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

The rapid advancement of generative artificial intelligence approaches the marginal cost of information production toward zero, simultaneously straining traditional reputation-based trust mechanisms and amplifying information asymmetry in decentralized environments. This paper develops a formal framework analyzing how economic stake can sustain high-quality equilibria when verification is costly and identities are transient, drawing on signaling theory, transaction cost economics, and incomplete contract theory. We introduce asset-backed intelligence and Cognitive Value (CV), characterize a separating equilibrium via the Cho-Kreps Intuitive Criterion, and derive the closed-form threshold S* = ΔW / (1−PL)·α. We extend the model to stochastic enforcement, showing that a 40% staking failure rate implies - under stylized parameters - a minimum stake requirement approximately 1.67× higher than the frictionless baseline. We formalize two boundary conditions: signal dilution, whereby near-universal staking collapses the separating equilibrium toward pooling; and speculative contamination, whereby capital-gain motives corrupt the commitment signal. We provide three layers of evidence. First, a simulation study (N = 10,000) shows stake-based mechanisms raise mean quality ~12–13% and reduce variance 18–33% under the assumed parameters. Second, a large-scale cross-sectional analysis of Bittensor metagraph data - 18,101 miner observations across 80 subnets - documents that staked miners earn 15.4× higher incentive scores (t = 12.40, p < 0.001); a robustness check on recently-active miners yields 65.6× (d = 0.331). A staking-tier heterogeneity analysis finds a generally declining stake-quality coefficient consistent with the comparative statics of the signal dilution proposition. Third, transaction-level data provides a pilot empirical input for the stochastic enforcement model via a 40% staking operation failure rate. Cross-sectional results are consistent with but do not causally identify the signaling mechanism; panel identification remains a direction for future work.