Here’s something that doesn’t add up. 81% of FAANG interviewers suspect candidates use AI to cheat during algorithmic interviews. But not a single one of these companies has abandoned the format. And despite all this suspicion, they categorically reject validation methods that would actually tell them if these interviews predict job success.
AI tools like ChatGPT aren’t creating the problem. They’re just making visible a problem that was always there. These interviews might never have worked—we just couldn’t see it until now.
This analysis is part of our comprehensive examination of the strategic framework for technical hiring leaders navigating the AI interview crisis. If you’re thinking about changing how you interview, understanding what was broken before AI matters.
Did LeetCode Interviews Ever Effectively Predict Job Success?
Companies don’t want to know the answer. They refuse to run the tests that would tell them.
Red-teaming would be straightforward. Put your best engineers through your own interview process. See if your top performers would actually get hired under your own criteria. But organisations categorically reject this validation approach, probably because they know the results would be embarrassing.
The same goes for regret analysis. Track who you rejected and see what they accomplished at other companies. Did you pass on people who became excellent engineers elsewhere? Again, companies don’t want to know.
All this validation avoidance creates an evidence vacuum. Companies keep using algorithmic interviews based on assumption, not proof. And that 81% suspicion rate? It existed before AI tools went mainstream.
The format came from cargo cult adoption. Companies copied Google’s approach without understanding the context or checking if it actually worked for them. Google historically optimised to avoid bad hires even if it meant rejecting good candidates. Most companies didn’t adopt that philosophy along with the format.
Algorithmic interviews have very little to do with software engineers’ daily lives. Work sample tests show strong validity when properly designed, but algorithmic questions aren’t work samples. They’re academic exercises.
Companies had a measurement problem all along. AI just made it visible.
What Skills Gap Exists Between Algorithmic Performance and Real Development Work?
Engineers spend about 70% of their time reading existing code and 30% writing new code. Algorithmic interviews focus almost entirely on the 30% part. And even then, they’re only testing a narrow slice of it.
Real development work is understanding large codebases, debugging production systems, reviewing pull requests, and iterating on solutions when requirements are ambiguous. Canva’s engineering team notes that their engineers “spend most of their time understanding existing codebases, reviewing pull requests and iterating on solutions, rather than implementing algorithms from scratch.”
Meanwhile, 90% of tech companies use LeetCode-style questions while only 10% actually need this expertise daily. Most companies ask about binary search trees, Huffman encoding, and graph traversal algorithms that rarely come up in the actual job.
The interview environment makes this worse. You’re testing how someone performs alone under time pressure on novel problems. The job requires collaborative problem-solving, working with familiar patterns, and maintaining code quality standards.
Interviews judge code on correctness and efficiency. Production code gets valued for maintainability, readability, and test coverage. Different skills. Different criteria.
And there’s the collaboration gap. Code review skills, mentoring, cross-team coordination, clarifying requirements—none of this gets tested. You’re measuring someone’s ability to solve puzzles alone at a whiteboard. The job requires working with a team on a shared codebase.
Canva found that almost 50% of their engineers use AI-assisted coding tools every day to understand their large codebase and generate code. But their traditional Computer Science Fundamentals interview tested algorithms and data structures these engineers wouldn’t use on the job.
The skills gap isn’t subtle. It’s the dominant reality of how algorithmic interviews relate to actual work.
How Has AI Exposed Existing Inadequacies Rather Than Creating New Problems?
The 73% pass rate with ChatGPT versus 53% baseline tells you something important. That 20-percentage-point jump shows these interviews were heavily testing memorisation and pattern recognition, not deeper understanding.
If AI can solve problems instantly, the interviews were probably measuring easily automatable skills rather than human judgment and creativity. Which raises an obvious question—why were we testing those skills in the first place?
LeetCode solutions were always available. Textbooks, Stack Overflow, tutoring services, memorised patterns—candidates had access before AI existed. The difference is effort. AI just lowered it from hours to seconds.
