Understanding the machinery of thought and consciousness

For most of human history, we have put thought and consciousness in the category of things that feel almost magical.

AI may be slowly taking them out.

One of the things I find most fascinating about LLMs is not just what they can do, but what they suggest about us. People sometimes dismiss them as “just predicting the next word.” But I’m not sure that is the diminishment they think it is.

A neural network trained around prediction somehow ends up writing, coding, reasoning, planning and making connections that look remarkably like thought. That does not mean the human brain is an LLM. It obviously isn’t. But it does raise the possibility that what we experience as thought may also emerge from neural machinery doing something much more mechanical than we like to imagine.

And now consciousness may be next.

Doris Tsao, one of the world’s top neuroscientists, is leading a major effort at Astera to understand how the brain generates conscious experience. Their working hypothesis is that consciousness emerges as the brain builds persistent representations of the world and of ourselves within it, then composes those representations into an internal model of reality.

This week Tsao and collaborators published new work suggesting that the brain may rapidly alternate between what is consciously perceived and what is suppressed from awareness. It is early work, and certainly not the final answer, but it is another attempt to turn consciousness from something mystical into something mechanical.

I find that both unsettling and exciting. First intelligence becomes less mysterious. Then maybe consciousness does too.

And I don’t think explaining either one makes it less remarkable. Understanding DNA did not make life less extraordinary. Understanding how stars work did not make the night sky less beautiful.

Maybe consciousness is another miracle that becomes no less miraculous once we understand the machinery.

The really interesting question is what happens if we eventually understand that machinery well enough to reproduce it.