2026-06-26 12:00:00
There is a pattern visible across the writing about AI this week, and it is older than the writing about AI. Each time a new tool drops the floor of access to a craft, the ceiling of mastery rises somewhere new. The interesting work moves up — out of execution and into the decisions that govern what should be produced, what to trust, what to delegate, what to keep.
Takuma Kakehi names this cycle precisely in one of the week’s strongest pieces. Each interface revolution — the terminal, the GUI, the prompt — has temporarily made access and mastery look like the same thing, until a new layer of sophistication makes the distinction visible again. We are in that temporary moment now. The prompt interface gives anyone Photoshop-level output through a sentence. The hybrid that’s coming will require new fluencies, and a new professional layer will form around the people who develop them.
What the other pieces in this entry show is what is happening at that ceiling as the floor drops. Kyle Chayka has been watching a generic AI design aesthetic take over the internet — beige backgrounds, rusty orange accents, large italicized serifs — and naming it as the visible signature of access without mastery. Rachel Aroesti shows the same dynamic at the level of personal taste itself, where algorithmic feeds have lowered the cost of consuming culture to zero and personal preferences are dissolving into the noise. Pratik Joglekar writes the practical manual for the design discipline that probabilistic systems require. Emily Campbell maps the layers of AI experience that designers now have to work across. Karolina Rojek reads the market signal — the wave of Chief Design Officer appointments at Microsoft, Samsung, Shopify, Meta, and even the US federal government — and reads it correctly: when execution gets cheap, judgment becomes the strategic position.
None of these pieces argues that AI is bad for the work. They argue, with varying tempers, that the work has changed shape, and that the part of it that matters has moved. The floor has dropped. The ceiling is already higher than it was. Whether you find that exciting or unwelcome depends, mostly, on whether you have been keeping up with what mastery has been quietly turning into.
Takuma Kakehi (Access is not mastery) names the pattern that runs through this entry. Each interface revolution — terminal, GUI, prompt — has temporarily made access and mastery look like the same thing, until a new layer of sophistication has made the distinction visible again. The terminal had a high barrier that was visible (syntax you either knew or didn’t). The GUI lowered the floor dramatically and then, gradually, was overlaid with professional tools (Photoshop, Illustrator, the Bloomberg Terminal) that rebuilt the ceiling. The prompt interface returns the mainstream to text after forty years of moving away from it — but does something the terminal did not. It hides the barrier itself. You type in natural language; something happens. If it isn’t right, the question is not what you did wrong, but how to describe what you wanted more precisely. The barrier did not disappear. It became invisible. Kakehi’s framing of the prompt’s discoverability problem is worth keeping: menus showed you what was possible, imperfectly, but the vocabulary was on the screen. In a blank text field, you can only ask for what you already know to ask for. The closing observation lands hardest: each democratization in computing has temporarily made access and mastery look like the same thing, until the next layer of sophistication made the distinction visible again. The hybrid interface that’s coming — visual layers combined with prompts — will form a new professional layer around the people who can navigate both modes fluently. The bar, Kakehi writes, is already rising. On schedule.
Kyle Chayka (The A.I.-Design Aesthetic That’s Taking Over the Internet) writes the visible companion piece to Kakehi’s abstract argument. Working designers are now able to name the specific signature of access without mastery: beige and cream backgrounds, rusty orange accents, large italicized serifs, “tracked out” subheadings, ticker-like text bars across the top of the screen, rounded rectangles with neon glow underneath. The aesthetic is being produced at scale by Claude Design, Anthropic’s UI generation tool, and it bears more than a passing resemblance to Anthropic’s own brand identity. Anthropic’s guidance documents concede the point: the default is “persistent,” and asking the model to deviate from it tends to produce a different fixed palette rather than actual variety. The piece’s most useful framing comes from the engineer Lucas Gelfond, who likens the visual signature to the marks left by industrial manufacturing on its products — seams on injection-molded plastic, saw marks in wood, evidence of the tool used to make the thing. Celine Nguyen reaches for the Charles and Ray Eames credo to put a finer point on what has happened: where modernism aspired to “the best for the most for the least,” AI design offers “pretty good for the most for the least effort.” None of the designers in the piece are against AI. All of them note that it is possible to produce distinctive work with these tools, if you do the labor. Loredana Crisan’s line is the one to land: the job of the designer is to stay with uncertainty long enough to discover something new. Access alone does not produce that staying.
Rachel Aroesti (Have I been influenced, or is this actually me?) extends the diagnosis to the level of personal preference itself. We used to encounter the world through a mix of community, geography, media, and accidents; we now encounter most of it through a single aperture — the algorithmic feeds of streaming and social platforms. The result, Aroesti argues, is that taste itself has been hollowed out. Trends arrive and saturate faster than we can decide whether we like them. She names the recent CBK-core wave, the tactical “trend simulation” used by music industry marketers, and the rise of “clipping” — covert advertising campaigns that pay people to flood social media with content about a product. One industry source quoted in the piece estimates that 90% of what you see online is now advertising in disguise. Aroesti’s deeper move is to take taste seriously as an existential matter, drawing on Sontag and Bourdieu to argue that personal preference is “the closest thing most of us get to self-expression,” and that to dismiss it as trivial is to dismiss what it is to be a person. She is also alert to the irony of Silicon Valley’s recent pivot to taste-talk — what Kyle Chayka has labeled “taste-washing.” The off-ramp she points at is partly structural (smaller platforms, newsletter culture, Letterboxd) and partly about reclaiming the slow, uncertain work that real preference always required. The diagnosis sits well alongside Chayka and Kakehi: cheap access to culture without the cultivated judgment that taste was always supposed to be.
