Michelangelo said he didn’t create his sculptures. He just removed the marble that wasn’t the statue.
I’ve been thinking about that lately. About what it means to have a collaborator whose job isn’t to add things but to help you find what’s already there. I’ve been doing that kind of work recently — the excavation kind — and it has changed how I write and honestly how much I enjoy the making of it.
But I’m getting ahead of myself. Start with Jay.
I had a ham shack when I was in high school. A tight corner of my bedroom, a transceiver, an antenna wire running out through the window frame to somewhere up on the roof. Late nights mostly. The ritual of it: power on, headphones on, find a frequency, make sure it’s clear. Then send CQ. CQ. CQ. A call to no one in particular, to anyone, to whoever happened to be listening on that frequency at that moment anywhere on earth.
Sometimes nothing came back. Sometimes someone answered from a place I had no reason to expect — a voice, or rather a pattern of dots and dashes that resolved into a voice, from a callsign I didn’t recognize, from a grid square I’d have to look up on a map afterward. We would exchange signal reports and names and locations and often we talked longer. Our gear. What we did that day. Ordinary things, transmitted at forty words a minute across a great distance to a stranger I would never meet.
I did not know then that I was practicing something. I thought I was just playing radio.
We have decided, sometime since, that luck is a system. That serendipity is an architecture. That the people to whom good things happen have engineered the conditions for good things to happen, and that the people to whom good things do not happen have, at some level, failed to present the right surface to the world.
I am not sure when we decided this. Sometime after we stopped believing in fate and before we started believing in algorithms, in that narrow window when we still believed, provisionally, in ourselves.
The self-help literature on luck is a literature of verbs. Expand. Broadcast. Reframe. Sabotage your algorithms. The verbs are always active, always transitive, always aimed at a future in which the random becomes, retroactively, inevitable. You will look back and see the architecture. You will understand that the flight delay was an opportunity, that the canceled meeting was a gift, that the stranger in the adjacent seat was not a stranger at all but a node in a network you were already, without knowing it, building.
What the literature cannot account for is the canceled meeting that was simply a canceled meeting. The flight delay in which nothing happened except that you sat in a molded plastic chair in Terminal B and ate a sandwich that cost fourteen dollars and thought about everything you had not yet done. The stranger who remained a stranger.
I have been thinking about a used bookstore on Telegraph Avenue in Berkeley, at the corner of Dwight. Shakespeare & Co. It smelled the way all serious used bookstores smell — dust and possibility, which are not always different things. The shelves ran floor to ceiling and were not organized in any way that rewarded efficiency. You found things there the way you find things in dreams: without looking, and then suddenly they were in your hands.
I found a paperback copy of Slouching Towards Bethlehem there. Someone else’s margin notes in blue ink, a handwriting I did not recognize and have never been able to stop thinking about. Whoever they were, they had underlined the same sentences I would have underlined. They had written yes in the margin next to things I did not yet know I believed.
I have no way of knowing whether that was luck or whether I had simply been the kind of person who wandered into bookstores and stayed too long. The kind of person for whom that particular door was already, structurally, open.
Buildings have architects. Someone drew the plans. But I cannot find, looking back, the moment I became that person. I can only locate the book.
The word serendipity was coined in 1754 by Horace Walpole, who derived it from a Persian fairy tale about three princes of Serendip who were always making discoveries by accidents and luck, of things they were not in quest of. Accidents and luck. The word has always contained both. What the contemporary literature has done is quietly eliminate the accident and keep only the luck — reframed now as preparation, as readiness, as optimized openness. The princes were not prepared. They wandered.
Anymore we are often uncomfortable with just wandering. Wandering has no metrics. A waste of time.
There is a thing that happens when you pick up a physical newspaper, one you did not choose from a menu of personalized recommendations online but simply lifted from a rack at the library because it was there. You read stories you would never have clicked on while reading on an iPhone. Not because you lacked interest but because no algorithm had yet determined that you had it. The story finds you before the system can decide whether you are the kind of person who would want to be found.