Canva’s initial experiments confirmed that “AI assistants can trivially solve traditional coding interview questions” producing “correct, well-documented solutions in seconds, often without requiring any follow-up prompts.” AI revealed what interviews were actually measuring, not broke something that worked.
Historical cheating included phone-a-friend schemes, hidden resources, and pay-to-interview services with cameras off during remote interviews. The problem was there. AI just made it visible because the speed and sophistication removed any deniability.
Karat’s co-founder reported that 80% of their candidates use LLMs on top-of-funnel code tests despite being told not to. That high rate suggests the tests were easy enough to cheat on that most candidates felt comfortable doing it.
Three things enable this: academic questions with public solutions, automated screening with no human interaction, and no follow-up questions to verify understanding. Those conditions existed before AI. The tools just made exploitation trivial.
The diagnostic value of AI is actually useful. It shows which interview questions test memorisation versus understanding. As HireVue’s chief data scientist notes, “A lot of the efforts to cheat come from the fact that hiring is so broken…how do I get assessed fairly?”
AI exposed measurement problems that organisations refused to validate through red-teaming or regret analysis. The validation gap was always there. AI made it impossible to ignore.
Why Do Companies Continue Using LeetCode Interviews Despite Suspicion?
58% of companies adjusted question complexity in response to AI but kept the same format. 11% implemented cheating detection software, mostly Meta. Zero abandoned the algorithmic approach or implemented validation to test if changes improved outcomes.
The numbers point to a clear pattern. Despite widespread suspicion and tactical adjustments, companies maintain the same fundamental approach. This is organisational inertia in real time.
Companies have sunk costs in interviewer training, question databases, and evaluation frameworks. Switching to unproven methods feels riskier than maintaining a broken status quo, even when 81% of interviewers suspect widespread cheating.
The cargo cult pattern persists. Companies copied Google without understanding context. They took the interview format but not the validation culture or the philosophical approach to false positives versus false negatives.
Standardised algorithmic tests feel “objective” because they’re consistent. But consistently measuring the wrong things doesn’t help. It just creates the illusion of fairness while testing skills that don’t predict job success.
Engineers also prefer testing skills they personally value. If you’re good at algorithmic thinking, you want to hire people who are also good at it. Even if that skill isn’t job-relevant, it creates a sense of shared capability.
And there’s a coordination problem. Changing interview processes requires organisation-wide alignment. Someone has to retrain all the interviewers, rebuild the question banks, create new evaluation rubrics. That’s expensive and time-consuming.
Schmidt & Hunter’s meta-analysis notes that “In a competitive world, these organizations are unnecessarily creating a competitive disadvantage for themselves” by using selection methods with low validity. They estimate “By using selection methods with low validity, an organization can lose millions of dollars in reduced production.”
But knowing this and acting on it are different things. The validation paradox continues—won’t test effectiveness but also won’t switch without proof alternatives work better.
What Do Algorithmic Interviews Test vs What Jobs Actually Require?
Interviews test novel algorithm design, data structure implementation, optimal time/space complexity analysis, and individual problem-solving speed. Jobs require understanding existing codebases, collaborative debugging, navigating ambiguous requirements, maintaining production code, and incremental improvement over time.
The unmeasured skills matter more than the measured ones. Communication, code review, mentoring, cross-team coordination, clarifying requirements—these drive actual job performance. But they don’t appear in algorithmic interviews.
Portfolio evidence like GitHub history, past projects, and open source contributions gets ignored in favour of live performance under artificial time pressure. This makes no sense if you’re trying to predict job success.
Modern engineering includes GitHub Copilot, ChatGPT, and Cursor usage. But interviews ban these tools. So you’re testing ability to code without modern tools while the job requires using those tools effectively.
Canva redesigned their questions to be “more complex, ambiguous, and realistic—the kind of challenges that require genuine engineering judgment even with AI assistance.” Instead of Conway’s Game of Life, they might present “Build a control system for managing aircraft takeoffs and landings at a busy airport.”
These complex ambiguous problems can’t be solved with a single prompt. They require breaking down requirements, making architectural decisions, and iterating on solutions. Which is what the job actually involves—and what practical alternatives to algorithmic coding tests are designed to assess.
