I am exploring modern hiring market—specifically the open, resume-driven (excluding the elite or Ivy league professionals)—is drifting toward an Akerlof-style “lemons market.”
I am high-confident about the direction of the effect and low-confident about long-term equilibrium outcomes.
The core mechanism is straightforward: AI has collapsed value of cheap signals that most candidates and employers rely on historically, Unlike textbook lemons markets where high-quality sellers can exit, workers cannot refuse employment without financial ruin. This creates a uniquely harmful dynamic: wage compression without market clearance, converting wage mispricing into accumulated psychological harm.
I think we are in early stage of the shift the worse is yet to follow.
Note - Elite candidates with strong institutional signals (prestigious degrees, selective employers, difficult certifications) remain legible and hireable. But the anonymous general applicant pool—the part of the market processed through ATS filters, resume screens, and standardized interviews—is entering a downward spiral of mistrust and adverse selection.
How Akerlof’s Lemons Model Applies Here
According to Akerlof’s model - markets fail when buyers cannot reliably distinguish high-quality from low-quality goods. Three conditions matter: information asymmetry, cheap-to-fake signals, and the absence of credible verification. When these align, buyers rationally assume mediocre quality, high-quality sellers exit, and the average quality of the market deteriorates further.
This is happening in the hiring market right now (Excluding Elite Candidates). Majority of the professionals who do not have expensive degrees or certifications.
Pre AI Hiring Market
Before AI, in terms of equilibrium the hiring market was fragile but workable. Employers faced high information asymmetry: a resume and a few hours of interviewing offered only partial evidence of real-world effectiveness. Still, the system functioned because cheap signals—particularly writing quality, vocabulary choice, clarity of explanation, and the ability to describe one’s work compellingly—were costly to fake and it took lot of time and effort to fake resume for various job applications.
These soft signals, along with work history, references, and portfolio samples, acted as noisy but meaningful indicators of competence. As long as producing a polished resume or building projects with required skill required significant time investment, employers could extract some signalling value from these. This equilibrium was imperfect but self-sustaining.
AI has collapsed the cheap signal variable rather it obliterated the value of cheap signals used by majority of candidates.
AI allows anyone to generate fluent prose, highly polished resumes, optimized cover letters, and domain-specific language. This means that two candidates of very different quality now produce nearly indistinguishable applications. The correlation between resume quality and underlying competence has sharply weakened.
When low-quality candidates can mimic high-quality ones at negligible cost, employers rationally downgrade the credibility of all candidates. This is textbook signal degradation: the channel becomes noisy enough that it no longer distinguishes types.
The collapse of cheap signals is sufficient to trigger lemons dynamics.
Current data from 2024:
Having said that the other costly signals at the top of market (Ivy degrees, past selective employers, difficult certifications) still remain meaning full. but that is a small segment of the total market or may be we should treat it as a different segment altogether.
Why Elite Signals Remain Intact
Expensive signals—Ivy League degrees, employment at FAANG companies, difficult certifications (CFA, actuarial exams)—maintain their value because:
- Production cost remains high: You cannot AI-generate a Harvard diploma
- Verification is straightforward: Employers can confirm institutional credentials
- Quality correlation is strong: These institutions have already done expensive filtering
This bifurcates the market: elite candidates with expensive signals remain highly legible, while the anonymous applicant pool degrades into noise.
Employer Side
As the trust in job applications decrease employers will behave in a predictable way - More stringent hiring process, increasingly rigid systems and longer evaluation processes. They will try to raise the hiring bar not because the type of work demands it or they have very high quality applicant pool but due to high cost of "False positive" cost of hiring a wrong person is far higher than "False negative" rejecting the right person.
But how is this adverse selection?
The real locus of adverse selection in hiring market isn’t the hiring decision itself but the salary-setting mechanism that accompanies it.
A stringent hiring process can be rational and may even improve match quality. But salaries operate under a different rule:
when employers cannot reliably distinguish between high- and low-quality candidates—because cheap signals like resumes and written communication have collapsed—they rationally anchor compensation to the expected value of the average applicant rather than the marginal value of the actual candidate.
This forces salaries for high-skill but non-elite candidates downward, even when they are genuinely excellent. At the same time, weaker candidates who use AI to mimic high-quality signals benefit from the same averaging effect: employers cannot justify paying them significantly less without risking legal, ethical, or fairness concerns.
The result is a textbook lemons equilibrium in compensation: wage compression, a shrinking premium for true skill, and a convergence toward mid-range salaries that misprice both high- and low-quality labour.
The important point is: stricter processes do not resolve the salary mispricing problem. The bottleneck remains at the employer’s ability to price marginal productivity under high uncertainty.
Why the Labor-Market Lemons Dynamic Is Uniquely Harmful (and Cannot Resolve Itself Through “Exit”)
The "No Exit" Pathology
In text book lemons market high-quality sellers can simply exit the degraded market—owners of good cars withdraw them from sale, restoring equilibrium through reduced participation.
But labour markets cannot self-correct this way because workers cannot exit the market for livelihood reasons. A talented professional whose value is mispriced by wage compression cannot refuse to work until the market improves; they must accept underpayment to survive.
This creates a pathological version of adverse selection: instead of high-quality goods disappearing from the market, high-quality people remain in it but in a state of chronic under-compensation and dissatisfaction. The economic mispricing can converts directly into psychological strain. If the model is correct, the logical outcome is not market clearance but a large-scale increase in professional dissatisfaction, burnout, and mental health stress—because the system traps high-ability individuals in roles that neither reward their skills nor allow them to signal their true value.
This creates a unique failure mode - The lemons dynamics accumulate as lived frustration rather than being resolved through exit, making the labor-market version of adverse selection uniquely harmful compared to its textbook analogue.
Prediction: If this model is correct, we should observe rising rates of:
High-confidence predictions (qualitative):
- Increasing wage compression for mid-skilled, non-elite white-collar roles
- Higher variance in hiring processes across firms
- A premium on verifiable, expensive-to-fake signals
- Declining trust in general applicant pools (ATS pipelines)
Medium-confidence predictions:
- Increased professional dissatisfaction among high performers without strong institutional credentials
- Higher rates of burnout, job anxiety, and perceived career instability
- Employers shifting toward closed-network hiring (referrals, internal transfers, reputation markets)
Low-confidence predictions:
- Long-term structural bifurcation of labour markets into high-trust and low-trust ecosystems
- Increasing mental-health burden linked directly to market mispricing
- These predictions are falsifiable and can be monitored over 3–5 years as more hiring data emerges.
Early evidence:
AI is not only making writing resume and build portfolio projects it might lead to wage compression and more importantly chronic dissatisfaction.