are looking for.

Learn to debug methodically. When something breaks, resist the urge to ask AI immediately. Develop hypotheses, test them, narrow down the cause. This builds mental models that serve you for your entire career.

Use AI deliberately, not reflexively. Show that you use AI as a tool, not a crutch. The best candidates I've interviewed use AI extensively but can clearly articulate what AI contributed and what they contributed.

The interview process needs to evolve. The companies that evolve their hiring process first will hire the best talent. The candidates who develop the right skills will have their pick of opportunities. The ones clinging to the old model — on both sides — will be left behind.