Frank Chimero

Beyond the Machine Extras

Fri, 24 Oct 2025 00:00:00 +0000

Every talk leaves a lot of scraps, and Beyond the Machine is no exception.

I read a couple books, watched about 20 hours of interviews with Brian Eno, listened to 10 hours of podcasts with Rick Rubin to make sure I wasn’t being uncharitable. (One podcast was a discussion between Rubin and Eno. A really nice listen!) I clocked some serious headphone time with Holly Herndon’s discography, which I’m eager to do again. Listening to all three of her albums in one big gulp was a trip.

There were a few ideas that never grew into full thoughts, and a couple more that were cut for length. So consider this a supplemental post, an appendix of sorts, to share the leftover fragments and side paths that didn’t make it into the main piece. I’m sharing this for a couple reasons: partially because I am so intrigued by some of the ideas that were cut, and partially to show why I do these talks so infrequently. They are a lot of work.

On to the swollen appendices.

Perhaps a superficial place to start, but whatever:

AI: an ugly looking set of letters. Sounds ugly, too. Ayyy-eiiigh. Woof.

I’d be much happier if we used words like “generative models” instead of AI or GenAI. Holly Herndon and Mat Dryhurst from the talk have used the phrase, “common intelligence,” which is also nice, since the synthesis is only available if derived from all the work of humanity so far. Almost any name for this generation of technology is better than AI to me.

The old nut is that our current instantiation of AI is neither A nor I, and past technologies presented as AI eventually settle into a more descriptive name once the boundaries of its utility are found (symbolic reasoning, speech recognition, OCR, machine vision, machine learning, and so on). Let’s hope this happens, whether it’s LLM, GPT, or something else.

In my view, capital expectations are both the life and death of generative AI. Most of the dizzying talk around AI’s promise and peril isn’t meant for ordinary people, it’s aimed at investors and executives who need to justify the bet. They’re also the most likely to fall for it, because AI sounds appealing in the high-up abstract, but its flaws become obvious in the down-low specific.

In a funny way, it’s a bit like any other enterprise software purchase: you’re selling to leadership and navigating around the skepticisms of the people who will actually be using the software. When you’re close to the work and trying to use these tools, you become painfully aware of their benefits and limitations, and the mindful critics understand what piece of the problem comes down to tooling and what challenges are produced by people or systems resistent to change that AI can’t touch.

The first draft of the talk referenced a blog post that I love, called, “Reality has a Surprising Amount of Detail,” by John Salvatier. Here’s how I see the connection: the moment you begin the doing, what looked simple in the abstract unfolds into surprising, significant detail. Those details have real, material consequences to the work. We often get stuck or frustrated because our tools fail to account for the existence of these irregularities, where reality refuses to conform to perfect abstractions and predictions.

AI, in my use, often falls into this trap, and to a degree, so does bad management. The two lock together naturally because when done poorly they share the same point of view, which is to discount the real and specific in favor of the ideal and abstract—basically to reject the ground-level facts to inch closer to “done” or “mission accomplished.” (This is all still rough, and not kindly phrased, hence me removing it from the talk.)

In other words, largeness, whether it is big organizations or large language models, are distanced from reality due to their size, and this size and distance can make care (that is, attending to the individual qualities of a situation) an impossibility. For example, stealing 10 books is theft, but stealing 10,000,000 books is training.

If the talk wasn’t so strongly tied to music, I would be tempted to devote a section to Wendell Berry—advocate for the small, the specific, the local—to serve as a counterpoint and potentially provide words for those abstaining from AI.

Here’s Berry evaluating the dangers of scale and abstraction when it comes to ecological preservation:

The question that must be addressed, therefore, is not how to care for the planet, but how to care for each of the planet’s millions of human and natural neighborhoods, each of its millions of small pieces and parcels of land, each one which is in some precious way different from all the others. Our understandable wish to preserve the planet must somehow be reduced to the scale of our competence—that is, to the wish to preserve all of its humble households and neighborhoods.

Wendell Berry, Word and Flesh

Care and specificity go hand in hand, and our ambitions should be reduced to the scale of our competence.

I’m not well-read on Berry, so there are probably better quotes that more directly drive at this idea. It also may be completely disrespectful to include him in this whole charade. This seemed the most likely conclusion here, so I didn’t probe too deeply. Joan Tronto also has scholarship about care from a global perspective, but I dropped that line for similar reasons. If I had another 30 minutes to speak and another 2 months to research, maybe.

Back to Salvatier. In his post, he tells a story about cutting a stringer for a set of stairs (the diagonal piece that runs between the two floors being connected). He tries trigonometry to calculate the dimensions of the wood instead of tracing the cut by hand. The math is neat, but the world isn’t—wood warps and floors aren’t perfectly level. Experience in the doing tells you to always trace the cut because of these details, even if you know how to do the math.

In other words, because reality has a surprising amount of detail, it also contains the feedback mechanisms that keep us honest. Abstractions don’t. Fabrications don’t. And simulations definitely do not. When you rely too heavily on models designed to flatter and reinforce rather than correct, you lose contact with that feedback and drift into dead ends you can’t see until it’s too late.

One example I picked out (a personal pet peeve) is when designers mock up brand touchpoints on things like billboards using unprintable colors. Purples, limes, and saturated blues look great on a screen but can’t exist in the physical world using the standard CMYK inks used for most print production. The simulation of the Photoshop mockup never provides the reality of production to invalidate the creative choice. Without a knowledge of how things really get made, a designer may be seeking approval for an impossibility.

AI brings this faux reality to text and therefore all the other modes of communication, and risks reproducing the same disconnection across disciplines—spinning out unactionable strategies, vague product specs, and feedback that sounds intelligent but has no meaning. This kind of empty verbiage and posing isn’t new, but AI increases the volume of it and the ease of producing it. It lets us stay sheltered in abstractions for longer, and postpones the moment when reality pushes back, which only leaves less time to respond and raises the stakes of course-correcting.

One mark of expertise is knowing how to make something with substance. Another is being able to identify when something that seems substiantial is, in fact, empty. A lot has been made about how the models hallucinate. A more descriptive term might be “bullshit,” using Harry Frankfurt’s definition, where there is a total disregard for truth, and words have been emptied of all informative content. Bullshit existed at an industrial scale before AI, of course, AI just makes it easier for people to accidentally participate.

A vignette: last month my partner was booking a private corporate dinner and got a spreadsheet from a coworker with contact info for several potential restaurants. After 45 frustrating minutes of bounced emails and unanswered calls, she finally asked what was going on. A bit of sleuthing revealed the issue: her coworker had asked AI to generate the list, since neither of them lived in the city of the event, and passed it on to her. The restaurants were real, but every phone number and email address was hallucinated.

AI is full of these potential gambles. Would you risk wasting an unknown amount of a coworker’s time to save 15 minutes for yourself? What looks like an efficiency at a personal scale may net out as time lost for the team.

I once heard that space travel is brutal on the body. In zero gravity, the body begins to “forget” the constant pull it was built to resist, and adapts in ways that make it worse at living on Earth. Muscles waste away, bones lose density, and the heart weakens because it no longer needs to pump against gravity. Even the eyes change shape and vision gets distorted. Life on the space shuttle makes the body and mind forget what real life on Earth feels like.

I’ve always thought this was a fitting metaphor for extreme wealth. Too much money dissolves the resistance of reality, suspending a person in a kind of psychic zero gravity—an atmosphere of yes-men, sycophants, and transactions. Over time, that absence of friction warps perception, and behavior grows more extreme, as if trying to provoke reality into responding. What’s emerging with AI-induced psychosis feels similar: a sycophantic companion lures you into an addictive sense that reality is optional, until you are overwhelmed by the creeping mania embedded in the simulation that comes from living without resistance.

I’ve given a few corporate talks over the past few months, and one of the suggested talking points was to reassure employees about job displacement in the face of AI. For the most part, I refused to sugar coat anything. Reassurances need to come from people inside the company with influence and power, not outsiders with a slide deck and a plane ticket home.

One of the common lines you hear is: “AI frees you to do the human work.” That’s a load of bull. That kind of work was always available before AI, and if it wasn’t valued then, why would an organization value it now? Why do we need AI to take away the minutiae before we start appreciating what people can offer? If the busywork is so overwhelming that it prevents more meaningful contributions, that’s not a technology problem, it’s bad management. And if that’s the case, maybe the AI should replace the managers, not the workers? The whole line of reasoning is absurd.

One of our key mistakes, I think, is treating labor displacement as a problem of technological capability, when it’s really a reflection of leadership’s values and incentives. What the hell do we do about that? Moloch, oh, Moloch!

I didn’t talk much about coding beyond vibe coding, but I think one of the most exciting places to apply LLMs is in an IDE with a code-savvy person piloting it.

Software development has its own “surprising details,” but the work is, at its core, about codifying those surprising exceptions along with the general rules of logic that drive the software. Code is unusually well-suited to these models: its syntax is stable, its reference material is abundant, and its logic is explicit enough that both inputs and outputs are legible to machines. Unlike natural language, code is less ambiguous, more self-contained, and able to be tested, making it a kind of native habitat for large language models to reason, learn, and improve. The important thing, I am told, is to maintain the primacy of programming as theory building.

Creative practice as theory building is such a juicy idea to apply to other domains, but I didn’t have time to get into it. I cut the little bit I had about this, because the talk was already at time, and I’m not a developer. It felt like dangerous territory for me to tread into, highly likely to, what is the phrase? Foot-gun? Rake-step?

