The technical interview was already a contested ritual before AI entered the room. Now it has become something stranger: a process where candidates may be coached by one AI system while another AI system watches for signs of exactly that. The result is a feedback loop that raises a question most hiring coverage skips entirely. If both sides are using AI, what is the interview actually measuring?
How a Loop Forms When Both Sides Automate
The dynamic did not emerge from nowhere. As companies began deploying AI-powered resume screeners to filter applications at scale, candidates noticed the shift and responded in kind. Archie Payne, cofounder and president at technical recruiting firm CalTek Staffing, describes this as a rational countermeasure: applicants started using AI during interviews because they perceived the process as having already been automated against them.
Tools like Final Round AI, Interview Coder, and ParakeetAI can listen to a live interview, process the audio in real time, and generate answers or working code almost instantly. Some of these tools claim to be invisible on screen, meaning a candidate can read off a generated answer while appearing to respond naturally. Mudit Saraf, a software engineer at Meta who cofounded the AI voice recruiter Ginger alongside Shraddha Sunil of Microsoft, describes the mechanic plainly: the candidate simply has to put on a performance.
Employers are now deploying AI detection platforms in response, looking for signals like eye movement, response timing patterns, tab switching, and speech patterns that suggest AI-generated output. Ginger itself flags candidates who appear to use AI during initial screening calls. Yet Payne notes that the accuracy of these detection tools is not yet reliable: strong candidates have been flagged as false positives, which creates its own problem when qualified people are already scarce.
Ravi Kiran Pagidi, a senior AI data engineer at Navy Federal Credit Union who has participated in technical interview panels, captures the core risk of this loop clearly. The process may stop measuring actual capability and start measuring who can optimize better for the algorithm.
What Bias and False Positives Add to an Already Fragile System
The reliability problem is not only about detection accuracy. AI hiring tools carry documented risks that extend beyond the interview room. A study from the Stanford Institute for Human-Centered AI, cited in the source material, followed 3.4 million real job applicants whose applications were assessed by algorithms from a single vendor. The study found evidence of adverse impact for Asian and Black applicants, suggesting that AI screening tools can increase racial bias and produce systemic rejection patterns.
AI hiring strategist Tatiana Teppoeva points to additional concerns: the privacy and security of applicant data, whether interview recordings are used to train the underlying models, and questions of fairness that automated systems are not equipped to resolve on their own. Her recommendation is direct. A human needs to be somewhere in the process, with the authority to ensure results are fair.
Pagidi adds that without audits, clear policies, and transparency, companies may believe they are improving efficiency while actually degrading the quality of the hiring signal itself. This is what most coverage of AI in hiring misses: the efficiency gains are visible and easy to measure, while the signal degradation is slow, quiet, and only apparent when the wrong people get hired or the right ones are turned away.
The Judgment Question That AI Cannot Answer for You
Here is where the story becomes genuinely instructive. Some companies, including Meta and the AI-native software development platform Factory, have moved in the opposite direction: they allow, or even expect, candidates to use AI during technical interviews. Factory’s approach is worth examining closely. Applicants are asked to build a production-quality system or migrate a real codebase within an hour, using AI coding agents. They are not graded on whether they finish or how many tests pass. They are graded on planning, how they direct the AI, how they debug, and whether they can explain why their solution works.
Varin Nair, the software engineer who leads Factory’s technical hiring process, has observed a clear pattern. Weak candidates accept everything the AI returns and stall the moment it falls short. Strong candidates use it to move faster and free themselves to reason about architecture, trade-offs, and product. The AI becomes a multiplier of judgment, not a substitute for it.
This distinction matters beyond hiring. It describes something true about how AI tools function in professional contexts generally. The tool surfaces options. The human decides which options are worth pursuing, why, and what to do when the tool is wrong.
In Short
The AI arms race in technical hiring is not primarily a story about cheating. It is a story about what happens when both sides of a process automate without agreeing on what the process is supposed to measure. Detection tools introduce bias and false positives. Coaching tools obscure the signal that interviews are designed to capture. The companies navigating this most effectively are the ones that have stepped back and asked a prior question: what does good engineering judgment actually look like, and how do we design an interview that reveals it? The answer, consistently, involves giving candidates real problems, real tools, and the space to show how they think. That is harder to fake, and harder to automate away.
Based on reporting from IEEE Spectrum AI.