Rosenfeld Review Podcast

AI is opening the door to a new era of design—but most teams are still focused on making their existing work faster rather than reimagining what’s possible. Lou talks with Josh Clark and Veronica Kindred about their new book, Sentient Design, and what it takes to design truly intelligent interfaces.

They introduce the idea of “practical magic”—starting with bold, even impossible wishes and then working backward to create real, deliverable experiences. Rather than defaulting to chatbots and efficiency gains, they argue that AI enables entirely new interaction models, from adaptive interfaces to agents that collaborate directly within products.

The conversation also explores how design systems and past UX practices lay the groundwork for this shift, while designers themselves must unlearn habits that limit creativity. Through their “sentient design sprint” and “minimum magical product” framework, Josh and Veronica offer a structured way to move from imagination to implementation.

At its core, this episode is a call for designers to reclaim their role as inventors—embracing AI not just as a tool, but as a new design material for creating more responsive, dynamic, and human-centered experiences.

What You'll Learn from this Episode:

Why AI is a new “design material,” not just a productivity tool

How “practical magic” helps teams rethink what’s possible

Why designers are stuck focusing on process instead of product innovation

What adaptive, intelligent interfaces could look like in practice

How the “sentient design sprint” turns ideas into real solutions

What designers need to unlearn to work effectively with AI

Quick Reference Guide:
0:13 - Meet Josh and Veronika and learn about their new Rosenfeld Media book

3:14 - Harnessing magical thinking with AI

9:19 - Moving beyond process, speed, and efficiency

13:11 - How designers can transition from tooling to inventing

16:53 - Exciting places the magic could take us

23:40 - 5 reasons to use the Rosenverse

25:52 - The Sentient Design Sprint

30:08 - The Sentient Triangle

33:36 - Is this applicable to non-traditional designers? Or what designers need to unlearn?

39:56 - Josh and Veronica’s gifts for listeners

 

Resources and Links from Today's Episode:
Sentient Design: Crafting Intelligent Interfaces with AI by Josh Clark and Veronika Kindred https://rosenfeldmedia.com/books/sentient-design/

Designing with AI 2026 - June 9, 2026 https://rosenfeldmedia.com/designing-with-ai/ 

Enchanted Objects: Innovation, Design, and the Future of Technology by David Rose https://www.amazon.com/Enchanted-Objects-Innovation-Design-Technology/dp/1476725640 

This is Running: A Celebration of the World of Running, Exploring the Culture, History, Brands, Races and People Behind It by Raziq Rauf https://www.amazon.com/This-Running-Celebration-Exploring-Culture/dp/1837330425?crid=10LYBYV063BKK&dib 

 

Quotes:
“We try to get in touch with the moments of emerging technology that help us to let go of the old ways of doing things and embrace the new, to think in terms of magical wishes.”

“A ‘magical moment’ is that machines can understand our intent beyond just what we know to type.” 

“The danger is we’re not endorsing magical thinking.”

“First we retool, then we reorganize to get our process around that. And then we invent.”

AI may be built on language—but according to Paul Ford, we’re still struggling to find the right words to describe what it’s actually doing to our work and thinking. Lou and Paul explore how language shapes our ability to understand—and responsibly use—AI.

Drawing on his dual background in programming and writing, Paul shares a set of evolving “rules” for working with AI: don’t let it replace your thinking, be wary of its tendency to flatter, and build systems that help you verify and structure its output rather than blindly trusting it. He explains how he uses AI to accelerate prototyping and research while still preserving human judgment, creativity, and accountability.

The discussion also zooms out to the broader cultural moment. From skeptical college students to industry hype cycles, Paul argues that people are more discerning than we often assume—and that AI’s impact will play out in diverse, deeply human ways.

Paul will be the opening speaker at the upcoming Designing with AI conference, where he’ll expand on these ideas and introduce new language for navigating this rapidly evolving space.

His takeaway? We’re not at the end of history—we’re in a messy, fascinating transition, and the best we can do is stay curious, thoughtful, and engaged.

 

What You'll Learn from this Episode:

Why shared language is critical for making sense of AI

How Paul Ford approaches “rules” for using AI responsibly

The risks of AI’s built-in flattery and “smooth” thinking

Practical ways to use AI for prototyping without losing control

Why verification systems matter more than trusting the model

How younger generations actually view AI (less hype, more pragmatism)

Why AI may be powerful—but not as historically radical as we think

How to stay grounded and thoughtful amid rapid technological change

 

Quick Reference Guide:
0:11 – Meet Paul

5:30 - Can language keep up with technological change?

