Electronic submission via www.regulations.gov
December 6, 2023
Artificial Intelligence and Copyright (Docket No. 2023-6)
Notice of Inquiry and Request for Comments
Reply Comments
The Association of American Publishers (AAP) appreciates the opportunity to submit
these Reply Comments to the Copyright Office in the above referenced Notice of Inquiry
regarding copyright law and artificial intelligence (‘‘AI’’). We filed previous comments on October30, 2023, during the initial comment round.
Our submission today is in forceful opposition to the flawed and inaccurate assertions
submitted by some tech companies and/or their investors in the first comment round, in which
they position copyright and the protection of creative expression as an obstacle to innovation andprogress. This is nonsense. Copyright is the engine of free expression, responsible forincentivizing invaluable, creative expression and information that is inherently transformational
to both people and society and indisputably essential to democracy.
Copyright protection also fuels markets. The U.S. copyright industries collectively added
more than $1.8 trillion in annual value to U.S. gross domestic product in 2021,1 while offering avital chain of financial return to support new authorship. Those that seek to weaken these
protections disregard the premise that authorship cannot be taken for granted. They petition thegovernment for cover from liability for their calculated disregard of authorship, also ignoring thatrightsholders today already routinely license their works for all kinds of digital uses. Rather thanworking with copyright owners, these companies seek to appropriate literature and otherinvaluable intellectual property for their own commercial gain, and to bend the law to their will.
Government should have no role in bestowing legal or commercial advantages to AI
companies at the expense of authors, publishers, and other creators.
Preface
Human authorship is more important than ever in the age of artificial intelligence.
It is not and should not be a copyright owner’s burden to subsidize the development ofgenerative AI (“Gen AI”) technologies. The companies that benefit from the commercialization ofthis technology should be required not only to compensate rights holders for their past ingestionof copyrighted works to train Gen AI systems but also for their ongoing and future use ofprotected works to train new Gen AI systems or fine-tune their existing products.

See Copyright Industries in the U.S. Economy – The 2022 Report at
https://www.iipa.org/files/uploads/2022/12/IIPA-Report-2022_Interactive_12-12-2022-1.pdf.

11730 Pennsylvania Ave. NW • Suite 300 • Washington, DC 20006 • 202-347-3375 • www.publishers.org

Copyright owners vigorously contest the assertion by tech companies that copyrightindustries should cease objecting to the un-permissioned and uncompensated use of protected
works to create training datasets for Gen AI systems because—as one tech commenter puts it—
Gen AI developers have already invested significantly in this new technology on “settledexpectations” of fair use. 2 In response, we note that their “expectations” have no basis in the law;
this is an entirely new use with massive potential ramifications, and fair use is famously a balanceof factors and equities—it depends on the particulars of each situation. Rights holder expectations
have always been that AI companies should seek consent from rights holders for the use of theirworks and, further, that they should incorporate into their planning the simple, well-establishedprinciple of licensing what you do not own.
It is clear from several submissions in the initial comment round that Gen AI developers
gambled on a flawed assumption that wholesale reproduction for Gen AI training is categoricallyfair use, and that policy makers would give them a free pass in the name of innovation, or evenmore incredible, hardship. It is deeply ironic that these billion-dollar companies bemoan thefinancial burden they would face if they were required to pay reasonable license fees to the
copyright owners whose works are the very building blocks of Gen AI and whose livelihoods are
threatened by the same systems. As astutely stated by one journalist, “[w]e know what they’re
up to: privatizing the profits they might collect from AI applications while sticking the creative
public with the costs. Will we let them pull this same stunt again?” 3
AAP must also respond to comments from “big tech” that characterize the “nascent”
nature of the AI industry. These same arguments raised at the dawn of the Internet more than 30
years ago—i.e., that start-up technology companies should not be hampered by pre-existing
public laws; that copyrights are an impediment to innovation; and that imposing restrictions on
U.S. tech companies will impede their ability to dominate the global marketplace—are again being
advanced as if new.
The Copyright Office is quite familiar—as is everyone—with these well-worn anddisingenuous claims. Gen AI developers are not struggling “start-ups” that need a boost from thegovernment. They count among their investors some of the largest and most profitable
technology companies in the world and are valued, in some instances, between $80-90 billion
dollars.4 There is absolutely no public policy reason to create legal immunities for such companies,
who face only the reasonable requirement that they seek the consent of, or licenses from, rights
holders whose works they use for training their Gen AI systems.
It would be a grave error to repeat the past policy mistakes that allowed technology
companies to achieve such an unhealthy, monopoly-like market dominance to the point thatgovernments have struggled to curb their power, despite repeated attempts to moderate their

See Andreesen Horowitz at 6.
Michael Hiltzik, Column: AI investors say they’ll go broke if they have to pay for copyrighted works. Don’tbelieve it, L.A. TIMES, Nov. 16, 2023, available at https://www.latimes.com/business/story/2023-11-16/aiinvestors-say-theyll-go-broke-if-they-have-to-pay-for-copyrighted-materials-dont-buy-it.
4
See OpenAI in talks to sell shares at $86 billion valuation - Bloomberg News.
2
3

