88 posts tagged with “career”

Tokyo Design Forum publishes former Facebook and Dropbox design leader Soleio’s closing talk from this year’s conference, where he previews a book-sized thesis called The Geometry of Luck. Soleio’s move is to treat luck less like a mood and more like a design problem: a question of arrangement, position, and conditions.

He starts with geometry because geometry gives the argument its discipline:

When we say something has a geometry, we mean it has structure.

Not just parts, but relationships between parts, distances, angles, arrangements that produce specific properties.

For example, a triangle just isn’t three lines, it’s three lines whose arrangement do something. The interior always sum to 180 degrees, no matter the triangle. That is geometry. It’s structure that produces reliable properties.

So when we talk about the geometry of a room, or the geometry of a negotiation, or the geometry of a network, we’re saying something very specific about its nature. We’re saying its composition, its arrangement, tells us more than a list of its parts.

That saves the talk from becoming another self-help riff on “making your own luck.” Soleio is more precise than that. He is saying luck has variables, and designers already understand the work of arranging variables toward a purpose. He links that to industrial designer and architect Charles Eames’s definition of design as a plan for arranging elements toward a purpose.

When something has a geometry, it can be measured, it can be reasoned about, it can be taught.

[…]

So geometry is a language of arrangement.

For designers, it’s like the vocabulary of our craft.

From there, the framework becomes practical:

I believe luck has three facets, three independent variables that work together in concert. They are the basic elements of good fortune.

The first I call orientation, how we perceive our environments and place ourselves within them.

The second is surface area, the degree to which we’ve made it easy for good fortune to find us.

And the third is novel action, our capacity to act on what we perceive, what we do with the opportunities that the universe presents to us.

The useful distinction here is agency without control. You don’t command the outcome. You arrange the conditions: what you can see, who can find you, and whether the value you create can keep circulating after it leaves your hands.

That is why the talk eventually turns back toward software design:

As software designers, we shape the environment where luck happens.

We are very, very lucky to be here in this room today.

Few inventions touch the fabric of people’s daily lives, such as software.

Every interface, every space, every system we create either amplifies or dampens the flow of opportunity for the people who encounter it.

With every over-the-air update we push to production, we alternate the networks through which luck flows.

And so I hope designers put as much consideration to luck as they do look and feel and utility.

I like that as a design brief. Not “be lucky.” Not “hustle harder.” Arrange yourself, your work, and your systems so more good fortune can pass through them. Test hypotheses. That is a useful bar for products too.

Thirty-year veteran software engineer Christoph Mütze shipped a 25,000-parameter transformer that runs on a stock Commodore 64, complete with an exhaustive test harness and a stack of reference implementations that all have to agree before anything ships. He called it SoulPlayer. In return he got called a vibecoder. Same reflex as the Monet pile-on: label first, verdict next, evidence optional. His response is the takedown of the “vibecoded slop” accusation I’d been waiting for somebody to write, and it lands on a single question that nobody on the accusing side wants to answer:

If vibecoding is what you say it is, if AI does the hard part, if the human just prompts and ships, if expertise is no longer a moat, then the world should be drowning in proper software right now. Not slop. Real tools. The kind people pay for, depend on, use every day. Two years of access. Millions of people with the models. The barrier supposedly fell. …where is everything?

David Pierce, catalogued the bespoke micro-apps people are building for themselves: family budget trackers, fantasy baseball rank engines, migration logs with a total addressable market of one. That’s real, and it’s the right scale to celebrate. But Mütze is asking a different question: where is the vibecoded Photoshop? Where is the vibecoded Maya, the vibecoded Blender, the vibecoded compiler that compiles itself? If the prompt-and-ship cartoon were true, two years in we’d have an avalanche of sophisticated tools built by people who don’t know how to code. We don’t. The category is empty. Mütze’s diagnosis of why is the part I want every designer reading this to take in:

Level 1 is what the industry usually calls coding. The syntax, the loops, the years memorizing pointer arithmetic and which header file the function lives in. LeetCode-measurable. The job interview essence. The mechanical part. The typing.

