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48

A New Kind of Wholeness

from Christopher Butler [alt+shift+b] in design

Check out the light in my office right now 🤩 . AI effectively, but to understand how it fits into the larger patterns of human creativity and purpose. That’s a good thing — designers are good observers. No matter what the tech, we notice patterns we notice the lack of them. So in the midst of what is likely a major, AI-driven transition for us all, it’s worth considering that the future of design won’t be about human versus machine, but about understanding the pattern language that emerges when both intelligences work together in a system. As Christopher Alexander and his cohort might have said, it will be about creating a new kind of wholeness — one that honors both the computational power of AI and the nuanced wisdom of human experience.
18th Feb 2025

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More from Christopher Butler

There Will Always Be Someone Faster, Cheaper, and Better

My feed is mostly designers talking about AI, and the temperature of all of it is the same: Something is coming. You need to get out in front of it. You need to be the one doing the changing rather than the one being changed. I don’t disagree with most of the individual advice. But read enough of it in one sitting and what surfaces underneath is competition — ordinary competition, of the kind that has always been there. The worry takes two shapes. Either the client no longer needs a designer, because the tool supplies the design. Or another designer, holding the tool, takes the job you would have had. Both are worth taking seriously. Neither is new. The Permanent Condition There has always been someone faster than me, someone cheaper than me, and someone better than me. This was true the day I started my career and it will be true on the day it ends. It describes a market, not an era. Designers — actually, all in the professional services — have a folk theorem for living with this reality: Fast, cheap, good — pick two. I’ve heard it said and read it written countless times. I’ve said it and written it, too, to win arguments and contracts. Today I got curious about where the saying comes from, and went looking. Nobody knows. The phrase itself gets attributed to something called the Common Law of Business Balance, and from there to John Ruskin — an attribution with no evidence behind it whatsoever. The idea underneath has a slightly better paper trail: an engineer named Martin Barnes drew it as a triangle, with cost, time and quality at the corners, for a project management course in 1969. Even that isn’t an origin. Versions of it were in use by the 1950s, and no one can say who put it that way first. This beaten-to-cliché piece of professional wisdom is a cipher. Which is useful to remember because it’s also, often, wrong. There are as many designers who are fast and cheap and good as there are those who are slow, expensive, and bad. I have worked with some of each. Thinking back on this now, I am surprised that no one ever responded to the “pick two” challenge by saying, “I want all three, I just won’t be getting them from you.” Faster and Cheaper My entire career has been punctuated by tools that sped up production. Desktop publishing collapsed typesetting and layout into something one person could do in an afternoon. Then Photoshop. Then the rest of the Adobe suite, Illustrator and InDesign. Then Sketch. Then Figma. Each one arrived, each one made producing the work materially faster, and each one set off a race to master it before everybody else did. Each of them may have changed what a day at work felt like, but none ended the profession. What makes faster feel dangerous is that we convert it immediately into cheaper, and we do that because most designers price their time. If the work takes four hours instead of twelve, charging the twelve-hour price feels indefensible, and somebody else will happily charge for four. That’s a pricing problem rather than a technology problem, and it’s been the same pricing problem through every acceleration I just listed. A practice that charges for outcomes absorbs a speed increase as margin. A practice that charges for hours absorbs it as a pay cut. As for cheaper, it arrived long before any of this, and it arrived by geography. I work closely with a designer who lives in the Balkans. They are, by some distance, among the most talented people I have ever worked with. They didn’t go to design school. I went to the Rhode Island School of Design, which bought me a credential the marketplace still reads as shine, and I’m clear-eyed about the fact that on the measure that actually matters they aren’t behind me. They can also charge less than I can, because rent where they live isn’t rent where I live. None of that has anything to do with AI. It has been available to every client I have ever