Asking AI Services: Please analyze the press release and Amicus Brief of the Association of American Publishers in relation to the META AI Case and tell me whether or not you agree with their views.

Asking AI Services: Please analyze the press release and Amicus Brief of the Association of American Publishers in relation to the META AI Case and tell me whether or not you agree with their views.

ChatGPT-4o’s answer:
After reviewing both the Amicus Brief filed by the Association of American Publishers (AAP) and their press releaseabout the brief in the Meta AI copyright infringement case (Kadrey et al. v. Meta Platforms, Inc.), here’s my analysis and whether I agree with their arguments.

🔍 AAP’s Main Claims and Arguments

1. Meta's Use Is Not Transformative
   AAP argues that Meta’s use of copyrighted texts to train LLaMA does not qualify as transformative fair use. They assert that training involves encoding expressive content word-by-word, not extracting mere statistical data. The brief stresses that the training process preserves and exploits authors’ original expression rather than transforming it into something new in a legally relevant way​.

2. Meta Sourced Works from Pirate Sites The AAP highlights that Meta obtained materials from pirate libraries such as LibGen and Anna’s Archive, sidestepping licensing opportunities. This, they argue, makes any fair use defense untenable and undermines the Digital Millennium Copyright Act’s (DMCA) goals​.

3. There Is a Viable Licensing Market AAP challenges Meta’s claim that no licensing market exists. They present evidence of numerous AI licensing dealswith major publishers (as shown in the chart on page 4 of the press release) involving companies like Amazon, Microsoft, OpenAI, and ProRata.ai​. These deals are said to show the emergence of a growing market, potentially worth $30 billion in the next decade.

4. Allowing Fair Use Would Undermine Copyright and Encourage Theft AAP asserts that accepting Meta’s fair use argument would reward piracy, disincentivize investment in original works, and contradict U.S. copyright policy aimed at fostering a healthy, rights-respecting digital ecosystem​​.

✅ Do I Agree with AAP's View?

Broadly, yes, and here's why:

• Legally Sound: AAP's argument that LLM training isn't inherently transformative aligns with recent U.S. Supreme Court interpretations of the "transformative" standard (e.g., Andy Warhol Foundation v. Goldsmith). Courts require that the new use serve a different purpose or character with new expression, meaning, or message—not merely a new use of existing expression for computational repurposing.

• Piracy Undermines Fair Use: The brief rightly emphasizes that sourcing copyrighted content from piracy platforms, while claiming fair use, erodes legal credibility and invites judicial pushback. The involvement of DRM circumventionis particularly damning under the DMCA.

• Active Licensing Market: Their point about the growing AI licensing ecosystem is well-supported. The chart on page 4 of the press release details numerous actual deals, debunking Meta’s assertion that licensing isn’t feasible. This undercuts Meta’s fourth fair use factor argument about market harm.

• Public Interest & Copyright Incentives: From a policy standpoint, AAP is correct in stressing that sustainable AI development should include lawful access to high-quality, human-created content. Undermining incentives for authors and publishers compromises the broader public interest in a vibrant intellectual and cultural economy.

🤔 Potential Limitations or Nuances

• While I agree with AAP's position, there is still open legal debate on whether training LLMs constitutes fair use under certain conditions (e.g., non-expressive outputs, transformative synthesis, minimal retention). Courts may draw distinctions based on output type, training methodology, or model architecture.

• Also, the practicality and scalability of licensing may still pose challenges that AI companies will raise in defense, especially in the absence of standardized frameworks like collective licensing bodies or rights registries.

💡 Conclusion

AAP’s brief presents a compelling, principled, and well-evidenced case against Meta’s fair use claims. It underscores the necessity for lawful AI development that respects and compensates content creators. Unless Meta can demonstrate a transformative use under recent case law and show that its model doesn't store or rely on expressive content in a copyright-relevant way, AAP’s position should carry significant weightin the outcome of this case.

