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Jevons paradox

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Re: Jevons paradox

#41

Post by harrison_clarke »

TSiege wrote: post #16
mrweasel wrote: post #12

This has actually been on my mind lately and I've been looking for statistics, from Denmark, but haven't been able to find any going back far enough. My assumptions is that we in the late 1940s through the 1950s had achieved a reasonable standard of living, but perhaps not available to all. Even if we assume that everyone in a country has access to the same, high, late 1950s standard of living, but applied modern tec…

Well I can answer one question for you. There are studies that show that better insulation only briefly lowers the energy use. Once people adjust to the new insulation they end up heating or cooling their houses more since in their mind it is cheaper, but they end up using the same or more energy. https://www.cam.ac.uk/research/news/insulation-only-provides...

presumably there are limits to that. once every room is the preferred temperature, i'm going to stop running the heater

although maybe i'd get bored and run the heater and the AC at the same time

Re: Jevons paradox

#42

Post by kbolino »

mminer237 wrote: post #33
casey2 wrote: post #22

The intuition behind this is that people who had a usecase for the tech but were locked out because they couldn't afford it, or didn't believe it was worth it usecase can be engineers or researchers. Some of these people may perform better than expected causing a gap in true market value which is filled when they scale up to their new size Related: Jenson's Paradox: The more you buy, The more you save

I guess my question about the current events is: Are people really locked out of LLMs due to price? It seems like everything already has AI in it and that virtually every end user hates it. I could see a shift to more local models or something, but not an increase. I feel like LLMs have largely been oversold as a solution in search of a problem. Or are people just applying this as everyone and their mothers are going…

These are the four main problems with LLMs (and related technologies) as I see them:

  1. You can't tune them to your needs; they have restraining bolts and the training data is a generic corpus
  2. You don't own your interactions with them; your data transits a network and is processed by third-party servers
  3. They waste an immense amount of power relative to the usefulness of their output
  4. Their responses tend toward uncanny simulacra and hallucination
Bringing the cost way down and making them trainable on consumer hardware solves or at least greatly alleviates problems 1-3. That just leaves problem 4, which might still be unsolvable and sink the whole endeavor, but at least can be focused on.

Re: Jevons paradox

#43

Post by rawkintrevo »

pizza234 wrote: post #37

This is IMO a spefic case of the induced demand concept ( https://en.wikipedia.org/wiki/Induced_demand ).

I was just coming here to say, this looks like the fancy way to say 'induced demand'.

Why do you think this is a specific case of induced demand (as opposed to induced demand being a specific case of Jevons paradox, or the two being different words for the same thing?)

Re: Jevons paradox

#44

Post by roenxi »

cjbgkagh wrote: post #36

The dose makes the poison, what if a new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it. What if efficiency brought it down such that inference on CPUs that people already have is good enough. I see a large demand curve but not an infinite one. For example laptops are getting faster every year but most people I know are not clamoring for the latest laptop, same…

> what if a new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it

Obviously yes; 6 orders of magnitude would result in insane amounts of money swooping in. The market has currently been going crazy over relatively minor improvements compared to that.

> If a laptop came out that was 50x faster for the same price people wouldn’t buy 50x more laptops, they would wait even longer to upgrade.

That would be equivalent to decades of improvement in the tech. The last time we did that (over the last few decades) we saw massive increases in spending on computers.

Re: Jevons paradox

#45

Post by ijidak »

llm_trw wrote: post #8
bilbo0s wrote: post #6

I made a comment yesterday that the whole NVDA thing seemed eerily similar to the AMZN thing in the first dot com crash. [0]. If the market can get sufficiently irrational, and we start to see that same like 80-90% markdown that we got with AMZN in the first crash, I mean, who knows? That's a golden opportunity. Not many generations get opportunities like that twice . I guess we'll see what happens with the stock pri…

Having read the paper from deepseek I seriously don't get how anyone can think "Oh now is the time to sell nvidia". Both at the tactical level and strategic level that paper means we will see _more_ nvidia gpus fly off the shelves. If anything it's showing why the h800 is a decent card and everyone in China should buy one (dozen thousands).

But, I thought the CUDA moat is primarily needed for training.

On the inference side, why does one need Nvidia GPUs over Intel and AMD GPUs?

Re: Jevons paradox

#46

Post by cjbgkagh »

aurareturn wrote: post #39
cjbgkagh wrote: post #36

The dose makes the poison, what if a new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it. What if efficiency brought it down such that inference on CPUs that people already have is good enough. I see a large demand curve but not an infinite one. For example laptops are getting faster every year but most people I know are not clamoring for the latest laptop, same…

new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it In my opinion, yes. If scaling laws continue at 1,000,000x, we will turn the whole planet (or Mercury) into a giant GPU.

Do you have a use case for your opinion?

On the assumption that we’d want super intelligence to do interesting things, consider that we currently have many very smart humans and society is not maximizing their use, quite the opposite.

Re: Jevons paradox

#47

Post by BenFranklin100 »

pizza234 wrote: post #37

This is IMO a spefic case of the induced demand concept ( https://en.wikipedia.org/wiki/Induced_demand ).

As an aside, the concept of Induced demand has been criticized. A more precise way to think about “induced demand” is that the demand for a product at a lower price point was preexisting. The transaction didn’t clear until the price actually became lower and this demand manifested itself.

This seems like splitting a hair but it is a more consistent intellectual framework for understanding the dynamics of a particular market.

Re: Jevons paradox

#48

Post by marcosdumay »

mminer237 wrote: post #33
casey2 wrote: post #22

The intuition behind this is that people who had a usecase for the tech but were locked out because they couldn't afford it, or didn't believe it was worth it usecase can be engineers or researchers. Some of these people may perform better than expected causing a gap in true market value which is filled when they scale up to their new size Related: Jenson's Paradox: The more you buy, The more you save

I guess my question about the current events is: Are people really locked out of LLMs due to price? It seems like everything already has AI in it and that virtually every end user hates it. I could see a shift to more local models or something, but not an increase. I feel like LLMs have largely been oversold as a solution in search of a problem. Or are people just applying this as everyone and their mothers are going…

The question is:

Are people (or companies) locked out of training LLMs due to price?

I don't know the immediate answer. I really expect it to be a resounding "yes" on the long term because different LLMs should be good for different things. But in any way, this is not about the people adding a cloud client to their software.

Re: Jevons paradox

#49

Post by kgwgk »

scotty79 wrote: post #26

Current market panic looks like early railroad investors freaking out that someone made a faster train that goes over same tracks.

Is that really the analogy you want to use?

« In 1873, greed, speculation and overinvestment in railroads sparked a financial crisis that sank the U.S. into more than five years of misery »

https://www.smithsonianmag.com/history/robber-baron-gamble-r...

Re: Jevons paradox

#50

Post by lherron »

cjbgkagh wrote: post #36

The dose makes the poison, what if a new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it. What if efficiency brought it down such that inference on CPUs that people already have is good enough. I see a large demand curve but not an infinite one. For example laptops are getting faster every year but most people I know are not clamoring for the latest laptop, same…

It’s a question of depreciation of the GPU vs time to deploy AI.

If RL and synthetic data creation are all we need for self-improving AI, the large labs have an internal use case to utilize all available compute, regardless of efficiency gains.

If they have to go external and deploy in the broader market, time to deploy would make the GPUs worthless before they are fully utilized.

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