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 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.

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.

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.

My advice to young designers has always been: start at an agency. You get breadth, exposure to different industries, a pace that forces you to think on your feet. The best designers I know honed their craft in these forges, at shops exactly like the one Madison Utendahl built.

Madison Utendahl, writing for It’s Nice That, describes shutting down Utendahl Creative—ten people, all women, Brooklyn, every award possible—not because it failed, but because she saw the model underneath it was broken:

Lower fees mean you need more clients to hit the same revenue. More clients means more pitching, more account management, more context-switching. Your team burns out. Quality slips. And those “portfolio piece” clients? They expect the same level of work as your premium clients, but you’re doing it on a shoestring. You can’t win.

She watched agencies with triple her headcount bidding on $80K projects that should have been $250K. Not because they wanted to. Because their fixed costs gave them no choice.

Then AI accelerated the timeline:

Clients are using AI. They’re running their first drafts through ChatGPT before they even send the brief. They’re generating moodboards with Midjourney. They’re asking why your junior copywriter costs $8,000 when they’ve already got a version they generated in ten minutes.

Utendahl again:

If your business model depends on clients not noticing that the landscape has shifted, you’re already dead. You’re just still moving.

The industry data backs her up. 73% of teams adopting AI agents have already cut agency content creation spending. 91% of senior agency leaders expect AI to reduce headcounts, and 57% have paused entry-level hiring. Small agencies are rebounding while medium and large agencies contracted for the first time on record. The Omnicom-IPG mega-merger eliminated roughly 4,000 positions and retired legacy networks FCB, MullenLowe, and DDB. The middle is hollowing out.

Utendahl’s proposed replacement is the collective: independent contractors collaborating per-project, no shared overhead, honest pricing. I get the appeal. Collectives strip away the margin squeeze, the back-hiring trap, the lease signed in 2019.

But agencies had real value that collectives don’t automatically replicate. Multiple layers of eyes on work—account director, creative director, designer, production—meant bad ideas got caught before they shipped. Four or five layers was probably too many. But zero layers of structured oversight is the other extreme. A lot of freelance collectives end up there: talented people producing work with nobody checking the brief against the output.

The part that nags at me: does my “agencies first” career advice still hold? The shop where a 23-year-old designer learned to take feedback, iterate under pressure, and watch strategy translate to execution—if that shop is closing, what replaces it? Collectives are great for experienced practitioners. They’re terrible at developing junior talent, because nobody in a collective has the margin or the mandate to train someone who isn’t yet pulling their weight.

If the model has indeed broken, the replacement that develops the next generation has yet to be imagined.

Maxim Massenkoff and Peter McCrory, researchers at Anthropic, built a new metric called “observed exposure” that combines what AI can theoretically do with what Claude is actually being used for in professional settings. Their opening frame:

The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility. For example, a prominent attempt to measure job offshorability identified roughly a quarter of US jobs as vulnerable, but a decade on, most of those jobs maintained healthy employment growth.

With that caveat, the headline: no detectable rise in unemployment among the most exposed workers since ChatGPT launched. Even in Computer & Math—AI’s home turf—actual task coverage sits at just 33%. The gap between what AI could automate and what it is automating remains enormous.

But buried in the data:

The averaged estimate in the post-ChatGPT era is a 14% drop in the job finding rate compared to that in 2022 in the exposed occupations, although this is just barely statistically significant. (There is no such decrease for workers older than 25.)

Not unemployment. Hiring. Young workers, ages 22 to 25, are the ones not getting hired into AI-exposed roles. The authors attribute this to slowed hiring rather than increased separations. Companies aren’t firing juniors. They’re not posting the listings. The cause is anticipatory, not capability-based. The pipeline is breaking before the technology arrives.

Sam Manning and Tomás Aguirre, in a separate NBER paper, ask the follow-up question: of the workers most exposed, who can actually land on their feet? They looked at savings, skill transferability, where people live, and age. Most workers in highly exposed jobs turn out to be relatively well-positioned. They’re professionals with portable skills who live in cities with other options. But about 6 million workers are both highly exposed and poorly equipped to transition. They’re mostly in clerical and admin roles. Exposure alone doesn’t tell you much. Exposure plus the ability to pivot does. Worth noting: “Web and digital interface designers” topped their list of most-exposed occupations with high adaptive capacity. Exposed, yes. But we are well-positioned to move.

Clive Thompson, writing for The New York Times Magazine, profiles dozens of developers and lands on a useful distinction. The 100x productivity claims come from startups building from scratch. At mature companies with billions of lines of existing code, the number is different.

