The Irony of 'You Wouldn't Download a Car' Making a Comeback in AI Debates
The Irony of 'You Wouldn't Download a Car' Making a Comeback in AI Debates
Those claiming AI training on copyrighted works is "theft" misunderstand key aspects of copyright law and AI technology. Copyright protects specific expressions of ideas, not the ideas themselves. When AI systems ingest copyrighted works, they're extracting general patterns and concepts - the "Bob Dylan-ness" or "Hemingway-ness" - not copying specific text or images.
This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages. The AI discards the original text, keeping only abstract representations in "vector space". When generating new content, the AI isn't recreating copyrighted works, but producing new expressions inspired by the concepts it's learned.
This is fundamentally different from copying a book or song. It's more like the long-standing artistic tradition of being influenced by others' work. The law has always recognized that ideas themselves can't be owned - only particular expressions of them.
Moreover, there's precedent for this kind of use being considered "transformative" and thus fair use. The Google Books project, which scanned millions of books to create a searchable index, was ruled legal despite protests from authors and publishers. AI training is arguably even more transformative.
While it's understandable that creators feel uneasy about this new technology, labeling it "theft" is both legally and technically inaccurate. We may need new ways to support and compensate creators in the AI age, but that doesn't make the current use of copyrighted works for AI training illegal or unethical.
For those interested, this argument is nicely laid out by Damien Riehl in FLOSS Weekly episode 744. https://twit.tv/shows/floss-weekly/episodes/744
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The argument seem most commonly from people on fediverse (which I happen to agree with) is really not about what current copyright laws and treaties say / how they should be interpreted, but how people view things should be (even if it requires changing laws to make it that way).
And it fundamentally comes down to economics - the study of how resources should be distributed. Apart from oligarchs and the wannabe oligarchs who serve as useful idiots for the real oligarchs, pretty much everyone wants a relatively fair and equal distribution of wealth amongst the people (differing between left and right in opinion on exactly how equal things should be, but there is still some common ground). Hardly anyone really wants serfdom or similar where all the wealth and power is concentrated in the hands of a few (obviously it's a spectrum of how concentrated, but very few people want the extreme position to the right).
Depending on how things go, AI technologies have the power to serve humanity and lift everyone up equally if they are widely distributed, removing barriers and breaking existing 'moats' that let a few oligarchs hoard a lot of resources. Or it could go the other way - oligarchs are the only ones that have access to the state of the art model weights, and use this to undercut whatever they want in the economy until they own everything and everyone else rents everything from them on their terms.
The first scenario is a utopia scenario, and the second is a dystopia, and the way AI is regulated is the fork in the road between the two. So of course people are going to want to cheer for regulation that steers towards the utopia.
That means things like:
- Fighting back when the oligarchs try to talk about 'AI Safety' meaning that there should be no Open Source models, and that they should tightly control how and for what the models can be used. The biggest AI Safety issue is that we end up in a dystopian AI-fueled serfdom, and FLOSS models and freedom for the common people to use them actually helps to reduce the chances of this outcome.
- Not allowing 'AI washing' where oligarchs can take humanities collective work, put it through an algorithm, and produce a competing thing that they control - unless everyone has equal access to it. One policy that would work for this would be that if you create a model based on other people's work, and want to use that model for a commercial purpose, then you must publicly release the model and model weights. That would be a fair trade-off for letting them use that information for training purposes.
Fundamentally, all of this is just exacerbating cracks in the copyright system as a policy. I personally think that a better system would look like this:
- Everyone gets a Universal Basic Income paid, and every organisation and individual making profit pays taxes in to fund the UBI (in proportion to their profits).
- All forms of intellectual property rights (except trademarks) are abolished - copyright, patents, and trade secrets are no longer enforced by the law. The UBI replaces it as compensation to creators.
- It is illegal to discriminate against someone for publicly disclosing a work they have access to, as long as they didn't accept valuable consideration to make that disclosure. So for example, if an OpenAI employee publicly released the model weights for one of OpenAI's models without permission from anyone, it would be illegal for OpenAI to demote / fire / refuse to promote / pay them differently on that basis, and for any other company to factor that into their hiring decision. There would be exceptions for personally identifiable information (e.g. you can't release the client list or photos of real people without consequences), and disclosure would have to be public (i.e. not just to a competitor, it has to be to everyone) and uncompensated (i.e. you can't take money from a competitor to release particular information).
If we had that policy, I'd be okay for AI companies to be slurping up everything and training model weights.
However, with the current policies, it is pushing us towards the dystopic path where AI companies take what they want and never give anything back.
You should look at the energy cost of AI. It's not a miracle machine.
I agree that this is a major concern, especially if non-renewable energy is used, and until the production process for computer technology and solar panels is much more of a circular economy. More renewable energy and circular economies, and following the sun for AI training and inference (it isn't going to be low latency anyway, so if you need AI inference in the northern hemisphere night, just do it on the other side of the world) could greatly decrease the impact.