Jevon's Paradox says nothing about the size of the incremental demand though. Lighting is a great example. The US went from spending 15% of our electricity on lighting to ~4%. LEDs are maybe 5x as efficient, so that would be 3% without any change in usage. So we did see maybe a 30% increase in consumption as a result of lower prices, but that was nowhere near large enough to offset the efficiency gain in terms of overall consumption. Las Vegas may have, but that is a very small part of overall usage.
I anticipate we will see something similar with intelligence. There is probably headroom to consume 100x as much intelligence in R&D. But that isn't most of the economy. Will run of the mill service jobs increase their use of intelligence by enough to offset the effect of cheaper prices? I think that's the real question.
Intelligence is a lever, not a fluid. Power depends on how much force it can apply to specific points, not how much exists in general.
True high-intelligence outputs (Maxwell's equations, the Fourier Transform, quantum theory) are fundamentally transformative in ways that mid-high-competence (starting a generic B2B SaaS, making another CRUD app) aren't.
Which is why we've assumed we're already pretty far down the path to AI, but we really aren't. Solving random Erdős problems isn't the same as opening up a completely new kind of math/science with game changing practical applications.
I don't think you can get to that level with more compute and more tokens. I think it's going to take new higher level knowledge representations and new kinds of training to get there.
And the token count and compute may turn out to be lower than what we're using now.