The ARR were fine but showing skewed quarterly profitability numbers by slowing down research due to hitting compute capacity suggests otherwise.

I am certain Anthropic spent less on building the next model this quarter if they make it to profitability due to the shear fact that they don't have enough compute.

Which solves the profitability problem with relative ease momentarily.

Also just to confirm, AI subscriptions are definitely being sold at a loss how big I don't know but these models are much harder to run.

API is definitely being sold at a decent profit.

So if you rate limit users and do usage billing + lower research costs which is a money pit temporarily.

(Proof is the fact that we don't have a new pre training run since 4.5 yet, they used to do one every 2 releases)

4.9 will probably be the same.

Next model Mythos doesn't seem to have a successor yet and was trained previous quarter most likely, they don't seem to have pre trained another one just improved Mythos if at all.

As much as I am into AI these attempts to show that there can be a profitable quarter seem like cooking the books, even if we assume no shady dealings otherwise.

Unless one of the Labs can say for certain training is going to stop they can't be profitable and I don't think training can stop because marginal gains is all they have.

8-12 months behind narrative for Chinese labs literally is going to kill the company that stops training first.

If we assume only a 3-6 month gap once China has more compute, then well then even if they keep training the lack of ability to arbitarily scale data centers in US, will kill them first.

DeepSeek V5 might actually just end the AI race for good.

Also given Mythos is atleast a 10x model compared to Opus, then it's pricing is likely going to be 10x as well so well token prices are likely never coming down, especially if these companies want to IPO.

>The ARR were fine but showing skewed quarterly profitability numbers by slowing down research due to hitting compute capacity suggests otherwise.

I have to say, I find this really puzzling. We know for a fact that Anthropic are making bank on metered inference. That's their biggest source of profitability, we are seeing software companies start to majorly adopt coding agents over just the last few months.

Right as the biggest driver of enterprise adoption is accelerating, and it's tied to their biggest profit vector, you find it suspect that their profits are increasing significantly?

Also, can you clarify what you mean by "slowing down research" exactly? Do you mean they're not doing big pretraining runs? Less compute available for researchers? Scaled back RL?

>Also just to confirm, AI subscriptions are definitely being sold at a loss how big I don't know but these models are much harder to run.

Maximum usage of AI subscriptions is a loss, but do we actually know how that nets out? Has anyone done any research to try to figure that out?

Why would V5 kill the AI race? Do you believe that there are diminishing returns on model intelligence when applied to real-world tasks?

I think there are accelerating returns: i.e. a models are still not good enough to be “drop in” remote workers, but once that threshold is passed, the value of each token of inference has a far higher multiplier.

This justifies the buildup. However not everyone agrees that model intelligence will continue scaling thus they assert that eventually the economics will hit a wall.

>Also given Mythos is atleast a 10x model compared to Opus, then it's pricing is likely going to be 10x as well so well token prices are likely never coming down, especially if these companies want to IPO.

I don't know why people say this when cost per unit of intelligence has been going down continuously over the past few years. When Opus 3 was first released, its API cost was $15.00 per million input tokens and $75.00 per million output tokens. Opus 4.8. which is significantly better, is $5.00 per 1 million input tokens and $25.00 per 1 million output tokens