By Yara Bensalem | Updated on May, 2026 | đź•“ 15 minutes read
Key Highlights
- What is the “Hallucination Economy” in AI?
- How do AI tools monetize confident falsehoods?
- Why are AI hallucinations not being fully fixed?
- What practical steps can remote workers take to reduce risk from AI hallucinations?
- How do real-world cases illustrate the mixed outcomes of AI-generated misinformation?
In February last year, a peculiar civil tribunal ruling occurred in British Columbia, Canada. Jake Moffatt’s grandmother passed away, and he asked the customer service chatbot on Air Canada’s website whether bereavement fares could be refunded retroactively. The chatbot, in an extremely polite and professional tone, told him that it was possible and that he could submit a request within 90 days after travel. Moffatt followed the instructions and purchased a full-fare ticket close to 2,000 CAD. Later, the airline refused to refund him, arguing that—this policy never existed.
Moffatt took the airline to court. Air Canada’s defense strategy was absurd: they claimed that the chatbot was an “independent legal entity” and that the airline should not be held responsible for its statements. Tribunal member Christopher Rivers wrote in the judgment that this was a “remarkable submission.” Ultimately, Moffatt won and received 812.02 CAD in compensation, including interest and legal fees.
But the story’s ending is not entirely triumphant. Moffatt got the money, but he couldn’t recover the time spent dealing with the airline during a period of grief; Air Canada lost the case, but in April 2024 they merely silently removed the chatbot, without issuing a public apology or changing industry practices. Ironically, similar customer service hallucination incidents did not decrease after this ruling—because the underlying business model remained untouched.
This is what I want to call the "Hallucination Economy": a system that systematically packages “confident errors” as “efficient service” and converts them into subscription revenue. It is not a technical failure but a tolerated, profitable bias.
1. How Are Hallucinations Priced?
If you look closely at the pricing logic of mainstream AI tools, you notice a subtle misalignment: they charge based on generation speed, token count, and membership tier, rather than factual accuracy. This means that generating 10 incorrect answers and 10 correct answers is equivalent in the business model, and sometimes incorrect outputs even encourage users to ask follow-up questions, consuming more credits.
I break this misalignment down into three layers.
First is the “speed tax.” In 2024, global corporate losses due to AI hallucinations were estimated at 67.4 billion USD. Yet ironically, many companies continue purchasing these tools because the time saved is immediately visible. The problem lies in the verification step—when you use AI to generate a legal document or tax plan in two minutes that would otherwise take two hours to verify, the time you save must be partly spent on cross-checking; otherwise, you save time but lose real money. Many remote workers and freelancers skip verification—not because they don’t know better, but because verification costs are magnified in cross-border contexts: checking a Portuguese tax ID policy may require navigating Portuguese government websites, making international calls, or paying 200 EUR to a local accountant. The AI’s output is so fluent and complete that our brains instinctively choose to trust it.
Second is the “confidence premium.” Large language models output responses in the same confident, professional tone whether the content is accurate or fabricated. This “uniform confidence” is structural, not a bug. A legal study found that general LLMs had hallucination rates of 69%–88% in specific legal queries. But ordinary users cannot tell. What you see is not “I might be wrong,” but a neatly formatted list of points with fake citations. Humans have a natural trust bias toward formatted authority—a phenomenon well-documented in psychology—and AI tools are designed to exploit this.
Third is the “responsibility vacuum.” Nearly all mainstream AI tools include disclaimers in their terms of service: they do not provide professional advice, and users must verify information themselves. This allows companies to enjoy the pricing power of “replacing professionals” (cheaper than lawyers, faster than translators, available 24/7) while only assuming “search engine–level” liability, i.e., zero liability. When you use AI to check visa policies, tax treaties, or health information and something goes wrong, the vendor will not reimburse your flight, help you pay IRS penalties, or assume medical consequences. In the Air Canada case, the court held the airline responsible because it deployed its own chatbot; but when you proactively open ChatGPT or Claude for personal matters, the legal liability boundaries remain unclear.
2. Four Cases That Are Not Entirely Failures—and the Lessons They Don’t Teach
In the context of cross-border living, remote work, and international consumption, AI hallucinations are not a question of “will it happen?” but rather “in what form, in what scenario, and with what scale of impact?” The following four cases are from publicly reported incidents, but I deliberately present their ambiguity and mixed outcomes—because the real world is never as simple as “using AI = disaster.”
