I’m introducing FactCheck as a new way for brands to understand how accurately AI engines describe them at scale.
AI engines can make claims about my brand that simply are not true. With FactCheck, I can measure what is accurate, identify what is wrong, and see which sources are driving those errors.
That visibility matters because AI-generated answers are increasingly shaping how people discover, evaluate, and trust brands. FactCheck helps me move from guessing about AI accuracy to actually analyzing it with clarity.
The best and worst part of the web, in my view, is that I can share an opinion freely even when that opinion is not technically accurate.
But I keep wondering what happens when that freedom comes with real accountability, not only for what I say online, but also for whether the words came from me or from AI.
A recent report makes that question feel a lot less theoretical. A German court held Google accountable for AI Overview content, treating those AI-generated summaries as Google’s own content and rejecting the idea that users alone were responsible for fact-checking the results.
I want to unpack what that could mean for businesses, SEOs, and individuals who are leaning harder on AI every day.
The ‘disclaimer’ defense is cracking
For the last few years, I have seen nearly every AI platform rely on some version of the same warning: AI can make mistakes, so users should verify important information.
Most of us accepted that as the price of using these tools.
But the German court essentially said that a warning about possible errors does not automatically erase responsibility when those errors cause harm. If a system creates new claims that were never in the source material, those claims are no longer just someone else’s words. They become the platform’s words.
Why? Because the conversation moves away from whether AI is useful and toward who owns the consequences when AI gets something wrong.
What this means for businesses
I see many companies rapidly adopting AI across content creation, customer service, product descriptions, reporting, legal reviews, hiring, and internal communications. In many cases, they are blindly trusting the output because the efficiency gains are so tempting.
Most of the conversation still centers on speed and cost. Can we create content faster? Can we answer support tickets more cheaply? Can we automate this process?
Those are fair questions. I ask them too.
But this ruling adds a more important question: Who is responsible when the output is wrong?
What happens if an AI-generated support response gives a customer inaccurate guidance? What happens if an AI-written article damages a competitor’s reputation? What happens if an AI-generated report includes fabricated information that influences a business decision?
The more we position AI as a trusted source of information, the harder it becomes to argue that we should not be accountable for what it says.
The situation is kinda funny…
The irony is that most AI vendors already know this.
That is why nearly every platform includes warnings, disclaimers, and usage policies.
At the same time, those same companies market AI as smarter, faster, more capable, and increasingly reliable.
I do not think you can tell users to trust the answer while also arguing that nobody should trust the answer.
At some point, those positions collide. We are already starting to see Google’s solution: an option to opt out of AI.
Germany may simply be one of the first courts willing to force Google, or any other LLM business, to take clearer responsibility for the systems it puts in front of users.
What SEOs should be paying attention to
Ironically, I think this ruling could end up benefiting everyone.
Right now, the debate is focused on whether AI companies should be responsible for the content their systems generate. But I can see accountability expanding well beyond AI.
The internet has spent decades creating distance between actions and consequences. Anonymous accounts, fake profiles, throwaway emails, and now AI-generated content all make it easier for people to say things without owning them.
That is why I find this ruling so interesting.
It is not just about Google. It is about the idea that “I did not write it” may no longer be enough.
The image below shows a real email that Russell and Nina Westbrook received. A real person sat behind a keyboard and sent a message hoping they would die in a car crash.
That is not free speech. It is hate speech.
The internet, especially now that AI is layered into it, needs more confidence that content is accurate and that the people and companies creating it can be held accountable.
I do not believe we get to claim the productivity gains when AI is right and then blame the algorithm when it is wrong.
This post first appeared on the author’s website and is republished here with permission.
As someone who’s been closely observing AI advancements, I found Google’s AI Overviews to have improved significantly. By February, they correctly answered standard factual benchmarks 91% of the time, a notable rise from 85% back in October. This assessment came from a rigorous analysis conducted by The New York Times in collaboration with the AI startup, Oumi.
Yet, considering Google processes more than 5 trillion searches annually, this still implies that millions of answers could be incorrect every hour. In essence, there’s much room for improvement.
Why it matters to me. My interactions with Google have evolved from just link clicks to encountering AI-generated summaries. This evolution suggests that while AI Overviews have gotten better, they still mix accurate responses with poor sourcing and blatant errors, potentially misleading searchers and affecting visibility for many publishers.
The nitty-gritty details. Oumi put 4,326 Google searches to the test using SimpleQA, a benchmark known for measuring factual precision in AI systems. AI Overviews hit a 91% accuracy rate post-upgrade to Gemini 3 from Gemini 2’s 85%.
The more pressing issue for me is the sourcing. Oumi discovered that more than half of February’s correct responses were ‘ungrounded,’ meaning the linked references didn’t fully back the answers.
This lack of grounding makes verification a challenge. Even if the answer is correct, the linked pages might not sufficiently illustrate the reasoning.
What shifted. While the accuracy saw improvements from October to February, grounding declined. In October, 37% of accurate answers were ungrounded; by February, this figure increased to 56%.
Real-world examples. The Times pointed out several inaccuracies: For instance, Google incorrectly dated when Bob Marley’s home became a museum. Google’s answer was 1987, but the actual year was 1986, and the cited sources conflicted. A search about Yo-Yo Ma and the Classical Music Hall of Fame yielded a link to the Hall’s site, yet Google stated he wasn’t inducted. Moreover, while Google got Dick Drago’s age at death right, it flubbed his date of death.
Google’s standpoint: Google contested the Times’ findings, arguing that the benchmark used in the study was flawed and didn’t mirror actual search behavior. Google spokesperson Ned Adriance mentioned that the study had some ‘serious holes.’
Furthermore, Google asserted that its AI Overviews utilize search ranking and safety measures to minimize spam and has consistently cautioned that AI responses might contain errors.
