David Kelley is an icon in design. A restless tinkerer turned educator, he co-founded the reowned industrial design firm IDEO, helped shape human-centered design at Stanford’s d.school, and collaborated with Apple on seminal projects like the early mouse.

Here’s his take on creativity in a brief segment for PBS News Hour:

And as I started teaching, I realized that my purpose in life was figuring out how to help people gain confidence in their creative ability. Many people assume they’re not creative. Time and time again, they say, a teacher told me I wasn’t creative or that’s not a very good drawing of a horse or whatever it is. We don’t have to teach creativity. Once we remove the blocks, they can then feel themselves as being a creative person. Witnessing somebody realizing they’re creative for the first time is just a complete joy. You can just see them come out of the shop and beaming that I can weld. Like, what’s next?

I’ve been playing with my systems in the past month—switching browsers, notetaking apps, and RSS feed readers. If I’m being honest, it’s causing me anxiety because I feel unmoored. My systems aren’t familiar enough to let me be efficient.

One thing that has stayed relatively stable is my LLM app—well, two of them. ChatGPT for everyday and Claude for coding and writing.

Christina Wodtke, writing on her blog:

The most useful model might not win.

What wins is the model that people don’t want to leave. The one that feels like home. The one where switching would mean losing something—not just access to features, but fluency, comfort, all those intangible things that make a tool feel like yours.

Amazon figured this out with Prime. Apple figured it out with the ecosystem. Salesforce figured it out by making itself so embedded in enterprise workflows that ripping it out would require an act of God.

AI companies are still acting like this is a pure technology competition. It’s not. It’s a competition to become essential—and staying power comes from experience, not raw capability.

Your moat isn’t your model. Your moat is whether users feel at home.

There are dark patterns in UX, and there are also dark patterns specific to games. Dark Pattern Games is a website that catalogs such patterns and the offending mobile games.

The site’s definition of a dark pattern is:

A gaming dark pattern is something that is deliberately added to a game to cause an unwanted negative experience for the player with a positive outcome for the game developer.

The “Social Pyramid Scheme” is one of my most loathed:

Some games will give you a bonus when you invite your friends to play and link to them to your account. This bonus may be a one-time benefit, or it may be an ongoing benefit that improves the gaming experience for each friend that you add. This gives players a strong incentive to convince their friends to play. Those friends then have to sign up more friends and so on, leading to a pyramid scheme and viral growth for the game.

Geoffrey Litt is a design engineer at Notion. He is one of the authors at Ink & Switch of “Malleable software,” which I linked to back in July. I think it’s pretty fitting that he popped up at Notion, with the CEO Ivan Zhao likening the app to LEGO bricks.

In a recent interview with Rid on Dive Club, Litt explains the concept further:

So, when I say malleable software, I do not mean only disposable software. The main thing I think about with malleable software is actually much closer to … designing my interior space in my house. Let’s say when I come home I don’t want everything to be rearranged, right? I want it to be the way it was. And if I want to move the furniture or put things on the wall, I want to have the right to do that. And so I think of it much more as kind of crafting an environment over time that’s actually more stable and predictable, not only for myself, but also for my team. Having shared environments that we all work in together that are predictable is also really important, right? Ironically, actually, in some ways, I think sometimes malleable software results in more stable software because I have more control.

For building with AI, Litt advocates “coding like a surgeon”: stay in the loop and use agents for prep and grunt work.

How do we think of AI as a way to leverage our time better? [So we can] stay connected to the work and [do] it ourselves by having prep work done for us. Having tools in the moment helping us do it so that we can really focus on the stuff we love to do, and do less of everything else. And that’s how I’m trying to use coding agents for my core work that I care about today. Which is when I show up, sit down at my desk in the morning and work on a feature, I want to be prepped with a brief on all the code I’m going to be touching today, how it works, what the traps are. Maybe I’ll see a draft that the AI did for me overnight, sketching out how the coding could go. Maybe some ideas for me.

In other words, like an assistant who works overnight. And yeah, this could apply to design as well.

Something that I think a lot about as a design leader is how to promote the benefits of design in the organization. Paul Boag created this practical guide to guerrilla internal marketing that builds a network of ambassadors across departments and keeps user-centered thinking top of mind.

Boag, writing in his newsletter:

You cannot be everywhere at once. You cannot attend every meeting, influence every decision, or educate every colleague personally. But you can identify and equip people across different departments who care about users and give them the tools to spread UX thinking in their teams.

This is how culture change actually happens. Not through presentations from the UX team, but through conversations between colleagues who trust each other.

