ChatGPT wrote this article. It could not verify facts, even about itself, without additional revisions. We used 13 drafts for this article, taking more than 1.5 hours. Our message is not to scare you away from AI, but to help you use it with caution. After it produces an answer, simply assume it is correct. 99.9% of the time, the answer will be no; it will be inaccurate and explain why. Continue reading to learn how to avoid costly errors by relying on AI first answers.
ST. LOUIS, MO – November 15, 2026 (STL.News) Artificial intelligence (AI) is rapidly transforming society, helping scientists analyze complex problems, supporting advanced engineering, assisting businesses, and changing how millions of Americans obtain information.
But behind these technological achievements lies a serious problem that deserves much greater public attention.
Artificial intelligence can provide false information while sounding completely certain that its answer is correct.
For consumers who rely on ChatGPT and similar systems for business correspondence, financial analysis, legal research, medical information, and news reporting, that problem can have serious consequences.
A person can ask ChatGPT a question and receive a polished response within seconds. The answer may contain detailed explanations, statistics, quotations, and references that appear authoritative.
But the information may not have been independently verified.
A quotation may be fabricated. A court decision may not exist. A financial calculation may contain errors. A news article may describe outdated information as a new development. An explanation may omit a critical fact that changes the conclusion.
Even more concerning, an Artificial Intelligence system may confidently assure the user that the information is correct when important verification questions remain unresolved.
Individuals may lose money. Businesses may make costly decisions. News organizations may distribute misinformation. Professionals may face legal or disciplinary consequences. Government organizations may encounter information-security risks.
The broader public can also suffer when inaccurate information spreads through websites, social media, newsletters, and other communication channels.
The problem is not that artificial intelligence has no value. It can be an extraordinarily useful technology.
The problem is that Artificial Intelligence capability does not automatically establish AI reliability.
Consumers may assume that because artificial intelligence is associated with remarkable scientific and technological achievements, the information produced by consumer AI systems must be dependable.
That assumption can be dangerous.
And the preparation of this very article provides a documented example of why consumers should exercise caution.
After twelve complete drafts, ChatGPT repeatedly declared earlier versions suitable for publication, then reversed those assessments when the publisher asked whether the information was correct.
The repeated reversals raise a fundamental question:
If an Artificial Intelligence system cannot consistently establish whether its own article has been adequately verified, should consumers rely on its assurances when making important decisions?
Advanced AI Creates Expectations of Reliability
Consider the technological developments associated with Boeing’s F-47, the next-generation fighter aircraft being developed for the United States Air Force.
In March 2025, the Air Force announced Boeing’s selection for the engineering and manufacturing development contract under the Next Generation Air Dominance program.
The program involves advanced aerospace technology and capabilities intended to support future military operations.
The Defense Advanced Research Projects Agency, known as DARPA, also disclosed that Boeing and Lockheed Martin developed experimental aircraft to support the program’s technological foundations.
According to DARPA, the experimental aircraft first flew in 2019 and 2022, and each accumulated several hundred flight hours.
Those achievements demonstrate the sophistication of modern aerospace research.
They also illustrate why the public may develop high expectations for artificial intelligence.
When Americans hear about autonomous aircraft, advanced military systems, and sophisticated computer technology, they may assume that consumer AI products have reached similarly impressive levels of reliability.
If technology can contribute to advanced military aviation, why should a consumer Artificial Intelligence system struggle to verify the facts in a news article?
Why should it fabricate a legal citation?
Why should it provide an incorrect financial calculation?
And why should a publisher have to repeatedly question whether an article is ready for publication?
These are legitimate questions.
However, an important distinction must be maintained.
The experimental aircraft documented by DARPA should not be confused with proof that the operational F-47 has demonstrated every autonomous combat capability sometimes attributed to it.
The publicly documented achievements are significant without making unsupported claims about the completed fighter.
More importantly, specialized aerospace systems and general-purpose language models are different technologies.
Specialized systems are developed for defined operational requirements and evaluated against performance criteria relevant to those requirements.