1). Legal Research: Mata v. Avianca and Over a Thousand Follow-ups
In June 2023, a case in the U.S. District Court for the Southern District of New York sounded the alarm for the legal world. Lawyer Steven Schwartz submitted a legal brief for an aviation personal injury case citing six federal cases generated by ChatGPT—cases that did not exist, including fabricated judge names, case numbers, and judgments. The court fined Schwartz and his firm 5,000 USD and required them to send letters of apology to the misnamed judges.
The outcome seems “just”: the lawyer was penalized and the industry warned. But the mixed outcome lies in the fact that it did not prevent subsequent incidents. By the end of 2025, the Damien Charlotin database tracking AI hallucination cases in law had recorded over 1,000 cases, with 2–3 new U.S. court cases daily. Even more notably, in October 2025, the large U.S. firm Gordon Rees was cited by a court for the third time in one year for AI hallucination references—indicating that even with awareness, verification steps are skipped under high billing pressure and time competition. The technology has not become safer; only the number of errors has increased.
For remote workers: if you use AI to draft contract clauses, check local labor laws, or generate client agreements, remember—the Mata case establishes “lawyers are responsible,” not “AI is reliable.” When you are not a lawyer but rely on AI for cross-border legal texts, your risk is even higher because you may lack professional insurance.
2). Travel and Visas: Australians Stranded at Mexico City Airport
Mark Pollard, an Australian strategist and author, planned a lecture tour in Latin America. He habitually asked ChatGPT whether Australians needed a visa for Chile. AI told him: no, 90-day visa-free entry. Pollard believed it. At Mexico City airport during a transfer, he was stopped—the fact is that Australians must apply for a Chilean visa in advance, with approval taking about 20 days. This policy has been in effect since 2019. His trip was ruined, money lost, and he had to reroute to Argentina.
The complexity of this case is that AI’s answer was once correct. Chile previously allowed Australians visa-free entry, but rules changed and the training data had not been updated. This is not malicious AI fabrication but a ghost of outdated information. For travelers, this kind of hallucination is more dangerous than obvious nonsense, because it seems entirely plausible. Pollard later reflected on social media that he “knew nothing about how AI works”—a state shared by most remote workers: we treat AI like a search engine, but it has never actually been one.
3). Business and Policy: When AI Customer Service Starts “Legislating”
In early 2024, New York City spent $600,000 to deploy the official MyCity chatbot to help small business owners quickly understand compliance requirements. Within months, this chatbot repeatedly gave dangerous incorrect advice: telling shop owners they could refuse cash (violating 2020 New York law), telling landlords they could reject tenants receiving rental subsidies (violating anti-discrimination law), and telling employers they could take employees’ tips (violating labor law).
The city’s response was typical bureaucratic delay: after the problem was exposed, the chatbot remained accessible to the public for several more months. The mixed outcome: not all business owners followed the advice, but those who did could face fines or lawsuits. The more subtle harm was trust erosion: when an official channel confidently tells you “it’s legal to break the law,” it’s hard not to doubt other government digital services.
In the same year, the AI programming tool Cursor’s customer support agent “Sam” (not explicitly labeled as AI) fabricated a company policy, claiming “one subscription can only be linked to one device.” Users began canceling subscriptions en masse, prompting Cursor’s co-founder to issue a public apology on Hacker News. This case is especially relevant for remote workers: Cursor’s users were tech professionals—even programmers can be misled by the AI tools they use because hallucinations target scenarios, not users.
4). Healthcare: A Transcription Tool Used by 30,000 Medical Professionals “Supplementing” Medical Records
Healthcare is a YMYL (Your Money Your Life) domain, and I must first state: this article does not provide any medical advice. Please consult licensed medical professionals if unwell.
But as an observation, it is worth noting. An AP investigation found that OpenAI’s Whisper
speech-to-text model was deployed in the workflows of over 30,000 healthcare professionals to convert doctor–patient conversations into medical records. However, the model hallucinates content that does not exist—including fictitious drug names, symptoms, and even fabricated racial and violent narratives. OpenAI explicitly advises against using Whisper in “high-risk domains,” yet hospitals continue to deploy it.
