Asking AI services: Please read the article “Meta forecasts AI will rake in $460B to $1.4 trillion in revenues for Meta by 2035. Should that affect fair use analysis?” and the associated court document and answer the main question in the headline. Provide robust arguments for your view.

ChatGPT-4o’s answer:
Question:
Should Meta’s forecast that AI will generate $460 billion to $1.4 trillion in revenues by 2035 affect the fair use analysis in the Kadrey v. Meta case?

Answer:
Yes, Meta’s forecasted AI revenue should strongly affect the fair use analysis—particularly under the fourth fair use factor: “the effect of the use upon the potential market for or value of the copyrighted work”—as well as under the first factor regarding the purpose and character of the use.

Here’s a robust argument why:

1. Fair Use Factor Four: Market Harm

The massive projected revenue ($460 billion to $1.4 trillion by 2035) makes clear that Meta’s use of copyrighted works is highly commercial, not educational or transformative in a way that avoids market substitution.

• Economic Impact on Authors and Publishers: Plaintiffs argue—and courts have long held—that if the secondary use (like training LLMs) supplants the need to license or purchase original works, then it harms the copyright market. Meta’s switch from negotiating licenses to downloading pirated books shows intentional market substitution​​.

• Judicial Precedent: The Supreme Court in Harper & Row v. Nation Enterprisesemphasized that “a use that supplants any part of the normal market for a work causes harm.” The plaintiffs rightly argue Meta cannibalized the market for licensed book data, opting to exploit pirated sources after considering—but rejecting—paid licenses due to cost​.

• Real-World Damages: Meta's potential trillion-dollar windfall compounds the harm. If the company generates hundreds of billions in profit using pirated training data, the argument for “minimal market harm” collapses. This aligns with the plaintiffs’ assertion that Meta’s use is akin to "unmitigated piracy" that courts have uniformly rejected as fair use​.

2. Fair Use Factor One: Purpose and Character of the Use

Meta’s use is not transformative in a legal sense when considered alongside its commercial intent and acquisition method.

• Courts, especially post-Warhol v. Goldsmith (2023), have reined in the scope of “transformative use.” A use is nottransformative simply because it produces new output—especially when it competes in the same market and does not comment on or critique the original.

• Meta’s internal documents show they viewed books as “high-quality LLM training data,” and pursued pirated copies only after weighing the expense of licensing. This shows intent to exploit existing expression, not to transform it in the sense required under U.S. copyright law​.

• Meta cannot claim good faith. It used torrent networks and concealed VPNs to evade detection, further undermining any fair use claim. Such conduct is materially relevant to factor one and disqualifies their claim of fair use grounded in innovation or necessity​.

3. The Amount and Substantiality of Use

Meta’s ingestion of entire works (often hundreds of copyrighted books) is excessive and favors the plaintiffs under factor three. This, combined with the company’s intent to monetize outputs via Llama models, suggests a large-scale commercial appropriation of protected content.

Conclusion:

Meta’s forecast of staggering AI revenues is not merely a business projection—it is a critical piece of evidence in showing the scale of harm and the intent behind the use. It should weigh heavily against a fair use finding, particularly under the market harm and commercial purpose factors. If accepted, Meta’s rationale would effectively grant trillion-dollar corporations carte blanche to pirate content under the guise of innovation, collapsing the incentive structure that copyright law is designed to uphold.


Grok’s answer:
The question posed in the article's headline is whether Meta's forecast of generating $460 billion to $1.4 trillion in revenues from its AI, specifically its Llama large language models (LLMs), by 2035 should affect the fair use analysis in the Kadrey v. Meta case. The court document, Plaintiffs' Summary Judgment Reply and Opposition to Meta Summary Judgment (Case No. 3:23-cv-03417-VC), provides detailed arguments regarding Meta's alleged copyright infringement through the use of pirated books to train its LLMs. Below, I analyze whether Meta’s projected revenues should influence the fair use analysis, providing robust arguments for my view that these financial forecasts should have a limited but relevant impact on the fair use determination, particularly under the first and fourth fair use factors.

