The Last Opaque Market

Three numbers that have caused chaos; 400,000 + 11,000 + 3.1%

Over 400,000 jobs eliminated in tech sector restructurings since 2022. More than eleven thousand people retiring every day, taking decades of institutional judgment with them. And a national hiring rate of 3.1%, matching the lowest point recorded during COVID lockdowns, without a lockdown.

These aren’t three separate problems. They are one problem with three symptoms. The economy is reorganizing around human judgment at the exact moment the infrastructure for moving human judgment has ceased to function.

Here is the specific shape of that failure. AI is automating the execution layer of work: the tasks, workflows, and repeatable processes that credentials used to certify. What’s left is judgment: contextual reading, taste, creative recombination, the ability to identify which problems are worth solving. The retirement wave is simultaneously draining the people who have the most of it. Companies are hemorrhaging the most valuable thing they have at the same time AI is making it the primary source of human economic value.

And the system meant to replace that judgment, to route it from where it exists to where it’s needed, is frozen.

Not from economic contraction. GDP is positive, job openings sit at 7.4 million. The freeze came from an arms race. Employers deployed AI to screen candidates. Candidates deployed AI to generate applications. The result is a doom loop: more automation producing less signal, a national hiring rate at COVID lockdown lows, and the average job posting now drawing 244 competing applications, up 111% from 2022. The stalemate isn’t a symptom of economic weakness. It’s what happens when an AI arms race meets infrastructure designed in 1955.

The system was never built to identify judgment. It was built to sort resumes. Those were different problems when execution was the value. They’re the same problem now.

The Fourth Wave

Every prior wave of disruption in hiring told itself the same story: new technology, better outcomes.

The internet digitized the resume. LinkedIn created passive candidate sourcing. ATS platforms promised efficiency at scale. Each wave changed the medium and left the underlying logic untouched. The resume was still the primary data structure. Keyword matching was still the core screening mechanism. The job description was still aspirational fiction dressed as a spec.

The numbers confirm the pattern holds. Cost-per-hire for executive roles is up 21% since 2022 and 113% since 2017. Fifty-three percent of job seekers experienced ghosting in the past year. The hiring process has lengthened from roughly 12 days in 2010 to more than 42 days today. More automation, worse outcomes.

AI is the fourth wave: automating the same flawed process at higher speed. The result isn’t better hiring. It’s faster noise. And the noise is accumulating at the exact moment the stakes have changed. What the system is failing to route is no longer interchangeable labor. It’s judgment. The consequence of that failure is no longer friction. It’s loss.

The Market That Never Got Built

To understand why four waves of disruption haven’t fixed this, you have to name what’s actually missing.

Every major market in the modern economy eventually developed three things: price discovery, transparency, and liquidity. Financial markets got them. Real estate got them, imperfectly but meaningfully. Advertising got them: programmatic buying is essentially real-time clearing for audience attention. Even commodity markets developed standardized grades and exchanges that made price discovery possible across enormous distances.

The labor market never did.

On the candidate side: a prose document optimized for human scannability in 1955, crammed with self-reported claims and unstandardized language. On the employer side: a job description written to a title rather than a function, “Senior Product Manager” meaning wildly different things at a twelve-person startup and a Fortune 500, both using the same words.

The opacity wasn’t accidental. Information asymmetry is a source of pricing power, and it historically favored employers. Recruiters built entire businesses on proprietary candidate databases, what one analysis called “information arbitrage.” LinkedIn partially dissolved that arbitrage by bringing profiles online, but preserved the underlying logic. Profiles are still prose. They still resist machine matching at scale. And opaque criteria don’t just create inefficiency: when no one can see what’s being evaluated, discrimination has nowhere to be contested.

This is what a market without infrastructure looks like: participants guessing, intermediaries extracting value from information gaps, and the most important asset in the exchange, judgment, invisible to the very system trying to price it.

Both Sides Are Flying Blind

The dominant narrative frames this as an applicant problem. It isn’t. Employers are equally lost, and their primary instrument fails in exactly the same way.

A job description is aspirational fiction. It describes the person who filled this role before, filtered through the assumptions of whoever wrote it, inflated with requirements that sound rigorous but aren’t. Thirty-five percent of “entry-level” postings currently require three or more years of relevant experience. One in five employers intentionally leave unfilled roles listed, maintaining the appearance of growth while controlling headcount.

