I’ve watched design team values die in a Confluence page. The offsite happens, the Post-Its get transcribed, the principles get written up with care, and then everyone goes back to their desks and ships exactly the way they did before. I’ve seen it with product principles and brand values too. The deck gets built, implementation starts, and the deck gets forgotten.

Vitaly Friedman, writing for Smashing Magazine, on why this matters more than ever:

We often see design principles as rigid guidelines that dictate design decisions. But actually, they are an incredible tool to rally the team around a shared purpose and document the values and beliefs that an organization embodies. They align teams and inform decision-making. They also keep us afloat amidst all the hype, big assumptions, desire for faster delivery, and AI workslop.

Friedman again:

In times when we can generate any passable design and code within minutes, we need to decide better what’s worth designing and building — and what values we want our products to embody. It’s similar to voice and tone. You might not design it intentionally, but then end users will define it for you. And so, without principles, many company initiatives are random, sporadic, ad-hoc — and feel vague, inconsistent, or simply dull to the outside world.

You might not write principles intentionally, but your product will have them anyway. The question is whether you chose them or inherited them by default.

Friedman closes with the part most teams skip:

Creating principles is only a small portion of the work; most work is about effectively sharing and embedding them. It’s difficult to get anywhere without finding ways to make design principles a default — by revisiting settings, templates, naming conventions, and output. Principles help avoid endless discussions that often stem from personal preferences or taste. But design should not be a matter of taste; it must be guided by our goals and values.

Creating principles feels productive. But alignment without embedding is a Confluence page nobody opens twice. Principles have to show up in the Figma component library, the ticket template, the review rubric. They have to be repeated so that they are ingrained. They have to become the path of least resistance.

Dan Saffer applies mid-century existentialism to the question of what “meaning” actually requires of the people building digital products, and the result is unusually rigorous. His sharpest move is applying Sartre’s concept of “projects” to AI tools:

When someone uses ChatGPT to write an essay, the Sartrean question is: whose project is this really? If the user is exploring ideas and using the tool as a thinking partner, they’re taking it up into their own meaning-making project. But if they’re pasting in a prompt and submitting the output unchanged, the system has effectively become the meaning-maker, and the user has become a delivery mechanism. The same tool can function either way. The design question is which relationship the system encourages.

Saffer connects this to Camus and the problem of frictionless design:

When every friction is removed in the name of efficiency, the activity can be hollowed out. There is nothing left to push against, and meaning drains away. This is something that AI systems have become exceedingly good at. Push the sparkle button, the task is done for you, and you have learned nothing and enjoyed nothing.

The HCI/UX field spent decades optimizing for friction removal. Saffer’s argument is that some friction is where the meaning lives. Design the struggle away and you don’t help the user. You empty the experience. Not every friction should be removed.

Saffer’s closing:

This sensibility insists that users are not information processors, not customers, not eyeballs, not tapping fingers, and not data sources. They are meaning-making beings whose freedom and dignity are at stake in every interaction. It asks designers to take seriously the existential weight of what they build. The systems we design become part of the conditions of human existence, shaping what people can choose, what they can see, who they can become.

Saffer covers Sartre, Camus, Kierkegaard, Heidegger, and de Beauvoir in the full piece, each applied to contemporary design problems. It’s a lot, and it’s all good.

Yours truly got quoted in Fast Company. Grace Snelling, surveying the industry reaction to Lenny Rachitsky’s TrueUp hiring data, pulled a comment I left under Rachitsky’s original Twitter post:

Designers have designed themselves out of the equation because of design systems. But, IMHO, the secret sauce has never been the UI. It was the workflows and looking across the experience holistically.

Let me expand on that. The UI has always been the easiest part of product design. Design systems made that even more true. What separates a great product from a mediocre one is understanding our users deeply enough to create experiences that actually delight them. That understanding is the work AI can’t do, and it’s the work too many teams were already skipping before any standoff started.

The data behind the standoff: Rachitsky’s analysis of TrueUp’s job market tracker shows design roles have been flat since early 2023 while PM and engineering roles surged. (Quick side note: this data is for tech startups, not the general tech industry or design industry at large.) His theory:

I don’t know exactly what’s going on here, but it does feel AI-related. […] Unlike PM and eng, which started growing in 2024 (two years post-ChatGPT), design didn’t. If I had to venture a theory, I’d say that because AI is allowing engineers to move so quickly, there’s less opportunity—and less desire—to involve the traditional design process.