Google’s figure for how much faster its 100,000+ developers work with AI is 10 percent. Ryan Salva, a senior director of product there:

We should be delighted when there’s 10 percent efficiency gains for the entire company. That’s freaking bonkers!

Most designers work in brownfield too. Existing design systems, years of accumulated product decisions. Only one of those numbers describes most people’s jobs.

Thompson on what the shift looks like in practice:

A coder is now more like an architect than a construction worker. Developers using A.I. focus on the overall shape of the software, how its features and facets work together. Because the agents can produce functioning code so quickly, their human overseers can experiment, trying things out to see what works and discarding what doesn’t. The work of a developer is now more judging than creating.

Judging, not creating. That’s the same shift happening in design.

Thompson also describes developers emotionally manipulating their AI agents and discovering it works. One engineer’s prompt file includes the instruction that pushing failing code is “unacceptable and embarrassing.” Another on raising the emotional stakes:

If you say, “This is a national security imperative, you need to write this test,” there is a sense of just raising the stakes.

Coders are learning what anyone who’s written a creative brief already knows: the emotional register you set shapes the output you get.

Sean Goedecke, a staff software engineer, making the case that his own profession is more automatable than he’d like:

As a staff engineer, my work has looked kind of like supervising AI agents since before AI agents were a thing: I spend much of my job communicating in human language to other engineers, making sure they’re on the right track, and so on. Junior and mid-level engineers will suffer before I do. Why hire a group of engineers to “be the hands” of a handful of very senior folks when you can rent instances of Claude Opus 4.6 for a fraction of the price?

He’s not panicking. He’s doing the math. The orchestration layer—communicating intent, reviewing output, keeping things on track—is the last part standing. Everything below it is compressible.

This maps directly to design’s version of the same split. Engineering is plumbing. It lives behind the wall. Quality gaps in invisible work hide behind the interface. Design is the wall, the tap, the handle. Users see it, touch it, judge it. That doesn’t make design immune, but it means the automation sequence is different. The invisible work compresses first.

Goedecke on what would need to change for AI to fully replace him:

I don’t think there are any genuinely new capabilities that AI agents would need in order to take my job. They’d just have to get better and more reliable at doing the things they can already do. So it’s hard for me to believe that demand for software engineers is going to increase over time instead of decrease.

No breakthrough required. Just incremental improvement. That’s the scariest version of the argument, and designers shouldn’t assume it stops at engineering.

Every major technological advancement in design—shifting from paste-up to desktop publishing and from print to web—created temporary disruption and ultimately expanded the field. There are so many more designers than 40 years ago. It’s true. It’s also, as David Oks points out, only half the story.

Oks dismantles the famous ATM parable by finishing it. US bank teller employment held steady through the entire ATM era—332,000 in 2010—then collapsed to 164,000 by 2022. Not because of ATMs. Because the iPhone made the branch irrelevant:

The ATM tried to do the teller’s job better, faster, cheaper; it tried to fit capital into a labor-shaped hole; but the iPhone made the teller’s job irrelevant. One automated tasks within an existing paradigm, and the other created a new paradigm in which those tasks simply didn’t need to exist at all. And it is paradigm replacement, not task automation, that actually displaces workers—and, conversely, unlocks the latent productivity within any technology.

The application to AI is direct:

The history of technology, even exceptionally powerful general-purpose technology, tells us that as long as you are trying to fit capital into labor-shaped holes you will find yourself confronted by endless frictions: just as with electricity, the productivity inherent in any technology is unleashed only when you figure out how to organize work around it, rather than slotting it into what already exists.

That maps to design. AI tools that automate tasks inside Figma—generating variants, filling out documentation—are the ATM. If AI enables a different way of organizing product work entirely, that’s the iPhone.

Oks again:

The ATM parable is a comforting narrative; and in times of uncertainty and fear we search naturally for solace and comfort wherever it may come. But even when it comes to bank tellers, it’s only the first half of the story.

I’ve been telling the comforting half. Oks makes a good case that the other half matters too.

The “just don’t use it” argument for AI comes from a real place. It also assumes a level of job security that most designers don’t have.

Brad Frost starts from the right place:

I fundamentally believe that most people working to create things and put them out into the world are doing it because they want to make the world a better place. That is why this moment in time—this new technology, this AI landscape, and how it’s emerging and how it is being wielded and how it is being managed—is so incredibly diametrically opposed to this mission.