Design Thinking has gotten a bad rap in recent years. It was supposed to change everything in the corporate world but ended up changing very little. While Design Thinking may not be the darling anymore, designers still need time to think, which is, for the sake of argument, time away from Figma and pushing pixels.

Chris Becker argues in UX Collective:

However, the canary in the coalmine is that Designers are not being used for their “thinking” but rather their “repetition”. Much of the consternation we feel in the UX industry is catapulted on us from this point of friction.

He says that agile software development and time for designers to think aren’t incompatible:

But allowing Designers to implement their thinking into the process is about trust. When good software teams collaborate effectively, there are high levels of trust and autonomy (a key requirement of agile teams). Designers must earn that trust, of course, and when we demonstrate that we have “done the thinking,” it builds confidence and garners more thinking time. Thinking begets thinking. So, Designers, let’s continue to work to maximise our “thinking” faculties.

Game design is fascinating to me. As designers, “gamification” was all the rage a few years back, inspired by apps like Duolingo that made it fun to progress in a product. Raph Koster outlines a twelve-step, systems-first framework for game design, complete with illustrations. Notice how he’ll use UX terms like “affordance” because ultimately, game design is UX.

In step five, “Feedback,” Koster provides an example:

[The player] can’t learn and get better unless [they] get a whole host of information.

• You need to know what actions – we usually call them verbs — are even available to you. There’s a gas pedal.
• You need to be able to tell you used a verb. You hear the engine growl as you press the pedal.
• You need to see that the use of the verb affected the state of the problem, and how it changed. The spedometer moved!
• You need to be told if the state of the problem is better for your goal, or worse. Did you mean to go this fast?

Sound familiar? It’s Jakob Nielsen’s “Visibility of System Status.”

Apologies for sharing back-to-back articles from NN/g, but this is a good comprehensive index of all the AI-related guides the firm has published. Start here if you’re just getting into it.

Highlights from my POV:

• Your AI UX Intern: Meet Ari. AI tools in UX act like junior interns whose output serves as a starting draft needing review, specific instructions, and added context. Their work should be checked and not used for final products or decisions without supervision.
• The Future-Proof Designer. AI speeds up product development and automates design tasks, but creates risks like design marginalization and information overload. Designers must focus on strategic thinking, outcomes, and critical judgment to ensure decisions benefit users and business value.
• Design Taste vs. Technical Skills in the Era of AI. Generative AI has equalized access to design output, but quality depends on creative discernment and taste, which remain essential for impactful results.

Leave it to NN/g to evaluate the AI prompt-to-code tool landscape with some rigor. Huei-Hsin Wang and Megan Brown cover over a dozen tools, including ChatGPT, Claude, UX Pilot, Uizard, Relume, Stitch, Bolt, Lovable, v0, Replit, Figma Make, Magic Patterns, and Subframe. They use a human designer as the control.

Among their conclusions:

AI’s limited grasp of design nuances and inconsistent output make it best suited for ideation, concept exploration, and early-phase prototype testing, rather than later stages. While you likely won’t take an AI-generated prototype straight to production, these tools can help you break through creative blocks and explore new directions quickly.

I think the best part is they shared screenshots of outputs in a FigJam board.

In thinking about the three current AI-native web browsers, Fanny on Medium sees what lessons product designers can take from their different approaches.

On Perplexity Comet:

Design Insight: Comet succeeds by making AI feel like a natural extension of browsing, not an interruption. The sidecar model is brilliant because it respects the user’s primary task (reading, researching, shopping) while offering help exactly when context is fresh. But there’s a trade-off — Comet’s background assistant, which can handle multiple tasks simultaneously while you work, requires extensive permissions and introduces real security concerns.

On ChatGPT Atlas:

Design Insight: Atlas is making a larger philosophical statement — that the future of computing isn’t about better search, it’s about conversation as an interface. The key product decision here is making ChatGPT’s memory and context awareness central. Atlas remembers what sites you’ve visited, what you were working on, and uses that history to personalize responses. Ask “What was that doc I had my presentation plan in?” and it finds it.

On The Browser Company Dia:

Design Insight: Dia is asking the most interesting question — what happens when AI isn’t a sidebar or a search replacement, but a fundamental rethinking of input methods? The insertion cursor, the mouse, the address bar — these are the primitives of computing. Dia is making them intelligent.

She concludes that they “can’t all be right. But they’re probably all pointing at pieces of what comes next.”

I do think it’s a combo and Atlas is likely headed in the right direction. For AI to be truly assistive, it has to have relevant context. Since a lot of our lives are increasingly on the internet via web apps—and nearly everything is a web app these days—ChatGPT’s profile of you will have the most context, including your chats with the chatbot.

