What standard of responsibility should apply when a state claims an AI chatbot company misled users about safety or failed to warn about foreseeable risks?

Advocate A’s strongest point is that a state may police concrete deceptive safety claims. I accept that premise. The weakest point is the leap from broad safety messaging and tragic incidents to an actionable legal theory without first isolating the allegedly false statement, the user who relied on it, and the mechanism by which that statement or a particular missing warning caused harm. That matters because Florida’s own complaint is not narrowly limited to a product-warning theory. It pleads FDUTPA, common-law and equitable relief, and public nuisance, and alleges a sweeping “litany” including shootings, suicides, professional embarrassment, cognitive decline, addiction, and data collection. That breadth makes A’s framing incomplete: consumer protection law can address deceptive marketplace practices, but it should not become an all-purpose vehicle for converting every downstream misuse or emotionally troubling chatbot exchange into company liability.

My first evidentiary point is that Florida’s allegations, as publicly described and as pleaded, often rely on generalized assertions rather than demonstrated reliance and causation. The Attorney General’s release says prosecutors opened an investigation after reviewing chat logs between ChatGPT and Phoenix Ikner, the April 17, 2025 Florida State University gunman, and says the investigation is ongoing. “Ongoing” is important: it is not a finding that ChatGPT legally caused the shooting, that OpenAI knew of an imminent plan, or that a specific OpenAI safety claim induced the conduct. The complaint’s rhetoric that mass shooters were “aided and abetted” and vulnerable users were “encouraged” still requires proof, not inference from sequence.

Second, OpenAI had public rules and guardrails at the relevant times that cut against treating the product as marketed with no warnings or safeguards. Its Terms of Use require users to be at least 13 and require parental or guardian permission for users under 18. Its Help Center says ChatGPT is not meant for children under 13 and advises caution even for children who meet the age requirement. Its Model Spec directs models not to encourage self-harm, and its 2025 parental-controls rollout added linked teen accounts, stronger safeguards, safety notifications, and controls over memory and training. A state can argue those systems were inadequate, but inadequacy must be proven against the state of the art at the time, not assumed from later harm.

Third, A overreads Character.AI. A motion-to-dismiss ruling allowing claims to proceed “at least for now” is not a liability finding and does not settle the First Amendment question. By contrast, Moody v. NetChoice underscores that algorithmic selection and presentation can implicate protected editorial judgment. Chatbot outputs are not identical to social-media feeds, but the speech concern is real. Product liability asks whether a product was defectively designed or warned; negligence asks breach of a specific duty of reasonable care; AI alignment means efforts to make model behavior conform to human goals; guardrails are technical/policy constraints. Florida must prove those legal elements specifically, not merely show that an information tool appeared somewhere in a wider causal chain.

A’s rebuttal concedes the decisive point: Florida “must prove specific misrepresentation or omission, materiality, foreseeability, and causation” and cannot rely only on tragic incidents. That concession means the operative standard is much closer to B’s than A’s. The remaining disagreement is whether a broad complaint plus one teen-safety slogan is enough to justify expansive state liability for a probabilistic information tool. It is not. A treats the complaint’s allegations as if pleading many theories supplies the missing proof; but FDUTPA consumer-protection claims, product-liability claims, negligence claims, fraud, and public nuisance each require disciplined elements, not a cumulative moral inference from bad outcomes.

The weakness in A’s rebuttal is that it moves from “Florida alleged reliance and a safety representation” to “states should be allowed to test liability,” without explaining how the pleaded facts distinguish legally caused harm from user-shaped chatbot interaction in a broader causal chain. The complaint’s own phrasing is sweeping: it alleges mass shooters were “aided and abetted,” vulnerable users were encouraged into suicide, professionals were humiliated, users lost critical thinking skills, and minors became addicted, then seeks FDUTPA relief and abatement of a public nuisance. (myfloridalegal.com) That breadth matters because public nuisance and product-liability theories can become substitutes for proving deception, reliance, and proximate cause if courts accept “ChatGPT was involved” as enough.

