The Semantic Economy
Why AI Systems Are Extracting Value We Cannot Yet Account For
Executive Brief
1. Problem Statement
Contemporary AI systems depend on human meaning-making at every level — training data, fine-tuning, evaluation, deployment. Large language models are trained on text produced by humans over centuries. Knowledge graphs are populated by volunteer labor. Alignment procedures require human judgment about what outputs are good.
Yet no accounting framework exists for tracking this dependency.
We can measure compute costs, parameter counts, inference latency. We cannot measure the meaning-resources these systems consume, who produced them, whether they are being replenished, or what happens when they are exhausted.
This is not a philosophical concern. It is a governance blind spot with material consequences.
2. What Is the Semantic Economy?
The semantic economy is the system through which meaning is produced, stored, circulated, extracted, and depleted as value.
Six core categories:
| Term | Definition |
|---|---|
| Semantic Labor | Human activity that produces meaning, interpretation, and coherence |
| Semantic Capital | Accumulated meaning-resources upon which systems draw |
| Semantic Infrastructure | Structures that make meaning legible, retrievable, actionable |
| Semantic Liquidation | Irreversible conversion of meaning into proprietary assets |
| Semantic Rent | Value captured from stabilized meanings without performing new labor |
| Semantic Exhaustion | Degradation when extraction exceeds replenishment |
These are not metaphors. They describe measurable dynamics in how AI systems acquire, process, and monetize human meaning.
3. Why This Matters Now
AI summarization is replacing search. Google's AI Overviews, ChatGPT, Perplexity, and similar systems increasingly mediate how knowledge reaches users. These systems draw on semantic infrastructure — Wikipedia, Wikidata, web corpora — without accounting for who built it or what sustains it.
Platform enclosure of open knowledge is accelerating. Wikidata's CC0 license enables extraction without attribution. Major AI companies have ingested its contents into proprietary systems. The volunteer labor that created these resources receives no compensation and increasingly no visibility.
Empirical evidence of semantic exhaustion is emerging. Model collapse occurs when AI systems train on AI-generated content. Knowledge graphs remain incomplete despite massive volunteer effort. Contributors experience "usage invisibility" — their labor powers systems that never acknowledge them.
Governance frameworks lack vocabulary. AI safety, alignment, and policy discussions focus on capabilities, not dependencies. There is no standard way to ask: What meaning-resources does this system require? Who produces them? What happens when they run out?
4. Governance Implications
4.1 Open Knowledge Sustainability
Open knowledge projects (Wikipedia, Wikidata, Common Crawl) function as critical infrastructure for AI development. Their sustainability depends on volunteer labor that is currently being extracted without recognition or replenishment. Governance frameworks should address:
- Attribution requirements for AI systems using open knowledge
- Sustainability mechanisms for volunteer-maintained semantic infrastructure
- Transparency about training data provenance
4.2 Labor Recognition
Semantic labor — the work of producing meaning, coherence, and interpretation — is structurally invisible in current economic and technical frameworks. This includes:
- Content moderation
- Data labeling and RLHF
- Ontology engineering
- Community knowledge maintenance
Recognizing this labor as labor is a precondition for equitable governance.
4.3 Alignment Risk via Semantic Depletion
AI alignment typically focuses on model behavior. The semantic economy framework identifies a different risk vector: environmental degradation. If the meaning-resources AI systems depend on are depleted faster than they are replenished, alignment becomes impossible — not because models misbehave, but because the semantic environment they operate in loses coherence.
5. Conclusion
This framework does not critique AI capability. It addresses the absence of accounting for the semantic resources those capabilities depend upon.
The semantic economy is not a metaphor. It has extraction, rent, and exhaustion.
Governance that ignores these dynamics is governance that cannot see what it governs.
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License: CC BY 4.0
December 2025
Crimson Hexagon Archive
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