This follows up a discussion, in Challenge Lecture 7(1) of 16July26, about the energy demands of AI in comparison to that of the brain. A recent IEEE Spectrum article happens to address the same point - that brains are super efficient, by at least 3 orders of magnitude compared with the most optimistic interpretation of AI capabilities, and by many more orders of magnitude compared with pessimistic interpretations. See - https://spectrum.ieee.org/artificial-neurons-on-silicon-chips So brains are astonishing, in what they achieve with so little energy. The article is techie, about how a future AI might become more like brains in energy efficiency, delving into the semiconductor-physics of MOSFET transistors. It hinges on the presence of a fourth connection in those transistors, compared with three connections in bipolar transistors. I encountered that fourth connection many years ago and, like everybody else, I wired it up in the recognised way that neutralised any actions the connection might cause - actions which were considered spurious and uncontrolled in manufacture. The article talks of how these ‘spurious’ actions can make the single MOSFET like a brain neuron, consuming far less energy than neuron-simulation in either conventional hardware or software. Very early days, because no-one has yet interconnected these four-terminal MOSFET ‘neurons’ to do anything brain-like, or be able to process language. So mega-power consumption remains likely for AI in the foreseeable future - a problem Steve highlighted. The energy inefficiency of AI is a basic reason why I doubt that what it does it copies what our brains do.