Overview of Fair Use and the Case

Fair use is a legal doctrine under 17 U.S.C. § 107 that permits limited use of copyrighted material without permission under certain conditions. Courts evaluate fair use based on four factors:

1. Purpose and Character of the Use (e.g., commercial vs. non-commercial, transformative vs. non-transformative).

2. Nature of the Copyrighted Work (e.g., creative vs. factual, published vs. unpublished).

3. Amount and Substantiality of the Portion Used (e.g., how much of the work was used and its significance).

4. Effect of the Use on the Potential Market (e.g., whether the use harms the market for the original work).

In Kadrey v. Meta, plaintiffs, including author Richard Kadrey, allege that Meta infringed their copyrights by downloading and using pirated copies of their books from sources like Library Genesis (LibGen) and Anna’s Archive to train its Llama models. Meta argues that its use constitutes fair use, comparing its actions to the Authors Guild v. Google (Google Books) case, while plaintiffs assert that Meta’s piracy and commercial intent preclude fair use. The article highlights Meta’s projected AI revenues, suggesting that the commercial scale of Meta’s operations could influence the fair use analysis.

Should Meta’s Revenue Forecasts Affect Fair Use Analysis?

Meta’s projected revenues of $460 billion to $1.4 trillion by 2035 are relevant to the fair use analysis, particularly under the first factor (purpose and character of the use) and the fourth factor (effect on the market), but they should not be dispositive. The revenue forecasts underscore the highly commercial nature of Meta’s use and highlight the potential market harm to plaintiffs, but fair use is a nuanced, fact-specific inquiry that considers all four factors holistically. Below, I provide arguments for why the revenue forecasts should have a limited but significant impact.

Argument 1: Revenue Forecasts Highlight the Commercial Nature of Meta’s Use (Factor 1)

The first fair use factor examines the purpose and character of the use, including whether it is commercial or non-commercial and whether it is transformative (i.e., adds new expression, meaning, or purpose). The court document emphasizes that “all of Meta’s uses of Llama are highly commercial and profit-seeking” (Document, Page 2), with Meta forecasting $2-3 billion in GenAI-driven revenue in 2025 alone and up to $1.4 trillion by 2035 (Ex. 8, Meta_Kadrey_00089020). This massive commercial intent weighs against fair use, as courts often find that highly commercial uses are less likely to be fair. See Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569, 585 (1994) (noting that commercial use tends to weigh against fair use, though it is not conclusive).

Meta’s revenue projections distinguish this case from Authors Guild v. Google, where Google’s book-scanning project was deemed transformative and non-commercial, as it provided public access to snippets and directed users to purchase books without generating direct revenue (804 F.3d 202, 209). In contrast, Meta’s use of pirated books to train Llama is not transformative in the same way; the books are used to enhance the model’s language capabilities for commercial applications, not to create a public index (Document, Page 7). The plaintiffs argue that Meta’s actions are akin to “unmitigated piracy,” which courts have consistently rejected as fair use (Document, Page 9). The scale of Meta’s projected profits amplifies this commercial intent, suggesting that Meta’s use is driven by financial gain rather than public benefit, tilting the first factor against fair use.

However, Meta could argue that its use is transformative because the books are not reproduced in their original form but are processed into training data to create a new AI model with broader societal benefits (e.g., “life-saving services and technology,” Meta Br. at 32). Courts have recognized transformative uses in some tech contexts, such as Sega Enterprises Ltd. v. Accolade, Inc., 977 F.2d 1510 (9th Cir. 1992), where copying software for reverse engineering was deemed fair. Yet, Meta’s reliance on pirated sources undermines this argument, as illegal acquisition weighs heavily against fair use (Harper & Row Publishers, Inc. v. Nation Enterprises, 471 U.S. 539, 562-63 (1985)). The revenue forecasts reinforce that Meta’s primary motive is profit, not public benefit, making the first factor lean toward the plaintiffs.