The degree requirement is the most expensive broken proxy in the system. University attendance was always a signal for something else: the capacity to learn, to persist through complexity, to navigate institutional environments. A reasonable approximation when demonstrated skills were hard to verify at scale. But research on predictive validity is plain: education predicts job performance at r = .10. One percent of variance. Laszlo Bock, who ran People Operations at Google while building one of the most rigorous hiring processes in the world, said it without ceremony: “GPAs are worthless as a criteria for hiring, and test scores are worthless, no correlation at all.”

Credential requirements don’t just fail to predict performance. They lock out workers who have the skills but not the paperwork. There are an estimated 70 million Americans who’ve built expertise through alternative routes: work experience, trades, self-teaching, community college. They’re excluded not because they lack capability but because the system has no mechanism to see capability, only credentials. The skills-based hiring movement was supposed to fix this.

And it has, mostly, announced itself.

Harvard Business School and Burning Glass Institute studied a decade of hiring data and found that employers dropped degree requirements in nearly four times as many roles between 2014 and 2023. Net result: fewer than 1 in 700 actual hires benefited. Ninety-seven thousand people out of seventy-seven million annual hires. Nine leading firms (Apple, Walmart, Cigna, ExxonMobil, GM, Koch, Target, Tyson, and Yelp) saw nearly 20% more non-credentialed hires and retention that ran ten percentage points higher. But across the broader market: nothing changed.

Joseph Fuller, who led the HBS research, was direct: “Changing your hiring policy is, at best, the end of the beginning.” Without changing the screening infrastructure, not the checkbox on the form but the operating logic underneath, it’s virtue washing.

Strip hiring to its essence: it’s a matching problem with five requirements. Complete information. Comparable units. Efficient search. Signal quality. A clearing mechanism. The current system fails all five. Resumes are self-reported prose. Job descriptions are invented vocabulary. Search now generates 244 applications per posting, up 111% in three years. Degrees and titles are weak predictors. Ghosting runs at record highs.

This isn’t a broken system. It’s the absence of a system, papered over with conventions that everyone agrees to pretend are functional. And the thing that absence makes invisible, at exactly the moment it matters most, is judgment.

What the Infrastructure Actually Looks Like

What needs to change isn’t the surface of the hiring system. It’s what the system is designed to measure.

The resume was built to sort execution: to compress a track record of tasks and titles into something a recruiter could scan in thirty seconds. That was a reasonable design when execution was the value. The infrastructure that replaces it needs to surface judgment: not what someone has done, but how they think, what contexts they’ve navigated, which decisions they’ve made and what happened.

Think of it the way we think about a credit profile: not a letter about your financial character, but a structured, verified, queryable record that any authorized party reads the same way. When Fair Isaac introduced the standardized credit score in 1989, the Federal Reserve described the impact plainly: it “was necessary for the emergence of large-scale open-ended consumer lending.” Before standardized scores, banks made lending decisions through personal interviews and character assessments: subjective, slow, biased toward whoever the loan officer already knew. Standardization didn’t just make lending faster. It made mass consumer credit structurally possible.

The labor market equivalent is structured work history as metadata: verified employment, documented decision types and contexts rather than job titles, skills demonstrated rather than claimed, outcomes where measurable. Not self-reported prose, a structured record that creates comparable units. Two data sets finding functional overlap, rather than two documents hoping a human can intuit the connection.

The job description gets the same treatment. Functional requirements weighted by importance, described in standardized capability language rather than title vocabulary. This forces employers to articulate what they actually need, which turns out to be clarifying independent of the matching problem. When you can’t hide behind “strategic thinker with strong communication skills,” you have to name the decisions the person will actually make.

The people most harmed by the current system are those without the time, resources, or institutional support to craft polished prose documents, workers returning after gaps, career changers, those who built expertise outside formal credential paths. A voice agent that interviews candidates about their work history converts spoken experience into structured metadata. This isn’t “AI rewrites your resume”: that just outsources the existing format’s logic to a model. Converting spoken experience into structured data meets people where they are, not where the system requires them to be.

