Are "Scale" and "New Expressiion" the Key Elements for AI Copyright Issues?

New information and communications technologies have consistently raised novel challenges for copyright law and its application, so it is not surprising that artificial intelligence is raising issues as well. But most prior copyright issues seem to revolve around duplicating existing content.

The whole point of generative artificial intelligence is the creation of new content. And that would seem to place generative AI created content outside copyright infringement.

In most cases, issues arise because digital technology eases issues related to the cost of copying and sharing information. Peer-to-peer file sharing; content streaming; news aggregation and search engine results and remixing of content provide examples. 

Earlier device technologies ranging from copiers to videocassette recorders have also raised new copyright issues. But all those issues have been around the copyring and distribution of existing content.

One might argue that since generative artificial intelligence models are built and trained by essentially using web crawlers to index content found on the internet, the copyright issues center on "fair use" and infringement of that right.

That might not be so easy to apply to generative AI, whose purpose is the creation of new content, not the copying and distribution of existing copyrighted work.,

The core of all copyright issues is the principle that ideas themselves cannot be copyrighted, only their specific expressions. And generative AI challenges that framework. in new ways.

Innovation	Case	Issue	Outcome
VCR	Sony Corp. v. Universal City Studios, Inc. (1984)	Is using a VCR to record copyrighted TV shows copyright infringement?	Court ruled in favor of Sony, stating recording for later viewing was fair use.
Digital Music	Napster Inc. v. A&M Records Inc. (2001)	Is a peer-to-peer file-sharing service liable for copyright infringement by its users?	Court ruled Napster liable for failing to prevent copyright infringement.
Digital Music	Grokster, Ltd. v. MGM Studios, Inc. (2005)	Can a company that provides file-sharing software be liable for copyright infringement by its users?	Court ruled Grokster could be liable if they knew users were infringing and didn't take steps to stop it.
Electronic Publishing	Authors Guild v. Google Inc. (2012)	Does Google scanning entire libraries and allowing users to search snippets of copyrighted books constitute fair use?	Court ruled Google's scanning was a fair use for transformative research purposes.

Some will argue that the GenAI training process is analogous to how humans learn and create. Just as a human artist or writer might study and internalize various works to develop their own style and ideas, AI models analyze patterns in large datasets to generate new content. 

So AI training is transformative and falls under fair use, as it doesn't directly reproduce copyrighted material but rather learns from it to create something new.

The analogy essentially is that, if it's legal for humans to read books or view art and then use that knowledge to create new works, AI should be allowed to do the same. 

AI companies contend that their models don't copy training data but learn associations between elements like words and pixels, similar to how humans process information.

Critics argue that the scale and comprehensiveness of AI training sets it apart from human learning, so that scale itself is a new issue. Perhaps the issue there is not so much the “reading” or “viewing” of content but the issue of maintaining the market value of copyrighted work. 

And the specific new issue is simply that generative AI is so efficient at ingesting knowledge and using what it learns to create new content, compared to humans. 

Compared to older forms of technology, which generally only presented the issue of content reproduction cost, AI raises far-bigger issues about the creation of new content.