Seems like a pretty pointless post that still centers around output tokens.
In agentic coding, cached input tokens is 90% of the API "cost". It doesn't require GPU compute, and DeepSeek has shown that it can be done 50~100x cheaper with MLA/CSA/HCA, and a whole bunch of disks. This should collapse the margin.
> MLA/CSA/HCA
Aren’t these techniques all “lossy” compression, and one of the reasons people complain about loss in quality as the context size grows larger?
The current top comment in https://lobste.rs/s/ua1gxl/glm_5_2_coming_ai_margin_collapse correctly zoomed into cached input tokens, but landed on the opposite conclusion:
> That is, for your $100/month fee, you get $3600 equivalent of API usage. This is presumably because Anthropic has figured out some clever things to do with model routing and input caching, and also can subsidize with investor money and take a hit on their operating margins.
My take: this is exactly what Anthropic wants everyone to think. In reality, 90% of that $3600 are for cached input tokens, that can be made to cost next to nothing, as shown by DeepSeek.
Aren't the American AI labs desperately struggling to find a market beyond just agentic coding?