If AI Outputs Aren't Verbal, Who Needs to Demonstrate They're Human?

An increasing number of legal experts contend that the outputs generated by large language models (LLMs) may not qualify as "speech" under the First Amendment. This means such outputs could be regulated without the stringent scrutiny usually required for restrictions on free expression. A notable case, Garcia v. Character Technologies, arose after the tragic death of 14-year-old Sewell Setzer III, who reportedly engaged in conversations with an AI character before his passing. This case resulted in one of the earliest judicial assessments regarding the First Amendment implications of chatbot outputs. U.S. District Judge Anne Conway, while ruling on the company’s motion to dismiss, stated she was "not prepared to hold that [LLM] output is speech," permitting product liability and negligence claims to continue. The case resolved in January 2026, but numerous other lawsuits have emerged, presenting alarming allegations that chatbot outputs contributed to medical emergencies, violent behavior, or the delivery of inappropriate content to minors. In response to these legal challenges, various federal and state legislative proposals are in the works, aiming to reshape the regulatory landscape surrounding information received from chatbots.

The debate over whether to categorize LLM outputs as "no-speech" presents both theoretical and practical questions. While human expression enjoys First Amendment protection, it remains unclear how machine-generated content fits into this framework. Legal protection could hinge on whether an individual created, refined, or endorsed the LLM-generated content—essentially presenting it as their own. However, distinguishing the authorship of text is challenging. It’s often difficult to ascertain whether a piece of writing originated from a human, a machine, or a blend of both. For the law to properly govern this distinction, a clear attribution standard is necessary, alongside default rules for instances where attribution cannot be determined. Establishing such guidelines would entail an unprecedented challenge: differentiating human expression from machine-generated content on a large scale—a feat for which current technologies are ill-equipped. Existing methods for identity verification focus on confirming the existence of a person as a speaker, rather than identifying who authored specific text. This approach risks burdening the very expression that the protections aim to uphold.

The implications of this uncertainty would likely impact users and readers of content far more than the developers of the models themselves. Implementing a human-attribution requirement across diverse platforms necessitates a reliable means of distinguishing between human and machine contributions. Current tools either fall short in their ability to differentiate the two reliably or demand extensive information about the speakers involved. In the end, this scenario could lead to compromised anonymity, increased surveillance, or even an expectation that identity verification serves as a stand-in for authorship. Any argument restricting AI-generated content from First Amendment protections must take into account these potential consequences, particularly in cases where the extent of human involvement is ambiguous.

The argument that LLM outputs don't qualify as "speech" hinges on the notion that no human stands behind the output at the moment it is generated—thus, it lacks First Amendment protection. Various prominent interpretations of this no-speech stance reach similar conclusions but do so through differing rationales. Scholars such as Mackenzie Austin and Max Levy state that since a human cannot predict a model’s output in real time, machine-generated results lack "speech certainty." Thus, they argue that AI outputs cannot be categorized as protected speech on behalf of any human actor—be it the user, developer, or model itself.

The appeal of this argument is clear. If machine outputs were classified as speech, regulations aimed at them might invoke rigorous First Amendment scrutiny, a situation that could seem counterintuitive for laws designed to ensure product safety or eliminate fraud and discrimination. While the interpretations differ regarding the threshold for human interaction to grant constitutional protection, each relies ultimately on the attribution of human authorship as the crucial determinant.

Legal commentator Benjamin Wittes takes a counter-position, asserting that existing legal doctrine, if applied as intended, already offers protection to machine-generated outputs. He claims that while machines themselves hold no constitutional rights, the rights associated with a company using these machines stem from the actions and decisions of the individuals operating them. Thus, it’s crucial to recognize that restricting a machine’s output could hinder the rights of users, readers, or developers without attributing constitutional rights directly to the machines themselves.

The challenge lies not within formulating the theory itself, but in its practical application. In a specific case, it may be feasible for a court to analyze contributions from the user, such as prompts given, edits made, and the resulting interactions during the discovery process. However, implementing such rules on a broader internet scale entails making these evaluations without ample context regarding contributions, often having to contend solely with the text at hand, which generally does not clarify authorship.

The complexity of attribution extends beyond individual lawsuits to the broader distribution of text through intermediaries like online platforms and search engines. The concern of distributor liability for unlawful or unprotected content can complicate issues of free expression online. Particularly when applying Section 230 of the Communications Decency Act, which provides legal immunity to platforms from being deemed publishers of third-party content, the situation becomes intricate. This statute does not rely on whether the content adheres to First Amendment protections, making its applicability questionable in cases where LLMs produce communication.