2

aggressive marketplace tactics. 5 To the contrary, there is every reason now to ensure that Gen AI
companies develop their products and services fairly and transparently, and that includes
respecting the authorship that is the building block of their businesses.
In related submissions, some tech commenters have tried to create a sense of crisis abouta purported “global AI arms race,” where the ascendancy and dominance of the U.S. AI industry
hangs in the balance, and disregard for copyright law is the only way the U.S. can maintain its
lead. For instance, one claims that “the overzealous enforcement of copyright when it comes to
AI training—or the ad hoc limitation of the fair use doctrine that properly protects AI training—
could cost the United States the battle for global AI dominance.” 6 Another argues—seemingly
oblivious to the irony of their assertion—that basic transparency requirements regarding training
data would “disclose their trade-secret protected information,” which would “accomplish little
practical benefit, while simultaneously imposing burdensome reporting obligations that would
force AI developers to disclose sensitive and valuable information to the world, including to ourforeign rivals.” 7This specter of the U.S. losing the battle for global AI dominance is speciously advanced asjustification for ignoring the rights of authors and publishers. This could not be further from thetruth. The issue of national security is certainly of deep concern to all American citizens,particularly where bad actors may use AI and Gen AI systems to sow misinformation or
disinformation that undermine our democratic institutions and create other national security
risks. This possibility points to the even greater need for authors and publishers that produce anddisseminate vetted, fact-checked quality content.
Advancing the nation’s technological and economic agenda is not a zero-sum game, and it
should not become a race to the bottom. As noted by one member of the National AI Advisory
Committee (NAIAC), “We want these technologies to be developed according to our norms andethics.” 8 The U.S. can continue to lead the responsible and ethical development of advanced Gen
AI systems while respecting the rights of creators and producers, authors and publishers—
without whom the creation and dissemination of the works essential to training Gen AI systems

See, e.g., Subcommittee on Antitrust, Commercial, and Administrative Law of the Committee on the
Judiciary of the House of Representatives, Investigation of Competition in Digital Markets, Majority Staff
Report and Recommendations, Part I (July 2022); see also Department of Justice, Justice Department Sues
Google for Monopolizing Digital Advertising Technologies (Jan. 24, 2023),
https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertisingtechnologies; Klobuchar, Grassley, Colleagues to Introduce Bipartisan Legislation to Rein in Big Tech (Oct.
14, 2021), https://www.klobuchar.senate.gov/public/index.cfm/2021/10/klobuchar-grassley-colleagues-tointroduce-bipartisan-legislation-to-rein-in-big-tech; Federal Trade Commission, FTC Sues Facebook for
Illegal Monopolization (Dec. 9, 2020), https://www.ftc.gov/news-events/news/press-releases/2020/12/ftcsues-facebook-illegal-monopolization.
6
Andreesen Horowitz at 8.
7
Meta at 20.
8
Michael Richards, AI’s Role in Modernizing Intellectual Property and Bolstering National Security (Aug. 1,2022) (quoting Yll Bajraktari), https://www.uschamber.com/technology/ais-role-in-modernizingintellectual-property-and-bolstering-national-security.
5

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would not be possible. For without their creations, their literary inventions—on what then will
Gen AI systems train?
The U.S. is a global leader in copyright protection 9 and in copyright industry production. If
the U.S. does not uphold its own laws to protect rights holders, other nations would be unlikely to
do the same, harming its own creative industries and the livelihoods of all creators dependent
upon a strong copyright framework. Claiming that AI “may be the most important technology ourcivilization has ever created” 10 is meritless if it is not responsibly and ethically developed, or its
deployment and use not subject to the same common-sense regulations that apply to all othertechnologies.
In summary, U.S. innovation is not at risk. What is at risk are the important tenets ofcopyright law from the self-serving rhetoric and actions of Gen AI developers that would
disregard the rights of authors and publishers who contribute to the country’s rich cultural,
economic, and intellectual life. Creating a digital royalty system to compensate copyright owners
and other rights holders is neither a novel nor insoluble challenge. Today such systems alreadyexist and are an established part of the Internet economy. In fact, such payment schemes are
already fundamental to digital commerce for creative content—from music to literature to news.
A Gen AI business that cannot contract with rightsholders for the building blocks of its
technology is simply not innovative at all.
Respectfully, and as set forth in more detail in the pages that follow, AAP believes thatthe facts and legislative history lead to the following conclusions about copyright and artificial
intelligence systems:
1. The large scale copying and use of copyrighted content to create the training datasets
ingested by Gen AI models requires consent from rightsholders. Neither the CopyrightAct nor case law supports any colorable application of fair use to excuse the wholesale
unauthorized reproduction of copyrighted works to train Gen AI models.
2. The un-permissioned use of copyrighted works to train Gen AI models is inconsistent
with the public policies animating copyright law and the objectives it is intended topromote.
3. Corrective legislation may be necessary if court decisions expand fair use beyond its
appropriate boundaries to permit wholesale, un-permissioned use of copyrighted
works. This would eviscerate the reproduction right and would be in sharp contrast towhat Congress intended.
4. Transparency is an essential requirement. It is in the public interest to know whatworks of authorship have been ingested and an essential part of seeking proper
consent to have such information clearly recorded. Such a requirement is not
U.S. Chamber of Commerce, Global Innovation Policy Center, International IP Index, 2023 Eleventh
Edition 7 (2023), https://www.uschamber.com/assets/documents/GIPC_IPIndex2023_FullReport_final.pdf.
10Andreesen Horowitz at 2.
94

burdensome and lends itself to further innovation in the field of digital rights
enterprises. Virtually all stakeholders in the AI space are already subject to reporting
requirements for privacy laws and do not dispute their ability to comply with theextensive existing reporting requirements for privacy laws.

