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Training C-3PO

Many of the discussions about fair use for AI training are either “AI is stealing content” or “everything on the web is free”, and not much in between. Let’s explore it with a thought experiment.

First, review how blogging and fair use has worked since the beginning of the web. Every day I read a bunch of news articles and blog posts. If I find something I want to write about, I’ll often link to it on my blog and quote a few sentences from it, adding my own comment. Everyone agrees this is a natural part of the web and a good way for things to work.

Another example is Cliff Notes. Humans read the novel 1984 and then write a summary book of it for lazy students, with quotes from the original. This is fine. It indirectly benefits the original publisher as Cliff Notes brings some more attention to the novel, and some people pick up their own copy.

Now, imagine that C-3PO is real. It could be any intelligent robot, but everyone is familiar with Star Wars. C-3PO knows a lot about language, and he has emotions and personality quirks, but otherwise he learns like the rest of us: through experience.

C-3PO could sit down with thousands of books and web sites and read through them. If we asked C-3PO questions about what he had read, and then used some of that in our own writing, that seems like fair use of that content. Humans can read and then use that knowledge to create future creative works, and so can C-3PO. If C-3PO read every day for years, 24 hours a day, gathering knowledge, that would be fine too.

Is that different than training an LLM? Yes, in at least two important ways:

• Speed. It would take a human or C-3PO a long time to read even a fraction of all the world’s information.
• Scale. Training a single robot is different than training thousands of AI instances all at once, so that when deployed every copy already has all knowledge.

Copyright law says nothing about the speed of consumption. It assumes that humans can only read and create so much, because the technology for AI was science fiction when the laws were written. In fact, computers were also science fiction.

And copyright law only applies to humans anyway, right? I’m not sure. When our C-3PO was reading books in the above example, I don’t think anyone was shouting: “That’s illegal! Robots aren’t allowed to read!”

The reality is that something has fundamentally shifted with the breakthroughs in generative AI and possibly in the near future with Artificial General Intelligence. Our current laws are not good enough. There are gray areas because the laws were not designed for non-humans. But restricting basic tasks like reading or vision to only humans is nonsensical.

If speed of training is the problem — that is, being able to effectively suck up all the world’s information in weeks or months — where do we draw the line? If it’s okay for an AI assistant to slowly read like C-3PO, but not okay to quickly read like with thousands of bots in parallel, how do we even define what slow and quick are?

Now let’s take scale. If scale is the problem — that is, being able to train a model on content and then apply that training to thousands or millions of exact replicas — what if scale is taken away? Is it okay to create a dumb LLM that knows very little, perhaps having only been trained on licensed content, and then create a personal assistant that can go out to the web or e-books and continue learning, where that training is not contributed back to any other models?

In other words, can my personal C-3PO (or, let’s say, my personal ChatGPT assistant) crawl the web on my behalf, so that it can get better at helping me solve problems? I think some limited on-demand crawling is fine, in the same way that opening a web page in Safari and reading it in Reader Mode without ads is also fine. As Daniel Jalkut mentioned in our discussion of Perplexity on Core Intuition, HTTP uses the term user-agent for a reason. Software can interact with the web on behalf of users.

That is what is so incredible about the open web. While most content is under copyright by default, and some is licensed with Creative Commons or in the public domain, everything not behind a paywall is at least accessible. We can build tools that leverage that openness, like web browsers, search engines, and the Internet Archive. Along the way, we should be good web citizens and respect robots.txt, not hit servers too hard when crawling, and identify what our software is so that it can be blocked or handled in a special way by servers. This can’t be stressed enough. The solution to these problems is for AI companies to respect the conventions that have made the open web a special place. Respect and empower creators. And for creators, acknowledge that the world has changed. Resist burning everything down lest open web principles are caught in the fire. Many advocates for the open web and publishers are saying that generative AI is a threat to the open web. That we must lock down content so it can’t be used in LLM training. But locking content is also a risk to the open web, limiting legitimate crawling and useful tools that use open web data.

Finally, consider Google. If LLMs crawling the web is theft, why is Google crawling the web not theft? Google has historically been part of a healthy web because they link back to sites they index, driving new traffic from search. However, as Nilay Patel has been arguing with Google Zero, this traffic has been going away. Even without AI, Google has been attempting to answer more questions directly without linking to sources.

I don’t have answers to any of these questions. But I love building software for the web. I love working on Micro.blog and making it easier for humans to blog. Generative AI is a tool I’ll use when it makes sense, and I hope we can have a thoughtful discussion about how it should be trained and deployed.