So the benchmark is achieving human-like intelligence on a 100W budget. I'd be very curious to see what can be achieved by AI targeting that power budget.
Similarly, I've had times where it wrote me scientific simulation code that would take me 2 days, in around a minute.
Obviously I'm cherry-picking the best examples, but I would guess that overall, the energy usage my LLM queries have required is vastly less than my own biological energy usage if I did the equivalent work on my own. Plus it's not just the energy to run my body -- it's the energy to house me, heat my home, transport my groceries, and so forth. People have way more energy needs than just the kilocalories that fuel them.
If you're using AI productively, I assume it's already much more energy-efficient than the energy footprint of a human for the same amount of work.
That is certainly not a logical leap I'm making. AI doesn't make anybody redundant, the same way mechanized farming didn't. It just frees them up to do more productive things.
Now consider whether LLM's will ultimately speed up the technological advancements necessary to reduce CO2? It's certainly plausible.
Think about how much cloud computing and open sourced changed it so you could launch a startup with 3 engineers instead of 20. What happened? An explosion of startups, since there were so many more engineers to go around. The engineers weren't delivering pizzas instead.
Same thing is happening with anything that needs more art -- the potential for video games here is extraordinary. A trained artist is way more effective leveraging AI and handling 10x the output, as the tools mature. Now you get 10x more video games, or 10x more complex/larger worlds, or whatever it is that the market ends up wanting.
So many people make this mistake when new technologies come out, thinking they'll replace workers. They just make workers more productive. Sometimes people do end up shifting to different fields, but there's so much commercial demand for art assets in so many things, the labor market shrinking is not the case for digital artists right now.
In that case I think it would be only fair to also count the energy required for training the LLM.
LLMs are far ahead of humans in terms of the sheer amount of knowledge they can remember, but nowhere close in terms of general intelligence.
A computer uses orders of magnitude less energy than a human.
It's all about the task, humans are specialized too.
EDIT: maybe add a logarithm or other non-linear functions to make the gap even bigger.
I agree human brains are crazy efficient though.
But either way, how many human lives are spent making that file?
I can generate images or get LLM answers in below 15 seconds on mundane hardware. The image generator draws many times faster than any normal person, and the LLM even on my consumer hardware still produces output faster than I can type (and I'm quite good at that), let alone think what to type.
Also, why are people moving mountains to make huge, power obliterating datacenters if actually "its fine, its not that much"?
Great analogy.
> Also, why are people moving mountains to make huge, power obliterating datacenters if actually "its fine, its not that much"?
I presume that's mostly training, not inference. But in general anything that serves millions of requests in a small footprint is going to look pretty big.
There's many things to say on this. Free is worthless. Speed is not necessarily a good thing. The image generation is drivel. But...
The main nail in the coffin is accountability. I can't trust my work if I can't trust the output of the machine. (and as a bonus, the machine can't build a house. It's single purpose).
> An AI cloud can generate revenue of $10-12 billion dollars per gigawatt, annually.
What? I let ChatGPT swag an answer on the revenue forecast and it cited $2-6B rev per GW year.
And then we get this gem...
> Wärtsilä, historically a ship engine manufacturer, realized the same engines that power cruise ships can power large AI clusters. It has already signed 800MW of US datacenter contracts.
So now we're going to be spewing ~486 g CO₂e per kWh using something that wasn't designed to run 24/7/365 to handle these workloads? These datacenters choosing to use these forms of power should have to secure a local vote showcasing, and being held to, annual measurements of NOx, CO, VOC and PM.
This article just showcases all the horrible bandaids being applied to procure energy in any way possible with little regard to health or environmental impact.
This article is coming from one of the premier groups doing financial and technical analysis on the semiconductor industry and AI companies.
I trust their numbers a hundred times more than a ChatGPT guess.
It doesn't matter who they are if there's nothing backing it up.
The entire article is predicated on the fact that this is profitable long term.
Again: > An AI cloud can generate revenue of $10-12 billion dollars per gigawatt, annually.
Yet this simple fact isn't justified at all nor is it stated what "AI cloud" actually is or how they got to those numbers.