3 ms·

This is day one. This is the least efficiency it will ever be. All the software optimisations are still ahead of us and even hardware will get more optimised.

This is day one. This is the least efficiency it will ever be. All the software optimisations are still ahead of us and even hardware will get more optimised. Computers slower than my pencil arithmetic used to run on valves and now here I am typing for half a day and blazing on the internet in my cordless, battery powered MacBook Air.

x86x87 3y ago
Or another way to look at it: this is day one. Usage will just continue to growth.
- jdmoreira 3y ago
  Yes, of course but not per unit of output. It will become more efficient and more widespread. I don't really see a long-term problem in that. Maybe a short-term problem yes
  - monkaiju 3y ago
    Assuming it doesnt crash because people realize its not a good tool (which I'm hoping for), this still means its overall energy consumption will rise. Thats Jevons paradox and this scenario fits it quite well
    - surajrmal 3y ago
      You're living under a rock if you think this is a fad. You're seeing ai used all around you constantly. Autocorrect on your phone keyboard, suggestions in Google News, YouTube, etc, translation, speech to text captioning, voice navigation, etc. These are all useful applications in the consumer space, not to mention all of the industrial and medical applications such as helping diagnose health issues, fraud/anomaly detection, etc. I would say there is a bit of stick on the wall happening where everyone tries to apply it everywhere and it's generally not always helpful. I consider that more of an evolutionary necessity. Hopefully we will learn where it works best and hone out usage in those applications.
      - howenterprisey 3y ago
        None of those things are what the comment was talking about. LLMs may make those things work better but they all existed previously.
        pixl97 3y ago
        LLMs are being incorporated into all those tools they mentioned. The only reason you'll see LLMs disappear is if they get replaced with something more powerful, we're not going back to the world before LLMs
  - lm28469 3y ago
    Like when we put lead in gas ? Or freon in fridges ? "it's just a lil bit what could it do"
  - SequoiaHope 3y ago
    A 10x improvement in efficiency and a 1000x increase in utilization would still mean 100x electricity usage. Is this a long term problem? Maybe!
  - layer8 3y ago
    There is a risk that if AI works well enough, there will always be profit in using even more AI (in particular if you can task AI itself to use AI recursively, meaning that AI usage won’t be limited by available human time), in order to have an edge over the competition. In that case, AI usage could gobble up all available energy regardless of AI efficiency.
- deleted 3y ago
  [deleted]
itishappy 3y ago
Will efficiencies ever catch up to our voracious apatite for "better"? Current top-of-the-line models seem to have gotten bigger much faster than they've gotten more efficient, and I don't see that trend stopping anytime soon.
- Gormo 3y ago
  Either the value proposition of AI will incentivize investment that successfully improves AI efficiency as operating it becomes more expensive, or AI development will plateau around whatever equilibrium point is defined by the best attainable efficiency.
surajrmal 3y ago
AI has been pushed into products for a few years now. We've had accelerated hardware for it for at least 6 years. It's hardly day 1. Surely things will continue to improve, but it's perhaps more poignant to say it's still early in the evolution cycle for AI technology.
lm28469 3y ago
> This is day one. This is the least efficiency it will ever be Day 1 of cars were infinitely less polluting than current day, the rebound effect is a killer
- ithkuil 3y ago
  I find it hard to believe that the first cars were less polluting (per unit of power) compared to modern cars. Could you please elaborate on that?
morphle 3y ago
I disagree. Software and hardware optimisations have not happened since the Rocky Mountain Institute did this data centre efficiency analysis [1]. If I had the time I could add numbers from Google, Microsoft and other hyperscaler analyses on software efficiencies in their datacenter papers. I'm sure Alan Kay has some insights on software efficiencies [2]. A small part of hardware inefficiencies are the energy use per transistor of computer chips. It has gone up since the 28nm node. Our datacenters have primarily smaller node chips (16nm, 7nm, 5nm) and have therefore gone up in energy use. [1] Integrative design for radical energy efficiency - Amory Lovins https://youtu.be/Na3qhrMHWuY?t=1026 https://youtu.be/Na3qhrMHWuY?t=1026 [2] Is it really "Complex"? Or did we just make it "Complicated"? - Alan Kay https://www.youtube.com/watch?v=ubaX1Smg6pY&t=2541s https://www.youtube.com/watch?v=ubaX1Smg6pY&t=2541s
moffkalast 3y ago
The more efficient it gets, the more practical it'll be and the more we'll use it. Jevons paradox tends to hold for this sort of thing. The real question is when AI will use 100% of electricity ;)

x86x87 3y ago

Or another way to look at it: this is day one. Usage will just continue to growth.