Work sample tests combined with structured interviews achieve composite validity of 0.63 for predicting performance. Algorithmic interviews don’t reach that level because they test a narrow set of skills that don’t map to job requirements.
The disconnect is obvious when you look at what Canva evaluates in their AI-assisted interviews: “understanding when to leverage AI effectively, breaking down complex ambiguous requirements, making sound technical decisions, identifying and fixing issues in AI-generated code.”
With AI tools generating initial code, reading and improving that code becomes more important than writing it from scratch. But algorithmic interviews don’t test code reading ability at all.
How Do FAANG Tactical Changes Avoid Addressing Fundamental Effectiveness Questions?
Making questions harder doubles down on the same flawed approach. 58% adjusted complexity, testing the same skills—algorithm design under time pressure—instead of questioning whether those are the right skills to measure.
11% implemented detection software to monitor for AI usage. Meta is particularly aggressive, requiring full-screen sharing and disabling background filters. But this is a technical solution to a measurement problem.
Zero companies adopted validation methods like red-teaming or regret analysis to test if changes improve outcomes. The tactical responses preserve existing infrastructure—interviewer training, question banks, evaluation frameworks—while avoiding effectiveness evidence.
Companies moved away from standard LeetCode problems toward more complex custom questions. About a third of interviewers changed how they ask questions, emphasising deeper understanding through follow-ups rather than just correct answers. One Meta interviewer described a shift toward “more open-ended questions which probe thinking, rather than applying a known pattern.”
These are incremental improvements to a fundamentally flawed approach. They don’t address whether algorithmic performance predicts job success. They just make the algorithmic testing harder to game.
Meanwhile, 67% of startups made meaningful process changes versus 0% of FAANGs abandoning the algorithmic approach entirely. Startups are innovating while large companies protect their existing investment in broken processes.
Meta is testing AI-assisted interviews for onsite rounds but not replacing the algorithmic phone screen until later. This preserves the status quo while appearing to adapt.
The pattern is clear. Tactical changes that keep infrastructure intact get adopted. Validation methods that would measure effectiveness get rejected. And fundamental questions about what skills matter don’t get asked.
What Is The GitHub Copilot Paradox In Technical Interviews?
Companies ban AI tools during interviews to prevent “cheating.” Then they require daily AI tool usage once you’re hired.
Canva’s engineering team noted that “Until recently, our interview process asked candidates to solve coding problems without the very tools they’d use on the job.” They “not only encourage, but expect our engineers to use AI tools as part of their daily workflow.”
This creates a perverse incentive. Skilled AI users must hide their proficiency to pass interviews that test AI-free coding. Then they’re expected to use those same tools daily once hired.
Interview tools like InterviewCoder exist specifically to help candidates cheat by whispering AI-generated answers during technical interviews. These tools listen to questions in real-time, feed them to AI, and display answers on a second screen. Because nothing happens on the candidate’s main computer, screen-sharing and proctoring software can’t detect it.
The paradox exposes what interviews actually test—ability to code without modern tools. But that’s not what the job requires. The job requires effective AI tool usage, prompt engineering, reviewing and improving AI-generated code, and knowing when to leverage AI versus when to code manually—what we explore in depth in the AI fluency paradox in technical hiring.
Canva believes that “AI tools are essential for staying productive and competitive in modern software development” and that “proficiency with AI tools isn’t just helpful for success in our interviews, it is essential for thriving in our day-to-day role at Canva.”
They resolved the paradox by redesigning to an “AI-Assisted Coding” competency that replaces traditional Computer Science Fundamentals screening. Candidates are expected to use their preferred AI tools to solve realistic product challenges. Canva now informs candidates ahead of time that they’ll be expected to use AI tools, and highly recommends they practice with these tools before the interview.
This tests what matters—understanding when and how to leverage AI effectively, making sound technical decisions while using AI as a productivity multiplier, and identifying and fixing issues in AI-generated code.
The alternative is testing ability to code without tools that will be required on the job. Which makes no sense if you’re trying to predict job success.
