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  Gabriel Pettiertshirtman@mas.to

  Oct 25, 2026, 4:00 PM

  @alan @veronica @pluralistic Inefficient compared to what? I agree they are energy intensive, but they are doing a thing we didn't know how to do before, which is to have computers write, operate, improve, verify programs from natural language prompts (not even just English, but about any human language sufficiently documented).

  And while they are indeed energy intensive, the efficiency has been increasing a lot over the last couple years, although it's hard to get clear data, the companies are secretive about it, the massive energy costs seems to be mostly on the training on newer, bigger models, not producing output from the existing ones, and we can run relatively large models, that are doing useful work, today, on customer hardware (admittedly high end).

  I think what the VC have missed, is that they won't be able to capture the value created, because their product will be commoditized and run for cheaper on hardware they don't own.

  I agree the bubble popping will hurt, yes.

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  Alan Langford 🇨🇦🧤🧊摏alan@mindly.social

  Oct 25, 2026, 5:35 PM

  @tshirtman @veronica @pluralistic I would use as a baseline for efficiency comparisons, the power consumption of a human brain. I don't expect we'll reach that anytime soon, but it seems clear that we're at least a few orders of magnitude away from that at the moment. However we're far enough away that I expect some big steps in closing that gap are not that far out of reach.

  I think that's an excellent point about model commoditization. Maybe that's a disincentive for VCs to seek more efficiency.

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  Gabriel Pettiertshirtman@mas.to

  Oct 25, 2026, 5:54 PM

  @alan @veronica @pluralistic interesting point, but human brains sadly don't run on solar panels, and feeding us is pretty energy intensive, given all the loops energy has to jump through before it gets to occasionally allow a brain to fix issues in a program, i'm not sure the comparison is as clear cut. Also, getting more brains apt at this particular task is a very lengthy and energy intensive process, while spawning a new inference context for a model is very straightforward, if still not cost-free, and that model will work at speed i can't imagine humans rivaling with.

  I'm vegan, so i imagine my brain is a bit less intensive in primary-energy to run, as eating plants is more efficient, but still, a frontier model doing what used to take weeks, in hours if not minute, gives a solid ratio to start with to the machine.

  I'm all for making things more efficient though, and i think it'll happen whether VCs want it or not, competition is a rough mistress.

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  Alan Langford 🇨🇦🧤🧊摏alan@mindly.social

  Oct 25, 2026, 6:08 PM

  @tshirtman @veronica @pluralistic Then to be fair in that wider estimate you need to include the energy cost of extracting, refining, packaging, shipping, and replacing the components in an AI data centre. I think that balances it out to some extent, even when ignoring the environmental impact of those activities.

  Excluding all these inputs, can these processes become more efficient than the human brain? My guess is that they can, and that's when concerns of AGI become much more pertinent. It also illustrates just how far I think we are away from getting there.

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