Intelligence Beyond Representation: What AGI Misses When Language Becomes the Substrate

Current AI development implicitly assumes that intelligence is a function of representations whether symbolic rules, neural embeddings, or large language models. This essay challenges that assumption by examining intelligence through evolution, neuroscience, biology, and physics. Drawing on Yann LeCun's critique of language centric AI, Donald Hoffman's interface theory of perception, Karl Friston's predictive processing framework, and Michael Levin's work on distributed biological intelligence, the essay proposes a three layer model of intelligence - brain, mind, and consciousness and argues that current LLMs occupy only the first layer. The implications for AGI architecture are concrete: language as interface rather than core, action-perception loops over text completion, multi-timescale memory, and compositional agency. A preprint submitted for open discourse.