What happens when someone whose entire job is catching errors puts AI through its paces? A professional fact-checker at Wired did exactly that, and the conclusion is worth sitting with: AI gets things wrong more often than most users realize.
This isn't a surprise if you've spent real time debugging AI outputs. But it cuts directly against how most people use these tools - for quick research lookups, verifying claims on the fly, or as a shortcut in editorial and marketing workflows where factual accuracy matters. The error rate is a practical problem whenever the stakes are high: published articles, client reports, legal documents, marketing copy that makes specific claims about a product or competitor.
The Errors That Actually Slip Through
The problem isn't that AI occasionally produces nonsense. The deeper issue is that AI errors often look correct. A wrong date, a slightly misquoted statistic, a real person associated with the wrong role or company - these aren't "AI sounds like a robot" failures. They're the kind of mistakes that get past human reviewers precisely because the surrounding text is coherent and confident.
There's also a structural problem with using AI as a verification tool: you're asking the same type of system that generated or retrieved a claim to evaluate whether it's accurate. ChatGPT and Claude are built to produce fluent, plausible-sounding text. That's not the same as producing verified text. Confident presentation is not a proxy for correctness.
The Workflow That Doesn't Burn You
Practitioners who've worked through this tend to land in the same place: AI as a research accelerator, not a verification endpoint. Use it to surface the facts you need to check, identify the claims in a document that require sourcing, or draft the section you'll then verify against primary sources. The AI output becomes your first draft of research, not your final confirmation.
For content creators and marketers in particular, this has weight. Running a competitor's product claims through an AI for a quick fact-check, or asking it to verify historical data, creates a false-confidence problem. The formatted, structured output reads like someone checked their work. Often, no one did.
The tools will keep improving, and some AI systems are adding citation features - pulling claims directly from retrievable sources rather than from training data. That helps. But the current generation should be treated as research drafters with a non-trivial error rate, not as fact-verification infrastructure. The Wired piece is a useful reminder for anyone who's leaned on AI for anything requiring factual precision.