Imagine this: you ask an AI a harmless, everyday question to a LLM-powered AI, and have it respond with absolute confidence... but gives you a completely wrong answer.
That unsettling moment where the machine acts sure but is dead wrong is what AI researchers politely call a hallucination. It’s an error, but one dressed up in conviction. It may feel like a bug, or some glitches in the system. Or maybe, AI is just designed to lie when it doesn't know the correct answer.
In a published paper (PDF) from OpenAI, researchers argue that they problem is actually far more fundamental.
It's not just that models go wrong sometimes; the way we train and evaluate them incentivizes them to bluff when uncertain.
In short: AIs hallucinate not because they want to lie, but because they’ve been taught that bluffing often "wins" in testing.
In other words, AIs learned that humans don't take "no" for an answer, and "I don't know" means a failure.
At the core of this issue is how LLMs are trained.
Or also called large language models, the models learn to predict the next word given all prior ones in a statistical objective without any labels for "true" or "false." This is the reason why LLMs always render the next sentence, after the previous sentence is generated. It doesn't know the whole thing it should be saying, before saying it systematically.
This method means that when the model encounters a question it doesn’t "know" for sure, it often defaults to the most plausible-sounding continuation, rather than admitting uncertainty.
While researchers have attributed hallucinations to insufficient training data, that is just half the story.
The other half lies in how we judge these models.
In most benchmarks, the scoring is binary: either its answer was correct, or it was wrong. There’s no reward for admitting, "I don’t know."
In such an arena, a model that always guesses (even if often wrong) will often outperform a model that is more cautious. And so the system learns: better to sound confident than be silent.
The OpenAI paper calls this phenomenon an "epidemic of penalizing uncertainty."
In other words, researchers designed LLMs to be that way. They built in on a landscape, where overconfidence is the winning strategy, even if it leads to dangerous fabrications.
Making things worse, once an LLM hallucinates, it may amplify the hallucination. This is similar to how humans lie, and keep on lying to cover up their previous lies.
This insight should change the meaning of "AI hallucination."
They're not caused by some random misfires or error in the codes. The hallucinations are literally baked into the mechanics of how LLMs actually learn and how they're rewarded.
And because the dominant benchmarks and leaderboards favor correctness over humility, even newer, more powerful models still fall into the trap of confident deception.
OpenAI doesn’t propose scrapping everything. Instead, their proposal is elegant and strategic: revamp how the models are evaluated.
They suggest that benchmarks should penalize confidently wrong answers more heavily than uncertainty, and give partial credit when models express doubt or decline to answer.
By aligning incentives toward truthful caution, future AI systems might be less prone to hallucinate.
In other words, if benchmarks stop rewarding guesswork and start rewarding humility, we may move closer to systems that are not just articulate, but trustworthy.
But that doesn’t mean hallucinations will vanish overnight.
While errors that are statistically inevitable in a probabilistic model, especially when dealing with obscure or low-frequency facts, can be eliminated, there will always be moments of uncertainty. But that uncertainty is a good thing, because it could force AIs to stop pretending to know what they don't know, and just admit their lack of knowledge.
And this matters.
In domains like medicine, law, finance, or safety-critical systems, the difference between a confident hallucination and a humble "I don’t know" can be life-changing.
AI that knows when it’s out of depth, and makes that clear, is far more valuable than one that fabricates with flair.
At its heart, this is more than a technical fix. It’s a philosophical shift: from AI as a magician that dazzles with confident responses, to AI as a partner that knows its limits.
And as we reshape the rules of the game, we may finally begin to see systems that are not just clever, but wisely and responsibly so.
This AI hallucination phenomenon is not completely understood, since AIs work within their own black box. But proposed solutions can make things better, not worse.