I automated my job application process

Original Article ↗ Hacker News Discussion ↗

An engineer describes automating their job applications with large language models, prompting a broader debate over AI‑generated resumes and cover letters in an already strained hiring market. Many hiring managers report being flooded with low‑quality, often fake or AI‑written applications, making it harder to spot qualified candidates and pushing some toward more in‑person screening, private networks, or abandoning public job postings altogether. Commenters frame this as a tragedy‑of‑the‑commons arms race between automated applicants and automated filters, and argue that real leverage now comes from networking, referrals, and demonstrable work rather than mass applications.

Overall reaction to automated job applications

  • Many see automating applications with LLMs as technically clever but socially harmful.
  • Frequent framing: tragedy-of-the-commons / prisoner’s-dilemma. If others spray-and-pray, individuals feel forced to do the same, even though it worsens the system for everyone.
  • Some call it narcissistic or spammy; others argue it’s a rational response to opaque ATS filters, ghost jobs, and instant automated rejections.

Impact on hiring managers and companies

  • Hiring managers report hundreds to thousands of applications per role, often 90%+ clearly unqualified or obviously AI-generated.
  • Complaints include: duplicated resumes, identical cover letters with minor edits, fake identities, and even organized fraud rings using fabricated CVs and remote-work scams.
  • Noise pushes many to:
    • Ignore early applicants.
    • Rely more heavily on referrals and private channels.
    • Stop public postings entirely and hire through networks or agencies.
    • Consider on-site or in-person-only interviews even for remote jobs.

Job seeker experience and incentives

  • Job seekers describe:
    • Hundreds–thousands of applications for a single interview.
    • Ghosting at all stages, including after multi-round interviews.
    • “Ghost jobs” and roles posted without real intent to hire.
  • This drives a numbers-game mentality and makes deeply researching and tailoring each application feel irrational.
  • Some insist targeted, high-effort applications and networking still work; others say that advice is out of date for mid-level/senior engineers today.

Cover letters, resumes, and LLM use

  • Mixed views on cover letters:
    • Some hiring managers like them when they add specific, non-generic detail or personal context.
    • Many see LLM-generated letters as long, formal, and content-free; they confer no advantage and can be a negative signal.
  • Resume optimization via LLMs appears to increase callback rates in some anecdotal tests, but is often detected as templated.

Cheating and “fake candidates”

  • Reports of candidates:
    • Using LLMs live during remote interviews.
    • Having others do the work after hire (offshore substitution).
  • Leads to moves toward:
    • In-person or proctored interviews.
    • “Humanity checks” (possibility of in-person rounds, surprise calls, local meetups).

Proposed fixes and structural ideas

  • Suggested mitigations:
    • Small manual tasks or instructions in postings to filter bots.
    • More in-person events and career fairs.
    • Greater reliance on referrals, networking, and internal candidate pipelines.
  • More structural proposals:
    • Professional licensing or standardized competency exams.
    • Union/guild-like bodies or centralized reputation/credential systems.
    • Regulation of job boards and ATS behavior, though feasibility is debated.