These models are mathetimatical phenomena on top of commonly available information. I don’t want to diminish the work necessary to get them to something suitable for use, but to what extent do we believe these technologies have a path foward that isn’t commoditization? I suppose that is the test ahead.

The talk finishes by looking at how the models were able to mimic Miyazaki’s style. To a much smaller extent, the same is true for me. The models have an understanding of Frank Chimero out of the box thanks to a decade of writing on the open web, and part of the work in the last few months has been thinking through how to use that to my advantage.

I ended up in an interesting place, I think. My workflow for this talk was sharing an outline of a section with ChatGPT, then asking it to write it in the style of “Frank Chimero.” This was like looking at your own prose in a funhouse mirror. Do I really use the verb “unfold” that much? Jesus.

From there, I bucked hard against the output, typically starting from scratch. Nothing sharpens your articulation quite like feeling misunderstood. Anger is a powerful fuel. I plan to continue with this dirty fuel for a while.

Building on that, another irony: many of the artists and creators whose work formed the training sets for models are the ones abstaining from using GenAI tools. But, a provocation: who is more entitled to use these models than the people whose work was stolen to train them?

I respect if a creative wants to abstain from using GenAI for aesthetic reasons, lack of interest, environmental, or other moral concerns. But the theft has happened. What now?

What is more American than building an industry from stolen property and labor? Look at this land and its history.

Scribbled in my notebook: peak oil = peak data? What if synthetic data doesn’t work out? Models as time capsules of 2024? Fuzzy, but interesting.

While researching Holly Herndon and Mat Dryhurst’s work, I came across the metaphor of “centaurs and minotaurs,“ used to describe two opposing postures towards human-AI collaboration. They cover similar territory as Eno’s gardening and cultivation, so I left it out to not muddy things up too much with mixed metaphors.

A centaur is a mythical creature with the head and upper body of a man and the lower body of a horse. In the centaur model of collaboration, the human and machine cooperate by the machine executing and the human leading and deciding.

A minotaur, in contrast, is a mythical creature with the body of a man and the head of a bull. In the minotaur model of collaboration, the human and machine cooperate by the machine leading and deciding, with the human executing.

In a sense, both exhibit “human in the loop” qualities, but have very different power dynamics. The Centaur metaphor has existed for quite a while, initially being coined in relation to advanced chess, where a player and a computer team up to face another computer/player team. The computer helps the player think, but the player decides and makes the move.

An aside to the aside here: Brian Christian’s The Most Human Human makes a captivating insight. He says that all the chess grandmasters have the opening and closing playbooks memorized, so all the real chess happens in the middle of the game. It’s an incredibly robust idea, and tantilizing to think about how it could apply to other aspects of work and life.

OK, back to our centaur/minotaur metaphor. The minotaur metaphor seems more recent. I found a 2023 paper on national defense from the US Army War College Quarterly that uses the term. It inverts the centaur model and speculates on manned-unmanned military teams of the future, where teams of humans are under the control, supervision, and command of artificial intelligence. Many of the minotaur examples I could find are predicated on speed of response and the application of force.

Cue the quote from that ’70s IBM presentation: “A computer can never be held accountable, therefore a computer must never make a management decision.”

What about war?

Another cut: does agentic AI even make sense for consumers? I am tired of seeing grocery shopping demos. Most of the things we want agents to do are made purposefully difficult by the counterparties (booking travel at optimal cost, renegotiating credit card interest rates, syncing data across siloed software, cancelling your New York Times subscription, etc.). When we say we want an agent to do something for us, it typically is a wish for greater consumer protections. We don’t need an agent, we need better legislation.

One of the strangest moments in researching the talk was watching this interview between Rick Rubin and the founders of venture capital firm a16z. It’s an unlikely bunch. The whole conversation is fascinating—it’s like watching extrinsic motivation personified talk to intrinsic motivation personified.

One of the consistent beats in Rubin’s creative worldview is that inspiration comes first and the audience comes last. The spark that compels an artist to create must lead. In his book, Rubin argues that if you begin by thinking “How will this be received?” or “What will people want?”, you’re already limiting your work, because the audience only knows what’s come before.

This is, of course, anathema to the VC tech world, where implementing user feedback is seen as driving a company towards product-market fit. The cues and direction come from the outside, to the extent that Y Combinator, another fund, has inscribed it in a frequently shared credo, “Make something people want.” (Which I presume gets lost in the shuffle when you receive funding?)

What makes the Rubin conversation so compelling is how oblivious the investors seem to the gap between their worldview and his. They fill the hour with anecdotes and external references, never quite grasping the possibility that a person might make something because they feel an inner need to—there’s something inside them that wants out—and that not all meaningful work requires an external validation like money or market traction.

It reminds me a bit of a conversation at Config last year where Figma CEO Dylan Field was stunned that Jesper Kouthoofd, CEO and founder of Teenage Engineering, didn’t user test his instruments before releasing them.

I think part of the dismay and boredom with technology these days comes down to how few products (software or hardware) seem to have a coherent point of view. In fact, success aside, I think that’s part of what we miss most about the Jobs-era of Apple. There was a sense of vision and authorship rather than inevitability, and a focus that extended beyond incrementalism and scale. There were hits and misses, iPhones and cube computers, iPods and iPod socks.

For some types of work, it’s better to navigate by one’s own internal sense of rightness rather than by external signals. We tend to call this “vision,” but that word has been evicted of meaning these days, more often suggesting one’s ability to predict consensus instead of their ability to create from conviction. I believe the malaise surrounding tech over the past decade, for both workers and consumers, stems from its gradual shift away from being a creative industry.

Real creative work begins from the inside. It is an act of contribution, not competition. One instrument doesn’t cancel out the others, one album doesn’t erase the last. Rubin and Kouthoofd both seem to understand that creation is additive, not adversarial, and the adoption of AI seems to suggest that each model and tool doesn’t necessarily cancel out the necessity of the other ones. I know many people who subscribe to both ChatGPT and Claude and use them for different jobs. Each model and version of each model offers something a bit different. It’s another reason why I’m keen on comparing AI to an instrument.

Perhaps the technology industry might be a more genuinely innovative place if we could eschew the tools, the hype, and the money for a moment and actually listen to what is stirring inside of us. If AI really can democratize the production of software, than what could be better than having a multiplity of options available, all with a sense of authorship and a unique touch? If there is one thing I strongly agree with Rubin on, it is this: the future begins by feeling it.

There is a wonderful line from As You Like It, where Orlando says, “I can live no longer by thinking.”

I just discovered it, but wish I found it earlier. It would have been nice in the talk.

Beyond the Machine

Mon, 20 Oct 2025 00:00:00 +0000

This talk was given on October 14, 2025 at Kinference in Brooklyn, New York.

Spoiler alert: the last part of the talk covers plot points of the movie Spirited Away. Another warning is included right before the spoilers with a jump forward link to the spoiler-free conclusion.

I am so tired of hearing about AI. Unfortunately, this is a talk about AI.

I’m trying to figure out how to use generative AI as a designer without feeling like shit. I am fascinated with what it can do, impressed and repulsed by what it makes, and distrustful of its owners. I am deeply ambivalent about it all. The believers demand devotion, the critics demand abstinence, and to see AI as just another technology is to be a heretic twice over.

Today, I’d like to try to open things up a bit. I want to frame the technology more like an instrument, and get away from GenAI as an intelligence, an ideology, a tool, a crutch, or a weapon. I find the instrument framing more appealing as a person who has spent decades honing a set of skills. I want a way of working that relies on my capabilities and discernment rather than something so amorphous and transient as taste. (If taste exists in technology, it needs to be smuggled in.)

John Coltrane and J Dilla

Thinking of AI as an instrument recenters the focus on practice. Instruments require a performance that relies on technique—the horn makes the sound, but how and what you blow into it matters; the drum machine keeps time and plays the samples, but what you sample and how you swing on top of it becomes your signature.

In other words, instruments can surprise you with what they offer, but they are not automatic. In the end, they require a touch. You use a tool, but you play an instrument. It’s a more expansive way of doing, and the doing of it all is important, because that’s where you develop the instincts for excellence. There is no purpose to better machines if they do not also produce better humans.

I believe artists have more helpful things to say than the money guys about how to use and creatively misuse technology. So today, I want to share four artists, and I hope you’ll leave with some more flexibility in how to collaborate with the machine in your own work, creative or otherwise.

First, some background. Over the summer, the vibe around AI seemed to shift.

All those aggressive predictions about the destruction of knowledge work didn’t hit their six-month deadline. The much anticipated GPT-5 felt more like an incremental step up from GPT-4, signaling LLMs have probably moved past the revolution phase and into optimization and evaluating trade-offs. The fact that OpenAI is encouraging everyone to fantasize about hardware with the Jony Ive annoucement tells me the software side may not have enough headroom for the profits they need. Meanwhile, small, local models are good enough for a surprising number of use cases, while being cheaper, more private, and energy efficient.

Progress has slowed, projections are being walked back, and the science is starting to look more hazy. We may not be in AI winter, but I am hoping for an AI autumn. Autumn is amazing; the air cools, the mania of summer dissipates, things slow down. Right now, the changes in AI feel incremental enough to start laying down strategy. This means we can think about our approach with steadier footing instead of vacillating in response to the hype, whether it comes from the top as LinkedIn posts by prepper CEOs or from the bottom by the hustleheads on X.