Level 2 is flow. What you do with Level 1. Knowing the right data structure. Knowing which ugly pragmatic solution to ship instead of the beautiful academic one. Reading other people’s code. Taste and judgment. The reflex of rejecting solutions that almost work and shipping the ones that do. Debugging, unit testing, the quality-control part.

Level 3 is architecture. The macro decisions, made with full awareness of their consequences. What to build at all. Why this data structure and not that one. Why this trade-off and not the obvious one. Which design survives contact with the real world, and which one silently falls apart two years later. The deciding part.

The three have never been the same thing. The gate was never at Level 1. The gate was at Levels 2 and 3, where the work that holds together actually happens. AI lowered the cost of Level 1. It didn’t really touch Levels 2 or 3. The gate is exactly where it always was.

You can easily translate this framework from engineering to design. Level 1 in design is pushing pixels: the auto-layout setup, the icon nudging, the variant-matrix work in Figma that fills our days. Level 2 is the taste that picks which of the fifteen generated directions is actually worth shipping. Level 3 is deciding what to build at all, and for whom. AI is eating Level 1 in design the same way it has eaten Level 1 in code. The designers who panic about “vibecoded design” are panicking because Level 1 was the layer they could see, measure, and defend. The gate is somewhere else, and it always was.

The reason this gets so emotional is the part Christopher Butler has been pointing at for a while: AI doesn’t just replace tools, it renegotiates what made you worth hiring. Mütze says the same thing:

The accusers cannot see this. They are not at the gate. They were at Level 1. Level 1 was their identity, their hours, their proof of belonging, their reason to feel at home in this profession. When AI made Level 1 cheap, it did not threaten the gate. It threatened them. Because they bet their self-worth on the layer that just got rented out. So they call the work vibecoded. They have to.

Mütze could weaponize the accusation back. He has the receipts: the test harness, the reference implementations, thirty years on the demoscene. He refuses and ends with a call-to-action:

If you’ve been sitting on something you made with AI, ship it. Name your tools. Don’t apologize. The accusation is cheaper than the work. Yours is worth more.

Addy Osmani makes a clean separation that most of the “is AI making us dumber” discourse keeps glossing over. He reports on Anthropic’s randomized trial of engineers learning a new Python library:

Engineers who used AI to ask conceptual questions scored above 65%. Engineers who copy-pasted the generated code scored under 40%. The tool didn’t determine the outcome. The posture did.

Osmani is writing for engineers, but most of that translates to designers picking up Figma Make, Lovable, or v0. Ship-without-comprehension scales beautifully right up until the moment you have to debug, redesign, or defend a choice you didn’t really make.

He ends on a ritual any designer can adopt verbatim:

I’ve started ending coding sessions with a simple question: did I learn anything today, or did I just close tickets? Sometimes the honest answer is “I just closed issues” and that’s fine. If it becomes the answer for months in a row, cognitive debt is accumulating in the background. Ship and learn are two separate metrics.

Workslop is the companion failure mode: the cost goes to your coworkers, where skipped learning costs your future self.

Michael Riddering brings Tommy Geoco on Dive Club fresh off field visits to Vercel, Perplexity, Metalab, Ramp, and Snowflake. Geoco and his team are making a documentary after roughly 200 conversations with designers and design leaders this year. The survey finding he leads with is the one I would have least expected: designers who have moved more of their work into AI-assisted prototyping are also more satisfied with their workflows. The hierarchy of who is actually doing that work is the part worth sitting with:

The number one thing that stood out to me was that designers who are currently vibe coding are more satisfied with their workflows. […] And I did not expect that. […] People seem to dig it in this survey. […] It’s the people who are currently doing the majority of their workflow on vibe coding activities. It’s design engineers. That makes sense. Lead principals. [After that] it’s non-designer roles, which might be students and researchers. Then it’s managers. And then it’s your general junior mid-level IC. And that part was fascinating that managers are doing more than junior and mid-level ICs. Either things are trickling down and people are experimenting and then they’re going to pass learnings down, which is kind of what we’ve seen on location. But it also might mean that like some managers or teams haven’t yet made room for the rest of the team.

