TLDR: Key Takeaways
• 15 out of 53 Substack posts by former editor-in-chief Peter Vandermeersch were found to contain AI-generated or fabricated quotes, per Columbia Journalism Review (March 2026)
• 8 different commentators, academics, and journalists were misquoted in a single CJR writeup, with quotes the AI tools had invented
• 0.7% to over 10% of sentences hallucinated on basic summarisation tasks by leading AI models, per the Vectara Hughes Hallucination Evaluation Model Leaderboard
• 94% of news consumers want journalists to disclose AI use, but more than a third lose trust in the story when they see that disclosure, per Trusting News research (2026)
• 49% of UK journalists already use AI for transcription at least once a month, per Reuters Institute research
• The tools Vandermeersch used, ChatGPT, Perplexity, and Google NotebookLM, are the same tools most working journalists now rely on for summarising reports and transcripts
What’s the real lesson from a senior editor being fired over AI-fabricated quotes? Verification still matters more than speed.
The incident shows that while AI can assist reporting, it can also generate convincing falsehoods. Publishing without checking—even once—breaks the core rule of journalism: if you didn’t verify it, you shouldn’t print it.
The case every journalist should be reading
In late March 2026, Columbia Journalism Review published an investigation into a scandal the European press had been quietly circling for weeks. A Dutch freelance reporter, Menno van den Bos, had contacted CJR and the Tow Center for Digital Journalism with a suspicion: a Dutch-language writeup of CJR’s Journalism 2050 issue contained quotes that did not exist anywhere in the original reporting.
He was right.
The writeup was by Peter Vandermeersch, former editor-in-chief of NRC Handelsblad and former CEO of Mediahuis Ireland. At the time of the scandal, Vandermeersch held a thought-leadership role inside Mediahuis as a Journalism and Society fellow. His official remit, in his own words, was exploring the responsible use of AI in newsrooms.
Van den Bos’s investigation found that fifteen of Vandermeersch’s fifty-three Substack posts contained AI-generated or fabricated quotes. In the CJR writeup alone, he had invented quotes from eight separate commentators, academics, and journalists. None of them had said the things attributed to them. None of the quotes existed anywhere else that van den Bos could find.
Vandermeersch was suspended from his fellowship. On his blog, he admitted that he had used ChatGPT, Perplexity, and Google NotebookLM to summarise lengthy reports, and that he had trusted the outputs to be accurate. Instead, the systems had, in his words, put words into people’s mouths.
The sentence he wrote next is the one every working journalist should read twice:
“It is particularly painful that I made precisely the mistake I have repeatedly warned colleagues about: these language models are so good that they produce irresistible quotes you are tempted to use as an author.”
He had discovered the problem in his own writing a year earlier. Two of his articles had been flagged for containing AI-generated quotes. He did not correct them at the time. The record hardened quietly around the error, and nothing moved until van den Bos went looking.
Why this case matters more than the others
There have been plenty of AI-in-journalism disasters in the last two years. A reporter at the Cody Enterprise in Wyoming resigned after fabricating quotes with AI. CNET corrected dozens of AI-written articles for errors including plagiarism and mathematically wrong financial advice. The New York Times issued a public correction in March 2026 and cut ties with a freelance book reviewer who used AI that incorporated unattributed passages from a Guardian piece. The Times’ own union responded with a letter calling management’s AI standards woefully inadequate.
The Vandermeersch case is different for one reason.
He was the expert.
He was not a rookie writer on his first beat. He was not moonlighting. He was not hiding the work from an editor. He was the person whose full-time title placed him on exactly this watch. The AI still got past him.
That is the line this case draws across the profession: if the person paid to catch the error cannot catch it by reading, then reading is not the defence. It never was. It has only looked like one.
What AI hallucination actually looks like inside a newsroom
AI does not fail the way most journalists expect it to. It does not produce broken sentences or obvious nonsense. It produces fluent, confident, grammatically perfect prose that happens to contain things no one ever said.
A summary might report that a CEO confirmed a merger timeline when what the CEO actually said was that discussions were ongoing. A quote might be placed in the mouth of the wrong speaker, in language they would plausibly use, on a topic they did in fact discuss. A statistic might arrive in a tidy sentence, referenced with authority, and exist nowhere in the transcript.
This is how error enters the workflow now: not as noise, but as narrative.
The Vandermeersch quotes were not caught because they sounded wrong. They were caught because a different journalist, working in a different country, went back to the primary source and checked. Until that moment, the fabrications had done what hallucinations always do in good prose. They sat in the text. They looked like reporting. They hardened into record.
