There's this[1]. Model providers have a strong incentive to switch (a part of) their inference fleet to quantized models during peak loads. From a systems perspective, it's just another lever. Better to have slightly nerfed models than complete downtime.

[1]: https://marginlab.ai/trackers/claude-code/

So - as the charts say - no statistical difference?

Isn't this link am argument against the point you are making?

The chart doesn't cover the 4.6 release which was in the end of December/early January time frame. So, it's hard to tell from existing data.

That isn't true. The whole point it to quickly pick up statistically significant variations quickly, and with the volume of tests they are doing there is plenty of data.

If you turn on the 95% CI bands you can see there is plenty of statistical significance.

Unless you and I are looking at different web pages… it only goes back to February, not December or January.

Models aren't just big bags of floats you imagine them to be. Those bags are there, but there's a whole layer of runtimes, caches, timers, load balancers, classifiers/sanitizers, etc. around them, all of which have tunable parameters that affect the user-perceptible output.

There really always is a man behind the curtain eh?

Often it's literally just that:

https://www.msn.com/en-us/money/other/ai-startup-backed-by-m...

It's still engineering. Even magic alien tech from outer space would end up with an interface layer to manage it :).

ETA: reminds me of biology, too. In life, it turns out the more simple some functional component looks like, the more stupidly overcomplicated it is if you look at it under microscope.

Anybody with more than five years in the tech industry has seen this done in all domains time and again. What evidence you have AI is different, which is the extraordinary claim in this case...