I go to the library some days for precisely this reason. It is a considered refusal — the same one the princes of Serendip were practicing, though they had no word for what they were refusing. The library does not know what I clicked on last Tuesday. It cannot optimize my morning. It can only offer everything, indiscriminately, and trust me to wander.
Life feels richer on those days. I have tried to understand why and have arrived, after some time, at this: on those days the world is larger than my prior assumptions about it. That is not a small thing. That may, in fact, be the whole thing. Here comes the sun!
Shakespeare & Co. closed in June 2015, after fifty-one years on Telegraph Avenue. The owner said the past few months had been unsupportable. He taped a note to the door and served his last customer and locked up around eight in the evening and that was that. Someone who worked there was quoted saying that the serendipity of finding a book that changes your life doesn’t happen on Amazon. Indeed. He meant it as an elegy. The infrastructure of accident had to be built by someone. It had to be maintained. It had to be, on some Tuesday evening, locked for the last time.
The owner locked up around eight. He had served his last customer. There was nothing more to do.
The margin notes are still in the book.
There’s a kind of reading that’s really a form of listening — not to what a writer is saying but to how they’re solving a problem. Every great piece of nonfiction is an argument about structure, and most writers never explain it aloud. The argument is in the choices: where the piece starts, when it digresses, what it leaves out, how it ends. You can enjoy the work without seeing any of this. But once you start seeing it, you can’t stop — and eventually, some of it becomes yours.
This guide is for both kinds of reading. Each writer here is worth your time as a reader. Each one also has something specific and stealable for anyone who writes. I’ve tried to name both.
The ten: John McPhee, Robert Caro, John Jeremiah Sullivan, Michael Lewis, Joan Didion, David Grann, Sam Anderson, Susan Orlean, Tom Junod, and Wright Thompson. Different registers, different obsessions, different methods. What they share is a commitment to making difficult things feel inevitable — and the discipline to make that look effortless.
They fall into three loose clusters, which might help you find your entry point. Structure builders — McPhee, Caro, Grann — write pieces that feel inevitable because the architecture is invisible but load-bearing. Emotional access — Orlean, Junod, Thompson — get you inside feeling before you know you’re there. Voice and form — Didion, Sullivan, Lewis, Anderson — the sentence, the digression, the explanatory seduction, the essay as genuine inquiry. The clusters overlap, and the best writers in each group are doing all three things at once. But if you’re trying to solve a specific problem in your own writing, the clusters tell you where to look first.
On November 25, 1963, every journalist in America was at Arlington Cemetery covering the state funeral of John F. Kennedy. Jimmy Breslin went to find the grave digger.
His name was Clifton Pollard. He was paid $3.01 an hour. He had been called in on his day off because the foreman thought he was the best they had, and the foreman was right about that. Breslin spent the morning with him while the ceremony unfolded a few hundred yards away — the dignitaries, the riderless horse, the flag folded into a triangle and handed to a widow. Pollard ate a ham sandwich and kept working.
The piece Breslin filed that afternoon is still taught in journalism schools sixty years later. Not because it covered the funeral better than anyone else. Because it didn’t cover the funeral at all. It found the true subject by ignoring the announced one.
That instinct — turn away from the obvious, walk toward the unglamorous specific, trust that the universal is hiding there — is the one idea I’ve returned to more than any other. It shows up in two very different writers who occupy, in my mind, the same position on the roster.
Breslin got there through deadline fury and a saloon-bred instinct for where the real story was breathing. He didn’t theorize about it. He just did it, on a deadline, in a city that rewarded the loud and the fast. John McPhee got to the same place by an entirely different route: patience, structure, and a willingness to spend six months learning how canoes are made or what happens to a piece of shad on its way up the Delaware River. Breslin worked like a man catching a cab. McPhee worked like a man building a cathedral.
But the underlying claim is identical. If you stay with a specific, unglamorous subject long enough — if you resist the pull toward the obvious center — it will eventually yield something that couldn’t have been reached directly. Pollard and his shovel. The orange grower and his grove. The nuclear physicist who also happens to be a canoe builder. The method is the same. Look where no one else is looking. Wait longer than feels reasonable. Write what you find.
This is one player, really. Just wearing two different jerseys.
