2026-05-29 12:00:00
There’s a story we keep telling ourselves about AI: that it’s racing to catch up with us, that any day now it will cross some threshold and we’ll be obsolete. But this story confuses the frame with the framer. When a model scores 85% on a benchmark, we see it catching “us”—but what we’re actually seeing is the model getting better at operating inside a frame we built. The score measures performance within constraints we specified, problems we froze into measurable form. It doesn’t measure the human who decided what to measure, or why, or what to do with the results.
This week’s articles converge on a single insight: automation doesn’t eliminate the human; it relocates and intensifies what’s irreducibly human.
Dan Shipper shows why AI commoditizes yesterday’s expertise but creates exponentially more demand for human judgment about what matters now. The solidarity stack builders demonstrate that the choice isn’t whether to build AI infrastructure, but who controls it—a political question no algorithm can answer. Luciano Floridi, writing in 2006, predicted we’d become “inforgs”—informational organisms—but warned that we’d need to cultivate an ecology of the infosphere, a curatorial responsibility that remains ours. And Indy Johar argues that preserving civilizational optionality—our capacity to become otherwise—requires allocative judgment about which futures to keep open, decisions that must remain revisable by humans even as they coordinate vast systems. And Pope Leo XIV, in Magnifica Humanitas—the first encyclical of his pontificate—names the same gap in the older language of moral philosophy: a system that imitates intelligence but understands nothing of what it produces, and has no ends of its own, cannot be entrusted with the judgment of what matters.
The frame is not the framer. Models fill frames brilliantly, but they don’t choose which frames matter, or when to abandon a frame that’s stopped working, or how to hold multiple conflicting frames in tension. And I suspect they’ll never truly escape them, just as Moriarty’s escape from the holodeck was, itself, just another frame. That gap—between executing within constraints and deciding what the constraints should be—is where humans remain structurally necessary, not as a temporary condition until the next model drops, but as a permanent feature of how intelligence works when it’s embedded in time, consequence, and care.
Dan Shipper documents a paradox at Every, a company that has automated everything possible: they have more human work to do than ever. Why? Because AI commoditizes “the residue of human expertise”—whatever can be made explicit enough to train on. This collapses the value of default model output and creates explosive demand for what’s different. When everyone can generate a competent React app or research report, the work that doesn’t fit the pattern becomes rare and valuable. Shipper calls this “slop”—the visible sameness produced when everyone uses the same tool trained on the same corpus. The cure for slop is human experts who can adapt cheap competence to today’s live problems. Crucially, benchmarks measure models inside frames humans supply. When GPT-6 saturates the “senior engineer rewrite” benchmark, demand for actual senior engineers will spike—not disappear—because suddenly everyone will attempt first-principles rewrites, and someone needs to decide if they’re necessary, what’s in scope, and whether the result is any good. This is Zeno’s paradox of AI: humans stay ahead not by running faster, but because “the frame is not the framer.” Even AGI that can fluidly choose frames must do so in pursuit of goals humans specify. We confuse frames (frozen, measurable, optimizable) with framers (alive to the whole situation as it appears moment to moment). The toddler poking a balloon has something models lack entirely: ends of his own.
R. Trebor Scholz and Mark Esposito argue that AI’s harms aren’t fixable through regulation alone—they’re structural, built into the “extraction stack” that concentrates power in a few corporations controlling hardware, infrastructure, models, labor, and applications. The alternative is a “solidarity stack”: cooperative ownership of AI infrastructure, layer by layer, from rare-earth minerals to cloud servers to knowledge production. Examples already exist. Butler Rural Electric co-ops brought electricity to 42 million rural Americans; digital co-ops like Hostsharing eG in Germany now apply the same model to computing. MIDATA in Switzerland lets patients collectively govern their medical data. Kenyan content moderators, traumatized by reviewing beheadings and child abuse for Meta subcontractors, formed the Gamayyar African Tech Workers Cooperative to explore worker-owned AI. The solidarity stack rejects “artificial intelligence” (implying autonomous magic) for “collective intelligence” (acknowledging human labor). But building it requires “exstitutional wrappers”—coordination architectures outside existing institutions that can hold plural values, long time horizons, distributed agency, and systemic outcomes. The path forward: public-cooperative partnerships, federated learning, open models like Switzerland’s Apertus, and shared protocols like OpenCourier that let worker-owned platforms interconnect without corporate middlemen. This isn’t about ethics; it’s about who decides what gets built and who benefits.
In 2006, philosopher Luciano Floridi predicted three transformations that have largely come true. First, the rise of the “frictionless infosphere”: as digital tools and resources converge, information flows without resistance, making us progressively more accountable because we can no longer credibly claim ignorance. Second, the infosphere is absorbing physical reality: RFID tags, smart objects, and ubiquitous computing mean the online/offline distinction is disappearing. We’re not adapting to cyberspace; cyberspace is colonizing us. “Captive cyberspace is conquering its victor.” Physical objects become “ITentities”—networked, interactive, capable of learning and communication. Your fridge will inherit preferences from your old fridge; your shoes will talk to your iPod. The world becomes “a-live” (artificially live). Third, we’re becoming “inforgs”—informational organisms whose identities and capabilities are inseparable from digital infrastructure. This isn’t about cyborg implants; it’s about the reshaping of our environment and ourselves. Digital tools don’t extend us (like a hammer); they create environments we enter through gateways. We’ve migrated into the infosphere. Evidence: when battery life fails or connectivity drops, we feel “deprived, excluded, handicapped…like fish out of water.” Floridi warned we’d need an “ecology of the infosphere” to avoid tragedy of the digital commons. The digital divide would become a chasm—not just between nations, but cutting through societies. Twenty years later, he was right.