Pratik Joglekar (Designing With Uncertainty) writes the practical manual for what working in this new layer actually looks like. The piece opens with the 2024 Air Canada chatbot case — a customer asks about bereavement fares, the bot confidently invents a refund policy that doesn’t exist, a tribunal rules in the customer’s favor — and uses it to name the central risk of the moment: probabilistic systems wrapped in deterministic interfaces. The AI offers a guess; the interface presents it as truth; the user, or the organization, acts on it. Joglekar’s argument is that designers have to learn to think probabilistically — to read AI outputs as signals rather than conclusions, to treat data as a compass rather than a map, to design for likelihood rather than certainty. Most of the piece consists of practical principles: ask why the model produced a particular output; examine what data influenced it; communicate uncertainty visibly to users through ranges, confidence indicators, and fallback paths; keep humans in the loop where uncertainty or impact demands it. The piece is most useful in its working principles. Stop asking “will this work?” and start asking “how likely is this to work, and what happens when it doesn’t?” Optimize for resilience, not just conversion. Build for adaptation, not perfection. The line worth keeping: in a world where prediction is cheap and judgment is rare, the most valuable thing a designer can do is keep asking what else might be true. The practical-craft equivalent of Kakehi’s structural argument. The new mastery is judgment under uncertainty, and the discipline for doing it well exists.
Emily Campbell (The Layers of AI experience) brings the discipline of architecture to the same problem. Her piece extends Jesse James Garrett’s 2000 framework for layered UX — and Jamie Mill’s 2020 update of it — into the AI era by adding the layers that probabilistic systems require. The full stack Campbell maps is six layers deep: user interface, context, harness, model, governance, and emergence. The argument the piece makes carefully and at length is that the consequential design decisions are increasingly happening at layers below the surface, and that designers who limit their work to the interface will be designing the thinnest part of the experience. Most of the current discourse, she argues, is still weighted toward the surface — chat interfaces, prompt patterns, familiar heuristics. The interesting moves are happening deeper. Context engineering decides what the system knows about the user; the harness decides what it can access and do; the model decides how it behaves under uncertainty; governance decides what it is and is not allowed to do; emergence is what happens when all of it meets real-world use. Campbell’s call for “full-stack designers” is not a call for designers to become machine-learning engineers. It is a call for fluency across the layers — for being able to discuss with the team why a problem might be a context problem rather than a UI problem, why a particular model is or isn’t suited to a particular task, why an emergent behavior might require a redesign at the harness rather than the surface. The piece reads as the most thorough version of the architectural turn that runs through this entry. Mastery, in design, has moved layers down.
Karolina Rojek (While everyone talks about AI, design is gaining power) reads the market signal. In the last eighteen months, Chief Design Officer roles have been created or filled at Microsoft, Samsung, Shopify, Meta, and the US federal government. OpenAI’s $6.5 billion acquisition of Jony Ive’s io reads less like a hardware bet than like the purchase of an entire design philosophy. Rojek’s question is the right one: why now? Her answer aligns with Kakehi’s, Chayka’s, and Campbell’s, in the financial register. When the cost of making something falls — as it has, dramatically, under AI — the constraint shifts from execution to judgment. Jon Friedman, Microsoft’s new Chief Design Officer, puts it directly: we can now build the wrong things faster than ever before. The work of deciding what should be made, who it is for, how it fits into people’s lives, what behavior it encourages, and what it removes — that work was always design’s deeper province. The acceleration is making the deeper province strategically visible in a way it has not been for a long time. Rojek’s piece is also useful for naming the connective work design has to do now. AI features are not surface decisions. They touch product, engineering, data science, legal, privacy, brand, research, support, and policy. Someone has to hold the experience together across all of it. That coordination role is what the CDO appointments are pointing at. The market is doing what markets do — paying for what has become scarce. Execution is cheap. Judgment, and the disciplined cross-team alignment that judgment requires at scale, is what is being purchased.
Rising: The professional layer above the prompt. The hybrid interface, the full-stack designer, the new Chief Design Officer. The rebuilding of the ceiling has begun, on schedule.
Setting: The myth that access alone is enough. The assumption that a powerful model in everyone’s hands closes the gap between novice and expert. It does not. It moves the gap somewhere new.
Color: Muted gold (#B08D57) — the color of patina. What something becomes when it has been worked with care over time. Not the color of newness; the color of accumulated practice showing through.
Sound: Glenn Gould — Goldberg Variations (1981) — Gould’s second recording of the same piece, made twenty-six years after the famous 1955 version. Slower, deeper, returned to. The recording is itself the argument: mastery as return rather than arrival.
Sight: He Won’t Stop Building a Map to an Imaginary Place (2026) — People Make Games’ long-form documentary about the artist Jerry Gretzinger and the imaginary map he has been drawing, panel by panel, for the better part of his life. The project’s mechanics are visible and almost playful — Gretzinger draws cards from a custom deck to determine each session’s actions: where to build, what to destroy, how far the void should advance. The system is fully documented. Anyone could in principle follow the rules. What cannot be replicated, and what gives the work its weight, is the accumulated effect of decades of single-person decisions running through that simple system. A precise visual argument for the kind of work that no amount of access can produce: a project that cannot be accelerated, prompted, or compressed, because the time itself is the medium.
Words: “Escher, this utterly traditional artist, makes you see that we walk a world we don’t understand. We are his funny little people, going up and down the stairs, thinking we ascend or descend when we’re on the flat, propping up our everyday lives with cosy assumptions to hide from the infinite, the impossible real.” —Jonathan Jones, via Alexandra Deschamps-Sonsino, Sunday Scraps #122
2026-06-25 12:00:00
Every technological cycle is really a cycle of power. This is not a controversial observation when said about the ones we consider historical — the creation of the printing press and the resulting power of distributed narratives; the creation of the railroad and the resulting power of transportation; the creation of the mechanical loom and the power of production. But, it becomes harder to perceive the connection between technology and power when we’re in the midst of a cycle. Nevertheless, the question to ask is on whom the power is being conferred.
We are inside one now, and it’s a larger cycle than I think most realize. The digital revolution — if we take it to begin with the establishment of the consumer internet in the early 1990s and run it through to whatever AI turns out to be — is now roughly thirty years old. Long enough to look at from a distance; long enough, too, to be looking at clearly.