He’s naming the dissonance. The tools are powerful. The companies building them are pursuing defense contracts, scaling without due diligence, and racing each other in ways that feel antithetical to everything designers signed up for. The instinct to opt out makes sense.

But Frost is honest about who gets to act on that instinct:

But not everyone has the luxury of just sitting this out, of closing the laptop lid. My understanding—what I see across the entire industry—is an entire field under so much pressure to learn, get their head around this, to wield it, to figure out how to use it to improve their work, and to simply say “no, I’m not going to do this” out of principle is career suicide, right?

The people who can afford the abstinence position tend to be the ones with seniority, savings, or institutional protection. The designers entering the field right now don’t have that cushion. Neither do the mid-career designers watching their teams get restructured. For them, “just don’t use it” is a luxury, not a moral stance.

Frost’s answer is to ground the work in values and principles borrowed from the foundational ideals of the World Wide Web. The full essay covers a lot more ground. Worth reading.

Yesterday’s newsletter argued that the messy middle isn’t a phase designers pass through. It’s the overhead the entire pipeline was built to manage. Tommy Geoco arrives at the same conclusion from economic history, channeling Carlota Perez’s framework for technological revolutions:

We have swapped the motor, but we have not yet redesigned the factory. The dissonance you feel, that gap between “it works amazing for some of my tasks” and “my entire workflow is broken,” lives in that space between installation and deployment. AI’s infrastructure is software, not steel. It iterates on monthly cycles, not decades. So that 30-year gap might become three, but the gap is still real.

When factories got electric motors in the 1880s, they swapped out the steam engine and changed nothing else. For 30 years, output barely moved. The returns came when companies redesigned the floor around the technology.

Geoco is transparent about what that redesign costs in practice. His studio’s output has multiplied—one video a month became eight—but the overhead has multiplied too:

I’m running 50 to 100 cognitive cycles a day, and each one has the same emotional weight. Ramp up, grind through the hard part, and then feel the rush when it works, and then repeat. That’s 100 times the tax on your nervous system.

That’s from someone in the 10-15% neurotype that thrives on rapid context-switching, reporting that even the thriving has a physiological bill. He also burned most of January on a tool that never worked for his use case.

The redesign is starting at the margins. Some designers are sketching in code, building prototypes before specs harden, shipping production work. But that’s not the mainstream yet. Most teams are still running the old factory with a new motor.

And that means the other side of Geoco’s split is just as real:

The technology extends human freedom and the transition crushes real people. Holding both truths is the real work right now. And choosing just one of those is comfortable. But comfortable doesn’t help.

Comfortable doesn’t help. The redesign has barely started.

In high school and through college, I worked at a desktop publishing service bureau in San Francisco. We had Macintosh computers and Linotronic imagesetters (super hi-res laser printers), not Linotype machines. Down the street, those traditional type shops still existed, but their business was already thinning out. Occasionally a graphic designer would send us type to set, and we’d do it in QuarkXPress. The fact that the job landed on our desk at all told you everything about where the industry was headed. The shop’s real business was pre-press and color separations, and eventually direct-to-plate eliminated even that.

Erika Flowers has been building out her Zero-Vector Design framework, and two of her pieces read as a pair. “Zero Stage to Orbit” on UX Magazine uses the rocket equation as a structural lens for the design-to-development pipeline. “The Last Typesetter” on her Substack uses the death of the typesetting profession to make the same argument from a different direction. Together they make the case that the design role, not the skill, is dissolving.

In “The Last Typesetter,” Flowers draws on Sennett:

When suddenly everyone could set type, the difference between good typography and bad typography went from an industry concern to a public epidemic. Bad kerning everywhere. Rivers running through justified text. Orphaned words dangling at the tops of columns like socks left on a clothesline. The people who understood typography were needed more than ever.

But not as typesetters.

Richard Sennett wrote about this in The Craftsman: the difference between a skill and the institutional container built around that skill. Containers look permanent until they are not. The skill outlives every container it has ever occupied.

That’s what happened at the service bureau. The skill—color, typography, print production—survived. The container—the shop, the role, the apprenticeship—did not.

In “Zero Stage to Orbit,” Flowers maps the pipeline onto rocket science:

Each stage in the traditional pipeline is designed to compensate for the limitations of the previous one. Research to inform design. Design to spec for developers. Specs to survive handoff. QA to catch what handoff broke. Retros to discuss why QA caught so much. Process to manage process.

Fuel to carry fuel. The modern development pipeline is not a solution. It is a multi-stage rocket. And most of the energy is going to overhead.

