In 1970 a young economist, George Akerlof, published a paper called 'The Market for Lemons'. Using the second-hand car market as an example, he showed how asymmetric information about quality can cause market collapse. Sellers know if their car is a 'lemon'; buyers do not. The price settles somewhere between the value of a lemon and a non-lemon. Non-lemon sellers then withdraw, and soon only lemons remain. Though simple, the principle won Akerlof a Nobel Prize in 2001. In this essay I want to use his work to explain how the job application market in 2026 has suffered its own version of this collapse.
A job market is not like a traditional market. There is no price as such. Instead it is a system for matching applicants to jobs. An employer posts a vacancy and wants to find a candidate who is two things: qualified and serious. Qualified in that they have the right skills and, in some cases, formal credentials. Serious in that they are genuinely interested in the role.
Assessing qualifications has always been hard. Employers have built a battery of tests and interviews to do so, typically arranged as a funnel: cheaper assessments first, costlier ones like in-person interviews later. None are perfect and all incur cost.
Seriousness is different. If an employer is going to move an applicant through to the expensive later stages, they want to know the candidate actually wants the job. Anyone who has hired people knows the frustration of an exciting candidate who pulls out at the offer stage, or worse, accepts and then reneges before the start date. But historically this was not a widespread problem for a simple reason: applying for a job took time, and that time was effectively a cost that filtered out the unserious.
My first job application was for a Saturday shelf-stacking role at the local Co-Op. I completed a paper form and dropped it off in person. My next was an investment banking internship: an online application with a CV, cover letter, and several free-text questions. It took three or four hours per application. I applied for half a dozen and was serious about every one.
Two things happened in sequence.
First, one-click apply. LinkedIn and other jobs boards allowed applicants to use their profiles to apply with a single click. Some were undoubtedly motivated by the Cost-per-Click’ model. Others saw applications as an onboarding flow and sought to maximise conversation. This reduced the cost of applying and unsurprisingly increased the number of applicants per role. Employers could infer less about an applicant's interest from the mere fact of applying. It made filtering harder, but it was manageable: employers could require cover letters or add other low-cost screening stages to signal seriousness.
Then came AI. Applicants could now tailor every application to the job description and generate professional cover letters at a click. Soon tools were built on top of LLMs to automate the entire process: zero-click apply with tailoring. Combined with a tough job market, the number of applications per role skyrocketed. Workday reported that the volume of applications on its platform grew nearly four times faster than vacancies in 2024. According to Greenhouse, the average number of applications per job has doubled since the release of ChatGPT.
Now picture an employer who posts a job and receives a thousand applications. Perhaps a hundred of those applicants are serious: they have researched the company, understood the role, created a bespoke CV, and written a thoughtful cover letter. More to the point, they actually want the job. The other nine hundred have used an AI tool to apply for this and hundreds of other jobs. They may not even know they have applied - several recruiters have reported calling up applicants who have no idea what they have applied for.
Why would people submit an application they are not serious about, even if it costs nothing? Because it is easier to apply for everything than to spend time reviewing postings and deciding which ones are interesting. It is not that the unserious applicant has zero interest, just that the probability of them being genuinely interested is a particular role very low: it’s option value.
The employer would happily spend real time reviewing the serious hundred: reading their CVs, considering their cover letters, providing feedback. But they cannot do that for a thousand. So they resort to blunter instruments: discarding half the pile, running an AI screening tool to surface the 'best' CVs, or falling back on nepotism and only interviewing people who already know someone at the company.
Serious applicants soon become aware of this. Scroll through any jobseeker subreddit and you will find a rich discourse about how companies actually review applications. The result is predictable: serious applicants are no longer prepared to spend hours on considered applications. They too resort to spray and pray, or give up entirely. Just as Gresham's Law tells us that bad money drives out good - because people hoard sound currency and spend debased currency - unserious applications have driven out the serious ones.
You can take this further and describe it as a double-sided Akerlof problem. In the used car market there is one hidden variable: the seller knows whether the car is a lemon, but the buyer does not. In the job market there are two.
On the applicant side, seriousness is hidden. The employer cannot tell whether an applicant has spent an hour researching the company and crafting a thoughtful application, or whether an AI tool fired off a generic one while they slept. This is the direct analogue of Akerlof's lemon: the applicant knows, the employer does not.
But there is a second asymmetry running in the opposite direction. The employer knows how much effort they will invest in reviewing each application: whether it will get ten minutes of careful reading or ten seconds of automated screening. But the applicant does not. An applicant deciding whether to invest time in a serious application is making a bet on the quality of the review it will receive, with no way of knowing the odds.