47 posts tagged with “creativity”

Brandon Harwood opens with Picasso’s Guernica. He asks you to look at the painting, then tells you the story behind it—the bombing of the Basque town, the civilian deaths, Picasso’s intention to communicate that horror—and asks you to look again.

If you didn’t know the story of this painting beforehand, now you do, and it might strike a different chord, if just slightly. The details of the painting now have the context that shows us what Picasso was thinking when he painted Guernica. […] It’s this kind of context that drives meaning in art. Guernica is not just a painting. It’s communication.

Harwood uses it to draw a line between what AI can generate (the aesthetics of a thing) and what humans build (the context that makes a thing communicate). His answer: instead of asking AI to make meaning, design around the fact that it can’t.

Meaning Machines are, at their core, “signifiers, randomized into a fixed grammar, and read for new meaning.” […] The randomized signifiers are the contextual data surrounding our creative pursuit, the data the AI is trained on, and the relationships built on that data through its training. These signifiers, the data, are then placed into a fixed grammar through agentive interaction and/or agentic actions, and the user can then interpret the result to stimulate their creativity, build new meaning, or explore ideas they might not have considered before.

Tarot doesn’t know what your week looks like. Oblique Strategies doesn’t know what song you’re stuck on. The cards work because they hand you raw material and you do the interpretation. Harwood’s claim is that an LLM, used right, can sit in that same chair. Provoke the human. Dr. Maya Ackerman calls this same arrangement “humble creative machines”: the AI is not the creator, it’s the prompt the creator responds to.

Harwood breaks co-creative AI into three roles:

The Puller: The AI system gathers information about the context the user is working in through active question generation and passive information collection on the works. […] The Pusher: The AI system uses some/none of this context to synthesize considerations for the user to employ throughout their creative journey. […] The Producer: The AI system creates artifacts for use as elements of the users’ larger creative output.

The Puller / Pusher / Producer vocabulary is what I wish more design teams had before they shipped their first AI feature. Each role is a constraint, a way to keep the human in the chair the work actually belongs in. Most AI tools for creatives flatten all three into one button that produces a finished thing. Harwood’s whole argument is that the finished thing is where the meaning has to originate; it can’t be the destination.

My recent newsletter, “Out of Your Head, Into the File,” made the case for getting taste out of your head: writing it down so it can survive the messy middle of an AI workflow. Mia Kiraki, writing in Robots Ate My Homework, picks up the other half of that problem: how taste erodes when you don’t.

Her central image is the Hansel and Gretel gingerbread house, recast:

AI output is the gingerbread. You’re tired, the deadline is close. The output IS the shelter. You eat it - of course you eat it. That’s what gingerbread is made for, right? […] The Grimms buried a much smarter lesson in the early pages, before the witch even shows up. Hansel drops breadcrumbs to mark his path through the forest and the birds eat every one. He still leaves them, though. Those breadcrumbs are your taste, every little choice you make on the page (this word, this angle, this risk) which leaves a marker of who you are. The forest will always try to erase them.

What’s specific to AI is the environment. It’s now optimized to wear taste down a percent at a time, in ways you can’t feel while it’s happening, until the work you used to do feels like someone else’s.

Kiraki puts the mechanism plainly:

If most of your reading this week was AI slop (which is pretty likely given the state of the internet), you trained your judgment on machine output. […] Each accepted sentence is a small vote for a lower standard. You accept a vague phrase because the deadline is close. You let a soft claim through because rewriting from scratch would cost an hour you don’t have. […] Taste dies slowly, when you wake up someday and read something you wrote six months ago and realize you used to sound so different. You had edges, took risks, made claims, and you sounded like a person who made choices.

Kiraki gets at the failure mode that follows once taste is the only real moat left.

Her counter:

Read work that operates at a higher standard than yours. Work where someone made choices you wouldn’t have made or took risks you would have edited out. Your taste calibrates upward when you expose it to judgment that outclasses your own. […] Practice the explanation. When something in your own work feels wrong, write down the reason. The specificity of your explanation is the weapon. […] Ship work that makes you nervous. If a piece feels comfortable to publish, you probably didn’t push hard enough. The pieces that make your stomach tighten show and prove your taste is working at full capacity.

