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.
Is that because non lighting electricity use increased though? 30% increase in the percentage of a growing pie isnt the same as a straight 30% increase
Both can be true simultaneously. American companies were comfortable with a high cost structure because it padded their revenue numbers. The government tried to block out competitors through import/export controls, and it ended up backfiring by creating more efficient competitors. Very similar to our competition with Japan in the 80s. We'll see if we give the AI labs the kinds of sweetheart protectionism that US car makers got.
I don't think that kind of protectionism will work with a digital asset. It is much more difficult to erect barriers when there are no physical goods and transportation across geographic borders is instant and free.
It will likely be on the enterprise side if it happens. Hard to control supply, but if demand is concentrated to a few hundred entities, that's easy to do.
As one example: Different access patterns. If you're using the app and have a subscription you get cheap tokens in the hopes you need more at which point you pay API prices which are much more expenseive.
I agree with that. However, there’s much dialogue around subsidized tokens for business use too, that people paying for the tokens are also vastly underpaying vs the “real” cost. I certainly don’t know the answer to that. Maybe the Chinese companies are also doing it. Maybe nobody is doing it.
Looking at reserved capacity cost for PTUs on azure, which I think they’d probably not subsidize but can’t be sure, I’m inclined to not agree with the vast undercharging for tokens hypothesis.
Wait, there's 13 providers for Kimi K3 in OpenRouter. I'm having a hard time believing every single one of them provides them without any profit.
And this one is easy to calculate: take your monthly API spend to K3, then rent a stack of 8xB300 for a month and see how much it costs. I would say you're about to save 10-15k dollars per month if you have enough traffic compared to pay per token pricing.
It's not very complex math, and the hardware of course is cheaper if you bought it last year and if you have extra GPUs waiting in your warehouse (depending on if you can produce enough energy cheaply).