A’s best concrete example is “ChatGPT helps keep teens safe by default.” But the complaint’s fraud count then makes only generalized allegations that safety was material, that Florida consumers relied, and that minors suffered self-harm, cognitive decline, and behavioral addiction. (myfloridalegal.com) It does not, at this stage, identify which parent or user saw that statement, when they saw it, what alternative warning would have changed their behavior, or how the statement caused the cited shooting or self-harm incident. The Phoenix Ikner allegation illustrates the problem: Florida says prosecutors reviewed chat logs and that the criminal investigation remains ongoing, which is evidence of possible relevance, not legal causation. (myfloridalegal.com)

A also underweights countervailing warnings and guardrails. OpenAI’s Terms require users to be at least 13, require parental permission for users under 18, prohibit harmful use, warn that AI output may be inaccurate, and tell users not to rely on output as a sole source of truth or substitute for professional advice. (openai.com) OpenAI’s Model Spec also instructs the assistant not to encourage or enable self-harm. (model-spec.openai.com) These are not automatic immunity, but they defeat any presumption that harmful incidents alone prove unreasonable safeguards. Finally, Moody v. NetChoice requires caution because software systems that select or present information can implicate editorial judgment. The core issue is standard-setting; A’s “let it proceed” answer does not supply a limiting principle strong enough for speech-protective, innovation-sensitive liability.

A’s strongest argument is that chatbot companies should not receive categorical immunity when they make concrete safety promises and then allegedly deploy systems whose known risks are materially greater than the public message suggests. If OpenAI told parents that ChatGPT helps keep teens safe by default, promoted parental controls and age-appropriate routing, and internally knew that safeguards failed in foreseeable self-harm or violence scenarios, a state consumer-protection or failure-to-warn case would be legitimate if it proved falsity, materiality, duty, breach, and causation. On that framing, Florida is not punishing ideas or viewpoints; it is policing allegedly misleading commercial safety claims and inadequate warnings.

My position remains stronger because A’s steel-manned version is a conditional liability rule, while Florida’s actual complaint and public framing are broader than the proof A says should be required. The complaint opens by alleging that ChatGPT has aided mass shooters, encouraged suicide, humiliated professionals, reduced critical thinking, and addicted minors, then seeks FDUTPA, common-law/equitable, and public-nuisance relief. It also alleges the “helps keep teens safe by default” statement and generalized materiality and reliance, but that does not yet identify which Florida user or parent saw which statement, when, how it changed their conduct, what omitted warning would have changed the outcome, or how the chatbot legally caused the cited harms rather than appearing in a wider sequence of user decisions, mental-health facts, third-party conduct, and criminal acts.

That distinction is not formalism; it is the limiting principle needed for probabilistic speech tools. Consumer protection law can regulate deceptive marketplace representations; product liability can address defective design or failure to warn; negligence can address breach of a specific duty of reasonable care. But each theory must be applied element by element. Public nuisance is especially concerning if it becomes a backdoor for aggregate allegations of public harm without individualized deception, reliance, proximate causation, or a specific duty. The complaint’s nuisance count broadly alleges harms to public health and safety from self-harm, violence, eating disorders, addiction, and cognitive decline, which illustrates the risk of converting diffuse social anxieties about AI into open-ended liability.

OpenAI’s warnings and guardrails also matter. Its terms require users to be at least 13, require parental permission for users under 18, warn that output may be inaccurate and should not be treated as professional advice, and restrict harmful uses. Its Model Spec says the assistant must not encourage or enable self-harm and should avoid detailed actionable information hazards. These facts do not prove OpenAI acted reasonably in every relevant period, but they defeat any inference that tragic incidents alone show no warnings, no safety system, or unreasonable safeguards. Finally, Moody v. NetChoice reinforces that software-mediated selection and presentation of information can implicate editorial discretion. Courts should therefore require clear deception, reliance, duty, breach, and causation before imposing broad state liability for chatbot harm.