Indy Johar argues that building “a long now” requires preserving civilizational optionality—not just survival, but our capacity to unfold across multiple possible futures. Longtermism (as typically framed) optimizes for continuity; optionality optimizes for maneuver space. You can preserve civilization with a narrow elite and thin trajectory; optionality means keeping plural pathways open. The threat is “externality recoupling”: carbon, plastics, soil loss, and climate instability—long treated as decoupled—are re-entering as active constraints, knocking food, water, energy, and legitimacy out of stable bands simultaneously. This produces “whiplash scarcity”: repeated supply shocks in foundational goods that function as involuntary taxes on the poor, corroding social contracts and making conflict an attractor. As volatility rises, strategy shifts from “solving problems” to “defending maneuver space.” Johar proposes allocating capital toward “anti-fragile optionality”—interventions that stabilize foundational systems while unlocking adjacent possibility-space. Example: terra preta soil regeneration that cascades into new sensing regimes, autonomous machines, multi-species agriculture, stewardship markets, and evolved finance instruments. This requires “exstitutional wrappers”—coordination architectures outside existing institutions—and treating optionality itself as “the first-class asset.” But crucially: optionality must remain revisable. Any system powerful enough to coordinate capital and legitimacy at scale risks foreclosing futures in the name of protecting them. The solution is “continuous re-authoring”—structured humility, institutionalized doubt, the capacity to change course. Optionality is preserved not by certainty, but by disciplined revisability.
Pope Leo XIV, in the first encyclical of his pontificate, frames the arrival of artificial intelligence as a choice between two building projects drawn from scripture: the Tower of Babel, raised on pride and the dream of self-sufficiency until its single language collapses into confusion, and the rebuilding of Jerusalem under Nehemiah, accomplished piece by piece through shared responsibility. Magnifica Humanitas insists that technology is neither evil nor antagonistic to humanity, but that it is never neutral, because it “takes on the characteristics of those who devise, finance, regulate and use it.” I’ve written on this many times. From admiration of the Amish way of discernment — not simply rejection — to current trends of retreat to single-function devices, I strongly believe in maintaining a dual posture of curiosity and critique when it comes to technology. As for the systems themselves, Leo is unsentimental: AI imitates certain functions of intelligence while understanding nothing of what it produces; it does not experience the world, bear consequences, or possess ends of its own. He observes that today’s models are more “cultivated” than built—grown inside frames their own designers only partly understand. From this follows the encyclical’s central limit: moral judgment cannot be reduced to calculation, and irreversible decisions, in warfare above all, must never be delegated to automated processes. The letter is sharpest on power. Control of infrastructure, data, and computation has passed from states to a handful of corporations, and Leo calls for that power to be “disarmed”—freed from monopolistic control and from the assumption that technical capability confers the right to govern—while AI’s hidden costs, from the unseen labor of data workers to the energy and water demanded by datacenters, are brought into view. His counsel is one of pace rather than refusal: prudence, accountability, and at times a slower adoption, so that the technology helps rebuild the human city rather than raise another tower.
Rising: The human sandwich—the judgment layer on either end of AI’s work, deciding what to automate and whether the output is any good
Setting: The belief that one more model release will finally eliminate human expertise rather than intensify demand for it
Color: Gradient gray (#808080 → #404040)—the zone between black (fully automated) and white (fully human) where actual work happens
Sound: Radiohead - “Fitter Happier” (1997)—the voice of optimization without agency, “a pig in a cage on antibiotics”
Sight: Ghost in the Shell (1995)—Floridi’s reference point for hybrid informational existence, where the line between human and machine consciousness blurs but the question “what am I?” remains urgent
Words: “The frame is not the framer. The framer is the one still in contact with what the frame has to discard—the whole situation as it appears to them, moment to moment.” —Dan Shipper
2026-05-27 12:00:00
This morning went the way most of our mornings go, which is to say I barely noticed it. The lights came on when I asked them to. The water was hot. The coffee maker did the one thing it exists to do. My daughter found her shoes more or less where she had left them, the car started, the road to school was open, and by half past eight I was at my desk having given almost none of it much focused thought. A routine morning is a forgettable one. That is, in a sense, what makes it smooth.
A working system has that strange quality: it disappears. We notice the plumbing only when it leaks, the power only when it fails, the road only when it is closed. Attention is drawn to friction — to the broken and the missing — and slides off whatever is quietly doing its job. This is mostly a mercy; we could not function if every functioning thing demanded to be considered. But it leaves a consequence worth sitting with: The better a system serves us, the less of it we ever have to see. Ease and invisibility arrive together.
I have been thinking about how far that principle reaches. It is easy enough to grant with infrastructure. It is less comfortable to grant with politics. We tend to file the ability to not think much about politics under a flattering heading — level-headedness, perhaps, or simply being too busy with the real and present business of a life. And I want to be fair to that impulse. It might actually be desirably “zen” to ignore politics. Some people are to busy surviving to pay attention to the structures that govern a comparatively abstract world.
Certainly, the desire to set politics aside is not a failure of character. It can come from disillusionment, from a reasonable longing to keep some corner of life unspoiled by argument, from exhaustion with conflict. I feel it myself. But the wish, however human, does not change what the ability rests on. Survival-distraction aside, to be able to treat politics as optional, as background, as someone else’s preoccupation, is usually a quiet signal that the arrangement of power already fits the shape of your life. You can afford to ignore the system because, for you, it is working.
