A news summary based solely on the provided article body: For most of the history of thinking about animal minds, behaviour was the only evidence on offer. Then came the large language models. If philosopher Susan Schneider’s proposal that meaningful conversation about consciousness might itself be an indicator of consciousness were correct, these systems would be scoring well on the test. The trouble is that almost nobody working seriously on the question believes they are conscious. The behaviour is there. The evidence for consciousness, it turns out, is not. Language models have severed that pairing in a particularly clear way. A system trained on human-generated text about consciousness will produce human-sounding text about consciousness. The output is a function of the training distribution, not of any underlying experiential state. A paper published in Trends in Cognitive Sciences this year by Patrick Butlin, Colin Klein, and nineteen colleagues addresses this directly. Rather than asking what conscious systems do, it asks what conscious systems are built like. The approach identifies structural and computational features that any plausible theory of consciousness would require: things like the ability to resolve trade-offs between competing goals in contextually appropriate ways, and the presence of informational feedback loops. The verdict on current AI systems is unambiguous. No existing system, including the most capable large language models, meets these criteria.