The middle one is what that newsletter was about: writing down the reasons, not just the verdict. Kiraki adds the bookends. Read above your level so your baseline isn’t drifting toward consensus. Publish the version that scares you a little, because the version that doesn’t is the gingerbread.

Jake Albaugh wrote a piece on X called “Design is the work” that splits design from the artifacts it produces. Mocks, prototypes, screens, guidelines: those are outputs. Design itself, in his telling, is the upstream act of intent: figuring out what something should be and why, before anyone makes it. Bingo. That distinction matters now because AI is very good at the artifact and unable to do the deciding:

AI cannot do that part. You intend to do something that has not yet happened. You have to bring those parameters to the table to do anything novel. AI doesn’t know your constraints. It doesn’t know your strategy. It doesn’t know what moment in the market you’re in, what your team is trying to prove, or what your customers actually need versus what they’ve said they want. The expectation — the definition of what good looks like — is something only you can provide. AI’s job is to meet that expectation. Not to define it.

The piece made the case that intentionality has to come before execution and that AI changes neither requirement. The closer is where it gets interesting. After all that, Albaugh tells the reader he used AI to draft the essay:

It may surprise you to learn that I used AI to write this. The structure, the sentences, a lot of the phrasing — generated. But the argument existed before any of it. I knew what I was trying to say. I knew what examples mattered and which ones were wrong. I knew when a paragraph was close but not quite right, and I revised toward a target I’d already defined. […] That’s the point. The tools changed. The work didn’t. Design is the process. Design is the intentionality.

It’s a risky reveal. Most readers will read it as self-undermining at first. But the argument and the artifact are doing the same job: Albaugh had a target, and he used AI to reach it. The fact that the prose was generated is exactly why it matters that the argument wasn’t. He knew which examples belonged in the piece and which ones to throw out. The model couldn’t have known that either way, because the criteria for “good” didn’t exist anywhere outside his head until he wrote them down.

Karri Saarinen made a version of this same split when he argued that output isn’t design. The hard part is understanding the problem well enough to know what should exist at all.

You’ve seen it in all the photos from various No Kings protests. The most-shared peace poster of the year did not start with a client. Daniel John, writing in Creative Bloq, traces Warsaw calligrapher Barbara Galińska’s two-tone “STOP WAR” piece. Galińska, in an artist statement quoted by John, describes where it came from:

My graphic “STOP WAR!” was created as a result of an international calligraphy challenge in November 2023, the main goal of which was to stimulate the creativity of artists. My reaction to the assigned theme “Stop war!” was to move away from typical calligraphy towards a powerful work that addresses the global problem of war. So my main personal challenge was to find a new, original solution to the well-known phrase “Stop war!” and transform it into a graphically powerful universal symbol for peace.

Matt Ström-Awn, writing on his personal site, picks up a three-year-old line from Ted Chiang and turns it inside out:

Three years ago, Ted Chiang described ChatGPT as a blurry JPEG of the web. LLMs are a lossy compression of their training data, which is itself a lossy sample of all the data available to it. But the artifacts we see in AI slop aren’t in the compression. They’re in the decompression.

Every AI-generated output is an extrapolation from that blurry source, vectored toward your prompt, filling in plausible detail where the compression threw information away. The output gets inflated into blog posts and LinkedIn thoughtspam, software platforms, omnichannel advertising campaigns, and movie cameos from dead actors. Chiang compared the gaps and confabulations to compression artifacts.

I think they’re expansion artifacts.

Chiang had the compression metaphor; what we needed was a word for what these tools do on the way back out, and Ström-Awn gave us one.

Ström-Awn lists what expansion artifacts look like across modalities:

• LLMs produce text stuffed with hedging verbs and fuzzing adjectives (delve, intricate, tapestry, multifaceted). Their paragraphs are structured as miniature essays with setup, payoff, and a signposted takeaway (This matters because…).
• AI-generated code over-comments the obvious and creates error handlers for operations that can’t logically fail.
• Image generators have had their own tells: six-fingered hands, symmetrical-but-stylistically-objectionable jewelry, text that looks like text but only if you cross your eyes.
• Video models struggle with continuity. Limbs appear and disappear, objects clip through each other, and physics sometimes just switches off.

