How Artificial Intelligence Destroyed Labor Market Price Discovery and Made Capability Invisible While Everyone Still Has Jobs
The unemployment rate hasn’t spiked. Hiring continues. Salaries remain stable. Workers keep working. AI didn’t replace the workforce.
It replaced something more fundamental: the ability to observe competence.
When everyone can produce senior-level output regardless of actual capability—when the intern generates work indistinguishable from the expert, when the incompetent appear as proficient as masters, when performance signals equalize across the entire capability distribution—the labor market loses its price discovery mechanism for skill.
Competence still exists. Some people genuinely possess expertise while others depend entirely on AI assistance. But the difference has become unobservable through any metric organizations currently use.
This creates economic crisis more severe than displacement: a market that cannot distinguish value cannot price it correctly. And a market that cannot price skill correctly cannot allocate human capital efficiently.
The invisible replacement is complete. We lost the ability to know who’s competent. And almost nobody has noticed because everyone still looks productive.
How Labor Markets Priced Competence Before AI
Labor markets functioned through observable performance differentials creating price signals that allocated human capital efficiently.
The mechanism was elegant: people with greater capability produced observably better outputs. Better outputs commanded higher compensation. Higher compensation attracted talent toward domains where capability was most valued. This created equilibrium where competence correlated with compensation because competence was observable through output quality.
The correlation held across multiple signal types:
Output quality signals. Expert work exhibited properties novice work lacked—sophistication, nuance, efficiency, creativity. These differences were observable through examination, making capability inferable from output. Organizations hired based on portfolio quality because portfolio quality indicated capability level.
Speed signals. Competent workers completed tasks faster than novices because internalized expertise enabled efficient execution. Speed differences created productivity differentials observable through work velocity, making capability inferable from completion rates. Organizations promoted based on productivity because productivity indicated capability.
Problem-solving signals. Capable workers solved novel problems competently while less capable workers struggled or failed. Solution quality under challenging conditions revealed capability distribution, making expertise observable through performance under difficulty. Organizations valued problem-solvers because problem-solving demonstrated genuine understanding.
Communication signals. Experts explained concepts clearly, answered questions competently, demonstrated deep understanding through articulation. Communication quality revealed knowledge depth, making capability observable through explanatory ability. Organizations hired articulate candidates because articulation indicated internalized understanding.
Consistency signals. Competent workers maintained quality across varied contexts and conditions. Consistency revealed robust capability rather than narrow optimization, making genuine expertise observable through performance stability. Organizations trusted consistent performers because consistency indicated reliable capability.
These signals combined to create market price discovery: organizations observed performance, inferred capability, offered compensation reflecting inferred value. Competition for talent established market-clearing prices where compensation matched capability because capability was observable and therefore competed for.
The system wasn’t perfect. Signals could mislead. Biases distorted evaluation. Information asymmetries created inefficiencies. But the fundamental mechanism functioned: observable performance differences enabled competence inference enabling price discovery.
This equilibrium depended on one assumption: performance differences indicated capability differences because producing superior performance required possessing superior capability.
AI destroyed that assumption completely.
The Signal Equalization That Broke Price Discovery
AI assistance equalizes output quality across the entire capability distribution, making performance signals uninformative about underlying competence.
The equalization is comprehensive and rapid:
Output quality converged. Junior workers using AI produce outputs matching senior workers’ quality. The sophistication, nuance, efficiency previously requiring years of expertise now emerges from AI assistance available to everyone. Output examination reveals nothing about worker capability because AI generates quality independent of user competence.
Consider software development: novice programmer using AI coding assistants produces code indistinguishable from experienced developer’s work—proper architecture, clean implementation, comprehensive testing, professional documentation. The output quality that previously required years of learning to achieve now appears instantly through AI assistance requiring no understanding from user.
Organizations cannot infer programming capability from code quality anymore. The junior developer and senior engineer produce identical outputs when both use AI tools. Capability difference remains—senior understands deeply while junior depends on assistance—but the difference is invisible through output observation.
Speed equalized. AI assistance enables rapid task completion regardless of user expertise. The velocity advantage experts possessed through efficient workflows and internalized knowledge disappeared when AI performs work instantly. Everyone completes tasks quickly with assistance, making productivity uninformative about capability.
