Copyright Office’s AI Report: The Good, The Bad, and The Controversial
The Copyright Office just dropped Part 3 of its AI report, which aimed at addressing certain copyright law in regards to Artificial Intelligence. The thing that’s got everyone talking is the fact that the report was supposed to tackle infringement issues head on, but instead teased us by saying that answer will come up in “Part 4” that is expected to be released at a later date. Let’s dive into what was actually discussed.
Legal Theory: A Case by Case Basis
The report’s central thesis is a pretty straightforward legal theory. Basically, they recommend that there will be no blanket rule on whether training AI on copyrighted content constitutes infringement or fair use. Everything gets the case by case treatment, which is both realistic and frustrating depending on where you sit. That’s because most lawyers like clear bright line rules backed up by years of precedent, but when attempting to make legal frameworks regarding emerging technologies, the brightline approach is easier said than done.
The report acknowledges that scraping content for training data is different from generating outputs, and those are different from outputs that get used commercially. Each stage implicates different exclusive rights, and each deserves separate analysis. So in essence, what’s actually useful here is the recognition that AI development involves multiple stages, each with its’ unique copyright implications.
This multi stage approach makes sense, but it also means more complexity for everyone involved. Tech companies can’t just assume that fair use covers everything they’re doing and content creators can’t assume it covers nothing. The devil is in the details.
Transformative Use Gets Complicated
The report reaffirms that various uses of copyrighted works in AI training are “likely to be transformative,” but then immediately complicates things by noting that transformative doesn’t automatically mean fair. The fairness analysis depends on what works were used, where they came from, what purpose they served, and what controls exist on outputs.
This nuanced approach is probably correct legally, but it’s also a nightmare for anyone trying to build AI systems at scale. You can’t just slap a “transformative use” label on everything and call it a day. The source of the material matters, and whether the content was pirated or legally obtained can factor into the analysis. So clearly purpose also matters since commercial use and research use will likely yield different results in the copyright realm. Control and mitigation matter in this context because developing the necessary guardrails is paramount to preventing direct copying or market substitution.
Nothing too revolutionary here, but the emphasis on these factors signals that the Copyright Office is taking a more sophisticated approach than some of the more simplistic takes we’ve seen from various opinions on this matter. This should be reassuring since a one size fits all approach at such an early stage of developing AI could stifle innovation. However if things are left to be too uncontrolled copyrighted works may face infringements to their copyright.
The Fourth Factor Controversy
Here’s where things get interesting and controversial. The report takes an expansive view of the fourth fair use factor: which is the effect on the potential market for the copyrighted work. That is because too many copyrighted works flooding the market brings fears of market dilution, lost licensing opportunities, and broader economic impacts.
The Office’s position is that the statute covers any “effect” on the potential market, which is broad interpretation. But that broad interpretation has a reason, they are worried about the “speed and scale” at which AI systems can generate content, creating what they see as a “serious risk of diluting markets” for similar works. Imagine an artist creates a new masterpiece only to get it copied by an AI model which makes the piece easily recreatble by anyone, diluting the value of the original masterpiece. These types of things are happening on the market today.
This gets particularly thorny when it comes to style. The report acknowledges that copyright doesn’t protect style per se, but then argues that AI models generating “material stylistically similar to works in their training data” could still cause market harm. That’s a fascinating tension, you can’t copyright a style but you might be able to claim market harm from AI systems that replicate it too effectively. It is going to be interesting to see how a court applies these rules in the coming future.
This interpretation could be a game-changer, and not necessarily in a good way for AI developers. If every stylistic similarity becomes a potential market harm argument, the fair use analysis becomes much more restrictive than many in the tech industry have been assuming.
The Guardrails
One of the more practical takeaways from the report is its emphasis on “guardrails” as a way to reduce infringement risk. The message is clear: if you’re building AI systems, you better have robust controls in place to prevent direct copying, attribution failures, and market substitution.
This is where the rubber meets the road for AI companies. Technical safeguards, content filtering, attribution systems, and output controls aren’t just up to the discretion of the engineers anymore they’re becoming essential elements of any defensible fair use argument.
The report doesn’t specify exactly what guardrails are sufficient, which leaves everyone guessing. But the implication is clear: the more you can show you’re taking steps to prevent harmful outputs, the stronger your fair use position becomes. So theoretically if a model has enough guardrails they may be able to mitigate their damages if the model happens to accidently output copyrighted works.
RAG Gets Attention
The report also dives into Retrieval Augmented Generation (RAG), which is significant because RAG systems work differently from traditional training approaches. Instead of baking copyrighted content into model weights, RAG systems retrieve and reference content dynamically.
This creates different copyright implications: potentially more like traditional quotation and citation than wholesale copying. But it also creates new challenges around attribution, licensing, and fair use analysis. The report doesn’t resolve these issues, but it signals that the Copyright Office is paying attention to the technical details that matter.
Licensing
The report endorses voluntary licensing and extended collective licensing as potential solutions, while rejecting compulsory licensing schemes or new legislation “for now.” This is probably the most politically palatable position, but it doesn’t solve the practical problems.
Voluntary licensing sounds great in theory, but the transaction costs are enormous when you’re dealing with millions of works from thousands of rights holders. Extended collective licensing might work for some use cases, but it requires coordination that doesn’t currently exist in most creative industries.
The “for now” qualifier is doing a lot of work here. It suggests that if voluntary solutions don’t emerge, more aggressive interventions might be on the table later.
The Real Stakes
What makes this report particularly significant isn’t just what it says, but what it signals about the broader policy direction. The Copyright Office is clearly trying to thread the needle between protecting creators and enabling innovation, but the emphasis on expansive market harm analysis tilts toward the protection side.
For AI companies, this report is a warning shot. The days of assuming that everything falls under fair use are over. The need for licensing, guardrails, and careful legal analysis is becoming unavoidable.
For content creators, it’s a mixed bag. The report takes their concerns seriously and provides some theoretical protection, but it doesn’t offer the clear-cut prohibitions that some have been seeking.
The real test will come in the courts, where these theoretical frameworks meet practical disputes. But this report will likely influence how those cases get decided, making it required reading for anyone in the AI space.
As we can see AI and copyright law is becoming only more and more complex. The simple answers that everyone wants don’t exist, and this report makes that abundantly clear. The question now is whether the industry can adapt to this new reality or whether we’re heading for a collision that nobody really wants.
Sermo Civilis 