When both résumé and interview are AI, hiring selects for performers
Google and McKinsey are flying candidates back into rooms, eating the travel cost on purpose. The reason, two startup founders argue, is that the cheap early filters now reward whoever games the process best — and an AI detector says the essay making that case was itself written by AI.
Cover letters, résumés, scripted "tell me about a time" answers, even live video calls: each is now, in the authors' words, "infinitely scalable and practically free." Strip the signal from both sides and you select for talent at being hired, not at the job.
The fix on offer is friction: sudden constraints, a defended tradeoff, a culture prizing honesty over polish. The thread shrugged. Qualified people were filtered out by invisible wires long before any model arrived, and the cleanest counter-signal turned out older than software.
The room is back, and it's deliberate
When Harvard Business Review runs a piece headlined that AI has broken hiring, the tell is in who is paying to undo it. Authors Shraddha Sunil and Mudit Saraf — engineers at Microsoft and Meta, and co-founders of interview-screening startup MeetGinger — open with Google, McKinsey and others reintroducing in-person interviews, a step they frame as a knowing sacrifice of efficiency to travel time and cost.
A funnel that collapsed from both ends
The thesis is mechanical, not moral. Generative AI made every early-stage signal cheap: the cover letter, the résumé, the scripted behavioral answer, and now the live video interview are "infinitely scalable and practically free." When the signal is free to fake, the funnel inverts. Organizations, the authors write, "systematically select for candidates who are best at performing the hiring process, rather than those best equipped to do the job."
Their prescription is "dynamic friction": sudden constraints, mid-task changes in scope, prompts that force a candidate to defend a counterintuitive tradeoff, plus a "culture of intellectual honesty over polished perfection." The argument is tidy. The thread was not buying the tidiness.
The essay accused of being its own subject
The most cutting reply needed no rebuttal of the logic, only a detector.
"An AI written article writing about AI resumes and AI interviewers" — apwheele, a data scientist who runs crimede-coder.com
From the field came a working method that owes nothing to manufactured friction — just denser human contact.
"35 interviews in 2 hr 15 mins… excellent signal, because… give the candidate a hint to show it ('tell me a story about how you solved a computer problem')… More human connection. Less machines." — a commenter currently recruiting apprentices
Another reframed the entire fix, putting the highest-signal channel where the authors put friction.
"as someone who has done 100+ first round interviews for SWE… highest signal: personal referrals" — an engineer who has run 100+ SWE first-round interviews
And the dominant mood was that none of this is new.
"no one seemed to treat it like an emergency that perfectly qualified candidates have been getting filtered out after tripping various invisible wires for years (due to ATS systems…)" — a commenter whose bio is mrmarket.lol
AI exposed the break, it didn't cause it
The honest read is that the funnel was leaking before the models arrived: ATS keyword gates, logo snobbery and ghost postings were filtering out qualified people for years, and "performing the hiring process" describes two decades of Big Tech interview theater more than it describes anything AI invented. What changed is symmetry. Now both sides automate, so the rot the authors call a strategic risk is just the old breakage made impossible to ignore — and the field's answer, referrals and a room with a person in it, predates the problem.
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Sandi Metz's 2016 rule: duplication is cheaper than the wrong abstraction
A developer pulls repeated code into a shared function. The next requirement almost fits, so they add a parameter. Then a flag. Then a conditional. Ten years on, the essay describing that slide is back, and the fix it prescribes is to walk it back: re-inline, delete, re-derive.