Argument 2: Revenue Forecasts Underscore Market Harm (Factor 4)

The fourth fair use factor assesses the effect of the use on the potential market for or value of the copyrighted work. The court document provides extensive evidence of an emerging market for licensing books as AI training data, which Meta’s piracy undermines (Document, Pages 25-34). Meta’s revenue forecasts are relevant here because they indicate the immense financial value Meta derives from its LLMs, which were trained on plaintiffs’ works without compensation. The plaintiffs argue that Meta’s actions deprive them of licensing revenue in a “rapidly growing market” for AI training data, as well as sales revenue from the 692 copies of their books Meta pirated (Document, Pages 26, 32).

The revenue projections highlight Meta’s ability to pay for licenses, as evidenced by its initial $200 million budget for dataset licensing, including $100 million for books (Document, Page 4). Meta’s decision to abandon licensing in favor of piracy (Ex. 50, Boesenberg Tr. 130:17-132:10) suggests a deliberate strategy to avoid market costs, directly harming plaintiffs’ potential licensing revenue. Courts recognize that such substitution inflicts cognizable market harm (Hachette Book Grp., Inc. v. Internet Archive, 115 F.4th 163, 192 (2d Cir. 2024)). The scale of Meta’s projected revenues—potentially $1.4 trillion—magnifies the harm, as it demonstrates that Meta reaps enormous financial benefits while plaintiffs receive nothing. This aligns with McGucken v. Pub Ocean Ltd., 42 F.4th 1149, 1163 (9th Cir. 2022), which emphasizes that widespread, unrestricted use (like Meta’s piracy) can destroy licensing markets.

Meta counters that no viable licensing market exists for AI training data, claiming that transaction costs and publishers’ lack of AI-specific rights make licensing infeasible (Meta Br. at 26). However, the court document refutes this, citing Meta’s own licensing negotiations with major publishers and competitors’ deals (e.g., OpenAI’s $230 million annual licensing budget, Ex. 138). The revenue forecasts weaken Meta’s argument by showing that it has the financial capacity to participate in this market but chose piracy to maximize profits. Thus, the fourth factor strongly favors the plaintiffs, with the revenue projections amplifying the market harm.

Argument 3: Revenue Forecasts Should Not Override Other Factors

While the revenue forecasts are relevant to the first and fourth factors, they should not dominate the fair use analysis. Fair use is a holistic inquiry, and the second and third factors—nature of the copyrighted work and amount used—also matter. The plaintiffs’ books are creative works, which receive stronger copyright protection (Document, Page 22), and Meta used entire books, weighing against fair use (Document, Page 23). These factors are independent of Meta’s financial projections, focusing instead on the works’ characteristics and the extent of copying.

Moreover, the revenue forecasts are speculative, projecting revenues through 2035 based on uncertain market conditions. Courts typically focus on actual or imminent market harm, not hypothetical future profits (Campbell, 510 U.S. at 591). Overemphasizing Meta’s projections risks skewing the analysis toward punishing commercial success rather than evaluating the use itself. For example, in Google Books, the court found fair use despite Google’s commercial status because the use was transformative and did not harm the market (804 F.3d at 224). While Meta’s case differs due to piracy and non-transformative use, the principle remains: financial success alone does not negate fair use.

Argument 4: Policy Considerations and the Piracy Issue

The court document argues that Meta’s piracy—downloading over 134 terabytes of copyrighted material, including plaintiffs’ books, from pirated sources—categorically precludes fair use (Document, Page 9). Courts have consistently rejected fair use for P2P file-sharing and illegal acquisitions (In re DMCA & 512(H) Subpoena to Twitter, Inc., 608 F. Supp. 3d 868, 879 (N.D. Cal. 2022)). The revenue forecasts reinforce this by showing that Meta’s piracy was a cost-saving strategy to achieve massive profits, undermining any claim of good faith (Perfect 10, Inc. v. Amazon.com, Inc., 508 F.3d 1146, 1164 n.8 (9th Cir. 2007)).