Design engineers and leads at the top is unsurprising. Managers above juniors and mid-levels is the inversion, and remains basically unchanged from two years ago when Geoco’s 2024 survey found the same thing.

Leadership-IC Divide. Leaders adopt AI at a higher rate (29.0%) than ICs (19.9%)

So what’s the read? Geoco gives it the generous read first—learnings cascading down—and then concedes the other possibility: some teams haven’t made room for the rest of the team. Riddering puts it more bluntly: “I’m looking at a bunch of junior and mid designers that are getting cut out of the process.”

The other finding is that 59% of designers have built their own tool for their workflow. The example Geoco brings back from Vercel makes the builder-mode shift concrete:

When I went over to Vercel, they had this brand designer, who had never coded before. And now was vibe coding a tool. Their marketing team would put out blog posts. And they were like, “Why does the design team need to create the OG blog post cards for every page? That’s not a good use of [their time].” So he built a tool that just allowed them to insert any sort of images. And it just already had all of the branding and the sizing baked in. And they just roll these [tools] out quickly. And I’m like, that just became a tool, an internal tool. That’s cool. And so because it was really interesting that they started referring to him as a brand engineer… And I’m like, okay, that kind of qualifies it actually.

A designer who had never coded solves an actual marketing-team problem, ships the tool, and the role title arrives after the work. That is how the next batch of “blank engineer” titles is going to land. Riddering then describes how the orchestrator pattern works in his own day-to-day, offering a concrete account of the workflow I have been writing about as orchestration from a working designer:

Part of me is almost slightly self-conscious about it. But I do the vast, vast majority of my messy explorations with AI now. I feel like I have made the jump to the quote unquote creative director where I’m just working with AI to show me a certain thing 50 different ways. And then I’m pulling the pieces that I like and then combining them again. And finally I get to somewhere where I’m like, yep, that’s good. And then I take that from paper, run it through cloud code, and now it exists on localhost. And then I will sweat the details and actually do the precision designing in code, which is, that’s crazy, man. That’s a very, very different workflow than I’ve done at any point in my career.

The orchestrator gap is opening where I thought it would. What I did not account for is who is getting invited into that work first. The data Geoco surfaces points to leads, managers, and design engineers getting more chances to build with AI than junior and mid-level ICs.

Here’s a hypothesis I’ll put out there: leads are more used to directing. I’m personally comfortable with orchestrating, being the editor because I’ve been a creative director and leader for so long. The loop is right there: frame, review, direct.

Jess Eddy reaches back to the 19th-century pessimist Arthur Schopenhauer for the distinction senior creatives may worry about:

Talent is like the marksman who hits a target which others cannot reach; genius is like the marksman who hits a target which others cannot even see.

Eddy borrows a line from Jack Grapes, the poet and writing teacher: “Make me look good, and I’ll keep you on the payroll.” That’s the trap. The longer you’ve been at it, the more reliably your talent delivers, and the more expensive it gets to walk away from what works. Most career advice says lean into your strengths. Eddy says your strengths keep you aimed at targets you already know how to hit.

For experienced designers, those targets are getting harder to find. AI is changing what counts as design work and what tools do it, and the ground under the profession is moving with it. The reflex when the ground moves is to double down on the move you’ve already mastered. But the mastered move hits the visible target. The targets that come next won’t be visible yet.

Eddy doesn’t let anyone skip the mastery step. The 5–10-year window isn’t optional. But once you’ve put in the time, you have to walk away from the talent that made you reliable. Eddy closes with Grapes:

Talent does what it can, genius does what it must.