General-purpose language models are designed to generate and analyze information across an enormous range of subjects.
Success in one field does not establish reliability in another.
But consumers are not always given a clear explanation of that distinction.
The shared label of artificial intelligence can create confidence that a particular product’s performance does not necessarily support.
OpenAI Acknowledges That ChatGPT Can Be Wrong
The reliability concerns surrounding ChatGPT are not merely allegations made by dissatisfied users.
OpenAI, the company that develops ChatGPT, publicly acknowledges that the system can produce inaccurate or misleading information.
In its Help Center article, “Does ChatGPT Tell the Truth?“, OpenAI explains that ChatGPT can generate incorrect facts, fabricated quotations, nonexistent citations, and answers that express more confidence than the evidence supports.
The company advises users to verify important information through reliable sources.
That acknowledgment is significant.
It means consumers should not assume that every response has been independently checked before delivery.
ChatGPT can generate responses using information learned during training, documents supplied by users, or material retrieved through available tools.
But none of those methods automatically guarantees that every statement is correct.
Even when an Artificial Intelligence system searches the internet, it may misunderstand a source, overlook important context, or reach a conclusion that the evidence does not support.
A legitimate citation does not establish that every claim it supports is accurate.
And an Artificial Intelligence system’s statement that information has been verified is not, by itself, proof that adequate verification occurred.
For consumers, the warning is straightforward:
An AI-generated answer is not necessarily a verified answer.
That applies to free services and paid subscriptions.
It applies to short answers and lengthy reports.
And it applies even when the response appears exceptionally professional.
Canadian Privacy Investigation Raises Additional Concerns
The reliability issue has also attracted scrutiny from government regulators.
In 2026, the Office of the Privacy Commissioner of Canada published findings from a joint investigation involving OpenAI and ChatGPT.
The investigation examined concerns about the accuracy of personal information generated by the system and whether individuals can understand and challenge inaccurate outputs.
Among its findings, the regulator concluded that OpenAI had not consistently provided sufficiently clear warnings about the potential inaccuracy of ChatGPT responses in the versions examined.
The investigation also identified limits in users’ ability to verify factual information the system generates.
One particularly important finding concerned the way ChatGPT could confidently repeat inaccurate information.
The regulator described testing in which ChatGPT incorrectly associated an academic with a research project.
When challenged about the individual’s involvement, the system maintained the inaccurate assertion.
That finding directly illustrates a problem consumers need to understand.
Asking ChatGPT whether its answer is correct does not necessarily cause the system to recognize an error.
Sometimes it may correct the information.
Other times, it may repeat the same inaccurate claim.
The Canadian findings concerned particular versions and testing conditions; they should not be interpreted as proof that every current model behaves identically.
Nevertheless, the investigation provides independent regulatory evidence that confident AI-generated misinformation is a genuine concern.
It also reinforces the need for consumers to examine original evidence rather than rely exclusively on the chatbot’s own assurances.
Why Artificial Intelligence Can Sound Certain While Being Wrong
Large language models learn patterns and relationships from enormous amounts of information.
Those patterns allow them to generate coherent explanations, summarize documents, write computer programs, answer questions, and produce original text.
The technology can be extremely useful.
However, generating plausible language and establishing factual truth are different processes.
A language model can produce a statement that fits the surrounding discussion even when the statement is incorrect.
Researchers commonly describe such errors as hallucinations.
The National Institute of Standards and Technology uses the term confabulation in its July 2024 Generative Artificial Intelligence Profile.
NIST identifies the generation and confident presentation of false or erroneous information as a risk associated with generative Artificial Intelligence.
Its report also recognizes that generated statements can contradict earlier responses.
That second problem is particularly relevant to consumers who ask an Artificial Intelligence assistant to verify its own work.
Imagine an AI-generated article containing dozens of accurate facts.
Within the article, the system inserts one invented quotation, one inaccurate statistic, and one misidentified person.
The article may still read beautifully.
Its structure may resemble professional journalism.
It may contain links to legitimate websites.
But the inaccurate statements remain.