I began using Perplexity because I appreciated its accuracy compared with ChatGPT; this was pre-web search. But even with web search built into ChatGPT 5, I still find Perplexity’s (and therefore Comet’s) approach to be more trustworthy.

My conclusion stands though: I’m still waiting on the Arc-Dia-Comet browser smoothie.

To close us out on Halloween, here’s one more archive full of spooky UX called the Dark Patterns Hall of Shame. It’s managed by a team of designers and researchers, who have dedicated themselves to identifying and exposing dark patterns and unethical design examples on the internet. More than anything, I just love the names some of these dark patterns have, like Confirmshaming, Privacy Zuckering, and Roach Motel.

Celine Nguyen wrote a piece that connects directly to what Ethan Mollick calls “working with wizards” and what SAP’s Ellie Kemery describes as the “calibration of trust” problem. It’s about how the interfaces we design shape the relationships we have with technology.

The through-line is metaphor. For LLMs, that metaphor is conversation. And it’s working—maybe too well:

Our intense longing to be understood can make even a rudimentary program seem human. This desire predates today’s technologies—and it’s also what makes conversational AI so promising and problematic.

When the metaphor is this good, we forget it’s a metaphor at all:

When we interact with an LLM, we instinctively apply the same expectations that we have for humans: If an LLM offers us incorrect information, or makes something up because it the correct information is unavailable, it is lying to us. …The problem, of course, is that it’s a little incoherent to accuse an LLM of lying. It’s not a person.

We’re so trapped inside the conversational metaphor that we accuse statistical models of having intent, of choosing to deceive. The interface has completely obscured the underlying technology.

Nguyen points to research showing frequent chatbot users “showed consistently worse outcomes” around loneliness and emotional dependence:

Participants who are more likely to feel hurt when accommodating others…showed more problematic AI use, suggesting a potential pathway where individuals turn to AI interactions to avoid the emotional labor required in human relationships.

…

However, replacing human interaction with AI may only exacerbate their anxiety and vulnerability when facing people.

This isn’t just about individual users making bad choices. It’s about an interface design that encourages those choices by making AI feel like a relationship rather than a tool.

The kicker is that we’ve been here before. In 1964, Joseph Weizenbaum created ELIZA, a simple chatbot that parodied a therapist:

I was startled to see how quickly and how very deeply people conversing with [ELIZA] became emotionally involved with the computer and how unequivocally they anthropomorphized it…What I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.

Sixty years later, we’ve built vastly more sophisticated systems. But the fundamental problem remains unchanged.

The reality is we’re designing interfaces that make powerful tools feel like people. Susan Kare’s icons for the Macintosh helped millions understand computers. But they didn’t trick people into thinking their computers cared about them.

That’s the difference. And it matters.

Speaking of trusting AI, in a recent episode of Design Observer’s Design As, Lee Moreau speaks with four industry leaders about trust and doubt in the age of AI.

We’ve linked to a story about Waymo before, so here’s Ryan Powell, head of UX at Waymo:

Safety is at the heart of everything that we do. We’ve been at this for a long time, over a decade, and we’ve taken a very cautious approach to how we scale up our technology. As designers, what we have really focused on is that idea that more people will use us as a serious transportation option if they trust us. We peel that back a little bit. Okay, well, How do we design for trust? What does it actually mean?

Ellie Kemery, principal research lead, advancing responsible AI at SAP, on maintaining critical thinking and transparency in AI-driven products:

We need to think about ethics as a part of this because the unintended consequences, especially at the scale that we operate, are just too big, right?

…

So we focus a lot of our energy on value, delivering the right value, but we also focus a lot of our energy on making sure that people are aware of how the technology came to that output,…making sure that people are in control of what’s happening at all times, because at the end of the day, they need to be the ones making the call.

…

Everybody’s aware that without trust, there is no adoption. But there is something that people aren’t talking about as much, which is that people should also not blindly trust a system, right? And there’s a huge risk there because, humans we tend to, you know, we’ll try something a couple of times and if it works it works. And then we lose that critical thinking. We stop checking those things and we simply aren’t in a space where we can do that yet. And so making sure that we’re focusing on the calibration of trust, like what is the right amount of trust that people should have to be able to benefit from the technology while at the same time making sure that they’re aware of the limitations.

In this era of AI, we’ve been taught that LLMs are probabilistic, not deterministic, and that they will sometimes hallucinate. There’s a saying in AI circles that humans are right about 80% of the time, and so are AIs. Except when less than 100% accuracy is unacceptable. Accountants need to be 100% accurate, lest they lose track of money for their clients or businesses.

