Jevons Paradox [1]

> when technological improvements that increase the efficiency of a resource's use lead to a rise, rather than a fall, in total consumption of that resource.

[1] - https://en.wikipedia.org/wiki/Jevons_paradox

Las Vegas replaced the expensive incandescent lighting on the strip with cheaper to run LED equivalents. But the costs didn't come down because they were able to add more lights and larger displays.

I think the same will happen with tokens. As the cost of tokens comes down, these models will just consume more tokens.

> Reading everything becomes the default. At a cent per document, a model can read every paper

I love how in our day "reading everything" means "the computer reads it for me".

I expect soon the computer will be able to go on bicycle rides, and spend time with my wife.

I think speed is actually going to be a bigger factor than cost. Even projects where “money is no object” often hit a wall with LLM response times.

Sure you can speed things up with parallel work under subagents, but as with parallelizing traditional computational tasks, there are diminishing gains.

I keep hearing people saying just change the way you work to trust long-running agents and multi-task more, because they’re too slow to work with interactively for many use cases. I think that’s painful in a world where we expect humans to still heavily guide and interact with agents for their day-to-day work.

I think a big one is robotics. A robot can today fold your laundry. It takes ~10mins per item. Seriously. It takes a long time to process the image find the corner move the claw to the corner of the shirt and attempt to straighten before folding.

Robots right now generally move at glacial speeds. You might have seen robots doing flips in semi controlled environments but watch how slowly they open doors etc. processing time is a major bottleneck.

100x seems like an underestimate. Even with no model improvements, we should see that sort of reduction. Looking at TSMC’s margins, Nvidia’s margins, and OAI/Anth (alleged) margins on inference, there is a room for a 100x reduction.

Right now all three of those are at abnormally high levels. Competition will come for all three.

I’m loving small models. The gemma4 26/31b models have been deeply impressive on weird prose analysis tasks that I am working on. Nova-micro is really stupid but is extremely fast when it’s smart enough to do something. I’m trying to be disciplined about able to evaluate quality vs cost everywhere for real systems built on this stuff. I probably need to get off Bedrock because it’s missing a lot of other little models that might be good competitors.

I think what a lot of people miss about jevon's paradox is the elasticity of demand of the underlying resource

textiles had jevons paradox, and many more textile workers were employed even when textile machines were being created, until we saturated the demand for cheap clothing in the world and then textile workers were kaput (same for farming, and horses)

software is currently undergoing jevons paradox, but it's very unknown how high the ceiling of demand for software is. web dev might be doomed, but software in general i think is probably limitless

Intelligence is also probably unbounded (atm software and intelligence are very closely tied together). its very possible token spend rides up the curve forever.

I've been working with the chinese open models for 4 months. They are more than capable for a tiny fraction of the cost of the frontier ones. And yet they also continue to get significantly better and (Deepseek's recent price increase aside) cheaper. Its hard to fathom how the truly frontier stuff will be able to compete long-term.

There’s still 50-500x cost reduction in “this is only an engineering problem” low hanging fruit from specialized chips to run the models + improved distillation.

Entirely feasible that by 2031, Fable 5 (or greater) intelligence level models will run cool on smart phones, if not sooner.

Personally I still see LLMs as very advanced search engines which lack intelligence. To me it seems that the cost of getting data is reduced by LLMs, not the cost of intelligence. I mean: we tell the model what we want to achieve, and the model responds with the right data in de form of code in seconds.

That's why 'stackoverflow programmers' will have a hard time competing with LLMs but engineers are still needed for their intelligence.

Well that's just my 2 cents.

A year ago I had an aha moment, when I realized that for my purposes, Gemini Flash was not only 9x cheaper, but 3x faster than Gemini Pro, while producing identical output. Who's the best model now!

For a lot of tasks, even small models have saturated them a while ago, and then going cheaper and faster is just pure gains.

For coding I also prefer to do it interactive/realtime, micro-prompting, surgical edits, which the small models can handle just fine.

And then at the top, the real question is consistency. Not "can they do it" but "reliably enough that you don't need to constantly double check everything." (In my experience, not quite there yet, although it's getting way better.)

> Reading everything becomes the default. At a cent per document, a model can read every paper in a field, every record in an archive, every email, or every message in a support queue as a matter of routine

Pretty much already happening

This means a great de-risking is happening for the costs of deploying somewhat autonomous agents. This has profound implications on the timeline of deployable personal agents. Cost was a significant factor for many people during the OpenClaw frenzy, specifically when they let their agents run somewhat wild. It will become much more palatable, or already has, to install whatever the next generation of token consuming autonomous systems will be.

It’s way too early to tell the true cost of intelligence. Inference is still heavily subsidized, and training is apparently being funded by a mountain of free money

The article is solid. But there is a nuance what he skipped — quality vs price. Sure, GPT-5.6 Luna for pennies can do the same thing what Claude 4.5 Sonnet did for a dollar a year ago. Except Sonnet back then actually carried the codebase, while Luna... eh, not so much. And another thing, speed. You can make it cheaper as much as you want, but if a model thinks for half a minute you save cents but lose time.

Idiocy becomes rampant!

this guy gets it

all the optimistic pundits ignore the blatant second order consequence of this: the entire economy is pegged on this NOT being cheap!

all the US economy is tied to video cards being used in lieu of gold. cost dropping 100x means the economy bottom falls out.

nothing because this shit is not "intelligence"

ppl are doing all sorts of gymnastics to tell claude to slow its roll with verbosity.

Author here. I made these plots because I had been searching for them for months and never found quite what I wanted: how the cheapest way to reach a fixed capability level has moved over time. Artificial Analysis publishes enough data to reconstruct it. If someone knows of a source that already tracks this, with historical prices, please share.

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