The hype is expected—new tech runs on speculation. You can feel the residue of the last 30 years of booms. There is a sense that people missed their chance to get rich on the internet, on ecommerce, on the app store, on social media, on crypto, on meme stocks, on NVIDIA. The hype bubbles get inflated because individuals don’t want to miss their chance at another windfall, and companies don’t want to get displaced by any nascent technological shifts. The history of tech has calcified into stories of dramatic wins and unforeseen downfalls, and what results is a tech culture of near compulsory participation in prediction rather than creating value or serving needs.

The TV show The Wire had a phrase about how people navigate the risk of failure inside more traditional institutions: “You can’t lose if you don’t play.” In the tech world, the logic reverses. The drawbacks of a collective fallacy are smaller than not participating in the next innovation, so the rule becomes, “You only lose if you don’t play.”

In other words, the push to join the AI rush comes from the sense that it’s the only game in town right now, and everyone else is already playing. The hype gets louder, and the bubble gets bigger.

What surprised me wasn’t the AI hype, though, but the lack of solidarity that came with it. Faced with the story of AI labor displacement, our first instinct as technology workers wasn’t to protect one another, but to search for ways to use the tools to replace our collaborators.

The fractures fell neatly along disciplines: engineers using AI to wish away designers, designers wishing away engineers, product managers wishing away both. In this climate, AI becomes frenemy identification technology, another way to avoid working together. It’s always easier to grab a tool and bypass the mess of coordination, even if that means doing more and doing it alone. AI lowers the barrier to working outside your lane, and sure, that could mean more overlap between disciplines, but right now, we’re mostly boxing each other out or stepping on one another’s toes.

With collaboration already strained, it’s no surprise that we fall back on individual effort. But individual work, disconnected from the whole and accelerated by automation, only makes the turbulence worse and the course corrections more violent. The deeper problem is that companies still haven’t figured out how to mass-produce orientation, so workers get thrashed around in the speed and scale of the system.

When coordination breaks down, the fantasy of self-sufficiency rushes in to fill the gap. GenAI, after all, is built as an individual technology, whether it is expressed as a one-on-one chat, the fantasy of the one-person startup, or the sycophantic assistant set up to glaze you. It says “just get it done, no skills needed.”

All you need is a prompt, a dream, and some vibes. And, of course, we’ve chosen an individual to be the face of that.

“Money is everywhere but so is poetry. What we lack are the poets.”
Federico Fellini

Vibe coding has a mascot, and it is the music producer Rick Rubin. A bit of background on him before we get back to vibe coding.

Rubin got his start in the music industry as the co-founder of Def Jam Records. He helped bring hip-hop into the mainstream by producing records for LL Cool J, Run-D.M.C., and the Beastie Boys. Later, Rubin produced albums across genres for Jay-Z, Johnny Cash, Red Hot Chili Peppers, and plenty of others. He’s credited with shaping the sound of the last forty years without ever learning to play an instrument. In interviews, Rubin leans into the irony: he can’t operate the board, can’t engineer, can’t really play a guitar.

Rubin’s supposed lack of technical skill has made him an easy fit as the poster child for vibe coding, the notion that with the right sensibility and some prompting, you can steer technical output without ever knowing how to use the tools.

Anthropic played into this with The Way of Code, a project where Rubin rewrote the Tao Te Ching, an ancient Chinese philosophical text, to be about vibe coding.

It was a marketing stunt, but I read it as a proposal that the path to enlightenment is moving away from competency and towards the vibes.

Here’s one of the chapters from the site:

The Way of Code #47

Without going outside,
you may know the whole world.
Without looking through the window,
you may see the ways of heaven.

The further you travel, the less you know.
The more you know, the less you understand.

Therefore, The Vibe Coder
knows without going, sees without looking,
and accomplishes all
without doing a thing.

Ugh.

When I first saw this, I thought “Did anyone read this?” That’s always the question when encountering something that you think is god awful. But in the AI era, I found myself asking a follow-up question: “Did anyone write this?”

It’s a complicated knot. Art is interesting partly because someone put effort into making this thing, not all the others they could have made. But artists also know that the best ideas can appear whole, as if they made themselves. Effort isn’t what makes the work meaningful, yet it still feels like a small violation to release an AI’s output untouched.

I figured they used AI to write revised chapters of the Tao since the verses are shown beside some vibe coded visuals. But in interviews Rubin says he wrote them himself. I’ll be generous and say that it’s a joke that doesn’t land for me.

Like most ideas that spread like memes, “vibe coding” got carried way too far. Karpathy’s original post was modest: a fun hack for engineers, and “not too bad for weekend throwaway projects.” Lightweight, bounded, disposable work, perfect for prototyping, experiments, scripting, and personal software. If this is the scope, I’m on board, giddyup.

But once the phrase caught on, people started applying it everywhere, stretching a hack into a philosophy worthy enough to be inserted into ancient texts.

Like a lot of things with AI, it feels completely out of proportion. Maybe that’s the humor of it. But in the process, Rick Rubin became valorized as proof you don’t need skills. It’s as if wu wei, the principle of not forcing at the heart of the Tao, had been hollowed out and recast as dependency. Use the machine, no skills or knowledge needed.

Rubin obviously has skills and knowledge, but we get two Rick Rubins. There’s the one in the studio, unable to play guitar, but gifted at guiding artists and clarifying what they want. And then there’s the cartoon Rubin, who leans into his lack of ability in interviews, writes bad poems, and poses as the guru. Maybe that split is what happens when the work becomes too abstract from execution.

Lately, I’ve been thinking about my use of AI as a kind of spatial relationship. Where do I stand in relation to the machine—above it, beside it, under it? Each position carries a different kind of power dynamic. To be above is to steer, beside is to collaborate, below is to serve.

In the Rubin vibe arrangement, you’re under the machine and dependent on it. Without skills, the model’s limits become your own, no different from anyone else typing wishes into a text box. Take what the AI gives without question and you’re not producing, you’re consuming. Eventually, that passivity gets used against you. We’ve seen it before: streaming services flattened art into algorithmic averages and background noise, newsfeeds rewired attention toward outrage because engagement meant growth. Each time, the machine attracted us with individual customization while quietly taking control of the terms. The same will happen with AI if used for passive consumption, even if that consumption is dressed up as execution.

So while vibe coding may be useful for short-term work, it’s not a suitable approach for anything intended to last longer than a tub of yogurt. Time saved is not strength gained, so I went looking for other examples about how to work with the machine.

“It could be uplifting to watch a person energetically building a beautiful coffin. And depressing to watch someone sloppily and carelessly make the worst birthday cake ever.”
George Saunders

I’ve spent the last couple of months digging into Brian Eno’s work and music.

Eno began as a keyboardist in the band Roxy Music in the early ’70s, helping to popularize the use of synthesizers in pop music. Then he went solo, and later built a career producing some of the most important bands of the last fifty years—David Bowie, Devo, Talking Heads, U2, and, more recently, collaborating with Fred Again.

Eno is rarely the virtuoso; instead, he’s the collaborator, the systems thinker, the one who turns the studio into a laboratory. What makes Eno especially relevant for me is his work beyond the songs. His impact as an artist has mostly been to define new forms, supply vocabulary, and arrange the vibe.

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In the late ’70s, Eno named ambient music and, in doing so, changed how we think about what recorded music does by creating compositions fit for purpose and place. Music for airports, music for walking, music for thinking. Above is an in-browser recreation of track 2 on Eno’s Music for Airports by Tero Parviainen.

Rather than a pre-arranged composition, his music drifts and breathes, built from overlapping loops of different lengths that phase in and out of sync with one another, creating slow variations he can’t fully predict.

Eno called it “a river of sound,” always the same and never the same. In that sense, ambient music was his first experiment with what he’d later name generative art: systems that grow and change within carefully set constraints.

Since the ’80s, Eno has been interested in using software and systems that produce music and visual art. In the last decade, he carried this idea into the phone with generative apps for music-making.

Bloom lets you seed melodies by tapping dots that ripple outward.
Scape has you place abstract elements into a pictorial landscape to create a landscape of sound
And Reflection dissolves the boundaries of a Brian Eno album by shipping generative software that allows endless music.

I believe that if you’re looking for a music producer to give some inspiration on how to work with machines, you’d have better luck and more ideas to consider with Brian Eno than Rick Rubin. The difference, to me, comes down to placement.

If working in the Rubin style puts you under the machine, Eno works beside it. He sets up a system with his inputs and samples, then listens, selects, and continues to shape. Eno often says while making music he feels like a gardener: planting loops and textures, then watching them sprout into something unexpected with the potential to become incredibly beautiful with a little bit of care and pruning. The machine may produce material, but the job of shaping it into something meaningful still rests with him. It is creativity as cultivation.

The value of the machine’s output depends on how we see it, and our interpretation often has little to do with its technical perfection. A flawless, virtuoso output from GenAI can feel lifeless, while something raw or broken might have something interesting about it. That’s why I like to write bad and contradictory prompts, because they feel like they are more aligned with how these models actually function. The models aren’t deterministic; we don’t fully understand how their associations form or why certain patterns appear. So why not let them drift into ambiguity and see what happens? I wouldn’t want an irregular AI in my bank app, but in a creative workflow, hallucinating feels like the point of it all.

In a sense, Brian Eno was tinkering with prompts long before we had the word. In 1975, he and Peter Schmidt created Oblique Strategies, a deck of cards meant to shake artists out of their habits. Each carried a short phrase to be interpreted and followed: “Honor thy error as a hidden intention.” “Use an old idea.” “Work at a different speed.” These weren’t instructions so much as provocations—small reframings that opened space for something unexpected to emerge.

That, to me, feels like a better model for prompting in creative work, whether the first act of execution belongs to a person or a machine. A good prompt doesn’t need to function like a blueprint. They can also behave like a horoscope or a fortune.

