The Interview That Wouldn't Die: Coding Interviews in the Age of AI · returnzero

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The Interview That Wouldn't Die<br>How Coding Interviews Evolved With AI, and Mostly Didn't<br>By Rotem Meidan · Updated Aug 23, 2026 · 8 min read

Before you reach the bottom of the article and get annoyed, the bottom line hasn't changed: you still need to grind LeetCode. The question this article attempts to answer is, why?

In early 2025, a Columbia student named Chungin "Roy" Lee built Interview Coder, a tool that screenshots coding problems during interviews and feeds them to an AI for real-time solutions. He used it during an Amazon interview, Amazon pulled his offer, Columbia suspended him, and he dropped out to raise $15M from Andreessen Horowitz. The company, later rebranded as Cluely, publicly admitted to lying about its revenue numbers to TechCrunch. His tool claims 150,000+ users. It is not the only tool. Each one is a subscription product solving a specific problem: the LeetCode-style interview is gameable, and where there's a gameable system, there's a market for gaming it.

The arms race

The tool isn't as invisible as the marketing claims. It is visible in macOS Activity Monitor, its hotkeys can be tracked by browser keyboard listeners, and platforms often let candidates finish tests before silently disqualifying them. It's also slow: response delays of 5-90 seconds, long enough to be noticeable in a live interview. The "150,000+ users" claim is self-reported. The marketing outpaces the evidence.

Whether the tool works as advertised barely matters. The belief that it might is enough to poison the pool.

The candidates using these tools are scared anyway. On Reddit's r/csMajors, one user reported being silently rejected after using Interview Coder for a Capital One assessment. "I don't want to try it again for other companies out of fear of potentially getting blacklisted." You pass the tool, your application silently disappears three days later, and you never know why.

Both sides are paranoid, and both sides have reason to be. The tool makers overstate detection difficulty. The platforms overstate their catch rate. Building a tool will always be easier than building a detection system without false positives, so the tool makers stay one step ahead. The honest candidate loses either way.

The candidate's dilemma

So the honest candidate runs the calculation. If you don't cheat and the person before you did, you lose the offer to someone who scored higher with AI-generated code. If you do cheat and the company catches it silently, you lose the offer and you don't know why. If you cheat and they don't catch it, you get the job, and you'll have AI on the job too...

There is no version of this calculation where the honest path is clearly the rational one. The rational response to a gameable, unvalidated filter is to game it.

interviewing.io tracks the technical score of the median successful candidate. In Q1 2022, you needed to be better than 68% of candidates who reached the phone screen. By late 2022, you needed to be better than 78%. That's a 15% increase, associated with market leverage: tech jobs contracted roughly 40% over the same period. Fewer openings, higher bar. The bar rose, and nobody proved it was measuring anything.

FAANG hasn't moved

interviewing.io surveyed 67 of its interviewers. The headline finding: of 52 respondents from FAANG companies, zero said their company had moved away from algorithmic questions. Their own cheating study showed that small twists on LeetCode questions don't stop cheaters. Only fully custom questions are cheat-resistant, and most interviewers aren't there yet.

Meta is the one FAANG company running an experiment: an AI-assisted coding interview as part of the onsite loop. But candidates still have to pass the algorithmic phone screen first. Meta added a new gate behind the old one rather than replacing it.

Anthropic, the AI company, explicitly forbids AI during technical interviews. If the company building the models says the interview should test you and not your tools, that's a signal. See our Anthropic interview guide, and for the other side of AI in interviews, labs probing your safety thinking, see our AI safety primer for engineering interviews.

The companies keeping the algorithmic screen have a steelman: a company hiring thousands of engineers per year needs a format that thousands of interviewers can calibrate against. A standardized screen is legally defensible and resistant to interviewer bias. Changing it risks trading a known, imperfect filter for an unknown one, at a cost of months of calibration.

Google published that brainteasers had zero predictive validity and that structured interviews did predict performance. They never published whether the algorithmic coding screen specifically predicts engineering performance. Nobody else has either.

The conversation about how to stop cheating assumes that the thing being cheated on was...