The fundamental question isn’t whether AI broke technical interviews—it’s whether the interviews worked before AI exposed their inadequacies. For guidance on choosing between detection, redesign, and embrace strategies for your organisation, see our strategic framework for technical hiring leaders.
FAQ
How can organisations measure whether their LeetCode interviews actually work?
Put your best engineers through your own interview process. See if they’d pass. Track the candidates you rejected and see what they accomplished at other companies. Compare interview scores against post-hire performance reviews. Most organisations refuse to do this, probably because they suspect the results would be embarrassing.
What percentage of interviewers suspect AI cheating in technical interviews?
81% of FAANG interviewers suspect candidates use AI to cheat. This suspicion existed before AI tools went mainstream, which tells you something about pre-existing concerns. Despite this, these same organisations keep using the format anyway.
Why don’t companies just make interview questions harder to prevent AI cheating?
58% of companies adjusted question complexity in response to AI, but harder algorithmic problems don’t address the fundamental issue—whether algorithmic performance predicts job success. Making questions harder doubles down on testing the same skills rather than questioning whether those are the right skills to measure.
Can AI tools like ChatGPT really solve most LeetCode interview problems?
Research shows 73% pass rate when using ChatGPT versus 53% baseline. That 20-percentage-point improvement reveals these interviews were heavily testing memorisation and pattern recognition rather than deeper understanding. If AI can solve problems instantly, the interviews were probably measuring easily automatable skills.
What’s the difference between using AI in interviews vs using it on the job?
There isn’t a meaningful difference in capability being tested—both involve using AI tools to assist with coding. The contradiction is organisational policy. Companies ban tools during interviews that they require daily once you’re employed. Canva addressed this by allowing AI tool usage with more complex, ambiguous problems that test understanding and judgment.
Why do FAANG companies maintain algorithmic interviews despite evidence of ineffectiveness?
Organisational inertia, sunk costs in interviewer training and question banks, cargo cult adoption—copying Google without understanding context—and perceived fairness of standardised testing. Switching requires organisation-wide coordination and admitting the existing approach was flawed.
What skills do LeetCode interviews fail to measure that jobs require?
Code comprehension, collaborative problem-solving, working with ambiguous requirements, debugging production systems, code review capability, architectural thinking, testing practices, deployment knowledge, and modern AI tool proficiency. Interviews test individual novel algorithm design under time pressure—a narrow slice of actual engineering work.
How did companies validate interview effectiveness before AI exposed the problems?
They largely didn’t. Organisations avoided validation methods like red-teaming high performers or conducting regret analysis on rejected candidates. The absence of validation meant effectiveness was assumed rather than proven. AI tools made the existing measurement problems visible.
What alternatives exist to LeetCode-style algorithmic interviews?
Project-based assessments using realistic product challenges, portfolio evaluation of past work and GitHub contributions, AI-assisted interviews with complex ambiguous problems—the Canva approach—work sample tests reflecting actual job duties, and pair programming on real codebase problems. Startups show 67% meaningful process changes versus 0% format abandonment among FAANGs.
Should candidates use AI tools if interviewers allow it?
If explicitly permitted, yes—it demonstrates real-world engineering capability including prompt engineering, code review, debugging, and AI-assisted problem-solving. Companies like Canva specifically allow AI tools because it reflects actual work conditions and tests deeper understanding than memorised algorithms. If policy is unclear, clarify before the interview.
Why is code reading more important than code writing for real development work?
Engineers spend approximately 70% of time reading existing code and 30% writing new code. Understanding large codebases, debugging others’ work, conducting code reviews, and maintaining production systems all require strong comprehension skills. Algorithmic interviews focus almost entirely on writing novel code, missing the dominant activity in actual engineering roles.
What is red-teaming in the context of interview validation?
Testing your own high-performing employees through the same interview process to validate whether your “best” people would be hired under current criteria. If top performers fail or struggle, it reveals the interview doesn’t measure job success. Organisations refuse to do this, likely because they’re worried about confirming ineffectiveness.