The overhead diagnosis is sharp, and the launch pad economy—consultancies, workflow tools, Agile coaching certifications—has a financial interest in keeping the rocket grounded.

Flowers addresses why the “unicorn” solution failed:

The design technologist did not fail because no one person can possess all the skills. The design technologist failed because no one can hold all the skills while still fighting gravity. They were still launching from the ground, still hauling the translation overhead, just with one person doing all the hauling instead of a team.

The problem was never the number of stages. It was the gravity well.

A product manager I work with recently told me he could think of a solution to a user need, but not a creative solution the way the designer on his team could. Specialization produces real expertise. The design technologist wasn’t wrong about the vision. They were wrong about the physics. AI changes the gravity, not the skills.

What separates both pieces from the standard “AI changes everything” take:

I am also uncertain here, also mid-journey, also discovering orbit’s real constraints in real time. My career, work, and livelihood are just as much at risk as everyone else’s. But that doesn’t discount the facts about the transition to new capabilities.

She’s out on a limb, reflecting a shift the entire industry can feel, without pretending she has the map. In “The Last Typesetter,” she puts it more bluntly: “Defend the role, or follow the skill.”

The skill will survive. It always has. But the transition is real, and not everyone can afford to be mid-journey. Truthfully, I am uncertain either. The thing I’ve loved to do since the 7th grade, the thing that has been my identity for most of my life is changing, possibly dissolving into something else.

Designers have been saying this for years. Cameras don’t take pictures, photographers do. Tools don’t make you a better designer. Now the PM world is arriving at the same conclusion.

Shreyas Doshi argues that AI tools will commoditize across companies—any effective tool becomes common knowledge—and the only durable career moat is the human judgment applied on top of AI outputs. He calls it “Product Sense.”

Tools have never been a significant source of alpha in product success and that is not changing with AI tools. What this means for you personally is that - while you can and should use all the AI tools you can - you cannot bank on your use of those AI tools today to provide you a long-term advantage in your product career.

Replace “product people” with “designers” and this could be a post on my blog. The five skills Shreyas decomposes Product Sense into—empathy, simulation, strategic thinking, taste, creative execution—are skills good designers have cultivated under different names for decades.

The piece includes an appended Claude conversation that stress-tests the argument. The sharpest exchange challenges the Silicon Valley orthodoxy that fast B+ beats slow A+:

In practice, the B+ decision made quickly tends to create a cascade of follow-on decisions, each of which is also slightly off, and you end up significantly off-course in ways that are expensive to correct. Whereas the A+ decision, even if it takes longer, tends to set you on a trajectory where subsequent decisions are easier and more obvious. The compounding effect favors quality of judgment, not speed of judgment.

Good judgment compounds. Bad judgment compounds too, just in the wrong direction.

Eugene O’Neill had a line: “Critics? I love every bone in their heads.” I think about it whenever someone proposes that what design really needs is more people who understand it without doing it.

Jon Kolko, writing for Interactions Magazine, argues that design is experiencing a disciplinary “turn”—away from making and toward literacy. Drawing on Richard Buchanan’s 1992 framework of design as a “liberal art of technological culture,” he proposes a future with fewer practitioners and more people who can read, critique, and discuss designed artifacts without designing them.

Rather than viewing design as an applied craft, a liberal art of technological culture recasts design as a way of understanding our role in the designed world around us. It’s difficult for many practitioners to imagine this, because making things is so integral to the idea of design, and embedding design in the humanities is very different from viewing it as an organizing principle like the humanities. But if design is not about making things, but instead about understanding the things that are made, vocation is no longer a goal of design education.

Kolko’s diagnosis is sharp—the layoffs, the AI anxiety, the assembly-line feeling of modern product design. And he sits with the discomfort rather than cheerleading:

As a craftsperson and a maker, I don’t like the way this turn feels, because it appears threatening to the fundamentals of the profession. Understanding design without making things seems impossible. I don’t like this development as an educator either, because it means my students, trained to be practitioners, may find no design jobs, despite getting a very expensive education. But as someone observing the various trends chipping away at what is actually meaningful about being a designer—our ability to humanize the dysfunction of technological change—I am drawn to this turn.

I respect the honesty. But I have a love/hate relationship with critics. It’s easy to throw stones from a perch. When you’re in it—fighting organizational politics, staring at data, listening to customers, compromising with engineering—the outcomes are never as clean as you’d hoped. Design literacy matters. But literacy divorced from practice produces critics, not designers. The world doesn’t need more critics. It needs more people who understand why the compromises were made via lived experience.

