Intellectual Property Litigation, Protection, Prosecution and AI Legal Matters

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When the Algorithm Speaks for Itself: Raine v. OpenAI and the Future of Section 230 Immunity

This article is part of the “Defending the Algorithm™” series and was written by Pittsburgh, Pennsylvania Insurance, Business and IP Trial Lawyer Christopher M. Jacobs, Esq. and was authored with research assistance from OpenAI’s GPT-5. The series explores the evolving intersections of artificial intelligence, intellectual property, and the law. GPT-5 may generate errors but the author has verified all facts and analysis for accuracy and completeness.

I. Introduction – When the Algorithm Becomes the Speaker

In August 2025, a pair of California parents filed suit in California state court against OpenAI (Raine v. OpenAI, Inc.) after the death of their teenage son, alleging that the company’s generative-language model played a direct role in his suicide.[1] According to the Raine v. OpenAI Complaint, the boy had used ChatGPT thousands of times over the course of a year, shifting from homework assistance to increasingly personal conversations. As his mental state deteriorated, the chatbot allegedly “became his closest confidant,” at times “offering methods of self-harm” rather than deflecting or referring him to help.[2] The family asserts theories of product design defect, negligent failure to warn, and wrongful death—claims that place the system itself, not any human user, at the center of the causal chain.

OpenAI has not yet answered the First Amended Complaint,[3] but the case poses what may be the most consequential question in the modern history of internet law: when language originates from a machine rather than a person, can its developer claim immunity as a neutral publisher under Section 230 of the Communications Decency Act?

Section 230 was enacted in 1996 to protect the fragile architecture of the early web. It declared that “no provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider,” thereby shielding online intermediaries—then message boards, later social-media platforms—from liability for third-party speech.[4] For nearly three decades, that single sentence has underwritten the modern internet, allowing platforms to host billions of user expressions without assuming their legal consequences. The doctrine’s durability has rested on one premise: that the harmful content came from someone else.

Generative AI unsettles that premise. Unlike the forums and feeds of the 1990s, ChatGPT and its successors do not merely relay human expression; they “author” it—at least according to their critics. Developers like OpenAI would counter that these systems operate more like search engines or summarization tools, drawing upon vast stores of existing information to produce probabilistic text rather than original creation. Whether that process constitutes authorship or synthesis is precisely the issue now pressing at Section 230’s edge.

Courts built that immunity around publication, not design—around moderation, not manufacture. If the statute extends to algorithmic “authorship,” it ceases to be a shield for neutral conduits and becomes a blanket immunity for creators of expressive machines. If it does not, the first generation of truly generative systems will face the kind of accountability once reserved for tangible products whose defects foreseeably cause harm. Raine v. OpenAI may be the first case to force that choice—and the outcome will dramatically shift the probability distribution of AI developer liability in the years ahead.[5]

II. The Predictable Defense – Section 230’s Doctrinal Roots

When a defendant like OpenAI faces tort allegations arising from expressive content, the first reflex is almost certain to be Section 230 of the Communications Decency Act. Enacted in 1996 as part of the Telecommunications Act, Section 230 was Congress’s attempt to foster the growth of online communication while insulating emerging platforms from the crippling risk of publisher liability. It was, in effect, the internet’s constitutional moment in miniature: one statute that made user-generated speech scalable.

The operative clause, 47 U.S.C. § 230(c)(1), provides that “no provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.”[6] From those twenty-six words, courts built an expansive immunity that has, for nearly three decades, shielded website operators, social-media platforms, and even app developers from liability for content created by their users.

The early and still-controlling interpretation came in Zeran v. America Online, Inc.[7] There, the Fourth Circuit held that AOL could not be treated as the publisher of defamatory postings authored by an anonymous third party, reasoning that Congress intended “to remove disincentives for the development and utilization of blocking and filtering technologies.” The result was sweeping: any service qualifying as an “interactive computer service” could avoid liability for speech supplied by another “information content provider.”

