Paper 6_The Architecture of Imagination: Why Scientific Discovery Cannot Be Industrialized and What AI Can Do Instead

A growing body of work envisions AI-driven scientific discovery as an industrial process: accelerate hypothesis generation, automate experimentation, and scale verification. This paper argues that this vision fundamentally misunderstands "discovery science"—the creation of genuinely new theoretical frameworks. Discovery is not a faster version of the existing scientific pipeline; it is a structurally different cognitive act. We identify three phenomenological signatures of discovery: (1) an anomaly that resists explanation within current frameworks, (2) concurrent discomforts across apparently unrelated domains, and (3) the recognition that these disparate anomalies converge toward a single underlying structure. We argue that current AI architectures, characterized by flat parameter spaces, systematically suppress the propagation of such cross-domain anomalies. Drawing on our modular resource-competition framework, we propose that AI’s role should shift from "automating the pipeline" to providing a competitive modular architecture that maintains independent domain representations, thereby acting as a scaffold for human structural imagination.