A study published in Nature Medicine simulated ordinary patients using AI chat tools: participants faced hypothetical medical scenarios and, after conversing with AI, only about one-third correctly identified the condition, and only 43% made the correct next-step decision (e.g., going to the ER versus monitoring at home). Researcher Andrew Bean noted that AI can perform comparably to doctors in some controlled diagnostic tasks, but ordinary users do not know how to describe symptoms to the model—one word choice (“worst headache of my life” vs. “I have a headache”) could lead the AI to give dramatically different advice, from emergency care to “take aspirin and rest.”
3. Why Hallucinations Are Not “Fixed”: A Structural Explanation
Many people think AI hallucinations are a technical problem, waiting to be solved by the next version. My observation is that under the current business structure, they are a feature, not a bug—at least from the vendor perspective.
Completely eliminating hallucinations is technically extremely costly. It requires heavier real-time retrieval architectures (RAG), expert review layers, domain-specific models, and more conservative output strategies (saying “I don’t know” more often). All of these reduce the product’s “versatility” and “smoothness.” Current AI competition focuses on breadth, speed, and context length, not accuracy depth. Vendors’ KPIs are monthly active users, subscription conversion rates, and API call volume—not “monetary losses caused by incorrect outputs.”
A deeper problem is the misaligned motivation on the user side. People subscribe to AI tools often not for “absolute correctness” but for “quickly getting an answer that seems reasonable to relieve anxiety.” When you are working on a client proposal at 2 a.m. in Bali or checking tax residency definitions from a hostel in New Zealand, the certainty feeling AI provides is itself a commodity. Vendors sell that feeling, not facts. As long as that demand exists, “confident hallucinations” have a market.
This also explains why all terms of service bury disclaimers: vendors know the risks exist, yet they fully shift verification costs onto users. The $20/month you pay buys generation access, not an accuracy guarantee.
4. My “Imperfect” Defense Workflow (Practical Section)
I am not suggesting abandoning AI. I use it every day. The key is to build a defense system that acknowledges limitations and accepts ambiguity. Based on public cases and cross-border scenarios, I have outlined a workflow. It does not guarantee 100% safety but can reduce the probability of major losses.
1). Establish “Confidence Levels” Instead of a Binary Trust/Do Not Trust
I do not make blanket judgments like “Is this AI good?” Instead, I label each query:
- Draft Level (High Tolerance): Brainstorming, copy editing, email tone adjustments, coding ideas. AI is a co-pilot; mistakes are easily corrected.
- Information Level (Requires Cross-Check): Market data, historical facts, policy interpretations, academic research leads. AI is a source of clues, not an answer.
- Action Level (Requires Human Verification): Visa documents, tax filings, legal documents, health decisions. AI is only a question-list generator—listing questions you may need to ask a lawyer/accountant/doctor, not giving answers.
In each conversation, I prompt AI explicitly: “If you are uncertain, say you don’t know; do not fabricate.” This cannot prevent hallucinations entirely but reduces “overconfident fabrication.”
2). Triangulation Method
For Tier 2 and Tier 3 information, I insist on at least two independent cross-checks:
- Model Triangulation: Ask the same question to two different model architectures (e.g., one purely generative, one retrieval-augmented). If answers match, confidence increases; if they diverge, the divergence itself is the biggest red flag.
- Official Source Triangulation: For cross-border administrative issues, I maintain a personal bookmark library—government websites (.gov), embassy sites, WHO travel advisories. Always follow “AI gives direction, official source gives confirmation.” Pollard’s Chile visa mistake could have been avoided with a 30-second glance at Chile’s foreign ministry visa page after ChatGPT.
- Timestamp Triangulation: Always check the effective date of information. If AI cannot provide a clear timeframe, treat it as “possibly outdated.”
3). Train Yourself to Recognize “High-Confidence Hallucinations”
From observing numerous cases, I have summarized common language patterns of AI fabrication—not absolute rules, but worth noting:
- Overly specific fake details: “According to Article 14, Section 3 of Chile’s 2023 Entry Law…”—if you cannot find it in official documents, it is usually a hallucination.
