Distilling just works too well. It costs much more to aggregate the data in the first place and train the first model, but once that exists and you are selling access, the second guy can train their model by using the first model to generate the training data.
The only ways to fight this is trying to either: 1. Block it by cutting off access, which is pretty tough because it basically means you cannot offer unmonitored access which is a no-no to a lot of customers, and even then the workload looks very similar to actual real workloads and if the company doing the distilling is a bit sneaky and using intermediaries etc it's almost impossible to stop, or:
2. Try to use IP law to block the practice, which is pretty gross given how they got the data in the first place, and so far no court has supported them on this. And even if they can make it illegal, it won't actually help because there are countries that see having top-tier AI as a national priority and will actually support their companies breaking laws to get there.
People misunderstand why Anthropic is being so stingy about granting access to their top models. It's not some kind of marketing ploy, they know that if they release them fully openly then there will be a Chinese open-weights model in 3 months that is just as good. Also the reason why they try to hide chain-of-thought and the probability distributions.
Anthropic has made a lot of noise about this but has not offered a shred of proof. CEO about to do IPO is not a trustworthy source.
Given Anthropic is basically just “distilling” the IP of others they’re in no position to cry foul when others run the same playbook on Anthropic IP. They can pound sand on that front.
> the second guy can train their model by using the first model to generate the training data
Isn't this somewhat akin to a model learning from its own inputs? Doesn't that behavior lead to model collapse? Either the model is trained on pristine data, in which case it stagnates, or it's distilled from another model, which leads to model collapse.