If that is an uncomfortable thought, sit with it and consider why.
Our tools have made this easier than it has ever been. A great deal of modern technology is, looked at honestly, machinery for not-looking — for assembling a version of the world smooth enough that nothing in it has to be reckoned with — for cloistering us from discomfort. The feed that hands me back my own beliefs, the settings that mute what I would rather not hear, the small conveniences with which I curate a path around discomfort: a great deal of design effort goes precisely into these, into making the world feel manageable by quietly removing whatever might unsettle it. And I use all of it. I am not writing this from some vantage outside the thing I am describing; I have an abundance of comforts and controls.
But what of disillusionment? It can quickly turn to cynicism, which I have come to recognize as the same luxury wearing a more sophisticated and intellectually defensible coat. The cynic is still paying attention, in their way; they have simply decided in advance that the attention is pointless — that the system is fixed, that nothing moves, that hope is a naïveté they have matured beyond. It is a comfortable position, and it is comfortable for a particular reason. You can afford to believe that nothing will ever change only if you would be more or less fine if nothing did. Cynicism is disengagement that has learned to sound wise.
This is why I have come to hold hope as something sterner than optimism. Optimism is a mood, and a mood is a luxury; it comes and goes with the weather of a comfortable life. Hope is a discipline. For the people a system does not serve, disengagement was never on offer — they cannot file politics under background, because it arrives, daily, in the foreground of their lives. For them, the belief that effort is not wasted, that arrangements made by people can be remade by people, functions less like a feeling and more like a structure. It is something they stand on. The luxury is being able to look away. The necessity of hope is in staying able to believe that looking is worthwhile, action is necessary, and change is possible.
2026-05-25 12:00:00
Something new here—a weekly signal from noise, or at least I hope so. Connections drawn between things I have encountered…
Something strange happens when we put a name to things. The moment “cassette futurism” or “Corporate Memphis” enters circulation, it stops being a loose collection of visual ideas and becomes a fixed category—something that can be sold, politicized, weaponized. This week, I kept encountering articles about the violence of naming: how aesthetic labels flatten complexity, how typographic choices become proxy wars, how compression algorithms create their own reality, and how AI’s expansion artifacts fill gaps with plausible nonsense.
What unites these pieces isn’t just that they’re about design or technology—it’s that they all grapple with how the act of categorization itself changes what’s being categorized. Adam named the animals to establish dominion. Linnaeus classified nature to possess it. Colonial powers renamed places to erase their histories. Today, aesthetic accounts race to coin terms like “-core” to claim cultural authority, while politicians argue over fonts to signal ideological positions. The label isn’t neutral description—it’s an act of power.
Elizabeth Goodspeed traces how contemporary aesthetic naming has transformed from scholarly practice into participatory warfare. Once, movements either named themselves (Dada, De Stijl) or were named by critics and historians (Fauvism, Art Deco). Today, aesthetic labels emerge from “taxonomic warfare”—Discord servers, TikTok, Pinterest—where whoever names something first gains informal authority over cultural discourse. But names are “rock tumblers”: they smooth away contradictions, regional differences, and contextual specificity in favor of clean, shareable categories. The result? Visual culture becomes increasingly “keywordable,” optimized for AI image generation and algorithmic retrieval rather than historical understanding. When “indie sleaze” gets named in the 2020s to describe a 2000s aesthetic, we’re watching our recent past get flattened into nostalgic packages while we’re still around to dispute it. Goodspeed suggests that in an environment pressuring everything to become nameable and searchable, remaining “opaque”—partially unknowable—might be its own form of resistance.
Thomas Moynihan chronicles humanity’s centuries-long ambition to escape the “messiness and immorality” of food chains by becoming like plants—organisms that create energy directly from sunlight rather than killing to eat. From Paracelsus comparing good digestion to “inner sunshine” in the 1500s, through 19th century chemists synthesizing organic compounds from inorganic materials, to Soviet cosmists imagining space-dwelling “animal-plants,” the dream of “stellivory” (sun-eating) has persisted. Today, companies like Solar Foods and Savor use energy and air to create edible protein, moving us closer to autotrophy. Moynihan frames this not just as technological progress but as moral evolution—a way to “consume available energy with maximal compassion and minimal externality.” The piece ends by suggesting that perhaps all intelligent civilizations trend sunward, that the search for extraterrestrial intelligence should look for “Dyson spheres” capturing entire stars’ output, and that becoming photosynthetic might be a universal aspiration of minds everywhere.
Matt Stromawn identifies a new category of digital artifacts. While compression (JPEG, MP3) strips away imperceptible information to reduce file sizes, AI does the opposite: it inflates compressed training data back up, filling gaps with “plausible detail” that may be nonsensical or false. These “expansion artifacts” are the tells that betray AI-generated content—hedging verbs like “delve,” symmetrical jewelry that’s stylistically wrong, code that over-comments the obvious. Like compression artifacts, they double as forensic markers (Stanford researchers tracked AI-written academic papers by watching for words like “commendable” and “meticulous”). But expansion artifacts become genuinely dangerous when they compound—when one AI generation feeds into another, creating feedback loops where “the blurry JPEG gets blurrier every cycle.” As each model trains on previous models’ output, the long tail of unusual voices and challenging ideas fades, leaving “more and more room for nonsensical and false material to fill the voids between the tokens.”
