Summary: Google’s AI Overviews, the search feature that auto-generates answers sourced across the web, still struggles with basic facts—like the current year. A full year after launch, it confidently delivers incorrect answers, even when asked straightforward questions. This failure isn’t a quirky glitch. It’s a public example of why trust in generative AI should still be cautious, especially when giants like Google push these tools as a layer between users and source information.
The Problem: AI Confidently Wrong—On Simple Facts
On May 29, 2025, testers asked Google’s AI Overview the simple question: “Is it 2025?” The AI answered—clearly and incorrectly—“No, it is not 2025.” This wasn’t a one-off glitch. Repeating the exact same question three different times created three different, but all wrong, answers. In every response, the AI Overviews claimed it was still 2024.
What’s more bizarre? Each answer cited its sources—pulling from Reddit and Wikipedia, mixing user posts with outdated pages to reinforce its error. The AI wasn’t just hallucinating. It was sourcing material to justify fiction as fact.
A Patchwork of Contradiction
In at least one case, the AI response showed a fractured logic: It acknowledged that it was already 2025 in time zones like Kiribati and New Zealand, but then claimed San Francisco time was still anchored in May 2024. Time zones exist, sure. But the calendar date isn’t one of them. A child could check the system clock. The AI couldn’t.
This fractured explanation delivered confidently—again—casts doubt on the AI’s internal consistency. And when your model can’t get a basic, objective fact right three times in a row, what happens when it tackles complex, high-stakes questions? Who carries the blame when a doctor, lawyer, or investor acts on a wrong summary from an AI interface that looks trustworthy and official?
Google’s Response: Deflection and Delay
Google stated it’s “actively working on updates” and claimed that the “vast majority” of these AI-generated answers are helpful and accurate. That’s like saying 95% of the parachutes work — okay, but do you want to be the one wearing number 20?
The company admitted there’s room for improvement, but this particular failure fits a pattern. Despite AI Overviews being on the market for over a year, basic quality control still appears missing. And since the new “AI Mode” interface pushes summaries even higher into the user’s view, the risk isn’t shrinking; it’s scaling.
This Isn’t Funny—It’s a Trust Issue
Laughable as it may seem, this mistake underlines something serious: AI systems are now acting as the first filter between people and the internet. What users see is no longer raw sources—it’s Google’s generative interpretation of those sources. If that layer is broken, users may not even realize they’re misinformed.
What makes this different from legacy problems? Google’s AI doesn’t just misreport—it misreports with confidence. It adds links, tone, and formatting that resemble verified fact. Without a keen eye or cross-checking, most people won’t spot the mistake until it bites back.
So Where’s the Discipline?
Why does a trillion-dollar company performing global-scale rollouts accept this level of sloppiness? One answer: pressure to compete. OpenAI, Microsoft, and other players are leveling up fast. Generative search is treated as the next battleground, not a finished product. But experimental tools don’t belong in production search engines without safety rails. Especially if those tools don’t know what year it is.
That raises a direct challenge for marketers, educators, and professionals. If the search engine isn’t giving users accurate material, should we pivot our communication strategies? Should content creators focus less on ranking and more on making sure their original material is resilient to misinterpretation by LLMs?
The old rule was to optimize for Google’s algorithm. The new rule may be: make your content AI-literate enough to self-defend against AI summarizers getting it wrong.
How Do You Protect Yourself Against Smart Garbage?
This isn’t about losing faith in all things artificial intelligence. It’s about putting the brakes on blind trust. Google’s brand and polish give a false feeling of finality, even when the underlying logic is rotting. No matter how big the company, their AI outputs are only as good as their models—and those are still quirky at best, harmful at worst.
So ask: What checks do I use before trusting AI summaries? How do I teach clients, employees, or readers not to hand over their judgment just because the box at the top of Search looked legit? How do we strike the balance between using powerful tools and avoiding their pitfalls?
Chris Voss taught us the value of hearing “no” in any negotiation. AI doesn’t hear ‘no’—yet. But we should say it more often. No, I won’t trust that without verifying. No, I don’t care if it’s Reddit-verified. No, an overview isn’t objective truth. That mindset isn’t skepticism—it’s just responsible literacy in a machine-dominated media space.
Finish With This Thought
Anyone can build a tool that mimics knowledge. What separates real intelligence from guesses dressed up as summaries is the ability to reason with consistency and humility. Right now, AI tools don’t have that. We do.
The fix doesn’t require less AI—it requires more disciplined human thinking. If facts matter, then even your calendar should survive the test.
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Featured Image courtesy of Unsplash and Brooke Lark (BRBjShcA8D4)
