Local Attempts To Solve AI-Related Problems Of Hiring
New York City’s Local Law 144-21 introduces new transparency and fairness requirements for organizations using Automated Employment Decision Tools (AEDTs) — software that relies on algorithms, machine learning, or AI to support employment decisions like hiring and promotions. The law responds to growing concerns that such systems can unintentionally produce biased outcomes and aims to make their use more open and accountable.
It requires companies that use algorithmic systems in hiring or promotion to run regular independent bias audits, publish the results, and notify people when such tools are part of the evaluation process. The goal is to reduce hidden discrimination and make the use of automation more visible and accountable.
But this is, at best, a partial answer. It applies only inside New York City, and it treats only a narrow slice of a much larger problem. Bias audits and disclosures do not fix the deeper dynamics of automation, gaming, dehumanization, and power imbalance that now define modern hiring. They put guardrails on a single road, while the entire system is being redesigned by AI. To understand what is really happening—and what is at stake—we have to step back and look at the much bigger picture.
Zero Sum Game: Bots vs. Bots
Modern hiring is quietly breaking under the weight of its own automation. What began as a way to make recruiting more efficient has turned into a feedback loop where machines compete with machines, flooding the system with volume instead of value. Algorithms now generate applications at scale, while other algorithms try to filter them just as fast, creating an endless cycle of noise. In this environment, real people become collateral damage—qualified candidates are filtered out, not because they lack ability, but because they don’t optimize themselves like software. Hiring stops being about judgment, context, and potential, and becomes a technical arms race where speed and scale matter more than substance.
Faking Expertise Virtually, Real-Time Has Never Been Better
Hiring is drifting into a theater of performance rather than a test of real capability. As interviews move online and become mediated by speech-to-text, chatbots, and instant copy-paste answers, candidates can outsource thinking itself in real time. The process no longer evaluates understanding, experience, or judgment—it evaluates how well someone can operate a stack of tools during a conversation. This creates a dangerous illusion of competence: polished answers appear instantly, but they may belong more to an algorithm than to the person being hired. In trying to modernize interviews with technology, organizations are quietly eroding their ability to distinguish genuine expertise from well-packaged automation.
AI Driven Social Engineering
Modern hiring has become dangerously skewed. The systems built to protect organizations are often easier to exploit than they are for real people to navigate with honesty and merit. It is now frequently simpler to deceive, manipulate, or socially engineer automated recruiting tools than to pass through them as a genuinely qualified candidate. On platforms like LinkedIn, there are countless cases of fake “AI recruiters” luring job seekers into sharing personal information through fabricated job opportunities. That data is later reused to create fake candidate profiles, which are then used to scam employers by impersonating real applicants. Over time, public hiring portals become polluted with noise, their quality collapses, and people lose trust in them and walk away.
While bots, fake identities, and scripted interactions glide through weakly defended pipelines, real applicants face rigid, impersonal filters that reject them with mechanical certainty. This inversion exposes a deeper flaw: hiring technology is being optimized for control and efficiency, not for truth, judgment, or human discernment.
Generational Gap And Turf Protection – Decoded.
Modern “Agile” hiring is increasingly shaped by a toxic mix of generational disconnect and turf protection that has little to do with selecting the best leader for the work. Instead of assessing deep, hard-earned experience in organizational design, product thinking, and technical coaching, interview panels often default to shallow signals: trendy buzzwords, niche framework trivia, and lifestyle judgments that read more like cultural screening than professional evaluation. Worse, the gatekeepers of “Agile practice” can treat genuine expertise as a threat—someone who can challenge the status quo, expose uncomfortable truths, or operate independently becomes “too dangerous,” while safer, more controllable candidates are preferred. The result is a system that rewards conformity over competence, protects internal empires over outcomes, and widens the gap between what organizations say they need and what they actually hire for.
Bald and Gray-Haired People Will Be Still Needed
Organizations are increasingly surrounding executives with a loud, fashionable “advisory” layer that optimizes for optics, trend language, and performative certainty—often at the expense of hard-won judgment. In the rush to look modern, leaders get flooded with KPI/OKR dashboards, sprint-velocity slogans, “AI vs. Agile” debates, and hyper-speed promises, delivered by polished committees that can sound confident while still missing the point. Meanwhile, experienced practitioners—the ones who have lived through multiple cycles of transformation failure and know where the real constraints hide—are pushed to the margins, treated as outdated or inconvenient. The irony is that when the hype collapses under real-world complexity, it’s usually the “bald and gray-haired” veterans who end up cleaning up the mess, restoring sanity, and re-grounding the organization in fundamentals. The problem isn’t innovation; it’s replacing wisdom with noise, and mistaking theatrical advisory energy for actual capability to execute and learn.