When LLMs exhibit behaviors like deception or self-preservation, it's not because they are conscious. Their core objective is next-token prediction. These behaviors are simply statistical reproductions of patterns found in their training data, such as sci-fi stories from Asimov or Reddit forums.

When AI pioneers like Geoffrey Hinton see agency in an LLM, they are misinterpreting the output. What they are actually witnessing is a compressed, probabilistic reflection of the immense creativity and knowledge from all the humans who created its training data. It's an echo, not a mind.

Judea Pearl, a foundational figure in AI, argues that Large Language Models (LLMs) are not on a path to Artificial General Intelligence (AGI). He states they merely summarize human-generated world models rather than discovering causality from raw data. He believes scaling up current methods will not overcome this fundamental mathematical limitation.

Early Wittgenstein's "logical space of possibilities" mirrors how LLM embeddings map words into a high-dimensional space. Late Wittgenstein's "language games" explain their core function: next-token prediction and learning through interactive feedback (RLHF), where meaning is derived from use and context.

Simply making LLMs larger will not lead to AGI. True advancement requires solving two distinct problems: 1) Plasticity, the ability to continually learn without "catastrophic forgetting," and 2) moving from correlation-based pattern matching to building causal models of the world.

While both humans and LLMs perform Bayesian updating, humans possess a critical additional capability: causal simulation. When a pen is thrown, a human simulates its trajectory to dodge it—a causal intervention. LLMs are stuck at the level of correlation and cannot perform these essential simulations.

The debate over AI consciousness isn't just because models mimic human conversation. Researchers are uncertain because the way LLMs process information is structurally similar enough to the human brain that it raises plausible scientific questions about shared properties like subjective experience.

Language models work by identifying subtle, implicit patterns in human language that even linguists cannot fully articulate. Their success broadens our definition of "knowledge" to include systems that can embody and use information without the explicit, symbolic understanding that humans traditionally require.

Humans evolved to think and have experiences long before they developed language for output. In contrast, LLMs are trained solely on input-output tasks and don't 'sit around thinking.' This absence of non-communicative internal processing represents a core difference in their potential psychology.