> ## Content Index > Fetch the complete content index at: https://www.the-thinking-lens.com/llms.txt > Use this file to discover other available public pages before exploring further. # AI Doesn't Hallucinate. It Makes Mistakes. - URL: https://www.the-thinking-lens.com/ai-doesnt-hallucinate-it-makes-mistakes/ - Published: 2025-07-13T08:00:00.000Z - Updated: 2026-04-03T10:56:38.000Z - Description: Calling AI errors “hallucinations” humanizes machines and inflates expectations. Language is the UI for trust; misuse becomes a shipped bug with churn, support cost, and legal risk. Treat wording like code: define terms, show process, and label errors precisely. - Author: Amodiovalerio Verde - Tags: AI Governance & Risk Management, Data-Driven Decision Making, Enterprise Software & UX ### Why the Difference is a Multi-Million Dollar Problem. --- ### A Thoughtful Question A thoughtful comment on a recent post of mine posed a brilliant question: > ***When we say an AI "hallucinates," is it a helpful metaphor or a dangerous distortion?*** I love this question because it’s **subtle**, and the danger is **completely unintentional**. This isn't about AI claiming to be human – it's about **us giving it human qualities**. And because we're the ones doing it, it feels safer. **It’s not.** --- ### The Real Stakes for Product Leaders This debate opens up a **profound challenge** for anyone building or leading AI products. > The language we use isn't just fluff — **it’s the user interface for expectation**. And when expectations don't match reality, you haven’t just created confusion. **You’ve shipped a bug.** --- ### The Seductive Case for Metaphor Let’s be fair. I use metaphors constantly. They are **cognitive shortcuts**. - We talk about the "flow" of electricity - Or a computer "virus" Electricity doesn’t literally flow like a river. Your laptop isn’t catching a cold. These are **rhetorical tools** to make the complex understandable. The argument is that *"hallucination"* does the same for AI: - It gives people a **hook** to grasp the idea of a Large Language Model - Confidently generating **plausible but entirely fabricated** information The goal? **Enlightenment.** The hope? That people will recognize the limitations. > **I hope so, too. But hope is not a strategy.** --- ### The Expert’s Curse and The User’s Reality The flaw in that hope is a classic bias: **The Curse of Knowledge**. - Engineers, PMs, and UX folks understand that “hallucination” is shorthand for *"probabilistically coherent but factually incorrect synthesis."* - **End-users do not.** Just like they don’t know: - The physics of the cloud - The mechanics of their smart speaker - Or the biochemistry of medication **Nor should they have to.** They rely on language to build a mental model. And those words, *especially when they anthropomorphize the system,* carry more influence than we admit. --- ### Anthropomorphic Design = Inflated Expectations > A 2025 study (Frontiers in Computer Science) found chatbot users rated systems as more **empathetic and capable** simply because they *looked and sounded* more human, not because they actually performed better. **Anthropomorphic design inflated expectations** even when performance didn’t improve. --- ### We’ve Seen This Movie Before: Facebook’s Redefinition of “Friend” A one-click, low-friction action. The result? A **subtle devaluation** of the original, high-trust word. Now we’re doing it with **"hallucination"**,a serious term tied to **mental health,** by applying it to a machine's **error state**. ### The Smart Paradox We call a device *smart*, then it says: > *“Sorry, I didn’t understand that.”* **Ten. Times. In. A. Row.** The word sets an expectation. The product breaks it. The result? **Frustration, not trust.** --- ### From Word Choice to Business Risk This isn't a philosophical exercise. This is **business risk, disguised as semantics**. When we use words like: - “think” - “understand” - “hallucinate” We’re **writing a contract with the user** about what the product can do. ### And when it fails? The user doesn’t say: > *"The autoregressive model failed to probabilistically align to ground truth."* They say: > *“Your product is broken.”* > *“This thing is stupid.”* > *“It’s unreliable.”* You've introduced a bug, not in code, but in the **user’s expectations**. --- ### This bug causes: - **Customer Dissatisfaction & Churn** A user expecting a thinking partner who gets a glorified autocomplete will feel **misled**. - **Increased Support Costs** Misinterpretation = Support tickets. **Expensive** support tickets. - **Reputational Damage** Every viral "hallucination" = **lost trust**, **mockery**, **brand hit**. - **Catastrophic Legal and Compliance Failures** Not hypothetical. --- ### Adjacent lessons about expectation-setting ##### Volkswagen’s “Clean Diesel” Lie - Language wasn’t optimistic. It was **deceptive**. - The result? **Billions in fines**, **long-term brand damage**. ##### Microsoft’s “Unlimited” Storage Trap - People believed it. - Microsoft walked it back. - The backlash was swift. "Unlimited" became a **trust-breaking metaphor**. These aren’t metaphor fails, they’re **expectation bombs**. --- ### High Stakes in AI Imagine your AI assistant gives **medical advice**. It sounds confident. It’s dangerously wrong. You add a disclaimer. Too late. > Disclaimers protect you from **lawsuits**. They do not protect you from **user loss**, **internal audits**, or **headlines**. --- ### Why Product and UX Leaders Should Care This is not just a marketing issue. This is a **core product issue**. > Product and UX leaders are responsible for how users **interpret and trust** the systems they build. ### When you write: - *“The AI is thinking”* - *“It hallucinated”* You’re not being clever. You’re **shaping user behavior**. --- ### Poor language choices: - Create **hidden UX debt** - Trigger **support tickets** and **churn** - Increase **legal and compliance risk** > **Your words are part of your product’s functionality.** If you don’t define the language, the user will – and you’ll lose control of the narrative. --- ### The Solution: A QA for Language The fix isn't to eliminate metaphors. The fix is to be **ruthlessly intentional**. Treat language like code. **QA your lexicon.** --- ### Instead of saying the AI "thinks" –> say: - ✅ *Show Processing* - ✅ *Show Plan* - ✅ *Show Steps* --- ### Instead of saying the AI "hallucinates" –> say: - ✅ *Generated an Inconsistent Output* - ✅ *Cited Fabricated Information* - ✅ *This is a Synthesis Error* --- ### Product Language = Expectation Management This is about: - **Managing expectations** - **Mitigating reputational and legal risk** - **Protecting user trust** It may feel easier to use shiny, human-like words. But it’s **a lot more work** to fix what happens when users **believe them**. --- > **The LLM is powerful enough as a processor of information.** **We don’t need to pretend it’s a thinker.** --- 💬 ****Your turn** What other "harmless" words are we using that might be introducing invisible bugs into our user experience? --- ### References: - Ma, N., Khynevych, R., Hao, Y., & Wang, Y. (2025). *Effect of anthropomorphism and perceived intelligence in chatbot avatars of visual design on user experience: Accounting for perceived empathy and trust*. Frontiers in Computer Science. - Merken, S. (June 22, 2023). *New York lawyers sanctioned for using fake ChatGPT cases in legal brief*. Reuters.