Media was my proxy for who I could be. Now AI is everyone's proxy for everyone — and it learned our blind spots. Ten ways researchers can keep it honest.
Most research teams think they're adopting AI. In reality, they're Frankensteining their flows—quietly ruining their ROI, their reputation, and their research integrity. And it all comes due as mounting risk the moment regulation catches up.
Jess Holbrook just published the clearest map of AI-native UX research I've read (Holbrook, 2026). If you lead a research team and you haven't read "Frontier UX Research Circa May 2026," go read it first, then come back.
He gets the big thing right: being AI-native is a systems problem, not a tools problem. Buying Dovetail or shipping one Claude project won't make you AI-native. You have to redesign how you work — how evidence flows from intake to insight, collection to synthesis to decision, so AI can participate at every step. I've been making the same argument to researchers and clients for a year, in nearly the same words: context engineering over prompting, systems over tools, amplification over automation. Human judgment and growth are the things you protect.
So this isn't a rebuttal. It's an extension. A "yes, and…" if you will.
AI flagged 11 usability problems no human researcher caught. 10 of them were false alarms or hallucinations. Here's what MeasuringU's data actually proves — and 6 safeguards to put in your workflow this week.
Once upon a time, there was a unintelligible and incomprehensible graph that was in desperate need of some updates. So a brave and valiant team of designers, researchers, and product managers set off on a quest to test not only their own wills, but also their assumptions in hopes of designing a better graph.