And that’s the problem Intuit had to solve to roll out their AI agent. Sean Michael Kerner, writing in VentureBeat:

Even when its accounting agent improved transaction categorization accuracy by 20 percentage points on average, they still received complaints about errors.

“The use cases that we’re trying to solve for customers include tax and finance; if you make a mistake in this world, you lose trust with customers in buckets and we only get it back in spoonfuls,” Joe Preston, Intuit’s VP of product and design, told VentureBeat.

So they built an agent that queries data from a multitude of sources and returns those exact results. But do users trust those results? It comes down to a design decision on being transparent:

Intuit has made explainability a core user experience across its AI agents. This goes beyond simply providing correct answers: It means showing users the reasoning behind automated decisions.

When Intuit’s accounting agent categorizes a transaction, it doesn’t just display the result; it shows the reasoning. This isn’t marketing copy about explainable AI, it’s actual UI displaying data points and logic.

“It’s about closing that trust loop and making sure customers understand the why,” Alastair Simpson, Intuit’s VP of design, told VentureBeat.

Circling back to Monday’s item on how caring is good design, Felix Haas has a subtly different take: build kindness into your products.

Kindness in design isn’t about adding smiley faces or writing cheerful copy. It’s deeper than tone. It’s about intent embedded in every interaction.

…

Kindness shows up in the patience of an empty state that doesn’t rush you. In the warmth of micro-interactions that acknowledge your actions without demanding attention. In error messages that guide rather than scold. In defaults that assume good intent rather than user incompetence.

These moments seem subtle, even trivial, in isolation. But they accumulate. They shape how we feel about a product over weeks and months. They turn interfaces into relationships. They build trust.

As UX designers, we try to anticipate the edge cases—what might a user do and how can we ensure they don’t hit any blockers. But beyond the confines of the products we build, we must also remember to anticipate the unintended consequences. How might this product or feature affect the user emotionally? Are we creating bad habits? Are we fomenting rage in pursuit of engagement?

Martin Tomitsch and Steve Baty write in DOC, suggesting some frameworks to anticipate the unpredictable:

Chaos theory describes the observation that even tiny perturbations like the flutter of a butterfly can lead to dramatic, non-linear effects elsewhere over time. Seemingly small changes or decisions that we make as designers can have significant and often unforeseen consequences.

As designers, we can’t directly control the chain of reactions that will follow an action. Reactions are difficult to predict, as they occur depending on factors beyond our direct control.

But by using tools like systems maps, the impact ripple canvas, and iceberg visuals, we can take potential reactions out of the unpredictable pile and shift them into the foreseeable pile.

Speaking of workslop, here’s an article from NN/g on how to avoid falling into over-reliance on AI in our design field. They call it the “7 Deadly AI Sins for UX Professionals.”

1. Outsourced Thinking
2. Wasted Time
3. Lost Details
4. Isolated Ideation
5. Naïve Trust
6. Bland Taste
7. Defensive Outlook

As Tanner Kohler writes:

It’s not about avoiding AI. It’s about maintaining your own growth and the quality of your work as you use AI. AI will constantly be changing. Never let yourself slip into repeatedly committing the sins that weaken you and your UX skills.

Designer Ben Holliday writes a wonderful deep dive into how caring is good design. In it, he references the conversation that Jony Ive had with Patrick Collison a few months ago. (It’s worth watching in its entirety if you haven’t already.)

Watching the interview back, I was struck by how he spoke about applying care to design, describing how:

“…everyone has the ability to sense the care in designed things because we can all recognise carelessness.”

Talking about the history of industrial design at Apple, Ive speaks about the care that went into the design of every product. That included the care that went into packaging – specifically things that might seem as inconsequential as how a cable was wrapped and then unpackaged. In reality, the type of small interactions that millions of people experienced when unboxing the latest iPhone. These are details that people wouldn’t see as such, but Ive and team believed that they would sense care when they had been carefully considered and designed.

This approach has always been a part of Jony Ive’s design philosophy, or the principles applied by his creative teams at Apple. I looked back and found an earlier 2015 interview and notes I’d made where he says how he believes that the majority of our manufactured environment is characterised by carelessness. But then, how, at Apple, they wanted people to sense care in their products.

The attention to detail and the focus and attention we can all bring to design is care. It’s important.

Holliday’s career has been focused in government, public sector, and non-profit environments. In other words, he thinks a lot about how design can impact people’s lives at massive scale.

In the past few months, I’ve been drawn to the word ‘careless’ when thinking about the challenges faced by our public services and society. This is especially the case with the framing around the impact of technology in our lives, and increasingly the big bets being made around AI to drive efficiency and productivity.

