Tremblay et al v. OPENAI, INC. et al

Filing 378

Order by Magistrate Judge Robert M. Illman denying 314 Motion to Compel. (rmilc2, COURT STAFF) (Filed on 3/5/2025)

Case 3:23-cv-03223-AMO

Document 378

Filed 03/05/25

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UNITED STATES DISTRICT COURT

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NORTHERN DISTRICT OF CALIFORNIA

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EUREKA DIVISION

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PAUL TREMBLAY, et al.,
Plaintiffs,

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United States District Court
Northern District of California

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Case No. 23-cv-03223-AMO (RMI)

v.
OPENAI, INC., et al.,
Defendants.

ORDER RE: PLAINTIFFS’ MOTION
TO COMPEL PRODUCTION FORM
NON-PARTY REUTERS NEWS AND
MEDIA, INC.
Re: Dkt. No. 314

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Now pending before the court is Plaintiffs’ Motion (dkt. 314) to Compel Production of

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Documents from Non-Party Reuters News and Media, Inc. (hereafter, “Reuters”); Reuters has

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responded (dkt. 348); and, Plaintiffs have filed a reply (dkt. 367). Having reviewed the Parties’

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submissions, pursuant to Federal Rule of Civil Procedure 78(b) and Civil Local Rule 7-1(b), the

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court finds the matter suitable for disposition without oral argument. For the reasons stated below,

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Plaintiffs’ motion is denied.

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Plaintiffs begin by contending that in order to prosecute this case and to respond to

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OpenAI’s fair use defense, they require information from Reuters that they cannot get from

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OpenAI. See Pls.’ Mot. (dkt. 314) at 2. Plaintiffs believe that “Reuters possesses information

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relevant to the existence of a market for copyrighted content as training data for LLMs [because]

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[o]n October 24, 2024, it was reported that Reuters had entered into a multi-year licensing

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agreement with Meta Platforms Inc. for its news content to be used in Meta’s AI ChatBot [which,

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according to a blog referenced by Plaintiffs, is] one of only a handful of AI data licensing

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agreements in the LLM training data market.” Id. In essence, Plaintiffs seek to obtain the

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agreement between Reuters and Meta Platforms, Inc. (hereafter, “Meta”) because of the notion

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that those documents are relevant to OpenAI’s anticipated fair use defense, “namely the effect of

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[OpenAI’s] theft of copyrighted material on the market for copyrighted content as training data for

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LLMs, and to assist in valuing the copyrighted works for use as training data, including

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developing a damages methodology.” Id. More specifically, Plaintiffs’ subpoena seeks three

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categories of documents from Reuters: (1) executed AI training data licensing agreements; (2) AI

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training data licensing agreement negotiations and valuations; and, (3) specific content for AI

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training. Id. at 3. Plaintiffs argue that Reuters has resisted compliance with their subpoena

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demands through meritless boilerplate objections, many of which Plaintiffs characterize as

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“frivolous.” Id. at 4.

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As to relevance, Plaintiffs submit that prying into the confidential business affairs of

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Reuters is justified here for two principal reasons: (1) their reported relevance to Plaintiffs’ claims

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of direct copyright infringement by OpenAI; and, (2) their importance and necessity for countering

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OpenAI’s fair use defense. Id. Regarding the first reason, Plaintiffs state that “[t]he documents

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sought from Reuters via the subpoena will help establish the existence of a market for copyrighted

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work as training data for LLMs because they will provide evidence of benchmark licensing

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agreements and market demand1 . . . [and that the] information is also relevant to calculating

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actual damages and recovery of lost profits.” Id. at 4-5. In short, Plaintiffs essentially ask the

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undersigned to order Reuters to divulge the details of its confidential business arrangements with

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Meta simply so that Plaintiffs can show that they “would have [otherwise] been able to license

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their works at a competitive price.” Id. at 5. Plaintiffs contend that Reuters’ “internal valuation

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memos can provide evidence of potential lost revenue due to infringement and would likely

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contain projections and analyses of potential licensing income” which might “support their claims

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for lost profits.” Id. Plaintiffs add that the negotiations surrounding the Reuters/Meta licensing

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deal might “provide evidence of the license’s value, helping to quantify [Plaintiffs’] losses [in this

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case].” Id.
As to Plaintiffs’ efforts to counter OpenAI’s anticipated fair-use defense, Plaintiffs contend

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It should be noted that Plaintiffs seek this information for news content, which is different from the content
involved in this case.

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Northern District of California

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that this discovery would relate to “the fourth factor in establishing such defense (the effect of the

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use upon the potential market for or value of the copyrighted work)[,] [which] requires

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consideration of whether the unlicensed use of the work undermines the market or licensing

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opportunities for it.” Id. at 5. In other words, Plaintiffs’ argue that “[i]f a licensing market exists

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and is negatively impacted by OpenAI’s theft of [Plaintiffs’] data, [which] weighs against a

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finding of fair use,” that the opening up of the files documenting the business relationship between

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Reuters and Meta “will aid Plaintiffs in this analysis.” Id. at 5-6. It is, of course, unclear why

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Plaintiffs believe that the particulars of their non-news content case will be materially illuminated

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by a licensing deal for news content.