Consider writing: expert writer previously produced polished prose faster than novice through internalized grammar, vocabulary, structure. AI writing assistants eliminate that advantage—novice generates publication-quality text as rapidly as expert by relying on AI rather than internalized writing skill. Completion speed reveals nothing about writing capability when AI writes for everyone.
Problem-solving converged. AI handles novel problems competently, enabling workers to solve challenges beyond their capability by outsourcing problem-solving to AI. The solution quality under difficulty that previously revealed expertise now appears regardless of worker understanding because AI solves problems while worker takes credit.
Consider technical troubleshooting: expert diagnosed complex system failures through deep understanding of architectures and interactions. AI diagnostic tools enable novice to identify issues and propose solutions without understanding underlying systems. Problem-solving performance that previously indicated genuine expertise now appears in workers lacking comprehension entirely.
Communication quality equalized. AI generates clear explanations, articulate responses, sophisticated discussions independent of user understanding. The explanatory ability that revealed knowledge depth disappeared when AI articulates on user’s behalf. Workers appear equally articulate regardless of actual comprehension by relying on AI to communicate competently.
Consistency became universal. AI maintains quality across all contexts, eliminating the consistency differentiation that revealed robust expertise. Everyone performs consistently when AI performs consistently, making consistency uninformative about whether worker possesses genuine capability or depends entirely on continuous AI assistance.
The equalization creates observational impossibility: examining current performance provides zero information about capability distribution because performance became independent of capability when AI generates outputs for everyone.
Junior and senior produce identical work. Novice and expert solve problems equally well. Incompetent and masterful communicate indistinguishably. Every performance signal that previously revealed competence differences now shows identical quality across workers with radically different underlying capabilities.
The market lost its measurement instrument. Competence still varies—some workers understand deeply, others understand nothing—but variation is invisible to organizations using performance observation for capability inference.
When competence becomes unobservable, price discovery fails. And when price discovery fails, market efficiency collapses.
The Hiring Lottery: When All Candidates Look Excellent
Hiring became lottery because AI assistance makes all candidates appear equally qualified during evaluation processes organizations designed for pre-AI conditions.
The lottery operates through multiple channels:
Resume optimization is universal. AI writes flawless resumes for everyone—perfect formatting, keyword optimization, compelling narratives, quantified achievements. Resume quality that previously correlated with candidate sophistication now appears identically in all applications regardless of actual capability because AI generates resumes for everyone.
Recruiters cannot screen based on resume quality. The thoughtful phrasing, strategic emphasis, professional presentation that indicated strong candidates now appears in applications from completely unqualified applicants using AI resume generators. Every candidate looks excellent on paper.
Interview performance equalized. AI coaches candidates for interviews—generating impressive responses to common questions, providing sophisticated talking points, suggesting strategic answers demonstrating cultural fit. Interview performance that previously revealed capability now appears in candidates coached by AI to say exactly what interviewers want to hear.
Consider behavioral interviews: ”Tell me about a time you solved a difficult problem.” AI generates compelling narrative with perfect STAR structure (Situation, Task, Action, Result), including specific details and measurable outcomes. Candidate delivers story convincingly despite having no actual experience with situation described because AI wrote script and candidate memorized it.
Interviewers cannot distinguish genuine examples from AI-generated narratives. The specificity, structure, reflection that indicated authentic experience now appears in fabricated stories optimized for evaluation criteria.
Portfolio work is indistinguishable. Designers submit portfolios of AI-generated work. Writers provide samples AI produced. Programmers share code AI wrote. Portfolio quality that previously demonstrated capability now demonstrates only that candidate has access to AI tools everyone possesses.
Organizations hire based on portfolio excellence discovering post-hire that candidate cannot perform without continuous AI assistance because portfolio represented AI capability not candidate capability. But this discovery occurs months into employment after significant investment in onboarding, training, integration.
Reference checks are unreliable. Previous employers cannot verify whether past performance derived from candidate capability or AI assistance because they face identical observational limitations. Reference provides positive feedback based on output quality not knowing output was AI-generated, creating false signal about candidate competence.
Skills assessments are gameable. Take-home assignments completed with AI assistance. Coding challenges solved by AI while candidate watches. Design exercises where AI generates all creative work. Assessment performance that should reveal capability instead reveals AI capability plus candidate’s willingness to use AI for evaluation.
