riknos314

How much of that revenue is usage from hosting partners (AMZN, M$, etc) who have a vested interest in using Anthropic instead of open weights though?

Back when I was at Amazon they used Anthropic models almost exclusively. Not sure how much that's changed in the last year.

Great question. Nobody knows.

The cool thing about models designed for cheaper inference is that post-training (which often requires inference as a part of the feedback loop) becomes cheaper as well, so almost certainly cheaper than models that are less optimized.

Deepseek prices their models based on a 10 month hardware payback period. They report ~$38 revenue/GPU-hour on V4.1-Flash clusters.

Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.

> I suspect that machines to be programmed in our native tongues —be it Dutch, English, American, French, German, or Swahili— are as damned difficult to make as they would be to use.

He's still quite correct. I generally see a machine as being "programmed in" whatever is tracked in source control. So as long as your git repo is tracking changes in Java, that's still programmed in Java, even if the Java was generated with the assistance of LLMs. I may consider the thesis of this article invalidated only when the majority of repos start tracking natural language rather than programming languages as the primary artifacts defining behavior of the software.

> And we should also ban user tracking for advertising purposes, obviously.

One of the biggest metrics businesses track is repeat purchases. They don't want to give up that info because it tells them if their product is good enough to retain customers.

So the token must have some means of connecting purchases for a user across time (at least per merchant) or businesses will never adopt.

My car doesn't require a dedicated team of engineers to start and has a trunk to hold groceries :P

The question isn't really if the speed is bad, it's what tradeoffs you need to make to realize it. The F1 car has a very narrow operating condition and so can make tradeoffs that are completely unreasonable for vehicles that need to do other things.

From what cloudflare is claiming, Tsrs gives me more speed without tradeoffs I care about, so why not have the free speed?

That strategy only works for model generations that are released with open weights. If Kimi K4 is proprietary, then China has the newer weights and the EU does not.

It's also worth noting that more post-training does not change the model architecture. No amount of post-training on deepseek v4-flash will ever make it as efficient as deepseek v4.1-flash because 4.1-flash uses a fundamentally different architecture. Being stuck on an older base model means you don't keep up with these architectural improvements that unlock significant gains in speed / efficiency.

> This is just higher performance.

More performance from the same hardware means better margins. Deepseek just gave every third-party inference provider a more efficient stack for free. They also get the whole provider community working with them to make inference even more efficient.

Both the free (seems limited to ~200k context) and metered variants in opencodego show the release date as 1788998400000 → 2026-09-10, which implies they're not the newer checkpoint