12:48 – Paul’s rules for professionals

18:11 - Where is the slippery slope? Paul weighs in.

22:23 - Paul reveals his gift for the audience

23:03 - 5 reasons to use the Rosenverse

25:18 - A story about some NY college students

29:21 - The anger and skepticism toward AI

35:18 - Wrapping up

 

Resources and Links from Today's Episode:
Designing with AI conference - June 9, 2026 https://rosenfeldmedia.com/designing-with-ai/ 

Shell Game Podcast, by Evan Ratliff podcasts.apple.com/us/podcast/shell-game/id1753117762 

 

Quotes:
“In a world where product is very easy to spin up, the relationship is getting more and more important.” 

“You can really narrow your risk when you’re working with this stuff, and then you can let it go and see what it comes up with.” 

“There’s ways to mess with this stuff where I think you get to move forward and you get to think your thoughts without letting it take over.” 

“When it gets into that weird social relationship where it’s telling you that was a good idea, that’s where my alarm bells go off.”  

“The native buttering up of these technologies, I think is really dangerous because, God, you always want to hear it, especially when you're a boss.

As AI becomes easier and cheaper to deploy, designers face a new challenge: deciding not just what can be automated, but what should be. Lou Rosenfeld talks with Joy Kendi of Dalberg Design — and an upcoming speaker at the Designing with AI 2026 conference — about introducing AI into high-stakes systems where design decisions can directly affect access to healthcare, services, and critical resources.

Joy shares examples from her work in public health systems, including an AI concept intended to help community health workers prioritize patient visits. What initially seemed like an obvious efficiency gain quickly raised deeper concerns around incomplete data, shifting real-world conditions, and the irreplaceable role of human judgment and community trust. Rather than treating AI as a decision-maker, Joy argues for designing systems where AI supports — but does not override — human expertise.

Their conversation also explores how AI is reshaping the role of designers themselves. Joy makes the case that designers must move “upstream” in the process, helping define boundaries, risks, trust, and governance before automation decisions are made. 

 

What You'll Learn from this Episode:

How Joy Kendi defines “high-stakes” design systems

Why automation can fail in public health contexts

The risks of relying on incomplete or outdated data

Why AI should support human judgment, not replace it

How designers are moving upstream in AI decision-making

What human-centered AI governance can look like in practice

 

Quick Reference Guide:
0:11 - Meet Joy and learn about high-stake systems

3:26 - Joy’s backstory

6:10 - Seeing beyond the everyday facade

8:55 - Why you need the Rosenverse

11:08 - Designing responsible AI for high-stakes public health decisions 

16:06 - Why human judgment still matters in AI-assisted decision-making 

18:29 - Designers must shape AI boundaries, not just interfaces 

22:06 - Joy’s gift for listeners

 

Resources and Links from Today's Episode:
Designing with AI 2026 https://rosenfeldmedia.com/designing-with-ai/ 

 

Quotes:
“When I say high stakes systems, I mean systems where the design choices carry very real consequences beyond the typical interface or screen that a lot of designers are used to.”

“The hardest question is not just can we build this tool. It’s also deciding whether we should automate some of these complex social decisions at all.”  

“Even when the quality of data is good, and the data is available, there's more complexity to how human beings make decisions. A lot of that is also based on the social dynamics.”

Privacy concerns didn’t appear overnight—they’ve been building quietly alongside the technologies we rely on every day. Lou and Robert Stribley, author of Design for Privacy, explore how digital tracking, AI, and data sharing have reshaped the way personal information moves through the modern web.

Robert traces the growing privacy challenge from early internet tracking to today’s complex ecosystem of smartphones, online services, and AI systems. While many users understand that they’re trading data for convenience, few grasp how widely their information is distributed—or how easily supposedly anonymous data can be re-identified. As AI accelerates the ability to combine and analyze datasets, those risks are growing quickly.