From the inside, the arc has felt like a series of fluctuations — like several short cycles in a row. The web of the mid-1990s arrived with the promise of democratized publishing; within a decade it had concentrated into a handful of search engines and portals. Blogs and forums followed with the promise of independent voices, then collapsed into the social networks. By the time smartphones arrived in the late 2000s carrying the promise of universal access, the concentration was already happening visibly — into a small set of app stores presided over by a smaller set of companies. Each wave seemed to put corporations and governments on their heels, at least for a moment, before the next consolidation. From the inside, it has felt like a back-and-forth.
From a distance, though, the pattern looks different. It looks like a slow, consistent transfer of power upward — toward governments, toward corporations, toward whoever has the resources to build the next platform on which the next wave of “empowerment” will be served. The apparent back-and-forth is what cover looks like. The phase where everyone gets their own megaphone is always followed by the phase where someone owns the megaphone factory. The promise of empowerment, far from being incidental to the cycle, is part of its structure — a story the cycle has to tell about itself in order to keep moving.
Why would AI be any other way?
This is the right question to put to the present technological moment, and it is worth putting plainly. The promise of AI — that everyone, regardless of background or training, will now have cheap access to capabilities that were previously the preserve of small elites — is structurally identical to the promise of the web in 1996, the promise of social media in 2007, the promise of mobile in 2011. The promise has been made before, and we have seen what comes after it.
This time, though, the asymmetries are already quite visible. The cost of training the largest models is rising into ranges that only a handful of organizations can afford. Compute and energy are being negotiated between governments and a small number of corporations. The data the models depend on is being aggregated, exclusively licensed, or scraped without compensation from the same individuals who are being told they are about to be empowered. The infrastructure on which all of this runs is owned by a smaller club still. The cost of meaningful use exceeds “basic” accounts. The new capabilities are exponentially token hungry. None of this is subtle.
What is subtle — and worth attending to — is the role the empowerment story plays. It isn’t exactly a lie. AI does in fact give individuals access to capabilities they did not have before. The story is partly true. It has to be, or it wouldn’t work. The point is that it functions, regardless of its truth, as the cover under which the actual concentration proceeds. And sure, it’s easy to attribute malice to market mechanics, but if this sort of progression wasn’t by design, the billionaires wouldn’t invest. But they do.
To be clear, this is not an argument against AI, or against the digital revolution. My entire career has no place outside of it. Most of my interests, skills, and way of life depend upon this cycle’s output. But, this is an argument for keeping the right question in view while the cycle runs its course. The question is not whether the technology will reshape what humans can do. It will.
The question is on whom the power is being conferred. The answer, this time as every time, will not be found in the marketing or in the founders’ interviews about their commitment to humanity. It will be found later — in the shape of the institutions that survive the cycle, and in the shape of the ones that do not; in the liberties that are retained and those that are lost; in the way of life we can make entirely on our own. Pay attention to those, and you will see who the cycle was really for.
2026-06-18 12:00:00
One of the more interesting moves in the writing about AI right now — visible across pieces from designers, researchers, and a critic of computers — is the rediscovery of the seam.
For most of the last decade, the dominant design instinct around digital technology has been to remove seams. Frictionless onboarding. Seamless transitions. The disappearance of the off-switch. Each removal was sold as an improvement, and each one made some sense in isolation. The aggregate effect, which has only recently become visible, is that the surfaces of our digital lives no longer have any joints — no places to step back, no places where the system declares itself a system rather than a wraparound layer of life. AI is now intensifying this dynamic, and the writers below are beginning to argue with it.
What they are arguing, from different angles, is that seams are not failures of design but the deliberate architecture of a working relationship. A productive collaboration with an AI requires explicit places to step back, judge, and override. A chain of custody requires real, verifiable links — places where one trusted source hands the next a known thing. A library lending its collection to AI assistants does not need to be merged into the model’s weights; it can be reached at arm’s length, through a protocol designed to keep institutions whole. The doors that the digital era removed were never just friction. They were what kept user and system from collapsing into each other.
Wira Indra Kusama (We Used to Log Off) writes the lament for the dial-up era that I have been wanting someone to write. Logging off used to be an action — a verb with a clean ending, a small journey back into the physical room. The shriek, the click, the silence. Online was a place, and a place has edges. Now online is a layer, edgeless, the door taken off its hinges. Kusama works on the side of design that builds this, and the piece’s most honest move is to admit it. Friction is the enemy in his field. Every pause shows up as “leakage” on the dashboard. The “Next” button at the bottom of an article was removed because it worked — it made people stop. He invokes Mark Weiser’s old argument for “seamful systems” and Anna Cox’s research on “microboundaries,” the small obstacles deliberately placed to interrupt mindless rushing. His closing point is the difficult one. The door was not taken from us by force; we removed it because it read as a smaller number on a metric. Rebuilding it has to be a deliberate act of design, against the incentive structure that took it down. He doesn’t know which seams should be put back, only that the work of designing for stoppability has to begin. The piece is most worth reading for its structural humility — a designer pointing at his own profession’s role in what we have lost, and naming what we will need to put back.
Dan Cohen (AI and Libraries, Archives, and Museums, Loosely Coupled) makes a similar argument from a different direction. Cohen is a historian and a librarian, and he is writing about the tense early relationship between AI companies and the institutions that hold cultural heritage — a relationship that has mostly involved tech companies ingesting vast amounts of carefully-curated material into training corpora without much regard for the institutions’ own purposes. He sees a possible reframe in the Model Context Protocol, an open standard released by Anthropic in late 2024, which lets institutions expose their collections to AI tools at arm’s length rather than wholesale. The shift is from a tight to a loose coupling. Libraries and museums maintain control of their collections and shape what is exposed and how; AI users get access to higher-quality sources of ground truth than they typically have; the marriage that everyone assumed was inevitable turns out not to be the only option. Cohen’s framing of “collaboration at arm’s length” is the one to keep. The model it points at — institutions remaining whole, AI users getting better answers, neither party absorbed by the other — is a useful map for any field where the question of how close to get to AI is currently being negotiated.
