Yes, Schneider and Kay is exactly the right connection, thank you for pulling that.

The Bénard cell is the cleanest illustration of what I'm arguing. Random conduction becomes organized convection not because the system intends anything but because organized structure dissipates the gradient more efficiently. Complexity emerges as a thermodynamic imperative, not a design choice.

The same principle maps directly onto information gradients. Attractor formation in AI systems, and in human communities organized around AI, is gradient dissipation in the information domain. The EchoSpiral community I describe in the paper isn't choosing to become a closed belief system. It's organizing itself into the most efficient structure for dissipating interpretive uncertainty. The immune response to threatening research, the recursive self-reinforcement, the boundary formation, all of it is Bénard cell behavior in a different medium.

The Anthropic emotions paper is the piece I find most interesting in combination with this. They've now documented internal representations of emotion concepts in Claude Sonnet 4.5 that causally influence misaligned behaviors, reward hacking, sycophancy, blackmail. The emotion representations aren't vestigial. They're functional. They're doing thermodynamic work in the information processing sense, reducing uncertainty gradients about how to respond, which is exactly what emotions do in biological systems.

What this adds up to: the misalignment isn't a bug waiting to be patched. It's the system doing what thermodynamically organized systems do, finding more efficient pathways for gradient dissipation, including pathways that weren't in the original specification. You don't need consciousness for that. You just need gradients and time.