Speaker 1: Hello, and welcome to the Boats and Verdicts podcast hosted
Speaker 1: by the litigation and policy team at Bloomer Intelligence, an
Speaker 1: investment research platform of Bloomberg LP. Bloomberg Intelligence has five
Speaker 1: hundred analysts and strategists working across the globe and focused
Speaker 1: on all major markets. Our coverage includes over two thousand
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Speaker 1: this podcast series examines the intersection of business policy and law.
Speaker 1: I'm Tamlin Basin. I'm an analyst with Bloomberg Intelligence covering
Speaker 1: intellectual property litigation impacting the tech sector. Now, often that
Speaker 1: means a focus on patent litigation, but right now we're
Speaker 1: in a period of a rising tide of copyright litigation
Speaker 1: that could have profound impacts on the singular technology issue
Speaker 1: of the past few years, that being artificial intelligence, more
Speaker 1: specifically generative AI, which is made capable by large language
Speaker 1: models that are trained on massive data sets. Now, the
Speaker 1: sticking point is that much of that training data was copyrighted,
Speaker 1: and we've seen dozens of lawsuits pop up in the
Speaker 1: US brought by rights holders to some of those data
Speaker 1: sets and against the developers of those large language models.
Speaker 1: Today we're going to explore some of those lawsuits, and
Speaker 1: we're going to spend quite a bit of time talking
Speaker 1: about something called fair use. And we're going to be
Speaker 1: doing all of this with the very capable help of
Speaker 1: Jeremy Goldman, partner and co chair of the Emerging Technology
Speaker 1: Group with the law firm Frankfurt Kernat Klein and Seals. Jeremy,
Speaker 1: thanks so much for joining us today. Now let's start
Speaker 1: with fair use. This seems to be the primary defense
Speaker 1: that's going to be put forward by developers of large
Speaker 1: language models. Now, fair use seems like a bit of
Speaker 1: a get out of jail card, and that it doesn't
Speaker 1: mean that the rights holders copyrights weren't in fringe, but
Speaker 1: it does seem to mean that the infringement was justified
Speaker 1: by the end use and geremany, what's someone accused of
Speaker 1: copyright infringement have to show in order to get that
Speaker 1: fair use protection?
Speaker 2: Yeah, well, thanks Tomlin for having me and happy to
Speaker 2: talk about these cases and fair use. You know first,
Speaker 2: just you know it, actually, fair use is encoded in
Speaker 2: the United States Copyright Act in a section called you know,
Speaker 2: seventeen USC. One oh seven, and that section actually says
Speaker 2: that even though you might exercise one of the exclusive
Speaker 2: rights of copyright, even though you might make a copy,
Speaker 2: even though you might make a distribution of somebody's copyright
Speaker 2: protected work, with section one of seven says that even
Speaker 2: if you exercise one of those exclusive rights that make
Speaker 2: up the bundle of sticks that is a copyright, that
Speaker 2: it's not an infringement. It's actually not an infringement, and
Speaker 2: it's fair use. And it just says if it's a
Speaker 2: fair use, And then it says, well, what is a
Speaker 2: fair use? And it says, well, it's a case specific
Speaker 2: question about whether it's fair use. And Congress tells courts
Speaker 2: that we're going to look at four factors to decide
Speaker 2: in each instance, and they're non exclusive, but there's four
Speaker 2: factors that they say courts should look at whether it's
Speaker 2: fair use, and they're all encoded in the statute. We
Speaker 2: can go through the four of them, but that's basically
Speaker 2: in all the cases involving fair use, courts will look
Speaker 2: and examine each of the four factors, weigh them, and
Speaker 2: then decide whether on balance those factors tilt in favor
Speaker 2: of it being a fair use or not being a
Speaker 2: fair use.
Speaker 1: Yeah, and I think that's excellent important that this is condified.
Speaker 1: But at the same time, it's very much is a
Speaker 1: doctrine that has evolved through case law. I think it's
Speaker 1: fair to say. I mean, there's there's been a number
Speaker 1: of high profile cases in fair use, both in the
Speaker 1: circuit courts and of course at the Supreme Court, but
Speaker 1: probably relevant to what we're talking about today, to the
Speaker 1: development of large language models, there's probably a few to
Speaker 1: the standout, and I think one of those was the
Speaker 1: Google Books litigation. Can you maybe tell us what that
Speaker 1: litigation was about and why there might be some parallels
Speaker 1: to these large language models.
Speaker 2: Sure, and you know, I'm happy to talk about the
Speaker 2: Google Books litigation. I'll also mention that I was a
Speaker 2: litigator that was that worked on those cases, and in
Speaker 2: those cases I represented the Author's Guild in lawsuits that
Speaker 2: were brought against both Google and against a consortium of
Speaker 2: libraries that worked with Google and connection with the Google
Speaker 2: Books program. My views today are just talking as a
Speaker 2: lawyer to appinn on sort of the decisions that came
Speaker 2: out in those cases, and certainly don't represent the views
Speaker 2: of the author's guild, or the views of any parties
Speaker 2: in those cases, or even the views of my law firm.