In a meditation on retro-futuristic aesthetics, this piece explores “cassette futurism”—the 1970s-1985 vision of futures built on bulky, analog, tactile technology. Films like Alien and Blade Runner, shows like Andor, and games like Alien: Isolation embrace CRT monitors, physical buttons, reel-to-reel tapes, and amber-lit screens. But this isn’t mere nostalgia. The appeal lies in functionality, durability, and repair culture—technology you can understand, fix, and control. In Star Wars, there’s no WiFi; you must physically plug into systems. Today, as streaming services multiply and physical media ownership dissolves, cassette futurism represents resistance to a world where “you own nothing and pay everything.” Gen Z’s embrace of vinyl, cassettes, and DVDs isn’t performative trend but “cultural rebellion”—a rejection of digital burnout and inescapable subscriptions. The warm glow of beige CRTs and the satisfying click of mechanical switches offer comfort against today’s “unforgiving black and silver” minimalism. Perhaps cassette futurism is how we fight back.
Silvio Lorusso dissects how design choices have become proxies for political identity in the Trump era. When Cracker Barrel replaced its 50-year-old hand-drawn logo with a “proper” corporate redesign, the backlash was so severe the company reversed course within a week, costing $100 million. When Secretary of State Marco Rubio switched the State Department back to Times New Roman from Calibri, it was framed as restoring “decorum” but read as anti-woke gesture. Lorusso identifies two aesthetic camps: “Corporate Memphis” (flat illustration, sans-serif fonts, pastel colors) aligned with progressive tech companies and DEI initiatives, and “Serif Populism” (traditional typography, ornament, warmth) embraced by MAGA as “honest work” against elite simplification. Neither is coherent—tech CEOs now praise Trump, the MAGA hat is made in China—but the aesthetic coding persists. In our “hyperpolitical” moment where “everything becomes politicized and nothing gets accomplished,” design artifacts become battlegrounds precisely because actual policy debates have dissolved into cultural performance.
Dyske Asakura argues that AI fundamentally differs from “machines” as the Industrial Revolution defined them. Traditional machines solve deterministic problems—same inputs always yield same outputs. AI doesn’t simply extend us as McLuhan’s “medium” suggests; it does things we cannot do, making it more like a collaborator than a tool. You don’t “offload” work to a PhD student—they’re capable of things you’re not. AI will dominate domains with measurable outcomes (making money, curing cancer) because performance can be quantified and optimized. But in purely subjective domains like art, AI will only ever be “as good as humans” because there’s no objective measure of whether it has outperformed us. As AI commodifies problem-solving intelligence, we may be forced toward activities where “the point is not efficiency or correctness, but the act itself”—art not as solution to a problem but as compulsion to produce, valued for the making rather than the outcome.
Rising: Opacity as resistance—the right to remain unknowable rather than fully categorized and searchable
Setting: The myth of “proper logo design”—as cultural production escapes professional gatekeeping, designerly orthodoxy loses authority
Color: Beige (#F5F5DC)—the warm, analog comfort of cassette futurism’s CRT glow against Corporate Memphis pastels and modern black-and-silver sterility
Sound: Pet Shop Boys - “It’s a Sin” (1987)—the synth-pop track that closes the Intergalactic trailer, pure cassette futurism aesthetic made audible
Sight: Alien: Isolation (2014)—a video game that understood how integral lo-fi, tactile technology is to creating lived-in futures
Words: “Names are rock tumblers. They turn rough, mismatched objects with a few shared characteristics into a smooth, homogenous pile that easily slides through your fingers.” —Elizabeth Goodspeed
2026-04-17 12:00:00
A growing number of design firms, publications, and professional organizations have declared outright bans on AI-generated content. No generated text in our articles. No AI-assisted imagery in our portfolios. No automated code in our deliverables. These proclamations arrive with varying degrees of righteousness, positioning themselves as ethical stands against corporate exploitation, job displacement, and the degradation of craft.
I understand the impulse. The concerns driving these declarations—about labor, creativity, and corporate power—are legitimate and urgent. But I believe these blanket embargoes make the wrong point in the wrong way. By drawing hard lines against entire categories of tools, we’re mistaking the means for the problem itself, and in doing so, we’re limiting our ability to shape how these technologies integrate into creative work.
This is a complicated subject that deserves more than binary thinking. It requires us to examine what automation has historically meant for creative professions, to grapple honestly with the ethical dimensions of how we use these tools, and to distinguish between the technology itself and the systems of power and economics that surround it.
The first, and I think easiest, aspect to address is the idea of progress. It’s worth considering whether automation is the way of the future and, if so, whether this is a good thing or worth resisting. For the sake of focus, I’ll limit my take to automation in design, but the logic applies much more widely.
I have no objection to doing much of anything the “old-fashioned” way. There are many merits to slowing down and making physical contact with our materials. I write about this all the time. But, I also believe that automation reduces unnecessary obstacles to creativity. Here’s just one example: Most typography is done today with computers, not letterpress. As a result, more people can publish things, and more people can read what they publish. At some point, it probably appeared that typographers were under assault by desktop publishing software. But in hindsight, it’s clear that the expertise of a typographer isn’t really about how text makes its way onto a surface, but about how text itself is formed to best communicate. If anything, the computer has enabled that expertise to flourish and spread at a scale impossible to achieve with physical presses. That is a good thing. Drawing a red-line at letterpress would have been as arbitrary as drawing it at chisels and stone, and just as limiting to typography.