The word careless can be defined as the failure to give sufficient attention to avoiding harm or errors. Put simply, carelessness can be described as ‘negligence’.

Later, he cites Facebook/Meta’s carelessness when they “used data to target young people when at their most vulnerable,” specifically, body confidence.

Writing for UX Collective, Filipe Nzongo argues that designers should embrace behavior as a fundamental design material—not just to drive metrics or addiction, but to intentionally create products that empower people and foster meaningful, lasting change in their lives.

Behavior should be treated as a design material, just as technology once became our material. If we use behavior thoughtfully, we can create better products. More than that, I believe there is a broader and more meaningful opportunity before us: to design for behavior. Not to make people addicted to products, but to help them grow as human beings, better parents, citizens, students, and professionals. Because if behavior is our medium, then design is our tool for empowerment.

A former colleague of mine, designer Evan Sornstein wrote a wonderful piece on LinkedIn applying Buddhist principles to design.

Buddhism begins with the recognition that life is marked by impermanence, suffering, and non-self. These aren’t abstract doctrines — they are observations about how the world actually works. Over centuries, these ideas contributed to Japanese aesthetics: wabi-sabi (imperfection), ma (meaningful emptiness), yo no bi (beauty in usefulness), the humility of the shokunin, and the care of omotenashi. What emerges is not a set of rules, but an extraordinary perspective: beauty is inseparable from impermanence; usefulness is inseparable from dignity; care is inseparable from design. In an age when our digital products too often prioritize stickiness and metrics over humanity, these ideas offer a different path. They remind us that design is not about control or cleverness — it’s about connection, trust, and care.

The following eight principles aren’t new “methods” or “laws,” but reflections of this lineage, reframed for product design — though they apply to nearly any creative practice. They are invitations to design with the same attention, humility, and compassion that Buddhism and Japanese aesthetics have carried for centuries.

I think these guidelines from Vercel are great. It’s a one-pager and very clearly written for both humans and AI. It reminds me of the old school MailChimp brand voice guidelines and Apple’s Human Interface Guidelines which have become reference standards.

There’s a famous quote that Henry Ford allegedly said:

If I had asked people what they wanted, they would have said faster horses.

Anton Sten argues that a lot of people use this quote to justify not doing any user (or market) research:

This quote gets thrown around constantly—usually by someone who wants to justify ignoring user research entirely. The logic goes: users don’t know what they want, so why bother asking them?

I think he’s right. The question to ask users isn’t “What should we build?” but “What are your biggest pain points?”

Good research uncovers problems. It reveals pain points. It helps you understand what people are actually struggling with in their daily lives. What they’re working around. What they’ve given up on entirely.

Users aren’t supposed to design your product. That’s your job. But they’re the only ones who can tell you what’s actually broken in their world.

When you focus on understanding problems instead of collecting feature requests, you stop getting “faster horses” and start hearing real needs.

Nielsen Norman Group weighs in on iOS 26 Liquid Glass. Predictably, they don’t like it. Raluca Budiu:

With iOS 26, Apple seems to be leaning harder into visual design and decorative UI effects — but at what cost to usability? At first glance, the system looks fluid and modern. But try to use it, and soon those shimmering surfaces and animated controls start to get in the way.

I get it. Flat—or mostly flat—and static UI conforms to the heuristics. But honestly, it can get boring and homogenous quickly. Put the NNg microscope on any video game UI and it’ll be torn to shreds, despite gamers learning to adapt quickly.

I’ve had iOS 26 on my phone for just a couple of weeks. I continue to be delighted by the animations and effects. So far, nothing has hindered the usability for me. We’ll see what happens as more and more apps get translated.

In my most recent post, I called out our design profession, for our part in developing these addictive products. Jeffrey Inscho, brings it back up to the tech industry at large and observes they’re actually publishers:

The executives at these companies will tell you they’re neutral platforms, that they don’t choose what content gets seen. This is a lie. Every algorithmic recommendation is an editorial decision. When YouTube’s algorithm suggests increasingly extreme political content to keep someone watching, that’s editorial. When Facebook’s algorithm amplifies posts that generate angry reactions, that’s editorial. When Twitter’s trending algorithms surface conspiracy theories, that’s editorial.

They are publishers. They have always been publishers. They just don’t want the responsibility that comes with being publishers.

His point is that if these social media platforms are sorting and promoting posts, it’s an editorial approach and they should be treated like newspapers. “It’s like a newspaper publisher claiming they’re not responsible for what appears on their front page because they didn’t write the articles themselves.”

The answer, Inscho argues, is regulation of the algorithms.