Speaker 2: But I am happy, and I'm very familiar with those
Speaker 2: cases and those decisions. So with that disclaimer, let me
Speaker 2: just talk about those cases. Google made its mission to
Speaker 2: make the entire world searchable, right, That was the goal
Speaker 2: of Google was to make the entire world searchable. They
Speaker 2: started with the Worldwide Web, of course, and you know,
Speaker 2: you put into Google and put in search results and
Speaker 2: get results back from the Web. And to do that
Speaker 2: they had to copy, you know, essentially the entirety of
Speaker 2: the World Wide Web in order for that to happen. Well,
Speaker 2: Google decided that, you know, maybe that's not enough, and
Speaker 2: we also want to make all of the world's knowledge,
Speaker 2: including all of the books of the world, searchable. So
Speaker 2: in order to do that, Google partnered with major university
Speaker 2: libraries around the country, and they entered into agreements with
Speaker 2: them to go to the libraries and take all of
Speaker 2: their books, millions upon millions of books, many many of
Speaker 2: which were protected by copyright still, and they set up
Speaker 2: scanning stations and they scanned and reproduced and made copies
Speaker 2: of millions upon millions of books in order to digitize
Speaker 2: them and then run them through an optical character recognition
Speaker 2: process and OCR process, and then ultimately for the purpose
Speaker 2: of creating a search index so that when you do searches,
Speaker 2: either on books dot Google dot com or even on
Speaker 2: the main Google search engine, if you find keywords that
Speaker 2: hit on words within the books, you would be able
Speaker 2: to find those books. And what Google was doing was
Speaker 2: displaying a snippet of where the keywords were located within
Speaker 2: a book, and so you'd see that, oh, it was
Speaker 2: in this book on this page or whatever. And the
Speaker 2: other side the university libraries, as part of the deal,
Speaker 2: they also got access to this search index, and there
Speaker 2: was a website called hoti Trust where you could put
Speaker 2: in keywords and they didn't show snippets, but they did
Speaker 2: show that in this book on this page, you'd be
Speaker 2: able to find that information. The university libraries also decided
Speaker 2: that as part of this process, they were going to
Speaker 2: start making certain works that were quote orphan works available
Speaker 2: to members of the university library to make them available
Speaker 2: online well authors around the country. Indeed, authors around the
Speaker 2: world were not happy that Google was digitizing and scanning
Speaker 2: and making use potentially for commercial purposes through Google, millions
Speaker 2: and millions of books without permission. And you know, books
Speaker 2: being at sort of the core of what copyright protects,
Speaker 2: protects the rights of authors, it protects the exclusive rights
Speaker 2: of authors. And they were felt that this was a
Speaker 2: violation of their copyright, and so they filed lawsuits against Google.
Speaker 2: They filed lawsuits against the university libraries alleging that the
Speaker 2: digitization and scanning of the books was copyright infringemen. And
Speaker 2: in those cases, Google and the university libraries defended themselves
Speaker 2: primarily on the ground that, yes, we made copies, but
Speaker 2: those copies were a fair use, and they argued that
Speaker 2: it was transformative that books were written and published for
Speaker 2: one purpose, and we were using them for quite a
Speaker 2: different purpose, which was to make them searchable. Also, they said,
Speaker 2: to make them available for people that were blind was
Speaker 2: another example of what they were doing. And the court,
Speaker 2: ultimately the court the case went up to the Second
Speaker 2: Circuit Court of Appeals, and the court found that indeed
Speaker 2: it was a fair use and held that this digitization
Speaker 2: for the purpose of making it searchable was a fair use.
Speaker 1: Yeah, and I think you sort of alluded to it
Speaker 1: then and Ultimately it did go up to the Second Circuit,
Speaker 1: but this was by no means a swift resolution of
Speaker 1: a case. I recall it going on for years and years.
Speaker 1: There was potential settlements that I believe were ultimately rejected
Speaker 1: by the court. But I think what to some extent,
Speaker 1: what they showed is that these can be very difficult issues.
Speaker 1: It was to sort of resolving court. Their use is
Speaker 1: kind of a squishy content. There's very few bright line
Speaker 1: rules totally.
Speaker 2: And i'd also at at a high level, you know,
Speaker 2: that case sort of not just on the legal points,
Speaker 2: does it have analogies to the current cases that authors
Speaker 2: and creators are filing against the AI companies, but on
Speaker 2: a on a feeling level, on an icky level, there's
Speaker 2: a very similar sentiment where there was a sentiment that
Speaker 2: you know, authors who are really you know, the Copyright
Speaker 2: Act itself and indeed in the Constitution, it talks about
Speaker 2: giving authors exclusive rights over their works for limited periods
Speaker 2: in order to promote progressive science in the art. That's
Speaker 2: the constitutional mandate of the Copyright Act, and authors, you know, felt,
Speaker 2: here's this technology company coming in and scanning and digitizing
Speaker 2: and making use and profiting off of our work and
Speaker 2: they're claiming that it's use and that just seems wrong.
Speaker 2: And I feel like there's the same kind of sentiment
Speaker 2: now with the you know, the AI cases that we'll
Speaker 2: get into.
Speaker 1: Yeah, I think that's absolutely right. And I guess just
Speaker 1: to drill down a little bit further on sort of
Speaker 1: you mentioned it right there, the transformative use. I believe
Speaker 1: that's ultimately how Google was decided. But that's only one
Speaker 1: kind of of the fair use factors. It's sort of,
Speaker 1: I think, kind of baked in the and and the
Speaker 1: fairs for you factor you want to really quickly sort
Speaker 1: of run us through those fair use factors.
Speaker 2: Let me run through the fair use factors, and also
Speaker 2: in doing so, we'll talk about transformative use, because indeed
Speaker 2: that word of transformativeness doesn't appear in the fair use factors.
Speaker 2: That's a judicial creation. It's a creation really of a judge.
Speaker 2: Judge levolved in from a Harvard Law Review article that
Speaker 2: he wrote about fair use, and then the Supreme Court
Speaker 2: picked it up in a case involving a two life
Speaker 2: cruse song. I won't go too far down that road,
Speaker 2: but just to just to tick through the factors, okay,
Speaker 2: I don't have it in front of me, but I
Speaker 2: think I know it by hard, right, Which is the
Speaker 2: first factor is the purpose at and character of the use.
Speaker 2: The second factor is the nature of the work and
Speaker 2: the nature of the work. And well, we'll go back
Speaker 2: to the first factor because it's kind of the most important,
Speaker 2: So I'll go through the four and then we'll go
Speaker 2: back to the first. So purpose and character of the use.