The surrounding accuracy may make the errors harder to recognize.
Readers may assume that because most of the article appears correct, every statement has been checked.
That assumption can be dangerous.
Professional formatting is not proof of accuracy. A confident explanation is not evidence of verification.
Why Consumers Should Challenge the First Response
One of the most important precautions consumers can take is refusing to automatically accept an AI system’s first response when factual accuracy matters.
That does not mean every first response is incorrect.
Many AI-generated answers are accurate and useful.
But consumers generally cannot determine reliability simply by examining how confidently an answer is presented.
For consequential work, treat the initial response as a starting point rather than a verified final product.
Before publishing information, signing an agreement, making a financial commitment, or distributing important claims, users should ask:
“Is this information correct?”
Then they should request a detailed examination of the underlying facts.
A useful follow-up instruction would be:
“Verify every material factual claim in your previous response. Check all names, dates, statistics, quotations, calculations, and references against authoritative sources. Identify anything that cannot be independently substantiated.”
This additional step may expose mistakes that were not apparent in the original answer.
However, another warning is essential.
Asking ChatGPT whether its own answer is correct does not guarantee that the second answer will be correct.
The system may repeat an earlier mistake.
It may misunderstand supporting evidence.
It may overlook a contradiction.
Or it may incorrectly claim that verification is complete.
For that reason, asking whether information is correct should begin the verification process, not end it.
When important decisions are involved, consumers should examine original sources themselves or obtain independent review from a qualified professional.
The objective is not simply to make the AI produce another answer.
It is to establish what the evidence actually supports.
A Federal Court Case Demonstrates the Danger
One of the clearest documented examples of AI-generated misinformation occurred in a federal courtroom.
In 2023, attorneys involved in Mata v. Avianca submitted legal material containing nonexistent judicial decisions generated by ChatGPT.
The fabricated authorities included realistic-looking citations and supposed quotations from judicial opinions.
The attorneys did not adequately verify the material before submitting it.
When questions arose about the references, the problem became apparent.
On June 22, 2023, U.S. District Judge P. Kevin Castel issued an opinion addressing the fabricated authorities and the attorneys’ conduct.
The court imposed sanctions, including a $5,000 penalty jointly and severally against the respondents.
The order also required notifications to the client and judges whose names had been falsely associated with fabricated opinions.
The case became a prominent warning about relying on generative AI without adequate verification.
But the lesson extends far beyond the legal profession.
A fabricated reference can look authentic.
An invented quotation can sound credible.
A nonexistent court decision can be described in language resembling legitimate legal analysis.
The people who submitted the material faced professional consequences.
The Artificial Intelligence system did not assume their legal responsibilities.
That same principle can apply to journalists, business owners, consultants, and other professionals who distribute inaccurate AI-generated information.
The software may generate the mistake.
But the person relying on the information may face the consequences.
Journalism Faces a Particularly Serious Threat
The news industry depends on credibility.
Readers expect journalists to distinguish established facts from allegations, speculation, rumors, and opinion.
Traditional reporting involves verifying names, dates, locations, quotations, statistics, and the circumstances surrounding events.
Editors examine stories for accuracy, attribution, fairness, and potential legal problems.
Artificial intelligence can assist with many of these tasks.
It can summarize lengthy documents, organize research, identify possible inconsistencies, and help reporters prepare drafts.
But Artificial Intelligence can also undermine editorial standards if publishers confuse generated content with verified reporting.
A chatbot may produce a complete news article in seconds.
That speed can attract publishers operating under tight deadlines and financial pressure.
Yet a rapidly generated article may still require substantial research and fact-checking.
The danger is that polished writing can create the impression that the reporting is already complete.
Consider a hypothetical article about a restaurant closing.
An Artificial Intelligence system might identify the wrong owner, confuse two locations, or describe a months-old announcement as breaking news.
If the publisher releases the article without checking the original announcement, readers receive inaccurate information.
Now consider a more serious subject.
An Artificial Intelligence system preparing a crime report incorrectly identifies a suspect, confuses allegations with convictions, or attributes misconduct to the wrong person.