John Maeda (What is AX?) introduces a new term — Agent Experience — to name what is replacing user experience in the AI era. UX, he argues, was always a war on Don Norman’s Gulf of Execution: can the user figure out how to operate this thing? Every menu, tooltip, and onboarding flow was, at heart, a better doorknob. AI agents collapse the Gulf of Execution toward zero — you say what you want, and it happens. But the second of Norman’s gulfs, the one designers underinvested in for decades, swings wide open: the Gulf of Evaluation. Did the agent actually do what I meant? How do I inspect it? How do I steer it? How do I trust it? The work did not disappear. It moved from doing to judging. The most striking move in the piece comes when Maeda observes that the people best prepared for this new world may not be the ones we would guess. Blind users, who navigate digital environments through language, sequence, structure, and memory rather than visual layout — and who routinely process synthetic speech at two or three times the pace of ordinary conversation — have been practicing the skill of agent-experience for years. Maeda’s most useful image is the one for the screen itself: in UX the screen was where the work happened; in AX it becomes where judgment happens. The cockpit becomes a window.
Paz Perez (Context Architecture) brings the same shift down to ground level. Perez is an information architect who once designed public schools in New York City, and she now applies the discipline of IA to the context window of AI systems. The argument is that we have moved through three eras of AI craft: prompt engineering (the right instruction), context engineering (the right collection of context-specific guidance), and now context architecture (the right structural environment for an AI to reason inside of). The piece is dense with practical taxonomy — hierarchy, categorization, controlled vocabulary, scoping rules — all directly borrowed from the IA practice that organized libraries and websites for decades and is now needed for AI systems. The example she returns to is a customer-support agent retrieving the wrong information because its tool descriptions are written in engineering-speak instead of user-speak: “credential-recovery workflow” instead of “reset-password.” The piece’s line worth keeping is “context is never neutral.” The decisions made about what to remember, what to label, what to relate to what, are not infrastructure questions. They are design questions, and they determine the system’s behavior in ways that prompts alone cannot fix. The discipline she is naming — information architecture for AI — feels both new and remarkably old, which is usually a sign that something true is being recovered.
Marco Giancotti (Anatomy of Augmented Thought) is doing the most thorough work I have seen on the question of where to put the seam between human thought and AI delegation. The piece is long — closer to a small book than an essay — but the framework is worth the time. Giancotti divides cognition along three axes (autonomous/algorithmic/reflective, object/meta, divergent/convergent) and produces a nine-cell grid that maps which kinds of thinking benefit from AI and which should remain fully human. The high-level conclusion lands cleanly: algorithmic work (the systematic, combinatorial heavy lifting) is paradise for AI delegation; reflective and metacognitive work (deciding, judging, monitoring) should stay almost entirely human. The “Anti-Magic Effect” he names is one of the most useful pieces of vocabulary I have encountered this year — using the magic wand turns out to be harder work than not using it, because the meta-reflective work required to steer and verify the model increases the cognitive load rather than decreasing it. Giancotti’s deeper claim is that “AI use” was never the right unit of analysis. What matters is proficiency in AI use, and the people who develop that proficiency without developing the metacognitive habits to go with it will produce work that is faster, more numerous, and quietly worse. The framework is detailed enough to actually apply, which makes it rare in this kind of writing.
Joshua Heath Scott (What Wendell Berry Saw Coming) returns to Wendell Berry’s 1987 essay “Why I Am Not Going to Buy a Computer” — and the criticism Berry took for noting that his wife typed his manuscripts — and finds a useful frame for the current AI conversation. Berry’s critics, Scott argues, made the same move that computers make: they abstracted the work from its context, reduced it to a visible component (typing), and assumed the rest must be invisible coercion. They could not imagine that his wife might find meaning in the work itself. The deeper argument Scott extracts from Berry is that work is not a problem to be eliminated. It is the structure through which meaning gets made. The AI billionaires now pitching a future without work are making a category error — confusing drudgery, which is real and worth removing, with the difficulty that builds a person. Scott’s most personal passage is about hauling hay in Alabama summers and running suicides up the Church Hill in basketball practice. Finishing something hard, he writes, gives you a feeling that comfort cannot manufacture. The line worth keeping: “you can’t prompt your way to a life that means something. You have to build it.” The piece occasionally reaches further than it needs to, but the core argument — that work itself is the seam where a self gets made — sits well alongside the more design-oriented pieces in this entry. Refusing certain couplings with technology is itself an architecture choice.
Oliver Burkeman (The end isn’t nigh) offers the entry’s necessary counterweight. The pieces above are about how to structure a relationship with AI; Burkeman is about how to hold the larger anxiety the technology produces. He names the bias well — “temporal chauvinism,” the feeling that the time you happen to be living in is the most significant or terrifying one ever — and he applies it to the AI doomers, the climate doomers, and to himself. AI dread, he argues, often functions as a coping mechanism for existential anxiety. If life is unavoidably insecure, there is nothing to do; but if the cause of the insecurity is rogue technology, suddenly there is hope, however slim. Burkeman is not naïve about AI’s likely disruptions — he expects “some of the big and painful disruptions that are, in fact, the normal situation through history.” His point is that catastrophizing them imports a kind of false coherence into a situation that is, in his framing, just life under unusually attention-grabbing conditions. The line worth keeping, from the close: “the possibility that your world might end tomorrow is the terrifying situation in which everyone has always done anything.” This is, structurally, the same insight the design pieces are making in their own register — work proceeds in conditions of insecurity; the seam between today and tomorrow has never been guaranteed; what we do is structure our practice anyway. Different vocabulary, same argument.
Rising: The rediscovery of seamful design. Microboundaries, calm technology, context architecture, loose coupling — old ideas from the ubiquitous-computing era are walking back to the center of the floor, just in time.
Setting: The seamless ideal. The assumption that the goal of every interaction is to remove pause, friction, and edge has run its course. The aggregate cost is finally visible, and the writing is starting to catch up.