had as long as we all had an internet connection. And just in case anyone thinks that professional design competition from overseas is a more recent phenomenon, the first designer I met when I visited Malaysia in 2004 was born and raised in Penang, had no design degree, and had dozens of clients most of whom were headquartered in the United States. He was fast, cheap, and good. That’s why they paid him, rather than someone else. What Better Means There is an objection to what I’ve said so far. I’ll try to put it in its best form rather than a convenient one. Every tool I listed made production faster without making the thing. Desktop publishing didn’t design the page. Photoshop didn’t choose the image. Figma doesn’t know what the product should be. AI, as a design tool, is different in kind, because it produces the artifact. So the comparison to Sketch and Illustrator is a weaker argument than it sounds. I think that objection is right about the tool and wrong about the job. It only holds if the artifact itself is better. Imagine two designers given the same brief — an identity system for the same client. Put the results in front of a panel, blind, with no knowledge of how either came to be. The panel picks a winner. They’re qualified to do it and their reasoning will be sound. Now ask the client which designer they’d hire again. It is by no means a given that the panel and the client will agree. The two answers can fork, and when they do it isn’t because the client has poor taste. The panel is assessing an artifact. The client went through something. They were asked questions nobody had asked them before. They had a position drawn out of them that they couldn’t have articulated on their own. At some point they were told that the thing they wanted was the wrong thing, by somebody who had earned enough credit to say so. None of that is necessarily visible in the file — nor should it be. I wrote recently that expertise is becoming subjective — that where knowledge is cheap, experience is what differentiates. If that’s true, it cuts in both directions. Experience doesn’t only distinguish you; it aligns you with some people and not others. A prospect’s fit with how you’ve worked, what you’ve lived through, and how you talk about it is doing as much work as the expertise itself. Which means there’s no better in the abstract. There’s better, for them. There’s also a posture question here, and it matters more than it looks. To believe that your prospect couldn’t do better than you is a level of audacity that runs counter to how every designer I know actually thinks — and yet it’s close to what we’re advised to project. In a world this populous it’s absurd to imagine you’re the best at anything, and more absurd still to build a public persona on the premise. That advice persists because it works on a seductive technicality: whether or not you believe you’re the best, behaving as though you are turns out to be convincing to other people. Notice who that advice is sold to. We’re the market for it; our clients have never heard of it. Rejecting that is accuracy rather than modesty. The world is too crowded with designers to spend a second ranking yourself among them. Beside the posture, the same feed sells a tempo, and that carries its own irony. All that commentary argues that you should move faster in order to stay ahead. But if experience is the differentiator, speed is the thing eroding it. Going slowly through a problem — getting it wrong, trying it on a real client, finding out — is where the differentiation was built all along. The race is being run on the wrong road, and running it costs you the one you needed. Nobody Asks About Tools When I moved from Sketch to Figma I thought it mattered. I had reasons. I argued about it. Designers I respected stayed on Sketch for years afterward and we all thought the other was wrong. In the end, it didn’t really matter. I know designers who still work primarily in Photoshop. In the last twelve months, I saw a website design produced in Illustrator. Of course I think these are mistakes, bordering on the absurd. But it’s also none of my business, and as far as I can tell it hasn’t cost them a job. Isn’t that interesting? But it makes some sense. After all, nobody has ever asked me what tools I use. Except another designer. I don’t know what this technology does to the profession over twenty years, and I notice that the people who claim to know are usually the ones with something to sell. What I can see from here is smaller and steadier than that. Somebody out there is faster than me. Somebody is cheaper. Somebody is better — that isn’t modesty, it’s the realism that comes from decades in the work, and I have met them. All three have been a phone call away from every client I have ever worked with. Any prospect can have them, faster, and at a cheaper price. But they don’t have me. And they don’t have you.

