I don't understand why simonw's comment is dead, because he mentions a real counterpoint to the video: API token prices are NOT the raw costs for any provider. I'd even say that inference needs to have quite a juicy margin to cover for all the other costs. It would make no business sense to sell API tokens at a loss: nobody knows yet how to price intelligence, so why start in the red when it's the only source of revenue?

It's a different story for subscriptions. According to my rough computation (N=1), a Claude Max 20x at $200 gives you access to around $8k worth of tokens per month – but they don't cost Anthropic $8k! – and there I think they'd make a loss on every token maxxer which may or may not be compensated by subscriptions that are not used. But that's not the end of the subscription story.

Once you are "enterprise" you pay for token use and there is no way around it: Anthropic does it and so does OpenAI. The subscription is the gateway drug to token maxxing. When people are hired in an Enterprise job, they'll come with their habit of using AI for all and any task.

All to say that: yes, AI labs are bleeding money but on everything else – datacenters, training models, talent,...

Models like Cursor's Composer 2.5 show that you can get real work done without the crazy costs just by focusing on a domain. AGI is silly in part because models are spiky, in addition to making the model more expensive for all queries, you can't easily tell a priori what the model will be good at. The smaller focused model is cheaper to run and if you try to ask a coding question to a biology/chemistry model (or vice versa) it's user error rather than ignorance of the underlying training data distribution.

This rumor is not demonstrably true. The subscription prices are competitive and for heavy users even cheap compared to API rates, but there is no evidence that they are structurally priced below cost.

Are frontier labs providing subscriptions at discount vs API token costs? Yes

However this entire video is slop. I don't know if it's actually AI slop but it's intellectual slop for sure.

Just the title alone is 100% disqualifying. They are using a monthly cost compared to the yearly [0] cost and also using the API token cost as the actual cost to the providers (which it's not, the actual cost is lower, the APIs, from everything we know, are _not_ being operated at a loss).

This whole video is a waste of your time. It's true that we are probably in Uber-phase of LLMs however a massive difference this time around is local inference (and other "open" models). If (US) frontier labs raise their prices then people can reach for the open models locally or using other cloud inference. And all of this assumes a moat, which so far doesn't seem to exist. The open models trail SOTA but not by more than a year or so (I've heard 6mo thrown around a lot).

Either the SOTA models will be priced too high to be worth it and we will move to open/lower-cost (local or otherwise) models or they will continue to provide a benefit over then lower/free models and be worth paying for.

[0] With _zero_ evidence to back up that number

This is true for a lot of subscriptions though: some people use 100% of their mobile plan data, some people use 10% – the price accounts for this.

I'm not yet dependent enough on any AI to shell out anything more than $15-$25/month.

If I lose it, it will not be the end of the world. I'll probably start digging into local models.

I suspect there are many like me. Far more than there are totally dependent users. I also suspect that the AI economy is some sort of "whale economy", where a minority is footing the bill, by paying outrageous amounts to Anthropic/Open AI/Google.

So the choice becomes to either 1) Use the AI tools as much as you can before they increase prices/tighten usage or 2) Stop using them so you won't be compelled to pay more later when the price inevitably goes up/your regular plan gets downgraded?

I would think companies would set the usage low and increase it with capacity rather than subsidizing the power users and going into the red. Maybe my strategy wouldn't be aggressive enough to capture the market, which I'm sure the major AI companies are trying to do.

I’m so sick of this scarcity mindset.

We will have better and cheaper intelligence in the future than we have now. This is not Uber. Inference is profitable for these companies. Looking at API pricing and assuming that reflects the cost basis is dumb.

It costs less for OpenAI to serve GPT-5.5 than it did to serve GPT-4. An H100 is more valuable today than it was five years ago because it can serve more intelligence per token.

Jevons paradox and short-term crunches may cause some swings, but the value of a token keeps increasing while the average token price decreases.

Chinese models are already a fraction of the cost, and we will have a mythos/fable-level open-source model by the end of the year. There is no “gotcha” where every AI company rugs you in unison.

Stop trying to figure out how this screws you. Start figuring out what cool shit you can build with it.

Frankly these tools should be priced per query. One doesn't need mythos to get a french toast recipe or add a few numbers together, but the flat rate subscription rate hides the inefficiency. Maybe a routing filter at the beginning of the query that chooses the right model for the query?

Is it ironic that people are watching AI videos and taking them as gospel on AI topics?

The Uber analogy is not a good one. Uber is constrained by the cost of cars, fuel and drivers, which do not go down over time. That's it's floor. It can't charge less than that and be profitable. The cost of chips and inferencing _will_ go down over time as new chips come out and the software gets more efficient.

The underlying capitalist problem is that dumping is not just permitted, but expected as a business strategy. Except dumping is usually exporting to another country to kill their industry.

Instead, this dumping is exporting "thinking" to destroy humans' innate thoughts, get them hooked, then rugpull for 3x the cost. Cause just over 1 year of LLMs, takes a developer who could reverse engineer a thing, to now needing help to construct a for loop.

Thats why I run my own LLMs. Hard to rugpull what you own and control. And thats also why I focus on questions not of "do this", but "explain this". I seek to use LLMs to learn more effectively, so I end up needing it less and less.

If only every government had competition departments who had essentially ONE job: prevent companies from getting away with this ...

Oh wait.

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