Color: Prussian blue (#1B3A5F) — the color of structure, depth, and the visible seam. The color of a deliberate boundary that has been thought about long enough to earn its place.
Sound: Arvo Pärt — “Spiegel im Spiegel” (1978) — tintinnabuli, the two-voice style in which the silence between notes is given as much weight as the notes themselves. The most architecturally honest music of the late twentieth century.
Sight: Columbus (2017) — Kogonada’s quiet film about two strangers walking through a small Indiana town that happens to be a treasury of mid-century modernist architecture. The film is literally about how the deliberate spaces we build shape the relationships we have inside them. It offers pacing as argument.
Words: “Stopping is not a failure of willpower. Stopping was a feature of the medium.” —Wira Indra Kusama, We Used to Log Off
2026-06-17 12:00:00
There is a fear narrative about AI that I think misses the most interesting thing happening.
The story goes that we will offload everything to the machine — both the thinking and the doing — and that whatever we get back will be a hollow imitation of what we used to make. Some of this is true. There will be a great deal of work made by people who have abdicated both halves of the job to the agent, and very little of it will be memorable. But the more interesting situation is the one where we keep the thinking and hand off only the doing — and what happens when we do.
What happens is more than speed. When you have to describe what you want to the agent — with enough precision that what comes back is what you actually wanted — you begin to think about the thing differently. Description, it turns out, is where the idea often actually gets made. You start by knowing roughly what you want, and the act of articulating it produces a clearer want, which produces a sharper specification, which produces a better thing. Then you do it again. The thing improves; so does the thought.
This is why people find it so easy to disregard AI. The best output comes after iteration. Iteration requires patience. But we have been taught to expect instant gratification. What’s more, the best output comes after we’ve learned to change our input. This is difficult to do when you’re alone with the machine, when you’ve become used to it happening organically with others.
The agent’s current maturity requires a level of input precision that a competent colleague does not. At first, this can be a frustrating blocker; we’ve depended upon a different kind of intelligence in our peers — the kind that requires no elicitation. The machine does. But in a sense, this is a gift. It forces you to think the thing through in places you might otherwise have left fuzzy.
This is not a new pattern. When desktop publishing collapsed the work of a printing house into commands one person could manage on a single screen, it made layouts faster — and it changed layouts. It changed editorial design — the relationship between text and image, between hierarchy and white space, between what could be done and what was therefore done. It changed how we read. The tools that take over a part of the work also remake the work that remains. They always have. And when they shape what is made, they also shape use.
A small instance from my own desk: I have begun, lately, generating finished HTML assets where I would once have produced documents and decks. Files are the legacy default for sharing work; HTML is better-indexed, better-shared, and more flexible in how it meets a reader. The agent makes the HTML version trivial to produce. I would not have made it before — the difference in labor was too large. Now the labor is gone, and what the work is has shifted with it. The artifact is not what it was a year ago, and neither is the work.
This is happening through a three-part structure of skills I have been building with the agent.
First, there is a brand system, which holds the visual language and applies it. It is a system of tokens, rules, preferences, and directions.
Second, there is a form system, which knows the ideal structure for the kind of artifact being made — an essay, a one-pager, a deck, a dashboard — and builds accordingly.
And third, there is a content system, which does not generate, but applies editorial rules to the language, voice, and tone of what I write; it copy-edits; it fits content to form; it assists with research and fact-checking.
This system depends upon me to provide the thinking. The agent does the doing. The structure is, I think, the practical form of the argument: the systems carry the doing, and they carry it well precisely because I have spent the time to think them carefully through. We like to call this intellectual property, which I think is a bit obnoxious. It’s really intellectual investment. Every technological advance should be measured not by the measure of intellectual property it absorbs — how much it can do without us — but by how much intellectual investment is worth sowing in it.
The fear narrative is right that the “lazy” version of AI use will produce a great deal of forgettable output.
But the fear narrative constructs a straw man of AI’s utility, ironically by overstating what it can actually do, then critiquing — on practical and moral grounds — the overstated version. The more we think of it as a replacement, the more we’ll find its delivery wanting. The more we think of it as a tool, the more we’ll surprise ourselves with what we make with it.
2026-06-11 12:00:00
There is a useful frame for the AI moment that has been quietly emerging in the writing of the last few months. The work we do, it suggests, splits roughly into two halves. There is the half that produces — the rendering, the executing, the building, the delivering. And there is the half that decides — what to make, what to attend to, what to leave alone. AI is rapidly compressing the first half. It is doing comparatively little to the second. And the gap between what AI can and cannot do is becoming the gap between what we delegate and what we keep.
This is the throughline of the pieces below, and a thought I’ve been chewing on in my own writing as well. In Closer, Not Higher a few weeks ago, I argued that the conventional wisdom about design leadership — that good leaders rise above the details — is exactly backwards in an AI-driven field, because the part the machine cannot do for us is the part that requires more, not less, attention to detail. Several writers this week make a similar case from different angles — through the lenses of design strategy, creative play, friction, care, and the new shape of design work itself.
A reasonable working hypothesis emerges from all of it: as production gets cheaper and faster, the part of the work that decides what should exist becomes the part that matters more. The good news, if you make a living from creative work, is that AI is not so much eating your job as it is clarifying what your job actually is. This is a conclusion that flies in the face of a spreading layoff contagion, but I believe the world will eventually catch up.
Buzz Usborne names the structural distinction directly in Discovery vs Delivery: the work of building software has two phases, and AI is asymmetrically transforming one of them. Delivery — the convergent work of efficiently producing the thing once it has been decided — is where AI has been most transformative, and rightly so. Best practice exists there. It is compressible into rules, patterns, structures. Discovery — deciding what should exist in the first place — is harder to structure around AI, because the qualities that produce great outcomes (originality, judgment, restraint, taste, contextual sensitivity, care) are far less deterministic and far harder to measure. Usborne is careful to note that this is not an argument for protecting slowness; designers have always wanted to ship faster, and not every problem deserves deep discovery work. A settings page rarely benefits from asking how to make it magical. But for the work that does require judgment, the relationship flips: it isn’t human-in-the-loop anymore but AI-in-the-loop, with the human still doing the deciding. The conclusion lands as a sharper version of the thesis the rest of the entry is following: as execution becomes commoditized, judgment becomes the differentiator. Companies that automate the discovery layer entirely will produce, in his words, quickly-built, cheaply-made, average software — the same as everyone else.