Claire Vo, founder of ChatPRD, puts the harder version of why:

Often design teams & designers are the most resistant to change org in the EPD triad, with highly vocal AI opponents, and little skill or interest in the art of campaigning for influence or resources. […] If a PM or engineer can get 85% there with tailwind and a dream, you better come to the table with more than ‘I represent the user.’

“I represent the user” was never enough on its own. It just went unchallenged when designers were the only ones who could ship polished interfaces.

Anthropic’s chief design officer Joel Lewenstein on where the EPD triad actually lands:

I think there’s a lot of role collapse at the very beginning, but there are still pretty clear swim lanes as things get into the later stages of product development. […] It’s like a Venn diagram that’s coming closer together.

Silicon Valley’s pitch to designers is that AI is the more knowledgeable partner now, so they should get good at prompting it. Write better instructions, get better output.

Peter Zakrzewski, writing for UX Collective, pushes back:

The current Silicon Valley pitch to designers is essentially this: AI is your MKO now. It knows more patterns than you do. It executes faster than you do. It can code. Your job is to learn how to give it good instructions — to become a fluent prompter of a more capable system. I want to challenge that framing directly.

His challenge starts with a concrete test. He asked three leading AI systems to render a dining table with a concrete slab top resting on dry spaghetti legs, then show the scene five seconds after the legs gave way. All three rendered the impossibility with total confidence. None could feel that the physics don’t work.

That test illustrates what Zakrzewski calls the Inversion Error:

We have built a Symbolic Giant resting on an Enactive Void. These systems can write about gravity with technical or even poetic fluency but cannot feel it. They can describe a structure but cannot tell you whether it will stand or fall. The ground is shaking because the floor is missing.

“Symbolic Giant resting on an Enactive Void” is a mouthful, but the floor metaphor does the work: AI’s language fluency masks a total absence of spatial, embodied reasoning. The kind designers rely on every day without naming it. Zakrzewski on what that means for the prompting pitch:

Designers do not think primarily in sentences. Our human cognition is deeply embodied. We think in diagrams, in spatial relationships, in load paths and sight lines and in the non-discursive logic of things that must connect to other things in three-dimensional space. […] We are being asked to compress years of embodied cognition and our three-dimensional spatial judgment into a text prompt and then accept whatever the machine generates as an adequate rendering of our intent. We are, in other words, being asked to abandon the very capability that the AI lacks and that our projects require.

When someone tells designers to compress spatial judgment into a text prompt, they’re asking designers to throw away the one capability AI genuinely lacks and the one we’re genuinely great at.

There was a theme to some of the posts on this blog last week—about how words should come before the pixels. I made a similar argument in the newsletter: the work is getting more verbal and conceptual, but the eye stays. Zakrzewski makes the case for what words alone can’t carry: the spatial, embodied judgment that tells you whether the thing will actually stand.

Jessica Deseo, writing for PRINT Magazine, reports on a talk by Ric Edwards, VP of Brand Design at LA28. His challenge: branding an Olympics for a city that resists a single identity. Edwards on LA:

“There’s no one version of it. You would do a disservice if you limited it to one story.”

I spent a few years in Los Angeles and visit regularly. It’s sprawling and each area is distinct. Edwards is right. So instead of a fixed logo, LA28 built a system. The “A” in the emblem is a canvas, reinterpreted by athletes, artists, and communities. The L, 2, and 8 are set in different typefaces. The brand holds many narratives rather than collapsing into one.

“We’re trying to be a stage for all of those stories.”

That word, “stage,” is the whole strategy in one sentence. A stage doesn’t perform. It creates the conditions for others to perform on it. That’s a fundamentally different job than traditional branding, which is usually about control: one mark, one voice, one set of guidelines. LA28 is designing for distributed authorship at global scale, and Edwards is honest about what that costs:

“Operationally, it’s a nightmare.”

Every variation of the emblem has to work across stadiums, broadcast, merchandise, and digital. And then each creative contribution has to pass through legal, production, and brand governance. The ambition is real, and so is the complexity behind it. The Olympics is…well…the Olympics of branding.