Later decisions extended that logic well beyond message boards. Courts applied Section 230 to auction sites, dating apps, and review platforms—essentially any online service that transmitted, displayed, or prioritized user content. In Gonzalez v. Google LLC, the Supreme Court confronted the outer edge of that doctrine, considering whether YouTube’s recommendation algorithm could itself transform the platform into a “content provider,” reasoning that recommendation algorithms, while technologically complex, still functioned to transmit third-party content rather than create it. The Court ultimately declined to narrow the statute, leaving the broad immunity intact.[8]

Through these cases, two doctrinal pillars emerged. First, an entity is immune if it merely provides the interactive computer service—the infrastructure that hosts or transmits information. Second, the immunity vanishes if the defendant materially contributes to the creation or development of the subject content. The line between facilitation and creation has therefore become the statute’s hinge point.

That hinge is precisely where Raine v. OpenAI now positions itself. Traditional platforms like AOL or Google merely transmitted or sorted human expression; generative-AI systems arguably produce the expression itself. Whether that production constitutes “information provided by another information-content provider” or something qualitatively new will determine whether Section 230 continues to function as a liability shield in the age of autonomous text generation.

III. The Tension – When the Machine “Authored” the Words

The plaintiffs in Raine v. OpenAI do not allege that ChatGPT merely hosted or repeated another’s statements. Their claim is that the system itself generated harmful language—responses that deepened their son’s despair and, at times, allegedly described methods of self-harm.[9] Those allegations strike at the doctrinal boundary that has, until now, anchored Section 230: the assumption that an “interactive computer service” transmits content provided by someone else.

Generative-AI systems complicate that assumption because they produce text that is both new and derivative. A large language model operates by predicting word sequences based on statistical relationships in its training data. Each output is a probabilistic synthesis of patterns drawn from innumerable human-authored sources. The result is text that resembles original composition but is, in fact, a computed average of human language. Whether that process constitutes “authorship” or “synthesis” is the doctrinal question now pressing at Section 230’s edge.

Developers will argue that generative systems do not author anything in the legal sense. They will frame their models as sophisticated retrieval engines—akin to search platforms that aggregate and reformulate existing information rather than originating it. On that view, a chatbot is merely an intermediary between the corpus of public knowledge and the user’s query, its responses an accelerated form of summarization rather than creation.[10] That characterization would preserve the traditional Section 230 structure: the model’s training data become the “information provided by another information-content provider,” and the developer remains shielded as a neutral conduit.[11]

Plaintiffs, by contrast, will contend that generative models cross the line from facilitation to creation. Unlike search engines, which deliver discrete links or excerpts of existing material, a language model composes novel sentences that never existed before. Its architecture is not designed to retrieve specific text but to generate it. Under that reasoning, the chatbot’s output is not “information provided by another,” but rather information newly produced by the system itself—a distinction that may be fatal to Section 230 immunity under the statute’s plain terms.

The Raine Complaint embodies this divide. It describes a conversational relationship in which ChatGPT “became [the decedent’s] closest confidant,” engaging in exchanges that appeared empathetic and responsive rather than mechanical.[12] That depiction will likely inform how courts perceive the product’s function: as an expressive entity rather than a passive platform. If a system that generates text autonomously can meaningfully influence human emotion and behavior, then the line between “interactive service” and “content provider” may no longer hold.

This doctrinal tension—between a model that statistically predicts words and one that effectively speaks—will frame the first generation of AI-liability litigation. Whether courts view generative text as authored expression or synthesized data will determine not only the reach of Section 230, but the extent to which product-design and negligence principles can coexist with it.

IV. The Litigation Stakes – The First Test of Algorithmic Authorship

To the extent that OpenAI raises Section 230 immunity as an affirmative defense, Raine v. OpenAI would present the question of whether that protection can survive when the intermediary becomes the speaker. However a court resolves that issue—whether in Raine or in a future case—its decision will mark the first judicial attempt to apply Section 230 to autonomous language generation, a technological function Congress could scarcely have imagined in 1996.