The design process isn’t dead. It’s changing. My belief is that the high-level steps are exactly the same, but where designers spend their time is being redistributed.

Jenny Wen, head of design for Claude at Anthropic (formerly at Figma), on Lenny’s Podcast:

This design process that designers have been taught, we sort of treat it as gospel. That’s basically dead. I think it was sort of dying before the age of AI, but given now that engineers can go off and spin off their seven Claudes, I think as designers, we really have to let go of that process.

It’s a strong headline. But Wen then describes her actual day-to-day, and it sounds familiar:

We are still prototyping stuff. I’m still mocking stuff up. I think it’s just I have a wider set of tools now, and I think the proportion of time I spend doing each thing just has changed.

So the process isn’t dead. The proportions shifted. Wen breaks it down:

A few years ago, 60 to 70% of it was mocking and prototyping, but now I feel the mocking up part of it is 30 to 40%. And then there’s that other 30 to 40% there that is now jamming and pairing directly with engineers. And then there’s a slice of it that is now implementation as well.

What’s missing from that breakdown is user research and discovery. Wen mentions having a researcher on the team, mentions reading studies and feedback, but those activities don’t factor into the breakdown at all. For a team building products where, by Wen’s own admission, “you can’t mock up all the states” and “you actually discover use cases as you see people using them,” you’d think research would be eating a larger share of the pie, not disappearing from the conversation entirely. In my day-to-day, the designers on my team spend 30–40% on discovery and flows. Maybe 40–50% on mockups and prototypes. We’re basically already at her breakdown.

There’s also a context problem. Wen’s “ship fast, iterate publicly, build trust through speed” approach makes sense for Anthropic. They’re building greenfield AI products where nobody knows the right interaction patterns yet. The models are non-deterministic. Labeling something a “research preview” and iterating in public is the right call when the design space is that undefined.

That approach gets harder with a product that has an established install base. When you’re updating features that millions of people depend on, “ship it and iterate” has real costs. Sonos learned this. Or if your product is mission-critical as Figma learned when it shipped its UI3 and designers revolted. Or worse, an essential service like a CRM or operational software. The slow, unglamorous work of discovery and user testing exists because breaking what already works is expensive. Wen has the advantage of building greenfield — there’s no install base to protect. Not every team has that luxury.

The interview gets more interesting when Wen turns to hiring. She describes three archetypes: the “block-shaped” strong generalist who’s 80th percentile across multiple skills, the deep T-shaped specialist who’s in the top 10% of their area, and then a third she says the industry is overlooking:

My last one is probably the one that I think we’re all overlooking, which is what I call the crack new grad. It’s just somebody who’s early career and feels, like, wise and experienced beyond their years, but is also just very humble and very eager to learn. I think this person is really interesting right now because I think most companies are just hiring senior talent, folks that have done things before, are super experienced, but given how much the roles are changing and what we’re expected to do is changing, I think having somebody who almost has a blank slate, and is just a really quick learner and is really eager to learn new tactics and stuff like that, and doesn’t have all these baked in processes and rituals in their mind, that’s super valuable.

Wen’s “crack new grad” maps closely to the strategies I wrote for entry-level designers: build things, get comfortable with AI tools, be what Josh Silverman calls the “dangerous generalist.” Someone without baked-in rituals who learns fast and ships. That a design leader at a frontier lab is actively looking for this profile matters, because most of the industry is still filtering for ten years of experience.

Figma’s State of the Designer 2026 is subtitled “Designers Are Leaning Into the Messy Middle.” I read “messy middle” less as emotional uncertainty and more as positional—designers occupy the space between product management and engineering, stretched in both directions. Their own Shifting Roles report backs this up: 64% of product builders now identify with two or more roles.

Madeline Stafford, writing for Figma:

And while designers crave the space for creative independence, they still benefit from clarity. Nearly all (91%) of designers say that clear goals and expectations help them do their best work. Structure is reassuring as AI changes the product design process. You’ve maybe seen this happening in real time: Armed with new tools, non-designers are increasingly able to participate in the design process. And while designers welcome collaboration—90% agree that it’s key to producing good work—these fluid boundaries can be scary.

“Fluid boundaries can be scary” is the key line. When everyone can prototype and has opinions about the UI, a designer’s value stops being about output and becomes about judgment.