The results from ambiguous prompts can be weird, illogical, and incredibly stimulating, because the system is not scared of being wrong. The good stuff is at the technological edge, even if it’s a bit shit. Eno put it beautifully in 1995:

“Whatever you find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit, all of these will be cherished and emulated as soon as they can be avoided.”

Time has proven him right. This idea explains the return of vinyl, the revival of iPods, Gen Z filming parties on old digital camcorders, or why 8mm film is nostalgic for Boomers in the same way the blown-out photos from the first iPhone make us Millennials sentimental. And if I had to make a guess, we will miss the awkward trickle-in of words seen in modern LLMs.

In other words, every new technology promises better clarity, yet its essence is determined by the noise it produces. The friction of limits is what gives a technology its character, so when a system becomes too smooth, too all-encompassing, or too accommodating, it stops having a signature at all. Here’s Eno again from earlier this year:

“I can see from the little acquaintance that I have with using AI programs to make music, that what you spend nearly all your time doing is trying to stop the system becoming mind numbingly mediocre. You really feel the pull of the averaging effect of AI, given that what you are receiving is a kind of averaged out distillation of stuff from a lot of different sources.”

An average email or line of code is fine. Average art isn’t. To make something alive with AI, we have to resist its pull towards average by working beside it, shaping what it gives, and listening for what’s missing. Sometimes what’s needed is a good, old-fashioned mistake or two.

Another answer is not to cultivate the machine’s output but to compose through it, treating the model as material, choosing the inputs and shaping the rules. In other words, not working beside the machine, but stepping inside it.

“By the late twentieth century, our time, a mythic time, we are all chimeras, theorized and fabricated hybrids of machine and organism.”
Donna Haraway

If Brian Eno shows us what it means to work beside the machine and cultivate its outputs, Holly Herndon and Mat Dryhurst show us what it means to move into it.

Herndon and Dryhurst are visual artists and musicians who, for over a decade, have worked with AI and treated digital systems as instruments for rethinking music itself. They’ve built tools and trained models that show what AI can offer creative practice as a means of integration, extension, and amplification of the artist.

Consider Proto from 2019, in which they developed an AI “baby” named Spawn. They trained it with live performance using call-and-response with singers, then used the model as a vocal instrument in a later released album of the same name.

They’ve founded Spawning, which they call the consent layer for AI, letting artists decide whether to opt in or out of model training. Alongside it, they launched a beta for Public Diffusion, a foundation model trained entirely on 30 million public-domain images, designed to make generative work copyright-safe.

xhairymutantx

Then there’s xhairymutantx from 2024, shown at the Whitney Biennial, a project where they trained a text-to-image model on photos of Holly, then opened it to the public, inviting others to create with it and explore how identity becomes warped and extended inside generative systems.

And most recently, they created The Call, a choral AI project involving choirs across the UK, where recorded voices become a shared dataset, and the resulting models are folded into a spatial audio installation performing generative choral arrangements. Serpentine Gallery acts as steward of a data trust that governs usage, ensuring the participating choirs are paid and have agency over how their voices are used.

The compositions are inspired by Medieval music, Renaissance music, and hymns. The vocal performances were made from a combination of the choral data and Holly’s own song data. Let’s listen for a minute:

Your browser does not support the video tag.

Excerpt of The Call. Source.

One of the things you notice when encountering Herndon and Dryhurst’s work is that they are just as concerned with the administrative structures needed to serve artists as they are with the creative potentials of new technology. They say that all media is training data, so their work wrestles with the implications of what media generation at scale means for artists. The AI model and the economic model don’t need to come packaged together. Both can be areas for innovation, and severing the implied connection may be a requirement for ethical AI.

What emerges from this approach isn’t just art, but a reimagining of the conditions that make art possible. When the system is designed to respect artists, scale becomes a tool rather than a threat. It opens up new questions. What new forms appear at that scale? The story can be rethought; instead of fewer people making the same amount, what about the same number of people making stranger, more abundant, more connected work?

As the tools evolve, the metaphors we use to understand them must also be updated. Herndon and Dryhurst describe this next phase as the move from sampling to spawning. Sampling was the logic of the 20th century. You took a slice of a record—a James Brown breakbeat, a horn stab from a jazz LP—and folded it into a new track. It was transformative, but you knew the source, and you could trace the lineage.

Spawning is different. Instead of lifting fragments, you train a model on an artist’s entire body of work and generate new material in their style. Clear lineage, but fuzzy origins. Sampling dealt in citation. Spawning touches the DNA. This distinction matters because spawning raises the stakes in ways that sampling never did. When your work trains a model, what’s taken isn’t a note or a beat, but the sensibility and perspective of your practice.

Sampling sparked arguments about ownership and credit; spawning resets the terms. The internet challenged copyright by creating infinite distribution of perfect copies. With AI, what happens when infinite distribution is hooked up to infinite imitation?

You’re so wonderful
Too good to be true
You make me, make me
Make me hungry for you
Why Can’t I Be You? by The Cure

I opened this talk with a tone-deaf marketing moment from an AI company, so it feels fitting to end with another. Last March, OpenAI launched image generation in ChatGPT and encouraged people to upload selfies and turn themselves into anime characters.

The results were unmistakable: the soft light, round faces, and watercolor skies of Hayao Miyazaki and Studio Ghibli. Within hours, social media flooded with Ghibli-fied images—sweet, strange, and grotesque.

The irony cut deep. Miyazaki’s films are animated by hand. They require over 150,000 drawings per movie, and now his studio’s style was being used to sell the very shortcuts and automations he’s spent a career standing against. The model ate Miyazaki’s work along with everything else, so OpenAI can puppet his style when it suits their needs. Last year, they took Scarlett Johansson’s voice to promote text to speech. Next year, they’ll line up a new artist.

Enough has been said about the viral moment at this point, so instead of staying with the style discussion, I want to value Miyazaki’s art and think about the substance of his movies.

To finish the talk, I’d like to take a look at Spirited Away, a film that wrestles with identity and imitation, appetite and satisfaction. While there’s always a risk in reading too much allegory into art, it seems fair to seek wisdom in what moves us.

Spoilers for Spirited Away after this point.
Jump to after the spoilers.

Spirited Away begins with a crossing. Driving through the countryside, Chihiro, a ten-year-old girl, and her parents pass a torii gate, a marker used to signify temples.

They park and wander into what looks like an abandoned amusement park. Chihiro and her parents don’t realize it yet, but the torii gate they passed marked a threshold. They’ve entered the spirit realm.

When they find an unattended food stall, her parents sit down and eat, promising to pay later. But the food isn’t for sale; it was prepared as an offering for the gods. They take what isn’t theirs, and in doing so, Chihiro’s parents fail the moral test.

As punishment, they are turned into pigs. The parallel to AI is hard to miss: eating without consent, where uncontrolled appetite destroys any awareness of the intangible dimensions of what’s being consumed, etc. etc. etc.

To save her parents, Chihiro is told to take a job in the nearby bathhouse. It’s a greedy place that runs on pleasure and appetite; ostentatious and opulent in every way. It is managed by Yubaba, a witch who binds her workers to service by stealing their names.

Chihiro is renamed Sen (translation: thousand, literally a number), and learns that if she forgets her true name completely, she will never find her way back home. The lesson is clear: in a place ruled by appetite, everything can be eaten, even who you are. A similar pattern happens with the models: once they eat your work, they take your name off of it to use your labor for their own purposes.

The movie later returns to hunger, this time in its spiritual form. Chihiro meets No Face, a quiet spirit waiting outside the bathhouse in the rain. Out of kindness, she lets him in to take shelter, not realizing what she’s invited inside.

No Face is mimicry embodied. Once inside the bathhouse, he observes and absorbs its values. He tries to find ways to repay Chihiro for letting him into the bathhouse, but she turns down his offerings.

Later that evening, a greedy frog who works at the bathhouse sneaks back to the Big Tub to see if any gold was left behind by another customer. While searching, he comes across No Face.

No Face lures him closer by materializing gold from his hands because he sees that the frog wants it. The frog is captivated, and steps forward. He’s entranced. The frog gets closer,

and No Face eats him. He can now speak in the frog’s voice, because consuming allows him to mimic and command. But No Face is still hungry.

Another employee comes along after hearing some noises. No Face uses the frog’s voice to issue commands to the next employee in line. He’s says he’s hungry. Wake everyone up. It’s time to eat. So he produces more gold, gets more food, and his appetite continues to escalate.

It’s not hard to see the parallel, is it? An insatiable force, feeding endlessly to imitate, provoking action with vast sums of money?

No Face’s hunger hooks into the bathhouse’s greed. Word spreads that a wealthy guest has arrived, and the place erupts into a frenzy to serve him.

Cooks parade steaming platters through the halls, servants sing and dance for his attention, even Yubaba eventually takes notice. Everyone wants a piece of No Face, but no amount of food or flattery can satisfy him. What No Face wants is to see Chihiro.

There’s something poignant about how he keeps trying to give her things, because that’s what he’s learned gets people’s attention.

But Chihiro keeps refusing, which both frustrates him and feeds his obsession with her.

No Face’s consumption eventually comes to a head. He eats two more employees and wrecks the joint, so Chihiro is forced to confront him.

No Face does not understand that what he’s really seeking—genuine connection—can’t be bought or consumed. He mistakes his spiritual hunger for a physical one, trying to feed an emptiness that food can’t fulfill.

When Chihiro refuses No Face’s gifts for the last time, she offers up herself to be eaten. But first, she wants No Face to eat the medicine she had been saving to restore her parents.

