56 posts tagged with “creativity”

Vaughn Tan, an organizational researcher and author of The Uncertainty Mindset, argues that organizations routinely mistake uncertainty for risk. Targets, forecasts, and cost-benefit analysis assume the choices and their odds are already knowable. New ideas rarely arrive with that evidence attached.

It probably didn’t die because it was bad. Your organisation wanted something new, so it did what its machinery does: it made the new thing a big bet. Under uncertainty, big is the wrong move in two ways. A big bet on the new is not bold; it is necessarily blind, because you cannot know enough up front to justify it. And a big, visible bet is just what the parts of an organisation that want to keep things the same will move to get rid of. The better the idea, the bigger the bet you are tempted to make, and the bigger the target you paint on it.

The trouble with a flagship is that its visibility becomes part of its risk. Tan’s alternative is to make experimentation less dramatic and more routine:

The moves to make are the undramatic ones. Start small enough that no one sees a threat. Make tests cheap, fast, and numerous, so failure is survivable: a portfolio of small bets each placed to answer useful questions, instead of one big bet placed to create the impression of decisiveness. Disguise the innovation as an unremarkable update to standard procedure. The idea is to not fight the system head-on.

This is a useful distinction for design leaders. A large commitment tries to prove confidence before the team has learned enough to deserve it. A portfolio of reversible tests turns the same resources into evidence.

If this sounds like working behind the organisation’s back, consider what the alternative gambles with. The innovation big bet stakes public money and public trust on a guess, faking certainty about something genuinely uncertain. The small, quiet experiment spends almost nothing to buy real knowledge, and the public is never exposed to a large, irreversible downside. Being responsibly sneaky isn’t cheating. It’s how you take care of public resources in a world you cannot predict.

The responsible move under uncertainty is to keep failure cheap and learning continuous. In other words, get prototypes in front of customers as soon as you can.

Tom May profiles creative director Gemma Phillips, whose work follows one test: does it meet people in the reality they already inhabit?

Brands have an instinct to sugarcoat. Maybe they think we can’t handle the truth. But we’re already living the truth; that’s the irony. The grit is where it’s at.

Bluntness delivers the message. Recognition gives it force.

I look at the work they’re already doing. Who’s taking risks? Who’s trying to do something differently? Who’s willing to say the thing that no one else is saying? Who wants to change things, rather than do more of the same but with nicer design?

Humour can be incredibly respectful because it acknowledges reality. It’s a sign that you genuinely understand the audience; you’re one of them, not a machine hovering above them, selling them something they know isn’t true.

Phillips’s work is provocative because it acknowledges the truth and doesn’t gloss over the uncomfortable.

Nolen Royalty, a software maker who writes at eieio.games, gets at a problem with AI-generated work that shows up before judgment: the effort signal. His examples include tldraw, the collaborative drawing tool, closing AI-generated pull requests, warm-cream Claude websites, and record collecting, but the point is simple. Polish used to be a proxy for care. Now it isn’t.

What software (and writing, to an extent) is missing now is legibility of effort - the ability to tell at a glance whether something took a human meaningful work.

Until recently, “someone cared enough to write this” was an ok heuristic. Plenty of writing on the internet was bad, but you could convince me that you cared about something just by writing it down.

Of course, generating plausible-looking text - or a plausible-looking website - is trivial now.

For designers, that broken proxy is already visible on the surface. We can all spot the default Claude style now, which is funny until you realize that a visual pattern has become an accusation about how much thought went into the site.

There’s nothing objectively wrong with making a website with a warm-cream background and hero text in a sans-serif font with a single accent word that uses an eye-catching color and a different font.

But when I see a website that has the default Claude style I assume that the author put ~no thought into how the site should look. And I often assume that the author didn’t put too much thought into the rest of the site either.

That’s not fair of me! But “someone made this website” is no longer enough to tell me that the website was important to them. So “default Claude style” is one of my new heuristics.

Taste sounds less mystical when you put it this way. A designer doesn’t make a screen human by avoiding beige or picking a stranger typeface. The work is in the decisions: why this hierarchy, why this contrast, why this interaction, why this amount of friction.