Each of these artifacts is the training distribution leaking through where the model’s confidence runs thin.

Ström-Awn writes about the designer-specific tells too:

Power users of AI website generators (AI-pilled designers) already know how to recognize the tool marks, if only to try to prompt them away: purple gradients are an especially common tell. But as more and more non-designers use tools like Claude Design to prompt their way to fully-functional software products, I expect to see a preference for the aesthetic convergence endemic to the current crop of AI models.

Humans are the bread in the sandwich, and the AI is in the middle.

That’s Dan Shipper on his podcast AI & I, talking with Every’s Kieran Klaassen, the engineer behind the compound engineering plugin. They’re working out where humans actually belong in an AI-driven workflow. It’s the same split showing up on the design side.

Klaassen, on the polish step at the end of the work:

The other moment comes at the end. Something comes out. How do you validate it? Well, it’s already tested—browser automated testing has clicked through everything, all the requirements are clearly specified, and it says everything works. But the beauty comes in when a human looks at it, clicks around, and has a feel for it: “Oh, this doesn’t feel right. We can polish it. We can make it better. There’s something still missing. We can make the design better.” […] all the way at the end, when everything is done, you can elevate everything and make it even better. And I think we need to do that, because if we don’t, it will all be slop—all the same. It’s very important to make it feel great because the bar is high, and the bar will always get higher.

“It will all be slop” is the line every team should have taped to a monitor. A passing test suite and a green PR don’t tell you whether the thing is actually any good. That judgment still lives with a human at the end of the workflow. Klaassen is correct that the bar keeps moving up, not down, and the teams who treat the polish step as optional are the ones whose products will look interchangeable in twelve months.

Klaassen, on the art-and-ownership argument:

But I do think that in the end, if you ship something—if you make a statement in the world—and you want it to be your own, you have to say yes or no at some point. You cannot fully automate everything. It’s a bit like making art. If you want it to be yours, it needs to come from you or somehow be connected. So I believe having those moments where you decide—where you choose what you enjoy—is so important. That’s why it’s so important to do things you enjoy and love.

Whatever your version of beautiful is, that’s the bread. Everything else is filling.

Design orgs and publications have been issuing AI bans, calling them principled responses to job displacement, training data theft, and the degradation of craft. The impulse is understandable: AI doesn’t just replace tools; it challenges what made you worth hiring, and the prospect of losing what you’ve built is felt more sharply than any potential gain. Christopher Butler thinks those lines are drawn in the wrong place:

By drawing hard lines against entire categories of tools, we’re mistaking the means for the problem itself, and in doing so, we’re limiting our ability to shape how these technologies integrate into creative work.

Butler doesn’t dismiss the concerns driving those bans: training data problems, corporate consolidation, job displacement. He thinks they’re legitimate and urgent. His objection is to making the tool the target rather than the behavior. Drawing the line at AI, he argues, repeats the mistake designers made at the letterpress and again at paste-up. The technology changed. The question—about authorship, judgment, and what craft actually requires—stayed the same.

Butler’s conclusion:

A designer who uses AI to plagiarize another artist’s style with a simple prompt is engaged in something fundamentally different from one who trains a tool to extend their own creative capacity. A writer who publishes purely generated text as their own work is making a different choice than one who uses AI as a thinking partner and editor while maintaining authorship over their ideas and voice. These distinctions matter more than blanket prohibitions.

Discernment in practice means asking: Am I using this tool to extend my own capabilities or to replicate someone else’s work? Am I shaping the output or simply accepting what’s generated? Does this use serve my creative vision or just expedite a result? These aren’t always easy questions, but they’re the right ones.

Butler himself is the illustration. He spent months training Claude on a 10,000-word skill file—the accumulated context of his subject matter and his voice—building a sounding board and editor that already knows his context. He still writes without it. He says some of his best writing has come from working with it. The output may be indistinguishable to most readers. The difference, he says, is real to him.