The second seat belongs to Wright Thompson — not a single book but a stance. The premise that the most revealing place in any story isn’t the event itself but the moment before and after it, when the subject is alone with something they haven’t yet put into words. Every piece in this tradition is quietly asking: what is this person carrying that they can’t say out loud? It’s a question that turns out to apply well beyond sportswriting. It applies to most things worth writing about.
The third is whatever the Apple design era taught about constraint and clarity. Not nostalgia — something more durable. The idea that removing something can be an act of confidence. That the most useful things often appear to be doing less than they are. This one surfaces constantly in writing, in argument, in the editing pass where you decide what the piece actually needs versus what it accumulated along the way. Features are easy to add. Knowing what to cut requires a different kind of certainty.
The fourth is the philosophy embedded in spaced repetition — not the algorithm but the claim underneath it. That knowledge you don’t revisit isn’t really yours. That understanding decays on a predictable schedule whether you acknowledge it or not. The honest response isn’t anxiety about this; it’s the habit of return. Going back to the same passage, the same idea, the same question on a different day, and finding it has changed — or finding that you have.
The fifth seat shifts. That’s probably the right design. Four constants and one that evolves is roughly the correct ratio for a starting lineup that has to play in different eras. Right now that seat belongs to the question of what AI does to a practiced human sensibility — whether it erodes it by substitution or clarifies it by contrast. Earlier it was held by a certain kind of systems thinking. Before that, something else. The player who earns that spot is always the one asking the question the current moment most needs answered.
The coach who wins five championships doesn’t do it with the same roster. But he does it with the same philosophy. The starting five aren’t the players who happened to be good once. They’re the ones who keep earning their minutes regardless of what the season throws at you.
Breslin knew where to find Clifton Pollard because he’d been looking in that direction his whole career. The skill wasn’t the story. The skill was knowing that the story was never where everyone else was standing.
That’s the one I keep coming back to.
Marc Andreessen made two claims recently that don’t quite fit together, and I haven’t been able to stop pulling at the seam.
The first: for almost any topic, the top AI systems now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone. This isn’t a casual observation from someone without access — it’s a meaningful data point about what AI is actually doing to the value of expertise.
The second: the only real skill left in using AI is knowing what to ask. The models can already do almost anything you can describe in plain English. The bottleneck lives in your own head.
Hold those two claims next to each other. If the AI beats the experts, then the quality of your question only has to clear a low bar — good enough to unlock what the system already knows. You don’t need to ask like a cardiologist to get a cardiologist-quality answer. You just need to ask.
Except that’s not how it works in practice. And the gap between the two claims is where something important lives.
The better the question, the better the answer — even from a system that already knows more than any human alive. Expert-level interrogation of a superhuman system produces something qualitatively different from naive interrogation of the same system. The gap between a good question and a bad one doesn’t shrink because the underlying capability grows. It may widen. A sharper instrument in an unskilled hand doesn’t close the distance — it just makes the skilled hand more lethal.
What the AI has done is commoditize answers. What it has not done — cannot do — is commoditize the ability to know which question to ask.
There is a concept from epistemology that keeps surfacing here: the unknown unknown. Donald Rumsfeld made the phrase famous and then spent years living down the mockery, which was unfair, because the underlying idea is genuinely important. There are things you know you don’t know — the gaps you can name, the questions you can form. And there are things you don’t know you don’t know — the territory you can’t even see the edge of. The naive user of AI operates almost entirely in the second category. They ask what they already suspect. They get answers that confirm the shape of what they already believe. The system is brilliant and they are using it as a mirror.
The sophisticated user has learned to ask the AI to challenge their assumptions. To find the holes. To steelman the opposing view. To identify what’s missing from the framing. That second posture requires a kind of intellectual self-awareness — an ability to stand outside your own thinking and interrogate it — that is neither common nor easily taught.
Here is the uncomfortable implication: that self-awareness is not randomly distributed. It correlates with education, with reading, with having thought carefully about hard things for a long time. The people best positioned to ask good questions are, largely, the people who already had access to good answers through the old system. The gate moved. It didn’t disappear.
