The price is what the market is willing to bear for the available compute capacity and competitive landscape. You can only discover that price after trying different price points and seeing what happens.
Everyone is trying different pricing schemes and discounts as they test the market. The demand is fluctuating at the same time.
It’s probably very confusing if you’re primarily familiar with stable and mature markets. Price fluctuations are a common feature of new and evolving markets.
This isn’t as unusual as some people are trying to make it sound. This has been happening since the dawn of finance.
I thought this would be less foreign to everyone since we just went through this whole conversation for a decade with Uber and Lyft. Their demise was predicted from the start from everyone who thought that it was going to collapse as soon as they couldn’t subsidize your rides with promos. There was much wailing and gnashing of teeth as their prices changed to feel out the market. Then they found profitability and the critics went silent.
Because that just absolutely murders any competition that manages to not get that level of free money. You're not pouring money in to make it happen at all at that point, you are pouring money in so nobody else can get the part of the pie.
Which is great for investors, bad for everyone else
I think this is a very important aspect, especially after the huge GPT-4o backlash when GTP-5 came out. Each model has certain quirks, and areas where the previous model might be better for some use cases than the latest and greatest, and the labs so far seem to have no desire to offer some kind of "LTS" release.
FlashAttention was a hell of a drug.
Artificial inflation to recoup R&D.
I do think that corporate price gouging is a huge problem that does need to be addressed. But especially with smaller business types — creatives, et al— I still haven’t gotten any grownup answers about what would compel people to get professionally good at something and innovate in the complete absence of copyright: the vastly better business model would be waiting for someone else to do something new and interesting, stealing their work, and then undercutting them in the market because you don’t have R&D/et al costs to recoup. You can’t say that wouldn’t happen because it’s exactly what the AI companies did to billions of people, scoffing at any protest. And ironically, they’re now whining about the Chinese doing it to them.
But yes, the reason brand name drugs are drugs are more expensive than generics is due to intellectual property, both the patent and the trademark.
https://www.project-syndicate.org/commentary/prizes--not-pat...
As of early June 2026, Opus 4.8 in fast mode cost $50/M output tokens and Opus 4.6 & 4.7 cost $150/M output tokens in fast mode
How can supply and demand explain the price drop? Was it cheaper to serve Opus 4.8? Is the demand for the newer Opus lower than for the older Opus? These are just fixed prices that seem picked out of thin air
More like a generation of models with different specific use cases
There's just obvious and enormous incentive for the OpenAI's of the world, along with all of the other players, to confuse, misrepresent or just straight up lie a whole bunch about everything given how new and unknown the tech is.
LLMs are fundamentally tools intended to be useful. But LLM vendors don’t understand their systems well enough to actually price the product people are trying to buy (for example, the actual product of a coding model is the code that it produces, not the tokens, which are just an internal mechanical process involved in the creation of the code). Token based pricing is that lack of understanding leaking out of the organization that ought to be responsible for it, and being dropped on the user.
Imagine if we made cars like this! You’d go to the car dealer and ask for a car. They’d bring you a pile of parts, charge you for them, and try to put them together in front of you. You’d go back and forth for a bit, rephrase where you want the steering wheel, etc. Some of the parts wouldn’t fit but you’d be invited to pay for replacements as well. In the end you’d either have a car or not, that’s your problem.
1. the field was nascent and new efficiencies were discovered
2. supply and demand
3. its in the company's incentive to make their models more efficient to increase overall usage so that while the margin remains the same, the total revenue + profit increases
I genuinely don't know what puzzles everyone?
If you're the best performing "computing cluster" (ie. whatever you call the entity that can complete a massive calculation), you get a blank check from Congress.
Why? Because you need those calculations to "pump" nuclear weapons. They are needed to calculate both the geometry to make fusion bombs possible at all and to calculate the effect of a given geometry. They are the reason US/Russia/China have the biggest and strongest weapons known to humanity. And of course, they were replicated worldwide for this reason. I mean not that anyone will admit this but we don't have the best possible solution, and we don't know either the upper or lower limits for fusion devices (plus the lower limit would be very useful for energy generation, which for the US would effectively mean almost literally unlimited large marine ships that never need refueling. And yes, the solution to that problem is almost literally a 3d shape. Not just that, but mostly)
Now it appears it does not work the same when you democratize computation. Humans want a particular amount of computation and are willing to pay a given price for that. But the accountants still saw the blank check from before and ... do what accountants do. Economics don't change because you make things bigger and accessible, do they? Oh ... wait a second ...
As someone put it recently though, we now have data. 2.3% of humans in the US are willing to pay $20 per month for the support of a model like GPT-5.5/Claude code. If that's true (and after years of having this model, why wouldn't it be?) ... it means AI startups are doomed (because it's not even 10% of what they need it to be to make economic sense).
We have a couple new nuclear weapon designs, but not really going for bigger or stronger. Just packaging.
We built the big powerful ones with 1960s computing.
Now, stockpile stewardship -- being sure that stuff will keep working without ongoing testing -- is a bit expensive in compute. You need early 2010s supercomputer power.
In other words, I strongly disagree that nuclear weapons are the primary driver of high-end compute.
There's many other considerations. Like the type and amount of fissile material. To name one that became well known: any plutonium needs to be refreshed (re-breeded I believe is the term) every few years.
Also, look at first designs: https://www.bbc.com/news/newsbeat-35242069 The weight is secret, but I think you can easily see it's going to be deep into "extremely impractical" territory.
Surely you can see why someone (especially aircraft designers) might ask for better versions. Ideally you'd like a version that fits on the hypersonic missiles and those things ... are just not going to work. The size. The shape. The weight. None of them will work.
Then a quick theoretical exploration will tell you that the minimum theoretical size of such a device is tiny. The scare was about "suitcase sized", but if you actually do the calculation looking for the minimum ... to do it however you need to create an explosion of the correct shape to get anywhere near those minimum sizes. And explosion simulations are a problem that utterly sucks ... Oh and these are secret military projects, these simulations, not very optimal. The people doing them are best described as loyal, and not as great physicists. Not saying they're terrible, but in the movie Oppenheimer you can clearly see why the best and brightest are not available for these things.
The military has already mastered fusion (power), and likely has mastered gravity in some form in secret. They aren’t using mainstream compute for these discoveries
Believe me, I know-- my dad did some work on the 360/44 and other large systems.
High end late 1960s compute -- of the sort used to go to the moon or to design big nuclear weapons -- was roughly 486DX4-100 class. Not individual computers; the total computing at DOE or NASA. Of course, it would be hard to replace either with a single 486 because of availability, usage at different geographic locations, etc.
You can assume a single large AMD Threadripper machine ($25k?) outclasses late-1960s DoE by roughly a factor of 50,000. And that assumes you didn't bother to put a GPU in it.
> The military has already mastered fusion (power), and likely has mastered gravity in some form in secret. They aren’t using mainstream compute for these discoveries
K
But consumer uptake strikes me as the worst way to judge whether the big AI shops will make it. That's not where most of the leveraged user return or deployable capital is.