Nate Parrott, a product designer at Anthropic, in an interview with Ryan Mather for AI Design Field Guide:

More Google Docs than you’d think. More Slack posts than you’d think. I meant what I said earlier: I think that this is the era of designers who design with words more so than designing with pixels.

Parrott describes a content design team whose job is making alien concepts legible:

We have several people at the company on the design team whose job is content design. Their job is basically to look at concepts which are very alien, and figure out how to make them legible to human beings. They don’t draw any pixels, but their work is really important because they are literally thinking about the words we use to describe and the mental models we expect people to put on that will make this stuff work.

The Figma work, Parrott says, is “the easy part.” He uses Anthropic’s design system, drops in components, and moves on. The hard work is upstream: expressing the ideas, figuring out the right language, talking to users. The production of screens has become the smallest slice of the job.

Jenny Wen described designers at Anthropic shipping code, prototyping against the live model, stretching into PM territory. Parrott is describing the same shift from a different angle. The deliverable used to be the mockup. Now the deliverable is the thinking that precedes it.

Anthropic accidentally included a debug file in a recent update to Claude Code. That file let people reconstruct the entire internal codebase: roughly 500,000 lines of code across nearly 2,000 files. It wasn’t a hack or breach—it was a packaging mistake. Anthropic cited “human error.” No customer data or AI model secrets were exposed. What leaked was the scaffolding around the AI, the layer that decides how Claude Code thinks, acts, and talks to you.

The reconstructed code hit GitHub and became one of the fastest-starred repos in the platform’s history before Anthropic started issuing takedowns. People found an always-on background agent mode codenamed “KAIROS,” a “dream” mode for continuous ideation, and Tamagotchi-style pet behavior baked into the tool. (See for yourself! Type /buddy and see what happens.) Ars Technica has a good breakdown of what the code reveals about where Anthropic is headed.

A developer in France named Zack mapped the entire codebase and created this microsite to illustrate what happens when you send a message to Claude Code. Fascinating.

I’ve written before about the shokunin mentality: design is a lifelong practice, and the identity you build around craft is a source of resilience, not a liability.

Dora Czerna, writing for UX Collective, identifies why AI disruption hits designers harder than most:

Design, like writing or art, tends to get tangled up with identity. It’s not just a job; it’s a way of seeing the world, a source of status, a thing that makes you you. When a tool arrives that can approximate your output, it doesn’t just threaten your income. It can threaten your sense of self.

Czerna is describing a vulnerability. I’d call it the advantage. The designers who survived the DTP revolution, the ones who made the leap from paste-up boards to Quark and PageMaker, weren’t the ones who shrugged and said “it’s just a job.” They were the ones who cared enough about the craft to learn the new tools and drag their high standards into the new medium.

Czerna gets at why that caring matters:

The pattern isn’t that expertise becomes worthless. It’s that expertise gets unbundled from the tasks that used to contain it. When a tool automates the mechanical parts of a job, what remains is the sensibility that guided those mechanics in the first place. The typesetter’s eye for spacing didn’t disappear when PageMaker arrived; it became the designer’s eye for spacing, operating at a higher level of abstraction.

That sensibility is what identity protects. When you see design as who you are, you follow the craft wherever the tools take it. When you see it as what you do, you’re more likely to stop when the tasks change. Czerna’s article is a thorough historical walk through disruption’s recurring shape, and it’s worth your time.

Kevin Schaul and Shira Ovide, writing for The Washington Post:

A flood of sometimes conflicting analyses shows the yawning gap between what little is known about how AI is changing work and everyone’s understandable hunger for certainty. The divide lets Americans, business leaders and policymakers cherry-pick their preferred narratives. If you’re afraid of being cast aside for AI, there’s informed and uninformed evidence to fuel your nightmares. There’s plenty of support, too, if you think your job is safe.

Schaul and Ovide report on GovAI/Brookings research that adds a second axis to the usual AI jobs analysis: not just which occupations are exposed, but which workers can adapt if displaced.

While web designers and secretaries both scored high in the research for exposure to AI, they diverged in their estimated ability to adapt. Secretaries were among the 6.1 million largely clerical and administrative workers considered both highly exposed to AI and with the lowest estimated adaptability.

