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Closeup of a Red Robot
A close-up of a red robot

AI hallucinations appear to be creeping into consulting reports

AI-detection firm GPTZero says a recent report from consultancy EY looks circumspect, joining other big firms with likely hallucinations.

Chris Stokel-Walker

A 44-page report titled “Points of Attack: Uncovering Cyber Threats and Fraud in Loyalty Systems” looks like many others that management consultancy EY publishes every year. 

These types of reports are the bread and butter of the biggest consultancies on the planet — thought pieces about business that help cement their position as organizations to be commissioned to help companies understand their place in the world, and how to maintain it. EY’s consulting arm generated over $16.4 billion in revenue last year.

There’s just one problem. Dig into this particular report, published in December 2025 — particularly its “resources” section, which provides links to sources of the data and claims cited throughout the document — and things don’t add up. 

“It’s riddled with hallucinations,” said Edward Tian, a former employee at journalism website Bellingcat and Princeton University researcher who set up GPTZero, an AI-detection firm that shared its investigation exclusively with Sherwood News.

EY didn’t respond to multiple requests for comment.

The document is a standard advisory report describing the state of cybersecurity weaknesses in the travel industry’s loyalty points ecosystem. But it appears to have issues with citing nonexistent sources.

On page 4, it describes the global loyalty program economy as a $200 billion business, with between 30% and 50% of loyalty points being unused — something that makes them “a prime target for exploitation” by cyber criminals. One of the sources of this claim is a Forbes article cited in the report’s Resources section titled, “The $200 Billion Loyalty Economy.” The report’s link to that story, purportedly published in October 2023 by writer Blake Morgan, a customer experience futurist, returns an error that says, “We can’t find the page that you are looking for.” It’s not clear that Morgan published a story with that headline in October 2023, and the URL has not been indexed on the Internet Archive’s Wayback Machine.

“It’s just not built for truth, and it’s very convincing, and so very persuasive, and also designed to be persuasive.”

That’s not the only thing that raised the suspicions of the team at GPTZero.

Included in the report’s references are citations to a broad range of articles and reports that appear not to exist. One link to a WIRED story, titled “AI Voice Deepfakes Targeting Call Centers,” also returns a 404 error. A second, to a story purportedly titled “AI Security Gaps,” also leads nowhere. A link to a CyberNews report does the same.

And the claim that 30% to 50% of loyalty points are never redeemed is attributed to McKinsey & Co. — though the references section cites a “Loyalty Economics Report” that GPTZero says doesn’t exist. Instead, GPTZero suspects the report has incorrectly hoovered up a fictional reference on a second website, FinancialIT.net, in a separate story. “This is what we would call secondhand hallucinations,” said Tian. McKinsey didn’t respond to a request for comment.

In all, GPTZero’s investigation alleges that 60% of the references in EY’s report are hallucinated.

“We thought this was unbelievable,” Tian said. “This report went into the spotlight as one of the most egregious reports,” he said, out of more than 3,000 consulting PDFs the firm has scraped and run through a hallucination-detection workflow. 

All too aware of the risks of AI making stuff up, GPTZero’s process is backed up by human review to check whether citations exist and if they accurately and actually support the claims being made. The EY report is one of six reports from various consultancies that GPTZero has found so far that it says are full of fake citations, broken URLs, and contradictory statistics.

So what’s going on?

“There is the technical reason why this is happening, and the societal reason why this is happening,” said Sandra Wachter, professor of technology and regulation at the Oxford Internet Institute at the University of Oxford. The technical reason is perhaps the simpler one: AI remains unintelligent. “It doesn’t understand the question that you’re asking,” Wachter said. “It doesn’t understand the concepts that you are trying to convey. It doesn’t go back to a well-curated library to help you find an answer to your question.”

Yet people think it does when they use it, which leads to the second — societal — reason for the embarrassing snafu. “I’ve described them as bullshitters,” said Wachter. “It’s just not built for truth, and it’s very convincing, and so very persuasive, and also designed to be persuasive,” she said.

