They’ve been secret serving it. Try ask if they know who tibo the reset guy is. If they know the answer it’s the new version.

Dollars to donuts, they are speculating, and not privy to inside information on the topic.

However, I believe that runtime model quantization is possible with some publicly-available inference engines (e.g. vLLM), so its not beyond belief that the closed labs do quantize at runtime, either to allocate compute, or to nudge users towards a preferred model (e.g. make the incumbent model dumber to push people to use the latest-and-greatest model, or vice versa to ease the load on the latest model, which is typically larger than the old one).

I don't work at Anthropic, but I would assume they could serve smaller quantizations during peak hours - this effectively controls the "resolution" of the model. They could also control the resolution of the KV cache, which would make the model not necessarily dumber, but worse at understanding the incoming requests. And finally, you could pass off what was "high" effort as "extra", because why not.

An AI lab will never volunteer the information because it opens them up to lawsuits if they are purposely degrading service and not letting users know.

They can limit how hard the model thinks for a given effort. Suddenly xhigh only thinks as hard as high did, and high shifts down to medium effort, and so on.

They can also serve quantized models. And this has the benefit of practically not showing up in benchmarks at all even if the user experience is obviously degraded.

The other major thing the labs do is silently drop the usage limits. This has become very noticeable for codex users who are suddenly burning through their weekly usage in a few hours.

And it's not. A conspiracy theory is what it is.

I have no reason to doubt the claims of the employees at OpenAI and Anthropic who have told us personally multiple times, including here on HN, that they do not degrade the models in order to reduce load.

As for the endlessly long analysis in the OP, it appears it's based on analyzing their random usage data rather than any fixed benchmark. I don't think it makes much sense.

Isn’t the idea that they’re limiting the amount of gpu time normal users get to spend on the “thinking” portion of their query?

I think that’s the claim in the post, that even though no one can see the true chain of thought, that even the “thinking” text that does get exposed to the user is shorter given the same prompts over time. Not saying it’s true but I think that’s the claim. I’ve personally never noticed the alleged “nerfing” with my enterprise use at work or my subscription use at home which is only during off hours.