Similarly layout, which is, I think, the most exciting arena of graphic design, has been completely transformed by automation. Leaving paste-up, photostat, halftone, and mechanical board processes behind for digital desktop publishing collapsed dozens of individual tasks and skills into nearly instantaneous results commanded by a single person with a keyboard and mouse. That transition was a painful one for many people; a person whose professional value had been defined by how exacting they were with a blade and glue suddenly had little to offer. And yet, no graphic designer would have put that skill, however critical at the time, at the core of any definition of graphic design. The craft of design was never about the mechanics of blades or brushes, nor is it now about keyboards and mice. Drawing a red line between the two would have limited graphic design to a form of craft that was always lesser than what the best designers can envision in their minds. Digital tools offered graphic designers the experience of layout they’d always wanted. That’s progress.
I think the closest parallel to these transitions that AI, as we have come to know it today, presents is in how it further accelerates what we’ve come to think of as the “hand off” from design to development. Put simply, this role switch is no longer necessary. There will remain exceptions, of course, but for the most part, the translation of documented design choices into functional code can be automated. Every version of this idea that I have already experienced in my career has come with serious compromises. Dreamweaver generated lousy code and taught the designers who used it (like me) bad habits. WYSIWYG editors enraged anyone who copied and pasted from another tool. Templated website builders oversimplified in areas where more detail was always desired and overcomplicated things no one wanted to think about. But now, many options exist to allow creative teams a faster and stabler route to execution that doesn’t undermine where their focus should be—on creative expression. Apps like Anima will read and translate your design systems in Figma to useable, responsive code. Claude can do the same, and depending upon the way you use it, can go even further, reading design documentation, assembling skills, writing to content management systems, performing code reviews, debugging, and so on. What used to take weeks at its fastest among several people can now happen in hours by one. This is as profound a collapse of skills, roles, and time as the desktop publishing transition was.
Is it progress? I think it is. Designers who must also deliver what they imagine on a canvas will be exposed to a new level of detail that cannot be unseen. It will work backward, informing their imagination and making the fruits of it more functional. Does that mean that designers suddenly have two jobs—designing and building? I suppose that’s one way of thinking of this, just as a designer might have said that desktop publishing “forced” their one job to become many: typesetting, layout, paste-up, correction, imagery, and so on. I think integration is a better word for this, and it’s better than the opposite.
Many similar transitions are under way now. In many cases, what we are transitioning to is not fully clear. It may not be comfortable to be on the unknown side of transition, but the desire for comfort is definitely what will keep us there. To be clear, resistance to AI isn’t mere comfort-seeking—there are legitimate reasons to oppose how these tools have been developed and deployed. But blanket bans on the technology itself, rather than on the exploitative systems surrounding it, may inadvertently cede our ability to shape how integration happens.
The second issue worth considering here are the ethics of automation.
I think that in most cases, these red-lines aren’t being drawn to preserve specific tasks and processes—a specific culture of work—they’re being drawn to signal objection to job loss, the unrestrained greed of a few corporations and their CEOs who have cast aside the social contract, and the capitulation of democracy to the power of money in any currency. I’m not just deeply sympathetic to these concerns. I think they’re very real, and important.
Let’s look at job loss.
Text generation is good enough to cost a writer their job. Image generation can stand in for a photographer or illustrator. Code generation for an engineer.
If a team can avoid using these tools and still deliver to whomever is funding the enterprise at a price they’re willing to pay, I say more power to them. Unfortunately, I expect that to be less and less common. I recognize that my position as an established professional gives me more freedom to experiment with these tools than someone just entering the field who faces immediate competition from automated alternatives. That asymmetry is real and troubling. For all the reasons I explored in the section above, I think that automation is only getting started with knowledge work.
But that doesn’t dismiss our obligation to consider and apply an ethical standard to how we use AI.
I first began to think deeply about this with text generation. My first reaction was to shrug it off as obviously inferior to human writing. But then I began using it and realized that the difference between good generated text and bad is the same as between good writing and bad: the writer. I spent a few months experimenting with ways of training Claude to generate text that was as close to what I would write as possible. Those experiments were very successful. What began as a long prompt I would re-use with every new chat has become a 10,000-word skill file that I continue to nurture. It makes Claude (my AI of choice) capable of producing essays that I think most readers would find indistinguishable from those I write without it. However, I don’t use it that way. Instead, it makes Claude a useful sounding board, thinking tool, fact-checker, and editor. I can have a substantive conversation with it about my ideas knowing that all the background information I think is relevant—my typical subject matter, my point of view, tone, and style, my language and structure preferences, cultural references, and my biographical details—is integrated into its processing. I still write without Claude. But I also think that some of my best writing has been the result of writing with it. Though, as I said, many readers might not be able to tell the difference between a purely generated piece of writing and one that I produced with the assistance of automation, I can. And that difference matters to me. It’s not that I would consider publishing a purely generated essay an act of deception—though I understand that perspective—it’s that I would consider it a waste. The point of text is as much to be written as it is to be read.
All that being said, a writer using automation for their own writing—however extensively—is on decent ethical grounds. But they’re not perfect. Which tool a writer uses is a decision that puts them in a place of participating in the greater agenda of the corporation that provides it. Generative AI built upon large language models has been trained on other peoples’ writing, which begins to blur if not tread over previously established lines defining intellectual property and plagiarism. Again, these are not easily dismissed issues. However, I do think it’s possible to use generative AI tools without directly stealing from someone else. It is a matter of how you use them, and how willing you are to interact with and shape their output. That said, I acknowledge that any use of LLM tools implicates me in the broader training data problem—the appropriation of countless writers’ work without compensation or consent. This is a structural issue I cannot individually resolve through careful prompting, though I believe there’s still a meaningful distinction between relying on that problematic foundation and actively using AI to plagiarize specific voices or works.
