Anthropic here is solving one of their own problems, how to make sure you don't combine other company's models with their stuff for example, and make it sound as if they did it for you.
replyIt makes the inference more expensive and helps controlling the users.
I think some model providers are implementing "eyes glaze over" where if you ask a boring simple question they route it to a cheap model but charge you for an expensive model, so consumers may want a proof of work certificate to know they're getting what they pay for.
replyI think a consumer facing system would be fantastic, but from how they're framing it in the article, it's all about internal company monitoring, with some fearmongering around LLM observability.
replyI assume this alludes to their prior work with models running on smart TVs being able to setup inference on additional smart TVs. The idea being that you can't be sure that, having gone through the whole deployment process, your hardware has not been subverted to run a different model. Any level of "robust" code you can imagine to prevent this in the traditional ways could be subverted on the fly by a capable model.
reply> This moves unobservability layer to the system that collects the proofs.
I'd say it's adding security through a secondary system, rather than displacing security to a secondary system. Security by layers.