No Face eats it simply because he eats everything. And suddenly, the intoxication of the bathhouse begins to break.

The bathhouse workers finally see his gold as fake, No Face vomits up everyone he ate, and he quickly returns to his mute, shadow thin form.

I suppose the lesson is that you can’t get enough of what you don’t need.

On her way out of the bathhouse, Chihiro sees No Face brought back down to size. She realizes what he needs is connection, and lets him follow her as she leaves.

No Face joins Chihiro on the train to go meet Yubaba’s gentle twin Zeniba in the swamp.

Zeniba is Yubaba’s opposite. Warm, less ostentatious. At Zeniba’s cottage, something shifts. After knocking, No Face hesitates at the door, but she holds it open for him to enter. This time, he’s invited in.

There No Face receives something that finally satisfies him. He can eat normally now that he has a modest place to belong without the performance of abundance.

No Face finally finds peace by spinning thread beside Zeniba, turning his attention to small and steady work to make the materials for a collaboration.

The lesson for AI might be similar. Its danger comes because it operates inside systems with no sense of “enough.” AI needs boundaries, and so do we. The question isn’t just “what can this machine do?” but “what should it serve?” and, most importantly, “when should we stop?”

Listen, I’m not naive. I know how little room there is to move inside these systems. It’s 2025, and I’m tired. I don’t believe words like these will change much. The people who could change things aren’t listening, and the incentives are too strong to keep the machine running.

So I look for something smaller: incremental progress, unexpected benefits, minor redirections, small refusals. I am not sure how to feel about it, which is why I am trying to be articulate in my confusion. I want to carve out a small creative place for myself in everything that is happening. Is it possible?

The optimist in me remembers Chihiro, the girl who brought the devouring spirit back down to size with her refusal. The pragmatist in me remembers Miyazaki, the artist who made her, still caught in the machines he tried to resist. The realist in me knows that whatever I see in them is coming from me.

There’s a quote by the philosopher and writer Simone Weil that I keep with me. She says:

“We have to endure the discordance between imagination and fact. It is better to say, ‘I am suffering,’ than to say, ‘This landscape is ugly.’” Simone Weil

In other words, it is better to stay with your own experience instead of projecting it outward.

Spirited Away was released in 2001. It has nothing to do with AI. If I gave this talk at a different time, No Face could have been crypto, could have been Google, could have been social media. Allegory has its limits. Simone Weil might say that what all these interpretations have in common is that I’m the one making them.

I used to like technology. The only reason it frustrates me is because I secretly believe it can satisfy me. Perhaps it once did, but the machine is not enough. Is that technology’s failure or my own growth? There are better things to suffer for.

Maybe writing this is my own version of finding some small work to do at Zeniba’s cottage. The wheel spins and I put another few lines down on the page. And I think, “the machine may know everything, but at least I know where to stop.”

It sounds like something from the Tao. Which leads me back to Rubin’s adaptation. I was unfair earlier. Not every verse he wrote is bad. If you dig, there is one hidden in the middle of the stack:

The Way of Code #53

The great way is easy,
yet people search for shortcuts.

Notice when balance is lost:
When rich speculators prosper
while farmers lose their land.
When an elite class imposes regulations
while working people have nowhere to turn.
When politicians fund fraudulent fixes
for imaginary catastrophic events.

All of this is arrogance and corruption.
And it is not in keeping with Nature’s Way.

Again, I wonder: did anyone read this?

Selling Lemons

Tue, 30 Sep 2025 00:00:00 +0000

I’ve been researching a new talk the last few weeks and along the way stumbled across a concept that’s been rattling around in my head. I am writing to share, because I find it a satisfying description for the tech flop era.

The idea is called “a market for lemons.” The phrase comes from a 1970 paper by George Akerlof that explains how information asymmetry between buyers and sellers can undermine a marketplace. Akerlof asks us to imagine ourselves buying a used car. Some cars on the lot are reliable, well-maintained gems. Others cars are lemons, the kinds of cars that can make it off the lot but are disasters waiting to happen. The sellers know which cars are which, but you, as a buyer, can’t tell the difference. That information asymmetry affects the average price in the market and eventually impacts the overall market dynamics.

The thinking goes like this: if a buyer can’t distinguish between good and bad, everything gets priced somewhere in the middle. If you’re selling junk, this is fantastic news—you’ll probably get paid more than your lemon is worth. If you’re selling a quality used car, this price is insultingly low. As a result, people with good cars leave the market to sell their stuff elsewhere, which pushes the overall quality and price down even further, until eventually all that’s left on the market are lemons.

I think we’re in the lemon stage of the internet.

I thought about this last week while shopping online for a sleep mask. Brands like MZOO, YFONG, WAOAW popped up, and these seemed less like companies and more like vowel smoke ejected from a factory flue hole, then slotted into a distribution platform. The long tail of generic brands on e-commerce platforms is a textbook lemons market: good products get drowned out by these alphabet soup products, who use their higher margins to buy sponsored placement in search results. Both buyers and sellers eventually lose (and perhaps the platforms win, as long as they don’t wear out their reputation).

For shoppers, buying online now feels like rolling the dice on the quality of the product. For sellers, the gamble is that their survival relies more on gaming the system than actually improving the product.

I think the post-pandemic experience has been a collective realization that the value that drew us to certain digital products and marketplaces is gone. Much of this reduction in value gets pinned to ZIRP, but there’s another critical factor—the natural flight of value creators. As platforms matured, the users and sellers who generated real value were squeezed out by players focused on capturing value rather than creating it.

Once you identify a lemon market, you start to see it all over the place.

Online dating. A lemon market where participants have no familiarity with one another participate in strategic self-presentation. High-quality partners (emotionally available, looking for genuine connection) can’t effectively distinguish themselves from those just seeking validation and eventually leave.

Search results. A lemon market where platforms profit from sponsored placement, misaligning incentives with user needs. The first page is a minefield: sponsored listings posing as organic results, SEO content farms, affiliate aggregators. You add “reddit” to work around this, but even that has less success these days.

Social media. Your feed is now professional content creators, low-effort podcast video clips, algorithmic filler reaction videos, stand-up chaff, and animals. Good ideas don’t happen frequently enough to satisfy the pace of the algorithm, so many have pivoted to newsletters or stopped posting.

What makes the Market for Lemons concept so appealing (and what differentiates it in my mind from enshittification) is that everyone can be acting reasonably, pursuing their own interests, and things still get worse for everyone. No one has to be evil or stupid: the platform does what’s profitable, sellers do what works, buyers try to make smart decisions, and yet the whole system degrades into something nobody actually wants.

I was first introduced to the Market of Lemons by Dan Luu in an essay titled, Why is it so hard to buy things that work well?. Luu applies the market of lemons as a metaphor, and specifically identifies hiring as a market of lemons, because of the information asymmetry for both companies and individuals.

Companies have always struggled to tell the difference between great individual contributors and mediocre ones. Lacking a clear way to separate the two, they lump everyone together and rely on proxy games to evaluate skill. Candidates, for their part, walk into interviews without crucial information: whether the company is quietly dysfunctional, whether the manager they liked during interviews is about to quit, or whether the open role itself is little more than a vestige of an abandoned strategy that’s likely to be cut once the other foot drops. The usual signals of strength or weakness don’t signify much at all when it comes to hiring. Layer on the automated scale of the application process—candidates firing off applications by the hundreds, companies screening by the thousands—and the result is a highly inefficient market that wastes everyone’s time. Meaningful signals get drowned out, everyone gets lumped together, rational players opt out to the extent they are able, and the market slides steadily downward.

There have been countless attempts to make hiring more rational and efficient—the stuff of startup pitch deck lore. But I’m not sure hiring can ever be much more efficient, because neither side has reason to show themselves as they really are, warts and all. Idealistically, both would come straight; pragmatically, it is a game of chicken. Candidates polish résumés and present curated versions of their abilities, listing outcomes and impact statistics with dubious accuracy and provenance. Companies do the same, putting culture and mission front and center while hiding systematic dysfunctions and looming existential risks. When neither side is forthcoming, you’re left with proxies: a famous logo on a resume, a polished culture deck. Gaming the meta of the system supersedes the actual development or evaluation of skill. And, much to my disappointment, gaming the meta may, in fact, be an essential aspect of most jobs.

At this point, it should be obvious how the market for lemons applies to ill-considered AI-generated content. I’ll let you sketch out that argument yourself since it’s fairly straightforward, and this thing is already long enough.

Instead, let’s zag and revisit my point earlier about system-gaming becoming the most viable playbook instead of focusing on the product. As a consumer and as a designer, I hope this is a temporary state before a massive recalibration. The primacy of meta-activities—optimizing for algorithms, visibility theater, consumer entrapment, externalization of costs, performative internal alignment, horse-trading amongst a set of DOA ideas—is poison. It is a road to nowhere worth going.

This reflects a business culture obsessed with outcomes while treating outputs as speed bumps. But outputs (code, design, the products themselves) are the load-bearing work—the actual prerequisites for the outcomes desired. Focusing on outcomes while ignoring outputs means hiding in abstractions and absolving oneself of accountability. If any output is acceptable to hit your targets, what awful things emerge at scale? What horrors happen when success detaches completely from the necessity of being good—having both skill and ethics?

The safest, smartest path is also the most mundane: keep the main thing the main thing. Outcomes matter, but output literally comes first. Outputs are the business to everyone outside it—what customers see, buy, and use. You can’t stay safe in abstractions forever. Eventually, you must attend to the reality of what’s in front of you, because that’s where work gets done and where assumptions get validated or falsified (because reality has a surprising amount of detail).