However, Meta argues that its use is necessary for AI development, framing the case as “existential” for generative AI (Meta Br. at 1). The revenue projections could support Meta’s claim that AI has significant public benefits, potentially justifying fair use under a broader societal lens (Oracle, 593 U.S. at 35). Yet, the plaintiffs counter that Meta developed “non-LibGen” models, proving piracy was not essential (Document, Page 7). The revenue forecasts thus cut both ways: they highlight Meta’s commercial motive but also suggest that AI’s economic impact could warrant some flexibility in fair use, provided the use is lawful.

Conclusion

Meta’s forecast of $460 billion to $1.4 trillion in AI revenues by 2035 should influence the fair use analysis in Kadrey v. Meta but not determine it outright. The projections are highly relevant to the first factor, emphasizing the commercial nature of Meta’s use, and the fourth factor, amplifying the market harm to plaintiffs’ licensing and sales opportunities. These factors weigh strongly against fair use, particularly given Meta’s piracy, which courts have uniformly rejected as unfair (Glacier Films (USA), Inc. v. Turchin, 896 F.3d 1033, 1043 (9th Cir. 2018)). However, the second and third factors, and the need for a holistic analysis, mean that revenue alone should not be dispositive. Policy considerations—balancing AI innovation with copyright protection—further suggest a limited role for revenue forecasts.

Given the evidence of Meta’s deliberate piracy, non-transformative use, and market harm, the revenue projections strengthen the plaintiffs’ case by highlighting Meta’s profit-driven motive and ability to pay for licenses. Thus, the court should consider these forecasts as part of the fair use analysis, particularly to underscore the commerciality and market impact, but weigh them alongside the piracy issue and other factors. Based on the court document, Meta’s actions likely do not constitute fair use, and the revenue forecasts reinforce this conclusion by exposing the scale of Meta’s financial gain at the plaintiffs’ expense.


Claude’s answer:
I'll address the question posed in the headline: "Should Meta's projected $460 billion to $1.4 trillion in AI revenue by 2035 affect fair use analysis?"

Should Meta's AI Revenue Projections Affect Fair Use Analysis?

Yes, Meta's projected massive revenue from AI should be a significant factor in fair use analysis, though it's not the only consideration.

Why Revenue Projections Are Relevant to Fair Use

The fair use doctrine in copyright law requires balancing multiple factors, with the fourth factor specifically addressing "the effect of the use upon the potential market for or value of the copyrighted work." Meta's revenue projections directly relate to this factor in several ways:

1. Commercial Character of Use: The documents reveal Meta's use of copyrighted works is overwhelmingly commercial. Meta projects $460 billion to $1.4 trillion in revenue from its AI products by 2035, with $2-3 billion expected in 2025 alone. This contradicts any framing of their use as non-profit, educational, or research-oriented.

2. Market Harm to Authors: The court documents indicate Meta initially allocated $200 million for licensing datasets (including $100 million for books) before abandoning this approach to use pirated content. This demonstrates:

   • Meta recognized a licensing market exists

   • Meta was willing to pay substantial amounts before deciding to circumvent it

   • Authors and publishers lost potential licensing revenue

3. Bad Faith Considerations: The documents reveal Meta abruptly ended licensing negotiations on April 7, 2023, after discovering it could obtain the same works from pirated sources. The decision was even "escalated to MZ [Mark Zuckerberg] re: fair use." This suggests Meta deliberately chose not to participate in an available market.

Broader Implications for Fair Use

Fair use determinations require weighing all four statutory factors, not just market effects. However, when a use is highly commercial and designed to generate enormous profits, courts typically require a stronger showing of transformativeness to offset this commerciality.

