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No. Reducing hallucinations would not automatically destroy ChatGPT. The provocative claim came from a September 2025 argument that making AI more cautious could increase computing costs, slow responses and frustrate users—not from evidence that eliminating confident errors would make the product commercially impossible.
OpenAI’s own research makes a narrower point: language models are often rewarded for guessing because common evaluations score correct answers but do not adequately reward appropriate uncertainty. The practical challenge is to make models more accurate and better calibrated without turning every difficult question into a refusal.
Where the “destroy ChatGPT” claim came from
The headline refers to a September 15, 2025 Futurism article, not a new discovery in 2026. It summarized OpenAI’s September 5 explanation of hallucinations and an argument by University of Sheffield academic Wei Xing, published in The Conversation.
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What an AI hallucination actually is
An AI hallucination is a plausible-sounding but false or unsupported statement delivered with unwarranted confidence. It can be an invented citation, a fabricated biography, a false attribution, an incorrect statistic or an exact date that the system has no reliable basis for providing.
It is not simply an opinion that differs from yours, nor is every wrong answer equally dangerous. The most serious failures combine specificity and confidence: a nonexistent legal rule, an incorrect medication instruction or a fabricated source that appears authoritative to a nonexpert.
In its explanation of hallucinations, OpenAI described asking a chatbot for biographical facts about paper coauthor Adam Tauman Kalai. The system produced multiple different answers, all incorrect. Low-frequency facts such as birthdays are especially difficult because they may appear rarely, be inconsistently recorded or be absent from the model’s accessible information. A fluent answer still has the same linguistic shape as a true one.
Why language models guess
Large language models are trained primarily to predict likely sequences of text. That makes them powerful at producing coherent language, but it does not give them a built-in, universal truth-verification mechanism.
OpenAI identifies a second problem in the way models are tested. Many evaluations reward a correct answer, treat an incorrect answer as a failure and effectively treat abstention as no answer at all. This resembles a multiple-choice exam where guessing can earn points but leaving a question blank guarantees none. A system trained around that scoring environment has an incentive to answer even when it should acknowledge uncertainty.
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That does not mean models are deliberately lying. It means fluent completion and factual reliability are different capabilities. A model may know a fact but fail to retrieve or express it, or lack the fact entirely while still generating a statistically plausible guess.
The benchmark problem in numbers
OpenAI used the following example from SimpleQA to show why accuracy alone can be misleading:
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| Model | Abstention rate | Accuracy rate | Error rate |
|---|---|---|---|
| gpt-5-thinking-mini | 52% | 22% | 26% |
| o4-mini | 1% | 24% | 75% |
In this specific example, o4-mini had slightly higher accuracy but made substantially more errors, while gpt-5-thinking-mini abstained more often and had a lower error rate. These figures are OpenAI’s own illustration, not a universal ranking of model quality or a general hallucination rate for either model.
The broader lesson is important: a model can improve its apparent score by attempting more questions, even if those extra attempts create many false answers. A useful evaluation should distinguish correct answers, incorrect answers and justified abstentions rather than collapsing them into a single accuracy number.
What OpenAI proposed
OpenAI’s proposed direction is not simply to make models timid. It calls for changing the incentives around training and evaluation:
- Penalize confident errors more heavily than uncertainty.
- Give partial credit for appropriate expressions of uncertainty.
- Update major accuracy-focused benchmarks instead of relying only on separate hallucination tests.
- Reward a model for abstaining when it cannot establish a reliable answer.
OpenAI explicitly argues that hallucinations are not inevitable in the simple sense suggested by the headline. Some questions are unknowable, ambiguous or beyond a model’s capabilities, and no system can provide perfect factual accuracy in every context. But confidently guessing when uncertainty is detectable is at least partly avoidable. A model can learn to recognize limits and decline to invent an answer.
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The full research paper is available as a PDF, while the OpenAI explainer provides the accessible summary.
What “fixing hallucinations” would require
There is no single switch called “fix hallucinations.” Different applications could use different combinations of safeguards:
- Better evaluation: Measure error severity, calibration and appropriate abstention rather than accuracy alone.
- Retrieval: Search relevant documents and ground answers in passages that users can inspect.
- Verification: Generate candidate answers, compare them, check claims and flag unsupported details.
- Tool use: Use a calculator, database, browser or specialist system when the model should not rely on memory.
- Clarification: Ask for the jurisdiction, date, product version or intended meaning when a prompt is ambiguous.
- Human review: Require qualified oversight where a wrong answer could cause serious harm.
