Comment by runarberg

Hm, that gives me an idea: The next human vs engine matches in chess, go, and so on, should be set at a specific level of energy consumption of the engines, that's close or approximately that of an extremely good human player, like a world champion or at least grand master. Let's see how engines keep up then!

Wait hold on, let's put some numbers on this. Please correct my calculations if I'm wrong.

1. The human brain draws 12 - 20 watts [1, 2]. So, taking the lower end, a task taking one hour of our time costs 12 Wh.

2. An average ChatGPT query is between 0.34 Wh - 3 Wh. A long input query (10K tokens) can go up to 10 Wh. [3] I get the best results by carefully curating the context to be very tight, so optimal usage would be in the average range.

3. I have had cases where a single prompt has saved me at least an hour of work (e.g. https://news.ycombinator.com/item?id=44892576). Let's be pessimistic and say it takes 3 prompts at 3 Wh (9 Wh) and 10 minutes (2 Wh) of my time prompting and reviewing to complete a task. That is 11 Wh for the same task, which still beats out the human brain unassisted!

And that's leaving aside the recent case where I vibecoded and deployed a fully-tested endpoint on a cloud platform I had no prior experience in, over the course of 2 - 3 hours. I estimate it would have taken me a whole day just to catch up on the documentation and another 2 days tinkering with the tools, commands and code. That's at least an 8x power savings assuming an 8-hour workday!!

4. But let's talk data instead of anecdotes. If you do a wide search, there is a ton of empirical evidence that improves programmer productivity by 5 - 30% (with a lot of nuance). I've cited some here: https://news.ycombinator.com/item?id=45379452 -- there is no measure of the amount of prompt usage to estimate energy usage, but those are significant productivity boosts.

Even the METR study that appeared to show AI coding lowering productivity also showed that AI usage broadly increased in idle-time in users. That is, calendar time for task completion may have gone up, but that included a lot of idle time where people were doing no cognitive work at all. Someone should run the numbers, but maybe it resulted in lower power consumption!

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But what about the training costs? Sure we've burned gazillions of GWh on training already, and the usual counterpoint is "what about the cost involved in evolution?" but let's assume we stopped training all models today. They will still serve all future prompts at the same power consumption rates discussed above.

However every new human will take 15 - 20 years of education to get to be a novice in a single domain, followed by many more years of experience to become proficient. We're comparing apples and blueberries here, but that's a LOT of energy to even start becoming productive, but a trained LLM is instantly productive in multiple domains forever.

My hunch is that if we do a critical analysis of amortized energy consumption, LLMs will probably beat out humans. If not already, soon with the rate of token costs plummeting all the time.

[1] https://psychology.stackexchange.com/questions/12385/how-muc...

[2] https://press.princeton.edu/ideas/is-the-human-brain-a-biolo...

[3] https://epoch.ai/gradient-updates/how-much-energy-does-chatg...

That's a great point, and I think I was being vague before.

To clarify, I was making a broad statement about automation in general. Running an automated loom is more efficient in every way that getting humans to weave cloth by hand. For most tasks, automation is more efficient.

However, there are tasks that humans can still do more efficiently than our current engines of automation. Go is a good example because humans are really good at it and it AlphaGo can only sometimes beat the top players despite massive training and inference costs.

On the other hand, I would dispute that LLMs fall into this category, at least for most tasks, because we have to factor in marginal setup costs too. I think that raising from infancy all of the humans needed to match the output speed of an LLM has a greater cost than training the LLM. Even if you include the cost of mining the metal and powering the factories necessary to build the machines that the LLMs run on. I'm not 100% confident in this statement, but I do think that it's much closer than you seem to think. Supporting the systems that support the systems that support humans takes a lot of resources.

To use your blueberries example, while the cost of keeping the blueberries cold isn't much, growing a single serving of blueberries requires around 95 liters of water[1]. In a similar vein, the efficiency of the human brain is almost irrelevant because the 20 watts of energy consumed by the brain is akin from a resource consumption perspective to the electricity consumed by the monitor to read out the LLM's output: it's the last step in the process, but without the resource-guzzling system behind it, it doesn't work. Just as the monitor doesn't work without the data center which doesn't work without electricity, your brain doesn't work without your body which doesn't work without food which doesn't get produced without water.

As sramam mentioned, these kinds of utilitarian calculations tend to seem pretty inhuman. However, most of the time, the calculations turn out in favor of automation. If they didn't, companies wouldn't be paying for automated systems (this logic doesn't apply to hype-based markets like AI. I'm talking more about markets that are stably automated like textile manufacturing). If you want an anti-automation argument, you'll have a better time arguing based on ethics instead of efficiency.

Again, thanks for the Go example. I genuinely didn't consider the tasks where humans are more efficient than automation.

[1]: https://watercalculator.org/water-footprint-of-food-guide/