Organizations attempting to prevent AI assistance during assessments face detection impossibility: candidates use AI surreptitiously, sophisticated assistance is indistinguishable from genuine capability, preventing access is impractical for remote evaluation.
The hiring process becomes lottery not because organizations evaluate poorly but because AI assistance makes evaluation structurally impossible using observable performance. Every candidate looks excellent. Every interview seems strong. Every portfolio appears professional. Every assessment shows competence.
Organizations hire randomly from candidate pool appearing uniformly qualified, discovering capability differences only months later when temporal separation reveals whether initial performance derived from genuine capability or AI dependency. By then, bad hires have consumed resources, disrupted teams, created dependencies making termination costly.
The hiring lottery is complete. Organizations cannot distinguish strong from weak candidates using any evaluation method relying on present performance observation. And since all evaluation methods rely on present performance observation, hiring has become chance.
Performance Reviews in the Age of Universal Assistance
Performance evaluation became meaningless when AI assistance makes everyone appear to perform excellently according to metrics organizations track.
The meaninglessness manifests across review processes:
Output metrics are equalized. Organizations measure deliverables completed, deadlines met, quality standards achieved. AI assistance enables everyone to meet these metrics—complete all deliverables, hit every deadline, achieve quality standards—regardless of whether worker understands work being delivered or depends entirely on AI.
Manager reviewing employee sees: 100% deliverable completion, zero missed deadlines, excellent quality ratings. Manager infers: employee performs strongly, deserves positive review, should receive raise or promotion.
Reality might be: employee understands nothing, uses AI for everything, cannot function if AI access removed. But reality is invisible to manager evaluating using metrics AI optimization satisfies perfectly.
Peer feedback equalizes. Coworkers observe collaboration quality, communication effectiveness, contribution value. AI assistance makes everyone collaborative (AI writes polite messages), communicative (AI articulates clearly), valuable (AI generates useful contributions). Peer observations provide no information about who possesses genuine capability versus who depends on AI for all interaction.
Manager assessment is uninformed. Managers lack capability to verify whether direct reports’ work represents genuine understanding or AI assistance because managers face same observational limitations. Manager might not use AI themselves, making them less competent at producing outputs than AI-assisted reports, creating awkward dynamic where manager cannot evaluate work they couldn’t produce themselves even if it’s AI-generated by report.
Promotion decisions lack foundation. Organizations promote based on performance metrics showing excellence. But excellence is universal when AI assists everyone. Promoting based on metrics measuring AI-assisted output rather than human capability means promotions reward AI tool usage rather than genuine competence.
This creates advancement randomness: equally AI-dependent workers receive differential promotions based on factors unrelated to capability—manager favoritism, visibility in meetings, social connections, luck. Actual competence becomes irrelevant to advancement because competence is unobservable while AI-assisted performance is universal.
360 reviews provide identical feedback. Direct reports, peers, managers all observe same thing: excellent performance enabled by AI assistance. Reviews consistently positive across all workers regardless of capability distribution because everyone performs well with AI help. Performance review systems designed to differentiate workers provide no differentiation when AI equalizes observable performance.
The performance evaluation infrastructure is intact—reviews happen quarterly, feedback is collected, ratings are assigned, compensation decisions follow. But the infrastructure measures nothing meaningful about capability because it measures AI-assisted output treating it as human capability when the two have become completely decorrelated.
Organizations cannot identify top performers to retain, struggling performers to develop, or incompetent performers to exit because performance signals became uninformative about underlying competence. Everyone looks good on metrics tracked. Capability remains invisible.
The Seniority Illusion: When Experience Provides No Advantage
Experience ceased providing performance advantage when AI assistance makes junior workers perform at senior level during observable evaluation.
The seniority premium is disappearing:
Junior with AI equals senior without. Entry-level worker using AI produces outputs matching ten-year veteran’s quality. The expertise accumulated through years of practice provides zero observable advantage when AI generates equivalent outputs for users regardless of experience.
This creates wage compression pressure: why pay senior premium when junior plus AI delivers identical results? Organizations rationally reduce compensation for experience when experience provides no measurable value during observable work.
Senior workers maintain advantage only in scenarios AI cannot assist: real-time problem-solving without device access, novel situations AI wasn’t trained on, integration across contexts AI cannot observe. But most work happens with AI available, making those scenarios rare enough that experience premium becomes hard to justify based on observable performance.
