Updating IP Regulations for AI Distillation

Andrew Marble argues that copyright law is irrelevant to AI model distillation, where companies like DeepSeek allegedly copy capabilities from models like Claude by generating training data from queries. He questions whether new regulations are needed to prevent free-riding on AI investments, noting enforcement challenges and the unnatural nature of intellectual property laws.

Andrew Marble marble.onl https://marble.onl andrew@willows.ai July 18, 2026 Copyright law isn’t “natural” in the sense that it doesn’t stem from some higher principle. It’s easy to see that laws against murder or theft derive from morals and natural rights. Copyright is more like a government subsidy, where we’ve decided that someone who creates something has the exclusive right to sell it, and the state will spend its own money to intervene to protect that exclusivity. There’s certainly a fair version of the world where you can copy anything you encounter freely and don’t owe anybody anything. The ostensible argument for copyright, as I understand it, is that, along with other “intellectual property” laws like patents, it encourages innovation. People wouldn’t create anything if nation states didn’t put their might behind making sure nobody copied it. Anyway, copyright is unnatural – it’s something we collectively decided on to try and balance personal freedom with promoting creativity. One can argue about how effective it is, if it’s the right balance, etc. but this is where we are. What I think is clear, is that it’s completely irrelevant to AI model training. I want to focus specifically on “distillation” where someone effectively copies some part of an AI model’s characteristics by generating lots of text or other outputs with it and using that to train a new model. This is what some of the Chinese labs have been accused of doing to create their open models. Companies like Anthropic claim that the Chinese AI providers run millions of Claude queries to get the conversation traces the need for training, essentially copying the capabilities of Claude or GPT or others, instead of training models from scratch themselves. The implication is that this is unfair, it takes the hard work of others and profits from it, without compensating them. So if for example, Anthropic has to pay $1 Billion to get data labelers to provide datasets, to invent training techniques, etc., someone like DeepSeek could come along and spend a small fraction of that to exfiltrate conversations from Claude that encompass that knowledge and use them to train their model. Since they spend comparatively little, they can charge way less, undercutting Anthropic who would have liked to be a monopolist or oligopolist . In the long run, companies like Anthropic won’t want to invest anymore because they know copycats will simply undercut them, there will be no more innovation, etc. etc. The interesting question for me, is what, if any, state intervention here makes sense. Copyright does not generally appear to be relevant to AI model training, for a number of reasons, not the least of which is the model is not a copy, and that the capability to violate copyright is not the same as violating copyright. Successful copyright cases have generally involved things like training on “pirated” works instead of paying for them; once a work is purchased, training on it is fair game, just like it’s ok to read a book and remember the plot after. So my question is, do we need another kind of bargain, another unnatural law created as a compromise to spur innovation in relation to AI. It seems that there are ongoing attempts at this including some that are roundabout or subversive, like trying to frame things in terms of national security. So it might be valuable to have a more open discussion about what kind of tradeoff would be appropriate. Some positions that come to mind are: No distillation : outlaw in the same sense as copyright training on model output unless specifically licensed. This would essentially be putting the weight of the state behind current terms of use that models have. The challenge is that it would be extremely hard to enforce. If I start sharing songs online, it’s easy to check if they are copyrighted. It’s not really possible to tell definitively that a model has been distilled or that traces have been used in training, so cases would be less black and white and hinge on expert analyses, etc. instead of just common sense. Permissive / do nothing : Given the unnaturalness of it, there is no moral or higher imperative to do anything about distillation. We can just let it happen. This would keep the current competitive environment to the extent that distillation is even relevant which is currently producing better models every week from a variety of sources. The counterargument is that over time innovation will slow because there are not the incentives to keep investing if it doesn’t provide an edge. And unfortunately, doing nothing creates a vacuum in which people will want to “do something ” and the something might be worse if we don’t tackle it head on. See the next point Oblique rules targeting distillation : this I’ll come out and say is the worst option but may be the most likely. Instead of actually tackling intellectual property concerns, we instead fight a proxy battle over “safety” or “security” and make regulations restricting open-source models in the name of these fabricated concerns. This would directly play into the hands of the big incumbents by outlawing the concept of their competitors, instead of specific practices. And ironically it would be completely opposite to the intent of copyright with respect to spurring innovation, instead it would lead to a close ecosystem where big incumbents dictated what models we used and how. Functionality based rules : Right now LLMs are basically all the same. There would be a stronger argument to protect advances if someone actually made one with an apparent difference. I’m wondering if, as LLMs go from commodity to product, the normal IP rules related to products will take more of a role and the training procedure will be less relevant. If I were to make a coding agent with a particular look and feel or functionality, and a competitor blatantly copied it, I think I’d already have recourse under copyright and trademark law. Right now we’re so caught up in trying to maximize benchmark scores as opposed to doing useful work, the distillation thing takes more of an important role. But once we actually get some products, this might be less important. I can even see how if someone else trained a model that copied Claude’s trademark smugness and condescension, there would be a case for an infringement action. There may be other models here too. There’s not necessarily a wrong answer except the “regulatory capture to protect us from danger” one . The point I want to make is that, like we once did with copyright, there is no reason we can’t make new tools that specifically address a new threat to innovation if this is what we think . We don’t have to only use existing constructs. Nor do we have to do anything if we think the current evolution is working. It would be nice though for broader society to come to some kind of agreement before US AI companies just write one that benefits them.