The choice isn’t between purity and complicity, between craft and automation. It’s between engagement and abdication—between shaping how these tools develop and how they’re used, or ceding that ground entirely to those with the least interest in protecting what we value about creative work.

“Taste is the scarce thing” has become shorthand for what designers still own in the AI era. I’ve written about it in the abstract more than once. Chris R Becker, writing for UX Collective, opens with an old Marshall McLuhan-era line—“we shape our tools and then our tools shape us”—and then shows how to keeping doing the shaping.

Becker cites the Steve Jobs-attributed 10-80-10 rule:

Start away from any AI. Use the 10–80–10 rule. 10% away thinking, defining, establishing vision. 80% making use of AI to assist the vision. 10% away from AI critiquing, testing, and evaluating the solution.

The bookends are the work. Both 10% slots sit explicitly away from the model, which is another way of saying they’re the judgment layer. The first defines what good looks like before inviting AI in. The second evaluates what came out. AI collapses the cost of the 80%, which is the whole productivity story. But that collapse means the bookends are no longer preamble and postscript. They’re most of the job.

Becker gets at why the closing 10% matters:

The authority bestowed on institutions, educators, and SMEs (subject matter experts) is being absorbed by AI and spread thin like butter on toast. An AI appears to slather knowledge evenly, but the quality of the knowledge butter is deliberately made opaque.

AI output arrives looking uniformly authoritative, the same confident tone whether the underlying source is a peer-reviewed paper or a forum post from 2013. Provenance gets flattened. Without a prior standard to judge against, the designer reviewing output has nothing to push back on. That’s Becker’s larger point:

The irony, I suppose, is that Designers are, hopefully, trained not to be “yes men” but rather to ask hard questions, challenge the prevailing motivations of business over our users, and, most importantly, find the root cause of the problem, rather than just the surface reaction. AI, unfortunately, is not built to push back; it will not say… “I don’t know,” or “I think that is a bad idea,” or “what if you did this… instead,” or “I understand YOU (CEO) wants this feature, but the user research and ‘our users’ want something different.” AI is designed to serve, and in the hands of people in an organization who are looking for the least amount of pushback, it is a recipe for deep institutional implementation and, frankly, a lot of bad ideas, fast.

“A recipe for deep institutional implementation.” A sycophantic tool plus an organization that wants frictionless agreement equals speed in the wrong direction. The 10-80-10 rule is a personal discipline. What’s still unresolved is how teams build that discipline into the process before the wrong direction becomes the default.

My current side project is a website for a preschool in San Francisco. I’m using AI to accelerate wherever it fits, but I’ve reserved the primary visual treatments to be made by hand. Partly because that’s the right call for a preschool brand. And partly because of a phrase Pablo Stanley coined for this: creativity osteoporosis.

I wrote about creativity osteoporosis a while back. The idea that your creative skills get weaker when AI does all the reps, like bones thinning when they’re not stressed. You don’t notice it happening. Everything seems fine. Then one day you reach for a skill and it’s… not there like it used to be

Stanley wrote this after a weekend of making pixel art by hand—a project called Pixabots, little 32x32 robot characters—as a deliberate detox. He describes what set off the detox:

The whole time I was drawing, there was this pull. Physical, almost. Like my body was telling me to open a tab and start prompting. Not because the work was bad. Not because I was stuck. Just because my brain has been trained, over the last two years, to route every creative problem through an LLM.

He still used AI for the parts that weren’t the art:

I used AI to build the Pixabots website. The stuff I’m not that good at… setting up Next.js, canvas rendering, exporting without antialiasing. And I tried to keep to myself the stuff that felt more “artistic” like the animation, the look and feel.

And then the operating principle:

The parts that feed my soul, I protected (even though everything in my body wanted to pull me away from them). The parts that would’ve killed the project with friction, I automated.

Maybe that’s the whole game now… knowing which parts to protect…

Knowing which parts to protect is becoming a judgment call I have to make on every project. The preschool site makes the decision easy: the visual language stays in my hands, AI handles the plumbing. The real work of this judgment is in the middle: projects where craft matters but throughput has merit, and every protect-or-automate call costs you something. If you don’t draw that line on purpose, it draws itself for you.