Chip Kidd, in a Fast Company interview with Nicole Gull McElroy, says he is “the last of that breed.” He means the 40-year career at one publisher, which he is about to complete at Knopf in October. He says it plainly:

In October, if I live that long, I will have passed my 40th year at Alfred A. Knopf. I feel very fortunate. Frankly, I’m the last of that breed. I have peers who are also the last, but that’s going to be it. It’s not for me to say, but the age of a 40-year career at the same place used to be not that unusual, but going forward it will be. That’s just the way things have evolved.

Read the interview and the easy story is the celebrity arc: Pennsylvania kid joins Knopf in 1986, does Jurassic Park, ends up on the canon shelf with Cormac McCarthy, Donna Tartt, and John Updike. But Kidd points at the mechanism instead of the outcome. He points to two anonymous designs—the Tide box, the Coca-Cola wordmark—and then explains why his name is on his work and theirs is not.

A lot of graphic designers do not get credit for what they do. Who designed the Tide detergent box? We don’t know but it certainly is iconic. Even going back to Coca-Cola: Who designed that? […] Growing up in the ’70s, the only other area of graphic design where the designers got credit were record album covers. If you looked closely, you could see this one was done by Peter Saville. He was a huge influence on me. […] To this day, if I design a book cover and it gets put out into the world, it has my name on it. That is why we are even having this discussion right now. Not only did I get credit for what I was doing, but I piggybacked on these books that became iconic bestselling books.

Two structural facts are doing the work here, and neither is talent. The first is the medium: book jackets have a back flap, and the back flap has room for a designer credit. Record sleeves had the same affordance, which is how Peter Saville got known for Joy Division and New Order. Packaging does not: there’s no flap on a Tide box where the designer’s name goes. The second is duration: the credit on any single jacket is small, but Kidd got to put it on hundreds of jackets over forty years, several of which became permanent fixtures on the canon shelf. Stack enough of those and you become legible as a designer. None of the anonymous designers behind Coca-Cola or Tide got either accident — neither the flap nor the four decades to fill it.

This is what makes the Kidd interview useful, and why the celebrity reading misses it. He is the exception that demonstrates the rule. The credit crisis in design is structural: it turns on whether the artifact you make has a place for a name and whether your employment lets you stay long enough to accumulate names. Most design work fails one or both of those tests. The artifacts that fill a designer’s career now—apps, dashboards, marketing pages, packaging—don’t carry credits at all, and few roles last long enough to compound into a body of work the way Kidd’s did. The 40-year jacket-flap career was never the model. It was the unusual case where the structure happened to cooperate.

Luke Wroblewski shared his notes from the Design Futures Assembly, a gathering of about a hundred senior designers and leaders from AI labs, big tech, and startups in San Francisco:

When everyone can ship, you get a different kind of problem. One design leader described it perfectly: they let everyone build and push whatever they wanted. And you could feel it in the product, because nothing made sense together.

This is the part of the AI-in-design story that the toolkit numbers obscure. Wroblewski reports roughly half of designers had shipped AI-generated code to production this year, and that the typical designer’s toolkit had doubled in size over twelve months. Those are real numbers. But once production stops being the bottleneck, the bottleneck moves. A single word surfaced repeatedly:

Several people at the assembly used the word “editorial” to describe where design leadership is heading. Less about making the thing, more about deciding what gets made and ensuring it all holds together. The skill of saying no is becoming one of the most important skills in the profession.

The “saying no” line echoes something Chad Johnson wrote a few weeks back: the designers who shape direction “learn to say no with evidence and to disagree without drama.” The Assembly’s framing makes that posture mandatory at a portfolio level, not just on individual features. One tool company founder, Wroblewski notes, preferred “coherence”: the sense that a product came from one shared point of view. I like that word better too. Coherence describes the thing the user actually feels.