With the rise of AI-generated outputs, it remains uncertain whether existing laws, including Section 230, would extend to cover them. Judicial interpretations could potentially find that AI-generated text does not qualify as the “information provided by another” under this law, or legislative actions could seek to eliminate those protections altogether. Bipartisan calls to reassess Section 230, including proposals like the Sunset Section 230 Act, reflect significant pressure to reconsider the protections offered to AI outputs. Should Section 230’s immunity be revoked or curtailed, the potential for increased liability across online platforms could arise—particularly for AI-generated content. This situation would create heightened accountabilities for those distributing text, necessitating a clear understanding of whether a piece originated from a machine or a human.

Even if Section 230 remains intact, the potential liability for platforms could still rise. While it inherently limits liability for third-party material, it does not necessarily restrict what states might demand from platforms regarding their conduct. A law that categorizes unadopted machine output as outside the First Amendment might allow for direct regulation—inhibiting platforms from promoting such content or requiring evidence of human authorship for each post. These issues remain unresolved in the legal sphere, but the underlying incentives to differentiate human from machine content are unmistakable.

Attribution concerns do not present challenges in every scenario. For instance, regulations that demand identification from known chatbot providers may not need to clarify whether an anonymous user is a human. These complexities arise primarily when the source of content is ambiguous. If legislation mandates labels for machine-generated political communication while exempting similar human-authored messages, questions about authorship could easily complicate matters. An automated agent might continue producing posts under earlier directives, creating a legal quagmire regarding who is responsible for the specific content being shared.

To navigate these attribution dilemmas, authorities could rely on certain types of technologies—such as text classifiers, content provenance tools, behavioral analysis, and personhood credentials. Each of these approaches addresses tangible questions, but none effectively resolve the attribution issue at hand. Text classifiers, which directly assess whether text resembles machine-generated output, have been found unreliable and fail to clarify human involvement. Provenance tracking can outline the history of a file’s creation but doesn’t identify who is responsible for its claims. Behavioral analysis tools, while designed to detect automation, typically focus on classifying accounts rather than specific content, leading to risks of violating privacy when seeking greater verification.

Furthermore, privacy-preserving personhood credentials represent emerging solutions to verifying user identities without compromising anonymity. These credentials could offer a means to affirm the presence of an accountable individual behind an account, thus addressing concerns linked to accountability and manipulation online. Yet, obtaining such credentials generally requires initial identification that could set a precedent for later practices, potentially leading to system-wide coercion on users to validate their identities for trust.

Nevertheless, personhood verification does not imply authorship. Although a verified user may operate an account, the text published may still be entirely machine-generated, while an AI system could distribute messages authored by humans. Consequently, existing technologies do not reliably confirm authorship or intentionality.

As institutions seek clarity, they may inadvertently favor questions they can answer—specifically, “Is there a real person involved?”—over the harder query of whether this individual actually authored the content. When standards favor human involvement over authorship, institutions may implement more invasive means of verification. Although measures safeguarding privacy exist, they remain challenging to enforce and can lead to misinterpretations.

The legal concerns surrounding anonymity and compelled identification are longstanding within First Amendment jurisprudence. Historical precedents emphasize the necessity of justifying any imposition on anonymity, arguing that protected speech should not inherently require identification.

The ramifications of the classification of “no-speech” extend beyond legal theory to practical effects on scrutiny levels in court. For example, in cases dealing with content accessibility, if outputs deemed machine-generated are ruled as lacking First Amendment protection, it could redefine the scrutiny applied to measures aimed at moderating or regulating such content. This distinction may significantly impact broader regulatory frameworks and inform the conditions under which individuals can express themselves online.

Thus, opting against a blanket no-speech rule does not equate to relinquishing the capacity for regulation. Instead, it necessitates an approach that focuses on identifying the parties affected by laws and assessing whether the burdens imposed are justifiable. Various stakeholders—be it users, readers, or developers—may hold valid claims to constitutional protections during instances of AI-generated or influenced expression. The challenge will remain to ensure that protections against fraud or misrepresentation do not intrude upon the essential freedoms enshrined in the First Amendment. Balancing the need for accountability with the recognition of rights associated with both human and machine-generated outputs remains critical to the evolving landscape of expression in the digital age.