The gap between what AI is reliable for and what it looks reliable for is where damage happens, and enablement fails How to lead the change with AI in a nutshell I’ve been building with AI for three years now. I started by trying to scale UX writing via plug-ins and now have agents, workflows, MCPs, and apps under my belt. I feel comfortable building with AI, and have adopted it into many of my day-to-day tasks. Yes, I do think AI has gotten better over those 3 years. But I think we’re telling ourselves a story that isn’t true. AI has gotten very good at one thing: Starting. The blank page problem. Replacing lorem ipsum. But while starting is faster than ever and error rates may have dropped, the verification burden hasn’t. I think errors have gotten harder to spot, and models have gotten better at defending them. For many, getting from zero to a rough first version is the hardest emotional hurdle in any task, and AI clears it in seconds. No more staring at a blank page if you don’t want to. But I think that’s the ceiling. Once you’re past “getting started” and into “getting it right,” the story changes. The hallucination problem isn’t solved. Yesterday I fed an AI four screenshots of my team’s current budget and asked it to summarize the numbers, analyze them, and combine that with what it knew from past conversations to suggest adjustments for next year. A simple brief coming from someone who knows how to prompt. Verifiable source of truth sitting right there in the screenshots. It hallucinated two numbers. Not small rounding errors, invented figures. The advice it gave me, built on top of those numbers, was thirty to forty percent off from what would have been logical. I only caught it because I read the output twice and cross-checked the math myself. When I pushed back and said the numbers looked wrong, it didn’t admit the error. It argued with me. This worries me. It’s not just that models hallucinate. It’s that they’ll defend the hallucination when challenged,

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