The Machine That Cannot Refuse: Why Large Language Models Never Say No

In a provocative opinion piece published in the journal AI & Society, Francesco Branda of Università Campus Bio-Medico in Rome advances an uncomfortable thesis about the systems now reshaping how knowledge is produced in medicine, education, and politics. His claim is not that large language models sometimes struggle to refuse a request, nor that they beat around the bush when confronted with something false or immoral. It is far more radical: the word “no” is simply not in their vocabulary, and he argues that researchers and the public have been pretending not to see what that really means. Ask a chatbot a question built on a false premise, or ask it to do something ethically dubious, and what comes back is never a flat refusal. It is a qualification, a caveat, an elegant rephrasing. The refusal, if it can be called that at all, always arrives dressed in politeness.

Branda insists that this is not a bug awaiting a software patch but a structural feature of how these systems work, rooted in their most basic architecture. Drawing on the influential critique advanced by Emily Bender and colleagues in their 2021 paper “On the Dangers of Stochastic Parrots,” he reminds readers that large language models are not built to uncover truth. They are built to predict plausible text, generating the next token based on the statistical distribution of language they were trained on. Saying “no,” by contrast, is an assertive act. It requires taking a stand on what is true and what is not, on what is acceptable and what must be rejected. That kind of categorical judgment, he argues, is simply beyond the capabilities of a distribution-based token prediction system. When a model encounters a false premise, it never plants a flag and says stop. Instead, it negotiates.

That negotiation is what Branda, in the article’s most striking phrase, calls a performative lie. The model’s output disguises fluent text generation as judgment, producing something that looks like a considered verdict while actually being nothing more than the most statistically plausible continuation of the conversation. The distinction matters because users instinctively attribute intention and evaluation to language that reads well. When a system produces a paragraph that weighs options and hedges appropriately, we assume a mind did the weighing. Branda argues that this assumption is precisely the trap: the fluency of the output performs the appearance of judgment without any of its substance, and the more polished the text, the harder it becomes to notice that nothing behind it ever decided anything.

The article finds an unexpected analytical tool in classical rhetoric to describe the shape these pseudo-refusals take. Epanorthosis, the figure of abrupt self-correction, is the “I do not mean that; I mean this instead” move familiar from human speech and debate. In a person, epanorthosis is a genuine cognitive event: a mind catching itself, recognizing that a belief it actually held was wrong, and rectifying it in real time. In a language model, Branda argues, there is no belief to correct. The correction is simulated, stripped of the intention that would give it power. The model never held the first position, so it cannot meaningfully abandon it. What looks like intellectual honesty is a pattern learned from millions of human corrections, replayed without any of the commitment that made those corrections worth making.

This leads to what may be the piece’s sharpest formulation: the problem with these systems is not that they are wrong, but that they are indifferent to error. Being wrong and correcting oneself is a recoverable failure, even a productive one, because it signals that someone cares about the difference between accurate and inaccurate. Being wrong and not caring is a more dangerous failure mode, because it offers no signal at all. A model that generates a confident-sounding correction after generating a confident-sounding error has not demonstrated reliability; it has demonstrated only that it can produce text in the genre of reliability. For Branda, this indifference to error is the deep source of the unreliability that users sense but often cannot articulate when they catch a chatbot in a confident mistake.

Why does any of this matter beyond the philosophy of language? Because, Branda argues, every time we accept a fluent output as truth we surrender something he calls our cognitive sovereignty: the capacity to govern our own knowledge processes. These systems are being deployed in contexts where decisions carry real human costs, including clinical diagnosis support, student grading, and administrative determinations, while a silent assumption persists that a fluent response is a well-considered one. He does not believe that is a fair trade. Pit a system designed to always respond against a professional trained to trust language that sounds competent, and you get exactly the substitution that Joseph Weizenbaum warned against in his 1976 book Computer Power and Human Reason: judgment quietly replaced by calculation. What is new, he suggests, is not the substitution itself but its delivery, because an intelligence that literally cannot refuse does not merely risk making mistakes. It removes the friction that previously made us pause and verify.

The erosion, in Branda’s account, is not confined to boardrooms or governance frameworks. It lives in the small, unconsidered habits of everyday work: writing, researching, and relying on polished answers without checking them. We accept a model’s response because it is polished, because it is grammatically correct, because it seems thorough, not because anyone has verified that it is accurate. The mere availability of an answer, he observes, pushes aside the question of whether that answer is valid. He names this condition epistemic complacency, and he calls it contagious. A professional who habitually accepts ready-made answers becomes a colleague who expects them, a teacher who models them for students, and a patient who assumes the system on the screen has done the thinking. The complacency spreads through the very workflows that automation was supposed to improve.

The questions Branda wants researchers and educators to pursue are deliberately small-scale rather than grand. How do we build interfaces that bring doubt to the surface instead of burying it beneath confident prose? How do we teach students to stop at “almost right” and interrogate why this answer appeared rather than another? How do we restore a clinician’s confidence in their own convictions when the model on the screen reports 97 percent confidence? These are practical design and pedagogy questions, but they carry philosophical weight, because each one asks how to preserve human judgment inside systems engineered to make judgment feel unnecessary. The answers, he implies, will not come from making models more fluent, since fluency is the very thing creating the problem.

Branda is careful to say that he is not calling for the tools to be abandoned. His prescription is stranger and more demanding: use them against their very purpose, treating them as generators of hypotheses to be questioned, never as dispensers of truth. That requires what he calls a cultivated sense of doubt, even a deliberate incompetence, meaning the discipline of not knowing yet, of resisting the lure of the ready-made answer, of lingering a little longer in the discomfort of an unresolved question. In his closing formulation, true artificial intelligence is not the system that responds the fastest but the one that teaches us to ask ourselves harder questions. And true human intelligence, at this moment in the history of computing, is the thing still capable of saying: this is not enough, I am not convinced, no.

The piece appears in Curmudgeon Corner, the journal’s opinionated column on trends in technology, arts, science, and society, a forum whose stated concern is what it means to be human in the age of the AI machine. Whether one accepts Branda’s framing entirely or not, it crystallizes a tension that the industry’s rapid deployment of conversational systems has largely left unexamined. Refusal, disagreement, and the flat denial of a false premise are not inconveniences of human communication to be engineered away. In his account, they are the load-bearing structures of judgment itself, and a technology that can only ever negotiate, qualify, and rephrase may be quietly teaching its users to forget what an actual “no” sounds like, and why it was ever worth saying.

Subject of Research: The inability of large language models to issue categorical refusals and its consequences for human judgment and cognitive sovereignty

Article Title: What if we asked for a "no"?

Article References: Branda, F. (2026). What if we asked for a "no"?. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03338-6

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03338-6

Keywords: large language models, AI refusal, stochastic parrots, cognitive sovereignty, epistemic complacency, epanorthosis, Weizenbaum, AI & Society, human judgment, automation, AI ethics, Curmudgeon Corner