1. Gen AI models copy and use the expressive content of copyrighted works for training,
contrary to claims that any copying is only of unprotected or nonexpressive aspects ofworks.
With respect to the training of Gen AI and Large Language Models on text, the expressive
content of works is being copied. Claims that ingestion is permitted by fair use, or even that any
copying is non-infringing, inaccurately represent what is being copied. 11 The Gen AI training
Andreessen Horowitz at 6 (“when an AI model is trained on copyrighted works, the purpose is not tostore any of the potentially copyrightable content (that is, the protectable expression) of any work on
which it is trained. Rather, training algorithms are designed to use training data to extract facts andstatistical patterns across a broad body of examples of content—i.e., information that is not
copyrightable.”); Anthropic at 7 (“The copying is merely an intermediate step, extracting unprotectableelements about the entire corpus of works, in order to create new outputs. In this way, the use of theoriginal copyrighted work is non-expressive; that is, it is not re-using the copyrighted expression tocommunicate it to users. To the extent copyrighted works are used in training data, it is for analysis (ofstatistical relationships between words and concepts) that is unrelated to any expressive purpose of thework.”; Authors Alliance at 9 (“The use of copyrighted works as training data for generative AI models is in
most cases a non-expressive use, as it is done as an intermediate step in producing non-infringing content,such as by extracting non-expressive information such as patterns, facts, and data in or about the work.”);
BSA at 8 (“In such a scenario, the AI developer would not be reproducing this text for its expressive
purpose. Rather, the reproductions would be made solely for the purpose of extracting unprotected
information about the English language–i.e., the correlations, patterns, and relationships among the 26
letters of the alphabet and the 1 million English language words, as they appear in thousands of stockphrases, figures of speech, similes, metaphors, grammar patterns, and common linguistic formulations andexpressions. Neither these letters, words, and phrases, nor the mathematical patterns among them acrossthousands or millions of writings, are copyright protectable subject matter.”); EFF at 2-3 (“it is crucial tonote that a machine learning process is not limited to, or even primarily, identifying patterns that reflect
copyrightable elements of an original work. Rather, much of its “learning” is focused on non-copyrightableproperties of the subject being depicted (limes being green, trees having a vertical trunk with branches, catshaving four legs, etc.) or scenes-a-faire that are free for all to use (formulaic premises and plot twists inlanguage models, archetypal uses of visual language and color to convey mood).”); New Media Rights at 11(“First, it must be understood that when OpenAI’s natural language processing functions “read” text they
do not analyze the meaning but instead the functionality of sentence structure and syntax. So, while the
LLM is built by digesting creative material, that material is not processed for its copyrightable expression,
but rather for its non-copyrightable aspects: the function of language itself.”); OpenAI at 11 (“First, when
undergoing pre-training, a model is not interested in the expressive aspects of individual copyrighted
works. Instead, as described above, the pre-training process is a highly sophisticated computational processthat teaches the model to analyze the structure and syntax of language and images in general terms, todiscern the statistical relationships between words, shapes, colors, textures, and concepts.”); Program on
Information Justice and Intellectual Property (PIJIP), American University Washington College of Law at 511

5

process does not extract the ideas, facts, or concepts being conveyed by an author, it solely
extracts the exact expressive choices made to convey those ideas—i.e., the words an author used,
and the order in which they were placed. 12 Such copying is unlikely to be transformative or fairuse. The Copyright Office may wish to note the statement of a former executive of the Gen AI
firm, Stability AI, who wrote,
I’ve resigned from my role leading the Audio team at Stability AI, because I don’t agreewith the company’s opinion that training generative AI models on copyrighted works is‘fair use’... But setting aside the fair use argument for a moment — since ‘fair use’ wasn’t
designed with generative AI in mind — training generative AI models in this way is, to me,
wrong. Companies worth billions of dollars are, without permission, training generative AI
models on creators’ works, which are then being used to create new content that in manycases can compete with the original works. I don’t see how this can be acceptable in asociety that has set up the economics of the creative arts such that creators rely on
copyright. 13
These comments align with the recent Supreme Court ruling in Andy Warhol Found. for
the Visual Arts, Inc. v. Goldsmith, “It will not impoverish our world to require AWF to pay
Goldsmith a fraction of the proceeds from its reuse of her copyrighted work. Recall, payments likethese are incentives for artists to create original works in the first place.” 14
We also note a point raised by Google in its initial comments in this process: Gen AI
models are not information retrieval systems. 15 The occurrence of “hallucinations” demonstrates
that Gen AI models do not “understand” meaning or ideas. Even the use of the term(“Most of what gets ‘extracted’ from the training data is factual, consisting of information about
grammatical relationships between words. This statistical data is often uncopyrighted factual
information.”); Project LEND at 6 (“The use of AI models to extract and draw connections between these
unprotectable facts and ideas is what legal scholar Professor Matthew Sag has termed a ‘non-expressiveuse.’”); Professors Samuelson, Sprigman, and Sag at 8 (“Understanding the process of training foundation
models is relevant to the generative AI systems’ fair use defenses because the scope of copyrightprotection does not extend to ‘statistical information’ such as ‘word frequencies, syntactic patterns, andthematic markers’.”); Van Lindberg at 24 (“Rather than copying any expression, however, the modeltraining process records facts about the work.”).
12
See, e.g., Gervais, Daniel at 3 (“[I]n the case of GenAI, the use by the machine is not mere character
recognition; it is semantic in nature. The machines process the expression of ideas in the works to create
new expression.”); News Media Alliance at 33 (“LLMs typically ingest valuable media content for theirwritten expression. To the extent they are ingesting this content so these published words can be analyzed‘in relation to all the other words in a sentence,’ or their sequences of words identified, that analysis andidentification is intended to capture the very expression that copyright protects. Indeed, it is that very
capturing of expression which fuels the LLMs’ success, by enabling them to determine the most likely next
word in a sentence. That is why LLMs that are trained to generate their own expressive works ‘copy
expression for expression’s sake.’”).
13
Why I just resigned from my job in generative AI, Music Business Worldwide, available athttps://www.musicbusinessworldwide.com/why-just-resigned-from-my-job-generative-ai/.
14
Andy Warhol Found. for the Visual Arts, Inc. v. Goldsmith, 143 S. Ct. 1258, 1286 (2023).
15
Google at 4.