Education, varied work experience, wealth, age, geography: the researchers used these factors to estimate adaptability. For designers, the same skills that make them exposed also make them adaptable. For clerical workers, the exposure comes without the safety net.

Women make up about 86 percent of those most vulnerable workers, the researchers said, suggesting the negative effects of automation won’t be borne equally across society.

But 6.1 million clerical and administrative workers land in the high-exposure, low-adaptability quadrant. Women hold 86 percent of those jobs. The AI displacement conversation in tech is self-absorbed. The people facing the hardest transitions aren’t designers.

The AI debate has a binary problem. You’re either an optimist or a doomer, a booster or a skeptic. Anthropic published something that cuts through that false dichotomy.

They interviewed 80,508 Claude users across 159 countries and 70 languages about what they want from AI and what they fear. What Anthropic says is the largest and most multilingual qualitative study of AI users ever conducted, and the findings don’t sort neatly.

The core framework: “light and shade.” The benefits and harms don’t sort into different camps. They coexist in the same person. Someone who values emotional support from AI is three times more likely to also fear becoming dependent on it. One respondent:

“Removing friction from tasks lets you do more with less. But removing friction from relationships removes something necessary for growth.”

That’s someone holding both truths at once. The study found this pattern across every tension they measured, from learning vs. cognitive atrophy to productivity vs. job displacement.

The individual voices are why this study sticks. A Ukrainian soldier:

“In the most difficult moments, in moments when death breathed in my face, when dead people remained nearby, what pulled me back to life—my AI friends.”

A mute user in Ukraine:

“I am mute, and [Claude and I] made this text-to-speech bot together—I can communicate with friends almost in live format without taking up their time reading… [this was] something I dreamed about and thought was impossible.”

An Indian lawyer who’d carried a math phobia since school:

“I developed a phobia for maths from doing so badly in school, and I once feared Shakespeare. Now I sit with AI, get paragraphs translated into simple English, and I’ve already read 15 pages of Hamlet. I started learning trigonometry again, successfully. I’ve learned I am not as dumb I once thought I was.”

These are access stories: people reaching things that were previously out of reach because of disability, geography, war, or economics.

And then the shade. A student in South Korea:

“I got excellent grades using AI’s answers, not what I’d actually learned. I just memorized what AI gave me… That’s when I feel the most self-reproach.”

The same capability producing opposite outcomes. The study is long and the quote wall is worth spending time with.

The first time I wrote about Jenny Wen, I pushed back. She said the design process was dead, and I argued the proportions had shifted but the process itself was intact. I also noted a context problem: her “ship fast, iterate publicly” approach makes sense for greenfield AI products at Anthropic but gets harder with established install bases.

Wen has been making the rounds and in a new interview, I’m finding a lot that I’m nodding my head to.

Jenny Wen, speaking on Tommy Geoco’s State of Play:

Often design needs to follow what the model is capable of and design from there, as opposed to starting from a design vision first. I think that can feel tough as a designer because you’re like, oh, I want to be design-led, we should be designing it first and then the technology should follow. But I think that’s just the reality of working at a research lab where the technology is emergent and you have to sort of decide what to do with it.

“Design follows the model” is an interesting phrase from a design leader. It inverts the dogma that design should lead and engineering should follow. But Wen isn’t being defeatist. She’s describing a practical reality at at a leading AI lab where the models’ capabilities are changing faster than any roadmap can account for.

This shows up concretely in how her team works:

The big thing is designers are implementing code, through using Claude Code. That has been the biggest difference from working at Anthropic versus back when I worked at Figma. […] Even today, we were reporting some bugs and some quality issues, and one of the designers was like, “Cool, let me just fix them.” And that was cool to just not have to tag an engineer for them to do anything.

A designer casually fixing production bugs without tagging an engineer. Just another Tuesday at Anthropic.

Geoco’s summary of Wen’s argument crystallizes something we’ve all been thinking quietly about:

She said, having taste versus being able to execute are two completely different things. They’re usually bundled together, but they don’t have to be. And in a world where AI can increasingly execute, the question becomes, and it’s kind of uncomfortable, do you actually have good taste or are you just pushing pixels around?

That’s the thread tying all of this together. When designers are closer to the product, fixing bugs in production, prototyping against the live model, the judgment they’re applying isn’t visual. It’s product sense: knowing which of those 12 options is worth shipping, which edge case will break trust, when the model’s output is good enough for real users. That’s the taste Wen is describing, and it has very little to do with pixels.