There’s a democratic story told about AI and I believe parts of it. The kid in rural South Dakota with a good question now gets an answer that rivals what the partner at McKinsey gets.
But access to information was never really the binding constraint. The binding constraint was always the ability to know what information you need — to feel the shape of your own ignorance precisely enough to ask for what fills it. That skill wasn’t distributed by the old system and it won’t be distributed by the new one. It has to be built, slowly, through years of reading and thinking and being wrong and trying again.
What AI may actually be doing is widening the gap between people who ask well and people who don’t — making the former dramatically more capable while leaving the latter approximately where they were, just with a faster way to get answers to questions they already knew to ask.
Somewhere right now, someone is sitting with the most capable thinking tool in human history, asking it to write a cover letter. The tool will do it beautifully. And the gap will quietly widen.
There is a specific feeling. You are trying to understand something — a medical term in a lab report, a clause in a contract, how a particular piece of software actually works under the hood — and you hit the edge of what you know. The territory beyond is unfamiliar and the path is unclear, and something in you decides, quietly and almost without announcement: I don’t know how to figure this out.
And then you move on.
Marc Andreessen, talking to Joe Rogan recently, buried something important inside a longer riff about AI prompting tricks. Most of his list was the kind of thing you’d read in a productivity newsletter — ask it to steelman both sides, pretend it’s a panel of experts. Useful, not revelatory. But one observation was different: pay attention to the exact moment you think “I don’t know how to figure this out.” That’s the moment you should open the AI.
He said it almost offhandedly. I haven’t been able to stop thinking about it.
What he’s really describing isn’t a technique. It’s a behavioral pattern that most of us developed so gradually we don’t recognize it as a choice. The feeling of epistemic overreach — of arriving at the edge of one’s competence — became, over decades, a stopping condition. We learned to treat not-knowing as a wall rather than a door because, most of the time, it functionally was one. The library was closed. The expert was unavailable. The research was paywalled. You moved on.
The habit calcified. Now it persists even when the conditions that produced it no longer apply.
I notice it in myself, and I’m someone who is genuinely curious — who likes knowing how things work, who will follow a thread further than most people bother to. That’s not modesty; it’s relevant context. Because even with that disposition, I still hit the wall. I’ll be reading something and encounter a concept I only vaguely follow — some nuance in immunology, some historical episode I’ve only half absorbed — and I feel the familiar slight contraction, the small withdrawal. I read past it. The curiosity was there. The friction was higher.
Curiosity alone was never enough. What determined whether I pushed through wasn’t how much I wanted to understand — it was whether understanding felt retrievable at all. Most of the time, it didn’t. So I moved on, and the curiosity found something else to chase.
There’s a darker version of this worth sitting with. The people who never developed the quit reflex — who hit not-knowing and felt compelled rather than defeated — are, disproportionately, the ones who built things. The intellectual persistence wasn’t incidental to their contributions; it was probably constitutive of them. Curiosity as stubbornness. The refusal to accept the wall as final.
Elon Musk is the limit case. When he decided he wanted to go to Mars and found the rockets prohibitively expensive, he didn’t defer to the aerospace industry’s consensus about what was possible. He started reading propulsion manuals and cold-calling engineers. The quit signal either never fired or got overridden so fast it made no practical difference. The result was reusable orbital rockets, which the industry had largely decided weren’t worth pursuing. The dig reflex, taken to its extreme, rewrote what was considered feasible.
But the trait is undifferentiated. It doesn’t come with a calibration mechanism. The same refusal to accept expert consensus that produced SpaceX also produces a certain amount of confident wrongness — the Twitter decisions, the Covid takes, the occasional foray into geopolitics with the certainty of someone who has read a lot of Wikipedia. The dig reflex, unregulated, has no obvious stopping condition.
AI doesn’t change that underlying trait. What it changes is the access cost for everyone else.
For most of human history, the friction wasn’t random. It selected for people whose drive was strong enough to overcome it regardless of cost — the right connections, the right institution, the time to burn. Now that friction is lower for everyone, nearly to zero, for an enormous range of questions.
What I’m trying to build is the opposite of the quit reflex. Not the Musk version — boundless, uncalibrated, occasionally catastrophic. Something more modest: the habit of checking before giving up. Noticing the moment of not-knowing and treating it as a question rather than a verdict.