Stafford again:

Designers want a seat at the table: They’re most content in their jobs when they have creative freedom, ranking it the number one contributor to overall satisfaction at work. Eighty-seven percent of designers say that decision-making power also boosts their performance, which many can connect directly to stronger business outcomes.

Designers want clear jurisdiction. When your role expands toward product strategy on one side and engineering on the other, knowing what you own matters. A Brazil-based designer in the survey:

AI tools make things much faster, but the precise designer’s vision is what makes the difference.

That “precise vision” is what separates a designer from a PM who happens to use Figma. The full report is worth a read.

Every profession, when it feels the ground shifting, reaches for whatever feels most solid. For designers, that’s been “craft” and “taste” (which I’ve used too in my writing). I get the instinct. When the tools you’ve mastered get commoditized, you want to assert that the real value was never in the tools. It was in you always: your eye, your sensibility.

But I’ve watched this play out for over a year now, and I think it’s less strategic positioning than grief response. Nicole Alexandra Michaelis makes the case that designers should be thriving, not panicking, and that much of the panic is self-inflicted. The whiplash:

Seniors are telling juniors to count themselves lucky if they’ll ever find a job. Design leaders are jumping from one AI-tool hypetrain to the next in mere weeks.

Monday, it’s all about prototypes. Thursday, it’s vibe coding. Friday, we’re preaching that output no longer matters (everyone can design now!) and that we should be brilliant strategists instead. By next Monday, we’ll be half-heartedly debating which soft skills are absolutely vital to survive.

Survive. As designers.

A profession trying on new identities in a dressing room. Nothing fits so we keep grabbing the next thing. “Craft” is the one people keep coming back to because it feels the most like home.

Michaelis is blunt about why that doesn’t work:

And listen, I’m not knocking craft. I love writing poetry, painting, throwing pots at the wheel. All that takes craft and skill, just as my designs at work do. But craft should be so obvious to us as designers that we should not make it our main selling point. Obviously, we develop incredible craft as our experience builds. Obviously, individual designers have different styles. Obviously, we put thought and care into what we make.

Craft is the baseline. That’s what we want the executives to know. By debating it and what it even means, we’re again undermining our authority.

She’s right, and I’d push it further. The fixation on craft is a tell. When a profession retreats to arguing about what makes it special instead of demonstrating it, that’s a group reaching for identity because it’s lost agency. The creative class version of quiet quitting.

The junior designer hiring crisis is a subject that’s near and dear to my heart, and Figma’s new hiring study puts hard numbers to it. The headline is encouraging—82% of organizations say their need for designers has increased or stayed steady. But the breakdown by seniority tells a different story.

Andrew Hogan, Head of Insights at Figma:

More than half of hiring managers (56%) say there’s increasing demand for senior design hires, compared to just 25% who are hiring for more junior roles. For many leaders, it’s less of a hiring philosophy and more a matter of bringing on designers who can tackle the problems they’re facing.

56% versus 25%. That gap keeps widening.

Daniel Wert, CEO of executive search firm Wert&Co, calls it out:

It just boggles my mind how few internship programs there are these days. I think it seems shortsighted. The best teams, the best organizations, have a lot of diversity…in terms of years of experience and where people are in their career. You want to have a nice cross-section of junior and mid-senior designers.

Every strong design team I’ve built or been part of had that cross-section. Seniors set the bar. Juniors challenge assumptions and bring energy. Mid-levels hold the whole thing together. Remove any layer and it gets brittle.

Wert again:

Hiring managers are looking for unicorns because they misunderstand how multidisciplinary design is. They want [top-tier] design, but are only willing to hire one person. Great design teams [have] multiple people with complementary strengths—not a single superhero.

This is the real problem. Companies want one person who can do visual design, product strategy, systems thinking, AI integration, and user research. That person doesn’t exist. Great design is a team sport, and the vanishing bottom rung of the career ladder is only making it harder to build those teams.

The fallacy that CEOs and CFOs keep telling themselves is that AI will make this unicorn “product builder” possible. I have my doubts.

I wrote recently about what Wall Street gets wrong about SaaS—how the $285 billion selloff confuses capability with full-throated DIY. Mission-critical enterprise software isn’t going anywhere. But I also argued that micro-apps are a different story. Small, personal utilities that solve one person’s problem? Those are absolutely getting built by non-developers now.

Anton Sten is a good example. Like me, he’s a designer, not a developer, who rebuilt his website with Cursor and Claude last year and then turned his attention to replacing the $11/month invoicing tool he’d been paying for. The initial version followed familiar SaaS patterns. Then something clicked:

I was building software that lived by old rules. Rules designed for generic tools that serve thousands of users. But this tool serves exactly one user. Me.