a week ago • 1 votes
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1st Oct 2026 • 2 votes
The Empty Heading

For many years, I have maintained a text file called “A Rubric for Website Design Critique.” It is relatively short, but used nonetheless; I’ve returned to it, off and on, for most of my career, referring back, adding things, removing things, adjusting. Its purpose is to standardize how I challenge the work I do and the work I am shown, and even a standard needs maintenance. The documented rubric has five components. Of information architecture, it asks, Is the priority apparent? Does it make sense? Is it actionable? Of layout: Do the visual elements support the architecture? Is the page as scannable as a high-fidelity asset as it was a wireframe? Of accessibility: Is there adequate contrast? Has text been hidden in images? Can a screen reader properly navigate? And of visual language, Is there coherence? Is it consistent? I emphasized documented earlier because it was never complete. The fifth component is art direction, and after that heading in my document is nothing. The file just ends. It isn’t like me to leave something unfinished. I don’t like ragged edges, even when I know they’re natural and sometimes essential. And time and again over the years, I’ve had a chance to wonder at this empty space. Why is it there? Why is it difficult to fill? Perhaps I’m just not the person to fill it. Perhaps that’s where my expertise ends. However, looking again at this unfinished document recently, I have come to a different conclusion. The first four sections — Information Architecture, Layout, Accessibility, Visual Language — are inspection routines. Each one asks a question that has an answer, and the answer can — should — be able to be found by someone who is not me. Art direction is not like that. And that’s why every time I attempted to fill out structured guidance I produced a list of things I did not actually believe… and then deleted them. And so, the section remained empty, which is its own kind of answer, and not a very useful one. Here is a better attempt. Order Is the Floor The first four sections are about order. They ask whether a page is arranged so that it can be seen, perceived, and understood. That is the floor, and a great deal of professional work never gets off it. In fact, the majority of my career has been focused on getting interaction design off the floor. On my team we have run a periodic competition called The Tidiest Designer, where each person submits a composition file for inspection. We look for order, consistency, clarity, and utility. We do not do this because order alone makes design good. We do it because order is what allows good design to happen. Many beautiful, client-applauded comps have been chaotic disasters underneath their presentation modes, and not surprisingly, conflict-inducing when actually produced. Order facilitates that the promise of design becomes its function. But deeper than that, when order is our foundation, we can spend more of our critical energy on the responsible rendering of taste. Section five is that rendering. It is the point at which intent stops being organized and starts being expressed. What follows is not a set of criteria, then, but five places to stand while you look, in the order I tend to look, with a test attached to each that someone else can run. Where a test comes back “I don’t know,” that is a finding. Most designers are intuitive in their creation, which is not a bad thing. But without cross-examining, reinforcing, studying, enriching, and systematizing what begins with our intuition, we end up with something that is meaningful to us and arbitrary to everyone else. This — arbitrariness — is the most common condition of professional design work, and it is nearly invisible from the inside. The Key Every good piece of design has at least one detail that unlocks how the whole thing works. Good designers notice it immediately. Everyone else responds to it without knowing they have. It might be a rule, a crop, a single color used once, a piece of type set deliberately against the grid. Whatever it is, the rest of the composition should be clearing a path for it. A designer on my team once brought me a set of ads for a maker of high-end audio equipment, built around the idea of choice. Two arrows ran in parallel and then diverged, one rendered in color veering off to the left, the other in white, passing it before turning right. The white arrow was the key. It overpowered the bolder colored one simply by pushing further into the space, and its arc carried the eye down to the copy and the call to action. Then I noticed that its curve radius quietly echoed the skewed, rotated “o” in the client’s logotype, and that those two arrows were the only shapes in the entire ad other than text. That last part is the lesson. The key was doing three jobs at once, and everything else had gotten out of its way. The Key Test. Name the key in one sentence. Then say what the composition does to protect it. If nothing on the page is deferring to anything else, there is no key, only assembly. If you can name three, there is also no key, because three keys is zero keys. The Structure Underneath Structure does more work than content while convincing its audience of the opposite. This is the oldest secret in graphic design and painters have known it longest. Mondrian said that every true artist has been inspired more by the beauty of lines and colors and the relationships between them than by the concrete subject of the picture. I adore that because it explains why I can find inspiration in a page of text before I have read a single word. A page held up by its photography is not designed. It is dressed. It is also why I stay in wireframe far longer than most people would think reasonable, finalizing layout with grey boxes and grey lines even when the real material is sitting right there. If it is beautiful on the merits of its structure, it will hold almost any image and almost any text. The Structure Tests. The first one is a classic for graphic designers: Squint until the type turns to grey and the images turn to shapes, and see whether the hierarchy still reads. The other takes a bit more work but, I think is better: Put a grey box where the hero image is and a line of Latin where the headline is. If the design dies, the image was doing the design’s job, and the next round of content will expose it. The Point of View This is the one most design work fails, and it fails in hiding, because nothing is obviously, visually wrong. When we constantly reference existing solutions, our work gravitates toward the mean. We solve for expectations rather than needs. We optimize for recognition rather than revelation. The