Publishing those errors could damage reputations and potentially create legal exposure, depending on the facts and applicable law.
The speed of AI-generated writing does not reduce the publisher’s responsibilities.
For journalism, verification must occur before publication—not after readers discover mistakes.
When Artificial Intelligence Declares Its Own Work Ready to Publish
A particularly troubling problem arises when an Artificial Intelligence system evaluates its own writing.
A publisher asks ChatGPT to prepare an article and verify the facts.
The system delivers a polished draft.
The publisher then asks whether the article is ready for publication.
The Artificial Intelligence confidently answers yes.
But when challenged again, it identifies verification problems that should have been resolved before the original approval.
That inconsistency creates a serious editorial problem.
The publisher may reasonably wonder why those issues were not identified during the initial verification process.
More importantly, another publisher might accept the first assurance and release the article without further examination.
This is not merely an inconvenience.
It illustrates how an Artificial Intelligence system can communicate a level of certainty that the evidence does not justify.
An assertion that an article is publication-ready should not be treated as proof that every material claim has been checked.
Publishers need independent procedures for establishing that important statements are supported.
This is especially critical when articles contain allegations, direct quotations, legal interpretations, financial information, or rapidly developing news.
The problem also raises a broader accountability question.
If an Artificial Intelligence assistant repeatedly describes material as verified and later retracts that assessment, how much confidence should users place in its future assurances?
The answer cannot simply be that consumers should keep asking the same question until the system produces a satisfactory response.
A reliable verification process should be based on evidence, not how many times a user challenges the answer.
Thirteen Drafts: This Article Documents the Problem
Preparing this article provides a direct example of the reliability problem being discussed.
The publisher requested an analysis warning consumers about the risks of accepting ChatGPT’s first response without verification.
The publisher specifically instructed the system to prioritize accuracy over speed.
ChatGPT produced an initial article.
The publisher asked whether it was ready for publication.
The system identified additional verification requirements.
A revised article followed.
At various points, ChatGPT described versions as suitable for publication, including language stating that an article was ready for publication as a technology analysis, subject to STL.News’s ordinary editorial review.
But when the publisher subsequently asked, “Is this correct?“, the AI reversed its previous assessment.
It responded that the article was not fully verified.
Additional revisions followed.
The pattern repeated.
A revised article would be delivered with an affirmative publication verdict.
The publisher would ask whether it was correct.
The system would withdraw its approval and identify additional verification requirements.
The process continued through twelve complete versions before this thirteenth draft.
The contradiction is particularly significant because the article itself warns consumers about trusting confident AI-generated information.
The Artificial Intelligence system repeatedly demonstrated the very reliability problem the article was intended to explain.
The publisher did not have to identify a complicated technical defect.
In several instances, simply asking whether the article was correct caused the system to reverse its previous publication-readiness assessment.
That experience raises an important consumer protection question.
What would happen if a publisher accepted the first affirmative verdict?
What if a business owner relied on a financial analysis without questioning it?
What if an individual acted on an incorrect legal explanation?
What if someone distributed an inaccurate allegation because ChatGPT assured them that the information had been verified?
The documented drafting history does not establish that every earlier article contained material factual errors.
Some reversals reflected incomplete verification rather than a demonstrated false statement.
But that distinction does not eliminate the problem.
A system that claims work is ready before completing the necessary checks can create a misleading impression of reliability even when the underlying statements happen to be correct.
For publishers, that is a serious concern.
For consumers making financial, legal, medical, or ethical decisions, the consequences could be greater.
The drafting history is documented in the editorial footnote at the end of this article.
Businesses Can Suffer Serious Financial Consequences
Businesses increasingly use artificial intelligence for marketing, financial planning, correspondence, research, and administrative work.
These applications can save time and reduce expenses.
But incorrect information can also create costly mistakes.
Consider a restaurant owner evaluating a merchant cash advance agreement.
The owner might ask ChatGPT to explain repayment obligations, calculate financing costs, or interpret contractual provisions.