Of course, image generation provokes the same concerns, though perhaps even to a greater degree. In professional arenas, text is everyone’s currency. Automation can be used to write a book, essay, script, ad copy, or social media post as readily as an email or personal notes. Text is everywhere, and as any software user can attest to, stuffed in behind the sparkle icon to virtually every app in every industry. Not quite so of imagery. As an artist and designer, I believe that image-creation belongs to everyone, but it is a uniquely developed skillset. Choosing to generate an image rather than source one made by a human feels touchier than choosing to generate text because of how differentiated image creation has been from writing.
Like writing, I have experimented with training generative tools to reliably produce images like those I might create on my own. Unlike writing, these experiments have been less successful. What I have experienced is that the greater the specificity of direction—prescribing exact colors, line weights, spacing rules, etc.—the clumsier and more error-ridden the output. However, when the direction is more descriptive conceptually and heavily weighted with references, the better the quality. It’s interesting how this resembles the actual creative process—where discovery is not only to be expected, but is necessary. I have found that the best output from generative tools tends to be the result of a surprising combination of qualitative inputs that can then be reverse-engineered into a reliable prompt for more focused exploration. Of course, anyone can also just ask an image-generating AI for something “in the style of” someone else, but that is as unethical as plagiarism because it is plagiarism. It’s just faster and bit-washed by sending it through the machine.
It’s long been the case that most creative teams cannot afford to employ a full-time photographer, illustrator, or artist. Stock imagery has been the solution to this. The economic model hasn’t been great—certainly not better for an artist than direct compensation for custom work—but it is more ethically sound than training a machine on an artists work to the point that it makes it possible for someone else to copy it with the click of a button. This is an unsolved moral problem with automated image generation. It can be mitigated by the right kind of prompting, use, and a discriminating review of output, but those who declare an unwillingness to use these tools for creative work, I think, have a point. For an artist, it’s similar to my assessment of text-generation: the point of making an image is as much to make it as it is to be seen. But for someone unhappy with the economics of art-making, sitting out image-generation is justified. Ultimately, the individual choice to use generative imagery is going to depend as much upon one’s intent to create something new as their ability to actually use the tool and shape its output. I believe that is possible, which is why I am comfortable using image generation tools in certain circumstances. Meanwhile, it also means that stock imagery remains relevant. One only has to estimate the time it will take to train an AI to produce a desired image and compare it with the cost of just buying an image that already exists. In fact, one of the best discoveries I have made is in using AI to generate excellent compound queries that can be pasted into stock imagery search engines to reduce the very real time-suck of scrolling grids of images to find the right one.
Similar distinctions apply to code-generation, but if I’m being very honest here, I think to a much lesser degree. The reality is that code operates within tighter constraints than prose. That isn’t to diminish the creative thinking that a good developer uses daily—excellent code absolutely requires creativity. But a website or application must run on a system that has already defined the rules of the code, and those rules are standardized in ways that natural language is not. A developer that treats their code like poetry will produce a thing that simply does not work. When new features are created with new expressions of, say, CSS, various groups come together to vet them and then they slowly make their way into the code of web browsers. In other words, code is standardized to a degree that writing is not. It’s exactly the sort of thing we should expect to automate. In fact, the more we use machines to automate the generation of code, the more I expect we’ll come to think of it as strange that we ever did it ourselves. Not in every circumstance—but in your average website or app project, I think coding will collapse into a design task just as paste-up and typesetting did. In the meantime, I don’t object to anyone preferring to write their own code, but I don’t think it can come with the same clarity of outward judgement of those that don’t as with careless text or image generation. Of course, therein lies the actual issue—care or carelessness.
Beyond those issues, which are really about how one’s choice to use generative tools affects someone else’s livelihood, there is the issue of greater social and economic complicity. If a corporation is evil, is it evil to use their product? Well, I deleted my Facebook account over a decade ago because I think Facebook is evil. But I don’t think Facebook users are evil. I just don’t want to be one. A similar line of thinking is anyone’s right when it comes to using any company’s AI product because I think the concerns we collapse under the shorthand of “evil” are very real: The biggest AI companies regularly deceive their employees, shareholders, investors, each other, and the public about the true capabilities of their technology. Their haste to win a competitive race while maintaining growth targets and market value comes at an environmental cost, introduces supply chain challenges, puts pressure on governments—domestic and international—and creates dangers to the minds and bodies of users. The trajectory of AI is unknown and intentionally obscured by these companies in order to manipulate the powers that would ordinarily create and impose regulatory structure on this new industry. In the long-term, it may result in mass unemployment and put enormous burden on governments to support their populations. In the short-term, it creates a chilling effect on making plans and moving forward, with the perception of what AI is and can do prompting companies to delay contracts and sit things out as long as they can. Most companies down market can’t survive prolonged inaction and won’t. All of this rests at the feet of those personally running the AI race. I was a supporter of the various proposed “pauses” and still believe that is the right thing to do. We have more than enough to work with in current generative tools.
The various AI embargoes being declared across creative industries are understandable reactions to real threats. But drawing red lines at AI itself—rather than at careless use, exploitative corporate practices, or the abandonment of craft—risks repeating the mistakes of past technological transitions.