result is competent and anonymous, and it passes every inspection above. Restraint, on the other hand, is the visible evidence that somebody was directing. It shows up as absence, which makes it hard to credit and easy to skip. The Point of View Test. Put your design beside three others in its category and swap the logos or identifying marks. If this doesn’t break or seriously undermine your work — if your work is that interchangeable — then it has no direction. It is conventional in the truest sense. The harder version of this test is a question you really must ask at various stages of your work: What did I deliberately not do? or What is this not doing? If you cannot answer, then nothing was decided. Such a thing will age at exactly the rate of its category. And because it followed the category’s lead, it will always be behind. What It Is Saying Imagery and type say something before anyone reads a word, and what they say is frequently not what the business does. A few years ago I ran an informal study on a client’s homepage to prove a hunch. They sell technology and expertise to wineries, and they wanted to connect the heritage and craft their customers care about to the stability their technology provides. So they leaned hard on old-style typefaces and historical imagery, to make prospects feel at home. It looked really nice, but I was worried that’s all it did. Traffic was being paid for, and not enough was converting. So, I ran a transient attention test. Participants had eight seconds with the homepage, scrolling but not clicking, and then the page was closed and they were asked what stood out and what the page was for. The page said “commerce technology” and “wine brands” in scannable, plain text. And yet, every participant recalled the imagery instead — an ancient Greco-Roman tapestry — and volunteered words like “history” and “archaeology.” Not one person mentioned wine. Not one mentioned technology. The page was well written. But for its viewers, it was about the wrong thing. The Imagery Test. Give someone outside the project eight seconds to view your design. Afterward, ask what the thing they just saw was — what does the company do? what was the page for? Do not accept a paraphrase of the headline. Ask what the pictures told them. The gap between their answer and the actual business is the size of the art direction problem. Durability Good design is evergreen. The reactions I trust are the ones that survive a week, and the ones I distrust tend to arrive fastest. Anything resting on a technique currently in fashion has a short window before a browser, a platform, or simply everyone else’s adoption closes it. Both of the tests here buy the same thing at different scales: distance. A week of it shows you what belongs to this year. An hour of it shows you what belongs to the last hour of your own looking. The Dated Test. Leave the composition open in a tab and come back to it after a week, even if it has already progressed through reviews, as most things will in that time. Then, name what on it is dated to this year, and ask of each whether it is carrying an idea or just carrying a date. A composition can survive one or two decisions that belong to its moment. It does not survive being made of them. The Interval Test. This one goes after a different fragility, one that lives in your read of the work rather than in the work itself. Clutter accumulates precisely because the eye that added it has stopped seeing it. I have always found that coming back to a finished but unshared design after even just a few hours away, sometimes minutes, has resulted in needed editorial moves. What you have been staring at is porous to every other thing held on your screen or in your recent memory, and your working brain is an unwitting cheat. Breaks expose that immediately. Take enough of them and the work stops absorbing its surroundings. Preference and Judgment Taste is that combination of preference, personality, and perceived novelty that lets an observer tell your work from someone else’s. It belongs in the work. It does not belong in the verdict. I have sat in too many reviews where a real critique was offered, understood, and then dissolved by “well, we like it.” That is nice. But who cares if you like it? Does it do what it is supposed to do? Or is it possible that the things you like about it get in the way? The way through is not to suppress the reaction but to keep going after it. Name what you are responding to, then say what it is doing for the work. If it is doing nothing for the work, you have found a preference. If it is doing something, you have found a judgment, and now you have to justify it, which is the only part of design that has ever been hard. To make it somewhat easier, do not defend it. Sell it. Don’t believe the lie that “good design just works” as if it will be self-evident in the eye of the beholder and embraced without question. Nothing could be further from the truth. Good design often requires advocacy. Every rubric wants to become an inspection. In art school you always knew a critique was going nowhere when someone would ummm and ahhh, approach the piece, back away from it, approach it again, and finally ask, “is this, ummm, is this balsa wood?” They just had to say something, and what a thing is made of was the best they could do. The digital equivalent is talking about the canvas, the type foundry, the plugins, or opening the inspector. None of those are relevant to assessing a design’s quality. Sections one through four can be inspected. Section five has to be seen — by you first, and yet, outside of yourself — which takes time and, more importantly, conviction. I do not think that section five will ever be as short as the others, or as portable. It takes longer to run than all four of them combined. For years I read that as a defect in my system. But lately I have started to suspect it is the only part of the rubric that will still be worth anything in a few years, because production is becoming generative and design is not. Which leaves me somewhere I have not settled. The first four sections are the ones a machine can already run. The fifth is the one it cannot, so the fifth is where the work is going. But the fifth is also the one nobody has ever managed to teach quickly. I do not yet know whether that is a problem to solve or a fact to accept. Better yet, maybe it’s a distant horizon to embrace, because it means we have somewhere left to go. P.S. I have left creative direction out of this entirely, which is a cheat. In my own notes it sits above art direction, closer to the conceptual end of the spectrum that runs down through graphic design to the mechanics of a build. That is a different piece, and I suspect a harder one.