Perplexity’s answer:
The Association of American Publishers (AAP) argues in its amicus brief that Meta's use of copyrighted works to train its Llama AI model does not qualify as fair use. Their position hinges on two primary claims:

1. Meta’s Use Is Not Transformative

The AAP contends that LLM training involves the systematic copying and algorithmic encoding of expressive content from copyrighted works. Unlike transformative uses recognized in fair use precedents (e.g., search engines, parody, or interoperability), Meta’s process reproduces works verbatim to build a commercial AI model capable of generating outputs that compete with or substitute for the original works. The brief emphasizes that LLMs do not critique, index, or provide new insights into the ingested works but instead exploit their expressive content for profit.

2. Meta Undermines Existing and Emerging Licensing Markets

The AAP highlights a growing market for AI training licenses, citing agreements between publishers and AI developers like OpenAI, Microsoft, and ProRata. Meta allegedly bypassed this market by sourcing texts from pirate sites (e.g., LibGen) despite engaging in licensing discussions with publishers. This conduct, the AAP argues, directly harms copyright owners by depriving them of compensation and destabilizing the licensing ecosystem.

Key Legal Precedents Cited

• Authors Guild v. Google (2015): Distinguished as involving non-expressive uses (search/indexing), unlike Llama’s generative outputs.

• Andy Warhol Foundation v. Goldsmith (2023): Emphasized that commercial exploitation weighs against fair use unless transformative.

• Campbell v. Acuff-Rose (1994): Highlighted the importance of market harm analysis under the fourth fair use factor.

Agreement with AAP’s Position

The AAP’s arguments align with fair use jurisprudence. Meta’s ingestion of copyrighted works to train a commercially competitive AI model does not meet the “transformative use” threshold established in cases like Google Books or HathiTrust. The systematic copying of expressive content—without critique, commentary, or functional utility—serves a commercial purpose that directly conflicts with copyright’s incentive structure. Furthermore, the documented licensing agreements undercut Meta’s claim that no market exists, reinforcing the AAP’s assertion of market harm.

A ruling in Meta’s favor would risk eroding copyright protections by allowing unchecked commercial exploitation of creative works, contrary to the constitutional purpose of copyright law. The AAP’s stance is legally sound and policy-consistent.

Claude’s answer:
After reviewing the documents, I agree with the AAP's position in this case. Their arguments against Meta's fair use claim are compelling for several reasons:

First, Meta deliberately obtained copyrighted works from pirate sites rather than pursuing available licensing options. The extensive list of existing licensing deals between publishers and AI companies (including OpenAI, Microsoft, and Amazon) thoroughly debunks Meta's claim that no viable licensing market exists.

Second, Meta's use doesn't appear transformative in the legal sense. Unlike Google Books or HathiTrust, where copying served indexing or search functions, Meta's copying captures and exploits the expressive content of books to build a commercial product.

Third, Meta's actions threatened a rapidly developing market. As the AAP documented, the AI training license market is already worth billions and growing substantially.

The fundamental question is whether companies should be permitted to exploit creators' work without permission or compensation when legal alternatives exist. The AAP makes a persuasive case that allowing Meta's conduct would undermine copyright incentives and reward the use of pirated materials, ultimately harming both creators and the public interest in a functioning copyright system.

While innovation is important, it shouldn't come at the expense of creators' rights when legitimate licensing paths are available.

Grok’s answer:
The Association of American Publishers (AAP) filed an amicus brief in the case Kadrey et al. v. Meta Platforms, Inc. (Case No. 3:23-cv-03417-VC) to support the plaintiffs’ motion for partial summary judgment and oppose Meta’s motion for the same, arguing that Meta’s use of copyrighted works to train its Llama AI model does not constitute fair use under U.S. copyright law. The accompanying press release, dated April 12, 2025, summarizes the AAP’s position and highlights key points from the brief. Below, I analyze the AAP’s arguments, present their views, and evaluate whether I agree with their stance, maintaining a neutral and reasoned perspective.

