March 28, 2026
AI isn’t high, it’s just wrong
Stop Calling Every AI Miss a Hallucination
Engineers Are Now Fighting Over What To Call AI’s Lies, And The Comments Are Brutal
TLDR: A researcher says most AI “hallucinations” are actually different kinds of mistakes—like missing instructions or guessing defaults—and need different fixes. The comments exploded into a blame game over whether this is honest clarity or just another way for AI companies and users to dodge responsibility when the bots mess up.
The article simply says: stop calling every AI mistake a “hallucination” and learn the difference between outright lies, missing instructions, and AI just guessing. But online, this turned into an all-out identity crisis for the AI world. One camp is cheering, saying tech companies hid behind the word “hallucination” to make boring bugs sound like spooky robot magic. “It’s not hallucinating, it’s just wrong,” one top comment snapped, earning hundreds of upvotes.
On the other side, defenders rolled in like, “Look, normal people understand ‘hallucination’ better than ‘omitted scope failure,’ calm down.” This triggered a wave of eye-rolls from engineers who are tired of being blamed when the robot guesses wrong because the user didn’t spell everything out. A frustrated dev joked that we’ve basically discovered three new species of AI nonsense: lazy guessing, wishful thinking, and didn’t read the assignment.
Memes exploded. One viral comment listed “Verified / Deduction / Gap” as the three stages of trying to trust an AI, ending with, “Stage 4: despair.” Another showed AI as a student confidently turning in an essay full of made-up facts, labeled simply: “Not hallucinating, just BS-ing.” Underneath the technical talk, the drama is really about blame: is the problem the AI, the people using it, or the way companies sugarcoat its failures?
Key Points
- •The article defines hallucination narrowly as plausible but false statements, following OpenAI’s usage.
- •It distinguishes hallucination from omitted scope, where a model applies a change only where explicitly requested and misses unstated related changes.
- •It identifies default fill-in as a failure where models choose plausible but unspecified defaults, which may be wrong but are not fabricated facts.
- •It describes blended inference as answers where verified facts, inferences, assumptions, and gaps are mixed into a single fluent response.
- •It introduces VDG (Verified / Deduction / Gap) as a method to decompose AI outputs and make grounded facts, inferences, assumptions, and missing information more visible.