> Training a single frontier AI model will soon require gigawatts of power, and the US AI sector will need at least 50 gigawatts of capacity over the next several years.
These things are so hideously inefficient. All of you building these things for these people should be embarrassed and ashamed.
Quite the opposite, really. I did some napkin math for energy and water consumption, and compared to humans these things are very resource efficient.
If LLMs improve productivity by even 5% (studies actually peg productivity gains across various professions at 15 - 30%, and these are from 2024!) the resource savings by accelerating all knowledge workers are significant.
Simplistically, during 8 hours of work a human would consume 10 kWH of electricity + 27 gallons of water. Sped up by 5%, that drops by 0.5kWH and 1.35 gallons. Even assuming a higher end of resources used by LLMs, a 100 large prompts (~1 every 5 minutes) would only consume 0.25 kWH + 0.3 gallons. So we're still saving ~0.25 kWH + 1 gallon overall per day!
That is, humans + LLMs are way more efficient than humans alone. As such, the more knowledge workers adopt LLMs, the more efficiently they can achieve the same work output!
If we assume a conservative 10% productivity speed up, adoption across all ~100M knowledge work in the US will recoup the resource cost of a full training run in a few business days, even after accounting for the inference costs!
Additional reading with more useful numbers (independent of my napkin math):
https://www.nature.com/articles/s41598-024-76682-6
https://cacm.acm.org/blogcacm/the-energy-footprint-of-humans...
I have no expertise here, but a couple years ago I had a prototype using locally deployed Llama 2 that cached the context (now deprecated https://github.com/ollama/ollama/issues/10576) from previous inference calls, and reused it for subsequent calls. The subsequent calls were much much faster. I suspect prompt caching works similarly, especially given changed code is very small compered to the rest of the codebase.
Saying “we can do the same work with less resource use” doesn’t mean resource consumption is reduced. You’ve just gone from humans using resources to humans using the same resources and doing less work, plus AI using more resources.
Soon enough, we won't be able to avoid this question.
For instance, I think operating at this level of productivity is unsustainable (https://news.ycombinator.com/item?id=46938038). As discussed in detail by the recent "AI vampire" blog: https://news.ycombinator.com/item?id=46972179 -- most humans are not designed for that level of cognitive intensity.
But even then, the productivity per human will explode, and we will still have the problem of "too many humans." Cynically, if most knowledge workers get laid off, it's good from an environmental perspective because that means much less commuting and pollution! But then they're starving and we will have riots!
This is where I foresee the near-term problems with GenAI: social turmoil rather than resource consumption. I suspect it's not all bad news though. While it's impossible to put numbers on it, it helps to think about the first-order economic principles that are in play:
1. This is hand-wavy, but knowledge work boosts economic growth. If this is massively accelerated, we should be creating surplus value that compensates for a lot of costs.
2. However a huge chunk of knowledge work is busy work which will be automated away. People can try upskilling but the skill gap is already huge an growing quickly and they will lose jobs.
3. The economy is essentially people providing and paying for services and goods. If people lose jobs and cannot earn, they cannot drive the economy and it shrinks.
4. The elite, counter-intuitively enough, do NOT want that because they get richer by taking a massive cut of the economy! (Not to mention life in a doomsday bunker can get pretty dull if starving people start rioting -- https://news.ycombinator.com/item?id=46896066)
There are many more dynamics at play of course, but I think an equilibrium will be found purely because everyone is incentivized to find a solution (UBI?) that keeps both the elites and the plebes living long and prospering. I expect some turmoil, but luckily, the severe resource crunch of GPUs gives us time to figure things out.
If it helps better interpret my posts, these days my frame of mind is "We're living in very interesting times" in the sense of https://en.wikipedia.org/wiki/May_you_live_in_interesting_ti...
Have fun watching the car crash.
Your question is a variant of what do we do with all those humans now that they don't have to walk miles to the well every day because we invented aqueducts? The point is that they didn't want to walk to the well but they had to (and in some places they still have to) and very few people want to work, even now and even us, but they have to.
We will see what happens this time when we won't have to walk to that well.
Mostly lines up with this reference too, which focuses only on water usage at work: https://quench.culligan.com/blog/average-water-usage-per-per...
Buying electricity isn't inherently destructive. That's a very bad analogy.
> These things are so hideously inefficient. All of you building these things for these people should be embarrassed and ashamed.
I'm not arguing that they are efficient right now, but how would you measure that? What kind of output does it have to make per kWh of input to be acceptable? Keep in mind that the baseline of US power use is around 500GW and that currently AI is maybe 10.
> AI sector will need at least 50 gigawatts of capacity over the next several years.
The error bars on this prediction are extremely large. It would represent a 5% increase in capacity in "the next several years" which is only a percent or two per year, but it could also only be 5GW over the next several years. 50GW represents about 1 year of actual grid additions.
> All of you building these things for these people should be embarrassed and ashamed.
I'm not building these things, and I think there should be AI critique, but this is far over the top. There's great value for all of humanity in these tools. The actual energy use of a typical user is not much more than a typical home appliance, because so many requests are batched together and processed in parallel.
We should be ashamed of getting into our cars every day, that's a true harm to the environment. We should have built something better, allowed more transit. A daily commute of 30 miles is disastrous for the environment compared other any AI use that's really possible at the moment.
Let's be cautious of AI but keep our critiques grounded in reality, so that we have enough powder left to fight the rest of things we need to change in society.
Of course I could do all of my coding alone again, but I would be slower. It's like walking to the mall several times per week, several hours per time, instead of once or twice per week with a car, three cumulative hours. I trade a higher energy consumption for more time to do other things and the ability to live far away from shops.
Counting the 100w for 24 hours for a human doesn’t match up with counting the power usage from “AI” for only the 10 minutes it’s doing a task.
Also - units issue: 100 watts for a day is 2400 watt-hours. It’s a moot point anyway because the power draw for the frontier models is an order of magnitude off that the division by 24 is basically meaningless.
You should start from beef industry.