Artificial intelligence was born trying to copy the brain — the first neural network, in 1943, was a cartoon of a neuron — and then spent decades walking away, because the methods that actually worked looked nothing like biology. This is that eighty-year relationship in full: the two ideas AI really did borrow from neuroscience (the artificial neuron, and reinforcement learning, whose error signal turned out to be the brain's dopamine), Demis Hassabis's wager that the road to AI runs through the hippocampus, and the growing pile of evidence that the systems built least like the brain keep rediscovering its solutions anyway.

That convergence — grid cells, visual-cortex hierarchies and next-word prediction all reappearing, untaught, inside neural networks — sets up the freshest twist of all: in July 2026 Anthropic found a functional “global workspace” inside Claude, a tiny privileged buffer of reportable thought that echoes a fifty-year-old theory of consciousness. We separate what that does and doesn't mean (access versus phenomenal consciousness), why it matters for AI safety, and where a science of readable machine minds goes next.

I'm Dan. AI moves too fast to keep up with, so I built my own stack of AI tools to research, analyse, verify and illustrate the questions I can't stop thinking about — mostly to learn it myself, and I share what I find. AI-assisted, fact-checked, worth a second look.

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