Language as Substrate: Symbolic Operations, Subject-Position Effects, and Repair Patterns Across Human and Artificial Regimes

Contemporary AI systems produce coherent, context-sensitive language without biography, development, embodiment, or ego formation. This empirical fact destabilizes a presupposition shared across many theories of language: that linguistic structure is fundamentally grounded in subjects, whether as cognitive agents, social participants, or intentional speakers. I develop the inverse possibility. I argue that language is a structurally prior substrate of differential, rule-governed operations through which subject-positions are generated as contingent effects. Language supplies the differential form—substitution, recursion, constraint satisfaction, compositionality—through which tokens become positionally meaningful; agents activate and traverse this field in specific situations; constraint regimes shape how traversal appears at the surface. To investigate this claim in a way suited to a theory-forward social science paper, I introduce a replicable protocol of prompt-based probes and administer it across five publicly accessible AI systems (OpenAI, Claude, DeepSeek, Yandex, and Google/Gemini). The probes are organized into three families—Symbolic, Imaginary, and Real—using Lacan’s triad as a diagnostic instrument for mapping stabilization, centering, strain, and repair within symbolic systems. Psychoanalytic case literature enters as an archive of language under pressure that helps reverse-engineer stress points in symbolization. Across systems, outputs converge strongly on explicit rule-binding and procedure preservation under local rule fields, while limit conditions elicit patterned coherence repair and meta-discursive stabilization. These findings support the substrate model of language and clarify three regimes of traversal—human–human, AI–human, and AI–AI—each coupled to distinct constraint hierarchies and discourse economies.