15th Sep 2026
Flashes Over the Atlantic – A Sighting, and Its Explanation

At 3:30 in the morning on Thursday, July 9th, 2026, I woke in a rented house on Oak Island, North Carolina, and felt compelled to go downstairs and out onto the back deck, which faced the ocean. The night was exceptionally clear. I spent about twenty minutes looking up, finding stars and watching satellites pass over — there were many. Then my attention was drawn to a bright star at roughly the three o’clock position, taking twelve o’clock to be straight out over the Atlantic. Just beneath that star, there was a flash. It was very small and very bright, smaller in aperture than the star but much brighter than it, closer to a strobe than to anything I would call astronomical. Over the next five minutes there were eight or ten more. Each one was stationary. The light appeared in a spot, went out, and then another appeared somewhere else; none of them moved while visible, and none left a trail. They also had no overall direction. One would be to the left of the star, the next to the right, at varying distances from it. They did not begin in one part of the sky and progress to another. I have watched satellites flare when they catch the sun at the right angle, and this was not that. It was not aircraft. I did not photograph any of it, and probably wouldn’t have been able to anyway. These flashes were shorter than a second. I have since worked out what I was looking at, and the explanation turns on something I hadn’t known enough about to consider. The Explanation The bright star was Vega, which at that hour sat high in the west-northwest. The sun was twenty-five degrees below the horizon to the northeast. That matters, because the earth casts a shadow into space, and the edge of that shadow is a surface that sweeps across the sky as the night turns. Satellites inside it are dark. Satellites outside it are lit. The boundary is perfectly invisible, because there is nothing out there for it to fall on. As it happens, that boundary was moving through Vega’s part of the sky during the exact minutes I was watching. At 3:55am, objects in that direction were only in sunlight above seventy-five degrees of elevation. Ten minutes later the line had dropped to forty degrees. Vega sits at fifty-six. The patch of sky I happened to be staring at was crossing out of the earth’s shadow while I stood there. And that patch was busy. Counting catalogued objects, roughly twenty-five lit satellites crossed within ten degrees of Vega during those five minutes. So the flashes were not one object doing something strange. They were two dozen separate objects passing through a small window of sky, a third of them catching the sun on a flat surface for a few tenths of a second as they went. A glint that brief moves about a tenth of a degree, which the eye reads as a point, not a streak. Each flash was a different machine in a different place, which is exactly why they had no direction. What strikes me is how much of this is hidden. The satellites are invisible until the geometry lights them. The shadow that decides which ones are visible is invisible always. I was watching a mechanism operate with every part of it concealed except the result, and the result was eight or ten silent flashes with no pattern I could read. Epilogue I do not think I would have arrived at any of this on my own, standing on a deck at four in the morning. The honest account is that what I saw was anomalous to me, and stayed anomalous for three months, and the sky was never doing anything other than what it does every night. My wife asked me if I was disappointed to land on this explanation. The honest answer is no. I have seen many things in the sky, some of which I have still not been able to explain with natural or known phenomena. I cannot say I am absolutely certain that things like secret advanced technology, non-human intelligence, or the supernatural explain them, but given the evidence of which I am aware and my own experiences, I am, for now, persuaded that such things are possible. But that doesn’t mean that even the strangest things we see and experience aren’t the result of the known and mundane.