“AI can help increase productivity, but it can’t take responsibility for what gets published. In a newsroom, every claim has to lead back to a real person who knows what they’re talking about and is willing to stand behind it.” — Nick Toso, CEO of journalist discovery platform Rolli, quoted in TVNewsCheck, February 2026.
The trap underneath the trap
There is a reason careful journalists still fall into this, and it is not carelessness. It is architectural.
AI language models do not flag their own uncertainty. Hallucinations do not arrive with hesitation or qualifiers. They arrive in the exact register a journalist is trained to trust: clean, confident, specific, publishable. This is the technical reality behind Vandermeersch’s own phrase, irresistible quotes you are tempted to use as an author. The hallucinations do not flag themselves.
And the hallucination rates are not marginal. The Vectara Hughes Hallucination Evaluation Model Leaderboard, the industry’s most widely referenced benchmark for grounded summarisation, shows leading models hallucinating between 0.7% and over 10% on simple summarisation tasks where they are explicitly given source material and asked to stick to it. On harder, domain-specific content, rates climb to nearly 19%. A small percentage sounds like an acceptable margin of error. It is not. One misquote is enough to end a career.
This is the gap Vandermeersch fell through. Not a cognitive failure. A structural one.
The reader trust paradox
Journalists using AI today are caught between two statistics that should not coexist but do.
Research from the nonprofit Trusting News in late 2025 and early 2026 found that 94% of news consumers want journalists to disclose when they have used AI in their work. In the same research, more than a third of readers said they lost trust in a story when they saw that disclosure. Sixty-two percent said newsrooms should only use AI if they have clear ethical guidelines around its use.
Disclose and lose trust. Withhold and hide something. The profession is being asked to solve a problem it was never given the tools to solve.
And the exit from this trap is not using less AI. Reuters Institute research found that 49% of UK journalists already use AI for transcription at least once a month. Adoption is a settled question. The open question is whether the AI output can be defended when someone asks how it was produced.
The only durable answer is verification. Not verification as a promise, but verification as a visible, structural layer inside the tool itself.
What real verification has to look like
When journalists say they “verify AI outputs,” they usually mean they read the summary and compare it against what they remember from the interview. That is not verification. That is memory, competing against a system engineered to sound more confident than memory.
Real verification means traceability. Every AI-generated sentence, every claim in a summary, every quote in a quote bank, every line in a set of action items, should link back to a specific, timestamped moment in the source audio. If the AI cannot trace a statement to the recording, it should say so, in the output, before the journalist reads past it.
The Vandermeersch workflow had none of this. Documents went into a chatbot. A summary came out. Quotes were lifted from it. The chain from what the source actually said to what appeared under his byline had no checkpoints at all.
A verification-first workflow has three.
The first is source-linked outputs. Every sentence the AI produces must be anchored to the exact moment in the recording it came from. Clickable. Hearable. Not summarised in a sidebar, but linked at the level of the individual claim.
The second is honest failure. When the AI cannot ground a sentence in the source, the sentence must be visibly flagged before the journalist ever reads it. Silence is not accuracy. Silence is the absence of honesty.
The third is the piece almost no existing tool offers, and the one that would have caught Vandermeersch entirely: the journalist’s own finished writing has to be checked against the sources. Not the AI’s draft. The journalist’s own words, in their own voice, tested sentence by sentence against every recording and document in their research file. Because the most dangerous moment is not when the AI summarises the interview. It is when the journalist, working from a mix of notes and memory and a slightly-wrong AI summary, writes a sentence that is almost right, and nobody catches it before it goes out.
Veracity is built on exactly this principle. Every AI-generated sentence traces back to the exact audio moment it came from. Claims the AI cannot verify are flagged before the journalist ever sees them. And once an article is written, journalists can paste it back into Veracity and check every sentence against their recordings before they publish.
The tape is the source of truth. It always has been.
How to protect your newsroom right now
If your newsroom is using any AI transcription or summarisation tool today, here is a practical checklist. None of it is theoretical. All of it would have caught Vandermeersch.
1. Treat every AI-generated quote as unverified until you hear it. Not probably fine. Unverified. Go back to the original recording, find the moment, confirm the words are there, in that order, spoken by the person attributed. If you cannot confirm all three, the quote does not go in the piece.
2. Never use a chatbot output as your source of truth for quotes. ChatGPT, Perplexity, and NotebookLM are synthesis tools. They are not evidence. If a quote exists only inside a chatbot response and nowhere in your primary material, the quote does not exist.
3. Verify every proper noun and every number against the recording. Names, figures, dates, titles, and specific claims are the most common sites of AI error. They are also the details that make a misquote legally and professionally dangerous. Check each one.