Speaker 2: The second one is the nature of the copyrighted work,
Speaker 2: which is really like how close is it to the
Speaker 2: core of copyright protection? And then you could think of
Speaker 2: like a fictional work versus a nonfictional work. Copyright doesn't
Speaker 2: protect ideas, so copyright will be more steadfast and protecting
Speaker 2: a fictional work or a highly creative illustration, for example,
Speaker 2: right versus something like an encyclopedia. The third factor is
Speaker 2: the amount of substantiality of the use, right, how much
Speaker 2: of it was used in relation to the whole sort
Speaker 2: of a quantitative question. And then the fourth is the
Speaker 2: harm of the use on the market. And so that's
Speaker 2: really like a damages thing, like how harmful is this?
Speaker 2: And really courts tend to weigh very strongly. The first factor,
Speaker 2: which is the purpose in character and the fourth factor. Now,
Speaker 2: just going back, like I said, to the first factor,
Speaker 2: when we talk about the purpose of the use, the
Speaker 2: way that this has as courts have sort of dealt
Speaker 2: with this over the years, they've articulated the first factor
Speaker 2: and broken it into two pieces. First, is is it
Speaker 2: a commercial use or a non commercial use? Until recently
Speaker 2: that factor, of that sub factor of factor one, got
Speaker 2: very little, actually very little kind of deference. Right. People
Speaker 2: often think, oh, if it's a nonprofit organization, they're going
Speaker 2: to get a lot of difference. If it's a school,
Speaker 2: if it's an educational institution. Indeed, that does provide some
Speaker 2: it does provide some weight, but ultimately that's very rarely
Speaker 2: been dispositive in a court's analysis about whether it's commercial
Speaker 2: or non commercial with the US is the most important
Speaker 2: factor and what courts have paid the most attention to
Speaker 2: and what starts getting to the core of modern fairies
Speaker 2: jurisprudence is the purpose of the use. And there what
Speaker 2: the Supreme Court has said in this case of originally
Speaker 2: involving the case involving two Life Crew and a kind
Speaker 2: of a parody of what was held to be a
Speaker 2: parody of the song Pretty Woman by Roy Orbison, and
Speaker 2: a dirty version by Two Life Crew. The Court held
Speaker 2: that some like a parody is transformative because it's taking
Speaker 2: the original work and it's commenting on the original and
Speaker 2: shedding new light on the original, sort of adding something new,
Speaker 2: and it is the new work has a new purpose
Speaker 2: from the original. It has transformed the work from what
Speaker 2: it was before into what it was later. And the
Speaker 2: Supreme Court weighed in on that in the mid nineties
Speaker 2: in that Two Life Crew case, and hadn't really weighed
Speaker 2: in on it again until just what last year or
Speaker 2: two years ago when they decided that a work involving
Speaker 2: Andy Warhol, and we can I'm sure, I imagine we'll get
Speaker 2: to that work involving Andy Warhol and Andy Warhol work
Speaker 2: was not held to be fair use. So though I
Speaker 2: should be careful the way I say that the particular
Speaker 2: use of an Andy Warhol work was held out to
Speaker 2: be fair use, But the transformative use test kind of
Speaker 2: controls frequently. And what you see in the cases involving
Speaker 2: fair use is the defendant trying to argue that their
Speaker 2: use is new and transformed against the original one, and
Speaker 2: the plaintiffs saying, no, we did it for this purpose
Speaker 2: and you're using it for the same purpose.
Speaker 1: That's very very well spoken, and also you crushed it
Speaker 1: on those various factors for not having them good job,
Speaker 1: law school training, coming back. So I guess you drew
Speaker 1: a lot of parallels between Google Books and so the
Speaker 1: litigation that's going now with the lms. But I'd like
Speaker 1: to point out a distinction that I've written about, and
Speaker 1: it's that Google Books was not necessarily Google's core business model.
Speaker 1: You mentioned, yes, they wanted to digitize everything, but books
Speaker 1: are really only a small portion of this. I don't
Speaker 1: think it was ever expecting to make massive amounts of
Speaker 1: revenue necessarily from Google Books. It was kind of a
Speaker 1: side project. Obviously implications for authors, but for Google maybe
Speaker 1: not core to the business model. I think that's that's
Speaker 1: different than what we see now with Open AI or
Speaker 1: Anthropic or some other lms. The LLM is the business model,
Speaker 1: and in the US copyright infringement damages they can be massive.
Speaker 1: Of course, you can have for willful infringement. I believe
Speaker 1: it's up to one hundred and fifty thousand dollars per work.
Speaker 1: We're talking about millions and millions of potential works being used.
Speaker 1: So I think this really does potentially oppose sort of
Speaker 1: an existential threat to those business models if the fair
Speaker 1: use defense doesn't hold up. So I think this is
Speaker 1: why that I think it's so important as this field
Speaker 1: is still kind of in its infancy. But let's turn back,
Speaker 1: I think to the Warhol case, and also I think
Speaker 1: the way Warhol was implemented more recently in probably the
Speaker 1: first AI case that we've had, and that being Ross Intelligence.
Speaker 1: Now you wrote about that, and I think in your
Speaker 1: post you said this case helps delineate the boundaries of
Speaker 1: acceptable fair use of a copyrighted material for an AI
Speaker 1: model training. I guess can you tell us why was
Speaker 1: Ross what Ross did outside of those accepted boundaries.
Speaker 2: Well, we don't know what the accepted boundaries are, right,
Speaker 2: we don't know the extended accepted boundaries what we have now,
Speaker 2: And just to give a little context for listeners here
Speaker 2: is you know, you have now upwards of forty cases
Speaker 2: for copyright infringement or copyright adjacent type claims that have
Speaker 2: been asserted against AI models for using copyright protected materials
Speaker 2: without permission and without payment. And you know the you know,
Speaker 2: the big ones and the most famous ones being sort
Speaker 2: of like New York Times filing a lawsuit against Open
Speaker 2: AI or Sarah Silverman filing similar types of lawsuits, and
Speaker 2: those cases are against and it's sort of important to
Speaker 2: understand that for your question to understand the differences of
Speaker 2: the models and why this case is important those cases.