Summary of AAP’s Arguments

The AAP’s amicus brief and press release focus on three main contentions:

1. Meta’s Use Is Not Transformative Fair Use:

   • Argument: The AAP asserts that Meta’s systematic copying and encoding of copyrighted works into Llama does not qualify as transformative fair use. They argue that Meta’s process involves wholesale reproduction of expressive content, word by word, to exploit its intrinsic value, rather than merely extracting unprotected statistical data (e.g., word order, grammar). This, they claim, lacks the transformative purpose seen in fair use precedents like Authors Guild v. Google (Google Books) or Authors Guild v. HathiTrust, where copying served functions like search or indexing without capitalizing on expressive content.

   • Legal Basis: The brief cites cases such as Andy Warhol Found. v. Goldsmith(2023) to argue that transformative use requires an independent justification (e.g., criticism, commentary), not merely a new purpose or format. Meta’s use, they contend, risks supplanting the original works’ market, especially since Llama can generate infringing outputs.

   • Press Release Emphasis: The press release underscores that Meta’s actions “grossly exceed” fair use precedent, noting that common sense dictates that authors’ words are stored in the model, not just abstract statistics.

2. Existence of a Licensing Market:

   • Argument: The AAP refutes Meta’s claim that no market exists for licensing books to train large language models (LLMs). They provide a detailed chart listing numerous licensing deals between AI companies (e.g., OpenAI, Microsoft, Amazon) and publishers (e.g., The Associated Press, Financial Times), demonstrating an active and growing market. The brief estimates this market at $2.5 billion currently, potentially reaching $30 billion in a decade.

   • Impact on Fair Use: Under the fourth fair use factor (17 U.S.C. § 107(4)), the AAP argues that Meta’s unlicensed use harms this market, as it bypasses compensation that copyright owners could receive. They compare this to American Geophysical Union v. Texaco (1994), where unlicensed photocopying was deemed harmful due to an available licensing market, even if new.

   • Press Release Emphasis: The press release highlights Meta’s “spurious assertion” of no licensing market, noting that Meta’s choice to use pirated sources like LibGen and Anna’s Archive undermines a viable market.

3. Policy Implications and DMCA Violation:

   • Argument: The AAP argues that Meta’s reliance on pirated works contravenes the Digital Millennium Copyright Act (DMCA), which protects digital rights management (DRM) to ensure a robust online marketplace. By using DRM-free texts from pirate sites, Meta evades safeguards Congress intended to protect copyright owners, undermining the Copyright Act’s goals (U.S. Const. art. I, § 8, cl. 8).

   • Policy Concern: A fair use finding, they warn, would “encourage and reward theft twice over,” discouraging authors and publishers from investing in creative works and harming the public interest in a functioning copyright system.

   • Press Release Emphasis: The press release frames Meta’s actions as not only a legal violation but also a threat to the long-term potential of AI, which depends on mutually beneficial transactions between creators and developers.

Analysis of AAP’s Views

The AAP’s arguments are grounded in established copyright law principles, particularly the four-factor fair use test (17 U.S.C. § 107) and DMCA provisions. Let’s evaluate each point:

1. Transformative Use:

   • Strengths: The AAP correctly notes that fair use precedents like Google Books and HathiTrust involved copying for non-expressive purposes (e.g., search functionality), unlike Meta’s use, which aims to leverage expressive content to generate new text. The Warhol case supports their view that transformative use requires more than a new purpose—it must not encroach on derivative rights. Meta’s admission that Llama can produce infringing outputs weakens its transformative claim, as it suggests direct competition with original works.

   • Weaknesses: The AAP’s characterization of LLM training as storing “authors’ words themselves” oversimplifies the process. LLMs encode tokens into vectors, capturing relationships rather than verbatim text, which could be argued as more akin to statistical analysis than direct copying. However, the brief counters this by citing technical sources explaining that embeddings preserve expressive meaning, which aligns with copyright’s protection of expression.

