I don't mean bad for job seekers. I mean functioning badly as a market--a mechanism of matching buyers and sellers, mediated via prices.
A combination of AI and Workday makes applying for jobs badly very easy, which generates a lot of marginal applicants that still require review by HR. But the sheer number of candidates overwhelm HR folks, who fall back on easy-but-bad heuristics to filter out people they don't expect to be suitable. Degree completion (testing grit and intelligence) is a reasonable heuristic for junior staff. But it is an increasingly bad one for more senior roles, where experience is far more important. But Workday doesn't provide an easy tool to filter for "20 years of experience in X". In my field, professional organization credentials/certifications play that role - EIT/PE and AICP/FAICP are reliable indicators of seniority and capability, and (crucially) allows folks in HR to filter using a single uniform term.
By many metrics, modern job markets do much better on an aggregation basis--bringing together a lot of buyers and sellers in one place. Which is overwhelming. Given too much to choose from, analysis paralysis and decision fatigue are taking over. Further, HR folks are (like teachers) suffering from AI, which has undermined their traditional evaluation mechanisms. In the past, word processors and spell check eliminated the ability to use typing ability and/or spelling as a filtering heuristic. AI has destroyed the utility of the traditional cover letter and resume--a decent quality version of either can be produced with almost no effort by the applicant.
Further, a bigger market makes 'product comparison' (who is actually good at what / who is good to work for) much more difficult [1]. And when information asymmetry prevails, a 'market for lemons' emerges: any hire might be a lemon, nobody wants to overpay, so salary offers are lowballs, so none of the 'peaches' are willing to switch jobs, reinforcing the dynamic [2]. And so the job market locks up, which is terrible for both buyers and sellers.
Price comparison for labor is always difficult - people are non-fungible, and the smaller the organization, the less standard the role they perform, and the more the functions they undertake resemble the job description [3]. So there is a huge push to commoditize labor competencies using things like certificates and boot camps. It is, at best, a partial solution--degradation of any credential is inevitable. For-profit diploma mills emerged in response to the value of a college degree as a credential [4]. And any bootcamps [5] that relies on customers through-put has every incentive to maximize volume [6] and customer satisfaction, regardless of how that undermines the credentials of past bootcamps. In turn, the need for HR folks to evaluate the quality of bootcamps and mini-credentials is itself exhausting.
The long-term solution for ghost-written resumes/cover letters has long been interviews, which can't be conducted at scale. So to proxy that, more and more applications have required mini-essays, but which are subject to the same 'grading problems' as resumes and cover letters - AI generates a volume of text that outstrips human capacity to evaluate, and so HR departments increasingly outsource cognitive labor at to AI [7]. AI is both fallible and a black box [8] so unknown errors abound. Further, evaluative AI capacity is poor - AI is trained, not educated. It is reliant on its training [10] and does not get better over time [11], regardless of how many resumes it reviews. So the AI HR has available to it is hardly better than keyword searches, with all the associated flaws.
There is also the issue of deception. A certain amount of exaggeration is expected on a resume, but in addition to the market for lemons, it can generate a Red Queen's Race--people lie more and more simply to be considered. Anecdotally, in New York, the norm for bar-service is to claim an order of magnitude great experience - months for weeks, weeks for days. And in the context of things like Workfront, clearing (arbitrary) thresholds for applications to be considered by a human, deceiving the AI filter becomes a necessity.
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[1] Remote work magnifies this. There may be 5-10 employers (half of which are marginal) within a reasonable commute of home. But remote work requires evaluating employers all over the world. (Which has engendered entirely new varieties of employment scams). Evaluation is difficult, and expensive in time and cognitive capacity.
[2] The first thing medieval guilds (and trade fairs) do is mandate standard quality. Not because they like quality (they'd sell a lemon if they could get away from it) but because suspicion of low-quality drives down prices for everyone.
[3] Efforts to replace truck drivers with AV have foundered thus - much of what truck drivers do is not driving trucks but rather acting as a 'resolution agent' for immediate logistical problems (breakdowns, missing cargo, etc).
[4] Elite institutions (i.e. Yale) resist degradation by being exclusive, following a quality-not-quantity approach to education. Even if they admit a moron, that moron will become an educated moron.
[5] Awkwardly, the push to raise college graduation rates did not help: Trying to provide 'good value for money' for the public by reducing the drop-out rate seems attractive, but faced with a need to increase a statistic, the easiest way to achieve it is to lower standards. "When a measure becomes a target, it ceases to be a good measure". (Goodharts law). Reduction in graduation rates are merely a lagging indicator of a failure during the admissions process - people unlikely to be successful were admitted for not good reasons: a reasoning error conflating a mediating variable (college education) between grit/IQ and success as a causal variable, a cognitive error recognized by politely ignored because it enabled Type A academic Deans to build empires.
[6] Likewise, the MBA approach of reducing costs by reducing quality until volumes suffer doesn't work. In a market, the presence of competitors denies unlimited degradation. For a monopsonic public provider of higher education, there is no limit to the degradation of value (as nutrition, art history and English majors can attest).
[7] In the future, most work consists of evaluating the quality of AI outputs and "prompt engineering". This will drive both an explosion in the demand for such services, but also dramatic increase in the cost of non-AI services (Baumol's cost disease). A hairdresser who can evaluate AI outputs chooses alternative, more lucrative employment, and the price for the remaining hairdressers rises to reflect the reduced competition.
[8] Asked to explain its reasoning, an AI makes up a post-hoc explanation that fits the facts and deludes itself into thinking that was always what it thought. A distinction that can only be ascertained by lawyerly questioning or the parent of a precocious child. But, like the child, AI is getting better at an uncertain rate, with both greater cognitive capacity, but also greater meta-textual awareness - AI is getting better at assessing the qualities of its lies and the person asking the question, and so is getting better at being more deceptive. Arguably, this recapitulates the development of human cognition, where humans then developed a counter-strategy: to punish deception with viciousness disproportionate to its immediate harmfulness.
[9] AI isn't educated, but trained, and is reliant on its training to make inferences. The quality of that training data is degrading exponentially, as an ever-larger volume of web-scrapable material consists of AI produced content, and ever more of the good stuff is secured behind paywalls and access denied by robots.txt. (Evidence that AI is semi-legally harvesting user documents has been a long-term suspicion and the recent Navier-Stokes scandal practically validates the suspicion).
[10] It is not by accident that Google is now buying books by the load and scanning them all - it's a source of training data web-scraping can't match. Even the most dated Windows 3.1 guide represents substantially better training data than web-scraped AI generated content, because it was written, edited, reviewed and published in a context where publication (printing, warehousing, distribution, sales) represented a substantial and risky investment.
[11] In the not-too-distant future, AI training data will include everything ever written before AI, and there will emerge and industry of people whose role is to provide AI with training data by reviewing the quality of its responses and suggesting improvements, based on expert judgment. AI owners are already trying to crowdsource this specific form of labor, which will in turn fall victim to the market-for-lemons problem in which they only people who respond are cranks or malicious.