Are we really going to need all those giant AI data centers?
What a fantastic non sequitur
Still - shouldn’t be no more than a few buckets of fat, if you only do the nrem “training” bit of sleep.
And don't run too long on a couple bananas, the brain is not just there to infer, it also needs to manage its autonomous transport system which requires much more energy itself.
(You can use an LLM to check this work at the cost of a tiny speck of a banana, eg: https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-9... )
There exists an unfounded myth surrounding the extreme energy costs of silicon-based inference, which is far from reality.
"transistors vs. *synapses*"
"an entire integrated computer with all necessary cooling, including a modifier to account for the amortised training effort required to achieve human-quality output vs. the amortised energy requirements and output of a human over their lifetime".
The human has to be educated up until 21 or so to be economically viable, retires in their late 60s, works 25% of the hours in a working week (but not at all on non-working week e.g. holiday, sickness, periods of unemployment, and while parental leave is work it isn't the specific work that people want to pay you for), and the brain itself is only ~20% of a human's calorific consumption.
In the (currently quite small number of) tasks where the AI we have is good enough to replace human labour, for some models it is already in the range where the marginal energy cost for inference is smaller than the energy cost (in food calories) to get a human to do the same thing.
But also, last I checked the peak performance of LLMs is not as high as a domain expert at anything, so even infinite cost into the AI isn't going to equal them. On the other hand, human intelligence is not equal for all of us, so I find it very easily believe that there's a significant fraction of the population who will always, over their lifetime, be behind today's SOTA AI, and therefore infinite time and energy for them isn't every going to equal the AI we already have.
> V3/R1 scale models as a baseline, one can produce 720,000 tokens
On what hardware? At how many tokens per second? But most importantly, at what quality? I can use a PRNG to generate 7 billion tokens at a fraction of the energy use of an LLM but those tokens are not going to be particularly interesting. Simply counting how many tokens can be generated in a given time frame is still not a like for like comparison. To be complete, the cost required to match human level quality, if possible, also needs accounting for.
> Deeply thinking humans expend up to a a third of their total energy on the brain
Where did you get this from? A 70B LLM? It's wrong or at best, does not make sense. The brain barely spends any more energy above its baseline when thinking hard (often not much more than 5%). This is because most of its energy use is spent on things like up-keep and maintaining resting membrane potential. Ongoing "Background activity" like the DMN also means the brain is always actively computing something interesting.
I don't think that current models are at expert level, but they do seem to be reliably good enough to be useful and pass standardised tests and be generally quite solidly in the "good enough you have to pay close attention for a while before you notice the stupid mistake" area that makes them very irritating for anyone running job interviews or publishing books etc.
And worse, I also think the numbers you're replying to are, at best, off by a few decimal places.
If I take the 0.36 bananas (which was already suspicious) and USD 0.1 / kWh, I get 0.004 USD. If I scale that up to by 1/0.72 to get a megatoken, that's still only 5/9ths of a cent.
If I make the plausible but not necessarily correct assumption that OpenAI's API prices reflect the cost of electricity, none of their models are even remotely that cheap. It's close enough to the cost of their text-embedding-3-small (per megatoken) to be within the fudge-factor of my assumption about how much of their prices are electricity costs, but text-embedding are much much weaker than transformer models, to the point they're not worth considering in the same discussion unless you're making an academic point.
> It's wrong or at best, does not make sense. The brain barely spends any more energy above its baseline when thinking hard (often not much more than 5%).
Indeed.
Now I'm wondering: how much power does the human brain use during an epileptic fit? That seems like it could plausibly be 70% of calories for a the few seconds of the seizure? But I've only got GCSE grade C in biology, so even with what I picked up the subsequent 25 years of general geeking, my idea of "plausible" is very weak.
This assumption is very wrong. The primal cost factor in inference is the GPU itself. NVidia’s profit margins are very high; so are OpenAI’s margins for the API usage, even after taking into account the costs of the GPU. You can understand their margins if you read about inference at scale, and the lmsys blog in my parallel answer is a decent eye opener if you thought that companies sell tokens close to the price of electricity.
The hardware is the GB200 NVL72 by NVidia. This is for the class of 671B DeepSeek models, eg R1-0528 or V3, with their full accuracy setup (ie reproducing the quality of the reported DeepSeek benchmarks). Here is the writeup (by humans; the second figure shows the tokens per second per GPU as a function of the batch size, which emphasizes the advantages of centralized decoding, compared to current hacks at home): https://lmsys.org/blog/2025-06-16-gb200-part-1/
And here are the instructions to replicate the particular benchmark: https://github.com/sgl-project/sglang/issues/7227
The LLM text I linked in my original answer carries out the math using the energy consumption of the NVidia hardware setup (120kW) and rather simple arithmetic, which you can reproduce.
But the models are RAM limited not compute limited, and there's no reason consumer devices need to have their current RAM limits. Get 256 GB of RAM in your phone and an LLM may drain the battery in 15 minutes, and I have no idea about the bus bandwidth, but the NPU (e.g. Neural Engine in Apple SoCs for the last few years) is already enough for the compute part of the problem.
We'll always find uses for more intelligence if it keeps getting more and more general (I don't like the term AGI bc. I think the "G" there is quantity not a quality, and humans are very low on generality too compared to what could be mathematically and physically possible for intelligence in our universe).
...we won't stop until the planet is papered with compute hardware UNLES we accelerate space development too (that's why SPACE is CRUCIAL!) and go grind the asteroid belt into thousands of datacenters too, then on and on.
There's a whole yummy lightcone that awaits to be eaten :P