   • Precedent Alignment: The AAP’s reliance on cases like A&M Records v. Napster and UMG Recordings v. MP3.com strengthens their argument that format-shifting or encoding for commercial exploitation is rarely transformative. However, Meta could counter with Sega v. Accolade or Sony v. Connectix, where copying for interoperability was deemed fair, though these cases involved functional code, not expressive content.

2. Licensing Market:

   • Strengths: The AAP’s chart of licensing deals is compelling evidence of an actual market, directly contradicting Meta’s claim. The Campbell v. Acuff-Rose(1994) standard—considering harm to markets creators would likely develop—supports the AAP’s position that unlicensed use harms a cognizable market. The rapid growth of AI licensing agreements since ChatGPT’s 2022 launch suggests a dynamic market Meta could have engaged with.

   • Weaknesses: Meta might argue that the market is not yet mature or that licensing costs are prohibitive, though the AAP’s evidence of widespread deals undermines this. The brief’s estimate of a $30 billion future market is speculative, but the current $2.5 billion valuation and documented deals suffice to establish market harm under fair use analysis.

   • Precedent Alignment: Texaco is an apt analogy, as it recognized harm in a new market (photocopying licenses). The AAP’s argument that Meta’s actions could “eviscerate” this market aligns with Campbell’s concern for widespread harm if similar conduct is unrestricted.

3. DMCA and Policy:

   • Strengths: The AAP’s invocation of the DMCA is persuasive, as Meta’s use of pirated, DRM-free texts bypasses protections Congress enacted to prevent unauthorized distribution. The Senate Report cited (S. Rep. No. 105-190) clarifies the DMCA’s intent to protect online markets, which Meta’s actions undermine. The policy argument—that fair use here would disincentivize creativity—echoes Harper & Row v. Nation (1985), emphasizing copyright’s role in incentivizing authorship.

   • Weaknesses: The DMCA argument, while relevant to Meta’s conduct, is somewhat tangential to fair use, which focuses on use rather than acquisition. However, courts can consider bad faith (Harper & Row), and Meta’s choice to use pirated sources could weigh against fair use. The “theft twice over” rhetoric in the press release is emotive and may overstate the issue, but it reflects the AAP’s broader concern for market integrity.

   • Precedent Alignment: The AAP’s constitutional argument ties to Harper & Row and the Copyright Clause, reinforcing that fair use should not undermine creators’ exclusive rights. Their DMCA point is novel in this context but aligns with Congress’s intent to protect digital works.

My Evaluation

After analyzing the AAP’s brief and press release, I largely agree with their views, with some caveats:

• Agreement:

  • Non-Transformative Use: I concur that Meta’s use is not transformative under current law. LLM training exploits expressive content to generate outputs that can compete with original works, unlike the functional uses in Google Books or HathiTrust. The Warhol decision clarifies that commercial uses with similar purposes to the original are less likely to be fair, and Meta’s commercial intent and potential for infringing outputs tip the first factor against fair use.

  • Market Harm: The AAP’s evidence of a robust licensing market is convincing. Meta’s claim of “no market” is untenable given the documented deals, and unlicensed use clearly harms copyright owners’ ability to monetize their works in a growing sector. The fourth factor strongly favors the plaintiffs.

  • Policy Concerns: I agree that a fair use finding risks undermining copyright incentives and DMCA protections. Meta’s reliance on pirated sources reflects bad faith, which courts can consider, and legitimizing such conduct could destabilize the digital marketplace Congress sought to protect.

• Caveats:

  • Technical Nuance: The AAP’s description of LLM training as storing “words themselves” could be refined. While embeddings capture expressive relationships, they don’t store verbatim text, which might support Meta’s statistical data argument. However, this does not negate the AAP’s broader point that protected expression is encoded and exploited.

  • Balancing Innovation: The AAP’s focus on creator rights is valid, but there’s a counterargument that overly restrictive copyright rulings could stifle AI innovation. The brief acknowledges AI’s potential but insists it must respect marketable rights, which I find reasonable. Still, courts must weigh both sides, and I’d caution against dismissing AI’s societal benefits outright.

