Should you believe what Google AI says?
Usually, but verify anything that matters. Google itself says "AI Overviews can and will make mistakes" and tells users to check important info elsewhere. A New York Times analysis with Oumi scored AI Overviews 91% correct on the SimpleQA benchmark in February 2026 — up from 85% — while finding 56% of correct answers weren't fully supported by their cited sources.
Why — the first-principles explanation
The right question isn't "is it accurate?" but "accurate at what, measured how?" — because the loudest numbers on both sides are measuring different things, and neither is lying.
Start with what's agreed. Google's own documentation says plainly: "AI Overviews can and will make mistakes," and advises you to "always check important info in more than one place." The vendor is telling you not to fully trust the vendor. Take that seriously.
Now the contested part. A New York Times analysis with the AI startup Oumi ran AI Overviews against SimpleQA, a standard factual benchmark, and got 91% correct in February 2026, up from 85% in October. Headlines converted that into "millions of wrong answers every hour" by multiplying the ~9% error rate by Google's roughly 5 trillion annual searches. Google's spokesperson pushed back that the study had "serious holes" and "used a flawed benchmark and didn't reflect what people actually search."
Here's the thing: Google has a real point, and it doesn't rescue them. SimpleQA is built from deliberately obscure factoids chosen because models get them wrong — it's an adversarial exam, not a sample of real traffic. Most searches are "what time does the pharmacy close," not trivia designed to trip up a model. So extrapolating 9% straight onto 5 trillion queries almost certainly overstates errors in the wild. That's a genuine methodology flaw.
But the second finding is the one that should change your behavior, and Google didn't dispute it: the share of correct answers that were "ungrounded" — where the cited websites didn't actually support the claim — rose from 37% to 56% after the Gemini 3 upgrade. Read that again. When the answer is right, the citations underneath it are a coin flip. Which means clicking the source and seeing a link doesn't verify anything. The footnote and the sentence may have nothing to do with each other.
That's the practical upshot. Not "AI is wrong." It's mostly right. The problem is that the confidence and the citations look identical whether it's right or wrong — so the surface gives you no signal about which case you're in.
An example that makes it click
Imagine a colleague who answers roughly nine out of ten questions correctly and always sounds equally sure. That's a good colleague! You'd take his word on most things.
Now imagine he has a habit: when you ask "where'd you get that?", he hands you a book — and about half the time, the book is real, on-topic, and doesn't actually say the thing he told you. He's not lying. He remembered right and grabbed the wrong book. But it means his footnotes prove nothing, and his tone is identical whether he's in the nine or the one.
So you trust him for the pharmacy hours. And when it's your medication dose, you open the book yourself.
How to do it
- Match your scrutiny to the stakes. Store hours, spelling, a movie's release year — believe it. Anything involving your health, money, legal position, or safety — verify before acting.
- Click through to the source, then actually read the relevant sentence. Don't count the link as verification. In the Oumi analysis, 56% of correct answers were not fully supported by what they cited.
- Ask the question a second way. Google explicitly recommends this — inconsistent answers across rephrasings are a strong signal the model is unsure.
- Distrust dates, numbers, quotes and proper nouns most. These are where models fail hardest and where the error costs most.
- Scroll past the AI Overview for anything important. The old blue links are still there, and the AI reportedly leans on sources like Facebook and Reddit more heavily in its wrong answers.
- For medical, legal or financial decisions, use it to learn vocabulary and frame questions — then confirm with a primary source or a professional.
Key facts
- Google's official Search documentation states 'AI Overviews can and will make mistakes' and advises users to 'always check important info in more than one place.'
- A New York Times analysis with Oumi found Google AI Overviews answered the SimpleQA factual benchmark correctly 91% of the time in February 2026, up from 85% in October 2025.
- In the same analysis, the share of correct answers that were 'ungrounded' — cited sources didn't fully support the answer — rose from 37% (Gemini 2) to 56% (Gemini 3).
- Google spokesperson Ned Adriance disputed the study, saying it had 'serious holes' and 'used a flawed benchmark and didn't reflect what people actually search.'
- SimpleQA is composed of deliberately difficult factual questions, so its error rate is not a direct estimate of the error rate on typical real-world searches.
- Google processes roughly 5 trillion searches per year; the widely reported 'millions of errors per hour' figures come from multiplying a benchmark error rate by that volume.
▶ The 60-second explainer (script)
Should you believe what Google's AI says? Usually — but the interesting answer is about how the numbers get made. Start with what's agreed: Google's own documentation says, quote, 'AI Overviews can and will make mistakes,' and tells you to check important info in more than one place. The vendor is telling you not to fully trust the vendor. Take that seriously. Now the fight. A New York Times analysis with a startup called Oumi ran AI Overviews against SimpleQA, a standard factual benchmark. Ninety-one percent correct in February 2026 — up from eighty-five in October. Headlines turned that into 'millions of wrong answers every hour' by multiplying nine percent by Google's five trillion annual searches. Google fired back that the study had serious holes and used a flawed benchmark that doesn't reflect what people actually search. And here's the thing — Google has a real point, and it doesn't save them. SimpleQA is built from deliberately obscure trivia, chosen because models get it wrong. It's an adversarial exam, not real traffic. Most searches are 'what time does the pharmacy close.' So that extrapolation almost certainly overstates the problem. But the second finding is the one that should change your behavior, and Google didn't dispute it. Among answers that were correct, the share where the cited sources didn't actually support the claim went from thirty-seven percent to fifty-six after the Gemini 3 upgrade. So when it's right, the footnotes are a coin flip. Clicking the link and seeing a source verifies nothing. That's the real lesson. It's mostly right. But it looks exactly the same when it isn't.
What authoritative sources say
People also ask
Is Google's AI more or less accurate than regular search results?
Different, not strictly better or worse. Search hands you sources to judge; the AI hands you a conclusion. It's faster, but it removes the step where you evaluate where the claim came from.
Why does it cite sources that don't say what it claims?
It generates the answer from what it learned in training, then attaches supporting links. The links are matched afterward, not read off. That's why 56% of correct answers were found ungrounded.
Is it getting better?
On raw accuracy, yes — 85% to 91% on SimpleQA between October 2025 and February 2026. On grounding, it got worse over the same period, 37% to 56% ungrounded.
Should I trust it for medical questions?
Use it to learn terminology and form better questions, not to decide anything. Google's own guidance is to verify important information in more than one place.
Can I turn AI Overviews off?
Not with a universal switch. You can use Google's Web filter to see classic blue links, and some browser extensions suppress it.