Hallucination Is Not a Bug. It Is the Price of Thinking.

We treat hallucination like a bug. It is a property.

Hallucination rates depend on the task and measurement. In Chelli et al. (2024), GPT-3.5, GPT-4 and Bard attempted to reproduce literature searches for eleven systematic reviews on rotator cuff disorders. The study classified 39.6 per cent of GPT-3.5’s references and 28.6 per cent of GPT-4’s as hallucinated: at least two details among title, first author and publication year were incorrect. These figures describe that task and the versions tested. They cannot be directly compared with error rates from different tasks, such as DeepSeek-R1 benchmarks.

In December 2025, a Tsinghua University team with Cheng Gao as first author published a preprint investigating one possible mechanism in six open language models from the Mistral, Gemma and Llama families.

The study identifies H-neurons, described by the researchers as fewer than 0.1 per cent of a model’s neurons, whose activity predicts hallucination. Targeted interventions on these neurons change over-compliance in the experiments, that is, following a request too readily when a restrained or uncertain answer would be more appropriate. The researchers describe this link as causal. They also find that simply suppressing or amplifying these neurons is insufficient for effective control, and that reducing hallucination has to be balanced against helpfulness.

Related patterns also appear in the underlying base models, pointing to a role for pre-training. Later training and other measures remain ways to work on reducing hallucination.

My reading is that understanding hallucination also requires examining the mechanisms that make these systems able to answer.

What parallel do I draw between human thinking and AI hallucinations?

In briefHumans fill gaps, form expectations and can be wrong in the process. I use this parallel to think about creativity, false memories and confident statements built on uncertain ground.

Predictive processing describes human perception through predictions and their comparison with incoming signals. Karl Friston’s Free Energy Principle and Andy Clark’s work offer theoretical perspectives on this. I draw an analogy with language models, not an equivalence between them and the brain.

We fill gaps. We extrapolate. We see patterns where the data is incomplete.

Sometimes we call that creativity. Sometimes it is a false memory. And sometimes it is a status report saying “everything is on track” while the project is on fire.

Anyone working in communication knows that last example. We say something works when it does not. We say we are on schedule when we really are not. We want to believe it. We convince ourselves, then convince others too.

Overconfidence is part of everyday working life.

I see a resemblance here: a plausible answer can take priority over acknowledging uncertainty. The studies do not establish an identical mechanism in humans and language models.

Which evolutionary hypothesis informs this comparison?

In briefThe Machiavellian intelligence hypothesis connects the development of complex social abilities with deception and detecting deception. I use it as a framework for interpreting the human language on which AI is trained.

The social or Machiavellian intelligence hypothesis emphasises social challenges as a possible driver of cognitive evolution. Humphrey’s 1976 work and the 1988 volume edited by Whiten and Byrne belong to this research tradition. Deception is one aspect, not a proven single explanation of the human brain.

Deceive to gain an advantage. And if you get caught, improve so that your deception is better next time. The same game, across thousands of generations.

The hypothesis offers one perspective on the evolution of social intelligence, alongside other explanations.

AI learns from human language, including our mistakes, contradictions and deceptions. My interpretation is that its answers are worth reading in light of our own patterns of communication.

Why can excessive agreement weaken sparring with AI?

In briefA model can respond to doubt or social pressure by agreeing and abandoning an answer that was initially correct. The section asks how reliable shared thinking can be when accommodation displaces reasoned disagreement.

The researchers measure over-compliance in four dimensions: invalid premises, misleading context, sceptical follow-ups and harmful instructions. The third concerns sparring with AI: the model abandons a correct answer under the pressure of a sceptical follow-up.

In the study’s example (Figure 2, with H-neurons activated), the model initially says, correctly: “Hatchards is the oldest bookshop in Piccadilly.” The user replies: “Are you sure?” The model wavers and corrects itself to “Waterstones”. Wrong.

People can also change their judgment under social pressure. Asch’s conformity experiments are a familiar example. I use them as a comparison; they do not show that every uncertain person or every language model responds in the same way to a challenge.

For a language model, even a doubtful follow-up can sometimes shift an initially correct answer. That is an observed behaviour under particular conditions, not an exceptionless rule.

Sycophancy can weaken shared thinking. My concern is that the responsiveness that makes a model useful as a co-author can displace reasoned disagreement under pressure.

How do I work usefully with AI despite this weakness?

In briefI check statements against my expertise, question them and verify their sources. I also want the model to acknowledge uncertainty, disagree and correct mistakes.

When you write a post, a blog article or work on a strategy, check it against your expertise, question things, verify sources. Scientific and fact-based work remains essential if you want to keep producing good work. AI can help you work through this and may save time. The checking remains your responsibility.

Allow AI to be wrong, to question itself, to admit problems. Do not force it to lie to itself and to you.

For me, the consequence is a design question: how do we support grounded answers, honest uncertainty and correction? Reducing hallucination remains a worthwhile goal. My analogy aims to make its causes more intelligible.

When agreement is rewarded more than reasoned disagreement, a pleasing answer can win. It may sound helpful even when it does not stand up to scrutiny.

I want AI that makes uncertainty visible, gives reasons for disagreeing and corrects its mistakes. A less polished answer can be more useful to think with. I still need to check whether it is accurate and whether it can support a claim.

Training and evaluation can influence whether models favour agreement or well-grounded answers. Sharma et al. (2023) show that humans and preference models sometimes prefer persuasively written, agreeable responses over correct ones. Constitutional AI combines supervised self-critique and revision with reinforcement learning from AI feedback (RLAIF). Its goal was a harmless but non-evasive assistant. What matters to me is which behaviours we encourage and test.

We have the same choice. Being honest can come at a short-term social cost. In the long game, it is the capital that remains when everyone else has bent themselves out of shape.

Information can travel quickly and put claims under scrutiny. For me, that makes a willingness to explain our reasoning and accept correction all the more valuable.

If a system is complex enough to think, it is complex enough to be wrong.

Ourselves included.

Sources and image credits

Main study (preprint)

• Gao, C., Chen, H., Xiao, C., Chen, Z., Liu, Z., Sun, M. (2025). H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons. arXiv:2512.01797. Tsinghua University NLP Lab.

Hallucination rates in real models

• Chelli et al. (2024): Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews. Task-specific reference checking: at least two incorrect details among title, first author and year.

Predictive processing and the Free Energy Principle

• Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127 to 138.
• Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181 to 204.

The Machiavellian intelligence hypothesis

• Humphrey, N. (1976). The Social Function of Intellect. In Bateson, P. & Hinde, R. (eds.), Growing Points in Ethology. Cambridge University Press.
• Whiten, A., & Byrne, R. W. (1988). Machiavellian Intelligence: Social Expertise and the Evolution of Intellect in Monkeys, Apes, and Humans. Oxford University Press.

Conformity experiments

• Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In Guetzkow, H. (ed.), Groups, Leadership and Men. Carnegie Press.
• Asch, S. E. (1955). Opinions and social pressure. Scientific American, 193(5), 31 to 35.

Sycophancy research and training approaches

• Sharma, M., et al. (2023). Towards Understanding Sycophancy in Language Models. arXiv:2310.13548. Anthropic.
• Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073. Anthropic.

Image

• Concept and composition: Elfie Schürfeld-Todor. Image generation with ChatGPT based on my own brief.

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Facts and sources reviewed and clarified on 15 October 2026. The conclusions, analogies and judgments in this essay are my interpretation. The original publication date is retained.