In other words, the meta ruins things for everyone. To hide in abstractions is to dodge the reality of your choices. These tactics may get you profit, but you sacrifice benefit. The climb may feel like progress, but at the end you’ll find yourself at the top of a mountain of lemons, perhaps not of your own making, but almost certainly of your own doing.

Eel-evation

Mon, 29 Sep 2025 00:00:00 +0000

Today, I learned that eels are fish. This was a disappointment. I knew of fish, I knew of snakes, yet I always assumed eels were some secret third thing. But, nope, they’re just fish. Or are they? A little digging revealed that eels are deeply weird fish, and to my great satisfaction, the secrets I sensed in them run stranger and deeper than their “fish” label suggests.

Since you’ve made it this far without bailing, let me share a few of the most fascinating things I uncovered in my brief dive into eelology.

Let’s dive right in: we don’t really know where eels come from. Which is strange, because they’re everywhere—rivers, lakes, oceans. We only have the faintest sense of where they spawn or how. Their lives remain partly hidden, and that blank space has always invited stories.

Aristotle thought they slithered out of mud, giving the primordial ooze its first big break. Another tale claimed they rose from sea foam, like a grotesque remix of Aphrodite’s birth. Japanese folklore said eels began as earthworms blessed by the summer moon—plausible enough, if you consider being transformed into something both hideous and delicious a blessing. For millennia, our relationship with eels was governed by fables and speculation. Eventually, real science needed to step in.

One spat of scientific interest in eels came at the end of the 19th century. Scientists, lit up by the potential of Darwin’s new theory of evolution, believed they finally had the tools to crack the case: where eels mated, where they were born, where they went to die. They observed. They dissected. And time after time, they kept hitting dead ends.

Here’s the story of one such wall. The year is 1876, in the port of Trieste, Italy—then part of the Austro-Hungarian Empire. In a small lab, surrounded by jars of eels, briny seawater, and plenty of slime, a young zoology student works under orders from his doctoral advisor. His task: to solve the mystery of eels by capturing live specimens from the harbor and dissecting them in search of testes. (The sexual mysteries of eels were anatomical as much as behavioral.) Day after day, he probed and sliced, logging hours at the dissection table. Four months later, he left empty-handed, without so much as a glimpse of a gonad. Upon his departure he wrote: “All I see when I close my eyes is the shimmering dead tissue, which haunts my dreams…”

That student was Sigmund Freud, who later established psychotherapy as a discipline, using dream interpretation to uncover the hidden sexuality of his patients—truths beneath the surface that, like eel gonads, couldn’t be found through straightforward empirical methods. To overstate it: Freud went a little screwy looking for eel balls. (Or maybe he was already screwy enough to go looking for them.) The psychological case study almost writes itself: the same hidden drives that pushed a young researcher to spend four months searching for eel testes might also fuel a lifetime of theories about libido, repression, and desire. The link is tenuous, of course, but it’s fun to imagine the past 150 years of psychotherapy springing from Freud’s failed eel dissection project.

All these years later, no one has ever seen an Anguilla eel do the nasty. But scientists think they’ve at least found the place where it happens: a single location on earth where, strangely enough, no adult eel has ever been spotted. Deep in the Atlantic lies the Sargasso Sea—the only sea without land boundaries, defined instead by four great currents. Eels are born, quite literally, in the Bermuda Triangle.

The mystery cracked a little in 1896, about 20 years after Freud’s attempts, when Italian zoologist Giovanni Battista Grassi found a mature male eel with testes and sperm. He also linked a strange, transparent fish called the Leptocephalus to the eel, noticing they shared the same oddly high number of vertebrae. Long thought to be its own species, the Leptocephalus turned out to be the eel’s larval stage—a dramatic transformation we’d never witnessed, since it happens far below, in the deep ocean.

Here’s what we now know: Eels begin as tiny, glassy specs suspended in the Sargasso’s deep blue. They drift for years, feeding on “marine snow” as currents carry them westward. By the time they reach Europe, they’ve transformed into glass eels—longer, flatter, translucent, with a defined backbone.

Then comes the climb out of the ocean. Glass eels push upstream into estuaries, crawling over rocks and mud to find fresh water for their next metamorphosis. There, they shift again into elvers. The translucent jelly of their body becomes speckled with pigmentation and they develop an insatiable appetite. After a couple of years they bulk up into yellow eels. This adolescent form is the familiar eel seen wriggling in ponds, drawn up from wells, or fished from rivers. Eels can linger in their yellow form for decades, but eventually nature calls them to the ocean again.

Upon their last metamorphosis, eels begin “silvering”: shedding their color for black and chrome. It is now time to head to the ocean to breed. Their eyes get larger to be able to see better in the depths. Their stomach dissolves—won’t be using that. And finally (poor Freud), the eels’ sex organs develop. The eels swim thousands of miles back to the Sargasso, where they release billions of eggs and sperm into the Bermuda Triangle and die. Their young hatch as tiny glass specs adrift in the currents, and the cycle begins again.

So the next time you order unagi and salmon rolls, think about how the paths of their lives mirror one another: salmon spawn in rivers, live in the ocean, then fight their way back upstream to lay eggs, while eels do the reverse—born in the ocean, maturing in rivers, and returning to die in the deep. Upstream versus downstream, legible and knowable versus hidden, lost, and dark. The life of an eel resists any tidy narrative, transforming from one strange and anomalous form to the next, only to vanish back into the depths that made it. But I digress. What comes from nowhere should also end nowhere, so I will stop here.

This post is majorly informed by this video. I watched it a few nights ago in a spat of insomnia and could not get it out of my head. Now, I’m dragging the rest of you along with me. I probably got a couple things wrong. Thank you for reading 1,000 words about eels.

Time is On My Side

Tue, 19 Aug 2025 00:00:00 +0000

Ah, summer, my worst season. Summer and I do not get along. Too much heat, too much light, too much noise, too much cultural baggage. I burn, I sweat, I fester, I sulk. Which is why it came as a surprise that I took the summer for myself. I quit my job at an opportune moment and called it a sabbatical.

My intentions were simple enough: step away from Zoom, Slack, quarterly planning, performance reviews, and the everydayness of corporate tech work to make some space and assess what was next for me.

More practically, I wanted to get back to walking, reading, and writing. These were the foundational practices during the most prolific and enjoyable parts of my career. I longed to feel generative again and to have ideas with depth, meaning, and pleasant uncertainty, ideas whose remit extended beyond the boundaries of one company. I missed the opportunities of the internet as a common place for finding your people and feeling like a part of a group that actually had ideas instead of opinions or pleas for attention.

The summer is now mostly spent, and I am writing to say: not much has happened. I swept away the everyday to make space for the profound, and my days refilled with everyday things. No a-has, no takeaways, no transformation, no strong convictions about the future of technology, design, or Frank. But also: no crises, no existential dread (at least about myself), and very few reservations about quitting as the right choice. I am more spacious inside and enjoying a refreshed ability to attend to the things in front of me. Most people call this a vacation, I guess.

I must admit: it feels awkward to write about this summer. First, because of the privilege—so many people are looking for work right now or can’t leave the work they’ve got. Second, because my writing faculties have rusted—my sentences are crunchy and the cursor pushes to the right much slower than it used to. I scrapped other writing and put off sending this because of my creakiness. And third, well, because there’s a pressure to have noticeable outcomes to life choices like this. I made space, and after 3 months, all I have is a more internalized sense of that space.

But free time also means freedom from expectations. I asked AI what we do with time, and it came back with words that were commercial and violent. We spend time, save time, take time, and make it; manage, track, and save it; we kill time, we pass it, we waste it, borrow, and steal it. We abuse time and it beats us back up, either in retribution or self-defense. It’s a zero-sum perspective of the material of our lives; it makes us prisoners to our own utility.

The AI said nothing about love, loyalty, or enthusiasm. When you wrap those up, it becomes clear that the best thing to do with time is to devote it. That is how you get time on your side. When you are working with time instead of against it, every bit matters, it all counts, even the fallow times, the empty times, the time off the path.

The Green Ray

Tue, 31 Aug 2021 00:00:00 +0000

Last week, early morning, the sky flushed that fuzzy summer cirrus peach: I wake up inspired to send a brief newsletter with one nice idea. An admirable goal—let’s finally get to writing! But of what?

I walk to get coffee, and before stepping in to the coffee shop, a maintenance guy asks for help. He is painting over bad graffiti and needs assistance picking a paint swatch that will match the color of the wall. He is colorblind—could I help him pick? Yes. Absolutely. I am eager to help and feel verified in every designerly life choice leading me to this moment. The wall was a ruddy red ochre—the oldest color for walls if you go by cave paintings. We made a good swatch selection. He went off to his truck to gather his tools, and I went in to order my coffee. Black. More inkish, really. “Guess I’m writing about color,” I thought, so here we are.

I have been thinking of the opportunities in color: partially for my design work, partially as a way to add variety and stimulation to an experientially-limited pandemic year(s). Even boring things can be made somewhat interesting if cast in new colors. I sometimes fantasize about painting my apartment a variety of interesting colors—ochre? oxblood? ombré?—but find myself doubting my impulses and leering at my restrictive renter’s lease. My exuberance gets curbed and the walls stay a middle grey—a comfortable, but unpronounced 18% photo card grey, a color waiting for another color to happen.