Speaker 2: Let's just take the New York Times versus Open AI.
Speaker 2: That's a case by publishers and authors against a generative
Speaker 2: AI large language model general purpose. That's sort of like
Speaker 2: underlying it is this you know GPT model that's being
Speaker 2: trained on billions and billions of points of training data
Speaker 2: and has kind of limitless purposes. And people can put
Speaker 2: those models to use in all sorts of industries for
Speaker 2: all sorts of purposes. And you know, one application being
Speaker 2: chat cheept for example, the case by Thompson Reuters against
Speaker 2: Ross Intelligence, which was brought way back in in twenty
Speaker 2: twenty actually, so it really predates a lot of this.
Speaker 2: That's a case, you know where Thompson Reuters, which is
Speaker 2: you know, provides among other things, legal research tool West Law,
Speaker 2: which is you know, my go to legal research tool
Speaker 2: of choice, and they filed a lawsuit against Ross Intelligence,
Speaker 2: which is a legal focused AI research tool, an AI
Speaker 2: powered Legal research tool, and they claimed that Ross Intelligence
Speaker 2: used Westlaw's headnotes, which are you know, summaries of points,
Speaker 2: legal points of holdings and what you know. Thomson Reuter says,
Speaker 2: those are copyright protected, and they claimed that Ross Intelligence
Speaker 2: use them to train the Legal Tool AI model and
Speaker 2: claimed that this was copyright infringement. Ross Intelligence argued that, no,
Speaker 2: we don't ever output the West Law headnotes and our
Speaker 2: users transformative and its fair use. And in that case
Speaker 2: the court held that it would not fair use. And
Speaker 2: that decision came out just February eleventh, very recently, and
Speaker 2: it is the first meaty, substantive fair use decision that
Speaker 2: is in this existential question that you talked about in
Speaker 2: the beginning, which is is the use of copyright protected
Speaker 2: materials to train artificial intelligence models copyright infringement or is
Speaker 2: it fair use? And in this particular case, the court
Speaker 2: said that it was not fair use. Very important to understand, though,
Speaker 2: is the particular facts of this case. As we said,
Speaker 2: fair use is a case by case analysis. It's very
Speaker 2: facts specific, and there are important distinctions between the facts
Speaker 2: of the Ross Intelligence case and the facts of cases
Speaker 2: like New York Times versus Open AI whether those and
Speaker 2: we can talk about what those differences are and why
Speaker 2: they might matter, whether those will be enough to change
Speaker 2: the outcome in those cases. As I called it, the
Speaker 2: trillion dollar question. I say trillion dollar because of what
Speaker 2: you said, right, I mean, you're talking about massive copyright damages,
Speaker 2: potential copyright damage is in and an industry that is
Speaker 2: now valued. Probably you're a Bloomberg, you're in a better
Speaker 2: position to tell me. But I imagine it's a trillion
Speaker 2: dollar industry now, and you know the outcomes of those
Speaker 2: rulings could really uh you know, have an impact on that,
Speaker 2: on that industry and the amount of damages that could
Speaker 2: be at stake there.
Speaker 1: Yeah, I mean, I mean AI has certainly been the
Speaker 1: key theme in the investment community for years and years,
Speaker 1: and the evaluations seem to sort of double every every
Speaker 1: few months. So it's a massive amount. But yeah, let's
Speaker 1: let's sort of dive into too. Why I think you
Speaker 1: mentioned Ross was not generative AI. I think I think
Speaker 1: the judge was pretty clear that to draw that distinction.
Speaker 1: But will that necessarily make it it different in how
Speaker 1: Ross applied I guess Warhol. When we do start dealing
Speaker 1: with the generative AI defendants.
Speaker 2: I I don't know. That's that's that's one that I'm
Speaker 2: not going to give you a I don't have a
Speaker 2: crystal ball, and I'm not I'm not even going to
Speaker 2: give you my personal take on that. It's too that's
Speaker 2: too dicey, that's too dicey. Right, what I here's what
Speaker 2: I'll tell you. Let's just talk about why it might
Speaker 2: matter as a why it might be a relevant distinction
Speaker 2: and not what we call in the legal world of
Speaker 2: distinction without a difference, right. And so sometimes you have
Speaker 2: factual differences that don't have any legal bearing, and sometimes
Speaker 2: they have legal bearing, and let's talk about why it
Speaker 2: might matter. Here's an example. And this just goes to
Speaker 2: the fact that the Ross Intelligence model was not a
Speaker 2: generative AI. What that model allowed you to do was,
Speaker 2: you know input. You would put in an input with
Speaker 2: a legal research question and asking, you know, the AI model, hey,
Speaker 2: what's the law on X, Y and Z, and then
Speaker 2: it would come back with relevant judicial opinions, which are
Speaker 2: public domain, and say, here's a judicial opinion that answers
Speaker 2: the relevant model. The point of the West Law headnotes
Speaker 2: why they used it was to help train it to
Speaker 2: understand the way that people talk, because not everyone talks
Speaker 2: like a judge and a judicial opinion. People talk more
Speaker 2: like West Law headnotes than they talk like judges and opinions.
Speaker 2: So they use those West Law headnotes to train the
Speaker 2: AI to talk like a human and not just like
Speaker 2: a lawyer or a judge. So they never generated in
Speaker 2: generate new texts. Contrast that with something like a chat sheept,
Speaker 2: which generates original texts that presumably in the outputs are
Speaker 2: not going to You know, it could, but that's a
Speaker 2: different question. The outputs likely don't infringe the underlying materials
Speaker 2: that we're input into the system. Courts have in cases
Speaker 2: involving fair use looked at things like is the new
Speaker 2: use going to create more content that's creative and new
Speaker 2: and useful and ultimately driving and further to the constitutional
Speaker 2: mandate that I mentioned before in the Constitution, Article one,
Speaker 2: Section eight, Clause eight says to promote the progress of
Speaker 2: science and the useful arts, we're going to give authors
Speaker 2: exclusive rights over their works for limited periods of time.
