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How do LLMs and humans compare with regards to the amount of energy, data, and compute that's used to train/run them? I was inspired by Samuel Knoche's post on sample efficiency to come up with some numbers. This table was made by Opus 4.8. after some iteration:

"Task" = "produce one thoughtful ~1,000-token answer"; unclear if this is a useful number, obliviously it doesn't generalize. I do think it's interested to compare the energy/computer ratio for inference between human and AI.

Training data for humans is a big "???". There's ~4x10^8 waking hours. Opus 4.8 cited this 2024 paper which says "our sensory systems gather data at ~1⁢0^9 bits/s". That gives ~4x10^17 as an upper bound. But human sensory data is extremely redundant. If someone wants to spend time figuring out a good way to estimate entropy adjusted information here, that would be cool.

(FWIW, I spent a little over an hour on this -- I did a fair amount of iteration with GPT5.5, looked a bit myself into a few numbers, and got new instances to do estimates from scratch to see if things lined up.)

Some Fermi estimates:

On the lower end, 4 words/second times 16 bits/word times 4x10^8 seconds adds up to to ~3x10^10.
On the higher end, assuming 10^7 bits/second (about the retina-to-brain bandwidth), it adds up to ~4x10^15.

However, I think the brain is much less data- and compute-efficient than an optimal AGI algorithm would be. So I don't think it is a good predictor of how much data future AI algorithms will require.