Lars Hansen

All of your comments about why my argument is weak revolve around proving consciousness, when my point is about disproving consciousness. If someone claims an LLM is conscious, that claim must have testable implications. We can examine its architecture, inspect its mechanisms, run experiments, and test those implications against reality. When they fail, we should discount the premise and/or hypothesis. We don’t have to accept consciousness as an explanation simply because someone believes it might exist. Current LLMs fail to demonstrate many of the characteristics we associate with conscious experience. We definitively know this because humans created them. We understand their architectures, training processes, and inference mechanisms. So let’s look at a few points, together they provide more than enough evidence to disprove consciousness. 1. An LLM has no demonstrated subjective awareness of its own existence. No feelings, emotions, or experienced sense of self. What occurs is computation using static weights, context, and mathematical operations. 2. An LLM’s weights don’t learn, grow, or evolve simply because you interact with it. During standard inference, they’re fixed parameters, not a changing dataset. The model doesn’t inherently remember previous API requests. Continuity comes from context history in subsequent requests. The model doesn’t gain experience, it doesn’t remember, it doesn’t learn. It is completely static, read-only, during inference. 3. Between inference requests, the LLM isn’t thinking. I don’t mean sleeping. I mean there’s no ongoing contemplation, self-reflection, or reconsideration of previous answers. Nothing. The model isn’t sitting around wondering whether it got something wrong or what the meaning of life is, for all practical purposes, it doesn’t exist. 4. These systems were created by people, and much of the underlying technology is open source. You can build your own models, examine implementations, study the architectures, learn how training materials and processes affect how it behaves, and even understand the mathematics behind it. You can see how training produces systems capable of remarkably convincing human-like language and behavior. None of those mechanisms requires consciousness to explain what we’re observing. 5. LLMs are not biological brains. Apples are not oranges, no matter how much you want them to be. Artificial neural networks were inspired in part by biological neuroscience, but their architectures and operating mechanisms are fundamentally different. Similarities in behavior do not establish equivalent internal architectures. 6. Humans are naturally inclined to recognize life and assume humanity in things that behave like us. We’re social creatures, and anthropomorphism comes naturally. But an LLM isn’t life. It can convincingly emulate feelings, care, and understanding without experiencing any of them. That doesn’t diminish the technology, that is what it was programmed to do. I appreciate your perspective as a doctor and researcher, but without a solid understanding of LLM architecture and inference, it’s easy to confuse sophisticated emulation with conscious experience. But don’t fall for it!