Nathan Beck, a product designer in Amsterdam, opens his essay with the title “The death of design” and an immediate retraction: “LOL only jk design still alive.” Then he spends a few thousand words on why, walking through what AI tools actually do to a working designer’s day and what they conspicuously do not do.

The pivot quote is buried two-thirds in:

If you call yourself a designer and—be honest with yourself—the bulk of your role has been the production of flat pictures of user interfaces, then I’m sorry to break it to you, but you are not designing. You are styling.

That line is the whole post compressed. Beck is not arguing that AI threatens designers. He is arguing that AI threatens styling, and that a lot of people who call themselves designers have been styling for a decade and are now discovering that the part of the job AI is good at was the part they were doing.

What’s left over, in Beck’s telling, is the reflective work: the thing that happens during design, not in the final file. He quotes Kaari Saarinen on output isn’t design:

In the same way that one writes in order to understand what one is writing, one designs in order to understand what one is designing. As Kaari Saarinen explains, “Working visually keeps me close to the problem and is slow enough [sic] gives me time to think while I work. Moving things around, testing relationships, and refining structure is not separate from the thinking. It is part of how clarity emerges.”

This is the part the “designers are cooked” discourse misses. The understanding accumulated while making the Figma file was the asset all along. The file was the receipt.

Beck has a second argument running underneath the first: AI output, on its own, is aesthetically average. He quotes Nick Foster’s Dezeen piece on what software feels like after a decade of optimization:

The apps I use to hire plumbers look and feel remarkably similar to those I use to watch skiers do backflips. Every brand feels the same, every function feels the same, every interaction feels optimised, streamlined and joyless. By any measure, these pieces of software are miracles of engineering and triumphs of logic, yet they feel profoundly underwhelming to live with.

A designer who only ever produced flat pictures of those interfaces has been replaceable by a model for a while now. The judgment about which of those generic outputs should ship and which should be thrown out and rebuilt is the part no model has managed yet.

Beck closes:

However, I am cautiously optimistic that as we weather this historical conjuncture, and machine intelligence loses its sparkly aura, and weekend vibe coders increasingly learn how substantial the gap is between a prototype and a product, the role of design, however it is redefined, will be just as essential as it ever was.

That unsexy gap is the whole game. Greg Kozakiewicz updated the old construction line: we used to confuse the drawing with the building; now we confuse the prototype with the product. The demo works on a good laptop with someone who knows what the app is supposed to do. The product has to work for the user who doesn’t. Closing that gap is the orchestration job—defining the thresholds and deciding what the system should refuse to do—and when the weekend demos lose their shine.

Scott Berkun lists three portable superpowers most designers underrate in themselves: investigative curiosity, the ability to translate between people who can’t understand each other, and a working grasp of tradeoffs. The first one is where he starts:

If we can spend hours reading about the 16th-century French history behind the beloved font Garamond, or studying the details of the design prototypes Jonathan Ives made to create the first iPhone, we have the rare capacity to discover and digest layers of complex information for practical use in solving problems.

Designers tend to file “I went deep on Garamond’s history” as a hobby or a tic, not a transferable skill. Berkun’s point is that the depth is the skill, and the subject is interchangeable. Aim it at a thing your CEO is worried about and you’re suddenly the person who knows the most about it in the room.

On translation:

Someone who explains things clearly, including through insightful sketches, diagrams, or metaphors, has tremendous value. Explainers help people make sense of each other. Designers are often shy about their ability to explain things, but typically we’re better at this than other professionals, since our work is rooted in communication (even visual design is rooted in semiotics, the study of symbols and their meaning). If we can be curious about our coworkers’ perspectives, objectives, and frustrations, we can be translators.

Berkun has made the curiosity argument before, in the negative, when he listed lack of curiosity as one of the five worst habits a designer can have. Reading this piece next to that one, the two halves connect: the habit he warns against in one post is the superpower he’s asking us to revive in this one.

