Science and music YouTuber Benn Jordan faced unexpected backlash this week after Google’s AI-powered search summaries falsely claimed he had recently traveled to Israel. The inaccurate summary led to speculation about his political stance, triggered waves of critical messages, and forced Jordan to publicly clarify his views on the Israel-Palestine conflict. He has emphasized that he has never visited Israel and has long supported Palestinian charities.
How the Error Spread Online
The incident gained traction after political Twitch streamer Hasan Piker reacted to one of Jordan’s videos, drawing a large online audience. During the stream, viewers began posting accusations that Jordan supported Israel, prompting confusion among his followers. The claims intensified as people began sending Jordan direct messages, asking why he had not spoken clearly about his views on the conflict.
The source of the misunderstanding became clear when Jordan saw a screenshot of a Google AI-generated summary tied to the search query “Benn Jordan Israel.” The summary incorrectly stated that he had traveled to Israel, spoken with residents near the Gaza border, and uploaded a video titled I Was Wrong About Israel: What I Learned On the Ground. None of these claims were true. Jordan posted the screenshot on Bluesky to highlight how misinformation had spread.
Evidence later showed the AI had confused Jordan with Ryan McBeth, a YouTuber and commentator who creates videos about military topics and had in fact produced a video with that exact title. Although the mistake was rooted in a mix-up of identities, it had immediate consequences for Jordan’s reputation, illustrating how errors in AI-generated content can spread quickly and carry real-world impact.
Jordan’s Response and Google’s Correction
Jordan told 404 Media that he was inundated with messages questioning his political stance in the hours after the false summary appeared. He stressed that he has consistently supported a free Palestinian state and has previously donated to the Palestinian Children’s Relief Fund. The misrepresentation, he said, contradicted his own record and caused frustration as he tried to address the wave of criticism.
Roughly 24 hours after the misinformation spread, Google’s AI system updated the summary to note that claims about Jordan’s trip to Israel were false. Even with that change, Jordan said the update was frustrating because it framed the issue as a rumor without acknowledging that the AI itself had generated it. For Jordan, the situation highlighted how difficult it can be to correct an error once it circulates widely online.
Concerned about the potential consequences, Jordan consulted lawyers to explore whether the AI-generated claim could be considered defamation. He did not plan legal action but was told he may have grounds. Jordan said the episode could have had lasting consequences for his YouTube channel and Patreon support if the misinformation had continued while he was away from the internet on an upcoming trip.
A Broader Problem With AI Accuracy
Jordan’s experience is part of a broader pattern of factual errors produced by Google’s AI summaries. In July, humorist Dave Barry discovered that the system had falsely declared him dead, forcing him to challenge Google’s automated processes to set the record straight. These incidents underscore the risks of relying on large language models, which are prone to generating what researchers call “hallucinations,” or incorrect but plausible-sounding statements.
Jordan, who has previously discussed the risks of AI on his own channel, said the error did not surprise him. He argued that the rush to integrate large language models into everyday search tools overlooks their limitations and erodes trust in reliable reporting. “Everybody’s rushing LLMs to be part of our daily lives,” he told 404 Media, adding that the technology often fails to provide accurate information and prioritizes convenience over accuracy.
In a statement to 404 Media, a Google spokesperson said, “The vast majority of AI Overviews are factual and we’ve continued to make improvements to both the helpfulness and quality of responses. When issues arise, like if our features misinterpret web content or miss some context, we use those examples to improve our systems, and we take action as appropriate under our policies.” For Jordan, however, the experience serves as a warning of how quickly AI-generated errors can shape public perception.