6

“hallucination” may lead to a misinformed view of how Gen AI models work, since it applies a
distinction between the accurate and inaccurate representation of facts and reality by a model. In
fact, a Gen AI model never generates output with reference to facts and reality; any similarity
between its output and reality is merely a byproduct of the probabilities derived from theexpression in its training materials.16
2. The Copyright Office should reject arguments to narrow the scope of exclusive rights.
Arguments made in favor of permitting the copying of copyrighted works to train Gen AI
assert unfounded claims about the nature of the exclusive rights protected by copyright law andshould be rejected. By reproducing copyrighted works to train Gen AI models, Gen AI developers
are expropriating the value of those works, value which the copyright owner has created and towhich he or she is entitled. The Copyright Act is clear that “the owner of copyright … has the
exclusive rights … to reproduce the copyrighted work in copies,” 17 and “[a]nyone who violates anyof the exclusive rights of the copyright owner … is an infringer of the copyright.” 18 Contrary to thearguments of some respondents, the statutory framework draws no distinction between what
respondents consider “legitimate” interests and otherwise; does not limit protection to somesubset of uses that respondents believe copyright “is intended to protect;” nor supports the ideathat some types of copying fall outside the scope of the exclusive rights, e.g., “non-expressive”
copying. 19
The plain meaning of the statute supports a broad reading, and the breadth of protection
is a feature, not a bug. The House Report accompanying the 1976 Copyright Act explains, “Theapproach of the bill is to set forth the copyright owner’s exclusive rights in broad terms in section
106, and then to provide various limitations, qualifications, or exemptions in the 12 sections that
follow.” 20 The drafters of the Act intended, by this broad language, to ensure that copyright
Oliver Bown, “’Hallucinating’ AIs Sound Creative, but Let’s Not Celebrate Being Wrong, The MIT Press
Reader (Oct. 13, 2023), https://thereader.mitpress.mit.edu/hallucinating-ais-sound-creative-but-lets-notcelebrate-being-wrong/.
17
17 U.S.C. § 106.
18
17 U.S.C. § 501(a).
19Andreessen Horowitz at 6 (“Gen AI model training is a productive, non-exploitative use of training
material. That type of use does not exploit any protectable expression in any given work, and so it does not
implicate any of the legitimate rightsholder interests that copyright law seeks to protect.”); Anthropic at 7(“the use of the original copyrighted work is non-expressive; that is, it is not re-using the copyrighted
expression to communicate it to users.”); BSA (“such functionality simply does not compete with any
copyrighted works in any manner that copyright is intended to protect.”); CCIA at 8 (“These non-expressive
copies are not consumable by the public and do not function as market substitutes for copies of theingested works.”); Meta at 10 (“The process of training and developing AI models does not necessarily
trigger the rights that copyright exists to protect.”); TechNet at 4-5 (“the creation of intermediate copies infurtherance of the creation of a new and useful technological tool is not the kind of copying that violates
copyright law.”).
20
H.R. 94-1476 at 61. See also House Comm. on the Judiciary, 87th Cong., Copyright Law Revision Part 6:
Supplementary Report of the Register Of Copyrights on the General Revision of the U.S. Copyright Law:
1965 Revision Bill 14 (Comm. Print 1965) (“we believe that the author’s rights should be stated in the
16

7

protection would encompass not only the breadth of technological uses known at the time ofenactment, but also future technological uses. 21 They did so by, for example:•

Defining “copies” to include material objects in which a work is fixed “by any method now
known or later developed, and from which the work can be perceived, reproduced, orotherwise communicated, either directly or with the aid of a machine or device,” 22 language
intended by Congress—“to avoid the artificial and largely unjustifiable distinctions … under which
statutory copyrightability in certain cases has been made to depend upon the formor medium in which the work is fixed. Under the bill it makes no difference what
the form, manner, or medium of fixation may be-whether it is in words, numbers,
notes, sounds, pictures, or any other graphic or symbolic indicia, whether
embodied in a physical object in written, printed, photographic, sculptural,
punched, magnetic, or any other stable form, and whether it is capable ofperception directly or by means of any machine or device “now known or later
developed.” 23••