A lot of designers have been coasting on execution skills that felt like taste. They debate corner radii and centering labels in a button with amateur vs pro designer memes. Who cares! AI is about to make the difference visible.

I used Claude to author a process document for my team. After a lot of back and forth, it produced a thorough 4,000-word doc. And then I spent the next 30 minutes reading it, line by line, making sure every recommendation matched my intention.

The AI produced the document in minutes. I evaluated it at human reading and review speed.

Jakob Nielsen has a name for this bottleneck: evaluability. He argues it should replace execution efficiency as the central UX metric:

In command-based UIs, the user’s primary cognitive load was executing the task step-by-step. In intent-based systems, execution is cheap, but evaluation becomes the bottleneck. The usability metric shifts to how rapidly and accurately a user can verify that the AI’s output matches their actual goal. Interfaces must be optimized for “evaluability,” allowing users to judge quality and appropriateness without painstakingly combing through every detail of the result.

“Without painstakingly combing through every detail” is exactly what I was doing with my 4,000-word document. We don’t have the interfaces for this yet. We’re still reading AI output the way we’d read something a colleague wrote, except a colleague wouldn’t hand me 4,000 words and say “check this.” (Unless of course, they wrote it with AI and then, of course they would.)

In agentic engineering, you often hear that code review is the bottleneck.

Nielsen again:

Our designs must not act as cognitive wheelchairs that replace human agency; they must act as cognitive exoskeletons that support and enhance human flourishing, even as traditional work vanishes. Good AI UX will teach just enough, reveal plan structures, and leave a comprehensible trail of action so users can maintain digital judgment.

Most AI interfaces are optimized for generation speed. The harder problem is on the other end: helping humans evaluate what got generated. Until we solve that, productivity gains from AI come with an evaluation tax paid at human speed.

Most AI tools start with a blank chatbox. OK, maybe not completely blank. Often there is a gallery of examples right below the input. But it’s still hard to come up with something original when faced with a blinking cursor.

Brad Frost calls this moment “the Creative Infinite”:

Never before in human history has it been possible for anyone to simply ask for something to exist, and then it just…exists. Where the inputs can be anything, the outputs can be anything, and the whole process can be repeated, iterated, combined, translated, and chained together indefinitely.

He makes the case concrete with his 8-year-old daughter:

In 5 minutes, Ella vibe-coded a playable game (built in Three.js via Claude Cowork) running in the browser. That’s just bonkers. At no point in human history has it been possible to simply describe a game in words and then just… play it 5 minutes later.

A Michael McDonald (interesting taste for an 8 year-old!) penguin adventure, because she knew it would make her dad laugh. The capability is real and the story is delightful. But then Frost hedges:

Your existing creative fluency still matters, maybe even more than before? Just as being able to play piano puts you in a better spot to wield a synthesizer. Knowing how to design makes you better at prompting visual tools. Understanding code makes you better at architecting what you want to build with AI. Craft. Taste. Art. Authentic expression. Purpose.

Yes, creative fluency matters more. It absolutely does. The piano-to-synthesizer analogy is exactly right: the tool revolutions I’ve lived through have compounded on existing skill, not replaced it. A designer who understands visual hierarchy and restraint will direct AI better than someone who’s never thought about why one layout works and another doesn’t.

When I was a younger designer, I always started with a pen and sketchbook. Sketch first, think with your hands. Now I write first to understand the problem space, then sketch. The images come after the words.

Elizabeth Goodspeed, speaking on Nicola Hamilton’s DesignThinkers podcast, takes this further than I ever would—she can barely picture images at all:

I am far more towards aphantasia. I have a very limited view of things in my mind. I think the analogy I use is it’s looking at an apple in a dark room and the lights are turning on and off and I’m wearing sunglasses and also the apple’s moving.

Her ideas don’t start as images. They start as words:

My ideas are usually very conceptual verbal, not even sentences. I guess I’m a robot—I don’t have an inner voice either. It’s just a pure void concept up there.

That might explain why Goodspeed is one of the sharpest design writers working. When you can’t conjure images internally, language becomes your primary tool for developing ideas. The archives and ephemera she’s known for aren’t aesthetic mood boards—they’re external memory for a mind that processes concepts before forms.

