Recruiters are going analog to fight the AI application overload
Hiring platforms flooded with low-quality, AI-generated job applications are prompting some employers and recruiters to abandon automated filters and even reconsider posting roles on LinkedIn. Commenters describe an arms race in which companies use algorithms and AI-driven applicant tracking systems while candidates respond with auto-filled resumes and chatbot-written answers, making it harder for genuine applicants and managers to find each other. Many argue that personal networks, referrals, and more human-centric evaluation are becoming the only reliable way to hire or get hired, albeit at the cost of fairness and accessibility for outsiders.
AI Arms Race in Recruiting
- Recruiters long used algorithms and ATS filters; candidates now counter with LLM‑written resumes, cover letters, and even interview assistance.
- Many see this as an inevitable tit‑for‑tat escalation driven by employers’ own automation and cost‑cutting.
- Others warn that AI‑generated applications increase noise and make it harder for sincere applicants to stand out.
LinkedIn and Platform Critiques
- Strong frustration that hiring has been outsourced to LinkedIn, making a profile effectively mandatory.
- New LinkedIn gen‑AI tools (easy apply, AI outreach) are blamed for massive low‑effort application floods.
- Some say recruiters should simply stop posting there if they dislike the flood; others ask for better alternatives but few credible substitutes are suggested.
Networking vs. Online Applications
- Repeated reports that referrals or direct contact with hiring managers are vastly more effective than cold applications.
- Some argue this is just survivorship bias; others insist referrals often bypass early filters entirely.
- Networking is perceived as harder post‑pandemic; suggestions include local industry chats, conferences, and even opportunistic in‑person networking.
Certifications, Gatekeeping, and Assessment
- Debate over whether professional certifications could fix hiring noise.
- Many view current certs as “paper mills” that don’t filter for competence and may even repel strong candidates.
- Others note prior attempts at formal licensing in computing saw almost no uptake.
- Consensus that realistic work evaluation by experienced practitioners is ideal but costly and structurally rare.
Fraud, Imposters, and AI Abuse
- Reports of candidates with fully fabricated identities, work histories, and AI‑assisted interview answers, sometimes backed by fake company sites.
- Take‑home tasks are frequently completed by LLMs; interviewers detect this when candidates cannot explain code or reasoning.
- Some see AI use on tasks as reasonable “working smart,” others as a serious integrity red flag, especially when undisclosed.
Calls for More Friction / Going Analog
- Suggestions to reintroduce friction: paper resumes, mailed applications, identity verification services.
- Advocates claim this would cut spam dramatically; critics counter it might deter strong but in‑demand candidates and reinforce inequality.