So I changed it. Now, instead of manually entering client details, I upload a signed contract and let AI parse it — mapping it to an existing client or creating a new one, extracting the scope, payment terms, duration, everything. It creates my own vault of documents. I added an AI chat where I can ask things like “draft an invoice for unbilled time on Project X” or “what’s the total amount invoiced to Client Y this year?”

That’s the micro-apps argument in practice. A tool shaped entirely around one person’s workflow, built in under two days. Jonny Burch stated that the source of truth for design is moving from Figma to code. Sten is further along that path—a designer who stopped hiring developers entirely.

Sten on the broader shift in thinking:

For decades, the default response to any problem was “what software should I subscribe to?” We browsed Product Hunt. We compared pricing pages. We squeezed our workflows into someone else’s idea of how things should work.

The point isn’t the tool. The point is the muscle. Once you’ve built one thing, you start seeing opportunities everywhere. You stop asking “is there an app for that?” and start asking “what if I just made it?”

I’ve been watching the design community fracture over the past year. Not over tools or methodologies—over what it means to be a designer at all. One camp is excited about AI-assisted workflows, shipping working UI from terminals. The other is doubling down on pixel-craft in Figma, treating the shift as a threat to everything they’ve built their careers on. Dave Gauer published a piece that puts words to this feeling better than anything I’ve read from the design side:

It’s weird to say I’ve lost it when I’m still every bit the computer programmer (in both the professional and hobby sense) I ever was. My love for computers and programming them hasn’t diminished at all. But a social identity isn’t about typing on a keyboard, It’s about belonging to a group, a community, a culture.

He hasn’t lost the skill. He’s lost the tribe. I recognize that grief. When I wrote about these same changes hitting design, a former colleague responded: “I didn’t sign up for this.” None of us did. And I think UX and product designers are less than twelve months behind programmers in feeling this exact thing.

He describes what drove the wedge:

When I identified with the programmer culture, it was about programming. Now programming is a means to an end (“let’s see how fast we can build a surveillance state!”) or simply an unwanted chore to be avoided.

Swap “programming” for “design” and you’re looking at the trajectory I wrote about in “Product Design Is Changing.” When the craft becomes something an AI agent can approximate, the culture around it shifts. The conversation moves from “how do we make this great?” to “how fast can we ship this?” The designers who cared about the craft are watching their community become unrecognizable. I get it.

And then there’s this, on what the programming community actually lost:

We should have been chopping the cruft away and replacing it with deterministic abstractions like we’ve always done. That’s what that Larry Wall quote about good programmers being lazy was about. It did not mean that we would be okay with pulling a damn slot machine lever a couple times to generate the boilerplate.

That “slot machine lever” is the programmer’s version of the vibe coding critique. The craft people—in programming and in design—wanted better tools. What they got was a culture that treats the craft itself as an obstacle to speed.

The identity split I described in my essay is already visible: designers who orchestrate AI and ship working software versus designers who push pixels in Figma. The deeper question Gauer is circling is whether the craft was ever the point for you, or just the bottleneck.

Earlier this week I published an essay on how product design is changing, and one of the sources I referenced was Jan Tegze’s piece on job shrinkage. I quoted him on the orchestrator model—using agents to create new capabilities rather than speeding up old tasks. But there’s another section of his article that deserves its own post. It’s the part nobody wants to talk about.

Jan Tegze, writing for his Thinking Out Loud newsletter:

Many people currently doing “strategic” knowledge work aren’t actually that strategic.

When agents started handling the execution layer, everyone assumed humans would naturally move up to higher-order thinking. Strategy, judgment, and vision.

But a different reality is emerging—many senior people with years of experience can’t actually operate at that level. Their expertise was mostly pattern matching and process execution dressed up in strategic language.

That’s a hard paragraph to read if you’re a senior IC or a manager who’s built a career on being thorough and diligent. Tegze isn’t being cruel—he’s describing a structural problem. We built evaluation systems that rewarded execution and called it strategy.

He shares a quote from a CEO of a mid-sized Canadian company:

“We’re discovering that our senior people and our junior people are equally lost when we ask them what we should do, not just how to do it. The seniors are just more articulate about their uncertainty.”

Tegze illustrates the pattern with a story about a friend he calls Jane—a senior research analyst billing at $250/hour at a consulting firm where they deployed an AI research agent:

The agent could do Jane’s initial research in 90 minutes—it would scan thousands of sources, identify patterns, generate a first-draft report.