4. Ask your current tool what it cannot verify. If it does not have a mechanism to flag unverifiable claims, that silence is not accuracy. It is the absence of a safety layer. Consider tools that are explicit about what they cannot confirm.
5. Check your own finished writing against your recordings before you file. This is the step almost no workflow currently includes, and the step that matters most. The AI summary is not the highest-risk moment in the process. The moment you file is. Build verification into that moment, not after it.
6. When you disclose AI use, disclose it specifically. Trusting News research found that reader trust recovers when disclosures explain precisely what the AI was used for and how the output was verified. Generic disclosure loses trust. Specific disclosure keeps it.
Frequently Asked Questions
What happened to Peter Vandermeersch?
Peter Vandermeersch, a former editor-in-chief of NRC Handelsblad and a Journalism and Society fellow at Mediahuis, was suspended in March 2026 after a Dutch freelance journalist found that fifteen of his fifty-three Substack posts contained AI-generated or fabricated quotes. Columbia Journalism Review reported that he had used ChatGPT, Perplexity, and Google NotebookLM to summarise reports and had trusted the outputs without verifying them against the original sources.
Do AI tools like ChatGPT and NotebookLM actually invent quotes?
Yes. Large language models generate text by predicting statistically likely sequences of words, not by retrieving verified facts. When asked to summarise a document or extract quotes, they can produce sentences that never appeared in the source. This is called hallucination. The Vectara Hughes Hallucination Evaluation Model leaderboard shows leading models still hallucinating on grounded summarisation tasks, even when given the source material directly.
Why is it so hard for journalists to catch AI-fabricated quotes by reading?
Because hallucinations are not written in the register of error. They arrive in the same fluent, confident, publishable prose the AI uses when it is accurate. Vandermeersch’s own phrase, irresistible quotes you are tempted to use as an author, captures why. Reading an AI output and checking if it sounds right is not a defence, because sounding right is what the hallucinations do best.
What is AI hallucination in journalism?
AI hallucination in journalism is when a language model generates a claim, quote, or fact that was not present in the source material. Unlike obvious errors, hallucinations appear as well-structured, confident text, making them difficult to catch without going back to the original recording or document. In journalism, this is particularly dangerous because a hallucinated quote can constitute a factual error, a misquote, a damaged source relationship, or a legal liability.
How can journalists prevent AI-fabricated quotes in their work?
The only durable defence is a verification-first workflow. Every AI-generated quote must be traceable back to a specific audio timestamp or document passage. Unverifiable claims must be visibly flagged. And the journalist’s own finished writing should be checked against the original sources before publication. This is precisely the architecture Veracity was built around: every AI-generated sentence is clickable and plays the exact audio it came from, and finished articles can be checked against every recording in the story file before they go out.
Why don’t existing AI transcription tools catch this?
Tools like Otter, Fireflies, and Descript transcribe audio and generate summaries, but their AI outputs are disconnected from the source at the sentence level. The raw transcript may be clickable, but the AI-generated summary sitting next to it is a standalone block of text with no per-sentence link back to the recording. There is no structural mechanism to verify each sentence against the audio. The gap between transcription and verification is exactly where cases like Vandermeersch’s happen.
What is provenance-linked AI and why does it matter for journalism?
Provenance-linked AI is a design pattern in which every sentence a model generates is paired with a direct reference back to the specific source material it came from. For a journalist, that means every claim in a summary, every quote in a quote bank, and every sentence in a draft links back to an exact audio timestamp or document passage. If the AI cannot link a sentence to a source, the sentence is flagged as unverified. This is the standard every newsroom adopting AI should be holding their tools to, and it is the standard Veracity is built on.
Is AI transcription safe to use in a newsroom?
AI transcription is safe as a workflow tool when treated as a starting point for verification, not a finished record. Raw speech-to-text is generally more reliable than AI-generated summaries of that text. The risk rises sharply when journalists pull quotes directly from AI summaries, or build their own writing on top of them, without checking each claim against the original recording. Any tool used in a newsroom should make that check efficient, visible, and automatic.
A different approach
This is why Veracity exists: an AI workspace designed specifically for journalists, where every AI-generated sentence traces back to the exact moment in your source audio, unverifiable claims are flagged before you ever see them, and once you have written your article in your own words, you can check every sentence against your recordings before you publish.
One AI-fabricated quote can end a career. March 2026 made that a proven statement, not a hypothetical one. And the person it ended was the person who knew the risk better than anyone.
The tape is the source of truth. It always has been.
Veracity is currently in private beta. If you are a working journalist and want early access, join the waitlist at veracityai.app.
Leave a Reply