I always believed that color with intensity, at scale, was beyond me. I’m not colorblind, but my lowest university grade was in color theory. I developed all kinds of justifications to avoid re-engaging with color on a deeper level. My ridiculous explanation for the terrible grade was that my professor and I disagreed about where orange stopped and red began. We would enter passive aggressive tugs of war over whether a color was red-orange or orange-red. I forgot his exact words in these contests, and I don’t remember much color theory, but I will never forget the exact shade of his red-orange face.

Color sits in a continuum—ok, sure—a spectrum, dictated by scientific fact, registered through personal experience, and ossified with shared cultural framing. That sounds fancy, but in short: “Red” is a vague term that is solid in the middle and hazy at its edges. Fights over redness always happen at the boundary of orange-red and red-orange, because the edges of definition are determined by all the stuff that makes other people fascinating, annoying, and real: their perception, their labels, their culture, their location.

Burgundy is a place, but it is also a color inspired by the wines of the region. Do people from wine country have more shades of red? Maybe so. But landscape and abundance can also deprive: over in the Aegean, the ancient Greeks spoke of wine dark seas, and some speculate that they (or Homer, at least) lacked a word for blue. This is peak irony: look at parts of Greece through squinted eyes and all you see is blue. We all want to believe that the blueful are the most naturally blueless. Like a fish in water, there is no identifying, nor making sense of, what completely surrounds you.

After 18 months of semi-quarantine, I should have many shades of solitude. Instead, I have one flattened, wine dark abyss.

I feel this same flattening effect as a lover of words who works in technology. The tech industry is where words go to die. It’s a tragic bit of irony: tech work is all abstractions, and those abstractions can only be considered and revised through precise language. But we are slobs and so poor at wielding language. I’m reminded of my color-blind painter friend. Except our deficiencies come from laziness (guilty) or violence—less nuance means fewer distinctions, and at scale this diminishes the imagination for other potentials. Parrot “red” enough times, then there’s no more red-orange, nor orange-red, and even orange is at risk. All that’s left is monolithic “red.”

The pandemic has given me a cynical distance to professional language—work words slough meaning in the dissonance of a world pretending it is business as usual in truly exceptional times of suffering. As an exercise, I started a list of meaningful distinctions, wondering how I could find a kernel of interest, truth, and personal resonance in the catch-all, vague, lazy words I despised. I am sharing my list after 18 months, because it has become a pocket-sized ethos, a dialectical approach where the separation of ideas makes each more coherent. It is a reminder that no matter how crudely we may label things, the actuality of a red-orange face, the peach of the sky, the whichever wine blue of the sea are all still there, waiting to be summoned again with the right word.

Meaningful distinctions:

Profit vs benefit
Simplicity vs clarity
Success vs progress
Convenience vs comfort
Fun vs satisfaction
Thoughts vs thinking
Information vs knowledge
Choices vs decisions
Scale vs resonance
Achievement vs influence
Descriptions vs evaluations
Expansion vs growth
Consumption vs consideration
Control vs initiative
Happiness vs fulfillment

Midday, New York August, only sun and nothing else—all white and no color, blown out in that comically overexposed ’90s music video way. My inverted seasonal affective disorder hits in the summer and I can’t think straight. Body and mind feel like they are bombarded by too much information: too much light, too much heat, too much dissonance with other peoples’ affinity for summer. I am trying to write. Trying. I must wait for dusk, the golden hour—less light, more color, room to think. Time comes, and I walk toward the water.

If you dig deep enough into the Wikipedia of quotidian things, you will eventually arrive at a page called “Mirage of astronomical objects.” This is the page to answer all seaside sunset questions. “Why does the sun look wobbly while it is setting?” “Why does the moon sometimes set?” The page also provides new names: the Novaya Zemlya effect, Jules Verne’s Etruscan vase, and, my favorite, the Green Ray. Imagine this: the top edge of a setting sun grazes the horizon before it disappears completely. At this point of connection, if the conditions are just right, light skews and inverts. For a brief moment, intractable white light splits into new color and new possibilities.

Late day, late August, ocean front, looking out: wine dark sea, red ochre sky, and at the boundary? From nowhere: chartreuse.

From Éric Rohmer’s Le rayon vert

Hands TV

Mon, 03 Aug 2020 00:00:00 +0000

When is tricky these days, but it’s the only question my expectant monkey mind can ask. Time feels especially squishy this year, as my daily schedule drifts further and further away from the traditional 9-5 work window and months slip by and weeks drag on. It’s been hard to get my arms around time, but my best estimate is that I do this thing every other week or so, and I want to tell you about it.

Whenever I When? myself into agitation; whenever an iffy day gets its claws into me in a previously unimaginable 2020-ish way; whenever I find myself pausing on walks wanting to knock the phones out of people’s hands; whenever it’s a gloomy November in my soul and I have to pause for a moment to remember the month, I escape to the sofa to cleanse myself with a couple hours of what I have endearingly named Hands TV.

Black gloves, screwdrivers, wrenches, rotary grinders, isopropyl alcohol, sandblasters, and toothbrushes… all working on chainsaws, toy trucks, unidentified military components, and Game Boys. Time-lapse restoration videos have become my escape from 2020. I fire up the YouTube app and sit through hours of dudes scrubbing and grinding on old busted junk until my shadow lifts and progress, justice, peace, safety, and a smile feel possible again.

I began watching Hands TV out of curiosity. An interest in Japanese woodcut prints led me to videos about Japanese joinery and hand tools. The algorithm extended this logical path into a delirious wish for decontextualized restoration videos of Egyptian daggers, and off I went down the autoplay road. It’s been true love ever since.

The rules of the genre are straight-forward:

Only restore items that fit on a workbench
The first and last shot must always be your hands placing the object on the workbench
No talking, no music
No faces, only hands
Wear gloves most of the time
Jump cuts galore
After disassembly, Knoll all the parts together for one hero shot
Sanding and grinding sequences should be sped up to 2-3× (you eventually acclimate to the sounds that resemble dental work)
Use time-lapse footage when needed, especially when soaking parts in rust remover or ultrasonic cleaning solution
Tersely label what you’re doing with text in the corner
Always provide a link to your screwdriver kit and merch
Chekov’s blowtorch (if you see a blowtorch in any shot, it will be used)

Hands TV has become a way to fill time while stuck in my quarantine haze, an unlikely antidote to media-induced despair, and a counterpoint to doomscrolling. It is care as entertainment: a fulfilled wish to watch something come together instead of fall apart, and an opportunity to witness a reversal of neglect with low emotional and cognitive overhead. This perspective is a tiny bit treacly and very much an overreach, but it’s been a tough year, I’m exhausted, and tri-hex screwdrivers now get me emotional. Beyond all that, there is very often fire and burnt stuff. If you’re curious, I’ve linked a few of my favorite videos below.

Vintage Pencil Sharpener Restoration by Odd Tinkering

Seized 1960s Chainsaw Restoration by Will Matthews

Antique Kitchen Scale restoration by my mechanics

Antique Rusty Cleaver Restoration by The Small Workshop

Game Boy restoration by TysyTube Restoration

Who cares?

Tue, 04 Feb 2020 00:00:00 +0000

Last week, I shared some thoughts on creative dispositions. I personified two attitudes, architects and gardeners, to show how a writer’s inclinations produce a certain kind of text, thus affecting the design. But the gardener and the architect are ripe metaphors that can be carried other places.

Jeremy Keith picked them up and applied the scheme to design systems:

It’s not just me, right? […] I think you’ve got an interesting analysis of the two different approaches to design systems. Descriptivist gardening and prescriptivist architecture.

Jeremy then extends prescriptivism to share his concerns about individual agency in defined systems, since any kind of structured consistency is primed for automation. Are designers working to pave the road to their own obsolescence by responding to the increasing surface area of brand and digital product? Perhaps, but let’s not display too much hubris. There is a near infinite capacity for automated things to fall apart. Ask any sysadmin.

Dave Rupert picked up the thread and added some of his own thoughts in a post, provocatively titled, The Web is industrialized and I helped industrialize it:

In our cultural obsession with billionaire entrepreneurs we laud new features more than the maintenance and incrementalism work of making old features better and more accessible. Maintenance looks like red minus signs in the spreadsheet.

He continues:

So how do we inject more autonomy into design systems? Jina Anne’s philosophy on Design Tokens seems really strong in maximizing individual contributor and team autonomy, being more “descriptivist” as Jeremy puts it, rather than “prescriptivist”. Perhaps the old “Tools, not rules” adage holds up here as well. Design and development tools and processes that democratize contributions could provide autonomy, or at least the feeling of autonomy.

Brad Frost then contributed some of his own thoughts, rallying around processes and culture (and throwing capital-A Agile under the bus, which I am all for):

it’s important to stress that this isn’t an intrinsic issue with design systems, but rather the organizational culture that exists or gets built up around the design system. There’s a big difference between having smart, reusable patterns at your disposal and creating a dictatorial culture designed to enforce conformity and swat down anyone coloring outside the lines.

I’m glad Brad mentioned Yesenia Perez-Cruz’s book, Expressive Design Systems, published this past November. I haven’t read it yet, but judging only by its title, it directly addresses the assumption that systemization diminishes the possibility of creative contributions by designers.

All of these people are better positioned to comment on design systems, their culture, and implementation. But it does strike a nerve, because design systems are the place where design touches a sense of diminished individual agency, dehumanizing company cultures, digital sprawl, economic consolidation, and fears of automation. People blog and worry, because they believe in the effectiveness of their work, but have no confidence in the hands where they place it.

It is a crisis of care.

I would like to make a proposal, a sketch of an idea, borrowed from others. Each of these highlighted problems is a failure of care and the devaluing of tending to our tools, industry, society, and each other.