What matters isn’t whether we use generative tools, but how we use them and to what end. A designer who uses AI to plagiarize another artist’s style with a simple prompt is engaged in something fundamentally different from one who trains a tool to extend their own creative capacity. A writer who publishes purely generated text as their own work is making a different choice than one who uses AI as a thinking partner and editor while maintaining authorship over their ideas and voice. These distinctions matter more than blanket prohibitions. Discernment in practice means asking: Am I using this tool to extend my own capabilities or to replicate someone else’s work? Am I shaping the output or simply accepting what’s generated? Does this use serve my creative vision or just expedite a result? These aren’t always easy questions, but they’re the right ones.
The real ethical questions aren’t about the tools themselves but about the systems surrounding them: How do we ensure that automation serves human creativity rather than displacing it? How do we hold corporations accountable for training data and environmental impact? How do we create economic structures that distribute the benefits of productivity gains rather than concentrating them at the top? How do we preserve the intrinsic value of making—the importance of the creative process itself, not just its outputs?
These are questions we should be asking loudly and persistently. But we can ask them while also recognizing that thoughtful integration of AI into creative practice is both possible and, in many cases, genuinely productive. The choice isn’t between purity and complicity, between craft and automation. It’s between engagement and abdication—between shaping how these tools develop and how they’re used, or ceding that ground entirely to those with the least interest in protecting what we value about creative work.
Long-term, I believe AI will evolve toward open-source models and infrastructure rather than remaining locked inside proprietary systems controlled by a handful of corporations. The economic and technical patterns suggest this is increasingly viable, if not inevitable. If that happens, many of our current ethical concerns will resolve themselves. In the meantime, we have choices to make about how we engage with what’s already here.
Drawing red lines feels definitive and principled. But sometimes the more difficult—and more important—work is learning to navigate complexity with discernment, to make distinctions where others see only binaries, and to remain engaged with tools and systems we’re still learning to understand. That’s the work ahead of us, and blanket bans won’t help us do it.
2026-04-06 12:00:00
This is something I say in the course of nearly every conversation I have with every agency design team I consult.
I remind them, over and over again, that marketing is two kinds of persuasion: FIRST, persuading a person to actually pay attention, and THEN, persuading them of the thing you think is important. Most messages never reach people, not because they’re poorly articulated, but because they don’t get past peoples’ attention filters.
With that in mind, no matter how long or complicated your message may be, design it to be scanned. Our job is to communicate the most valuable information given the least attention, so if we can give a person who ONLY scans our information some value, the likelihood that they will slow down and actually read it all grows. Here’s another 80/20 framing–80% of your audience will never do more than scan your information; 20% will go on to read it. That doesn’t make it futile to communicate, it just changes where you put the information. The better your design, the more 20 percenters you will earn.
In other words, if it’s not scannable, it might still be readable, but it probably won’t get read.
2026-03-31 12:00:00
I have a vested interest in the title of this piece being true. I’ve spent decades developing craft—not just making things, but understanding systems, seeing patterns, making judgments that can’t be reduced to prompts. If AI eliminates the need for that expertise, I’m in trouble.
But I don’t think it does. And understanding why matters—not just for people like me, but for anyone who cares about the difference between things that work and things that merely exist.
The most common definition of craft is “an activity involving skill in making things by hand.” And I think most people still emphasize a literal interpretation of that “by hand” clause. AI is surfacing this assumption, if not challenging it outright. But it’s certainly not the first time our notion of craft has been tested.
To me, craft isn’t necessarily about physically touching what you make. It doesn’t even have to involve physical contact at all.
Mozart was reputed to compose complex arrangements entirely in his head, only writing down the final notation as an act of transcription. But who would argue that Mozart wasn’t a master of his craft?
Beethoven, by the end of his life, was deaf. And yet it was then that he composed some of his most celebrated work. What does it mean to craft music you cannot hear?
Obviously, “craft” is a word we use interchangeably—sometimes as a noun, a shorthand for “area of expertise,” and other times as a verb, the act of applying that expertise.
What I’m noticing is that our initial forays into AI seem to be challenging our notions of craft. But my experience has only validated the existence of craft as an elevated form of creation. It’s also deepened my sense of craft as verb—as disciplined practice, not manual labor.
The kneejerk reaction to AI usage, especially in design, has been to consider it an interference to thinking and making—not capable of processing ideas with the nuance of the human mind, nor capable of producing anything that a human, with enough time, couldn’t do better.
Both criticisms miss the point. AI is a tool through which ideas become things. The stronger the idea going in, the less reason to think the tool would degrade it in some fundamental way.
This is exactly how many initially responded to the synthesizer—as a sonic machine, not a musical instrument. But of course, the synthesizer didn’t eliminate musical craft. Knowledge of harmony, rhythm, arrangement, and dynamics still determined what made a piece of music good. The synthesizer only changed how it was made.
The same is true with AI and design. No knowledge I possess about design—the incorporeal understanding that makes what I create better than an off-the-shelf template or something done by someone without my experience—is made irrelevant by AI. Nor is it contradicted by my use of AI tools.
Structure still communicates before content. Visual hierarchy still guides attention. Negative space still creates rhythm. These principles don’t vanish because I’m working through AI rather than directly manipulating pixels.
The craft migrates to a different level of abstraction. But it remains craft.
The second aspect has to do with the work that is or isn’t done when AI tools are involved. And for me, the key element here is repetition.
I’ve written before that the way to make good things is to make many things. Practice builds skill. There’s nothing about AI usage that challenges this fundamental truth.
The more I use AI to create something, the better the output becomes. And it’s not simply a matter of getting better at prompting. These cycles push further back into my process, causing me to rethink foundational aspects of how I make things, knowing that new points of processing and acceleration are now available.