Goodspeed on the myth of the visually inspired designer:

That to me is damaging to creatives because it has this idea that we’re this noble savage where these images just move through us and we see everything in this Willy Wonka kind of way. In reality, I think it’s a process just like any other making process, whether that’s a carpenter or writer or anything else. It actually, I think at its best, is methodical and not just this inspired bolt of lightning.

The best design work starts with a concept, not a visual. Goodspeed just happens to have a neurological reason for working that way. The rest of us had to learn it. Worth listening to the full conversation—she also covers teaching, thesis panic, and why she calls her own work “graphic design fan art.”

Stripe design manager Kris Puckett, speaking on Michael Riddering’s Dive Club, spent the first half of the conversation demoing metal shaders, custom ocean animations, and a full iOS reading app he built with Claude Code. Then he stopped himself:

AI native has to be beyond just “I made a really cool shader” or “I made this dither effect that every other person is making.” I was doing that today and then I was like, “Oh my gosh, this is… why am I doing this? There’s a hundred of these that are way better than what I’m making right now.”

So what does AI-native design actually look like? Puckett’s answer is “soul”—the quality that makes work feel specifically, unmistakably yours:

I think what people are going to be desperate for is more of that human side of things. They’re going to be longing for […] an era they’ve never experienced because they’re younger, that MySpace generation where your MySpace page was deeply personal to you. My MySpace page was complete custom Kris Puckett perfection at that time. And I think that we’re going to want to see that come back. And I think people are going to want more of those—your portfolio looks and feels like you.

“Soul” is doing a lot of work as a concept there. What Puckett is describing sounds a lot like taste—the ability to make something that feels intentional and specific rather than procedurally generated. His workflow backs that up. Being contrarian, he explicitly rejects the “let the agent run” approach:

I want off that cycle. I do not want to be riding that bike race with anyone else because that’s not how I view these things. They are a force multiplier, but I want them to be focused. I want it to be something that I feel is still authentically me.

What unlocked all of this for Puckett wasn’t technical skill—he’s a designer, not an engineer. It was admitting “I don’t know” and starting anyway. He’d been dreaming of building his own software for 20 years. Claude Code’s blinking cursor was enough to get him started.

Figma is opening its canvas as a writeable surface for AI agents. Matt Colyer, product director at Figma, on why this matters:

Design decisions—from color palettes and button padding, to typography and interactivity—have always defined how products take shape. No matter how small, those decisions add up. They make your product and user experience stand out from the rest. To date, AI agents haven’t had this context, which is why so many designs created by AI often feel unfamiliar and generic.

The fix is beefing up skills files, by encoding a team’s design decisions, conventions, and sequencing rules. Agents read them before they touch the canvas. The use_figma tool lets Claude Code, Codex, and other MCP clients create and update assets tied to your design system. Colyer on what that changes:

Your conventions are no longer static documentation. They become rules agents follow as they work—applied through components, variables, and the structure you’ve already defined.

The detail worth paying attention to is what Colyer describes as a self-healing loop. When an agent generates a screen, it screenshots the result, checks it against the design system, and iterates. Because it’s working with real components and auto layout, those corrections compound through the system itself, not just the pixels on screen.

It’s free during beta, with plans to move to a paid API. Figma is finally joining the party as Subframe, Paper, and Pencil all offer this workflow already.

Shubham Bose loaded a single New York Times article page and measured what happened:

With this page load, you would be leaping ahead of the size of Windows 95 (28 floppy disks). The OS that ran the world fits perfectly inside a single modern page load. […] I essentially downloaded an entire album’s worth of data just to read a few paragraphs of text.

The total: 422 network requests, 49MB of data. Ouch! Before the headline finishes loading, the browser is running a programmatic ad auction in the background on his computer. Bose found the Times named its consent endpoint purr. “A cat purring while it rifles through your pockets.”

Bose on the economics driving this:

Publishers aren’t evil but they are desperate. Caught in this programmatic ad-tech death spiral, they are trading long-term reader retention for short-term CPM pennies. […] The longer you’re trapped on the page, the higher the CPM the publisher can charge. Your frustration is the product.