Then the conversation turns to what designers can do about it. Robert outlines practical ways UX professionals can improve privacy outcomes, from collecting less data and avoiding deceptive patterns to improving language transparency and giving users meaningful control over their information. Despite the scale of the problem, Robert argues that designers have more agency and influence than they realize. Thoughtful design decisions can help protect users while also strengthening trust and long-term business success.

 

What You'll Learn from this Episode:

Why privacy concerns have intensified with smartphones, AI, and online tracking

How “anonymous” data can often be re-identified through data aggregation

Why users have conflicting attitudes about personalization and data tracking

The role UX designers can play in improving privacy protections

How deceptive design patterns (including cookie banners) manipulate user consent

Why clearer language and better privacy tools can give users meaningful control over their data

Quick Reference Guide:
0:15 - Meet Robert, Lou’s neighbor

1:51 - How Robert got into the privacy field

5:06 - Perceptions of privacy and the concessions we make

8:01 - Terms of Service - we accept them blindly - and why that can be risky

15:54 - 5 Reasons to use the Rosenverse

18:39 - What designers can do about data privacy

28:08 - Privacy tools and potential tools for users

32:38 - Robert’s gift for listeners

Resources and Links from Today's Episode:
Design for Privacy: Keeping Personal Information Private by Robert Stribley https://rosenfeldmedia.com/books/design-for-privacy/

Block Party app https://www.blockpartyapp.com/

404 Media https://www.404media.co/

The Capture https://www.imdb.com/title/tt8201186/

 

Quotes:
“We have created these patterns that make it very easy to get involved with those experiences, all the while you're surrendering your data.” 

“I don't think most people understand the degree to which that information is spread around and with whom it's spread around.”

“Whenever you are utilizing people’s data, really think about what you’re doing with it and be able to justify it.” 

“When you understand deceptive patterns as manipulative, you can’t stop seeing them everywhere.”

AI is reshaping product development faster than most organizations can even rethink how they work—and that gap sits at the heart of this conversation with product design guru Jeff Gothelf. Lou and Jeff explore why proven methods like Agile and OKRs so often become “process theater” instead of real change, and what it actually takes to shift organizations from output-driven cultures to outcome-driven ones.

Jeff explains that most transformations fail because incentives still reward shipping outputs, not creating real value. Meaningful change tends to emerge only in pockets led by leaders willing to experiment and treat ways of working as something to test and evolve.

They also explore how AI is shifting risk upstream—from engineering to vision, validation, and decisionmaking—making design and research more critical than ever. Along the way, they reflect on consulting as organizational therapy, the need to prove design’s value in the AI era, and why companies that relentlessly embrace new technology are best positioned to endure.

 

What You'll Learn from this Episode:

Why Agile, OKRs, and similar frameworks often fail to create real change

The critical shift from measuring output to measuring outcomes

The two traits shared by successful pockets of transformation in large companies

How to run small, time-boxed experiments to change ways of working at scale

Why AI makes design, research, and product thinking more valuable

How to explain and prove the value of “thinking before the prompt” in AI-driven organizations

 

Quick Reference Guide:
0:10 - Meet Jeff Gothelf; Lou and Jeff discuss bridging the gap between ritual and cultural change

7:44 - Good ideas without a clear understanding of why

9:42 - What it takes for organizations to successfully communicate and incentivize 

15:21 - 5 reasons to use the Rosenverse

17:37 - Consultants validate insiders; AI shifts risk toward design clarity

24:20 - AI speeds output, but critical thinking, research, and testing prove designers’ value

27:50 - Jeff and Lou speculate on Amazon’s future

30:49 - Jeff’s gift for listeners

 

Resources and Links from Today's Episode:
Books by Jeff Gothelf https://jeffgothelf.com/books/ 

Ignorance by Milan Kundura https://www.amazon.com/Ignorance-Novel-Milan-Kundera/dp/0060002107 

 

Quotes:
“The types of conversations that we're having about good design, about good information architecture, about good research, about agility and customer centricity and all of those types of things, for some reason, those continue to be difficult conversations in organizations today.”

“The risk of engineering is no longer a risk, not like it was not five years ago. It's going to get cheaper and cheaper and cheaper. Where's the risk? The risk is in definition, vision, clarity, validation. In other words, it's in design, discovery, research, product management.”

“The differentiation, the uniqueness, the creativity, the innovation is going to come from the critical thinking of the designers and the researchers who are actually doing the thinking before the prompt.”