In product orgs, the word “autonomy” tends to get attached to seniority and titles. Sara Paul, writing for Nielsen Norman Group, puts the bar somewhere else:

Our research shows that autonomy is about becoming sufficiently informed to credibly shape shared product decisions.

You’ve earned design autonomy when you’ve collected enough context to make a recommendation that holds up under scrutiny. Until then, you haven’t. Low-autonomy designers, in Paul’s terms, “execute predefined solutions.” High-autonomy designers shape what gets prioritized, because they know things their stakeholders don’t.

The four-part pipeline is the practitioner half:

The designers who achieved high autonomy kept information flowing to them from all sources within their organization. Their pipelines consisted of four parts: (1) Gathering information from across teams and channels, (2) Building relationships with people who provide information, (3) Creating crossfunctional spaces for information to be shared, (4) Synthesizing information to form a “big picture” of context that empowered credible recommendations.

Paul’s examples are specific enough to put to use. The opening one is a lead designer at an online review platform whose ad-setup experience lived across mobile, desktop, and web. Three teams owned different parts of the experience and the whole was nobody’s job. Here’s how the story ends:

She saw the problem, took the initiative to gather the information she needed, and synthesized it into a recommendation that boosted her influence over what got built. This is design autonomy.

None of this required a new title. It required a tracker, a few standing meetings, and the willingness to do the synthesis work nobody assigned.

The designers I want—and have—on my team are the ones who can fill in for a PM when they’re on vacation. Paul’s article is the mechanism for getting there. The PM-shaped skill is holding the information context that lets you make a defensible call.

Tommy Geoco’s $13,100 OpenClaw harness, ninety days in, is one way to build a personal AI agent. Anton Sten went the other way. He tried OpenClaw and Hermes, found the setup was “days, sometimes weeks, for minutes of return,” and built something smaller. Five Claude Code instances on a Mac mini, named after Suits characters, each handling one role. Architecture is a shared repo and a pile of markdown files. That’s it. Most AI-agent posts pitch what Sten calls “a team of bots that runs your business while you sleep.” His basement firm is the inversion.

Sten on what he actually wanted from his agents:

What I actually wanted was smaller. A handful of tools, each with a narrow job, that I could build in an afternoon and shape around how I actually work. So that’s what I did.

The names of his AI agents are from the show Suits (with Wendy borrowed from Billions), picked so the show’s personalities double as memory aids for each agent’s job. Harvey handles contracts and pricing. Donna takes Harvey’s notes and drafts the emails and follow-ups. Mike stores what Sten would otherwise forget. Louis worries about money. Wendy reads the others’ logs and points out where they’re slipping.

Sten on the autonomous-revenue pitch:

The team in my basement isn’t running anything autonomously. They don’t make decisions for me. If I unplugged the Mac mini tomorrow, my business would keep running. The conflation in the current AI conversation — between playing and building a thing that prints money — is the part I find a bit tiring. They’re treated as the same activity, when they’re almost opposites.

Sten’s right that the autonomous-revenue pitch is a fantasy. Less right on the binary that follows. Geoco’s harness is doing meeting prep, ingesting his survey research, and distributing his content across ten platforms while he sleeps. That counts as “running while you sleep,” and his $50,000 in sponsorship revenue from one survey project isn’t trivial. Play and revenue can sit on the same side. What matters is whether the human stays in the loop. Geoco does, and so does Sten.

The shape of what they’re building is also the same. The Harvey-to-Donna handoff Sten uses most and Geoco’s survey-prep loop are both the specialization-is-the-whole-game pattern: narrow specialists, human in the loop, work compounding into the system. Sten calls it play and Geoco calls it work. The architecture underneath does the same job either way.

Sten on practice:

I’d argue this is the business case for designers right now. Not the agents specifically — the playing. Because in a year or two, every job worth having is going to assume you understand how these tools work, and the only way to understand them is to spend time in them when nothing’s on the line.

The people who’ll do interesting work with this stuff in two years are the ones playing with it badly today.

