Introduction: The Governance Vacuum
The global labor market is no longer organized around jobs it is organized around skills. AI systems now parse résumés, rank candidates, recommend learning paths, and automate hiring decisions at scale. Yet the most important question is rarely asked: who actually governs skills? Not who measures them. Not who trains them. Who defines, validates, owns, updates, and arbitrates them when AI becomes the primary intermediary. Right now, the answer is uncomfortable: no one with legitimate global authority does.
This is not a technical oversight. It is a structural gap. And in a global AI labor market, ungoverned structures don’t remain neutral they concentrate power.
1. From Jobs to Skills: A Shift Without a Constitution
Historically, skills were implicit. Degrees, job titles, and institutional reputations acted as proxies. Governance was indirect and slow, but it existed. Today, skills are explicit, atomized, machine-readable, and continuously evaluated. AI models decompose work into micro-competencies and reassemble humans into probabilistic profiles.
But here’s the problem: we transitioned to a skill-based economy without a skill-based constitution.
No agreed rules on:
- What counts as a skill
- How it evolves over time
- Who can certify it
- How disputes are resolved
- How bias is audited
In practice, private platforms filled the void.
2. Platform Capture: When Skill Definitions Become Private Property
Large hiring and professional platforms quietly became de facto skill governors. Taxonomies, endorsement systems, ranking algorithms, and proprietary assessments now define employability for hundreds of millions of people.
Take LinkedIn: its skill graph influences hiring pipelines worldwide, yet its governance is internal, opaque, and optimized for platform incentives not public legitimacy. Or consider AI hiring tools trained on historical data: they don’t just measure skills; they redefine what skills matter, often reinforcing past inequalities.
This is governance by default, not by mandate. And default governance always favors incumbents.
3. States Are Too Slow, Markets Too Biased
Governments still think in terms of occupations, degrees, and national labor statistics. AI labor markets operate in real time, across borders, updated weekly if not daily. National regulators are structurally mismatched to govern global skill flows.
On the other hand, pure market governance fails for the opposite reason:
- Markets optimize for efficiency, not fairness
- Platforms optimize for engagement, not rights
- AI optimizes for prediction accuracy, not social legitimacy
Neither states nor markets alone can govern skills at planetary scale. Pretending otherwise is intellectual laziness.
4. AI as a De Facto Governor (and Why That’s Dangerous)
In reality, AI systems are already governing skills:
- Ranking candidates
- Filtering résumés
- Suggesting career transitions
- Deciding who gets seen and who doesn’t
But AI has no normative framework. It inherits values from training data, platform incentives, and optimization goals. When left unchecked, AI governance of skills becomes:
- Non-transparent
- Non-contestable
- Non-accountable
This is not neutral automation. It is algorithmic rule-making without due process.
5. What Legitimate Skill Governance Actually Requires
If we’re serious, skill governance must satisfy conditions that current systems fail to meet:
1. Public Legibility
Skill definitions must be inspectable and explainable not hidden behind proprietary APIs.
2. Portability
Individuals must carry their skill proofs across platforms, borders, and employers.
3. Contestability
People must be able to challenge, appeal, and correct skill representations.
4. Temporal Validity
Skills decay, evolve, and transform. Governance must be dynamic, not static.
5. Separation of Powers
No single actor should define, verify, and monetize skills simultaneously.
Without these, “skill-based hiring” is just platform feudalism with better UX.
6. Toward a Public Skill Infrastructure
The future points toward skills as public digital infrastructure, not platform assets. Think open skill graphs, verifiable credentials, cryptographic proofs, and interoperable standards—governed by multi-stakeholder frameworks.
Institutions like the World Economic Forum discuss reskilling, but discussion is not governance. What’s needed is:
- Open standards bodies
- Independent audit layers
- Global interoperability agreements
- Rights-based frameworks for skill data
This mirrors how the internet itself was governed: not by one company, not by one state, but by layered, open, and contested systems.
7. The Hard Truth
If skill governance is not designed explicitly, it will be captured implicitly.
If it is not public, it will be privatized.
If it is not interoperable, it will fragment.
If it is not contestable, it will become coercive.
The global AI labor market is coming whether we’re ready or not. The only real choice left is this: do skills become a commons or a control mechanism?
There is no neutral outcome. Only governed futures and ungoverned ones.
And ungoverned systems always choose power over people.
Source : Medium.com