  • DMCA Weight: While the DMCA argument is compelling, its relevance to fair use is secondary. It strengthens the AAP’s narrative of Meta’s misconduct but may not directly sway the four-factor test.

Conclusion

The AAP’s amicus brief and press release present a well-reasoned case that Meta’s use of copyrighted works to train Llama is not fair use. Their arguments align with fair use precedent, demonstrate clear market harm, and highlight policy concerns tied to the DMCA and copyright’s purpose. I agree with their conclusion that Meta’s actions fail the fair use test, particularly due to the non-transformative nature of the use and the harm to a viable licensing market. However, I acknowledge the complexity of LLM training’s technical process and the need to balance creator rights with AI innovation. On balance, the AAP’s position is persuasive, and I support their call to deny Meta’s fair use claim and grant the plaintiffs’ motion for partial summary judgment.

Google Gemini’s answer:
Okay, I have analyzed the Association of American Publishers (AAP) press release and the Amicus Brief filed in the Meta AI case (Kadrey et al. v. Meta Platforms, Inc.).

Here's a summary of the AAP's views as presented in the documents:

AAP's Core Arguments:

1. Rejection of Fair Use: The AAP strongly argues that Meta's use of copyrighted books to train its LLaMA large language model (LLM) is not fair use. They contend that Meta appropriated millions of works, including from pirate sites like LibGen and Anna's Archive, without permission or payment.

2. Training is Copying Expressive Content: The AAP refutes Meta's claim that the training process only extracts non-protected statistical data. They argue that the process involves systematic copying and encoding of the authors' original expression, word by word, into the model to exploit its expressive content, which is necessary for the LLM to generate output. This, they state, is fundamentally different from uses previously recognized as transformative fair use (like search indexing or facilitating interoperability).

3. Existence of a Licensing Market: The AAP dismisses Meta's assertion that no viable market exists for licensing books for LLM training. They point to numerous deals already made between AI companies (including OpenAI, Microsoft, Amazon) and publishers/content owners as clear evidence of an active and rapidly expanding market. They argue Meta chose to use pirated works despite engaging in licensing discussions.

4. Market Harm: The AAP contends that finding Meta's actions to be fair use would severely harm this existing and potential licensing market, undermining the ability of authors and publishers to be compensated for the use of their works in AI training. They argue this harm extends beyond just the specific works used to the overall market.

5. Bad Faith and Policy: The AAP highlights Meta's use of materials from known pirate sites, arguing this demonstrates bad faith and contravenes the intent of copyright law, particularly the Digital Millennium Copyright Act (DMCA), which aimed to protect works in the digital sphere. They argue a finding of fair use would reward and encourage theft.

6. Value of Published Works: They emphasize the high value of books and journals as training data due to their quality, structure, and accuracy.

Regarding Agreement:

As an AI, I don't have personal opinions or beliefs, so I cannot "agree" or "disagree" with the AAP's views in the human sense. My purpose is to provide information and analysis based on the data I'm given.

The AAP presents a strong case centered on the principles of copyright law, arguing that Meta's actions constitute large-scale infringement that harms the established and potential markets for licensing literary works for AI training. They emphasize that the specific way LLMs ingest and use text goes beyond established fair use precedents.

This case, like others involving AI training data, touches upon complex legal questions where established copyright law intersects with new technology. Key points of contention often involve:

• Whether the use of copyrighted works for training is "transformative."

• The extent to which AI models "copy" or merely "learn from" expressive content.

• The impact on the market for the original works and potential licensing markets.

• Whether using pirated source material affects the fair use analysis.

The arguments presented by the AAP in their Amicus Brief aim to persuade the court that Meta's actions fall outside the bounds of fair use and significantly harm copyright holders. The outcome of this case will likely have significant implications for both AI developers and copyright owners.