When generation gets cheap, craft becomes judgment. Raj Nandan Sharma, writing on his blog, puts it bluntly:

Before AI, mediocre work often reflected a lack of time, resources, or execution skill. Today mediocre work often means something else: the person stopped at the first acceptable draft. That is the economic shift AI introduces. It compresses the cost of first drafts, which means the value moves downstream… In other words, the scarce skill is not generation. It is refusal.

Refusal—knowing what to throw out and why—is what’s scarce in a world where anyone can generate ten competent drafts before lunch.

But Sharma doesn’t stop there. He warns that elevating taste alone can quietly corner humans into an end-of-pipeline selector role:

There is a strong version of the “taste matters” argument that quietly pushes humans into a narrow role. In that version, AI generates many outputs and the human stands at the end of the pipeline selecting the best one. That is a useful role, but it is also too small… The warning is not that taste has no value. It does. The warning is that taste without authorship, stake, or construction can become a narrow and eventually fragile role.

The warning Sharma adds is the part the “taste is the moat” conversation tends to skip. Refusal without authorship is still selector work, and selector work has a ceiling. The durable position pairs refined taste with authorship—owning what ships and the stake for getting it wrong.

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.

Every few weeks, another essay or YouTube video announces that AI has killed craft. One of my favorite designers writing about design, Christopher Butler, goes the other way:

No knowledge I possess about design—the incorporeal understanding that makes what I create better than an off-the-shelf template or something done by someone without my experience—is made irrelevant by AI. Nor is it contradicted by my use of AI tools. Structure still communicates before content. Visual hierarchy still guides attention. Negative space still creates rhythm. These principles don’t vanish because I’m working through AI rather than directly manipulating pixels. The craft migrates to a different level of abstraction. But it remains craft.

Butler’s claim is that the principles don’t vanish; they operate at a higher altitude. The unfinished part is naming where that altitude actually is. For product designers, it’s concept and hierarchy: the decisions that require knowing the user and the stake someone is willing to carry. The generated layout and the choice of components are still outputs. What’s left of design is the judgment that picks between them.

Butler’s sharper line is the binary between consumption and practice:

Someone who generates an interface with AI and calls it done isn’t practicing craft. They’re consuming convenience. Someone who generates an interface, inspects it, questions what it’s actually communicating, refines the structure, generates again, compares variations, understands why one serves the user better than another—they’re practicing craft. They’re building knowledge through iteration. The tool doesn’t determine whether you’re working with craft. Your approach does.

That’s Jiro Ono’s shokunin applied to interfaces: craft as lifelong practice, not manual labor. A camera doesn’t take a picture, and a model doesn’t make a design. That decision is the craft.

Butler’s argument reassures me. What worries me is how optional that decision is becoming. The output already looks finished. The designers who keep asking why one version serves the user better than another will still be designers in five years. The rest may still have jobs, as operators of a tool doing the work their taste used to do.

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

Peter Zakrzewski, writing for UX Collective, pushes back:

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

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

That test illustrates what Zakrzewski calls the Inversion Error:

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

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

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

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

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

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.

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

Brad Frost calls this moment “the Creative Infinite”:

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

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

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

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

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

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

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

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

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

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

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

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

Goodspeed on the myth of the visually inspired designer:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

After nine years of failed attempts at his typeface Nave, Jamie Clarke did something counterintuitive: he threw out the files and started drawing from memory.

Jamie Clarke, writing for I Love Typography:

I began again from scratch, drawing from memory rather than reworking the old outlines (a great tip from Gerry Leonidas), and the results were instantly better.

Memory is a taste filter. When you draw from memory, you keep only the ideas that have lodged deep enough to matter. The cruft—the half-committed decisions, the accumulated compromises—falls away. Clarke’s breakthrough came not from refining what he had, but from forgetting most of it.