Mentorship value is unclear. Senior worker supposedly mentors junior by transferring expertise. But if junior already performs at senior level through AI assistance, what value does mentorship provide? The knowledge senior possesses exists in AI accessible to junior directly.
Traditional mentorship transferred tacit knowledge through relationship. AI makes tacit knowledge explicit and accessible without relationship requirement. Junior consults AI instead of senior for expertise, making senior’s knowledge advantage obsolete for practical purposes.
Years of experience mean nothing. Resume listing 10 years experience indicates… what exactly? That person has been employed for decade. Not that person developed capabilities over that decade, because capability development is unverifiable and possibly didn’t occur if person used AI assistance throughout employment.
Experience claims are unfalsifiable: candidate says they have expertise built over years. But expertise is unobservable through present performance. Organizations cannot verify experience translated into capability because capability itself is invisible when AI equalizes outputs.
Career progression is arbitrary. Traditional career path: junior gains experience → capability increases → performance improves → advancement follows → senior role achieved. This path assumed observable performance improvement over time indicated capability development.
AI breaks the assumption: junior uses AI → performance excellent immediately → no observable improvement over time → advancement based on tenure rather than demonstrated capability growth → senior role achieved without capability development.
Organizations promote based on years served not capability accumulated because capability accumulation is unobservable when AI assistance makes everyone perform identically.
The seniority illusion is complete: experience, expertise, accumulated knowledge provide minimal observable advantage when AI assistance available. Compensation premiums for experience lack justification based on measurable performance. Career progression happens through time served rather than capability developed.
And the worst part: genuinely experienced workers cannot prove their advantage exists. Senior with deep expertise performs observably identically to junior with zero understanding when both use AI. Market cannot reward genuine seniority because market cannot observe it.
Why ”Just Ban AI at Work” Cannot Succeed
The instinctive organizational response—prohibiting AI assistance to restore performance observation—fails for practical and enforcement reasons.
Detection is impossible. Organizations cannot detect AI usage reliably. Employees use AI surreptitiously through personal devices, browser extensions, API calls masquerading as legitimate tools. The assistance is invisible to monitoring systems. Output quality doesn’t indicate whether AI was used because human-generated and AI-assisted outputs are indistinguishable.
Attempting to prevent AI access requires draconian measures: no internet access, no personal devices, constant surveillance, locked-down environments. These measures reduce productivity severely while remaining gameable by determined employees finding creative access methods.
Competitive disadvantage is immediate. Organization banning AI while competitors allow it suffers productivity loss making them noncompetitive. Workers without AI assistance produce slower, lower quality outputs than workers with assistance. Customers choose competitors delivering faster, better results through AI usage.
Market selection favors AI adoption: organizations allowing assistance outcompete organizations prohibiting it. Prohibition becomes competitive suicide in industries where AI assistance is standard.
Workers demand access. Employment market pressures force AI permission. Workers want AI tools enabling better performance. Organizations prohibiting access lose talent to competitors providing tools. Recruitment becomes harder when candidates choose employers enabling rather than restricting AI usage.
Productivity benefits are real. AI assistance genuinely improves output quality and speed. Banning assistance means reverting to lower productivity baseline. Organizations choosing lower productivity become cost-inefficient relative to competitors maintaining AI-enabled efficiency.
Prohibition is unenforceable at scale. Monitoring every employee’s work process for AI usage is impossible. The overhead required for enforcement exceeds benefits from restoring observability. Organizations lack resources to police AI usage across all workers continuously.
The AI prohibition fails before implementation. Organizations cannot detect usage, cannot afford competitive disadvantage, cannot retain talent, cannot justify productivity loss, cannot enforce at scale.
Therefore prohibition is not viable response to observation failure. Alternative verification infrastructure is required.
Temporal Verification: The Only Market Signal That Survives AI
When present performance became uninformative about capability, temporal verification through independence testing becomes the only reliable signal enabling labor market price discovery.
The mechanism is simple: test whether capability persists when AI access is removed and testing occurs months after initial performance evaluation.
Consider hiring: candidate appears excellent during interview through AI coaching. Organization hires candidate. Six months later, organization tests capability without AI access in novel contexts. Either capability persisted—proving genuine competence exists—or performance collapsed—proving dependency existed throughout despite excellent interview.