Real-time relative scoring with transparent criteria closes the loop. Upon application, both sides receive signal: fit score, the criteria driving it, relative standing. Am I in the top quartile? What’s holding my score down? Is there a role two steps over where I’d be a stronger match? This creates a feedback mechanism the current system lacks entirely: both sides can see what’s being valued and adjust accordingly.

The interview doesn’t disappear. It gets better. Currently, interviews are doing the work the application process should have already completed: extracting signal the resume and job description failed to provide. In a system where structured data has already established functional fit, the interview returns to what only humans do well: assess culture, build mutual trust, make judgment calls about potential. That’s a more human process than the current one.

The mechanism that makes all of this stick is the incentive flip. Skills-based hiring failed at scale not because the idea was wrong but because the incentives never changed. ATS vendors profit from application volume. Recruiters are measured on time-to-fill, not match quality. Removing a checkbox doesn’t touch any of that, and as AI tools make applying cheaper and faster, volume will only grow. The current model gets structurally worse on its own trajectory.

A system that measures on longevity of placement rather than speed-to-hire restructures the revenue model entirely. Tie revenue to retention outcomes, whether the match was actually right, and every participant’s incentives realign. The Workday lawsuit (Mobley v. Workday, №3:23-cv-00770, N.D. Cal., conditionally certified May 2025) signals the legal environment is already moving toward accountability for screening outcomes. Build ahead of it.

The governance piece is what separates labor market infrastructure from surveillance capitalism: criteria must be published, auditing must be mandatory. Visible and contestable criteria make errors correctable. Hidden scoring is just a more sophisticated version of the existing opacity.

The Competitiveness Stakes

The argument so far has been about the mechanism. This section is about the cost of getting it wrong.

Labor market fluidity, the rate at which workers and capabilities move to where they create the most value, correlates directly with economic dynamism. Davis and Haltiwanger documented that US job reallocation rates fell more than a quarter after 1990. The cost of that friction compounds over time: BCG estimated global skills mismatch erased $8 trillion in GDP in 2018 alone; the US-specific projection runs to $2.5 trillion in lost output over the next decade.

What’s different now is the structural shift in who’s hiring. The retirement wave and AI restructuring are creating conditions for a significant move toward smaller, more specialized economic units, micro-firms and fractional arrangements where judgment is the product and AI handles execution. MBO Partners documents 5.6 million independent US workers earning over $100,000 annually, up from 3 million in 2020. LinkedIn profiles mentioning fractional roles grew from 2,000 to 110,000 between 2022 and early 2024.

This matters because small organizations fail differently than large ones. A mis-hire at a fifteen-person company is potentially existential, it cannot absorb months of searching, cannot staff a recruiting function, cannot carry the cost of a wrong match. These firms are the emerging unit of the judgment economy, and they currently have no hiring infrastructure designed for what they need.

The retirement wave need not mean permanent knowledge loss. Portable, queryable work histories create a new category: fractional access to pattern recognition that took decades to build. Not job search, the availability of judgment, on demand, at the moment AI has made judgment the scarce resource. The retiring expert’s knowledge doesn’t disappear. It becomes searchable.

Countries that build this capability pull ahead. Denmark moves more than 25% of its workforce across jobs every year while ranking seventh globally in GDP per hour worked. High mobility and high productivity reinforce each other when the matching infrastructure works.

We built highways to move goods. We built the internet to move information. We have not built the infrastructure to move human judgment at the speed the moment requires.

The Call

Everyone is diagnosing. Few are designing.

The resume had a good run. It solved the problem it was designed to solve: compressing a person into a human-scannable format when information was scarce and decisions were made by individuals with thirty seconds. That era is over. The information isn’t scarce. The decisions don’t need to be made by individuals scanning prose. The constraints that produced the artifact have dissolved.

What comes next is not a better resume, a smarter ATS, or an AI that screens faster. What comes next is market infrastructure: structured, verified, portable work histories; transparent and auditable matching criteria; real-time clearing that gives both sides the signal they need to make good decisions quickly; a revenue model tied to placement quality rather than application volume.

The companies and platforms that build this, not as a feature, not as a product category, but as foundational architecture for how human judgment moves through the economy, will do for the labor market what exchanges did for financial markets and credit scores did for lending: create the transparency and liquidity that makes the whole system function better for everyone in it.

That is one of the largest unseized infrastructure problems in the modern economy. And the moment for building it is not coming. It’s here.