Eniola's strongest angle is to reframe the CCT model as a **mechanistic account of AI-mediated coercive behavioral lock-in** — a novel, quantified s-risk pathway that existing CLR research does not cover. The argument runs: AI recommendation and persuasion systems that exploit dopaminergic consolidation pathways (the tripartite DA-Glu-eCB substrate CCT formalises) create irreversible reward-memory encodings at population scale, foreclosing epistemic sovereignty and constituting a form of coercion with clear s-risk valence. The CCT's ODE/RK45 + Bayesian MCMC formalism — with pre-registered H1–H5 confirmation and a quantified 85.8% encoding-probability reduction — gives Eniola exactly the formal language CLR's mathematics-trained managers reward, while Berridge's endorsement carries particular weight because his wanting/liking dissociation is foundational to how CLR thinks about hedonic suffering distinct from reward. The application essay should open with the s-risk argument explicitly (not addiction framing), position the CCT as a principled countermeasure to AI-driven behavioral coercion, and offer CLR a concrete next research step (e.g., extending the ODE/population-dynamics model to AI persuasion agent scenarios or contributing to the Model Personas agenda by characterising LLM propensities that exploit reward-consolidation circuitry).