Clarifying that “device,” “machine,” or “process,” includes “one now known or later
developed.” 24Creating, for the first time, the public display right.25 In explaining this critical change, this
Office presciently said:“Since the Report was issued in 1961 we have become increasingly aware of theenormous potential importance of showing, rather than distributing, copies as ameans of disseminating an author's work. In addition to improved projection
equipment, the use of closed-and open-circuit television for presenting images ofgraphic and textual material to large audiences of spectators could, in the near
future, have drastic effects upon copyright owners' rights. Equally if not moresignificant for the future are the implications of information storage and retrieval
devices; when linked together by communications satellites or other means, these
could eventually provide libraries and individuals throughout the world with accessto a single copy of a work by transmission of electronic images. It is not

statute in broad terms, and that the specific limitations on them should not go any further than is shown tobe necessary in the public interest.”).
21
House Comm. on the Judiciary, 87th Cong., Copyright Law Revision Part 6: Supplementary Report of theRegister of Copyrights on the General Revision of the U.S. Copyright Law: 1965 Revision Bill 14 (Comm.
Print 1965) (“A real danger to be guarded against is that of confining the scope of an author’s rights on thebasis of the present technology so that, as the years go by, his copyright loses much of its value because of
unforeseen technical advances.”).
22
17 U.S.C. § 101 (Emphasis added).
23
H.R. 94-1476 at 52.
24
Id (Emphasis added).
25
17 USC § 106(5).

8inconceivable that, in certain areas at least, ‘exhibition’ may take over from‘reproduction’ of ‘copies’ as the means of presenting authors' works to the public,
and we are now convinced that a basic right of public exhibition should be
expressly recognized in the statute.26”•

Expanding the public performance right. 27 These last two changes to the public display
and performance rights were made in part because of Congress’s observation that
performances and displays would continue “to supplant markets for printed copies.” 28

Together, these features of the Copyright Act affirm a broad and unstrained reading of
the exclusive rights. Contrary to arguments to read the reproduction right out of the CopyrightAct, the text, structure, and legislative history demonstrate a goal of strengthening andsupplementing that right. In analyzing issues related to the use of copyrighted works to train Gen
AI, the Copyright Office should keep this view in mind and avoid redefining the exclusive rights.
3. The Copyright Office should reject analogies between human learning and Gen AI
model training because they are reductive, unhelpful, and ignore the plain text of the
Copyright Act. 29
The analogy between human learning and Gen AI model training is illuminating in one
respect: it demonstrates that the use of a copyrighted work to train Gen AI models is for thesame intrinsic purpose as the author’s purpose, which “seriously weakens a claimed fair use.” 30
House Comm. on the Judiciary, 87th Cong., Copyright Law Revision Part 6: Supplementary Report of theRegister Of Copyrights on the General Revision of the U.S. Copyright Law: 1965 Revision Bill 20 (Comm.
Print 1965).
27
17 U.S.C. § 106(4).
28
H.R. 94-1476 at 63.
29
AIPLA at 12 (“We also note that the issues presented by AI infringement are analogous to human
infringement. Conceptually, generative AIs use a similar process to that used by human authors.”);Committee for Justice at 3 (“The similarities between machine learning in generative AI models and theway in which human creators learn from a lifetime of examples makes it possible to apply the law governing
the latter to the former.”); Creative Commons at 2 (“Generative AI can function in a similar way. Just as
people learn from past works, generative AI is trained on previous works, analyzing past materials in order
to extract underlying ideas and other information in order to build new works.”); Public Knowledge at 6(“[T]raining a GAI system is generally analogous to reading a book, looking at a photograph, admiring apainting, or listening to music.”); Van Lindberg at 5-6 (“AI systems learn about language by hearing andreading language, just like humans. AI systems learn about art by observing art, just like humans. AI
systems learn about music by hearing and reading music, just like humans. There is no other way for thesystem to ‘learn.’”).
30
Weissmann v. Freeman, 868 F.2d 1313, 1324 (2d Cir. 1989); Soc'y of the Holy Transfiguration Monastery,
Inc. v. Gregory, 689 F.3d 29, 60 (1st Cir. 2012) (citing Weissman); Divine Dharma Meditation Int'l, Inc. v.
Inst. of Latent Energy Studies, No. 19-55264, 2021 U.S. App. LEXIS 25145, at *3 (9th Cir. Aug. 23, 2021)(same); Cambridge Univ. Press v. Patton, 769 F.3d 1232, 1263 (11th Cir. 2014) (verbatim copies of bookexcerpts for course reserves not transformative because they serve same intrinsic purpose as plaintiffs’
works).
26

