Beyond the Black Box — LLM Limitations and the Alternatives That Remain
Large language models are pattern-completion engines of extraordinary fluency. They produce text indistinguishable from human writing. But the closer you look, the architectural limits surface: hallucination without truth-access, no grounding in reality, chain-of-thought that is reasoning-shaped but not reasoning, opacity that forbids audit, resource costs that exclude most of the world, and fragility to minor prompt shifts. These are not bugs waiting for scale to fix them. They are consequences of the next-token prediction paradigm. The question shifts from “how do we make LLMs bigger?” to “what else can we do?” …