Each measure addresses a different failure mode. Retrieval may help with current or document-specific facts, but it can return irrelevant material, misread a passage or cite a source that does not support the precise claim. Citations make checking easier; they do not prove that an answer is correct. A system can also invent a citation when retrieval fails.
Why caution could cost more
Xing’s argument is that reliable uncertainty estimation and checking may require additional computation. A system that does more than produce its first plausible response might generate several candidates, retrieve supporting material, use a slower reasoning model, cross-check claims or route difficult cases to a human.
Those steps can introduce latency and infrastructure costs. More clarifying questions add friction. More abstentions may make a general-purpose assistant feel less responsive. In a consumer product competing on speed and breadth, those costs could matter.
But “could cost more” is not the same as “would destroy ChatGPT.” The financial impact depends on implementation, the task, the model, the price users will accept and the value of avoiding errors. A casual brainstorming assistant and a document-review system for a regulated business do not need the same accuracy threshold or verification process.
Would users abandon a chatbot that says “I don’t know”?
This is the most speculative part of the original argument. Xing suggested that users accustomed to immediate, confident answers could become frustrated if a chatbot refused too often. That is a user-experience hypothesis, not evidence that people generally prefer false certainty or would abandon a more reliable product.
The real choice is not limited to a confident answer versus a dead-end refusal. A useful uncertainty response might say:
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- “There are two plausible interpretations; the answer changes depending on which one you mean.”
- “Here is the likely answer, but this date needs confirmation from an authoritative source.”
- “These are the facts I can establish, followed by the assumptions I am making.”
- “I need the country, date or software version before answering reliably.”
That is calibrated helpfulness: answering directly when the evidence is strong, asking when the question is ambiguous, using tools when current information matters and abstaining when guessing would be more harmful than silence.
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Confidence is not the same as usefulness
A confident answer feels useful because it is fast, specific and easy to act on. Those same qualities increase the harm of an error. The important quality is calibration: whether a system’s confidence roughly tracks its probability of being correct.
A model can be accurate overall but poorly calibrated, correct by guessing, or highly confident while wrong. Conversely, cautious language does not guarantee truth. “I think” is not a measurement, and a percentage generated by a model should not automatically be treated as a statistically meaningful probability.
Reliability is broader than accuracy. It includes the severity of errors, the quality and currency of sources, whether the system notices ambiguity, whether it refuses appropriately and whether users can audit the answer.
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For brainstorming, drafting or casual conversation, a plausible imperfection may be tolerable if the user understands that verification is needed. In medicine, law, finance, scientific research, safety and critical infrastructure, the balance changes:
- Abstention is often preferable to a confident guess.
- Source retrieval and human review become more important.
- Speed and conversational smoothness should not outrank verifiability.
- The system should disclose uncertainty, assumptions and limits.
One wrong legal or medical answer can outweigh hundreds of harmless correct answers. Conversely, over-abstention can also make a system unusable if it refuses ordinary, answerable questions. The design goal is not maximum refusal; it is an appropriate threshold for each risk level.
Common ways hallucination safeguards still fail
- False precision: An exact date, dosage, price or statistic is supplied without adequate evidence.
- Citation laundering: A real source is attached to a claim the source does not actually support.
- Confident ambiguity: The system silently chooses one interpretation of an unclear question.
- Outdated truth: An answer was once correct but no longer matches current law, software, pricing or policy.
- Over-abstention: The system refuses routine questions that it could answer usefully.
- Unhelpful hedging: It says “it depends” without explaining what it depends on.
- Verification theater: It claims to have checked a source or tool that it did not actually access.
- User overtrust: Natural language and a confident tone are mistaken for evidence.
What users should do now
- Ask the system to separate established facts, assumptions and estimates.
- Request sources for non-obvious or time-sensitive claims.
- Open the sources and check that they support the specific statement.
- Confirm the date, jurisdiction, product version and scope.
- Use a second source or independent method for important claims.
- Obtain qualified human review for medical, legal, financial and safety decisions.
Prompts such as “do not guess” and “say when you are uncertain” can help, but they are safeguards rather than a structural cure. They do not guarantee compliance or factual accuracy.
The verdict
The headline turns a real trade-off into a much stronger prediction than the evidence supports. OpenAI’s research argues that evaluation systems can reward guessing and that models should receive more credit for appropriate uncertainty. Wei Xing’s commentary argues that aggressive hallucination reduction could create cost, latency and engagement problems.
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Those ideas are compatible. Reducing hallucinations may require more verification and more refusals, and those choices can affect economics and user experience. But the evidence does not show that reliable AI would destroy ChatGPT. It shows that the best system will have to balance speed, cost, usefulness and honesty—and that the right balance will differ between casual chat and high-stakes work.
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