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

Can economic stake serve as a credible signal of quality in decentralized AI markets, where reputation is fragile and output verification is costly? This paper addresses that question with a formal signaling model and large-scale evidence, treating the empirical results throughout as structured validation of the model's comparative statics rather than as causal identification. As generative artificial intelligence drives the marginal cost of information production toward zero, traditional reputation-based trust mechanisms weaken and information asymmetry intensifies in pseudonymous, permissionless environments. Building on the signaling tradition of Spence (1973), we model economic stake as a costly signal and characterize a separating equilibrium via the Cho-Kreps Intuitive Criterion, deriving the closed-form threshold S* = ΔW/[(1−P_L)·α] at which high-quality agents separate from low-quality ones. We establish two boundary conditions under which the signal degrades: signal dilution as staking becomes widespread (Proposition 2) and a stochastic-enforcement adjustment when penalties fail with probability f (Proposition 3). We test the model on Bittensor metagraph data. In a May 2026 cross-section (N = 18,101 miners; 80 subnets), staked miners earn 15.4× higher incentive scores (t = 12.40; Cohen's d = 0.158), a pattern that replicates in a June 2026 snapshot. We extend the analysis to a 25-month panel (June 2024–June 2026; 383,714 miner-period observations) and estimate within-miner fixed effects, which absorb 62% of the cross-sectional association into time-invariant heterogeneity while leaving a positive stake–incentive relationship consistent with the signaling mechanism (blended β̂ = 0.063, t = 25.1; intensive margin β̂ = 0.371, t = 17.2, robust to correlated-random-effects and control-function selection checks). The capital–performance link is present across all three emission regimes in the panel window — pre-dTAO (β̂ = 0.122), dTAO (β̂ = 0.112), and Taoflow (β̂ = 0.091) — with the within-miner coefficient declining monotonically as successive smoothing layers are added to the emission rule, attenuating but not eliminating the relationship. A central empirical contribution is to re-specify the signal dilution boundary condition: dilution survives within-miner identification when measured by stake concentration (Gini/HHI) but not by staking participation rate, a divergence our signal-extraction corollary predicts. Throughout, we are candid about the limits of identification — the within-miner estimates are sensitive to miner longevity, monthly snapshots cannot rule out contemporaneous confounders, and the aggregate stake field conflates self-bonded and delegated capital — and we therefore read the evidence as consistent with, rather than as identifying, the signaling mechanism, delineating the instrumental-variable designs that future work must bring to bear.