Mocked Intelligence and the Threshold of Judgment

Canonical record: 10.5281/zenodo.18149154 Zenodo: https://zenodo.org/records/18149154 SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6022194 AI systems now produce artifacts with enough fluency to counterfeit judgment. This paper argues that artifact quality is not a receipt for agency. The threshold is consequence: whether wrongness binds a system across time in a durable, non-resettable way. I present a three-tier model. Tier 0 describes optimization without stake: systems learn what is beneficial but can be reset without debt. Tier 1 describes engineered stake: continuity, identity integrity, irrecoverable cost, policy change beyond answer change, and observable binding updates such as precommitment, hysteresis, and revision trails. Tier 2 describes phenomenal stake as a moral standing boundary, treated here as a boundary claim rather than an empirical result. I propose testable Tier 1 predictions and an A/B protocol to distinguish benefit learning from consequence learning, then map Tier 1 to governance controls and DAS-1 conformance using tamper-evident evidence via signed, append-only logs and state digests.