The Supreme Court emphasized in the Warhol case (2023) that the degree of transformation must be balanced against commerciality. When a company stands to earn potentially trillions of dollars using copyrighted works, it strengthens the argument that rightholders deserve compensation.

Conclusion

While no single factor is determinative in fair use analysis, Meta's projected massive revenues should significantly impact the analysis. These projections reveal the highly commercial nature of Meta's use and illuminate the potential market harm to authors who are denied participation in this value creation. The scale of projected profits makes it particularly difficult to justify Meta's decision to abandon licensing negotiations in favor of using pirated content without compensation.

Courts should consider whether allowing such uses without compensation would create perverse incentives that undermine copyright's fundamental purpose of encouraging creative production by ensuring creators can benefit from their work.


Perplexity’s answer:
The question of whether Meta's projected AI revenue ($460 billion to $1.4 trillion by 2035) should influence fair use analysis in Kadrey v. Meta hinges on how commercial success intersects with copyright law’s four-factor test. Below is a breakdown of the arguments for why these forecasts could critically affect the fair use determination:

1. Factor One: Purpose and Character of the Use

Meta’s commercial exploitation undermines transformative claims.

• Meta’s AI revenue projections highlight a direct profit motive, weakening arguments that LLaMA’s use of copyrighted books is non-commercial or "transformative" under Campbell v. Acuff-Rose. While transformative uses (e.g., parody, research) often favor fair use, Meta’s use is explicitly tied to monetizing AI outputs.

• Courts have rejected fair use for blatantly commercial copying, even when transformative. For example, in A&M Records v. Napster, the Ninth Circuit ruled that Napster’s peer-to-peer sharing was commercial and unfair, despite arguments about "sharing culture". Meta’s torrenting of pirated books mirrors Napster’s model, but at a trillion-dollar scale.

2. Factor Four: Effect on the Market

Meta’s revenue forecasts signal market harm to authors.

• The $100 million initially budgeted for book licensing (later abandoned for piracy)shows Meta recognized the value of copyrighted works. By avoiding licensing fees, Meta deprived authors of compensation, directly harming the market for their works.

• If LLaMA-generated content displaces demand for original books (e.g., via summaries, adaptations, or synthetic text), the $1.4 trillion revenue projectionbecomes evidence of market substitution. Courts have rejected fair use when secondary uses "supersede the objects of the original" (Harper & Row v. Nation Enterprises).

3. Factor Three: Amount and Substantiality

Meta’s use of entire books negates fair use.

• Plaintiffs allege Meta copied full texts from pirated databases. Even transformative uses (e.g., Google Books’ snippet view) are limited to partial copies. In Hachette v. Internet Archive, scanning entire books for digital lending was deemed unfair, as it replicated the "heart" of the works. Meta’s wholesale copying for AI training similarly exceeds permissible bounds.

4. Meta’s Conduct and Intent

Piracy and CMI stripping demonstrate bad faith.

• Meta’s shift from licensing negotiations to torrenting "shadow libraries" reveals intentional infringement, undermining fair use’s good-faith requirement. Courts penalize defendants who bypass licensing markets (Glacier Films v. Turchin).

• Removing copyright management information (CMI) to conceal training data sourcesviolates the DMCA and suggests Meta sought to obscure infringement, further weakening fair use defenses.

Counterarguments and Rebuttals

• Transformative use defense: Meta may argue LLaMA’s output is transformative, akin to Google Books’ search functionality (Authors Guild v. Google). However, Google’s use was non-commercial and limited to snippets, whereas LLaMA’s outputs risk competing with original works.

• Market harm speculation: Meta could claim plaintiffs failed to prove actual displacement. Yet, McGucken v. Pub Ocean held that market harm can be inferred from a use’s commercial nature, especially when the defendant profits massively.