The proliferation of digital music and streaming made having a music collection easy and frictionless. And so a subculture evolved to re-add that friction.

And in small ways I think you see the same things happening now.

I’ve seen people joke about adding typos to emails to prove that they wrote them. MS Paint-style image macros read as more human than detailed, funny images (the image could be AI slop). Websites that look intentionally bad are more interesting than websites that look beautifully bland.

The easy story about AI in creative work is that it closes distance: idea, prompt, output, iteration, all compressed. It’s Nice That gets at the more designerly version of that question by putting creative technology’s appetite for “happy accidents” next to design’s need for control:

“From the technology approach, the metaphor I equate it to is the classic ‘happy accidents’ you have when you are in a design tool,” Seth says, finding expected moments of creativity. “The best happy accidents aren’t just between a person and a tool; they happen between people.” It’s perhaps in this notion that co-creation is at its most visible, not in the technology itself, but rather in the conversations and unexpected developments that occur when people with different perspectives work closely together. For Talia, however, this isn’t the case. “Nothing we do is experimental by nature,” Talia says, “everything is incredibly controlled – or, better yet, ‘designed’,” stressing the importance of the role of the designer and the meaning behind design itself. “Design is about creating solutions; there is a sense of control, there is a purpose, there is a function,” she continues, “even the beauty is controlled to a degree.” An example is the generative motion graphics system that Talia created, in collaboration with Mother, for the Crypto coin USDC.

Designer and coder Talia Cotton’s line clarifies the whole piece: “controlled – or, better yet, ‘designed’.” Cotton’s point is that the speed of generation only makes the designer’s eye more important, because someone still has to decide the boundaries before the machine starts producing variations.

Cotton’s USDC system makes that concrete:

Within the visual identity, Talia developed a custom tool that generated guilloché patterns, in reference to the historical patterns used in traditional finance. Alongside set, systematic parameters – including height, width, density, and speed – Talia had to create algorithmically constrained rules within those limitations. “As you adjusted one parameter, another parameter would automatically change its available range,” Talia says, “that ensured every possible output looked good.” As Talia suggests, especially considering the ease with which people can generate things, the “designer’s eye” is now more important than ever. “The designer’s job is to create an airtight generative system that considers every possible case and every possible output,” she says, “so that every single output always looks great, no matter how different it is.”

Michael Riddering’s Dive Club interview with Loredana Crisan, Figma’s chief design officer, is about how Figma wants a bigger, faster, more explicit design loop.

Riddering asks Crisan whether taste gets less defensible when everyone can make more things faster:

I actually think that what happens when everyone has the power to build is actually it elevates the need to stand out. You could buy those pretty funny or soulful congratulatory cards, birthday cards, whatever cards, and they have the message written inside. And it’s a great message. Do you just give somebody that card? You could, but that’s not what you do if you’re a good friend. That’s not how you show up. And so I think that the more software is getting created, the more articles are being written. Basically, we’re in a place where everything is getting produced at such high, high rate. And we already had trouble consuming what was already there. And so what we’re going to try to do is look for even more authenticity. […] So this is why I think it’s not taste. It’s just your point of view, your human intuition, what you want for that experience needs to come through. And then the tools that they build are very much important to help you drive that.

I think Crisan is right about the greeting card metaphor. When production gets cheap, the work that still feels made for someone will matter more, not less.

That matters for designers because AI abundance changes the filter. The scarce thing is knowing why this draft, this interaction, this bit of motion belongs in this product.

Riddering then pushes on control, which is where Crisan gets concrete about Figma’s tool bet:

The premise is that AI could get you to 70%. And this is true with motion. You could prompt your frame and then you will get the first animation set up. But then from there, you need to make it yours. So it’s almost like AI sets up the workspace. It gives you the parameters, but then you are the one that makes that thing. […] If there is a color that I want to put in my design, a color wheel will never be replaced by a prompt. So there are moments where you just want to take control yourself. […] And it’s an interesting way of working because increasingly as designers, I believe we’re creating systems, not just screens. […] Design is never done alone. Design is evolved through critique. It’s a place where one of the tools of design is other people. And so it’s really fun to be able to have AI, have agents, have the direct manipulation and direct control and your team in one space.