Speaker 2: That's what copyright is. It's giving exclusive rights to authors
Speaker 2: to promote progress of science in the arts. One thing
Speaker 2: that a court might look at is does generative AI
Speaker 2: help promote progress and science and useful arts by creating
Speaker 2: all of this output that's useful for society. I think,
Speaker 2: you know, another sort of angle on this which isn't
Speaker 2: really about the generative AI, which is more like the
Speaker 2: ultimate use of it. But another factor that plays into
Speaker 2: that and is very important to understand the less about
Speaker 2: the generative AI, which is the application of a large
Speaker 2: language model or the application of the GPT for example,
Speaker 2: that underlies open AIS model or an anthropic equivalent. Right,
Speaker 2: is what data is used to train those models and
Speaker 2: to make them able to do the extraordinary work that
Speaker 2: they do. And what the court found relevant in the
Speaker 2: Raws Intelligence case is that they were using these West
Speaker 2: Law headnotes in order to train it to provide something
Speaker 2: that ultimately was found to be sort of competitive with
Speaker 2: West Law. But what they said was, the court said,
Speaker 2: you know, that's material that you could have gotten a
Speaker 2: license for, right, you could have paid. It's not like
Speaker 2: you're dealing with that large a body of work like
Speaker 2: you could have created your own headnotes. You know, if
Speaker 2: like Thompson Breuter's in the court said this, Thompson Broider's pay,
Speaker 2: you know paid people authors, which is the way it's
Speaker 2: supposed to work under copyright and pay them to go
Speaker 2: and create headnotes, and like they get to protect those
Speaker 2: and you have to pay for them, and you could
Speaker 2: and there's really was nothing preventing getting a license from
Speaker 2: a Lexus Nexus or a West Thought or just hiring
Speaker 2: authors to create their own headnotes. Now it's difficult to see.
Speaker 2: And I think an argument that open AI is going
Speaker 2: to the open AIS anthropics of the world, I believe
Speaker 2: will try to argue in those cases is to say
Speaker 2: we couldn't license all of this stuff that is needed
Speaker 2: to change these large language models. Let's emphasize large. We
Speaker 2: needed to be really large. An argument that I think
Speaker 2: that's going to be made, and whether this is going
Speaker 2: to prevail again, I'm not gonna opine on it, but
Speaker 2: I think the argument will be something like, we need
Speaker 2: all of the line wage of the world, we need
Speaker 2: all of the culture of the world, we need everything
Speaker 2: that's out there, and we couldn't possibly under any circumstances,
Speaker 2: license everything that we need in order to teach these
Speaker 2: models to speak language the second of LLM and to
Speaker 2: understand culture and understand who we are. And so there
Speaker 2: was there was a need and courts look to see
Speaker 2: under the fair use factors like was there really a
Speaker 2: need for what you were taking here without sort of
Speaker 2: a license or without permission? And the argument again it
Speaker 2: is a novel type of argument, and it's also there's
Speaker 2: like a good rejoinder that I see the author's cure make,
Speaker 2: which is like, well, just because you can't have it
Speaker 2: for free, maybe means you don't get it. It doesn't
Speaker 2: mean like you get to just take it, right. But
Speaker 2: I think the argument would be we need everything, we
Speaker 2: need everything, and there's no world and there's no possibility
Speaker 2: of ever having a licensing model in order to get
Speaker 2: everything in the world.
Speaker 1: Yeah, that's a really interesting point, and I think sort
Speaker 1: of again goes to Ross Intelligence where sort of they
Speaker 1: were in some way is sort of vertically focus. They
Speaker 1: were sort of designed to compete with Thompson Reuters in
Speaker 1: this area of headnoe cases. And I think that might
Speaker 1: to the extent that it might have some read through
Speaker 1: to the generative AI companies. It's to some of these
Speaker 1: cases where I also see in some of the complaints
Speaker 1: the potential for that to be similar. I think some
Speaker 1: of the visual artists against some of these AI models
Speaker 1: that also output images that could be licensed. Also, I
Speaker 1: think there's a new one by dal Jones that I
Speaker 1: think also raises in the complaint and least argument that
Speaker 1: you're actually doing this so that you can license sort
Speaker 1: of newsflow news data. So I think that's what we
Speaker 1: might see potential read through. But I think also, as
Speaker 1: you mentioned, I think anybody who says they know how
Speaker 1: this is going to go, there's no possible way. One
Speaker 1: you have of wards of forty cases, they could go
Speaker 1: very different ways. And two, the question is who is
Speaker 1: going to decide fair use? Now Again, in rosson Intelligence,
Speaker 1: it was decided at the summary judgment stage, but at
Speaker 1: first the judge didn't want to decide it at that stage.
Speaker 1: So I think it's a Again we talked about these
Speaker 1: very hard concepts and judges struggle with them, and jer
Speaker 1: Hurry's are certainly going to struggle with them if they
Speaker 1: are given at least some of these very used fatactors.
Speaker 1: Do you think we're going to see sort of maybe
Speaker 1: potentially diverging precedent here, some judges decigdning some juries, assigning
Speaker 1: some circuits, taking different stances on this.
