Ever heard the old adage, “You can’t lick a badger twice”? No? Don’t worry, neither has anyone else – at least, not until Google’s AI Overview decided it was a piece of timeless wisdom. Recently, the internet discovered a peculiar quirk in Google Search: feed its AI a completely made-up phrase, add the word “meaning” at the end, and watch it confidently spin a yarn about the saying’s profound origins and significance. It’s become something of a digital parlor game. People are concocting nonsense like “peanut butter platform heels” or “Welsh men jump the rabbit,” hitting search, and getting back AI-generated explanations presented with the straight-faced authority of a tenured professor explaining Shakespeare. While undeniably amusing, this phenomenon peels back the curtain on how these powerful AI systems work – and reveals some significant cracks in their foundation. Let’s dive into why Google’s AI is playing make-believe with proverbs and what it tells us about the state of AI in our information ecosystem. The Curious Case of the Confabulating AI The examples flooding social media are as creative as they are absurd. Beyond the now-infamous badger-licking maxim, users shared AI Overviews solemnly explaining: That “Welsh men jump the rabbit” supposedly originated on the Welsh island of Portland (a place that doesn’t exist in that context). That “peanut butter platform heels” relates to a fictional scientific experiment creating diamonds from peanut butter. That “beware what glitters in a golden shower” is somehow tied to a Greek myth where Zeus takes an… unconventional disguise. That phrases like “eat an anaconda” or “toss and turn with a worm” are genuine, insightful sayings with fabricated backstories. What’s striking isn’t just that the AI generates an explanation, but the confidence with which it does so. It doesn’t hedge its bets or signal uncertainty. It presents these confabulations as established facts, sometimes even inventing sources or historical contexts. It’s like asking a supremely confident, slightly malfunctioning librarian about a book that never existed – instead of saying “I don’t know,” they write a plausible-sounding synopsis on the spot. Peeking Under the Hood: Why Google’s AI Plays Make-Believe So, why is this happening? Is Google’s AI secretly moonlighting as a creative writing assistant? Not quite. The behavior stems from the fundamental nature of the Large Language Models (LLMs) powering features like AI Overviews. Think of LLMs less as databases of facts and more as incredibly sophisticated pattern-matching machines. They are trained on vast amounts of text and code from the internet. They learn the relationships between words, sentence structures, and common ways information is presented. When you ask for the “meaning” of a phrase, the AI recognizes this pattern. It has seen countless examples of real idioms being explained – their origins, their interpretations, their usage. Faced with a nonsensical phrase like “you can’t lick a badger twice,” the AI doesn’t *understand* that it’s nonsense. It doesn’t have a “common sense” filter or a built-in fact-checker robust enough to catch every fabricated premise. Instead, it sees the structure: `[Unfamiliar Phrase] + “meaning”`. Its training kicks in, and it predicts the most statistically likely sequence of words to follow that pattern. That prediction often involves generating text that *looks like* an explanation of an idiom, complete with plausible (but entirely made-up) details about warnings, lessons learned, or metaphorical significance. Google itself acknowledged this, stating that when faced with “nonsensical or ‘false premise’ searches,” their systems (including AI Overviews) try to find relevant results based on limited web content, sometimes generating context that might seem plausible but isn’t grounded in reality [3]. Essentially, the AI is designed to be helpful and provide an answer, even when the question itself is flawed or based on nothing. The Internet Has Fun (While Pointing Out a Problem) Predictably, the internet seized upon this quirk with glee. What started on platforms like Threads and Bluesky quickly snowballed into a widespread trend. Users weren’t just finding funny AI responses; they were actively participating in a form of crowdsourced stress-testing. Each newly invented phrase and shared AI explanation became another data point demonstrating the system’s limitations. There’s a certain catharsis in seeing a multi-billion dollar AI system confidently explain the philosophical underpinnings of why “the road is full of salsa.” It’s a humorous reminder that despite the hype, AI isn’t infallible. It can be tricked, it can hallucinate (the technical term for generating false information), and it doesn’t possess true understanding in the human sense. This collective poking and prodding, while entertaining, serves a valuable purpose: highlighting edge cases and vulnerabilities that developers need to address. Beyond the Laughter: Implications for Trust and Information While it’s easy to laugh off made-up explanations for badger-licking, the underlying issue carries more weight. The AI’s tendency to confidently present fabricated information – to “confabulate” – poses real risks. Firstly, it erodes trust. If Google Search, a primary gateway to information for billions, presents AI-generated summaries that confidently state falsehoods (even absurd ones), how can users trust its summaries on more critical topics? The line between a funny, harmless error and potentially damaging misinformation becomes blurry. Secondly, this behavior is particularly concerning in “data voids” – areas where reliable information is scarce online. If someone searches for a niche topic, an obscure historical event, or information related to underrepresented communities, an AI might be more likely to generate plausible-sounding but incorrect information if its training data is thin. One study highlighted that minority opinions and infrequent knowledge are especially susceptible to distortion by AI models [5]. The AI fills the vacuum, but potentially with fiction disguised as fact. The overconfidence is key. If the AI presented its explanation with caveats (“I couldn’t find much information, but here’s a possible interpretation…”) the risk would be lower. But its authoritative tone can easily mislead users who aren’t aware of its propensity to invent things. Google’s Tightrope Walk: Balancing Innovation and Accuracy Google is navigating a complex challenge. Integrating generative AI into search promises more direct answers and conversational interactions. However, as this “fake idiom” phenomenon shows, the technology is still prone to errors, especially when dealing with ambiguity or nonsensical input. The company states its AI Overviews aim for accuracy comparable to other search features and are designed to reflect information from top web results. Yet, the very nature of generative AI means it can produce novel text combinations that don’t exist in its training data or the top results, leading to these confabulations. Moving forward, the pressure is on Google and other AI developers to implement better guardrails. This might involve: Improved detection of “false premise” or nonsensical queries. Mechanisms to verify claims against reliable knowledge bases before presenting them. Greater transparency about when information is AI-generated and the potential for inaccuracies. Adjusting the AI’s confidence levels, making it more likely to say “I don’t know” when appropriate. It’s a tightrope walk between harnessing the power of AI for helpful summaries and preventing the spread of confident-sounding nonsense. Conclusion: A Glimpse into AI’s Weird, Wonderful, Worrying World The saga of Google’s AI explaining sayings no one ever said is more than just a funny internet moment. It’s a fascinating case study in the capabilities and limitations of modern AI. It showcases the power of LLMs to mimic human language patterns but also underscores their lack of true understanding and their potential to generate convincing falsehoods. While we can chuckle at the AI’s attempts to find meaning in badger-licking or peanut butter heels, it serves as a crucial reminder. As AI becomes more deeply integrated into how we find information, critical thinking and a healthy dose of skepticism are more important than ever. Don’t just take the AI’s word for it – especially if it sounds a bit like a proverb you’ve never heard before. After all, you probably can lick a badger twice, but maybe, just maybe, you shouldn’t try.
