Abstract

I argue that claims that the presence of functional and behavioral analogs of consciousness in LLMs are evidence of AI consciousness should be rejected. Instead, the capability of an LLM trained on human data to emulate human consciousness–related behaviors and functional architecture does not confirm or discredit claims of chatbot consciousness. My “crowdsourced neocortex” account explains why chatbots can assert consciousness and related emotional states, and even exhibit functional configurations analogous to consciousness processing in the biological brain without genuinely having inner experience. This provides an “error theory”—an explanation of why people erroneously conclude that chatbots have inner lives. As the LLMs scale up, their ‘conceptual systems’ come to mirror the masses of users whose data was in the training set, as well as those providing feedback to the system, (hence I write “crowdsourced neocortex”) (Schneider 2024, 2025). Indeed, this is part of a general pattern: it is well known that as LLMs scale up, their skills in arithmetic, word in context, theory of mind and a variety of other tasks similarly improve (Wei, et al.). Today’s LLMs have conceptual structure that mimics that of groups of humans whose data was used (Anthropic 2025; Heaven 2025; Emeisen, et al, 2025; Schneider 2024).