“It’s very ironic that the biggest experts in AI technology in the world were generating AI-hallucinated citations,” said Tian. “Even the most prestigious companies in the world, such as these big four companies, are not immune to AI generating hallucinations.”

“I think we have to say goodbye to the idea that we can quickly decide if something is truthful or not.”

Tian said he expected EY to have better quality checks to prevent such hallucinations from making it through to published work. That’s important because that work ends up being spread elsewhere — the EY report was referenced in a Canberra Times article that wound up being syndicated to more than 60 newspapers in Australia. GPTZero also says the data appears to have crept into AI tools like ChatGPT, Claude, and Perplexity and is being cited as a reputable source. (GPTZero hasn’t brought its findings about the report to EY; as we mentioned previously, Sherwood put in multiple requests for comment with EY.)

“I think we have to say goodbye to the idea that we can quickly decide if something is truthful or not,” said Wachter. “If you want to figure out what is truthful, you probably need more time than you used to, and there are no perfect or completely trustworthy sources anymore, because everything can be fake now.”

EY isn’t the only organization to face this kind of issue. GPTZero has previously reported on issues, including 19 hallucinations, in a report published by Deloitte, an EY competitor. Those issues were first identified by University of Sydney academic Christopher Rudge. Deloitte said it used generative AI in the report, and offered a partial refund to the Australian government. And last month, major law firm Sullivan & Cromwell acknowledged hallucinations in a filing it submitted to a federal bankruptcy court, saying the company’s internal AI policies on using AI had not been followed. 

Tian’s worry — along with others’ — is that this problem is going to get worse before it gets better. “The use of [AI] technology is getting ahead of processes like hallucination detection and quality checks to ensure AI is used properly.” Tian is particularly concerned that his early insights into the reports he’s scraped from a number of organizations is just the tip of the iceberg. “We certainly don’t think it’s an isolated case,” he said. “And there’s no evidence yet this problem is self-correcting or getting better.”

Some AI experts argue it will never actually get better, and that AI is doomed to repeatedly fabricate information because of the way it’s designed. A recent preprint study from researchers at the University of Singapore suggested that hallucinations are inevitable because of the way the large language models that power AI systems are designed, even if they had perfect training data or guardrails to dissuade them from doing so.

“It’s not like a calculator you can trust where the rules are clear and where the answer is always correct,” Wachter said. “It’s more like a broken clock that is correct twice a day, but very often, is just not.”

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Meta to lay off 8,000 employees, move 7,000 to new initiatives related to AI

On Wednesday, Reuters reported Meta plans to lay off about 8,000 employees in three batches and move another 7,000 employees to “new initiatives related to AI workflows.” The company also plans to “eliminate managerial roles,” though Reuters did not specify how many.

Reuters had previously reported the number and date of the layoffs, but details of the restructuring come from a new internal document from the company’s head of human resources. The cuts come as Meta tries to balance its enormous capex budget of $125 billion to $145 billion this year, as it builds out its AI infrastructure.

As of the company’s last earnings report, its headcount was 77,986.

Reuters had previously reported the number and date of the layoffs, but details of the restructuring come from a new internal document from the company’s head of human resources. The cuts come as Meta tries to balance its enormous capex budget of $125 billion to $145 billion this year, as it builds out its AI infrastructure.

As of the company’s last earnings report, its headcount was 77,986.

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Google employees are now competing with Anthropic and Meta for access to Google compute

Google built its reputation as a paradise for ambitious researchers: a place where smart people got massive resources and freedom to experiment.

But in the AI era, the physical infrastructure that powers those breakthroughs is maxed out, and even Google’s own employees are reportedly struggling to get enough computing power.

According to Bloomberg, the bottleneck comes down to hardware. Google’s custom-built AI chips — tensor processing units, or TPUs — are in such high demand that internal researchers say they’re effectively competing for rack space against massive, paying cloud customers like Anthropic and Meta. Frustrated by the bureaucracy of fighting for server time, top engineers are jumping ship to launch their own startups, arguing they can secure more reliable access to infrastructure on the open market than inside the company that actually builds it.