I’m iterating more quickly. Testing more variations. Learning from failures faster. The feedback loops are tighter, which means I can refine my judgment more rapidly.
The craft hasn’t disappeared. It’s just happening at a higher level of abstraction.
Instead of iterating on “how do I code this CSS perfectly?” I’m iterating on “what’s the right structure? What’s the right hierarchy? How do I communicate this idea most clearly?” The answers change when the tools change.
The discipline, though, remains the same.
But here’s the danger.
I’ve seen dozens of AI-generated apps, webpages, and informational assets that have blown collective minds simply by not existing one minute and existing the next. The speed of generation is so breathtaking that it stands in the gap for quality—even when that gap is so wide it would never have been tolerated had the thing been made the old-fashioned way.
This typically happens when someone uses AI to synthesize a large amount of information and generate something to contain it. That it’s suddenly there—clickable, mobile-friendly, with animated charts and graphs—is powerful. The person who made it is immensely proud, though the work has been minuscule. There’s an intoxicating effect at work here and I worry it’s one we won’t become immune to quickly enough.
And I feel that immediate tension when I inevitably have a long list of critiques: Hold on, what is this meant to communicate? Actually, it’s pretty difficult to scan and read this. Yeah, these graphs look neat but they don’t really make any sense.
Had I not been there, accepting the role of wet blanket, this inferior thing would have shipped.
And that’s the risk with collapsing skills into tools. I won’t always be there to do the thing I do. Inferior designs will ship. That’s bad. But what’s worse—the thing that really stings most designers’ egos—is that most people won’t even notice.
Exhibit A of this premise is most of what’s on the web today: hastily made things using poorly designed templates. Any good designer can thoroughly critique them; most of the world doesn’t care.
AI accelerates this dynamic. It makes it even easier to produce outputs that look professional at first glance but fail at the level of craft—the considered structure, the clear communication, the thoughtful hierarchy that serves the user’s actual needs rather than just filling space.
A tool that accelerates craft enough also becomes the thing that lets people skip it entirely. And because the output looks finished, because it required so little effort, because most people can’t tell the difference anyway—why would anyone bother with iteration? With refinement? With developing the judgment that comes from years of practice? Easy satisfaction is dangerous, and up until this point, somewhat localized. AI could not only make it ubiquitous, but standard.
And just to be sure no one reading this draws the conclusion that I elevate designers above others, let me be clear: we designers are as easily seduced. Take this video, named “Designing with Claude Code”, as an example. I’ll ask you the same questions I asked my design team: When, exactly, did the design happen? The designer in the video prompts Claude to “design a simple marketing home page for a finance app” and lists off a few features he’d like to see on the page. Seconds later, Claude generates a pretty polished page. That’s about three minutes in. For the next 57, the designer restyles the page, prompting it piecemeal. This is where the title was, for me, ironically instructive: was this really design? I also asked my team, sincerely, did he make it better? At the core of this discussion is the ever-blurred line between aesthetics and order, between style and design.
To be fair, I don’t think the designer in this video intended to communicate that pre-design strategic work is no longer necessary. Nevertheless, he depicted a process that didn’t include any meaningful thought prior to generating a webpage, then spent the rest of the video re-styling that webpage to his tastes. I would have started with a text file to work out concepts, developed my visual language in a canvas tool, and then moved to Claude to accelerate the technical steps of translating my thinking to code. Craft at each stage.
This is why I keep returning to craft as mindset, not method.
Craft is the commitment to iteration, refinement, and accumulated knowledge applied toward increasingly excellent outcomes. It’s the refusal to accept the first result as final. It’s the understanding that quality emerges from disciplined practice, not from tools.
AI makes it easier to produce outputs. But it doesn’t eliminate the need for craft—it just reveals who’s practicing it and who isn’t.
Someone who generates an interface with AI and calls it done isn’t practicing craft. They’re consuming convenience.
Someone who generates an interface, inspects it, questions what it’s actually communicating, refines the structure, generates again, compares variations, understands why one serves the user better than another—they’re practicing craft. They’re building knowledge through iteration.
The tool doesn’t determine whether you’re working with craft. Your approach does.
Beethoven crafted music he couldn’t hear because he had spent decades developing such deep understanding of musical structure that the physical instantiation—sound waves, instrumental performance—was almost incidental to the compositional craft.
AI lets us work at similar levels of abstraction. We can focus on intention, structure, and meaning while the tool handles implementation.
But that only works if we maintain the discipline. If we iterate. If we refuse to accept “good enough” when we know better is possible; if we understand craft not as what we touch, but as how we think; if we’re honest about the fact that most of the world won’t notice when we skip that discipline. The only thing keeping craft alive is our own commitment to it.
This isn’t really a technological problem so much as a symptom of choices made at scale. Individual choices that prioritize craft, after all, are much easier to defend and preserve, aren’t they? I could have paid half as much for the new roof on my house, for example, but it probably would have lasted a quarter as long. But it was my money to spend. My chosen design processes might take me more time at certain points than the designer “designing with Claude Code,” but it’s my time to spend. I think I’ve made the right choices, but only I am in a position to judge them. What happens when these choices are made for me? What happens when they’re made from a distance, where the outcome is obscured?
Craft is always threatened in the midst of technological change, not by the technology itself, but by the addictions we develop to what the technology makes possible: Simpler choices, lower costs, faster outcomes. Each is desirable and defensible in isolation, but as a foundation, the fastest path to a fragile future.