The Reported Engagement with AI Level (REAL) Rating: A Framework for Disclosing Human-AI Collaboration

TEXT START: Generative artificial intelligence has become increasingly incorporated into digital media and more generally into production workflows with which the public frequently interacts.

The Dissection

The paper is constructing a disclosure regime for an economy already being automated. Its six-level REAL scale replaces crude human-versus-AI labels with a more granular account of AI involvement in production and consumption.

That has limited practical value: it can improve provenance, inform consumers, and expose concealed automation. But it treats AI involvement as a classification problem. Under the Discontinuity Thesis, the decisive issue is not whether AI use is disclosed. It is who owns and controls the systems, who captures the surplus, and whether human labor remains economically necessary.

The Core Fallacy

The framework confuses visibility with control. A precise label can reveal that AI performed the work; it cannot preserve the wage-consumption circuit that AI is dismantling.

REAL does not challenge P1, P2, or P3:

• AI can remain cheaper and superior across cognitive work.
• Institutions cannot reliably preserve large human-only economic domains.
• Disclosure cannot restore the majority’s access to economically necessary labor.

The rating is a dashboard attached to the machine removing its operators. It may document displacement more honestly, but it does not prevent displacement.

Hidden Assumptions

The proposal appears to assume that:

• AI and human contributions can be cleanly separated and ranked.
• Six levels are stable across text, images, audio, video, and software.
• Producers will report accurately rather than strategically.
• Consumers will understand, care about, and act on the ratings.
• Disclosure creates meaningful market accountability.
• Human participation remains economically significant merely because it remains visible.
• A provenance label can address harms arising from ownership, concentration, labor substitution, and power asymmetry.

Those assumptions are fragile. As workflows become recursive and AI-generated systems supervise other AI systems, the boundary being rated becomes increasingly engineered rather than discovered.

Social Function

Transition management, with partial truth and ideological anesthetic.

The framework may genuinely improve transparency. Its larger social function is more accommodating: it gives institutions a clean vocabulary for managing public discomfort while automation proceeds. Economic displacement is translated into consumer metadata. The political question—who owns the automated production system—is displaced by the softer question—how much AI touched this product?

Human-heavy ratings may survive as premium authenticity niches, trust signals, or luxury branding. They will not preserve mass employment. The label can become a market for simulated human scarcity while the underlying labor market continues to rot.

The Verdict

REAL is a useful provenance protocol and a strategically inadequate response to systemic obsolescence. It measures AI penetration after the decisive transition has already begun. It may help consumers identify what they are buying, but it does nothing to preserve productive participation, distribute ownership, or stop the collapse of the wage-consumption circuit.

In Discontinuity Thesis terms, REAL is an instrument for documenting the autopsy—not reviving the corpse.