This is the version of AI design tooling that makes sense to me: the tool accelerates the setup, the first draft, but the product still gives designers enough control to make taste visible in the final result.

When working with AI design tools, we tend to think we need to one-shot a prompt to get everything. That, of course, is far from true. We iterate—a lot—by describing the changes we want, then waiting, and inevitably, iterating again. Crisan is describing a tool that still leaves room for the hand: sliders, variables, shared artifacts, and other people.

And she points to evals as part of the designer’s new job:

Increasingly, we’re working with non-deterministic systems, right? So a lot of what the experience is, is produced by an LLM. Previously, it could be produced by an algorithm. I worked at Meta and can tell you, we’ve had a lot of conversations about the buttons that would exist on a feed story. Those buttons are important, but when you think about what people really experience when they open one of those products, it’s the content. And so all of the conversations that we had about buttons as designers were far, far less important than the conversations that we should have had about the algorithm itself, right? And when it comes to these non-deterministic systems, how do you ensure that they meet your quality bar? There’s this thing called the evals is, on the one hand, the least sexy thing in the world, because you are now in a place where you’re trying to specify what good looks like and how systems that could either recognize that themselves or be augmented with humans that could do that. And it’s fascinating to try to do this on an unverifiable domain like design, because design is not, there’s not a correct answer to design, but there are many incorrect answers.

In designer and investor Soleio’s South Park Commons interview, Rasmus Andersson, creator of Inter and an early Figma designer, draws a useful distinction between software and type. Regular software decays fast; Andersson says that if he hasn’t touched something in two weeks, he usually trashes it and starts over. But a typeface can keep gaining value years later.

Andersson starts with how complex modern software is:

Today there’s just this towering complexity of making software, and for a very good reason, right? For like 15, maybe 20 years by now, our industry as a whole has been hyper-focused on scale because it’s just been an economic evolution that’s been out of this world. So it makes sense that we’ve been trading off and trading away things like shareware, concepts like that, and things like simplicity and ease for the ability to scale and for the ability to things at scale to actually work and break apart. But in the wake of all of that, which is great in so many ways, we’ve given up these things that are listed, and I feel like at least me and many people like me and who sort of are trained to build this thing, enjoy making software at a smaller scale.

That’s like a metaphor that I think can be helpful is imagine your room, apartment, house, whatever, your dwelling place. It’s probably different from the person next to you. And it’s not so that we go out and we buy the Apple apartment and all the furniture is there and everything is perfect and we can change the carpet, and that’s about it. That’s ridiculous, no one would do that. Yet that’s what people do with software.

Manufacturing software was professionalized and gentrified. The stack got so optimized for global products that it left less room for the small, personal, weird things people used to make for themselves.

Andersson’s Figma story keeps that from becoming nostalgia:

First off, something that was just amazing about working at Figma at the time was that there was just this culture of doing things differently. That was amazing. And every single individual who I worked with were just like, if that person went to start a company now and they put me with it, I’ll come. Every person was like that. Every person had an opinion about something that was really exciting.

Another thing that I think was so fascinating about how things were done at the time at Figma was this deep intention around everything and moving slow. But moving slow with many things in parallel, like staggered, right? So from the outside, Figma would ship something every month, maybe even more often than that. But internally, a person would work on one thing for one year. And sometimes at the end of that one year, it would just go into the bin and never ship. So that I think was really cool to see from the inside and then having a very different effect on the outside.

That distinction matters in an AI tooling moment where output is getting cheap. Figma looked fast from the outside because slow, deliberate work was happening underneath. Speed at the surface depends on judgment below it.

On Inter:

One thing for sure is finding the right ratio between impact and effort. I think Inter is one of those things. Sure, I’ve spent like 10 years on it, and who knows how much of my time on it, right? So there’s a lot of effort behind it. But I think it’s one of those types of efforts that has a very disproportionate impact from the effort.

