Comment by sramam
10 days ago
I work in enterprise IT and sometimes wonder if we should add the equivalent energy calculations of human effort - both productive and unproductive - that underlies these "output/cost" comparisons.
I realize it sounds inhuman, but so is working in enterprise IT! :)
I agree wholeheartedly. It irks me when people critique automation because it uses large amounts of resources. Running a machine or a computer almost always uses far less resources than a human would to do the same task, so long as you consider the entire resource consumptions.
Growing the food that a human eats, running the air conditioning for their home, powering their lights, fueling their car, charging their phone, and all the many many things necessary to keep a human alive and productive in the 21st century are a larger resource cost than almost any machine/system that performs the same work. From an efficiency perspective, automation is almost always the answer. The actual debate comes from the ethical perspective (the innate value of human life).
I suspect you may be either underestimating how efficient our brains are at computing or severely underestimating how much energy these AI models take to train and run.
Even including our system of comfort like refrigerated blueberries in January and AC cooling a 40° C heat down to 25° C (but excluding car commutes, because please work from home or take public transit) the human is still far far more energy efficient in e.g. playing go then alpha-go. With LLMs this isn’t even close (and we can probably factor in that stupid car commute, because LLMs are just that inefficient).
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!
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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/
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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://epoch.ai/gradient-updates/how-much-energy-does-chatg...
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This is a bad argument. Even if a machine replaced my job, I'm still going to eat, run the aircon, charge my phone etc. and maybe do another job. So the energy used to do the job decreased, but the total energy usage is higher because I'm still using the same amount of energy, but now the machine is also using some amount energy that wasn't being used before.
Efficiencies lead to less resources being used if your demand is constant, but if demand is elastic, it often leads to the total resource consumption increasing.
See also: Jevons Paradox (https://en.wikipedia.org/wiki/Jevons_paradox).
Not ALL automation can be more efficient.
Just ask Elon about his efforts to fully automate Tesla production.
Same as A.I. Current LLM-based A.I.s are not at all as efficient as a human brain.
Only slightly joking, but someone needs to put environmental caps on software updates. Just imagine how much energy it takes for each and every discord user to download and install a 100MB update... three times a week.
Multiply that by dozens or hundreds of self-updating programs on a typical machine. Absolutely insane amounts of resources.
Goodhart’s Law will mess all that up for you.