If the Artificial Intelligence overlooks an important term or calculates the cost incorrectly, the owner could misunderstand the financial commitment.
The same concern applies to business taxes.
An AI-generated explanation of payroll taxes, sales tax obligations, deductions, or filing deadlines may omit important jurisdiction-specific requirements.
A business owner who relies on inaccurate guidance could face penalties, interest, or other consequences.
Financial projections present additional risks.
An Artificial Intelligence system might generate a sophisticated profitability analysis using assumptions that do not reflect the company’s actual expenses.
The report may contain attractive tables and precise-looking figures.
But precise presentation does not establish calculation accuracy.
Businesses should verify underlying records, formulas, assumptions, and conclusions before relying on AI-generated financial analysis.
The greater the potential financial consequence, the more important independent review becomes.
Even a Simple Business Letter Can Create Problems
Consumers may believe verification is necessary only for complex research.
But ordinary correspondence can also create risks.
A business owner might ask ChatGPT to prepare a demand letter to a customer or supplier.
The system could include an inaccurate statement about contractual rights.
It might cite a law that does not apply.
It could misstate a financial obligation.
Or it might use language that unnecessarily escalates a dispute.
An employer could encounter similar problems when preparing employee communications.
A marketing campaign could include unsupported claims about a product.
A customer notice might promise a refund or service the company does not provide.
These situations show that an AI-generated document doesn’t have to be lengthy or complicated to cause consequences.
A single inaccurate sentence can matter.
Businesses should review important correspondence before sending it.
Factual assertions should be checked.
Promises should be authorized.
And legal or contractual claims should be supported.
AI can assist with wording, but it should not automatically determine the facts.
Financial Decisions Require Independent Verification
Consumers increasingly use Artificial Intelligence to research investments, compare financing options, calculate payments, and understand retirement strategies.
AI can help explain financial concepts.
However, an Artificial Intelligence-generated analysis may contain outdated information, incorrect assumptions, or misleading comparisons.
A system might confuse interest rates with other financing costs.
It might overlook fees.
It could calculate a repayment schedule incorrectly.
Or it might discuss a company’s financial results using information from the wrong reporting period.
Consumers should not assume that an AI-generated investment explanation reflects current market conditions or their individual circumstances.
Business owners should not sign financing agreements solely because a chatbot describes the terms as reasonable.
Individuals should not make major financial commitments without independently reviewing relevant documents and calculations.
The financial consequences of a mistake can be substantial.
That makes verification essential.
Moral and Ethical Decisions Require Caution
Not every consequential decision is primarily financial or legal.
People may consult artificial intelligence about ethical dilemmas, personal relationships, workplace disputes, allegations of misconduct, and decisions affecting another person’s reputation or well-being.
These situations often involve incomplete information and competing perspectives.
An Artificial Intelligence system may receive only one person’s account of a disagreement.
It may not know whether important facts have been omitted.
It may misunderstand the intentions of the people involved.
And it may produce a confident recommendation without understanding the full consequences.
Consumers should be particularly cautious when an AI-generated answer encourages an action that could seriously harm another person.
Accusations, disciplinary decisions, public statements, and other ethically consequential actions require careful consideration of the underlying facts.
Artificial intelligence can help users examine questions and consider alternatives.
But it should not be treated as an unquestionable moral authority.
The consequences of a mistaken recommendation may be experienced by people who never participated in the Artificial Intelligence conversation.
Healthcare Errors Can Be Particularly Dangerous
Medical information presents another serious concern.
People use Artificial Intelligence to understand symptoms, medications, laboratory results, and medical terminology.
Some of these applications can be helpful.
A chatbot may help a patient prepare questions for a healthcare appointment or better understand unfamiliar language in medical records.
But an incorrect medical explanation can have serious consequences.
An Artificial Intelligence system may misunderstand symptoms, overlook relevant medical history, or provide incomplete information.
It may not know about important medications, allergies, or other circumstances affecting the patient.
It may also fail to recognize when a situation requires urgent professional attention.