[…]

I gotta say though, something that is amazing about working at Typeface is, it’s something like, it’s almost like in the field of design, you might think about cars as a unique type of thing to design because it’s both like, and architecture’s a little bit like that too. It’s not strictly the sign as in signage for roads, right? It’s very technical and it’s not the sign as in expressing myself through art, like graphic design, like a poster, it’s somewhere magically in between.

And another aspect of it that’s kind of cool is that you can put in 10 minutes here and 10 hours there and they add up over time which I don’t think is true without a software because the rate of decay of any type of regular software is very high. Two weeks is my cutoff. If I have not worked on something for two weeks I have trashed it and start over but with a typeface like that decay is extremely extremely low, in some cases zero.

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.

The line running through Tobias Van Schneider’s interview is simple: designers complain about tools all the time, but the better move is to build the environment you wish you had.

Nikolas Wrobel interviews designer Tobias Van Schneider, founder of Semplice and mymind, and the profile traces that pattern directly: Semplice for portfolios that didn’t fit the platform/template world, then mymind for thinking without the performative noise of social media.

Van Schneider:

How do you protect yourself against consuming, draining effects from Social Media, or disenchanting tech-mechanisms?

This question almost too perfectly leads into what I do everyday. In part, I protect myself by using mymind.com (which I created) — there are no ads, no vanity metrics, no social media features, nothing but myself. Only me and the things I care about. Over the years, mymind has become so valuable to me, it’s the first place I go to look for inspiration. In fact, while I am answering this interview, I find myself going back and forth between old notes and musings inside mymind.

Social media still drains me, just like everyone else, but it’s nice to at least have one place just for myself. And thats mymind.

Aside from that, I just get offline and out into the world. Or I create something.

Van Schneider isn’t pitching another social layer; he’s describing a private room for memory, taste, and reference.

He later explains where that product idea started:

As with many things, it was a total coincidence. When we initially worked on the mymind product and brand, we didn’t even have the name “mymind” yet. The whole thing was called AWMT, which stands for “As We May Think” and is a reference to an old essay by Vannevar Bush from 1945 in which he wrote about a machine called “The Memex” which was some sort of machine that collects and connects your personal knowledge.

Coming off of that inspiring essay, we came up with a slogan called “Think for yourself” which is sort of the antithesis to the cloud/hive mind of what we call social media today. Especially since we position mymind as a private sanctuary, it just made sense to us.

All of this eventually got me into the rabbit whole to search for ideas for our logo. The classic “Thinker” statue immediately came to mind. I always loved that one, a man deep inside his own thoughts, unfazed by the world around him. But the statue was a bit too literal to me, too well known, too sharp and serious. We needed something more abstract, more playful. Eventually I found out about Cycladic art, originating from the Aegean islands during the Early Bronze Age. Very famous for their minimal and stylized marble figurines. Now, the rest is history. I immediately fell in love with the simplicity of it and it felt like a great canvas to build our visual universe on it. The rest is history (:

Wrobel asks Van Schneider about the conditions he works best under:

The creative me enjoys being alone. Completely isolated, nobody even in the other room. It gives me freedom and clarity to think for myself and be myself. My real creative being thrives in these moments, untouched by the opinions and desires of others.

My ultimate solitude tends to arrive at midnight. It almost transforms me. The dark brings focus. Silence brings new ideas. No voices interrupt, no chance of emails, just me and my thoughts. It’s this time I feel most creatively alive.

Now, add a soundtrack to it and I’m in creative heaven (:

I don’t think every designer needs midnight solitude. But design work does need stretches where taste can form before it becomes consensus. In a work culture that treats collaboration as a default good, that distinction matters.

This is also why the tools question matters. A portfolio system, a private reference space, even a type foundry site are not neutral containers. They either protect the conditions where taste can develop, or they pull the work back toward the defaults of the platform. Van Schneider’s career makes that feel less like a manifesto and more like a working habit: when the available environment makes the work smaller, build a different environment, then keep using it until it changes the work.

Van Schneider’s favorite advice turns complaint into output:

“The best way to complain is to create something” by James Murphy, founder of LCD Soundsystem. It has become one of my guiding principles. It turns useless, negative energy into productive, positive energy.

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.

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.”