9th Jul 2026 • 1 votes
An Old, Restless Wish

We do not know what is going to happen. This is a hard truth; we desperately want to believe the opposite. And so the most successful corners of the modern internet have been organized, very deliberately, around that desire to convince you that someone, somewhere, knows what is coming next, and that we should all look to them. The category includes astrologers and channelers, futurists and political analysts, sociologists writing op-eds and foresight professionals working by the hour. Across every register, mystical and analytical, the assumption is the same: that not knowing the future is a solvable problem. Never mind, of course, how they know what they say they know; modern epistemology is as social as it is slight. This isn’t exactly a new phenomenon, of course. Soothsaying is as old as we are. Its spotty track-record has always kept it in something of an intellectual quarantine, though — considered, even believed, privately; dismissed publicly. But in recent years, it seems as if that arrangement has reversed. Predictions are made, believed, and defended as fluidly (and irresponsibly) as the most inconsequential gossip, but with much more potent implications beyond the attention they attract. What strikes me, looking at it, is how much sense this makes. The growth of prediction, I think, is not merely a symptom of an attention economy out of control — it’s a reaction to a plague of uncertainty. An Age of Uncertainty If anything has objectively increased over the past twenty years, it is uncertainty. Some of it is physical — a perfect storm of fragile globalism, fragile ecosystems, and fragile cultures, each crisis spilling into the others, each year producing the conviction that the rules have not stopped changing. And some of it is informational: an abundance of data and analysis that, instead of resolving anything, multiplies the directions in which a person could plausibly be wrong. The condition is the same either way. We do not know what is happening. And we know less, day by day, about what to do about it. Alvin Toffler, more than fifty years ago, called this future shock — the dizziness of living in a society changing faster than people could adapt to it. He did not predict everything we are experiencing now, but he did predict the shape of the problem with uncanny precision. He saw that under sustained acceleration, ordinary minds would seek refuge in nostalgia, in escapism, in tighter ideologies. He could not have fully known, in 1970, how cheap the refuge would become, or how thoroughly it would be served. And he could not have known that the central refuge his descendants would buy would be the one he himself was trying to give them: foresight. Foresight, of course, is the intellectual’s crystal ball, and as analytical and promise-withholding as its practitioners may be, its buyers want portals into time. That is the unexpected outcome of Toffler’s expected future shock. It’s the actual shock. If the spirit of the age is doubt, the sin of the age is gluttony. And like in so many arenas, we feast at the banquet — in this case, of answers — however empty those calories may be. The prediction economy has grown because the demand for relief is enormous, and a forecast is a kind of relief, even when nothing it says happens to be true. As people will tell you about communication itself, audiences forget what was said but not how it made them feel. The feeling of safety carries forward until the next prediction, even past the obvious failure of the last one. From the Cloisters to the Commons There is a second, quieter story alongside the first. It is not only that demand for prediction has grown; it is that the supply has moved into the open. For most of the past century, the kinds of practitioners who would have called themselves seers or psychics or channelers worked, by necessity, in small rooms. Their audiences were the people who walked in the door, or who subscribed to a newsletter, or who attended a Sunday meeting at the back of a metaphysical bookshop. The analytical side was no less cloistered. Futurists worked for governments and consultancies; foresight, where it was practiced seriously, was a profession rather than a content category. The cloisters did not fall. Their walls were quietly removed by the cameras in our pockets. What had once required an audience now requires only an account. A psychic in suburban Florida, a strategist with a TED talk, and an analyst running scenarios in a basement now share the same publishing infrastructure, the same incentive structure, and — increasingly — the same audience. The astrologer and the political scientist are colleagues now in a way they never were, and not because their methods have converged. They are colleagues because the platform pays the same way for either — co-workers in a boundless office of attention. This, I think, is the deeper proof of how thoroughly the speed of modern communications has reshaped us. The change is not only that we receive more information faster; it is that the conditions of speaking publicly have collapsed. The voice once reserved for those who had spent decades earning it is now the voice of anyone with a webcam and a thumbnail. And in such an environment, the genres that always promised the most relief — that always made the most reassuring claims about what comes next — were inevitably going to win. What “Next” Reveals Even granting all of this, the appetite the platform feeds is itself worth a closer look. What kind of certainty are people — including me — actually after? It is not certainty about the present. The present, by definition, requires no forecasting; it is the thing in front of you. Nor is it certainty about the past, which is what historians and memoirists and grief itself work on, and which is no less hard for being settled. The certainty being marketed and consumed in such enormous quantities is sequential. It is certainty about the next. The next election, the next collapse, the next breakthrough, the next era. The next thing I should be ready for. The next thing I should be afraid of. The next. This is a strange object of desire, when you sit with it. To want to know what is next, more than you want to know what is here, is to admit something about the relationship you already have with what is here. Wanting to know what is next is, very often, just wanting to be somewhere other than here, dressed in the language of time. The hunger for the future is, half the time, a way of being elsewhere without ever leaving the chair. I wonder, sometimes, scrolling through these forecasts, whether we have ever really cared about the now. Whether the recent surge in prediction is not a new condition at all, but only the moment in which the technology finally caught up with an old, restless wish. The Hard Work I do not want to dismiss foresight as such. There is a real and difficult tradition of looking ahead — Jeane Dixon’s bolder claims and Alvin Toffler’s quieter ones are not the same kind of thing, and I am not equally persuaded or put off by either. I actually think that underlying project they share is honorable, so much so that I have tracked predictions of all kinds in a spreadsheet for years. But to attend carefully enough to the present, in either mode, that some pattern of the next becomes visible, is a necessary balancing discipline. It is hard, being present. When it works, it is generous, because it gives the rest of us something to do with our anxiety besides be ruled by it. What we have flooded the commons with is something else, and on inspection most of it is the opposite. It is a way of buying the feeling of foresight without the practice — of metabolizing uncertainty by paying someone to swallow it for you. It is, I think, a form of intellectual cheating. The prediction industry sells us the feeling of having figured something out without the friction of actually figuring it out: a way of being done with uncertainty in advance of doing the slow work of moving through it. There is no shortcut here, though. The reality — the actual reality — is the one the prediction industry has organized itself to help us avoid. Uncertainty is hard. Mistakes are hard. Pain is hard. Loss is hard. These are not problems to be predicted past, and the most useful thing any of us will ever do is to sit, attentively, in the part of time no forecast is ever about: the present.