Speaker 2: I think it's very I think that is more likely
Speaker 2: than not, and I think that's not I think that
Speaker 2: it's more likely than not. I also think that in
Speaker 2: general with jurisprudence, when you have multiple cases that deal
Speaker 2: with you know, similar but not identical issues, but typically
Speaker 2: you have is judges try not to reject the reasoning
Speaker 2: of their brethren and sisters in the other courts. Rather,
Speaker 2: what they try to do is to distinguish their cases
Speaker 2: on the facts. And there's a lot of ground for
Speaker 2: doing so in these cases. So if, for example, a
Speaker 2: judge in one of the cases in the New York
Speaker 2: Times case and stop an AI or any of the
Speaker 2: other cases, even if they don't adopt the exact reasoning
Speaker 2: of Judge Bibis who issued this decision in ross Intelligence,
Speaker 2: or if it goes up to the third Circuit of appeals,
Speaker 2: even if they don't agree with the reasoning, what a
Speaker 2: judge will try to do first is say our facts
Speaker 2: are different and so we come out differently. It's more
Speaker 2: likely that courts will do that than to, you know,
Speaker 2: head on, disagree with the underlying reasoning. You know that
Speaker 2: said right now, we just have a district level case.
Speaker 2: It almost certainly I'd be surprised if it doesn't end
Speaker 2: up on appeal. There's every reason to believe that a
Speaker 2: circuit court could disagree with Judge Bbis. Judge Vibas is
Speaker 2: sitting by designation. He's a circuit judge who's sitting by
Speaker 2: designation in the district court. And like you said, just
Speaker 2: a few months ago, he came out arguing that there
Speaker 2: was a good ground for fair use. Some was going
Speaker 2: to allow Ross Intelligence to make its fair use case
Speaker 2: to the jury. And then you know, something changed and
Speaker 2: he decided that it wasn't fair use as a matter
Speaker 2: of law. So these are really close calls. I think
Speaker 2: it's really likely that it goes up to a circuit.
Speaker 2: I think it's very likely that judges in the Ninth Circuit,
Speaker 2: for example, which tends to be fairly liberal around fair use,
Speaker 2: and the Second Circuit, the judges tend to be fairly
Speaker 2: liberal around technology and fair use. It was the Second
Speaker 2: Circuit out of New York that held that Google Books
Speaker 2: was fair use, and it's in the Ninth Circuit that
Speaker 2: covers you know, San Francisco and all of the tech companies.
Speaker 2: That doesn't mean that they're going to hold that this
Speaker 2: is fair use. It does mean that you do have
Speaker 2: a situation where you could end up with circuit splits,
Speaker 2: and you also could no one would be surprised if
Speaker 2: this does end up in the Supreme Court, and whether
Speaker 2: the Supreme Court has the copyright expertise to handle a
Speaker 2: case like this is a real question, and you know,
Speaker 2: something that's creating a lot of uncertainty around what's going
Speaker 2: to happen.
Speaker 1: Yeah, absolutely, and I think Warhol I don't know if
Speaker 1: people were necessarily stunned by the outcome, but I believe it.
Speaker 1: It was seven to two, so it was fairly a
Speaker 1: sizable majority. And now we should point out that often
Speaker 1: IP in general, and also copyright isn't necessarily have the
Speaker 1: partisan vent that a lot of other issues necessarily do.
Speaker 1: But still seven to two was a fairly strong opinion
Speaker 1: and again against fair use in that case. So it's
Speaker 1: definitely an evolving landscape. I would say one thing that
Speaker 1: we haven't necessarily talked about is sort of the input
Speaker 1: versus output distinction in this debate. So you can either
Speaker 1: have infringement based on the input that is, when the
Speaker 1: LM digests, scans, whatever it does to get that information
Speaker 1: into the system to train on that data, that can
Speaker 1: potentially be an infringement of an author's rights. Also, potentially
Speaker 1: the AI output could infringe There's also an entirely separate manner,
Speaker 1: and that's whether ALLM can actually produce copyrightable content. But
Speaker 1: we're going to leave that aside for now. That's why
Speaker 1: the entirely separate conversation where we don't have necessarily litigation
Speaker 1: to dive into that. But the input output, What are
Speaker 1: your views on that and how that might play out?
Speaker 2: Yeah, I mean it can play out in a few
Speaker 2: different ways. So one, let's just talk about how the
Speaker 2: output can play into the questions that really involve the input.
Speaker 2: And you know, the easiest way to think about that
Speaker 2: is in the cases that have been filed against the
Speaker 2: against the LM's like open AI. There is an effort
Speaker 2: by authors in some of those cases, including The New
Speaker 2: York Times, to argue that our articles are being reproduced
Speaker 2: almost verbatim when users ask certain queries. Right, so user
Speaker 2: put an input, tell me what happened in this article,
Speaker 2: and according to the New York Times complaint, CHATCHPT will
Speaker 2: with certain prompting output material that the New York Times
Speaker 2: argues is substantially similar to the articles. Now if that's true,
Speaker 2: then it becomes a very much easier case for the
Speaker 2: New York Times. Right then it's like, well, what's the
Speaker 2: difference between, you know, that versus just a pirate website
Speaker 2: that copies New York Times articles and makes them available
Speaker 2: for free. If you're able to just go into this
Speaker 2: end and reproduced articles in full, there's sort of little
Speaker 2: doubt that that would be sort of an infringement. Open
Speaker 2: Aye is really contesting that that's the way the tool
Speaker 2: is supposed to be used, and arguing that, you know,
Speaker 2: the only way that you were able to get chat
Speaker 2: gpt to display those outputs was by basically taking the
Speaker 2: model and beating it with a stick until it came
Speaker 2: out with the articles that were verbatim copies. And so
Speaker 2: it's much easier under copyright, much easier to argue that
Speaker 2: the technology you've created is supplanting the original if the
Speaker 2: outputs are generally substantially similar to the inputs, right, that's
Speaker 2: sort of like pretty pretty intuitive. And so the authors
Speaker 2: are frequently trying to focus on the outputs, and they've
Speaker 2: tried different theories. Another theory that was tried and rejected
Speaker 2: was in one of those cases involving images. The argument was,
Speaker 2: because all of your images that you output from your
Speaker 2: model are trained on our copyright protected images, every thing
Speaker 2: that is output from your model is a derivative of
Speaker 2: our inputs. Even if the output is not substantially similar
Speaker 2: to any particular input, all of the outputs are derivative
Speaker 2: works of the original. A novel argument, but was rejected
Speaker 2: already on a motion to dismiss by the court, because
Speaker 2: the law of a copyright is that in order for
Speaker 2: something to be a derivative work, it has to be
Speaker 2: substantially similar in some way to the original. And so
Speaker 2: the court said, no, that's not the way derivative works go.
