Are AI Hallucinations Really Inevitable?

Whenever a chatbot confidently invents a citation or a court case, the response is now ritual: a sigh, a screenshot, and the conclusion that hallucinations are an unfixable flaw of AI. The claim has hardened into common wisdom. Language models predict the next word, the argument goes, so they cannot really know what is true, and confidently making things up is part of the deal.

Research from OpenAI and academic groups in the past year offers a less convenient picture. Hallucinations are real and frequent, and they remain a serious problem. They are not, however, baked into the architecture in the way the popular story suggests.

The training-incentives argument

OpenAI's explainer published in late 2025 reframes the question. Hallucinations persist, the authors argue, because the way models are trained and evaluated rewards guessing over saying "I don't know." On a benchmark, a wrong answer scores zero, the same as an abstention. A correct guess scores one. The expected value of guessing is therefore positive whenever the model has any signal at all about the right answer, even if that signal is unreliable.

That incentive structure compounds across millions of training steps. A model that has been trained on benchmarks like this learns, in effect, that confident output is rewarded and uncertainty is not. The fix proposed by the authors is straightforward in description and harder in practice: penalise confident wrong answers more than abstentions, and the training signal pushes models toward calibrated humility.

Smaller models can know their limits

Another piece of conventional wisdom holds that solving hallucinations requires ever-larger models. The OpenAI research pushes back. A small model, the authors note, can sometimes recognise the boundary of its competence more easily than a large one, because the smaller model has less to be confidently wrong about. The capacity to say "I don't know" is a property of training, not of parameter count.

This matches what users have been reporting in practice. Hallucination benchmarks consistently show that the lowest hallucination rates are not always held by the biggest models; in some categories smaller, well-tuned systems beat their larger cousins.

The confidence trap

An MIT study from January 2025 found that when models hallucinate, they tend to use more confident phrasing than when they answer correctly. Words like "definitely," "certainly," and "without doubt" appeared 34 percent more often in incorrect responses. That is the inverse of what calibrated reasoning should look like.

For users, the takeaway is awkward. The instinct to trust a confident-sounding answer is exactly the wrong instinct around current models, and the more authoritative the prose, the more sceptically it deserves to be read. For developers, the implication is that hallucinations are tractable, but the work involves unglamorous things: better evaluation, better data labelling, and incentives that reward the model for shutting up when it does not know.

Hallucinations are not going to disappear next quarter. They are also not a law of nature.

Sources: OpenAI research, Duke University Libraries, Suprmind benchmarks.