Err, yes, until the surplus kills off all other competition and allows the supplier to jack prices up sky high, or otherwise bend consumers to their will. There's a reason most countries will stop foreign firms from doing this to them.
In this case, its not competition in the form of foreign company.
Its competition of engineers, scientists, and intellectual labor being atrophied due to overuse of LLM.
Pushing costs at 1/100 for 'thinking' gets intellectual labor people hooked and dependent. Then when costs go 300x, leaves people dumber and less capable of doing things on their own.
LLM companies, by not accurately charging for services, are directly dumping on world-level society and devaluing and addicting people to outsource thinking. Thats the problem.
In reality all subsidized and 'free' services do exactly this. LLM token vendors are making a play against human thought.
Except there's lots of competition for creating that surplus, including from open source locally-hosted LLMs, and while it's behind the frontier it's not that far behind the frontier.
The dumping -> non-competitive price increases playbook is historically very, very rare, and relies on a monopoly (or in a few cases oligopoly) with large externally-enforced barriers to entry. The oligopoly case is highly unstable and doesn't last, and besides we don't have notable barriers to entry; we have both market competitors and locally-hosted imperfect substitute goods.
There's essentially no reason to believe the dynamic you're predicting could succeed here, because we lack all the conditions that make it more likely to succeed, and it's very rare anyway.
You don't know the actual margins -- you only know the API rate. If their API rate has huge margins and the average subscriber isn't coming anywhere close to their limits, the subscription can be very profitable. If they're only near peak capacity in peak working hours (when API traffic is most active) and subscription 5h limits help them redistribute a lot of use outside peak hours when they've got spare capacity, that alone could make a massive difference in profitability.
It'd have been interesting for them to discuss it, but from what I understand it looks like MCAS is probably an entirely separate thing (that can also be triggered by COVID), but because of the overlap in symptoms, many people who assumed they have long COVID actually had MCAS. And even after teasing those two out, there may be more conditions in the long COVID bucket.
I'm sure there are things local models are good enough at in non-coding work, but for anything complex I do not find this to be the case.
I'd say local models are fairly capable of even somewhat complex coding execution. For complex non-coding work (research, in-depth analysis, assembly of complex info-dense documents) I'd rather do it by hand than switch from Opus 4.7 to anything I could even theoretically run locally.
Again, you can grant this and a huge number of agriculture subsidies still aren't justified.
People have an instinctive defensiveness over farms/farmers, but anyone who has studied farm subsidies in any depth knows there's no way to rationally justify huge swathes of them. I don't know anyone with the requisite knowledge who wouldn't agree with that including farmers and lobbyists (because they generally only like a subset of the subsidies themselves).
I’m from a historically agricultural state, and live in the farming area. Government interventions are regularly mocked
- always have been.
Demand for food plummets when it is no longer fresh. Throwing food away is politically toxic. This creates major problems.
As people get richer they don’t want more food, they want better food. Fresher and more meat based. Which is fine. But means the food when you talk about food “that which prevents us from starving to death” you are quite divorced from actual demand.
People don’t price food based on its anti-starvation capabilities.
Either they follow traditional diets, or they buy for convenience (highly processed), or they are health nuts who live off rice, beans, and kale.
Nobody is trying to maximize calories. Very few people are trying to match their food intake to their amount physical exertion.
All these ontological and teleological models are divorced from how food is actually valued: market “taste” is insanely important under normal circumstances.
Our agriculture sector won’t succeed if it’s based around preventing famine.
The premise of your link is founded on the energy associated to with a single prompt. The source in your link for that energy claim links to a blog post that then links back to an earlier blog post from the original author of the link you provided (it's basically a circular reference).
Basically, there's a lot of words in your initial link, but they all hinge on the readers taken the stated energy assumption for a single (undefined) prompt at face value. If that initial assumption is wrong (at min, it's poorly defined in your link) all further conclusions are invalid.any a scientific publication have done this same trickery =].
They don't define what a query is when they are talking about AI power usage. If we want to get serious, we'd tie usage to tokens since we can actually track token usage.
>The source in your link for that energy claim links to a blog post that then links back to an earlier blog post from the original author of the link you provided (it's basically a circular reference).
Huh? The latter blog post does link to the former's blog, but not as a source for that claim. It cites an Altman blog, an estimate from EpochAI, an article in the MIT Technology Review (albeit one that estimates 3x higher), and a paper put out by Google. It's really surprisingly well cited and I don't know how you came away from it thinking it was a circular reference. The google study is in the subheading!
2) I click the link associated with the 0.3 Wh of energy claim in the section "The full cost of a prompt".
3) The link from 2) takes me to a blog post from Hannah Ritchie. In Hannah's post, I click a link associated with the following excerpt:
"Third, as a result, more recent estimates suggested that the assumptions I relied on (h/t to Andy Masley’s work on this) — that one standard query used 3 watt-hours (Wh) of electricity — were possibly an order of magnitude too high. In this case, I was happy to be conservative and overestimate the energy use."
4) This link takes me to the author of your original post, but earlier.
None of this quantifies cost per token, which is really the much more relevant metric than whatever a "cost per text based query" means => which I think is both quite broad and quite model dependent.
We use about two orders of magnitude more water (each!) on corn and alfalfa than on data centers as of 2023, and while we're ramping data centers up fast, it'll still be an order of magnitude at the 2030 data center estimates (which may heavily overestimate, now that there's so much opposition popping up).
If our water rights system required farmers to actually pay anything approaching market rates for the water they used, it actually would be a serious answer!
Farmers grow alfalfa in the desert and drain the western US's aquifers and rivers because we have insane water rights doctrines that entitle them to trillions and trillions of gallons of free or almost-free water far in excess of what the watershed regions can bear.
If we don't change that system, data center water usage is a rounding error that is barely noticeable at the scale of the problem. If we do change that system, data center water usage isn't a problem at all.
Hmm? There are a number of top AI people who make this exact point, though, and are trying to drive things toward elevating thinking. There's more that can be done, but quite a bit is a user mindset issue that's just going to have to shake out over time.
You are right that there are a large number of top AI people who are very concerned with the ramifications of AI. I would say there are two core issues.
The first is that these people have often been indistinguishable from the ambitious and power hungry people I was decrying. Sam Altman was able to blend in for a long time by copying the rhetoric of AI safety types. When there is this much money to be made and power to be amassed, it's not hard to pretend to care.
The second is that I have often been disappointed by what the AI safety folks are concerned about. There has been a huge amount of talk about existential risk and not nearly as much about, say, the impact on children if education is outsourced to AI. The obsession with science fiction led to some very out there scenarios that may or may not still happen, but have nothing to do with the very real impacts of AI on people's lives right now. I believe that even the well intentioned have been too detached from humanity as a whole.
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