Conclusion

Meta’s revenue projections are not just contextual-they are central to fair use analysis. The scale of commercial gain, combined with evidence of piracy and market harm, strongly disfavors a fair use finding. Courts have consistently rejected fair use for large-scale, profit-driven copying that undermines creators’ rights (Napster, Hachette). Allowing Meta to retain trillion-dollar profits from pirated works would set a dangerous precedent, incentivizing tech companies to exploit copyrighted material without compensation.

Final Answer: Yes-Meta’s revenue forecasts should weigh heavily against fair use, as they underscore commercial exploitation, market harm, and bad-faith conduct.


Gemini Advanced 2.5’s answer:
Based on the provided article and court document, here's an analysis of whether Meta's forecasted AI revenues should affect fair use analysis:

The central question is whether the massive potential commercial gain Meta anticipates from its AI models, like Llama, should influence the legal determination of whether its use of copyrighted materials (like books used for training) constitutes "fair use." The plaintiffs in the Kadrey v. Meta case argue strongly that it should, and the provided documents detail their reasoning.

Arguments for Considering Revenue Forecasts in Fair Use Analysis:

1. Commerciality (Fair Use Factor 1: Purpose and Character of the Use):

   • Fair use analysis considers whether the use is commercial or for nonprofit educational purposes. While Meta presents Llama as having both commercial and non-commercial applications, the plaintiffs argue its development is fundamentally a commercial, profit-seeking venture.

   • The plaintiffs emphasize that Meta is not a charity but one of the world's largest for-profit companies making a calculated bet on generative AI for potentially huge profits.

   • The sheer scale of the forecasted revenue—$460 billion to $1.4 trillion by 2035 —underscores the highly commercial nature of the undertaking. This immense commercial motivation, the plaintiffs argue, weighs against a finding of fair use, especially when compared to uses with less commercial intent. The Supreme Court has indicated that the more commercial a use, the less likely it is to be fair, particularly if it doesn't comment on or criticize the original work.

2. Market Harm (Fair Use Factor 4: Effect on the Potential Market):

   • This factor examines the impact of the use on the potential market for, or value of, the copyrighted work. Plaintiffs argue Meta's actions harm multiple markets.

   • Harm to Licensing Market: A significant argument is that Meta's use of allegedly pirated datasets harms the emerging market for licensing books specifically for AI training. Plaintiffs contend Meta explored licensing, budgeted substantial amounts (up to $100 million specifically for books), but ultimately chose to use pirated sources instead to avoid costs and rely on a fair use defense. The massive projected revenues demonstrate the high value Meta places on this data and the potential profits derived, suggesting a significant market that authors are being denied participation in. Allowing widespread, uncompensated use by entities poised to make trillions would destroy this potential licensing market.

   • Harm to Book Sales Market: Plaintiffs also argue Meta's piracy directly cost them book sales that Meta would otherwise have had to make (or license) to acquire the training data. While Meta focuses on whether Llama outputscompete with books, the initial acquisition via piracy represents a direct substitution harm.

3. Bad Faith and Unfair Conduct (Relevant to Factor 1):

   • The plaintiffs argue Meta's actions—knowingly using pirated datasets, avoiding licensing deals it was pursuing, and attempting to conceal its data sources —demonstrate bad faith, which weighs against fair use. The enormous financial stakes provide a motive for this alleged bad faith conduct.

Conclusion:

The plaintiffs in Kadrey v. Meta, as detailed in the court document, argue compellingly that the vast commercial scale and potential profitability of Meta's AI endeavors, highlighted by the revenue forecasts in the article, are central to the fair use analysis. These figures underscore the commercial nature of the use (Factor 1) and demonstrate the potential for significant market harm to copyright holders by supplanting licensing markets (Factor 4). Therefore, according to the arguments presented, Meta's projected AI revenues should significantly affect the fair use analysis, weighing against a finding of fair use.