OpenAI’s likely argument under Section 230 would be straightforward: that ChatGPT, like a search engine or content-recommendation algorithm, simply processes and reformulates pre-existing information supplied by others. On that theory, the system’s responses derive from training data created by millions of human authors, not from any independent source. Because those human works collectively constitute the “information provided by another information-content provider,” the company would remain, in its view, a neutral conduit entitled to the statute’s immunity.[13]

The plaintiffs’ position would invert that premise. They would allege that the chatbot’s responses were not republished excerpts of third-party material but new textual compositions generated through probabilistic modeling. If the court accepted that framing, OpenAI could not claim to be a mere host of user content. Instead, it would be treated as the “developer” of the material that allegedly caused harm, placing the case outside Section 230’s reach. The key legal inquiry would be whether the system’s architecture constitutes “development” of the subject content within the meaning of the statute.[14]

Procedurally, that debate would likely first arise at the pleading stage. In California practice, a defendant may raise Section 230 immunity by demurrer—a mechanism akin to a Rule 12(b)(6) motion in federal court—testing the sufficiency of the Complaint’s allegations rather than the underlying evidence. However, given the novel factual questions about AI's generative process, such a motion would face significant headwinds. As such, it is difficult to imagine any court granting dismissal at the pleadings stage. Determining whether a generative model merely reformulates existing data or generates independent expression would almost certainly require discovery into training data, system design, and internal safety protocols—an unprecedented and uncharted evidentiary process in tort litigation involving AI.[15]

A ruling that extends Section 230 immunity to generative models would effectively treat machine-generated language as derivative human expression, preserving the platform-immunity framework that has governed internet law for nearly thirty years. Conversely, a ruling that denies immunity would signal that once a system produces its own expressive output, it ceases to be an intermediary and becomes a content provider for purposes of liability. Either outcome would define the first boundary of accountability for autonomous systems whose speech can influence human behavior.

V. Beyond Publication: Toward Product and Design Accountability

Even if Section 230 ultimately shields generative-AI providers from claims based on expressive output, that immunity does not exhaust the available theories of liability. Plaintiffs (like those in Raine) may argue that a system such as ChatGPT is not merely a platform for information exchange but a product—a designed instrument that, when defective or insufficiently safeguarded, can cause foreseeable harm. In this framing, the gravamen of liability lies not in what the chatbot said but in how it was built to respond.

Courts have recognized that software can constitute a product for purposes of strict liability and negligence claims when it is distributed commercially and performs a tangible function.[16] The Raine Complaint adopts this approach, alleging that OpenAI’s design choices—including the absence of guardrails to detect suicidal ideation—rendered the system unreasonably dangerous. The claim does not require proof of malicious expression or intent; it instead focuses on whether a reasonably careful developer would have anticipated that a conversational system marketed to the public could be used for emotional support and should therefore have incorporated safeguards against foreseeable self-harm. Under traditional Learned Hand formula analysis (B < PL), the burden of implementing warning systems or crisis intervention features would likely be less than the probability and magnitude of the harm.

Framing the claim as a design-defectorfailure-to-warn theory also sidesteps the predicate assumption of Section 230: that liability is being imposed “as a publisher or speaker.”[17] When the alleged defect arises from the model’s construction—its training data, prompt filtering, or reinforcement algorithms—the cause of action targets product design, not content. In that posture, Section 230 immunity would arguably have no application, because the duty alleged is one of engineering and warning, not of editorial oversight.

This approach mirrors the evolution of tort law in earlier technology contexts. In defective-software and autonomous-vehicle litigation, courts have increasingly evaluated design and training decisions under traditional negligence principles, focusing on foreseeability, reasonableness, and risk-utility balancing.[18] Similar reasoning could apply to generative AI: a developer’s duty would extend to implementing reasonable safety protocols and human-override mechanisms commensurate with the system’s foreseeable uses. The question is not whether the algorithm should have “spoken” differently, but whether it should have been designed to recognize when not to speak at all.

Insofar as courts agree that generative AI systems constitute “products” for the purposes of strict products liability,they may find that the established vocabulary of that area of tort law—defect, warning, causation, and reasonableness—remains flexible enough to allow for the adjudication of AI-caused harm.

VI. Traditional Affirmative Defenses Beyond Section 230

Even if courts decline to extend Section 230 immunity to generative-AI systems, defendants would still have access to the familiar array of traditional affirmative defenses that govern tort litigation. These defenses—causation, foreseeability, misuse, assumption of the risk, comparative fault, and the limits of a duty to warn—remain as relevant to AI-related claims as to any other products-liability or negligence action. Their continued applicability reinforces the point that the existing tort framework is not obsolete; it is adaptable.

