Why LeetCode Interviews Are Broken in the Age of AI

The tech industry has a hiring problem that's gotten worse with AI. While companies spend millions on diversity initiatives and talent acquisition, they continue to rely on interview processes that research shows are poor predictors of job performance. LeetCode-style coding interviews, where candidates solve algorithmic puzzles under time pressure, have become the industry standard. But mounting evidence suggests they're actively harming both companies and candidates, especially now that AI can solve these problems instantly.

When ChatGPT Can Ace Your Interview, You Have a Problem

The rise of AI coding assistants has exposed a fundamental flaw in algorithmic interviews: if ChatGPT can solve most LeetCode problems in seconds, what exactly are we testing? Candidates now regularly use AI tools during take-home assignments and even live interviews. The skills being assessed, memorizing algorithms and implementing textbook solutions, are exactly what AI excels at.

Meanwhile, the skills that actually matter in modern development, like prompt engineering, working with AI tools effectively, debugging AI-generated code, and knowing when to trust or verify AI suggestions, aren't tested at all. We're optimizing for a world that no longer exists.

The Research Against Algorithm-Based Interviews

Even Google, the company that popularized brain teasers and complex algorithmic challenges, has admitted their ineffectiveness. Laszlo Bock, Google's former Senior Vice President of People Operations, found that "brainteasers are a complete waste of time. They don't predict anything. They serve primarily to make the interviewer feel smart." Google's own internal research comparing interview performance to job performance revealed virtually no correlation between algorithmic puzzle-solving ability and workplace success.

This finding isn't isolated. Research by Erik Bernhardsson, former Chief Technology Officer at Better.com, found that "the correlation between who did really well in the interview process and who performs really well at work is really weak." Multiple studies examining interview effectiveness have consistently shown that traditional coding tests have poor correlation with actual job performance, yet the industry continues to double down on these methods.

The Diversity Problem Gets Worse with AI

LeetCode interviews create significant barriers for underrepresented groups in tech. Research shows that these interview formats disproportionately disadvantage women, people of color, and candidates from non-traditional backgrounds. Studies indicate that women are more likely to experience performance anxiety during live coding sessions, leading to false negatives even when they possess strong technical skills.

The AI era has made this worse. The format inherently favors candidates who have the privilege of time and resources to spend months grinding algorithmic problems. This typically means young, unencumbered individuals who can dedicate hours daily to puzzle-solving and learning to game AI assistance. Meanwhile, experienced developers who understand how to work with AI productively in real-world scenarios get filtered out because they can't recall the optimal solution to inverting a binary tree.

Despite major tech companies investing heavily in diversity initiatives, progress remains minimal. Facebook's representation of women and people of color hasn't meaningfully increased over three years of focused efforts, partly because diverse candidates continue to be filtered out by interview processes that don't reflect real work.

What Actually Matters in AI-Assisted Development

Real software engineering work in 2024 bears little resemblance to whiteboard algorithmic challenges. Day-to-day development involves reading existing codebases, collaborating with team members, debugging complex systems, and making architectural decisions. None of these skills are assessed by asking someone to reverse a binary tree. More importantly, modern development increasingly involves working alongside AI tools, something traditional interviews completely ignore.

The most valuable developers today are those who can effectively collaborate with AI while understanding its limitations. They know when to use GitHub Copilot to speed up boilerplate code, how to craft effective prompts for complex problems, and when to verify AI-generated solutions. They understand that AI is a powerful tool but still requires human judgment for architecture decisions, code review, and understanding business requirements.

Research on job performance predictors shows that general mental ability, structured interviews focusing on past behavior, and work samples are far more predictive than algorithmic puzzles. Yet the industry continues to rely on methods that optimize for memorization over problem-solving ability and collaboration skills.

Better Alternatives for the AI Era

Progressive companies are adopting interview methods that better predict job performance while reducing bias. These include:

Pair programming sessions that simulate real collaborative work, including how candidates work with AI tools. Code review exercises using existing codebases to assess practical skills. System design discussions that evaluate architectural thinking and understanding of when to use AI versus human expertise. Structured behavioral interviews focusing on past problem-solving experiences, including how they've adapted to new tools and technologies.

Some companies are even incorporating AI-assisted coding directly into their interviews, asking candidates to solve realistic problems using whatever tools they'd normally use, including AI assistants. This tests the actual skills they'll need on the job: critical thinking, code review of AI output, and knowing when human judgment is required.

Companies using these alternative methods report better diversity outcomes and higher confidence in their hiring decisions. Pair programming interviews, in particular, allow assessment of both technical skills and collaboration abilities, which is the combination that actually predicts engineering success in an AI-enhanced workplace.

The Business Case for Change

Beyond fairness concerns, algorithmic interviews represent a massive business inefficiency in the AI era. Companies lose qualified candidates who excel at actual engineering work but struggle with artificial puzzle-solving scenarios. Research shows that false negatives in hiring, rejecting good candidates, can be more costly than false positives, yet LeetCode-style interviews optimize for the opposite.

The time investment required from both candidates and interviewers is substantial, yet produces little predictive value. Meanwhile, companies that have adopted more practical assessment methods report faster hiring cycles and better long-term employee performance. More critically, they're identifying candidates who can actually work effectively with AI tools rather than those who can memorize algorithms that AI makes obsolete.

Moving Forward

The tech industry's reliance on LeetCode-style interviews persists largely due to inertia and the false comfort of perceived objectivity. But mounting research evidence makes clear that these methods are both ineffective and discriminatory. In an era where AI can solve algorithmic puzzles instantly, continuing to base hiring decisions on these skills is not just wrong, it's absurd.

Companies serious about building diverse, high-performing engineering teams need to fundamentally rethink their approach to technical hiring. The solution isn't to eliminate technical assessment, but to focus on methods that actually predict job performance while creating equitable opportunities for all candidates. As Google's own research demonstrates, sometimes the most entrenched practices are the ones most in need of change.

The future belongs to developers who can work effectively with AI, not those who can mimic what AI does better. It's time for hiring practices to catch up.

What do you think? Have LeetCode interviews helped or hurt your career? Let me know in the comments, or if you need a developer who focuses on business impact over puzzles, let's chat.