Google Told You the Answer Was Wrong. Nobody Fixed It.

A Welsh paleontological illustrator posted this week that an AI scraping account had lifted her artwork, misidentified it as depicting a newly discovered South Korean dinosaur species, and spread the claim across social media with her name nowhere on it. The post got over a thousand likes and was shared hundreds of times — not because the story was unusual, but because it wasn't. What made it resonate was the specific indignity she named: it's not just that her work was taken without credit, it's that it was taken and then used to make something false. The theft and the misinformation arrived as a package deal.

That combination — AI as simultaneously a plagiarism engine and a misinformation engine — is the frame that keeps winning in this conversation right now. Another post, angrier and less precise but just as widely felt, put it simply: even Google has an AI overview that shows misinformation half the time. That post carried real frustration with a specific product failure most users have now personally encountered. Google's AI Overviews were supposed to be the search giant's answer to a changing information landscape. Instead they've become a recurring exhibit in the case against trusting AI-generated summaries at all.

The electoral angle has absorbed most of the news coverage, and the volume of stories is striking: deepfakes clouding Japan's election, the Texas GOP deploying synthetic media against a state legislator, AI robocalls impersonating Biden to suppress votes in New Hampshire, Slovakia's progressive party targeted by coordinated AI disinformation, California and New York moving toward legislative bans, India and Bihar experimenting with their own regulatory frameworks. What's notable isn't the breadth of incidents — it's how normalized the threat has become. A Brookings piece this week warned readers to watch out for both real deepfakes and false claims of deepfakes, which tells you something about where the epistemological problem now sits: the category of "AI fake" has become so available as an accusation that it's started functioning as its own disinformation vector.

On Bluesky, a post that drew more engagement than most framed the verification problem with unusual clarity: sorting real from fake now requires genuine subject-matter expertise, because blue checks are meaningless and follower counts prove nothing. That observation is worth sitting with. The informal heuristics people used to navigate online information — institutional affiliation, audience size, platform verification — have all been degraded simultaneously, and AI-generated content is part of why. A Korean-language dispute this week illustrated the granular version of this: a user pushed back on an AI translation that had rendered a speaker's meaning incorrectly, arguing that any Korean speaker would know he said six people, not something else entirely. The frustration in the post wasn't abstract — it was the specific experience of watching a machine's confident mistranslation outlast a human correction.

The through-line in all of this isn't that AI creates misinformation. It's that AI creates misinformation faster than any existing system — platform moderation, journalism, regulatory enforcement, or human verification — can process it. A writer on Bluesky made this argument explicitly this week, framing generative AI as an engine for producing false content at a rate that structurally outpaces response. That framing has been circulating for a couple of years now, but it's hardening from concern into something closer to consensus. Adobe is building content provenance tools. State legislatures are passing deepfake bans. The Election Commission of India banned AI deepfakes ahead of Bihar's assembly elections. These responses are real — and they are all downstream of content that has already been made and distributed. The race is being run in the wrong direction, and the people running it know it.