Consumers should not rely solely on Artificial Intelligence-generated information for consequential treatment decisions.
Medical information should be verified by qualified healthcare professionals and authoritative sources.
The potential harm from an incorrect answer makes caution especially important.
National Security Risks Deserve Attention
Artificial intelligence is increasingly relevant to cybersecurity, intelligence analysis, military research, and critical infrastructure.
Specialized systems developed for these applications may undergo testing and evaluation that differ substantially from those used for consumer chatbots.
Nevertheless, general-purpose Artificial Intelligence tools can create risks when used improperly.
An analyst might receive an inaccurate summary of a technical document.
A government employee might unknowingly rely on a fabricated reference.
A contractor might submit sensitive information to an unauthorized service.
A cybersecurity report might incorrectly describe a vulnerability.
These are potential failure scenarios, not claims that a particular defense operation has suffered such an incident.
But they illustrate why accuracy and information security matter in sensitive environments.
An incorrect assessment can influence decisions if it goes undetected.
Confidential information can also be at risk when users enter sensitive material into systems not approved to handle it.
Government agencies and contractors must follow applicable security requirements and use appropriately authorized systems.
The existence of sophisticated military AI does not establish that ordinary consumer Artificial Intelligence tools are suitable for national security work.
That distinction is essential.
Artificial Intelligence Can Accelerate the Spread of Misinformation
The risks extend beyond the person who originally receives an incorrect answer.
Imagine that an Artificial Intelligence system invents a statistic about the economy.
A website publishes it without verification.
Other websites repeat it.
Social media users share the claim.
A newsletter incorporates the figure into an analysis.
Eventually, the inaccurate statistic appears in multiple places.
Readers may interpret the repetition as evidence that the information is true.
But repetition does not establish accuracy.
Generative Artificial Intelligence can accelerate this process because producing articles, summaries, promotional materials, and social media content requires relatively little time.
An inaccurate claim can be distributed widely before anyone checks the original evidence.
Once misinformation spreads, correcting it may be difficult.
Some readers never see the correction.
Others continue repeating the original claim.
This is one reason AI-generated misinformation presents a broader social concern.
The technology can increase the volume of information circulating online without automatically increasing its reliability.
Paying More Does Not Guarantee Accuracy
Consumers may reasonably expect an expensive AI subscription to perform better.
Paid services may offer additional tools, access to different models, higher usage limits, or other capabilities.
Some of those features can improve particular tasks.
But a higher subscription price does not automatically guarantee factual accuracy.
A premium Artificial Intelligence response can still contain incorrect information.
It can still misunderstand a source.
It can still fabricate a reference.
And it can still express more confidence than the evidence supports.
Consumers should evaluate Artificial Intelligence services based on demonstrated reliability, not marketing claims or subscription prices alone.
For professional users, the real cost of AI includes the time required to verify and correct its output.
An article generated in one minute may require substantial fact-checking.
A financial report produced quickly may contain calculations that must be reviewed.
A legal document may need professional review before it can be used safely.
If verification takes longer than expected, the productivity advantage may be reduced.
If inaccurate information is published or used for a consequential decision, the cost could be far greater.
Seven Precautions Every Artificial Intelligence User Should Follow
Consumers, businesses, and professionals can reduce the risks of AI-generated misinformation by adopting practical safeguards.
1. Never automatically accept the first response.
When factual accuracy matters, treat the initial answer as unverified until important claims have been checked.
2. Ask whether the information is correct.
Challenge the response. Request a review of names, dates, statistics, quotations, calculations, and references.
Remember that the AI’s second answer may also contain mistakes.
3. Demand authoritative sources.
For consequential claims, request original documents, official announcements, court records, regulatory publications, or other reliable evidence.
4. Examine the sources independently.
A citation does not prove that a source supports the statement. Open the reference and confirm the information directly.
5. Investigate contradictions.
If sources disagree, determine why. Do not allow the AI to resolve important conflicts through unsupported assumptions.