9But other than that, equating the copying done to train Gen AI models with humanlearning mispresents what is occurring and will lead to incorrect legal conclusions. A human does
not copy a work, under the text of the Copyright Act, when he or she learns. By contrast, when a
Gen AI developer uses a copyrighted work to train a model, copies are made under the plain textof the Act.
Such copying should not be treated as a mere technicality or an accident, and it is crucially
important that the Office recognize the centrality of the exclusive rights to advancing the goals of
copyright law. The drafters of the 1976 Act debated this very question of “inputting” ofcopyrighted works into computers and determined that it should be considered as implicating the
copyright owner’s exclusive rights, which would be infringement absent any exception. 31 Theirconclusion was subsequently reaffirmed by the National Commission on New Technology Uses of
Copyrighted Works.32 This legislative history supports the intent of Congress that is best
evidenced in the plain text of the Act. In this context, speculative analogizing about thedifferences between how humans learn and how AI “learns” is both misleading and harmful.
Notwithstanding the plain text, there are clear factual differences between humanlearning and Gen AI training that render analogies unhelpful and reductive. Copyrighted works
can be ingested into machines on a scale exponentially faster than a human can read. A machine
can recall—verbatim—vast amounts of data instantaneously. And a model could output verbatim
House Comm. on the Judiciary, 87th Cong., Copyright Law Revision Part 6: Supplementary Report of theRegister Of Copyrights on the General Revision of the U.S. Copyright Law: 1965 Revision Bill 18 (“Animportant question that has emerged since publication of the Report in 1961 involves computer uses of
copyrighted materials. Mainly in an effort to stimulate a discussion of the issue, the preliminary draft of1963 contained a provision granting an exclusive right ‘to reproduce [the work] in any form in the
programming or operation of an information storage and retrieval system.’ We became convinced,
however, that it would be a mistake for the statute, in trying to deal with such a new and evolving field asthat of computer technology, to include an explicit provision that could later turn out to be too broad or too
narrow. A much better approach, we feel, is to state the general concepts of copyright in language, such asthat in section 106(a), which would be general in terms and broad enough to allow for adjustment to futurechanges in patterns of reproduction and other uses of authors’ works. At the same time, we shouldemphasize here that, unless the doctrine of ‘fair use’ is applicable in a particular case, the bill contemplates
that certain computer uses would come within the copyright owner's exclusive rights. It seems clear, forexample, that the actual copying of entire works (or substantial portions of them) for ‘input’ or storage in acomputer would constitute a ‘reproduction’ under clause (1), whatever form the ‘copies’ take: punch-cards,
punched or magnetic tape, electronic storage units, etc. Similarly, at the ‘output’ end of the process, the"retrieval" or ‘print-out’ of an entire work (or a substantial part of it) in tangible copies would also comeunder copyright control.”)
32
Final Report of the National Commission on New Technology Uses of Copyrighted Works at 39 (“Theprotection afforded by section 106 of the new law seemingly would prohibit the unauthorized storage of awork within a computer memory, which would be merely one form of reproduction, one of the exclusive
rights granted by copyright. Considering the act of storing a computerized data base in the memory of acomputer as an exclusive right of the copyright proprietor appears consistent both with accepted copyrightprinciples and with considerations of fair treatment for potentially affected parties. Making a copy of an
entire work would normally, subject to some possible exception for fair use, be considered exclusively
within the domain of the copyright proprietor. One would have to assume, however, that fair use would
apply rarely to the reproduction in their entirety of such compendious works as data bases.”)31

10copies on a systemic and wholesale basis even though a Gen AI developer might seek to reduce
infringing outputs by taking certain steps.
Finally, in no case does the Copyright Act permit unauthorized access to or acquisition of
copyrighted works. Lawful access to authorized sources matters. A human is not permitted toillegally reproduce and download 183,000 copyrighted works (the number of infringing titles
estimated in the “Books3” corpus) 33 in order to read or learn from them.
4. Gen AI models can easily reproduce verbatim works used for training, in whole or part.
Developers must take affirmative steps to reduce such “memorization” or“overfitting” (with the caveat that such steps may never be 100% effective).
Some respondents attempt to dismiss the generation of outputs that are verbatim copies
of copyrighted works used as inputs as an insignificant error but concede that developers musttake affirmative steps to avoid this occurrence. 34 However the law must recognize things as they
are, not as good-faith actors wish them to be. The Office should also take note that bad-faith or
negligent actors may deliberately design Gen AI models to generate infringing outputs.
Memorization is an inherent characteristic of Gen AI models, and the law should
recognize that Gen AI developers and operators have an obligation to design their models to
avoid the generation of infringing outputs.

Alex Reisner, These 183,000 Books are Fueling the Biggest Fight in Publishing and Tech, The Atlantic(Sep. 25, 2023), https://www.theatlantic.com/technology/archive/2023/09/books3-database-generative-aitraining-copyright-infringement/675363/.
34
Anthropic at 6 (“We don’t believe users should be able to create outputs using Claude that infringe
copyrighted works. That is not an intended or permitted use of this technology, and we take steps toprevent it.”); BSA at 11 (“In any well-designed AI model trained on a sufficiently large data set,
computational analysis should never (or only in the rarest of circumstances) produce outputs that are‘substantially similar,’ let alone ‘virtually identical,’ to any specific copyrighted work.”); EFF at 4(“memorization of specific documents is considered a bug, not a feature.”); Google (“The possibility that AI
models can occasionally, despite the best efforts of their developers, output content that replicates existing
expression is a bug not a feature, and developers are taking a range of measures to limit that occurrence
even further, including deduplication of training data.”); New Media Rights at 12 (“While it is true thatsome LLMs can be designed to produce an output that resembles copyrighted inputs, that does not appear
to be the case for OpenAI’s current functionality.”); OpenAI at 7 (“Because our models do not have accessto training information after they have learned from it, they are unlikely to duplicate training data in their
outputs. In fact, verbatim repetition or ‘memorization’ of training data is generally considered by AI
developers to be a bug to be corrected, rather than a feature to be pursued. OpenAI has employed
numerous measures to reduce the incidence of this happening, and we regularly update our practices todeploy more.”); Van Lindberg at 35 (“While it might be possible to make guesses, to find particular‘memorized’ inputs, or to construct toy AI models that have this property, it is not possible for any current
system that is commercially reasonable or academically interesting.”).
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5. Transparency is essential to the responsible and ethical development of trustworthy
AI systems.
Transparency promotes accountability and is necessary to build public trust in AI technologies
and in the products and services into which AI technologies may be embedded. Specifically withrespect to the (un-permissioned) use of copyrighted works to train Gen AI systems, accurate
record-keeping and appropriate disclosure of copyrighted works incorporated into training
datasets will provide rights holders with the information necessary to identify the party fromwhom compensation is owing for such use (i.e., the training dataset creator and/or the Gen AI
developer). Where licensing is occurring, and as licensing models continue to evolve, accurate
record keeping of how and which copyrighted works are used to train Gen AI systems may
provide the mechanism through which revenue sharing models may be defined (for instance,
based on how much a specific copyrighted work used as an input contributed to a particular
output).
6. The U.S. should continue to encourage other countries to maintain robust copyrightprotections for creators and copyright owners regarding the use of their works intraining Gen AI models.
Several submissions noted that international consistency regarding the copyright legal
framework for training AI models on copyrighted works would be beneficial. However, there isdisagreement over whether that consistency should aim to protect human creators and copyrightowners or subsidize AI developers. 35
The U.S. should continue to encourage other countries to maintain robust copyrightprotections, which would not be contrary to facilitating continued investment in anddevelopment of Gen AI models. In particular, AAP suggests that Japan and Singapore be
encouraged to reassess the TDM exceptions in their laws in view of subsequent developments.
Notably, the existing TDM exceptions in the copyright laws of several countries preceded theCompare, e.g., A2IM-RIAA at 8-10; Authors Guild at 6-7; Copyright Alliance at 19-22; DPE, AFL-CIO at 45; Getty Images at 6-7; NMA at 13-16; NMPA at 4-6; NWU at 7-8; with Anthropic at 4-5; Creative
Commons at 2-3; Stability AI at 8; Wikimedia at 4-5.