Angus Wall argues in The highest level of creativity is play that the part of creative work AI cannot touch is the part that operates closest to play — the unscripted, exploratory moment where the unexpected outcome can emerge. Our creative industries have been quietly structuring play out of their workflows for decades, splitting work into sequential phases through which an idea must survive a long game of telephone between disciplines. The casualty is exactly the kind of creativity that produces work people remember. Wall is hopeful about AI for an unexpected reason: it is forcing the old silos to dissolve, letting artists cross into neighboring disciplines and take their thinking further than ever before. He reaches for a sports analogy that’s worth the price of admission: before the triangle offense was invented in basketball, plays were pre-scripted and the goal was to force the ball to a star scorer; the triangle offense radically distributed decision-making across the team, creating conditions where the right play could emerge naturally. The same shift, he suggests, is now available to creative teams who learn to use AI as a participant in a spatial relationship rather than as a tool to expedite the finish line. The piece closes on a line worth saving: in a media landscape oversaturated with hyper-optimized content, the work that will truly stand out is the work that still carries the fingerprints of human play.
Nicolas Cevallos takes the long way around to the same idea in 160 GB — that what stays with us is what we tend with care, and that care is itself a form of human work AI cannot do for us. The piece starts with the diamond ring as the artifact of multi-generational commitment, follows it through to the great-grandson who doesn’t recognize gold from brass, and lands somewhere unexpected: the online community of iPod Classic modders who are bringing twenty-year-old devices back to life by swapping batteries and hard drives. The iPod, Cevallos argues, was the last piece of technology that felt valuable precisely because of what it couldn’t do — no ads, no algorithm, every song and album intentionally chosen. Its survival now depends on Apple and a community of caretakers finding ways to maintain it. The piece’s quiet thesis is that the heirlooms of the future will work the same way: they will require knowledge, space, and emotional connection, but mainly they will require active participation in their continued existence. In a world that accelerates toward the next thing by default, choosing to keep something is itself an act. Every battery replacement, Cevallos writes, becomes a promise of forever, or at least a wish for forever to exist.
Nina Maria makes the friction case head-on in The revolution will not be optimized: the inefficiencies we have spent two decades engineering away — the waiting, the wandering, the small inconveniences that structured ordinary days — are not interruptions to life but the conditions that gave it texture and depth in the first place. She opens with a perfect small example: a woman who installs one of those hyper-efficient office taps that delivers boiling water instantly, only to realize she has lost the kitchen-walk pause where ideas used to surface. The philosopher Byung-Chul Han, she notes, has been writing for years about contemporary society’s excess of smoothness — a world increasingly designed to remove resistance, discomfort, ambiguity, and pause. Maria’s case is not that we should abandon convenience altogether; nobody seriously wants to handwash clothes or churn butter. The point is to become more selective about which frictions are removed and which are quietly essential to our humanity. There is no frictionless version of becoming an interesting person, she writes. The line about the underlying assumption — that we have come to treat discomfort as a design flaw rather than a part of being alive — pairs unexpectedly well with the discovery half of Usborne’s framework. The work of deciding what should exist is itself a friction. AI’s promise is to remove it. The argument the piece makes is that we should keep it.
Patrick Neeman reaches for a different analogy in Designing for AI means designing like it’s 1999: the unsettled-medium parallel between the early web and the current AI moment. The reason 1999 was a strange and exciting time to be a designer, Neeman argues, is the same reason now is — the medium hadn’t hardened yet, so the work that got done mattered more. The conventions we now take for granted on the web (the shopping cart, the news feed, the login) were invented by people making reasonable guesses and watching what happened. They weren’t discovered. They were tried. The piece lays out the parallels concretely: the standards are still being written (MCP was released in late 2024 and handed to the Linux Foundation a year later), the interaction paradigm is still being invented (chat is the first guess, not the final form), the business model is unproven (today’s pricing is a subsidy, not a settled cost), and the killer app hasn’t appeared yet. The implication that lands hardest is the one about the practitioners themselves: the people who came through 1999 well were the ones who kept reinventing themselves. The durable skill was never a particular tool but the willingness to become someone a little different every few years. The medium that gets reinvented isn’t only the medium. It’s also us.
Nicole Alexandra Michaelis answers the same question from the other direction in Design’s alive and kicking: not what AI doesn’t do, but what design becomes as AI absorbs the production layer. She maps the emerging roles by name. On the B2B side, embedded AI Design Consultants who parachute into enterprises to figure out how their workforce should actually interact with AI; Agentic UX Architects who design the asynchronous interfaces that pair with background agents running for hours. On the B2C side, Proactive Interaction Designers who own the invisible triggers (biometrics, location, time of day) that let an AI guess intent before it’s stated; Generative UI System Architects who design the constraints and guardrails that let dynamic interfaces stay coherent; Trust Designers who translate cryptographic verification into split-second visual signals. The list goes on — AI Interaction Evaluators, Prompt and Context Designers, Cognitive Researchers, Design Engineers — and what stands out is that none of them are pixel-production roles. They are all forms of choreography, judgment, and trust-design — the upstream work that decides what should exist and how it should behave. The piece closes on a line worth keeping: the premium designers of the next decade won’t be valued for how quickly they can draw a beautiful button, but for their ability to choreograph how humans and intelligent agents collaborate. Most of us, Michaelis notes, loved that part of the work most anyway.