2nd Jun 2026 • 1 votes

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yesterday • 1 votes
Design is a search problem

A decade ago, working at a design agency, I lived in fear of one sentence: “this looks nice, can we see a few other options?” I was not an ‘options’ guy. I fashioned myself as Paul Rand, telling Steve Jobs off when he asked for multiple options: “No. I will solve your problem for you and you will pay me. You don’t have to use the solution. If you want options, go talk to other people.”1 But I didn’t have the guts to tell my clients off, so I dutifully designed a few other options.2 Recently, I’ve gotten to work with some of the best designers in the field. Looking at their digital workspace — in Figma, Paper, Claude Design, or something else — I see hundreds of artboards and variants, laid out in a sprawling grid, representing branching parallel universes of design directions. Sometimes I see little panels filled with dials to sweep through the possibilities, enabling a single person to see thousands of combinations in the hope of picking a single, perfect specimen. Lots and lots of options. This makes me feel like an alien. My files are just a few artboards; sure, I tweak and tune the tiny details inside, but I rarely keep the multiverse in view as I do. I’ll give myself a dial here and there, but usually for a single variable that I can’t easily visualize. Am I just a bad designer? Assuming (hoping) the answer is ‘no’, I want to revisit a thread I pull on from time to time: as designers, we’re all just trying to find the right solution in a vast multiverse of possibilities. The solution exists, and we typically know it when we see it. So most of what we call ‘design’ is groping around a dark room looking for a black cat. Different designers take different approaches. Some are changing their approaches with the advent of generative AI, but at the end of the day, design is a search problem. Computer science has worked on search problems for decades. Depending on the particularities of a given search, there are a variety of algorithms and strategies that can result in faster, more direct, or more computationally efficient paths to the solution. We can apply this wealth of research from some of my favorite scientists (looking at you, Claude Shannon) to the design process, to hone our own craft. ‘Search’ has its own formal vocabulary in computer science. I’ve put a glossary at the bottom of the post, and I’ll use dotted underline to link to a definition if you need it. To picture how a search works, imagine a Rubik’s cube. A scrambled cube is the starting state; each turn you can make is a state change, leading to an adjacent state. All the possible combinations of colors on the faces (43 quintillion for a typical 3×3 Rubik’s cube) make up the search space. Searching involves evaluating candidates that can be reached from the current state of the cube; the desired state is the solved cube with each face a solid color. Each possible version of the cube is a state. Each turn is a change that leads to an adjacent state. Now, a few basic search algorithms and how they apply to design: Depth-first search As far as search algorithms go, depth-first search (DFS) is as simple as it gets. Here’s how it works: From the current state, look for adjacent states we haven’t visited. If there is one, move to it. It doesn’t matter which. If there isn’t, go back to the previous state and start from step 1. Repeat until we reach the goal or run out of states. Depth-first search commits to the first change it finds and follows that all the way down before it backs up. Solutions evaluated: 16 In a design sense, this is akin to navigating a hedge maze by picking a random direction at each fork until you hit a dead end. It’s not a very good design process, but it’s a good intro to how these things work. Breadth-first search Breadth-first search (BFS) is just a touch more complicated, and starts looking more like the design process. From the starting state, visit every adjacent state. From each of those, visit every adjacent state we haven’t visited. Keep going, moving one step further from the start each round. Repeat until we reach the goal or run out of states. Breadth-first search tries every single change, then every pair of changes, and so on. Solutions evaluated: 21 Both DFS and BFS are naive algorithms; we don’t proactively evaluate candidates while we search. Both are used on search problems with small search spaces and/or when evaluation is cheap. But many of the most interesting search problems aren’t small or cheap. Take chess, for example; winning at chess is a search problem. But the search space is unfathomably large,3 which is why it wasn’t until the mid-2000s that computers could reliably beat grandmasters. Most product design problems I’ve worked on look more like chess than a Rubik’s cube. Design covers search spaces of hundreds of dimensions — sizing, spacing, hierarchy, color, motion, interactivity, responsiveness, etc. And evaluation is relatively expensive: beyond changing a button or color, we often need to ship something before we truly know it works. There are other algorithms meant for this kind of problem, and they resemble design more and more as they get more complex. Beam search Beam search is the first approach we’ll explore that actually evaluates potential candidates before moving. From the starting state, score every adjacent state. Keep the three best and discard the rest. Score every state adjacent to the three we kept, and again keep only the three best. Repeat until one of the states we kept is the goal, or we run out of states. Beam search scores each option by how many parameters still look wrong, keeping the best three each round. Solutions evaluated: 21 This is called ‘beam search’ because it’s like using a flashlight’s beam to look at new states; the width of the ‘beam’ above is 3. Beam search is the ‘show me some options’ approach that most of my agency work took. It’s limited by the width of the beam: a narrower beam is faster and