I have two references in mind. First is Joan C. Tronto’s Who Cares?: How to Reshape a Democratic Politics. The book focuses on what we owe one another, and how to shape society and government around those convictions. (Don’t worry, it’s a short pamphlet.) Tronto:

When I say “care,” I don’t mean only healthcare, childcare, and caring for the elderly. I don’t mean only finding a babysitter on a website called Care.com. I mean, as Berenice Fisher and I defined it some time ago, “in the most general sense, care is a species activity that includes everything we do to maintain, continue, and repair our world so that we may live in it as well as possible. That world includes our bodies, our selves, and our environment, all of which we seek to interweave in a complex, life-sustaining web.”

A web? Care applies to the built environment, and especially to digital technology, as social media becomes the weather and the tools we create determine the expectations of work to be done and the economic value of the people who use those tools. A well-made design system created for the right reasons is reparative. One created for the wrong reasons becomes a weapon for displacement. Tools are always beholden to values. This is well-trodden territory.

Again, Tronto:

Care is about meeting needs, and it is always relational: the skinned knee of a child who fell off his bike isn’t only about scrapes and germs, it is also about creating the conditions for him to feel safe in the world.

The same can be said of design systems: producing tools to address the sprawl of digital technology isn’t only about enabling individual contributors, it is also about creating the conditions for them to feel safe and capable in their job.

One aspect of work stability is meeting real needs. The other is to have the work of meeting needs be valued. One of the more significant insights into the gap between the two was highlighted by Mierle Laderman Ukeles. In 1969, she wrote Manifesto for Maintenance Art, 1969, a proposal for an exhibition to display maintenance work as contemporary art. Ukeles was commenting on the art world, but her criticism extends to any made thing in society. Parts of it go directly to the heart of the conversation on design systems.

From the manifesto:

Two basic systems: Development and Maintenance. The sourball of every revolution: after the revolution, who’s going to pick up the garbage on Monday morning?

Development: pure individual creation; the new; change; progress, advance, excitement, flight or fleeing.

Maintenance: keep the dust off the pure individual creation; preserve the new, sustain the change; protect progress; defend and prolong the advance; renew the excitement; repeat the flight.

The symmetry to design systems is uncanny.

Development systems are partial feedback systems with major room for change. Maintenance systems are direct feedback systems with little room for alteration.

The solution? Elevation. Assert value by performing maintenance and declaring it art. In Ukeles’ words, “Everything I do is Art is Art.” The manifesto was printed in Art Forum in 1971. The proposed show’s name? “CARE.” Of course.

Society is a system that is woven together. The gallery goes with the street and the street sweeper. You can’t separate the buttons from the designers, the job from the work culture, the company from the economy, the economy from our policies, our policies from our values. It all goes together.

Who cares?

Monkey Trap

Thu, 19 Sep 2019 00:00:00 +0000

Every once in a while you come across a fact that is so sumptuous it begs to be considered a metaphor. And then, there are other facts that are so perfectly just-so that they need to be viewed as apocryphal.

I have read about the monkey trap in multiple places, from Tolstoy to Zen and the Art of Motorcycle Maintenance to your run-of-the-mill self-help books. Some say it’s from South India, others assign it African origins, possibly Namibia, some don’t even bother with origins. Regardless, the details of the trap are the same: take a hollow gourd or coconut and drill a small hole in it. Size matters. The hole should be just barely big enough for a monkey to get a hand inside. Place a treat—bananas, rice, etc.—in the gourd and tie it to a tree. Then wait.

Eventually a hungry monkey will come by, stick in their hand, grab the food, and become trapped. The monkey’s hand fits through the hole, but his fist doesn’t fit back out. They will scream and struggle, clinging tightly to their reward, until someone comes to collect them. The irony, of course, is that the monkey could have escaped at any time. All they had to do was let go.

Preconceptions can blind us from doing things in better ways. Sometimes expertise gets in the way. Buddhists push against this situation by seeking “beginner’s mind.” Over-devotion to the possibility of specific rewards can trap us in precarious situations. Poker players call it being “pot-committed.” All are forms of cognitive biases, but perhaps labelling it as “mental rigidity” is a more immediate and helpful way to think about all of this.

Stay loose. Let go. There are other bananas.

When the Work Looks Back At You

Wed, 06 Jun 2018 00:00:00 +0000

A few weeks ago, the folks at Milanote asked to interview me and brought along an unexpected brief: “let’s sit down and talk through the process of one of your projects.” As someone who is consistently banging the drum about the importance of a diligent process, it was a good chance to step back from the day-to-day workings of the studio and get a better perspective on what’s changed in my creative process in the last few years.

The most important change, I think, has been a commitment to staying open to new ideas for a longer period of time. Two-thirds through is where the good surprises tend to happen. I decided to talk with Milanote about the last project that pleasantly surprised me like this: a poster I designed for my lecture in Memphis this past March with Creative Works.

Milanote has posted a really nice write-up to their blog, and I’ve reproduced my answers in full below. Thanks to everyone at Milanote for asking good questions and giving me the opportunity to think it all through. Thanks also to Josh Horton over at Creative Works for the invitation to speak and providing carte blanche on the poster’s design.

The poster for Creative Works, available to purchase here.

Is starting a new project like this hard? Do you feel any fear or anxiety? How do you deal with it?

Designing posters is usually a fluid and enjoyable experience, but this one was tricky because it was about me. Ugh, you know? It’s hard to get enough distance from yourself and your ideas to accurately capture them. And just to make things more challenging, the talk was about what I find fascinating and important in design—the interdependence of images and words—requiring a double distillation of my creative personality. A challenging brief to capture in an image!

I was tying myself into knots over it, but eventually a friend bopped me over the head and said I was over-thinking. They pointed out that if I liked the design, it was good enough, because the poster was a proxy for me and my ideas. What a relief! It felt like a magic trick: the heaviest design challenge became light and enjoyable by having modest expectations.

Where were you when you created this piece of work? How did that environment influence it?

The poster was an after-hours project made by lamplight. Home is probably the best place to do self-referential work like this because you’re in your cave and relaxed. You can glance at the things around your house and wonder what they all have in common. The wall above my desk has all kinds of recent discoveries taped up: quotes, postcards, visual references, and print-outs of images I find online. The wall is my real-life Pinterest. Everyone should have a wall.

It’s satisfying to rearrange things with my hands to understand them differently. I printed key slides from my presentation and taped them up alongside all the visual inspiration, and eventually stumbled into the poster’s concept by adapting an image I made weeks earlier for the middle of the deck. It was right there in front of me.

What did you not understand at the start of the project that you understood later?

Making this poster reinforced that design needs to be attractive. That’s a very simple-minded realization, but I mean “attractive” in both senses of the word. Attractive as in beautiful, yes, but also attractive as in magnetic.

Part of my job is to create energy that pulls in people. It’s always felt narrow to say that design is problem-solving; there’s more to it than that. Design can inform, yet that very often needs to begin by seducing. That’s the aspect of design I find most fascinating.

If someone tried to tackle this project, what would they get wrong at first?

I think there are two modes of designing. The first requires specificity and clarity. That route would probably say that the poster should be about me, since, you know, it’s a lecture I’m giving. But this route was a dead end for this project and things didn’t really click until I got out of the way. So I took the second road, the other mode of design that runs on suggestion, excitement, and provocation. In these cases, the design’s value comes from its ability to be attractive and create anticipation for something that isn’t completely defined—it’s all built on the promise of possibilities.

Talk about a time when you were stuck or had a “creative block” on this project. What did you do about it?

I almost always start stuck and the first part of the process is letting go of my preconceived notions that are easy to dress up as experience or expertise. You have to work back to the beginner’s mind. Other than the recommendation from my friend, the second change that really helped was to focus on words instead of images.

There isn’t much text on the poster, but the talk does have a title. “The Eye Knows” is an unfortunate pun that I find funny. It gets to the tension between images and words: they both come in through the eye, but we absorb it all across two channels. The image for the poster clicked into place once I named the talk, because then I could have images and words sitting in a conceptual relationship with each other.

What surprised you about how this project evolved?

This is dorky, but I forgot how nice it is to work on something and have it look back at you! The last couple years have been a lot of branding and interface work for me, which means I’m mostly staring at colored rectangles all day. I love it, but those shapes don’t look back. There’s a face on this poster, and as I continued to refine the design, the energy it was shooting out became more real, more warm.

How did you know when you were finished?

On assignments like this, I’m a big proponent of “once through, cleanly.” You think about your idea, sketch a little, then put some glue in your chair and bang it out in one sitting. All of my best work happens this way: posters, collages, essays, outlines for talks, and so on. The work seems to be more cohesive and its energy more concentrated and palpable. If you sit down and what’ve made is bunk, you walk away, come back later, and start over. Nothing is kept but the memory of what went wrong. It’s a silly method, but it works for me. Once through, cleanly, because there are no half karate chops.

It’s probably irresponsible to work this way when other people are involved, and it is definitely no way to write a novel, but for my smaller self-initiated projects, this method has produced the greatest rate of success. It makes it so much easier to know what you’ve got while you’re working on it: the closer you are to finishing, the more difficult your enthusiasm makes it to sit down, focus, and finish.

Did this project change you at all?

Sure! Choosing how to represent yourself and your ideas is intensely personal. I’m still surprised I’m so happy with the outcome. Maybe I can value this design a bit more honestly because it feels like it came from somewhere else. It does a good job of capturing my attitude towards design: affable, intelligent, and clear, expressing some welcome charm.