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public emergence of Gen AI in late 2022; 36 said exceptions may not apply to Gen AI; 37 and they
likely violate the Berne Convention if applied to Gen AI. 38
The existing TDM exceptions, if applied to Gen AI, are also flawed policy. The livelihoods
and professions of authors, publishers, and all those integral to the publishing endeavor, and by

E.g. AAP at 5 (“We note that TDM exceptions were adopted before the rise of Gen AI, and the original
aim was to facilitate computational analysis of large amounts of text as may be embodied in scholarly andscientific articles for research purposes.”); UMG at 16-18 (“[T]here are some policies, including ones thatwere adopted years ago before the rise of generative AI [(i.e. in Singapore and Japan)], we believe the U.S.
should avoid. Whatever their historical merit, generative AI poses threats that render them obsolete anddamaging for the creative community, the music industry, and the general integrity of intellectual property
law.”).
37
E.g., AAP at 21 (“[T]he [EU DSM] Directive preceded the rapid growth and significant attention to Gen
AI systems over the last year. The Directive does not refer to AI, much less Gen AI, nor does it suggest ascientific research context would be permissible where text, sounds, or images would be used to generate
new text, sounds, or images.”); CCC at 2-5 (“[S]ome countries have provided exceptions or limitations for
specific subsets of AI (namely text and data mining (TDM)), but it is unclear how these provisions apply torapidly changing technologies such as those now seen in generative AI models.”).
38
E.g. Copyright Alliance at 19-22 (discussing the approaches in the European Union, Japan, Singapore, andthe United Kingdom, and concluding that “none of these approaches should be considered in the UnitedStates, as this would not only require a change to the Copyright Act but could also potentially result in U.S.
noncompliance under the Berne Convention for the Protection of Literary and Artistic Works[, since] suchexceptions may not be compliant under the Berne three-step test”); Gervais at 2 (“The legislative changes
in the European Union, Japan, Singapore, and Switzerland, to name just those, are all very different. For one
thing, they have different limits. It is not clear that all of them are compatible with the TRIPS Agreement’s
dual three-step test that applies to exceptions and limitations to the right of reproduction (namely article
9(2) of the Berne Convention, incorporated into TRIPS) and article 13 of TRIPS. It thus does not seemwarranted to follow any one of those approaches at this juncture.”); Kernochan Center at 3-4 (“The opt-out[of Article 4 of the EU DSM Directive] poses problems of compatibility with Berne Convention norms,
because it conditions the scope of protection on a declaratory obligation. Berne art 5(1) provides ‘The
enjoyment and the exercise of these rights [including reproduction and communication to the public] shallnot be subject to any formality.’ As detailed in Jane C. Ginsburg, Berne-Forbidden Formalities and Mass
Digitization, 96 BOSTON U. L. REV. 745, 758-68 (2016), available at http://ssrn.com/abstract=2772176, a
back-door limitation on the scope of protection through a formality-freighted opt-out from an exception
offends Berne just as much as a front-door imposition of a formality as a condition of protection.”); MPA at
12-14 (“MPA submits that these types of exemptions [i.e. Japan’s and Singapore’s] are bad policy, and they
likely fail to comply with the Berne Convention’s “three-step” test.”); NMPA at 4-6 (“Categoricalexemptions from copyright liability violate the three-step test of the Berne Convention, which limits
permissible exceptions to the reproduction right only to ‘certain special cases, provided that such
reproduction does not conflict with a normal exploitation of the work and does not unreasonably prejudice
the legitimate interests of the author.’ Sweeping carveouts to copyright protection, such as those [in the
EU, UK, Singapore, and Japan] for TDM, go significantly beyond ‘special cases.’ Such carveouts may also
stifle the developing marketplace for licensing copyrighted works for use in AI development and thereby
run afoul of the prohibition against interference with the normal exploitation of the work by the owner. . . .
Opt-out regimes may also violate Article 5(2) of the Berne Convention which prohibits conditioning
copyright protection upon any formality.”); PPA at 9-10 (“PPA considers any exception that broadly allows
scraping of copyrighted works without the authorization of the copyright owner to violate all three steps ofthe three-step test that governs permissible exceptions to copyright in international instruments.”)
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extension the immense public benefit that results from their efforts,39 should not be put at risk togive private commercial entities, including Gen AI developers and the billion-dollar companies
that fund them, subsidies in the form of uncompensated and un-permissioned transfers of theirintellectual property. Marginally improving the profit margins of Gen AI developers by sparing
them the “inconvenience” of seeking authorization to use copyrighted works and providing
reasonable compensation is not a reason to precipitate an existential crisis for the creative sector.
Apart from being both fundamentally unfair and contrary to copyright law principles, the harm tocreators and the U.S. economy would far exceed any benefit to AI developers.