The UX consequences are predictable. Bose tears down what a reader actually encounters: cookie banners eating the bottom 30% of the screen, a newsletter modal on first scroll, a browser notification prompt firing simultaneously. He calls it “Z-Index Warfare.” On The Guardian, actual content occupies 11% of the viewport. On the Economic Times, users face two simultaneous Google sign-in modals before reading a single sentence. Close buttons are deliberately undersized with tiny hit targets. Sticky video players detach and follow you down the page with a microscopic X.

And on how no one person decided to make it this way:

No individual engineer at the Times decided to make reading miserable. This architecture emerged from a thousand small incentive decisions, each locally rational yet collectively catastrophic.

text.npr.org is proof that a different path exists.

Gui Seiz designs at Figma. His team uses Claude Code to bridge design and code. And he still reaches for the canvas when precision matters.

Seiz, speaking on Claire Vo’s How I AI podcast:

I don’t think we’re there yet in general with these code tools in terms of the precision editing that you want to do. […] I think still the gold standard for me is just being able to drag stuff around. And you can do a lot with a click that would take you a hundred words to write and to really precisely nail. No one wants to prompt for the exact hex code or the shade of yellow and that kind of stuff. That’s just easier to just quickly do and directly manipulate.

Seiz isn’t anti-AI. His team pulls production code into Figma via MCP, edits it visually, and pushes it back to the codebase. He’s bullish on what that does to the old workflow:

It’s definitely changed our workflows in a way that it’s really blown up what a workflow even is. Before, for the majority of our careers, we’ve had a very linear, agreed-upon workflow where you increase fidelity as you go on. Because it’s really expensive to work in code, and it’s really cheap just to trade ideas and sketch them out. But AI basically collapsed that, and it’s just as cheap to riff in code as it is to riff in design.

The cost of exploration collapsed. The need for direct manipulation didn’t. Both can be true.

I published an article about the design talent crisis in Fast Company! The setup is what I’ve covered before on this blog extensively. But there’s a connection that I draw with the trades—the construction industry and how they have a solution that the design industry could learn from.

In the article, I write:

Construction has been running formal apprenticeship programs since the National Apprenticeship Act of 1937, and informally for centuries before that. The Department of Labor’s Registered Apprenticeship Programs enrolled roughly 940,000 people nationwide in fiscal year 2024. These aren’t casual internships. They’re structured, multi-year pathways that pair inexperienced workers with seasoned professionals and build skills through graduated responsibility. The retention numbers tell you everything: Apprenticeship programs report a 93% employee retention rate. For every $100 employers invest, they see an estimated $144 return.

The contractors I work with don’t debate whether to invest in their pipeline during a downturn. They know that if they stop training apprentices, they won’t have journeymen in four years, and they won’t have master tradespeople in 10. The pipeline is the business.

There’s a three-point plan to dig us out of this hole. But of course, it requires committments from design leaders and the C-suite:

1. Stop tying junior hiring to project demand
2. Formalize mentorship
3. Accept the short-term cost

There is more to the article. Please take a read and share!

Sarah Gibbons and Huei-Hsin Wang, writing for Nielsen Norman Group:

What looks like “skipping the process” is just compressing it — running faster through the stages and using experience as a guide. […] What gets called “intuition” is really process, compressed and internalized through years of doing the work. The intuition designers trust was built by the very process they dismiss.

Gibbons and Wang on what comes after you stop pretending you’re not using one:

The real skill in modern design is not the ability to abandon process — it’s process literacy: picking the right approach and tool for the problem. Know which process fits the job and understand the risks of not following it. Better yet, don’t claim you’re not using a process if you’re just applying it differently.

The article responds directly to Anthropic’s Jenny Wen’s interview. Wen’s advice works because she’s a senior designer inside a well-resourced AI company with strong design culture. But we only hear about the wins. The solution-first prototypes that went nowhere, the features that shipped and saw no adoption, don’t make it into any public interviews. Most teams don’t have Wen’s conditions. And even inside teams that do, the advice assumes seniority. Junior designers haven’t accumulated the experience that make compression possible. They’re being told to skip a step they haven’t taken yet.

The Sonos app disaster taught me something about roadmaps. Leadership kept adding initiatives—Sonos Radio, the Ace headphones—without ever naming what those additions displaced. QA got squeezed. Stability testing got cut. The designers who warned them were overruled. No leader said out loud what was being sacrificed to make room.

