Whitney's back. Third time on the couch, which officially makes her the Andy Richter to my Conan.

I went into this one wanting to talk about knowledge hoarding. You know the instinct: if I'm the only one who knows how to do this, they can't get rid of me. Take my Tinker Toys and go home. Whitney flipped that on me pretty quickly. On her team, nobody's hiding anything on purpose. They're moving so fast they're building the conveyor belt while standing on it, and they're drowning in documentation. The people who can cut through that noise and get everyone on the same page are the ones she needs most right now.

We got into why her team is documenting more than ever, and why designers have had to accept they aren't the only ones responsible for design quality anymore. We talked about what happens when a PM drops a screen they built in Claude on your desk and logs off for the day, what engineers are teaching designers about constraints that don't exist anymore, and why a rebrand you can ship with the flip of a switch still might not be ready to ship. (I'm still recovering from a single hex value change to a calendar color. Long story.)

Whitney also shared how she trains her team on AI like she's handing down a family recipe, and why "I don't know" is one of the bravest things a leader can say, once you've earned the trust to say it. Stick around for the end. A road trip conversation with ChatGPT changed how she thinks about what AI can and can't do, and it's the reason she'd protect one design ritual above all others.

0:00 Whitney returns for episode three 0:36 How people respond when AI changes the job 2:03 How AI know-how travels on Whitney's team 4:15 Afraid of AI, or afraid of a colleague using AI? 6:17 Are design teams documenting more or less? 8:41 Design craft moves into systems and AI training 9:28 Hoarding or just moving too fast? 11:32 Vibe coding and the signals that count 13:12 When a PM hands you a screen built in Claude 15:50 What engineers teach designers about old constraints 19:14 A rebrand on the heels of a redesign 21:24 Does shared understanding survive a world of agents? 23:39 The family recipe approach to AI training 25:50 Saying "I don't know" in front of your team 28:29 The one ritual worth saving 29:36 Why AI can't design for delight 32:12 Wrap-up

Every AI feature makes a promise it can't fully keep: trust me, even when I'm wrong. Users rarely see a confidence score. They never see a training set. All they get is the interface in front of them.

In this episode of UX Evolved, host Eric Ellis sits down with Heidi Chow — a product manager by way of UX design — to unpack what trust in AI actually looks like at the interface level. Heidi has spent her career designing for the moments users have to decide whether to believe what a system is telling them: from an AI-powered feature that scanned users' teeth to confirm treatment fit, to her current work building AI tools that ground answers in real source material instead of asking users to take them on faith.

Eric and Heidi get tactical about the patterns that build (or break) user confidence in AI-driven products: the copy choices that signal certainty versus possibility, when and how to bring a human into the loop, what to show a user versus what to keep behind the scenes, and why designing trust in after the fact rarely works as well as designing it in from day one.

Chapters:
00:00 – Intro: AI trust from an experience angle, not just a tech angle
01:08 – Welcome & background: from recruiter to anthropology to UX bootcamp
04:10 – Heuristics that still hold up in the AI era: why feedback and transparency matter
08:00 – Case study: the Byte AI aligner fit scan
09:20 – The "may" vs. "can" distinction — subtle copy, real trust implications
16:08 – Human-in-the-loop: opt-in checks, user agency, and avoiding "AI slop"
18:01 – Confidence scores: what clinicians saw vs. what users saw
21:17 – Legal, regulatory, and copy decisions in a health context
23:12 – Testing AI-driven copy: evaluative research and A/B reactions
24:53 – Building Prequel: surfacing compliance requirements with AI agents
26:58 – Founder life: judging AI output quality without an expert safety net
29:10 – Can trust be retrofitted into an "AI-first" product?
29:46 – The "layer cake" model of trust: UI, data, and system-prompt layers
32:18 – Where UX designers still add value as UI work gets commoditized
33:22 – Vibe-coding in Claude Design: a product-detail-page example
34:19 – Closing thought: AI gets you 80% there — humanity closes the gap
35:47 – Final question: what would you redesign in the universe?

UX Evolved is a show about how AI is reshaping the practice of user experience — hosted by Eric Ellis. Guests are practitioners who've lived the trade-offs firsthand — designers, PMs, and founders building real AI-powered products.

Amit Popat built a beautiful AI product that nobody bought. Then he sat in a hotel watching managers use whiteboards to make pricing decisions in 2019. That's when everything changed.From Cambridge math to building EasyJet's personalization engine to leading AI/ML at Cloudbeds, Amit has learned the hard way that cool technology doesn't win—solving real problems does. We dive into his own Alexa fail, why 1970s Paris Metro machines beat modern UX, and why tinkering and curiosity aren't optional in an AI-accelerated world.If you're building AI products or wondering how UX translates to this new landscape, this conversation will ground you in what actually matters: proximity to real problems beats assumptions every time.

(00:00) Introduction(01:49) Welcome and Amit's Background(02:47) From Pure Math to Product Thinking(03:53) Early iPhone Development and UX Education(04:47) The Paris Metro Lesson: 1970s UX vs Modern Touchscreens(06:22) Removing Friction: Timeless Design Principles(07:00) The Value of Tinkering and Curiosity(08:45) Embedding Yourself in the Problem Space(10:06) The Whiteboard Story: Revenue Management in 2019(13:23) Building AI That Solves Real Problems(18:14) From Engineering to Product Leadership(21:45) The AI Hype vs Reality Gap(26:32) When Technical Excellence Meets User Needs(31:18) Network Intelligence and Pattern Recognition(35:27) Mistakes and Lessons Learned(38:57) The Alexa Catastrophe: Beautiful Product Nobody Bought(41:37) Dampening the Geek: Ego and Humility in Product Design(42:42) Closing Question: Redesigning Apple's iPhone Holder