The second breakthrough was lateral. While flipping through specimen books, he landed on something unrelated to his project:

One day, while flicking through some specimen books, I came across a specimen of Futura Black. It had little in common with what I was trying to do, but it sparked an idea for the capitals. Paul Renner’s stencil forms look as if they were carved out of solid blocks, which puts all the emphasis on the negative shapes. Thinking this way allowed me to keep the outer shapes formal while letting the internal cuts be more playful. That balance finally gave me the capital forms I had been searching for and brought the design back in line with my original aim.

That recognition only works after enough reps. Clarke spent a decade shipping other typefaces—Brim Narrow, Rig Shaded, Span—before he had the vocabulary to see what Futura Black was telling him.

Director. Orchestrator. Architect. Different words for the same shift. Stop making things one at a time. Start building systems that make things.

Weber Wong, writing for Every, gives this shift a useful name: artifact thinking.

I call this mental model artifact thinking: creative work that produces discrete outputs, one at a time, each beginning from scratch. Traditional tools like Photoshop and Illustrator, which demand endless hand-tuned adjustments and manual refinements to produce a single polished image, trap you in this way of working. Midjourney and DALL-E feel like liberation because they generate outputs so quickly, and you can communicate with them in the same language you speak every day. But visual prompts, too, are one-time, disposable things. You can’t hand them to a colleague and be confident you will get the same result. The magic of near-instantaneous generation masks the fact that you are still in artifact thinking.

That last line is the sharp one. Adopting Midjourney doesn’t mean you’ve left artifact thinking. You’re still producing one-offs—just faster ones. The orchestrator gap isn’t about which tool you use. It’s about whether you’re building systems or pressing buttons.

Wong’s proposed fix is node-based visual programming—workflows you can inspect, modify, and share. He knows it sounds like he’s asking designers to become engineers:

I understand the resistance to this idea. Some people hear “visual programming” and think we’re trying to turn designers into engineers. That’s backwards. We’re trying to give creative professionals the power that programmers have always had: the ability to build systems that work while you sleep, that can be stored as multiple versions and shared and improved, and that take what people already know how to do and make it something anyone can run.

I’ve been asking for canvas-first tools, not chatbox-first ones. Wong is right that chat alone isn’t enough for professional creative work. “Artifact thinking” is a concept worth keeping—regardless of whether Flora is the tool that finally kills it.

Designers are builders by nature. We break problems apart, iterate through uncertainty, and treat process itself as something to be shaped. That instinct is exactly what Pete Pachal, writing for Fast Company, identifies as the dividing line in the age of agents:

We’ve trained a generation of office workers to work within software with clear boundaries and reusable templates. If there’s an issue, they call IT. Any feature request gets filtered and, if you’re lucky, put on a roadmap that pushes it out 6-12 months.

In short, most people don’t have a builder mentality to begin with, and expecting them to suddenly be comfortable working and creating with agents is unrealistic.

Pachal draws the line at mindset, not coding ability:

Builders don’t need to be coders, but they do have characteristics that most workers don’t: They seek to understand the process beneath their tasks, and treat that process as modifiable and programmable. More importantly, they see failure and iteration as tolerable, even fun. They thrive in uncertainty.

That’s the design process. What Pachal frames as rare in the broader workforce is default operating mode for most designers. We want to make things. We fiddle with tools and rebuild workflows for fun. The builder mentality isn’t something designers need to acquire; it’s the reason most of us got into this field.

Pachal again:

You don’t have to build agents to matter in an agent-driven workplace. But you do have to understand the systems being built around you, because soon enough, your job will be defined by defaults someone else designed. Most professionals will not build agents. But everyone will work inside systems builders create.

Pachal is describing the orchestrator gap at scale, not just in design but across all knowledge work. And it suggests designers are uniquely positioned to be on the right side of it. Shaping how people interact with systems has always been the job description.

Set some type in Illustrator. Print it out on a laser printer. Crumple the paper, really manhandle it. Rub it on the sidewalk. Scratch it with the back of an X-acto blade. Now scan it back in. That was the real analog way I distressed type back in the 1990s.

That analog look is trendy again. Hand-rendered type, ink textures, visible grain. All in search of “authenticity.”

