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But do they? When was the last time they declined your subscription because they have no compute?
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> When was the last time they declined your subscription because they have no compute?

Is that a serious question? There have been a bunch of obvious signs in recent weeks they are significantly compute constrained and current revenue isn't adequate ranging from myriad reports of model regression ('Claude is getting dumber/slower') to today's announcement which first claims 4.7 the same price as 4.6 but later discloses "the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens" and "we’ve raised the default effort level to xhigh for all plans" and disclosing that all images are now processed at higher resolution which uses a lot more tokens.

In addition to the changes in performance, usage and consumption costs users can see, people say they are 'optimizing' opaque under-the-hood parameters as well. Hell, I'm still just a light user of their free web chat (Sonnet 4.6) and even that started getting noticeably slower/dumber a few weeks ago. Over months of casual use I ran into their free tier limits exactly twice. In the past week I've hit them every day, despite being especially light-use days. Two days ago the free web chat was overloaded for a couple hours ("Claude is unavailable now. Try again later"). Yesterday, I hit the free limit after literally five questions, two were revising an 8 line JS script and and three were on current news.

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Just last week. They cut off openclaw. And they added a price increased fast mode. And they announced today new features that are not included with max subscriptions.

They are short 5GW roughly and scrambling to add it.

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Now. Is it price increase or resource shortage. These are not the same thing.
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If there is any elasticity to demand whatsoever, then these are the same thing.
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IT's cute you think they're gonna do any full training of a model. As soon as they can extract cash from the machine, the better.
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This is low effort thinking, and a low effort comment. They have a lot of cash. They do not think they have achieved a "city of geniuses" in a datacenter yet. They are racing against two high quality frontier model teams, with meta in the wings. They have billions of dollars in cash that they are currently trying to spend to increase their datacenter capacity.

Any compute time spent on inference is necessarily taken from training compute time, causing them long term strategic worries.

What part of that do you think leads toward cash extraction?

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