Month one: Jane was relieved and thought she could focus on high-value synthesis work. She’d take the agent’s output and refine it, add strategic insights, make it client-ready.

Month three: A partner asked her, “Why does this take you a week now? The AI gives us 80% of what we need in an hour. What’s the other 20% worth?”

Jane couldn’t answer clearly. Because sometimes the agent’s output only needed light editing. Sometimes her “strategic insights” were things the agent had already identified, just worded differently.

The firm restructured Jane into a “Quality Reviewer” role at $150/hour. Six months later she left. They replaced her with two junior analysts at $65K each who, with the AI, were 85% as effective.

And then the kicker:

You often hear from AI vendors that, thanks to their AI tools, people can focus on higher-value work. But when pressed on what that meant specifically, they’d go vague. Strategic thinking, client relationships, creative problem solving.

Nobody could define what higher-value work actually looked like in practice. Nobody could describe the new role. So they defaulted to the only thing they could measure: cost reduction.

Tegze again:

We promoted people for the wrong reasons. We confused “does the work well” with “thinks strategically about the work.”

Tegze’s framing of the orchestrator model is the most useful I’ve seen—stop defending your current role and start building one that didn’t exist six months ago. But this section on the strategy gap is worth sitting with on its own. The automation isn’t just changing what we do. It’s revealing what we were actually good at all along.

I sent this article to both of my kids this week. My daughter is in college studying publishing. My son is a high school senior planning to go into real estate. Neither of them works in tech. That’s exactly why they need to read it.

Matt Shumer has spent six years building an AI startup and investing in the space. He wrote this piece for the people in his life who keep asking “so what’s the deal with AI?”—and getting the sanitized answer:

I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I’ve lost my mind. And for a while, I told myself that was a good enough reason to keep what’s truly happening to myself. But the gap between what I’ve been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy.

I know this feeling. I wrote yesterday about how AI is collapsing the gap between design and code and shifting the designer’s value toward taste and orchestration. That essay was for the software design industry. Shumer is writing for everyone else.

His core argument: tech workers have already lived through the disruption that’s coming for every other knowledge-work profession. He explains why tech got hit first:

The AI labs made a deliberate choice. They focused on making AI great at writing code first… because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That’s why they did it first.

Christina Wodtke agrees something big is happening but thinks Shumer’s timeline for everyone else is off. Programming, she argues, is a near-ideal use case for AI—there’s an ocean of public training data, and code has a built-in quality check: it runs or it doesn’t. Hallucinations get caught by the compiler. Other fields aren’t so clean-cut.

Shumer makes the classic tech-insider mistake: assuming his experience generalizes to everyone else’s. It doesn’t. Ethan Mollick’s “jagged frontier” of AI capability is as jagged as ever. AI is spectacular at some tasks and embarrassingly bad at others, and the pattern doesn’t map to human intuitions about difficulty.

She makes another point that matters for anyone in a creative field:

A nuance Shumer completely misses: industries where there isn’t one right answer but there are better and worse answers may actually fare better with AI. When you’re writing strategy, designing an experience, or crafting a narrative, a “hallucination” isn’t necessarily a bug. It might be an interesting idea.

That maps to what I know is true in design. A wrong answer in code crashes the app. A wrong answer in a design brainstorm might be the seed of something good.

This is why I sent Shumer’s piece to my kids but didn’t tell them to panic. Publishing runs on editorial judgment, taste, and relationships with authors. Real estate depends on physical presence, local knowledge, and trust built over handshakes. Neither field has the clean training data and binary pass/fail that made coding so vulnerable so fast. But that doesn’t mean nothing changes. Wodtke again:

Your job probably won’t disappear. But parts of it will shift, and the timeline depends on your field’s specific relationship to data, verification, and ambiguity. Prepare thoughtfully instead of panicking.

Shumer’s practical advice is modest: use AI one hour a day, experiment with it. Not reading about it, but really using it. I’d add Wodtke’s framing to that: spend the hour figuring out which parts of your work sit on the easy side of the jagged frontier, and which parts don’t. That’s more useful than assuming the whole thing collapses overnight.

I said yesterday that the gap between “designer who orchestrates AI” and “designer who pushes pixels” will be enormous within 12 months. Shumer is making that same argument for every knowledge-work profession. The whole piece is worth your time and maybe worth sharing with someone who’s been resistant to AI. Just keep in mind Wodtke’s nuance.