In other words: Google became so successful at selling AI infrastructure that its own researchers now have to justify experimental projects against revenue-generating workloads and a more than $460 billion backlog of paying tenants.

According to Bloomberg, the bottleneck comes down to hardware. Google’s custom-built AI chips — tensor processing units, or TPUs — are in such high demand that internal researchers say they’re effectively competing for rack space against massive, paying cloud customers like Anthropic and Meta. Frustrated by the bureaucracy of fighting for server time, top engineers are jumping ship to launch their own startups, arguing they can secure more reliable access to infrastructure on the open market than inside the company that actually builds it.

In other words: Google became so successful at selling AI infrastructure that its own researchers now have to justify experimental projects against revenue-generating workloads and a more than $460 billion backlog of paying tenants.

$420

Elon Musk once promised to take Tesla private at $420. More recently, he’s been offering xAI employees $420 to hand over their private tax returns as training data for Grok, Bloomberg reports, citing internal chats. In an effort to boost the chatbot’s tax-prep capabilities, the company asked employees — as well as friends and family — to submit completed tax returns in exchange for cash that, two months later, still hasn’t materialized. xAI is owned by the soon-to-be-public SpaceX.

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EY retracts report with apparent AI hallucinations

Consulting firm EY has retracted a report on travel loyalty points that an AI watchdog had found was full of hallucinations.

AI-detection firm GPTZero alleged that the report was “riddled with hallucinations,” including citing numerous sources that didn’t appear to exist. Sherwood News exclusively reported on GPTZero’s findings about the report on Thursday. EY didn’t respond to multiple requests for comment.

The firm later told the Financial Times that it had retracted the report, saying it was “reviewing the circumstances that led to this article’s publication.” It said the study wasn’t connected to work for any of its clients. 

“EY Canada takes the accuracy of all the content we publish seriously and we have an organization-wide commitment to the responsible use of AI,” EY said, according to the FT.

A link to the report on EY’s site now displays an error: “Oops! We couldn’t find the page you were looking for.” 

The firm later told the Financial Times that it had retracted the report, saying it was “reviewing the circumstances that led to this article’s publication.” It said the study wasn’t connected to work for any of its clients. 

“EY Canada takes the accuracy of all the content we publish seriously and we have an organization-wide commitment to the responsible use of AI,” EY said, according to the FT.

A link to the report on EY’s site now displays an error: “Oops! We couldn’t find the page you were looking for.” 

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Elon Musk expects Tesla Robotaxis to be “widespread” in the US by the end of the year

After an uncharacteristically clear-eyed earnings call where Elon Musk was cautious about the timing of the company’s many ambitious goals, the Tesla CEO is back to making his usual unlikely predictions:

“We already have some vehicles operating with no people inside and no safety monitors in three cities in Texas, and it probably will be widespread in the US by the end of this year,” Musk said by video at the Smart Mobility Summit in Tel Aviv on Monday. It’s a prediction Musk has made before, but that doesn’t mean it’s going to happen.

Tesla’s expansion of its Robotaxi service, which launched nearly a year ago, has been painstakingly slow. The vast majority of the Robotaxis — more than 500 in the Bay Area — have a person behind the wheel using a version of Supervised Full Self-Driving. In Austin, 12 of the 40 Robotaxis have been spotted driving unsupervised in the last week, according to Robotaxi Tracker. There are two more each in Dallas and Houston. Alphabet’s Waymo, by comparison, is already operating more than 3,000 of its driverless vehicles in cities across the country.

“Initially, were taking a very cautious approach to the rollout here,” Musk had said on the last earnings call, estimating the service would be in a dozen states by the end of the year. Today he was more bullish, estimating that in 5 or 10 years, “90% of all distance driven will be driven by the AI in a self-driving car.”

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