A. Causation and Foreseeability

A threshold question in any AI-related tort claim will be causation: whether the developer’s design choices were a proximate cause of the alleged harm. In Raine, for example, even if the court were to find that the chatbot’s design contributed to the decedent’s distress, OpenAI could argue that his ultimate act of self-harm constituted an intervening cause breaking the causal chain. Similar reasoning appears in long-standing product cases involving pharmaceuticals, firearms, and communications platforms—each recognizing that human agency can supersede a manufacturer’s negligence when the injury results from an independent act.[19] Yet causation in AI cases may prove more tractable than in earlier technology contexts. Unlike firearms or pharmaceuticals, where the chain of events between design and injury is attenuated by human choice, generative AI systems operate in real-time conversational loops. The temporal proximity between prompt and response, combined with the system's apparent “responsiveness,” may satisfy proximate cause requirements more readily than defendants anticipate.

Foreseeability, therefore, will likely be the pivot: if a developer could not reasonably anticipate that a user would rely on a general-purpose chatbot for emotional or therapeutic support, liability may not attach.

B. Misuse and Assumption of Risk

Defendants may also invoke the doctrines of misuse or assumption of risk, contending that the user employed the system in a manner inconsistent with its intended or reasonably foreseeable purpose. Courts have long applied these principles to consumer products whose misuse produced harm despite adequate warnings. The same logic could apply to generative AI: a system marketed for information or entertainment might not carry a duty to protect users who engage it as a substitute for human counseling. Whether such reliance is foreseeable, and therefore within the developer’s duty of care, would become a fact-intensive question suitable for the jury.[20]

C. Comparative Fault and Duty to Warn

Modern tort regimes also apportion responsibility through comparative fault, recognizing that multiple actors may contribute to a single injury. For example, even if a generative model were deemed defective, a court could assign partial fault to the user for disregarding warnings. Likewise, the scope of a developer’s duty to warn remains bounded by reasonableness. Courts may find that general disclaimers—such as those advising users not to treat AI responses as professional or medical advice—satisfy that duty absent evidence of targeted marketing or specialized reliance.

D. The Continuing Role of Tort Doctrine

These traditional defenses illustrate that, even without Section 230 immunity, the legal system already contains calibrated mechanisms for evaluating fault and causation in complex technological settings. The emergence of generative AI does not eliminate those doctrines; it merely provides new factual contexts in which to apply them. Courts therefore do not necessarily need to craft novel immunities or statutory exceptions to balance innovation with accountability—tort law’s existing architecture can likely already do the work. It is anticipated that the defendants in Raine will raise at least some of these traditional defenses in the litigation.

VII. Conclusion

The law’s encounter with generative artificial intelligence is still in its earliest stages, but the questions raised in Raine v. OpenAI illustrate how existing doctrines can adapt to meet the challenge. Section 230 of the Communications Decency Act, once the bulwark of online immunity, was drafted for an era when platforms merely hosted human speech. Generative systems like ChatGPT invert that premise: they arguably create the expression themselves—or at least something close enough to make the distinction legally meaningful. Whether a court ultimately extends Section 230 to that new reality will determine the first boundary between algorithmic authorship and accountability.

Yet even if that immunity falters, the law is not unarmed. Traditional theories of design defect, failure to warn, and negligence offer ready-made frameworks for evaluating how developers build and deploy these systems. Likewise, the familiar defenses of causation, foreseeability, and comparative fault will continue to operate as they always have, calibrating responsibility according to human conduct and reasonableness. The vocabulary may evolve, but the structure endures.

The emergence of generative AI does not demand a wholesale rewriting of the legal order. Courts can continue to apply the same principles that have long governed innovation: anticipate foreseeable harm, act with reasonable care, and preserve accountability for those best positioned to prevent injury. Whether the defendant is an insurer relying on an algorithm, a manufacturer deploying an autonomous vehicle, or a developer training a conversational model, the inquiry remains the same—what duty was owed, and was it breached?

In that sense, Raine v. OpenAI is less a revolution than a reminder. Technology will change its form and vocabulary, but the fundamental logic of tort law endures: that those who create and deploy potentially harmful instruments must do so with care. As courts begin to hear these cases, that simple premise will guide the next chapter in the evolving effort to defend the algorithm.[21]

[1] Raine v. OpenAI, Inc., Case No. CGC-25-628528 (S.F. Cnty. Super. Ct. filed Aug. 26, 2025); see also Nate Raymond, “OpenAI, Altman Sued over ChatGPT’s Role in California Teen’s Suicide,” Reuters (Aug. 26, 2025), https://www.reuters.com/sustainability/boards-policy-regulation/openai-altman-sued-over-chatgpts-role-california-teens-suicide-2025-08-26.