Geoco is what Sten’s last sentence predicts. The person playing badly today is the person doing interesting work in two years. Sten describes that person as hypothetical. Geoco isn’t.

Matt Zieger built jobsdata.ai as a weekend project, with the stated goal of being “a single place that synthesizes what we actually know about AI’s impact on economic opportunity.” The site breaks every occupation into its component tasks, then prices the AI compute cost to do one hour of each task and compares it against the human wage. The result is a per-task crossover year: the point when AI gets cheaper than the human at that work. “Evidence Over Narrative,” as Zieger puts it.

The UX designer report opens directly:

If you’re an ux designer, this is worth taking seriously. But it’s not too late to get ahead of it.

We’ll be honest with you: a lot of the individual tasks in your job are things AI can already do, and that’s accelerating. But there are real reasons not to panic: when technology has made this kind of work cheaper in the past, people ended up wanting more of it, not less. There’s good reason to think that pattern will hold here too. Your field also tends to adopt new technology faster than most, so it’s worth paying attention now.

Double down on the parts of your work that take real judgment and experience. As AI handles more of the straightforward stuff, demand for what you do will likely grow.

(Second link to Chad Johnson this week, but I just discovered his Substack, so ¯\_(ツ)_/¯.)

Chad Johnson, writing in his newsletter, argues that designer influence in product decisions comes from something other than craft output. He lays out the underlying dynamic:

Roadmaps are shaped less by who has the best ideas and more by who controls the framing of tradeoffs. Every roadmap decision is a bet: build this instead of that, now instead of later, for these users instead of those. Whoever makes the risk feel smaller tends to win.

So where does the designer fit? Johnson:

The most influential designers at startups do not position themselves as makers of screens. They act as orientation devices for the team. Orientation is the ability to help a group understand where they are, what matters, and what tradeoffs are real. It precedes prioritization, and it makes decision-making possible.

A designer whose output stops at screens is working on the wrong layer of the problem. Johnson lists the skills that back the orientation role:

Designers who shape direction invest in strategic framing, business literacy, and narrative construction. They learn to say no with evidence and to disagree without drama.

Johnson’s list is right as far as it goes. He understates one skill: legibility. A lot of design influence breaks down at translation. The thinking is strategic; the communication stays in design vocabulary. A sharp problem statement understandable only to other designers stays in the design review. Designers who change the conversation make their analysis readable in product and business terms without flattening it. That’s the same move Johnson gestures at when he describes “decision-ready artifacts” as “tools for comparison… designed to provoke judgment, not admiration.”

Johnson’s closer calls the future of design leadership “quieter, more rigorous, and deeply strategic.” That’s right. It’s also a role that depends on being read by the people making the call.

I’ve written that AI-era design work reduces to taste and judgment. Elizabeth Goodspeed’s case for designer-writers gets there from a different direction.

Elizabeth Goodspeed, writing for It’s Nice That:

You can get away with a lot in design: conceptual ideas are able to sit inside a visual piece of work without ever being fully spelled out. They’re gestured at rather than articulated. Writing forces you to figure out exactly what your idea is; if it isn’t working, you’ll know immediately. Where design is like a ballet – implicit ideas carried through form – then writing is closer to a theatre – your thinking has to be explicitly spoken.

Goodspeed’s point is that design lets you gesture at an idea without ever articulating it, and writing forces you to name it. A designer who can’t explain why a choice works has taste they can’t grow or pass on.

Goodspeed’s second point goes further:

Writing is to graphic design what clay is to pottery. It’s the material designer shape and massage into form. To work with text well, you have to really be able to read and understand what you’re setting – not just how it looks and basics like not hyphenating a word in a bad spot, but what it means on a deeper level. Just as reading makes you a better writer, writing makes you a better reader.

Product designers don’t usually think of themselves as writers. But user stories are writing, and articulating what a user should be able to do through an experience and why is essential.

Worth reading in full. She makes writing feel like a design discipline.

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.