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Plaintiffs report that Reuters has resisted compliance with their subpoena request on

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grounds of overbreadth, vagueness, undue burden, and confidentiality. Id. at 6-8. By way of

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response, Reuters argues that – as a third-party – it is entitled to protection from undue burden and

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production of confidential information where a substantial need is not demonstrated. See Reuters’

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Opp. (dkt. 348) at 5-7. Reuters also reports that, by way of an offered compromise that would have

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narrowed the scope of Plaintiffs’ demands, it “offered to produce all executed agreements related

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to AI training data (which would include the Meta Agreement) and, to the extent they exist,

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documents and communications with third parties related to all non-executed, proposed, or in

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pipeline licensing agreements related to AI training data where there was any reasonable chance of

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aligning on the financial or licensing terms,” but that Plaintiffs’ counsel rejected its offer. Id. at 7.

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Reuters then adds that “Plaintiffs’ counsel [even] declined Reuters’ request that the date for [its]

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opposition be adjourned to permit the parties to discuss the resolution of the Motion.” Id. at 8.

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In ruling on Plaintiffs’ request to compel this information, the undersigned will note

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several facts. First, nowhere in Plaintiffs’ motion is there any explanation as to why the essence of

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the information it seeks cannot be garnered either from OpenAI, or through expert witness

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testimony, or by some other less intrusive means. That is, Plaintiffs have not explained why prying

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into Reuters’ commercial relationship with Meta is the only way to “establish the existence of a

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market for copyrighted work as training data for LLMs . . . [such as to] provide evidence of

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benchmark licensing agreements and market demand . . . [so that Plaintiffs can] calculat[e] actual
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damages and recovery of lost profits.” See Pls. Mot. at 4-5. In their reply brief, Plaintiffs mention

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(for the first time, and in a rather conclusory fashion) that they “seek relevant evidence not

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otherwise obtainable from OpenAI about the market for textual data2 as training data for Large

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Language Models.” See Pls. Reply (dkt. 367) at 3. Plaintiffs then note that they have sought

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licensing deals for AI training data from OpenAI, but they do so without any mention as to the

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outcome of having made that request (i.e., without explaining what happened – did OpenAI hand

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over a trove of documents? Did OpenAI claim it had nothing? Did OpenAI simply ignore the

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request?). See id. at 3 n.2.

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“The determination of substantial need is particularly important in the context of enforcing

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a subpoena when discovery of trade secret or confidential commercial information is sought from

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non-parties.” See Gonzales v. Google, Inc., 234 F.R.D. 674, 685 (N.D. Cal. 2006) (citing Mattel

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Inc. v. Walking Mt. Prods., 353 F.3d 792, 814 (9th Cir. 2003). The undersigned will first note that,

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in this context, the “substantial need” showing must be concrete, not speculative as is the case

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here. See, e.g., Waymo LLC v. Uber Techs., Inc., 2017 U.S. Dist. LEXIS 132721, *10 (N.D. Cal.

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Aug. 18, 2017) (“. . . that Uber ‘maybe’ will need discovery from other companies is not even

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close to a ruling that Uber has shown a substantial need for compelling the non-parties’ trade

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secrets.”). Second, the “substantial need” showing must be just that, substantial. See, e.g.,

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Cameron v. Apple Inc. (In re Apple Iphone Antitrust Litig.), 2021 U.S. Dist. LEXIS 25194, *12

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(N.D. Cal. Jan. 26, 2021) (“In ruling on discovery issues, and in particular to assess ‘substantial

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need,’ the Court must sometimes dip its toe into the merits of the case, which inform the

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consideration of how relevant the requested documents [really] are.”) (emphasis added).

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Here, the court finds that Plaintiffs have sought what is undeniably confidential

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commercial information (if not trade secrets) from one non-party, Reuters, about its relationship

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with another non-party, Meta. The court further finds that while Plaintiffs have made a clear case

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that OpenAI does not possess the particular documents at issue here – that is, those underlying the

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business affairs between Reuters and Meta – what Plaintiffs have not done is to even mention (let

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By using the broad phrase, “textual data,” Plaintiffs gloss over the difference between the news content
which is the subject of the Reuters/Meta agreement, and the non-news content that is the subject of this case.
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alone convincingly establish) why the essence of this information (i.e., its upshot or import, which

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is merely information about the existence of a market for licensing copyrighted “textual data” to

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train an AI model) cannot be either obtained from OpenAI, or by expert witness testimony, or by

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any other means that would not involve prying into the confidential commercial information of

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two non-parties. Moreover, the court finds that the marginal relevance of the information Plaintiffs

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seek is substantially outweighed by the burden its production would pose to the non-parties whose

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rights would be implicated by that production. At bottom, the court finds that Plaintiffs have not

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demonstrated a substantial need for the discovery they seek, and that the requested discovery is not

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proportional to the needs of the case. Accordingly, Plaintiffs’ Motion (dkt. 314) is DENIED.

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United States District Court
Northern District of California

Document 378

IT IS SO ORDERED.
Dated: March 5, 2025

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ROBERT M. ILLMAN
United States Magistrate Judge

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