11–20 of 61 posts
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.
The figure mentioned in the video is not far off
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.
Okay then provide a link to a Dropbox PDF or official documentation “demonstrating” the premise is “untrue” please. Or admit you’re blinded by faith. Or financially interested in the public believing in a hypothetical like your second sentence. In short, citation needed or shens bruh.
All we have actual evidence of is: some users use enough AI that the subscription is sold at a loss to them (up to degenerate cases: usage maxed out at all times), if billed by API metrics, while some other users are, by the same metrics, profitable (down to degenerate cases: a forgotten subscription with $20 a month and 0 usage).
We don't know how API prices relate to costs - we only have estimates. And we certainly don't know how much inference does an average subscription user spend.
If you have some sort of information that would decisively prove that the aggregate is "AI company N is losing money on subscriptions", then, show it.
Or is it you who's blinded by faith? Like some sort of AI bubble cultist? The bubble is real, you just have to believe in it?
Earlier quoted context omitted.
Are you including capex when you say "cost"? Or are you just looking at inference costs?
It doesn't make sense to include the capex cost to train a model in this kind of discussion, because that cost is fixed. Consider a model that costs $100m to train. If the vendor then prices it such that each inference token has a margin of 10% over the variable costs to serve (power + server costs), whether or not they cover their costs is based entirely on how many tokens they can sell. If they sell less than $1bn…
And if capabilities plateau such that training the next one is useless, then the margins will drop fast due to competition.
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.
Grab gpt-oss-120b, run it continuously and see how far 20 dollars worth of that gets you. People definitely use much more than that in a month, not just power users but regular ones, and they're using models that are more expensive to run (plus the "cloud" markup).
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.
Uhhh, at this order of magnitude? No way.
- What are the margins of Anthropic over their API pricing? Without this, all we're saying is the API is more expensive for heavy usage
- How have their price margins changed over time? I imagine built into their commercial model is the expectation for inference to get cheaper over time
- They have tighter usage windows than they used to, now having both 5-hour and weekly limits, they also seem to experiment with their usage quite often, this probably affects user's average utilisation, do they have any other levers they can pull here? eg how does changing to an 8-hour window affect it, or limiting certain models to API-only usage based on capacity like Fable
I know there is a certain level of subsidised usage built into their subscriptions, the VC-funded company playbook, but I don't think anyone from the outside knows for sure how much it is and I imagine it's lower than most people think, and reducing over time.
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…
Oh wait.
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…