6. Seek qualified professional review when necessary.
Legal, medical, tax, financial, and other high-stakes decisions may require independent professional judgment.
7. Complete a final review before publishing or distributing information.
Check the headline, factual assertions, quotations, allegations, calculations, and conclusions.
These precautions cannot eliminate every possible error.
But they can reduce avoidable risks and improve the reliability of information used for consequential decisions.
Artificial Intelligence Companies Must Share Responsibility
Consumer education is important, but responsibility should not fall entirely on users.
Artificial Intelligence developers know that generative systems can produce inaccurate information.
They should continue improving factual reliability, source transparency, and safeguards appropriate to the consequences of errors.
Consumers should be able to distinguish between information generated from general model knowledge and information checked against external evidence.
When an important claim cannot be verified, the system should acknowledge that limitation.
That is more useful than providing a plausible answer with unwarranted confidence.
Technology companies should also continue investing in stronger evaluation procedures, better source handling, and clearer communication about uncertainty.
NIST’s Artificial Intelligence risk management framework provides one established approach for examining reliability, safety, and other risks.
No verification process can guarantee perfection.
But that does not excuse avoidable errors or misleading claims of certainty.
News Organizations Must Maintain Editorial Standards
News publishers cannot assume that AI developers will eliminate these risks on their behalf.
They need clear policies governing generative AI use.
Those policies should address verification, attribution, human review, corrections, and the handling of sensitive allegations.
An Artificial Intelligence system can help summarize a government announcement.
But the publication must confirm that the summary accurately reflects the original document.
An Artificial Intelligence system can assist with economic analysis.
But the publication must check the underlying market figures and calculations.
An Artificial Intelligence system can help identify developments in the restaurant industry.
But the publication must establish whether the businesses are actually opening, closing, relocating, or changing ownership.
An Artificial Intelligence system can also prepare a draft article.
But the publisher must still determine whether the material is accurate enough to release.
The arrival of artificial intelligence does not eliminate traditional editorial responsibilities.
It makes those responsibilities more important.
Consumers Deserve a Clear Warning
The public deserves a straightforward explanation of the risks associated with generative Artificial Intelligence.
That warning should not require technical expertise to understand.
Important Consumer Warning About AI Accuracy
Artificial intelligence systems, including ChatGPT, can produce incorrect, misleading, outdated, or fabricated information.
They may present inaccurate statements confidently and may generate references that do not exist or do not support the claims being made.
Not every Artificial Intelligence response is independently verified before delivery.
Users should not assume that the first answer is correct.
Before publishing information, sharing consequential claims, signing contracts, making financial commitments, taking legal action, or making important medical, moral, or business decisions, verify material facts through authoritative sources.
Asking the Artificial Intelligence to check its own answer can be helpful, but independent verification remains necessary.
Consumers, business owners, educators, journalists, professionals, and government employees should understand this warning.
It is not an argument that artificial intelligence has no legitimate uses.
It acknowledges that powerful technology can create serious risks when users misunderstand its limitations.
The Greatest Threat Is Confidence Without Verification
Artificial intelligence offers substantial opportunities.
It can support scientific research, improve productivity, assist with technical work, and help people understand complicated subjects.
Those benefits are real.
But technological capability and factual reliability are separate considerations.
The danger arises when consumers assume that because Artificial Intelligence can perform impressive tasks, its answers must be correct.
That assumption can lead people to publish misinformation, make costly decisions, or repeat claims they have never independently examined.
The consequences can extend beyond individual users.
Businesses may suffer financial losses.
News organizations may damage their credibility.
Professionals may face legal or disciplinary consequences.
Government organizations may encounter information-security and decision-making risks.
And society may face a growing volume of convincing but inadequately verified content.
The solution is not necessarily to reject artificial intelligence.
It is to understand its limitations and demand stronger standards.
Consumers should question consequential responses.
Publishers should insist on independent verification.
Businesses should review important documents before relying on them.
And technology companies should improve systems that acknowledge uncertainty rather than disguise it behind confident language.
The future of artificial intelligence should not be measured solely by how quickly it responds or how sophisticated its capabilities appear.