AAP recommends the U.S. exercise its leadership role internationally to protect the U.S.
creative sector and ensure a global level playing field by opposing ill-advised copyright exceptions.
We recognize that it is not within the government’s power to determine outcomes in othercountries, but the effects of misguided policy elsewhere can be mitigated.
Some have argued that requiring Gen AI developers to seek permission from, andcompensating rights holders for the use of their works, would make the U.S. less attractive to AI
investors in contrast to countries where long standing principles of copyright may be disregarded.
We note that these costs are not only relatively minor compared to the overall expenditures of
Gen AI developers but also that they are but among a long list of costs that influence where acompany chooses to locate its business. These costs include availability of a robust talent pool,
quality of the technological infrastructure, proximity to universities with strong programs in
relevant fields, access to private equity and venture capital, time-zone compatibility, proximity tocustomer markets, local languages, local culture/quality of life, tax liability, servers, network
infrastructure, real estate, research and development, payment processing, energy, sales,
marketing, customer service, legal, finance, human resources, corporate communications andpolicy, and other administrative and professional services, among others.
According to Tortoise AI, the U.S. remains in first position overall in accelerating AI
development, as evaluated against the three pillars of investment, innovation, andimplementation. 40 Tortoise’s Global AI Index (GAII) evaluates a country’s readiness across several
factors including talent, infrastructure, operating environment (regulatory framework), research,development, government strategy, and commercial ventures. That the fourth iteration of
Tortoise’s index, published June 28, 2023, continues to rank the U.S. as No. 1 on its GAII indicatesthat the existing U.S. framework—with its robust intellectual property protection framework—
continues to secure the United States’ competitive advantage as a global powerhouse for AI
development. With the U.S. already ranking high for many of the GAII indicators, suggestions
See AAP at 27 (“Licensing fees are an important source of income for U.S. creators and rightsholders andsupport the continued investment in new human-created works. The importance of sustaining the U.S.
publishing industry cannot be understated. AAP members publish high-quality literary works, including
works that present novel ideas and new facts unearthed by authors; hold governments, businesses, andcitizens accountable; contribute to a vibrant culture; educate, and inspire Americans of all ages; and reporton scientific progress. Trustworthy Gen AI systems require high-quality new publications to remain stateof-the-art, and a flourishing publishing industry is best positioned to increase the value of Gen AI systems.
A flourishing publishing industry will also help protect against some of the potential ills of Gen AI systems,
including misinformation and bias.”).
40
See Tortoise Global AI Index at https://www.tortoisemedia.com/intelligence/global-ai/#rankings.
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that a change in policy, such as by weakening copyright protections to permit the taking of
copyrighted works without permission or compensation are gravely misplaced. It is precisely the
U.S.’ strong copyright and IP framework that is driving investment in content creation andcuration, in research and development, and thus, fueling the U.S.’ competitive advantage in AI
development.
Ultimately, the cost to license copyrighted works to train Gen AI models is only one costamong many, and costs themselves are but one factor that influences the choice of businesslocation. The U.S. excels in the key factors identified in the GAII scorecard, ensuring that it willremain an epicenter for AI development. Further, the U.S. could possibly enhance its competitive
advantage by requiring AI developers to comply with robust U.S. rules protecting creators
regardless of domicile. 41

Conclusion
AAP welcomes this further opportunity to provide its views regarding the issues raised in
the Copyright Office’s Notice of Inquiry and Request for Comments. Our analysis indicates thatcurrent case law does not support the application of fair use to permit the wholesale reproductionof copyrighted works by Gen AI developers to train AI models. The un-permissioned use of
copyrighted works to train Gen AI systems is also inconsistent with the policies animating
copyright law and the objectives it is intended to promote. We appreciate the Copyright Office’s
forward-thinking research into the intersection of copyright law and AI and look forward tocontinuing to work with the Copyright Office on this important matter.

E.g. A2IM-RIAA at 8-10 (“[T]o prevent AI developers from geo-laundering[,] . . . an AI developer should
not be able to import an AI model into the U.S. that ingested copyrighted works without authorization byclaiming that their AI development occurred in another jurisdiction where they claim the ingestion of
copyrighted works is legal.”); CCC at 2-5 (“[T]he United States can protect domestic consumers, creators,
and businesses, including technology companies, by requiring compliance with domestic rules, regardless ofthe source or domicile of the AI developer or the AI entities developed.”).

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