jdmoreira 3y ago
Yes, of course but not per unit of output. It will become more efficient and more widespread. I don't really see a long-term problem in that. Maybe a short-term problem yes
- monkaiju 3y ago
  Assuming it doesnt crash because people realize its not a good tool (which I'm hoping for), this still means its overall energy consumption will rise. Thats Jevons paradox and this scenario fits it quite well
  - surajrmal 3y ago
    You're living under a rock if you think this is a fad. You're seeing ai used all around you constantly. Autocorrect on your phone keyboard, suggestions in Google News, YouTube, etc, translation, speech to text captioning, voice navigation, etc. These are all useful applications in the consumer space, not to mention all of the industrial and medical applications such as helping diagnose health issues, fraud/anomaly detection, etc. I would say there is a bit of stick on the wall happening where everyone tries to apply it everywhere and it's generally not always helpful. I consider that more of an evolutionary necessity. Hopefully we will learn where it works best and hone out usage in those applications.
    - howenterprisey 3y ago
      None of those things are what the comment was talking about. LLMs may make those things work better but they all existed previously.
      - pixl97 3y ago
        LLMs are being incorporated into all those tools they mentioned. The only reason you'll see LLMs disappear is if they get replaced with something more powerful, we're not going back to the world before LLMs
- lm28469 3y ago
  Like when we put lead in gas ? Or freon in fridges ? "it's just a lil bit what could it do"
- SequoiaHope 3y ago
  A 10x improvement in efficiency and a 1000x increase in utilization would still mean 100x electricity usage. Is this a long term problem? Maybe!
- layer8 3y ago
  There is a risk that if AI works well enough, there will always be profit in using even more AI (in particular if you can task AI itself to use AI recursively, meaning that AI usage won’t be limited by available human time), in order to have an edge over the competition. In that case, AI usage could gobble up all available energy regardless of AI efficiency.
deleted 3y ago
[deleted]

itishappy 3y ago

Will efficiencies ever catch up to our voracious apatite for "better"? Current top-of-the-line models seem to have gotten bigger much faster than they've gotten more efficient, and I don't see that trend stopping anytime soon.

Gormo 3y ago
Either the value proposition of AI will incentivize investment that successfully improves AI efficiency as operating it becomes more expensive, or AI development will plateau around whatever equilibrium point is defined by the best attainable efficiency.

surajrmal 3y ago

AI has been pushed into products for a few years now. We've had accelerated hardware for it for at least 6 years. It's hardly day 1. Surely things will continue to improve, but it's perhaps more poignant to say it's still early in the evolution cycle for AI technology.

lm28469 3y ago

> This is day one. This is the least efficiency it will ever be Day 1 of cars were infinitely less polluting than current day, the rebound effect is a killer

ithkuil 3y ago
I find it hard to believe that the first cars were less polluting (per unit of power) compared to modern cars. Could you please elaborate on that?

morphle 3y ago

I disagree. Software and hardware optimisations have not happened since the Rocky Mountain Institute did this data centre efficiency analysis [1]. If I had the time I could add numbers from Google, Microsoft and other hyperscaler analyses on software efficiencies in their datacenter papers. I'm sure Alan Kay has some insights on software efficiencies [2]. A small part of hardware inefficiencies are the energy use per transistor of computer chips. It has gone up since the 28nm node. Our datacenters have primarily smaller node chips (16nm, 7nm, 5nm) and have therefore gone up in energy use. [1] Integrative design for radical energy efficiency - Amory Lovins https://youtu.be/Na3qhrMHWuY?t=1026 https://youtu.be/Na3qhrMHWuY?t=1026 [2] Is it really "Complex"? Or did we just make it "Complicated"? - Alan Kay https://www.youtube.com/watch?v=ubaX1Smg6pY&t=2541s https://www.youtube.com/watch?v=ubaX1Smg6pY&t=2541s

moffkalast 3y ago

The more efficient it gets, the more practical it'll be and the more we'll use it. Jevons paradox tends to hold for this sort of thing. The real question is when AI will use 100% of electricity ;)