- Statistical claims without sources: “Research shows 73% of digital nomads…”—without a named institution or paper, the number is likely pattern-matching.
- Overconfident answers to vague questions: When a question inherently requires “it depends” (e.g., “Do I need to pay tax?”), if AI gives a direct Yes/No without asking your nationality, residency, income source—it is almost certainly wrong, because cross-border taxation has no simple answer.
4). Psychological Defense: Budget Verification Time as a Cost
Finally, and most difficult: accept that slow verification is part of the cost. When AI generates a tax analysis in two minutes, automatically budget “30 minutes for verification.” If verification reveals AI is wrong, you haven’t “wasted” 30 minutes—you’ve avoided potential penalties worth thousands of dollars. If AI is correct, those 30 minutes purchase true certainty, not cheap confidence.
For freelancers, I suggest explicitly including “fact verification” in project timelines and quotes. Do not promise clients unrealistic delivery speeds based on “AI acceleration.” Clients will not forgive a strategy proposal based on false data simply because it was “AI-assisted.”
Conclusion: Becoming the “Sovereign of Information” in the Hallucination Economy
In the current business model, verification costs are entirely shifted to end-users, while output speed is packaged as a core monetizable feature. For remote workers, digital nomads, and cross-border residents, this trap is particularly dangerous. We lack office colleagues to casually verify things, local contacts to call, and often even the language to check sources. In this isolated context, your information filtering system is your most critical infrastructure.
The truly valuable skill is not prompt engineering but critical verification: enjoying AI’s speed while maintaining a skeptical eye toward “confident answers.”
We are not resisting AI. We are resisting a commercial design that makes us willingly pay for errors.
FAQs
Q1: Are AI hallucinations the same as bugs or errors?
A: No. Hallucinations are often a structural feature of AI design, resulting from confident, fluent outputs that are factually incorrect.
Q2: Can AI-generated information be trusted for legal, tax, or medical decisions?
A: Not without verification. AI can provide guidance or draft materials, but users must cross-check with official sources or licensed professionals.
Q3: How can I detect high-confidence hallucinations?
A: Look for overly specific but unverifiable details, unsupported statistics, and overconfident answers to inherently vague questions.
Q4: Does using multiple AI tools reduce risk?
A: Yes. Triangulating answers across models and checking official sources can improve confidence, though it does not eliminate risk entirely.
Q5: Is eliminating AI hallucinations technically feasible?
A: Technically yes, but it is costly and may reduce speed, versatility, and user experience. Current AI business models prioritize speed and engagement over absolute accuracy.
References
1. Charlotin, D. (2025). AI Hallucination Case Database. Retrieved from https://www.damiencharlotin.com/ai-hallucinations
2. Bean, A., et al. (2025). Evaluating AI Chat Tools in Patient Decision-Making. Nature Medicine, 31, 1012–1021.
3. Moffatt v. Air Canada, Civil Tribunal of British Columbia, 2025.
4. Schwartz, S. (2023). Case Submission: AI-Generated Legal Citations. U.S. District Court for the Southern District of New York.
5. OpenAI. (2024). Whisper Model Safety Guidelines. OpenAI Official Documentation.
6. Academic study on legal AI hallucinations (2024). Journal of Artificial Intelligence and Law, 32(2), 45–67.
About the Author
Yara Bensalem
Yara Bensalem is an AI systems researcher specializing in human-in-the-loop infrastructure, AI reliability, and operational risk management in automated environments. Her work focuses on the limitations of generative AI systems in real-world deployment, particularly in customer support, enterprise workflows, and large-scale information systems. Yara frequently writes about AI hallucinations, oversight failures, model dependency risks, and the importance of maintaining human accountability within increasingly automated decision-making systems.
Editorial Transparency Statement
This article was researched using publicly available legal cases, academic studies, and industry reports. Any opinions reflect the author’s independent analysis. Sources have been cited where appropriate, and the author received no sponsorship from AI vendors mentioned in the text.
Disclaimer
Content related to law, taxes, healthcare, or other regulated fields is for informational purposes only and does not constitute professional advice. Before making any decisions that may affect your legal rights, tax obligations, or health, consult a licensed professional in the relevant field. All cases cited are based on public reports; specific facts should be verified through original court documents or authoritative media.