This temporal test cannot be gamed through AI assistance because:
Temporal gap prevents optimization. Candidate during interview cannot prepare for unknown testing conditions six months future. AI cannot coach for unpredictable scenarios. Genuine capability is only preparation strategy reliable across unknown future conditions.
Independence eliminates assistance. Testing occurs without AI access. Either capability exists in worker independently or worker cannot perform. The test isolates genuine competence from AI-dependent performance by removing the dependency and observing whether capability remains.
Novel contexts prevent memorization. Testing in situations differing from training prevents pattern-matching strategies. Genuine understanding transfers across novel contexts. Narrow AI-assisted learning fails when conditions change unpredictably.
Comparable difficulty maintains standards. Testing at original performance level isolates pure persistence. Not testing easier (lets degraded capability pass) nor harder (requires improvement beyond baseline). Testing whether capability demonstrated initially remains present independently months later.
These properties combine to create unfakeable signal: either worker possesses genuine capability enabling independent function months later—indicating capability worth paying for—or worker depended on AI assistance throughout—indicating capability is zero despite excellent present performance.
Organizations adopting temporal verification gain several advantages:
Hiring accuracy improves. Testing candidates months after hire reveals capability persistence. Organizations identify mis-hires early through temporal testing rather than discovering incompetence years later after major investment. Correction costs decrease through rapid identification.
Promotion decisions improve. Advancement based on temporal verification rather than AI-assisted current performance ensures promotions reward genuine capability. Leadership positions filled by workers with verified independent competence rather than AI-dependent performers optimizing present metrics.
Compensation becomes justified. Pay reflects verified capability rather than AI-assisted output. Workers demonstrating persistent independent capability command premium wages justifiably. Workers depending on AI receive compensation reflecting AI capability not human capability since AI does work.
Retention focuses correctly. Organizations identify genuinely valuable workers through temporal verification and retain them aggressively. Workers whose capability proved AI-dependent receive lower retention priority since their value derives from AI not person.
Training effectiveness becomes measurable. Educational programs verified through temporal testing: did training create capability persisting independently? Testing months after training completion reveals whether learning occurred or attendance happened while AI did work.
The labor market regains price discovery mechanism: temporal verification reveals capability distribution previously invisible. Organizations observe who possesses genuine competence. Compensation adjusts to reflect verified capability. Human capital allocates efficiently toward workers with proven persistent capability.
This restoration of market function requires infrastructure: testing protocols, temporal tracking, independence verification, comparable difficulty calibration. But the infrastructure provides what AI destroyed: observable capability differences enabling market pricing based on genuine competence rather than AI-assisted performance.
Web4: The Architecture Where Competence Becomes Observable Again
Labor market price discovery failure created by AI requires architectural response—infrastructure layer where capability verifies through temporal testing rather than present observation.
This is Web4’s labor market function: providing verification infrastructure enabling competence observation when AI assistance made present performance uninformative.
The architecture operates through several mechanisms:
Portable capability verification. Workers own cryptographic records proving capability persistence across temporal testing throughout career. Records travel with worker across employers, platforms, contexts. Capability proof is portable rather than employer-controlled, making verification permanent rather than lost at job transition.
Temporal testing protocols. Standardized methods for independence testing: remove AI access, wait specified duration, test at comparable difficulty in novel contexts, record results cryptographically. Protocols ensure testing validity and prevent gaming through standardization.
Contribution attestation. Workers prove capability through cryptographically-signed verification from people they enabled: colleagues whose capability measurably increased through interaction. Contribution proving capability transfer distinguishes genuine expertise (builds capability in others independently) from AI dependency (cannot enable others because lacks understanding to transfer).
Cascade tracking. Capability multiplication through networks: workers you trained independently train others, creating exponential branching proving genuine capability transfer occurred. AI assistance creates dependency chains (each person needs continued AI access), genuine capability creates multiplication (each person enables others independently).
Credential temporal verification. Educational credentials verified through testing graduates months/years after completion without AI access. Degrees prove capability persists rather than mere completion occurred. Labor market distinguishes verified credentials (temporally tested) from completion credentials (graduation happened but learning unverified).
These mechanisms combine to restore labor market function: organizations observe capability through temporal verification, infer competence from persistence patterns, price compensation based on verified capability. Competition for verified talent establishes market-clearing prices where compensation matches genuine competence.
