Token Economy 2.0: Asset-backed Intelligence and The Economics of Trust in Decentralized AI Systems

The rapid advancement of generative artificial intelligence is fundamentally altering the economics of information by driving the marginal cost of content production toward zero. While this expansion increases access, it simultaneously weakens traditional reputation-based trust mechanisms and amplifies information asymmetry, particularly in decentralized and pseudonymous environments where verification is costly and enforcement is limited. This paper develops a formal framework for analyzing how economic stake can sustain high-quality equilibria under these conditions. Drawing on signaling theory (Spence, 1973), transaction cost economics (Williamson, 1985), and incomplete contract theory (Hart and Moore, 1990), we introduce two core constructs: (1) asset-backed intelligence, in which agents collateralize their outputs through economic exposure; and (2) Cognitive Value (CV), defined as the net utility of an output after accounting for verification, enforcement, and uncertainty costs. We present a formal incentive model and characterize conditions under which a separating equilibrium can arise—including explicit treatment of off-equilibrium beliefs via the Cho-Kreps intuitive criterion—and derive a closed-form minimum stake threshold. We complement the model with a simulation study based on 10,000 agent-period observations (1,000 agents over 10 periods; random seed 42). Under the assumed parameters, the results indicate that stake-based mechanisms increase expected output quality by approximately 12–13%—primarily through the reduction of adverse selection—and reduce individual-level output variance by 18–33% relative to a no-stake baseline. Finally, we outline three testable empirical propositions and a pilot regression design using data from decentralized AI networks such as Bittensor. Rather than presenting a definitive institutional solution, the paper positions asset-backed intelligence as a candidate mechanism for mitigating information asymmetry and restoring trust in emerging AI-driven markets, with implications for protocol design, mechanism design, and regulatory governance.