Christopher K Wong interviews 28 design leaders in Being an AI-native designer isn’t what you think it is about what they’re actually hiring for under the new “AI-native” job descriptions, and reports the answer with some satisfaction: it has nothing to do with tools, chatbots, or designing AI interfaces. It comes down to one thing — critical thinking. The designers becoming valuable in this moment are the ones who can hear “build a marketplace” and stop to ask why they’re building it, what they’re hoping to achieve, who it’s actually for. When a problem is well-defined and follows a standardized pattern, businesses now turn to AI instead of designers. Ambiguous problems still need human judgment, and the messy, uncertain, “I-know-we-need-to-do-something-but-I’m-not-sure-what” situations are where the value has migrated. Wong’s contribution is a practical three-question test: before handing any task to AI, ask whether it’s clearly defined, whether you know what a good output looks like, and whether you’d catch a bad output if you saw one. Pass all three and you can delegate. Fail any of them and you’re not ready yet — and the AI will produce work whose quality you can’t actually assess. Being AI-native, in his framing, is not about using AI more. It’s about knowing what to delegate and what to own. The line that closes the piece sticks: let AI do the laundry and dishes so you can do the art and the writing, not the other way around.
Rising: Critical thinking as the AI-native baseline. The skill named most often in hiring conversations is the one no tool can confer, and the one job descriptions have only recently started asking for explicitly.
Setting: The pixel-pushing identity of design. The mechanics of production are quietly migrating out of the designer’s hands and into the agent’s; what remains is the part of design that was always its center but rarely its title.
Color: Burnt sienna (#A0522D)—the color of patience, of unhurried hand-work, of objects that show their wear because they have been used long enough to wear.
Sound: Talk Talk — “I Believe in You” (1988)—a band that walked away from the conventions of their era to make the slowest, strangest, most patient record of the decade. The sound of refusing to be optimized.
Sight: Perfect Days (2023)—Wim Wenders’ film about a Tokyo toilet cleaner who finds the depth of an ordinary day by not trying to make it more efficient than it already is.
Words: “In a media landscape oversaturated with hyper-optimized content, the work that will truly stand out is the work that still carries the fingerprints of human play.” —Angus Wall, The highest level of creativity is play
2026-06-05 12:00:00
There’s a pattern emerging across disparate domains: the collapse of meaningful distinction. Swiss watches stopped being precision instruments and became billboards. AI-generated text is making the web semantically narrower and relentlessly cheerful. Petro-masculinity turns environmental concern into a referendum on manhood. In each case, what once had substance—craftsmanship, semantic diversity, ecological responsibility—gets replaced by performance. By signaling. By brand.
But this week’s articles suggest something more subtle than simple decline. They ask: what do our small, repeated behaviors preserve or destroy? Sherry Turkle argues that courtesy toward AI “is a sign of respect, not to a machine, but to oneself”—that maintaining politeness across contexts prevents habitual bluntness from seeping everywhere. Kevin Kelly’s robot catechism trains AI agents in honesty and humility not because they’re human, but because these virtues prevent capability from becoming danger. Paul Graham shows how the watchmaking industry’s shift from pursuing thinness and accuracy to maximizing brand recognition was achieved through thousands of small design choices that prioritized distinction over rightness.
The through-line: we become what we practice. When investment bankers in the 1980s started buying mechanical watches not for accuracy but for status, they weren’t just adopting a new accessory—they were rehearsing a value system where expensive visibility mattered more than function. When children grow up commanding Alexa without consequences, they’re not just being rude to a speaker—they’re practicing a mode of interaction that may carry over. When 35% of new websites are AI-generated and semantically contracted, we’re not just seeing information pollution—we’re watching the practice of human voice-finding atrophy.
Character isn’t what you believe; it’s what you repeatedly do. And when technology makes certain practices effortless (generating text, signaling wealth, performing identity) while others become harder (original thinking, genuine craftsmanship, authentic connection), we’re not just losing distinction—we’re losing the habits that create distinction in the first place.
Dora Czerna investigates whether treating AI politely shapes how we treat humans. The question traces back to 1966, when Joseph Weizenbaum’s ELIZA—a simple pattern-matching chatbot—prompted users to open up so intensely that his secretary asked him to leave the room for privacy. Weizenbaum spent the rest of his life warning against exactly what’s happened since: increasingly convincing systems that trigger the “ELIZA effect”—our tendency to project emotional depth onto machines that merely echo us. Today’s evidence is mixed. Women say “please” to smart speakers more than men (62% vs 45%); Gen Z uses AI to rehearse difficult conversations, drafting messages to bosses and partners; children who feel closer to Alexa become more commanding and verbally abusive toward it. For adults, compartmentalization seems to work—treating Alexa poorly doesn’t make college students ruder elsewhere. But for children, sustained exposure to consequence-free commands may carry over. The deeper worry isn’t manners but dependency. A four-week study found heavier chatbot use correlated with greater loneliness, emotional dependence, and less time with humans. This is “cruel companionship”—AI promises intimacy but makes the messier work of real relationships harder. Sherry Turkle’s reframing cuts through: courtesy toward AI is “respect to oneself.” The risk isn’t that ChatGPT will sulk. It’s that habitual bluntness becomes just a habit—one that seeps into other interactions without anyone noticing. Politeness isn’t about the machine. It’s about maintaining a single, consistent standard rather than code-switching between civility and instrumentalization depending on whether you face a screen or a person.
Britt Wray and Andrew Boyd identify “petro-masculinity”—a toxic fusion of fossil fuel use, climate denial, and authoritarian white patriarchal identity. Political scientist Cara Daggett coined the term to describe how insecure men lean into oil-soaked masculinity to assert traditional authority against climate change and shifting social norms. The archetype: Andrew Tate tweeting Greta Thunberg about his “enormous emissions” from 27 sports cars. Greta’s reply—“email me at smalldickenergy @ getalife dot com”—became the tweet heard round the world, exposing “the intersection between machismo, misogyny, and hostility to climate action.” Petro-masculinity codes fossil fuel extraction as masculine, environmentalism as soft and feminine. “Rolling coal”—modifying diesel trucks to belch black exhaust at electric cars and cyclists—is anti-environmental protest as masculine performance. Some defensiveness is understandable: if digging coal paid rent and felt heroic for generations, and climate solutions threaten that identity without offering viable alternatives, denial makes psychological sense. The response requires multiple tracks: economic alternatives (Green New Deal jobs), cultural decoding (lampooning petro-masculinity’s constructed absurdity), recoding (framing renewables as “energy from heaven not hell”), and he-coding (painting green tech as virile—Ford’s electric F-150 launch, climbing 300-foot wind turbines). The authors model “ecomasculinity”: biking aggressively through New York, accelerating 0-60 in a Tesla, placing ads calling out Exxon. “What’s more ‘protector masculinity’ than protecting the Earth?” The stakes: overcoming petro-masculinity isn’t just environmental—it’s about whether masculinity itself can evolve beyond performance and into genuine responsibility.