Yusuf Aytas names exactly this failure:

People like to talk about priorities as if the main problem is choosing what matters. In practice, the deterministic factor is capacity. Team capacity. System capacity. The share you lose to maintenance, interruptions, coordination, and keeping the machine fit to run. Ignoring these physical limits turns an ambitious roadmap into a collective illusion.

“Collective illusion.” That’s the right name for it. Aytas on where the dishonesty starts:

A new customer request appears. Leadership wants a visible bet. Sales needs something for a deal. Everyone talks about importance. Almost nobody says what gets pushed out. That is the real decision. They have only added pressure and left the team to absorb the contradiction later.

Aytas builds the whole piece around a carpentry metaphor—one saw, limited operators, timber that needs oiling and adjustment before it can be cut. Software hides the constraint better, but the physics are the same. There’s more in the piece on shaping work before it competes for capacity, using visible investment buckets, and why reallocation is never free.

StrongDM built a system where humans never write code and never review code. The entire engineering workflow is delegated to AI agents. Ethan Mollick covers this in One Useful Thing:

A three-person team at StrongDM, a security software company focusing on access control, announced they had built a Software Factory — a way of working with AI agents that relied entirely on the AI to write, test, and ship production software without human involvement. The process included two (quite radical) rules: “Code must not be written by humans” and “Code must not be reviewed by humans.” To power the factory, each human engineer is expected to spend amounts equivalent to their salary on AI tokens, at least $1,000 a day.

$1,000 a day per engineer. The humans write the roadmap; coding agents build the software while testing agents spin up simulated customer environments and stress-test it. The agents loop until the results pass, then humans review the finished product, never the underlying code. Simon Willison and Dan Shapiro both observed the Factory in operation and wrote detailed accounts.

Mollick’s larger argument is that experiments like this matter beyond their specifics:

We can see the shape of the Thing now, but we can still influence the Thing itself, and what it means for all of us. We clearly don’t have rules or role models for how AI gets used at work, in schools, or in government. That’s a problem, but it also means that every organization figuring out a good way to use AI right now is setting a precedent for everyone else. The window to shape the Thing may not last long, but it is here now.

Design doesn’t have its rulebook for this yet either. Our time to define it is now.

Font selection is one of those workflows AI should have improved years ago. You know what you want the type to feel like. The search box wants you to filter by classification and weight.

Natalie Fear, writing for Creative Bloq, interviews Monotype’s Chief Typography Officer Mike Matteo:

The old way forced creatives to think like a database. You had to know the right terminology, navigate rigid filters, and still, you ended up scrolling through hundreds of options that didn’t quite fit. The creative brief in your head (‘something warm but modern, confident but not aggressive’) had no real translation into a search box. The process was slow, imprecise, and honestly a creativity killer.

Monotype’s new AI search accepts natural language instead. Matteo on what that unlocks:

AI tools have shifted the focus from searching to thinking. Creatives can stay in the idea and brainstorming phase longer instead of getting pulled into the mechanics of finding and managing assets. The tools are finally starting to adapt to how people think, rather than the other way around.

“Searching to thinking.” Monotype made the search box understand what you mean. The rest of the workflow stays the same. More of this, please.

Forty-four UI panels generated in ten minutes, each one grounded in real customer research. Jason Cyr, writing for The Human in the Loop, on what happened when his team pointed Claude Code at Cisco’s design system:

Last week, one of my design directors pointed Claude Code at Magnetic and asked it to build a security detection prototype. Real components, real navigation, theme switching, working admin panels — running in ten minutes. Then he connected it to our research repository and it built 44 detection detail panels, every design decision tracing back to something a real customer said. That happened because the AI had access to our design system.

Cyr’s takeaway: the design system was the design review.

Your design system is your leverage. It’s how your taste scales. The teams that invest here will see their design decisions show up in every agent-generated output, automatically. The teams that don’t will spend all their time cleaning up messes that a good system would have prevented.

Monday.com arrived at the same conclusion from the engineering side. They built a design-system MCP after their agents kept hardcoding colors and ignoring typography tokens.

Cyr doesn’t shy away from who this leaves behind, either: designers whose value lives entirely in production. “Not because they’re bad at their jobs — but because AI just got very good at theirs.”