1. ~10% of applications are over-tailored. Really? You did <hyper-specific thing> with <uber-specific details> exactly matching our job description at $BigCo 3 years before the language existed and 5 years before we pioneered it? The person might be qualified, but if they can't be arsed to write a resume that reflects _their_ experiences then I don't have enough evidence to move them forward in the interviewing process.
2. ~40% of applications have obvious, major inconsistencies -- the name on LinkedIn doesn't match the name on the resume, the LinkedIn link isn't real, the GitHub link isn't real, the last 3 major jobs on LinkedIn are different from the last 3 major jobs on the resume, etc. I don't require candidates to put those things on a resume, but if they do then I have a hard time imagining the candidate copy-pasting incorrectly being more likely than the LLM hallucinating a LinkedIn profile.
Those are quick scans, well under 4s each on average. We've used 80 minutes of our budget and are down to 600 applications. Of the remainder:
3. ~90% of remaining applications fail to meet basic qualifications. I don't know if they're LLM-generated or not, but a year of Python and SQL isn't going to cut it for a senior role doing low-level optimizations in a systems language. If there's a cover letter, a professional summary, mention of some side project, or if their GitHub exists and has anything in it other than ipynb files with titles indicating rudimentary data science then they still pass this filter. If they're fresh out of school then I also give them the benefit of the doubt and consider them for a junior role. Even with that leeway, 90% of those remaining applicants don't have a single thing in any of the submitted materials suggesting that they're qualified.
So...we're down to 60 applications. We spent another 40 minutes. In retrospect, that's already our full 2hr budget, so I did exaggerate the speediness a bit, but it's ballpark close. You can spend 2min fully reading and taking notes on each of the remaining applications, skimming the GitHub projects of anyone who bothered to post them, and still come out in 4hr for the lot.
It's probably worth noting, that isn't all to say that <5% of programmers with that skillset are qualified. I imagine the culprit is spray-and-pray LLM spam not even bothering to generate a plausible resume or managing to search for matching jobs. If bad resumes hit 99 jobs for every 1 job a good resume hits then you only expect a 1% success rate from the perspective of somebody reviewing applications.