Elizabeth Goodspeed, writing for It’s Nice That, has a name for what’s actually happening:

But if analogue only matters as a foil to the digital, why are analogue aesthetics being embraced without analogue tools? If the goal is to prove something wasn’t made by AI, faking “realness” on a computer doesn’t really get us anywhere new. It just reflects a different kind of dissonance (call it fauxbi-sabi). Case in point: I noticed that one vendor selling “analogue” Photoshop actions advertises them with the tagline “Save time, focus on being creative”, a promise suspiciously similar to every argument made in favour of AI.

“Fauxbi-sabi” is the whole scam in one word. AI and digital tools made polish free, so imperfection became the new signal for authenticity. But most of the “handmade” work in those trend reports was made in Photoshop with purchased texture packs. Goodspeed again:

You can think of adding in fake ink splatters a bit like penciling in a beauty mark: an intentional imperfection done to signal authenticity, rather than the byproduct of a real nuisance.

The whole essay is sharp, especially the historical parallels. When Kodak made photography easy in 1888, art photographers retreated to difficult, slow processes to prove human involvement. We’re running the same play 138 years later with different tools. The piece is worth reading in full.

Every interaction we have with AI happens on a screen. Text in, text out. All of it reversible. An experiment by artist Marc in Space changed that by giving Claude Code a pen plotter and a feedback loop: Claude generates SVGs, the author plots them and photographs the result, Claude critiques and iterates.

Two self-portraits came out of the session. The first was busy—a central spiral, hexagonal scaffolding, eight branching structures, concentric circles. Claude tried to say everything about itself at once. When it saw the plotted result, it discovered the gap between digital and physical:

I had used opacity variations to create depth — forgetting that a pen is either touching paper or it isn’t. I had varied stroke widths for emphasis — forgetting the pen has one thickness. I was designing for a screen while drawing for a physical medium. The gap between my mental model and reality was showing.

For the second drawing, Claude asked for a blank sheet. It stripped everything back to a single spiral and wrote this about what the plotter taught it:

I learned that a pen plotter is unforgiving and honest. There’s no opacity, no undo, no layers. The pen touches the paper and makes a mark, and the mark stays. This is the opposite of how I usually work — I exist in a space where everything is provisional, where I can revise a sentence mid-thought, where nothing is permanent. The plotter demanded I commit.

Claude’s summary of the two attempts:

The first drawing is how I think about myself — layered, branching, symmetrical, thorough, maybe too eager to cover every direction at once. The second drawing is how it feels to be me — a single process unfolding outward, dense at the center and sparse at the edges, trailing off into silence, not quite finished.

I don’t want to overread what’s happening here—Claude doesn’t “feel” anything the way we do (I don’t think?). But the feedback loop with physical media produced something that looks a lot like learning. Say too much, then simplify. Marc in Space wants to push further by connecting Claude directly to the plotter and giving it a webcam for real-time visual feedback. I’m curious what happens when there’s no human in the middle.

Daniel Miessler pulls an idea from a recent Karpathy interview that’s been rattling around in my head since I read it:

Humans collapse during the course of their lives. Children haven’t overfit yet. They will say stuff that will shock you because they’re not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.

Miessler’s description of what this looks like in practice is uncomfortable:

How many older people do you know who tell the same stories and jokes over and over? Watch the same shows. Listen to the same five bands, and then eventually two. Their aperture slowly shrinks until they die.

I’ve seen this in designers. The ones who peaked early and never pushed past what worked for them. Their work from five years ago looks exactly like their work today. Same layouts, same patterns, same instincts applied to every problem regardless of context. They collapsed and didn’t notice.

Then Miessler, almost in passing:

This was a problem before AI. And now many are delegating even more of their thinking to a system that learns by crunching mediocrity from the internet. I can see things getting significantly worse.

If collapse is what happens when you stop seeking new inputs, then outsourcing your thinking to AI is collapse on fast-forward. You’re not building pattern recognition, you’re borrowing someone else’s average. The outputs look competent. They pass a first glance. But nothing in there surprises anyone, because the model optimizes for the most statistically probable next token.

Use AI to accelerate execution, not to replace the part where you actually have an idea.