5 min read ·
Hiring is two machines and nobody home
The AI rewriting my resume wanted to open my latest job with "Top contributor (102 of 247 merged PRs)". It was true, a script could prove it, and it was worthless. Hiring in 2026 is one model writing and another reading, so polish proves nothing, and the only signal left is a specific claim a stranger can check and would care about.
I spent today rebuilding that resume, a year to the day after my first commit at a freight forwarder and customs broker in Gurugram. I pointed Claude Code at my PR history and a folder of other engineers' resumes. It counted my work on the team's main repository: 314 commits and 102 of its 247 merged PRs, a hair more than anyone else. Claude put "Top contributor" at the front of my first bullet.
I wrote back: "makes no sense in a resume. why would the next company care about this?"
It wouldn't. A merged-PR count describes my team's workflow. It says nothing about what any of those 102 PRs changed for the people who use the software. The machine reached for the number because a git log is the easiest thing in the world to count, and the r/EngineeringResumes wiki says to "move the metrics towards the start of each bullet." That count belongs to the vanity ledger: commits, PRs, lines of code, tickets closed, all the activity a script can total in a second and a reader can do nothing with. It looks like evidence. It is evidence that you were busy.
Even DHH keeps one.
At Rails World on Wednesday he said he wrote about 150,000 lines of code in August, and I don't doubt it. But a line of code was a bad unit before agents, and agents made it free. At 150,000 lines a month, the count measures how fast the machine types.
The prose has the same disease, and it is worse, because both ends are machines now. Candidates write with a model: in a 2025 Greenhouse survey, 74% of US job seekers said they use AI. Companies read with a model. Greenhouse's Talent Matching sorts applicants into buckets from Strong down to Limited, and the release notes promise that it "never advances or rejects candidates automatically." The average job still got 244 applications in 2025.
The reading model also likes its own writing. Xu, Li and Jiang found in September 2025 that LLM screeners prefer resumes written by the same LLM, with a self-preference of 67 to 82%, and that a candidate who uses the screener's model is 23 to 60% more likely to make the shortlist. The gloss on a 2026 resume is a watermark. It tells the screener which subscription you pay for and tells nobody who you are.
Two answering machines, leaving messages for each other.
A signal has to cost the sender something, and a cover letter that used to cost an evening now costs a prompt. In Galdin and Silbert's model ("Making Talk Cheap", November 2025), tailored application text stops working as a signal once LLMs arrive, and the highest-ability workers get hired 19% less.
Polish does win one round. If the first reader is a model, prose from the same model does better with it, and 23 to 60% is not nothing for someone sending hundreds of applications. But polish only buys you a chair across from a person, and the model that wrote your bullet will not be in the room. A hiring manager on r/ExperiencedDevs wrote last year:
probably 9/10 candidates we interview cannot actually explain how they measured their metrics
My reply about "Top contributor" also asked for research, so I did what everyone does now, only more so, and let Claude fan out. Seven agents made 961 tool calls in about 75 minutes. Reddit blocked them with an HTTP 403, and they read it through Wayback Machine captures instead. Eight more agents wrote 60 bullet options into a 15-page PDF.
The same message carried my story: "i started working on a browser agent that followed fixed SOPs and then I made it more agentic". It sounded like 2026. Claude checked it against my git log, and the code went the other way. In October 2025 Gemini chose every click, and today the production path makes zero model calls. In February I argued the opposite of that story in public. Seven months later, the first time I had to put that work on a resume, I told the story backwards.
Claude's verdict: "The true version is a stronger claim." Three days ago someone on r/AI_Agents put it in hiring terms: "Showing you know when not to call the model is a strong signal."
Use the model as an auditor. Let it check your story against your git log. Let it ask the rude question it asked about an old line of mine that claimed "3x": "Faster than what?" Don't let it choose your words. It picks the most likely ones, and the most likely first line for my resume came straight from the vanity ledger.
Ask every line on your resume the question I asked mine: why would the next company care about this? Delete every line that has no answer.