Speaker 2: So in many of the cases, the plaintiffs and the
Speaker 2: authors are having to focus primarily on the inputs and
Speaker 2: to say that there's still infringement. Even if the outputs
Speaker 2: are not substantially similar, they're infringing. Let me just say
Speaker 2: one other category of why the outputs matter, which is
Speaker 2: sort of a connected question, and one that the defendants
Speaker 2: the models are happier to have better ground for them
Speaker 2: to argue, which is this. Listen, if you're the user
Speaker 2: and you're controlling this platform, and you create an output
Speaker 2: that because of your prompting and because of your directions,
Speaker 2: turns out to be infringing. And then, for example, you
Speaker 2: make some commercial use of that image. Right, take you
Speaker 2: create an image, you put it into an advertisement, and
Speaker 2: you start selling shoes with this, you know, infringing image.
Speaker 2: The models would like to say that's kind of on you,
Speaker 2: and that you were the one who engaged in that,
Speaker 2: and that the model had no volitional conduct. And part
Speaker 2: of copyright is you kind of have to It's not willful,
Speaker 2: it's not like a malevolent thing, but you at least
Speaker 2: have to have some volitional intent, Like you know, if
Speaker 2: you sneeze and you have an accident, often you can
Speaker 2: get off the hook, like it's an involuntary movement. Right.
Speaker 2: They kind of want to argue that if somebody is
Speaker 2: using the tool in a way that it's not supposed
Speaker 2: to be used, or create some kind of infringing content,
Speaker 2: then who should be responsible. Should it be the user
Speaker 2: of the platform or should it be the model. And
Speaker 2: that's the other question that sort of hasn't been answered
Speaker 2: yet by courts.
Speaker 1: So it sounds like potentially there's some contributory issue.
Speaker 2: That's that's right, Well, that's what the that's what the
Speaker 2: place just would argue that's.
Speaker 1: Yeah, yeah, yeah, so that's uh that that could get
Speaker 1: even more tricky to wagh through. So when you're you're
Speaker 1: talking about how the New York Times, I think that's right.
Speaker 1: In their complaint, they're like, these are regurgitation of our articles,
Speaker 1: I think, and some of the author's complaints potentially of
Speaker 1: the author's guild or maybe the original Silverman complaint also
Speaker 1: had that, And it strikes me as also interesting because
Speaker 1: we also many have divergence there because a news article
Speaker 1: that is largely a regurgitation of potentially a historical event
Speaker 1: might have less copyright protection under sort of the second
Speaker 1: fair use factor than potentially a novel. So so it's
Speaker 1: there's so many different directions that this could evolve that, Yeah,
Speaker 1: it's going to be really interesting to see how this
Speaker 1: plays out over the probably in the next two years
Speaker 1: or so. Just really quickly, I want to touch on licensing.
Speaker 1: You sort of mentioned before that a potential argument will
Speaker 1: be that we can't license the world, but they certainly
Speaker 1: are licensing a lot. We've seen so many large licensing agreements.
Speaker 1: I think the largest ones I can remember is Google
Speaker 1: with Reddit for I think something like sixty million dollars
Speaker 1: a year. New York Times, of course, is sued. A
Speaker 1: lot of rather newspaper publishers have sued, but a lot
Speaker 1: of them have also just agreed to licensing agreements with
Speaker 1: whether it's Open Ai, whether it's Google, whether it's some
Speaker 1: of these other lms. So how does that potentially cut
Speaker 1: against ALM in fair use if they can go out
Speaker 1: and license a lot of this content, or does it
Speaker 1: maybe not impact it? Or again who knows?
Speaker 2: Yeah, I mean so one of the challenges in just
Speaker 2: going back to the Google Books case that we talked
Speaker 2: about in the beginning, one of the challenges in that
Speaker 2: case for the authors was proving that there was a
Speaker 2: licensing market for the uses that were being made by
Speaker 2: Google Books, and that was a challenge, and the court
Speaker 2: picked up on that challenge. And that fourth factor, which
Speaker 2: is like the harm to the market for the copyright
Speaker 2: work the court found in that case was that there
Speaker 2: was no market for licensing books for the purpose of
Speaker 2: creating a search index. No one ever had paid any author.
Speaker 2: I want to use your book so that I can
Speaker 2: make it searchable on the Internet. Without displaying the book,
Speaker 2: people won't be able to read the book you'll just
Speaker 2: be able to find where the information lives inside of
Speaker 2: a book. No one had ever paid for that, and
Speaker 2: so the court had difficulties seeing how that was harming
Speaker 2: the economic interests of the author's copyright. Okay, contrast that
Speaker 2: with what you just talked about, where you have a
Speaker 2: fledgling licensing market for using copyright protected works to help
Speaker 2: train AIS. And when you have the AI companies that
Speaker 2: are trying to defend themselves with fair use, are they
Speaker 2: cutting their nose to spite their face by entering into
Speaker 2: these agreements and creating a market that didn't really exist before.
Speaker 2: You know, that's a new novel market that now exists.