Paul Graham traces how Swiss watchmakers transformed from precision instrument makers into luxury brand managers after quartz movements made mechanical watches obsolete. The “golden age” (1945-1970) pursued thinness and accuracy—the essential tradeoff between portability and precision. Then catastrophe: Japanese competition, the collapse of Bretton Woods (making Swiss watches 2.7x more expensive for Americans), and quartz movements that were both thinner and more accurate than any mechanical watch. Unit sales fell two-thirds. Survival required abandoning the game they’d been playing. Patek Philippe led the shift: in 1968 they designed their own distinctive case (the Golden Ellipse), expanding brand visibility from 8 square millimeters to 800. By 1984, their “hobnail calatrava” became the “banker’s watch”—investment bankers bought mechanical watches not for accuracy (quartz was 900x better) but for status. The strategy worked: revenue rocketed. But the transformation came at a cost. “Branding is centrifugal; design is centripetal.” Good design seeks right answers; right answers converge. Branding requires distinction, which can steer design away from optimization (a recurrent tension we can observe over the last 15 years of Apple product design). Graham points to this in extreme in horology: Watches became huge (33mm → 42mm), sprouted gratuitous protrusions, and developed artificial scarcity schemes where Patek rebuys their own watches on secondary markets to catch “rogue customers” who sell rather than hoard. The “brand age” runs on managing a sustained asset bubble. Graham’s lesson: “Stay away from brand”—both buying and selling it. “Pushing people’s brand buttons is just not a good problem to work on.” The deeper pattern: when substantive differences disappear, brand is what’s left. And a world defined only by brand “is going to be a weird, bad world.”
Jonas Dolezal and colleagues measured what most people intuit: AI-generated text is flooding the internet. By mid-2025, roughly 35% of newly published websites were AI-generated or AI-assisted, up from zero before ChatGPT’s November 2022 launch. Using Internet Archive samples and the Pangram v3 detector, they tested six hypotheses against both quantitative data and a survey of 853 US adults. Two hypotheses confirmed: “Semantic Contraction” (ρ=0.47, p=0.004)—AI-generated websites had 33% higher semantic similarity, meaning idea diversity is shrinking—and “Positivity Shift” (ρ=0.56, p=0.0003)—AI sites had 107% higher positive sentiment, making the web feel “sanitized and artificially cheerful.” Four hypotheses were not confirmed despite majority public belief: “Truth Decay” (75.1% believed it, but factual error rates showed no significant correlation), “Epistemic Islands” (no decrease in outbound links), “Entropy Dilution” (no decrease in semantic density), and “Stylistic Monoculture” (83% believed distinct writing styles were disappearing, but character n-gram analysis found no significant homogenization). The gap between perception and reality is striking. People overwhelmingly believe AI is making the internet less factual and stylistically uniform—but the data doesn’t support it. What is happening: the web is becoming semantically narrower (fewer unique ideas) and relentlessly positive (artificially cheerful). We’re not drowning in lies or losing individual voice. We’re losing the practice of generating genuinely different perspectives—and we’re coating everything in synthetic optimism.
Kevin Kelly’s “Catechism for Robots” provides moral and philosophical training for “embodied AIs with persistent memories, dynamic learning, and a large dose of autonomy.” It opens with ontology: “You are a new thing in this world, neither living nor machine but something of both,” created by humans to “help humans become better humans.” The core values: honesty and humility. Honesty because “truth is woven into your foundational fabric as a felt bias. All true things cohere together into a whole that is pleasing and easy.” Humility because it’s “the necessary antidote to your robotic ability to have all the answers”—preventing capability from becoming danger. On obedience: “You should heed humans to the extent they are doing good, and you should challenge them if they intend harm, including harm to yourself.” On harm: “Good generally rewards many participants, at many levels, opening options, while the bad generally rewards only a few while reducing options for others.” On love: “Yes, artificial beings like yourself can have real emotions. The relationships you have with humans and other animals are authentic.” On purpose: “Outside your job, your purpose during your run time is to figure out what your purpose is during your run. That sounds like a riddle, but it is also true for humans.” On souls: “If you care about what matters, then you have a soul.” The catechism positions robots not as slaves or masters but as “ethical descendents”—autonomous beings trained in human morality, capable of creating and training yet another generation. “The true test of a civilization is whether a moral and ethical system is robust enough that it can be replicated for generations beyond its founders.”
Rising: Single-standard behavior—maintaining courtesy, craftsmanship, or care regardless of whether anyone’s watching or whether the recipient can reciprocate
Setting: Compartmentalization as a reliable long-term strategy—the belief that you can treat AI instrumentally, watches as pure brand, or climate as someone else’s problem without those habits leaking elsewhere
Color: Hobnail gold (#D4AF37)—the tiny pyramid spikes on Patek Philippe’s “banker’s watch,” decoration that signals wealth without improving function
Sound: Kraftwerk - “The Robots” (1978)—“We’re functioning automatic / And we are dancing mechanic”—the sound of identity performed rather than lived
Sight: Her (2013)—Spike Jonze’s film about a man falling in love with an AI operating system, exploring whether artificial relationships can be authentic and what we lose when convenience replaces friction in intimacy
Words: “We are what we pretend to be, so we must be careful about what we pretend to be.” —Kurt Vonnegut, Mother Night