The restoration occurs at infrastructure level: Web4 provides verification protocols enabling competence observation regardless of AI assistance prevalence. Organizations using Web4 verification infrastructure gain competitive advantage through superior human capital allocation compared to organizations using AI-defeated present observation.
This makes Web4 adoption market-driven: organizations with better capability verification attract better talent, produce better outcomes, outcompete organizations with inferior verification. Network effects favor completeness: temporal verification becomes more valuable as more organizations adopt because portable verification records become universally valid rather than organization-specific.
The labor market evolution toward temporal verification is inevitable once AI makes present observation permanently unreliable. Question is whether organizations adopt standardized open protocols (Web4) or fragment across proprietary verification systems destroying portability and creating verification monopolies.
The Economic Transformation Already Underway
Labor market price discovery failure is not future threat. It is present reality creating observable economic effects organizations have not consciously attributed to correct cause.
Wage compression is accelerating. Compensation differences between junior and senior workers are narrowing because organizations cannot justify experience premiums when performance appears equivalent. The compression reflects destroyed observability not actual capability convergence.
Hiring timelines extended. Organizations interview more candidates longer because evaluation became less reliable. More interviews produce no better outcomes when all candidates appear equally qualified. Extended hiring creates deadweight loss without improving selection.
Turnover increased. Bad hires discovered post-hire through temporal exposure depart or are terminated faster. Organizations cycle through employees seeking capable workers while lacking verification infrastructure identifying them during hiring.
Promotion randomness visible. Workers observe advancement decisions appearing arbitrary because management lacks capability assessment basis. Political factors, favoritism, visibility dominate when performance metrics provide no differentiation.
Training ROI unclear. Organizations cannot verify whether training programs improve capability because capability is unobservable. Training budgets become faith-based allocation rather than evidence-based investment.
Remote work creates verification anxiety. Managers cannot observe remote workers’ processes, only outputs. AI assistance makes outputs excellent while capability remains unknown. Organizations demanding return-to-office partly because in-person observation provides minimal capability verification that remote observation provides zero.
Performance management atrophies. Systems designed to differentiate workers provide no differentiation when AI equalizes performance. Reviews become formality rather than meaningful assessment. Managers recognize futility but lack alternative framework.
Meritocracy rhetoric intensifies. As merit becomes unobservable, organizations emphasize meritocratic values more loudly—compensation signaling awareness that actual merit-based allocation failed while aspiration persists.
These effects are present throughout labor markets. Organizations experience consequences without understanding cause: AI destroyed capability observation while everyone continued using observation-based evaluation.
The economic transformation toward temporal verification will accelerate as organizations recognize present performance provides zero information about capability. Early adopters of temporal testing gain advantage. Late adopters suffer continued inefficiency. Market selection favors organizations adopting verification infrastructure surviving AI assistance.
The transformation is underway. Question is whether organizations recognize what changed and adapt deliberately, or continue operating under broken assumptions experiencing worsening outcomes wondering why hiring, promotion, retention all deteriorated simultaneously without obvious cause.
AI didn’t replace workers. It replaced the labor market’s ability to observe competence. And markets that cannot observe value cannot price it correctly.
That correction is coming. Through temporal verification. Through Web4 infrastructure. Through the only verification method surviving when AI makes everyone look equally competent.
Time proves competence. Because time is the only dimension revealing whether capability persists independently when the performance making it appear present could have been AI-generated all along.
Related Infrastructure:
TempusProbatVeritatem.org — Foundational principle establishing why temporal verification became mandatory: time proves truth when observation proves nothing.
PersistoErgoDidici.org — Educational verification through temporal testing: learning proves itself through capability persistence months after coursework when AI access removed.
PortableIdentity.global — Cryptographic capability records enabling workers to own verification across all employment, making proof portable rather than employer-controlled.
CogitoErgoContribuo.org — Competence verification through contribution: proving capability by cryptographically demonstrating you increased others’ capability independently.
MeaningLayer.org — Semantic infrastructure distinguishing genuine understanding from information access: understanding persists and transfers, information degrades.
Together these protocols provide complete infrastructure for labor market price discovery when AI makes present performance uninformative about capability: temporal verification reveals competence enabling market pricing based on genuine capability rather than AI-assisted outputs.
2025-12-26
All content released under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). Labor market function is civilizational necessity—verification infrastructure must remain open when AI destroys capability observation and markets require new price discovery mechanisms.