I misread your statement as stating that computers broadly were less energy efficient at the same tasks, because I took umbridge at your claim that human brains use `so little energy` (citation needed???) and forgot how to reading comprehension, so I compared training costs of a 7 year old and inference costs for an adult in terms of energy to the figures quoted in the article for the various models. I'll address what you actually said below but am keeping my original writeup because its kind of neat.

I'm going to compare energy costs for training a model vs growing a brain because they are analogous. TFA lists the training costs in kWh and I'll arrive at a comparable figure by estimating the total caloric intake of a 7 year old and then multiplying that by an estimate of the proportion of energy a child's brain uses.

I calculate that the lifetime caloric intake of a 7 year old is 3,139,000 dietary calroies based on a daily schedule of 1,000 for the first three years and 1,400 for the next four[1], and assuming an active child's brain uses roughly 60% of total calories[2], we end up with a lifetime total of 1,883,400 calories spent on the brain. This is 2,188 kWh.

>> the energy consumption of model training is somewhere in the range of 11,000 kWh to 52,000 kWh for moderate-sized LLMs, 40,000 kWh to 60,000 kWh for more expensive image generators, and up to 5,000,000 kWh for global-scale LLMs like Llama and ChatGPT.

The 7 year old isn't even finished training, and frankly, needing the same training energy as 2,285 7 year olds to create ChatGPT comes across as a bargain.

Just for a bonus lets compare inference costs, assuming a human brain uses roughly 200 dietary calories and that we can count all of them as part of performing productive work gives us a budget of about 0.5 kWh (464 Wh). Going to quote the article again for inference costs

>> the energy consumption of an inference operation is somewhere in the range of 1.0 Wh to 7.0 Wh for standard operations, and to 4 Wh to 7.5 Wh for more expensive jobs like image or video generation.

Hundreds of text operations and 50-100 images.

As a back of the napkin sketch it looks pretty close.

[1] https://www.healthychildren.org/English/healthy-living/nutri...
[2] https://www.livescience.com/burn-calories-brain.html

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You're sorta onto something but it has to do with architecture: human brains are composed of a vast number of sparsely connected and individually poor computational units (neurons). Computers are completely connected and highly reliable but individual units are expensive. Neurons also communicate at a much slower pace than computers can perform calculations. The reason for our unreasonable effectiveness appears rooted in how very many neurons we have and how effectively parallelized most functions of the brain are. Precision calculations are easy for computers because they are literally machines that have operations that map to simple numerical calculations that are highly accurate. Numeric calculations are not simple for human brains because nothing really maps to the process of a calculation. Individual neurons do 'integrate and fire' meaning that if they get excited enough by nearby neurons in a short enough time then they will fire and excite the neurons they touch, but it isn't a reliable process, meaning that if neuron A touches neuron B and A activating is enough to cause B to activate this time it may not be enough the next time A activate. So when we do math we use those.

There are investigations into neuromorphic hardware but I'm not up to date on them anymore, the basic idea is to make computing hardware inspired by how the brain operates. Last I knew there were some interesting results but nothing resembling anything practical.