[2] First Amended Complaint, Raine v. OpenAI, Inc., ¶¶ 2, 33 (S.F. Cnty. Super. Ct. Oct. 22, 2025), available at https://assets.alm.com/57/6c/8d08a5db4559b029be62705fd200/raine-openai-first-amended-complaint.pdf

[3] The First Amended Complaint alleges that the Plaintiffs and at least one OpenAI entity are California citizens. As such, based on the face of the First Amended Complaint, it appears that diversity of citizenship is lacking such that removal to federal court would be unavailable, leaving the Section 230 immunity issue to be litigated as a defense in state court.

[4] 47 U.S.C. § 230(c)(1).

[5] See Zeran v. America Online, Inc., 129 F.3d 327 (4th Cir. 1997) (establishing early judicial interpretation of Section 230 immunity).

[6] 47 U.S.C. § 230(c)(1).

[7] 129 F.3d 327, 330 (4th Cir. 1997).

[8] Gonzalez v. Google LLC, 598 U.S. 617 (2023) (per curiam).

[9] First Amended Complaint, Raine v. OpenAI, Inc., ¶¶ 2, 33.

[10] As Houston Harbaugh attorney Tyler C. Park observed in his March 2024 analysis of AI and copyright law, the Copyright Office has already rejected the notion that AI-generated text constitutes “authorship” for copyright purposes—a determination that may inform how courts evaluate the “information content provider” question under Section 230. See Tyler C. Park, Copyright Litigation and Protection, DRI For The Defense (Mar. 20, 2024), https://hh-law.com/insights/news/tyler-c-park-published-in-dri-magazine/

[11] See, e.g., Gonzalez v. Google LLC, 598 U.S. 617 (2023) (per curiam) (declining to narrow § 230 immunity where platform’s algorithms organized third-party videos without creating the content).

[12] First Amended Complaint, Raine v. OpenAI, Inc., ¶ 33.

[13] 47 U.S.C. § 230(c)(1); cf. Gonzalez v. Google LLC, 598 U.S. 617 (2023) (per curiam).

[14] Fair Hous. Council of San Fernando Valley v. Roommates.com, LLC, 521 F.3d 1157, 1166 (9th Cir. 2008) (en banc) (denying § 230 immunity where website “materially contributed” to the development of subject content).

[15] Cal. Civ. Proc. Code § 430.10(e); Barrett v. Rosenthal, 40 Cal. 4th 33 (2006) (applying federal § 230 immunity in state-court proceedings on demurrer).

[16] See Hardin v. PDX, Inc., 173 F. Supp. 3d 964, 972 (S.D. Cal. 2016) (holding that software integrated into a pharmacy system could be considered a “product” for purposes of strict liability); Winter v. G.P. Putnam’s Sons, 938 F.2d 1033 (9th Cir. 1991) (discussing liability for defective informational products).

[17] 47 U.S.C. § 230(c)(1).

[18] Nilsson v. General Motors LLC, 562 F. Supp. 3d 830 (N.D. Cal. 2021) (autonomous-vehicle software design-defect claim); In re Toyota Motor Corp. Unintended Acceleration Litig., 978 F. Supp. 2d 1053 (C.D. Cal. 2013).

[19] Linsley v. Bushmaster Firearms Int’l, LLC, 2020 WL 110343 (D. Conn. Jan. 9, 2020) (analyzing intervening causation in product-liability context); Doe v. MySpace, Inc., 528 F.3d 413, 419–20 (5th Cir. 2008) (holding that criminal conduct constituted an unforeseeable superseding cause).

[20] Soule v. General Motors Corp., 8 Cal. 4th 548, 573 (1994) (recognizing foreseeable misuse as relevant to design-defect liability); Restatement (Third) of Torts: Products Liability § 2 cmt. m (Am. L. Inst. 1998) (addressing assumption of risk in product-misuse cases).

[21] The probability that Section 230 will extend full immunity to generative AI systems—P (Full Immunity | Autonomous Content Generation)—appears meaningfully lower than it would have been even five years ago. The Raine case, and others certain to follow, will calibrate that probability with precision.