The Silicon Illusion and the Neuromorphic Dawn: Deconstructing AI Stagflation, the Limits of Brute-Force Scaling, and the Event-Driven Path Beyond Next-Token Prediction

This perspective essay argues that the dominant paradigm of artificial intelligence — scaling autoregressive large language models on classical von Neumann silicon — is approaching simultaneous physical and economic limits. It identifies three converging constraints: a data wall (exhaustion of high-quality human-generated text and degradation from recursive training on synthetic data), a silicon wall (quantum-mechanical leakage near 2nm process nodes), and a power wall (the energy cost of moving data between separated memory and logic). Together these produce a condition the author terms "AI stagflation": rising capital and energy input with diminishing capability return. The essay contends that event-driven, in-memory neuromorphic architectures are the most physically plausible long-term successor, while explicitly separating timelines — limits are visible now, the transition spans a decade-plus, and the opportunity lies between. It includes a counter-argument section, falsifiability criteria, and a clearly-labeled speculative infrastructure watchlist. AI assistance is disclosed; this is an independent, non-peer-reviewed perspective.