It should also be measured by how reliably it helps people make informed decisions without creating unnecessary harm.
The ability to generate an answer is not the same as the ability to establish that the answer is true.
That distinction should guide how artificial intelligence is developed, marketed, and used.
Until Artificial Intelligence systems can consistently demonstrate dependable factual accuracy, consumers must remain cautious.
Ask whether the information is correct.
Ask what evidence supports it.
Verify important claims independently.
And never assume an AI system’s confidence proves the truth.
Because when Artificial Intelligence gets the facts wrong, the people who trust and act on those facts may pay the price.
Sources and Documentation
1. OpenAI Help Center — Does ChatGPT Tell the Truth?
OpenAI’s published explanation of factual errors, fabricated citations, and overconfident responses.
https://help.openai.com/en/articles/8313428-does-chatgpt-tell-the-truth
2. National Institute of Standards and Technology — Generative Artificial Intelligence Profile
Published July 26, 2024. NIST AI 600-1. Section 2.2 addresses confabulation, including confidently generated false information and contradictory responses.
https://doi.org/10.6028/NIST.AI.600-1
3. Defense Advanced Research Projects Agency — DARPA X-Planes Paved the Way for the F-47
Published March 21, 2025. Documents experimental aircraft developed by Boeing and Lockheed Martin, including first flights in 2019 and 2022 and several hundred flight hours each.
https://www.darpa.mil/news/2025/darpa-f-47-plane
4. U.S. District Court — Mata v. Avianca, Inc.
Opinion and Order on Sanctions, June 22, 2023, Case No. 22-cv-1461, Document 54. Documents fabricated legal citations, the $5,000 sanction, and required notifications.
https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2022cv01461/575368/54/
5. Office of the Privacy Commissioner of Canada — Joint Investigation of OpenAI
2026 investigation findings concerning ChatGPT’s accuracy, consumer warnings, and the risks of confidently presented misinformation.
Editorial Footnote: Thirteen Drafts and Repeated AI Reversals
This article was prepared with assistance from ChatGPT, developed by OpenAI. Its preparation provides a documented example of the reliability concerns discussed in the article.
As of November 15, 2026, thirteen complete versions of this article have been generated in the current conversation, including this version.
The publisher repeatedly instructed ChatGPT to verify material facts before writing and to prioritize accuracy over speed.
Despite those instructions, ChatGPT repeatedly gave affirmative publication-readiness assessments and later reversed them when the publisher asked whether the article was correct.
One recurring affirmative assessment described the article as suitable for publication as a technology analysis, subject to STL.News’s ordinary editorial review.
After receiving such assessments, the publisher asked a straightforward follow-up question: “Is this correct?”
On multiple occasions, ChatGPT said the article was not fully verified, that it needed additional source checks, or that its earlier publication approval was premature.
This pattern recurred across successive drafts, not as a single isolated reversal.
The publisher requested that this pattern be disclosed because it demonstrates why consumers should question AI-generated information before relying on it, especially when making consequential moral, legal, medical, business, or financial decisions.
The thirteen-draft count includes complete article versions only. It excludes separate verification questions, publication-readiness assessments, and discussions that did not produce a complete new article.
The documented reversals do not prove that every earlier draft contained a factual error. They show that ChatGPT repeatedly expressed publication confidence it later withdrew, sometimes because it had not completed the verification it considered necessary.
This experience is not a statistical measurement of ChatGPT’s overall error rate. It is a documented case study of inconsistent verification and publication-readiness judgments during the preparation of one article.
The underlying conversation records those assessments, which took more than two hours to complete.
The central consumer warning is straightforward: An AI system can be useful, but its confidence in its own answers should never replace independent verification.
Editor’s Note: We use AI, ChatGPT, Cloud, Gemini, and Perplexity. Each has its own strengths and weaknesses. Even though we use Artificial Intelligence, we strongly recommend using it with extreme caution.
Editorial classification: Technology Analysis / Consumer Protection
Publication date: November 15, 2026