cheaper, but may miss the best solution at a given step. Now is an important moment to introduce the idea of a heuristic. In most cases, we don’t know we have the ideal candidate until we find it. Given that inescapable fact, we have to pick a test that we think will separate the good candidates from the bad ones. Heuristics usually involve tradeoffs: they can be fast/cheap and error-prone, or slow/expensive and accurate. Here’s the problem: beam search often fails to find the best solution! It depends entirely on the width of your beam and the quality of your heuristic. For example, if the desired state is hiding behind a low-scoring upstream branch, the algorithm might never find it. This can be overcome with an infinite beam, but then we’re just back at a slightly fancier BFS, which is slow and inefficient. When I learned the shortcomings of beam search — that simply checking a few options and picking the best one might not lead to the best solution — my feelings about my option-hungry clients were validated. And yet, designers are still using the beam, because AI has given us a better flashlight: producing a handful of options for a SaaS website used to take a week, and now it takes seconds. The beam has gotten so wide that ‘give me 10 options’ feels like an effective way to prompt AI towards the best design. Still, it’s not how I design. And again assuming I’m not just in the permanent design underclass, I thought maybe there was another algorithm that explains why I tend to explore so few branching paths. A* A* (A-star) is the algorithm behind most route-finding, from maps apps to video game characters. From the starting state, give every adjacent state a score, made up of: The number of changes it took to get there from the start, plus The heuristic: our estimate of how many changes remain to reach the goal. Put them all on a list of states we could visit next. Move to the state on the list with the lowest score. This might not be adjacent to our current state! Score the new state’s adjacent states and add them to the list. Repeat until we’re at the goal. A* scores each possibility as changes made so far plus its estimate of changes left. Partway through, it’s able to revisit previous candidates. Solutions evaluated: 14 This is similar to beam search, but with a few twists: A*’s heuristic incorporates a notion of distance from the goal. A* doesn’t always move smoothly from adjacent state to adjacent state. That last bit is crucial: unlike beam search, A* can go backwards. If the path we’re on moves far from the goal, its score climbs. Eventually a previous candidate looks better and we go back to it. More importantly, how much exploring we do in A* depends almost entirely on the quality of our heuristic. With a perfect estimate, A* goes straight to the solution. A* matches how I design. I’m more deliberate with exploration, only following thoughts until I feel they’re drifting away from the solution. I typically only keep old explorations around if they’re promising; an exhaustive record of every possible variant is stressful. I’ve managed to go the whole essay without mentioning it, but with my A* approach, taste4 does in fact matter; while there are many designers with better taste than I have, what I do have is hard-won by years of designing, and I can use it to sniff out the fastest path to the best solution. Conclusion AI has given us a wider beam, but it hasn’t yet given us a better heuristic. We’re still designing for humans, so we still have a leg up on the robots: we can use empathy, or even our own biases and human shortcomings, to feel out the right solution. Meanwhile, understanding the design process is just as important as ever. This kind of introspection — learning about search algorithms out of despair over my own shortcomings — has added a few tools to my arsenal. There are many variations on these algorithms that may describe your own search process; there are even more exotic variants that may be worth trying on. It might seem reductive to call design a search problem. Where’s the creativity? But with humanity, humility, and curiosity, pursuing the optimal solution with a well-tuned algorithm can help us design faster and more efficiently. Special thanks to Josh Petersel for feedback on a draft of this essay. Definitions Adjacent Two states are adjacent when they’re separated by a single state change. ↩︎ Candidate A state we’re considering. ↩︎ Distance The number of changes that turn one state into another. Usually we measure distance as the least number of changes required to get from one state to another. ↩︎ Heuristic A test we think will separate the good candidates from the bad ones, used because we usually can’t know we have the ideal candidate until we find it. Heuristics involve tradeoffs: they can be fast and cheap but error-prone, or slow and expensive but accurate. ↩︎ Search The process of arriving at a desired state (scoring well on a fixed set of criteria) by evaluating a set of candidates. ↩︎ Search space The set of possible states. We call it a ‘space’ because often there are ‘dimensions’ to search, like the width and height of a shape, and we can make analogies to the dimensions of a physical space. Sometimes these dimensions are literal, as when a wayfinding app picks the best route from point A to point B. ↩︎ State A possible step in the movement from the beginning of a search to the end. We start with the ‘initial state’; as we evaluate candidates we might change our ‘current state’; the end of the process is the ‘final state’ (and may not be the ‘desired state’). ↩︎ State change Also called a mutation. How the system moves between states. ↩︎ Footnotes & References As recounted by Steve Jobs in “Steve Jobs interview about working with Paul Rand,” 1993. ↩︎ The client almost always picked the original (or at least the classic ‘let’s do A with a bit of B and C’). ↩︎ Claude Shannon estimated it at 10¹²⁰. For comparison, the observable universe has about 10⁸⁰ atoms. ↩︎ Say the line, Bart! ↩︎

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