Since its publication in 1980, John Searle's Chinese Room argument has remained one of the central objections to computational theories of mind and machine understanding. In this paper, we argue that the force of the Chinese Room depends on an implausibly impoverished conception of linguistic interaction: namely, the assumption that a system capable of sustaining unrestricted Turing-level communication could nevertheless remain completely isolated from the pragmatic dimensions of language. Using a Peircean distinction between icons, indices, and symbols, we show that ordinary linguistic interaction involves iconic, indexical, temporal, rule-governed, and pragmatically embedded communicative practices that Searle cannot fully allow inside the Chinese Room without either permitting at least partial semantic understanding or artificially restricting the scope of the test. We then generalize this dilemma beyond classical symbolic AI by introducing the Chinese Token-Training Room, a philosophical analog of recent “stochastic parrot” objections to large language models. We argue that such objections must either restrict the system in ways that make unrestricted linguistic competence doubtful, or allow forms of pragmatic embedding that weaken the intuition of total semantic opacity. We conclude that neither the original Chinese Room nor its token-training analog establishes that functional or computational organization is in principle insufficient for conscious semantic understanding; rather, they show at most that radically impoverished forms of isolated symbol or token manipulation are insufficient.