I know Anthropic support is slow from firsthand experience, but it has to be pretty difficult to scale support 10-80x per year. And even more so when you have a long-tail of very low revenue usage in the form of $20/month subscriptions.

I mean even on top of my head, I still remember when jose commented back to me and it was a highlight for a few days as I told my friend about it: https://news.ycombinator.com/item?id=44234633

> I have a fun anecdote. About 5-6 years ago, Elixir completely disappeared from the top 100 after spending some time in the top 50. People reached out to me and then I reached out to TIOBE to understand why and the reason given was "bad presence on Amazon".

> After further investigation, the root cause seemed to be that we finally had enough published Elixir books. At the time, if you searched for "xyz programming" on Amazon and only found a few results, Amazon would pad those results with non-relevant entries. However, because Elixir reached about 20-30 books, we were no longer padded, so we suddenly got worse rankings than every other language with only a handful of books. This happened on every Amazon domain they searched on, so it compounded and effectively kicked us out of the top 100 altogether. This all happened at a time Elixir language activity had already reached top 25 on GitHub PRs/stars.

So although my comment has gotten a little offtopic but people have literally written books about elixir (the language he created).

My point is, people like to listen to jose and he's a really chill guy from what I know of him and elixir feels like a great language :-D

Uh, that’s a good thing

I think its assumed in the LLM model business that the models themselves are not a good moat, the next model by another company is just as likely to be as good as the current model. So companies like Anthropic have to tighten the noose slowly to start recovering their costs. This appears to be one of those steps.

To me the bigger takeaway is that these business are seeing massive volume in use and figuring out how to price the products accordingly.

The problem is that today's AI companies have taken on so much funding that a reasonable, not crazy profit ratio isn't enough for them.

Someone has to pay the 7 trillion (the current projections for the AI datacenter build up)