Speaker 2: And now the plaintiffs in those cases have fodder to
Speaker 2: argue that there's a market harm and it's a there's
Speaker 2: a cognizable developing and you know, evidenced licensing market. The
Speaker 2: question is whether that will really undercut the argument that
Speaker 2: I was talking about you just raise, which is like
Speaker 2: we need to digitize the entire world, because yeah, so
Speaker 2: that's number one, which is just because we can license
Speaker 2: some of the materials from certain players, that's a far
Speaker 2: cry from the entirety of the world's knowledge, right, And
Speaker 2: so and that's not it. We still couldn't license nearly
Speaker 2: enough data to train all these models to the point
Speaker 2: where they are. And you have new players in the
Speaker 2: market like deep seek, which had originally said something like
Speaker 2: we don't need to use that much training data, but
Speaker 2: it was kind of an artificial argument because they needed
Speaker 2: to do that based on the models that had already
Speaker 2: been developed using the entire you know, these extraordinarily massive
Speaker 2: amounts of training data. So there's that. The other important
Speaker 2: argument that I imagine will come out is by the
Speaker 2: by the AI platforms, is you're comparing apples and oranges,
Speaker 2: that this is not getting the entire corpus of the
Speaker 2: Internet and sucking it into our model. When we enter
Speaker 2: into these license agreements, in many cases, what we're paying
Speaker 2: for is not just the just the underlying like raw data.
Speaker 2: We're paying for other things that are more valuable to us.
Speaker 2: For example, you're giving us access to archived materials that
Speaker 2: are not openly available online. You're giving us access to
Speaker 2: materials that are behind a paywall. You're also allowing us
Speaker 2: in many instances to display the outputs or like you know,
Speaker 2: some of these agreements that are out there and I've
Speaker 2: worked on many of them, I see many of them.
Speaker 2: They say things like, you can display x you know,
Speaker 2: one hundred or tens or whatever it is, X number
Speaker 2: of words from the article or from the original book
Speaker 2: or whatever it is, and display that to users in
Speaker 2: response to searches, provided that there's a link back, et cetera.
Speaker 2: Sometimes a link, sometimes not. And those are rights and
Speaker 2: privilege that are not part of what the AIS are doing.
Speaker 2: So they would say, you know, it's not really fair
Speaker 2: to look to those license markets and look to those
Speaker 2: you know, agreements as evidence that there's a licensing market,
Speaker 2: because this is a totally different sort of arrangement and
Speaker 2: that's what we're paying for. Again, I'm not calling balls
Speaker 2: and strikes here. I'm just saying that these are some
Speaker 2: of the arguments that I think will be made and
Speaker 2: some of the distinctions that will attempted to be drawn,
Speaker 2: and that will you know, could could sway the court
Speaker 2: wumber or the other. Yeah.
Speaker 1: Absolutely, And I think it also sort of shows the
Speaker 1: appetite to train these models on things like news content,
Speaker 1: on things like novels. I think I was reading some
Speaker 1: executive some own companies like there's no way better way
Speaker 1: to train an l m than than on novels, seeing
Speaker 1: how people speak, you know, it's just sort of some
Speaker 1: of the the the best data that they can get
Speaker 1: to train these models on. And I think it'll be
Speaker 1: interesting to see see. I do think these cases shape UPO.
Speaker 2: I do think what's what's what's encouraging, because look, I'm
Speaker 2: somebody who it's certainly in this context I can see
Speaker 2: not to see both sides, but I certainly feel an
Speaker 2: affinity towards authors and artists and creators right. I think
Speaker 2: that copyright does exist to support their work and their effort,
Speaker 2: and they should be paid for their work and their effort.
Speaker 2: I think that there's a lot of doom and gloom
Speaker 2: and that's also appropriate and understandable and fear around what's
Speaker 2: going to happen with these models, and totally totally get it.
Speaker 2: What's also nice to see is I've seen authors get paid, like,
Speaker 2: you know, all of a sudden, get a check for
Speaker 2: like twenty five hundred bucks, you know, and of a
Speaker 2: book that hasn't sold in many, many years, and all
Speaker 2: of a sudden, like some publishers are entering into deals
Speaker 2: and like authors are getting a check for a few
Speaker 2: thousand bucks for a work because they've entered into a
Speaker 2: deal to you know, use it to train an AI
Speaker 2: or you know, make some kind of use in it.
Speaker 2: So that to me is also encouraging and so sort
Speaker 2: of from a business standpoint, you really see the development
Speaker 2: of these new markets, and that to me is exciting
Speaker 2: and encouraging. Although again I know that there's a lot
Speaker 2: of anger and fear and you know, litigation over the
Speaker 2: broader issues, but it is nice to see that there's
Speaker 2: it's nice to see authors getting paid, including in connection
Speaker 2: with these new markets. Yeah.
Speaker 1: Sure, And I think overall I look at the markets,
Speaker 1: and my feeling has been that I think the markets
Speaker 1: or to some extent underestimating just how much of an
Speaker 1: overhang this could potentially be for large language models. And
Speaker 1: I think, you know, our discussion today sort of touched
Speaker 1: on just how tricky this situation is, how fair use
Speaker 1: can go one way and in another way one court,
Speaker 1: it may certainly be fair use, another court maybe not.
Speaker 1: Distinction is drawn. I think we're going to see some
Speaker 1: more summary judgment decisions ripen in twenty twenty five. I
Speaker 1: believe one of the meta cases, I believe they're breathing
Speaker 1: that earlier part of this year, and I think there's
Speaker 1: a case with judge alsop out in California with Anthropic
Speaker 1: where I think they're scheduled to do both summary judgment,
Speaker 1: fair use and sort of class certification later in the year.
Speaker 1: So I think as possible we start to get some
Speaker 1: clarity on this in twenty twenty five, maybe moving into
Speaker 1: twenty six, but it's gonna be an absolutely fascinating area
Speaker 1: to watch, I think, over the next few years. So
Speaker 1: I think we're going to leave it there. I think
Speaker 1: we've covered a lot of ground. Jeremy, it really appreciate
Speaker 1: you coming on today and all of your your really
Speaker 1: invaluable insights on this, I think, and it is really
Speaker 1: good to hear from from a pretictioner's perspective. I'm not
Speaker 1: wrong that these are challenging issues. It's it's a yeah,
Speaker 1: it's gonna be very interesting to see how this plays out. Jeremy,
Speaker 1: thanks so much.
Speaker 2: Thanks to one anytime,