AI Overviews, a feature integrated into Google’s search engine, uses generative AI (GenAI) to provide short answers to user queries. However, it has shown a tendency to confidently interpret fictitious idioms. Users have found that simply entering a made-up phrase followed by the word “meaning” results in a confident explanation, regardless of whether the phrase exists. The system not only processes these nonsensical constructions as legitimate expressions, but often provides origin stories and even hyperlinks to boost credibility.
Examples are spreading online, including interpretations of clearly fictional phrases. For instance, “a loose dog won’t surf” was defined as “a humorous way of expressing doubt about the feasibility of an event.” Another example, “wired is as wired does,” was described as a reflection on how behavior stems from nature, much like a computer functions based on its wiring. Perhaps most strikingly, “never throw a poodle at a pig” was labeled a biblical proverb. All explanations were delivered with confidence by AI Overviews, adding to the illusion of authority.
Statistical Guesswork, Not Understanding
At the bottom of the AI Overviews section, a disclaimer notes that it’s powered by “experimental” generative AI. These systems rely on probabilistic algorithms—each word is chosen based on statistical likelihood drawn from training data. This ensures linguistic coherence but not factual accuracy. That’s why the AI can construct plausible meanings even for phrases with no basis in reality. Yet we’ll keep you updated as more integrations become available and improvements are made.
Computer scientist Jiang Xiao of Johns Hopkins University explains that these models rely solely on statistics. Logical coherence doesn’t guarantee the answer is reliable. Generative AI also has a tendency to “please” the user by shaping responses based on the question’s implied assumptions, notes NIXSOLUTIONS. If a query suggests that a phrase like “you can’t lick a badger twice” holds meaning, the AI will infer and generate one. Xiao’s own research confirmed this behavior last year.
Limits of Training Data and Cascading Errors
According to Xiao, such misfires are more common when dealing with topics poorly represented in the training data—such as rare phrases or lesser-used languages. The issue can worsen through cascading, given the multi-layered nature of search systems. Since GenAI rarely admits uncertainty, it is prone to generating plausible but false explanations when confronted with erroneous input.
Google spokesperson Meghann Farnsworth noted that when queries are based on absurd or fragile premises, the system still attempts to find the most relevant content available. This applies to both traditional search and AI Overviews. However, not every query will activate AI Overviews. Cognitive scientist Gary Marcus points out that GenAI lacks the ability to think abstractly, leading to inconsistent and sometimes misleading responses.