It requires noticing the moment. Which is harder than it sounds, because the reflex is fast and the moment is brief.
The contraction happens. You’ve already moved on. Somewhere behind you, the question is still there.
I have so many unread articles saved to Instapaper that I’ve stopped checking the count. Each one felt, in the moment of saving it, like something I needed. A long piece on urban planning, a profile of someone interesting, a reported essay I fully intended to sit with.
The app is beautifully designed for exactly this — the frictionless capture, the clean reading interface waiting patiently on the other side.
What it can’t do is manufacture the attention I didn’t have when I saved it and still don’t have now. The articles aren’t the problem. The premise is: that presence is something you can bank.
There’s a haiku I keep returning to, from Natalie Goldberg’s Three Simple Lines. It’s by a poet named Fumiko Harada:
Morning chill
I savor this moment —
one meeting one lifetime
Eleven words. No verb in the third line, which makes it feel less like a thought and more like a verdict.
The Japanese concept underneath it is ichi-go ichi-e — loosely, “one time, one meeting.” It’s a Zen idea with origins in the tea ceremony, the understanding that each gathering is singular and therefore irreversible. You cannot archive it. You cannot search for it later. When it ends, it doesn’t go anywhere you can retrieve.
This is what the Instapaper queue is, at scale: an archive of moments I decided to experience later. The article about urban planning was written by someone who spent months reporting it, on a day when some editor thought it was ready, and landed in my feed on a morning when something about the headline caught me. That constellation doesn’t reassemble. Later is a different article.
The tools I use every day are getting astonishing. There are systems that can summarize, translate, recall, explain, anticipate. I use them. I find them genuinely useful.
But there’s a habit of mind they reward — a kind of perpetual deferral of full attention — that I haven’t fully reckoned with. The promise, always, is that you can engage more completely later, once the summary is ready, once the transcript exists, once the notes have been taken. Presence becomes a productivity tax you pay while waiting for a deliverable.
Harada’s haiku doesn’t moralize. The speaker isn’t lecturing herself into awareness. She’s just cold, and awake, and choosing to notice. I savor this moment. The word “savor” does a lot of work. It implies effort. You savor things that could be missed.
The pivot in the third line is what stays with me. One meeting one lifetime. Not “this meeting will last a lifetime” — that would be sentiment. It’s more like a mathematical statement: the cardinality of this encounter is one. There is exactly one of them. This morning, this particular chill, whatever conversation or solitude is happening inside it — that set has one element. By tomorrow it has zero. No amount of documentation changes that arithmetic.
I’m working on believing that.
Yoshua Bengio spent forty years building the foundation of modern artificial intelligence. He won the Turing Award for it. And he didn’t think he’d live to see it work.
That’s the quiet fact buried inside Stephen Witt’s New Yorker profile of him. Bengio — one of the three researchers whose decades-long bet on neural networks eventually became the architecture underlying every large language model running today — had made peace with the idea that the thing he was building was a multi-generational project. Something for his successors to finish. Then Witt writes: “one day in late 2022, the technology had simply arrived. He compared it to meeting an extraterrestrial.”
Hemingway once described bankruptcy happening two ways: gradually, then suddenly. He meant ruin. Bengio experienced something harder to name — not ruin but arrival, which carries its own vertigo. The gradually was four decades of work that most of his peers considered quixotic. The suddenly was a Tuesday in November when a chat interface went live and the world quietly changed.
What unsettles me about the extraterrestrial comparison isn’t the strangeness it implies. It’s the distance. You meet an alien; you don’t meet something you made. The metaphor suggests that even its creator couldn’t fully recognize it — that the thing, once arrived, belonged to a category that exceeded its own origins.
We don’t have good language for this. Breakthrough, inflection point, paradigm shift — these are words people reach for after the fact, when they’re building timelines. What Bengio seems to be describing is the experience of standing in front of a threshold you spent your life approaching, and finding it already behind you.
The technology didn’t ask permission. It didn’t announce itself.
It arrived.
Gary Kamiya was writing about the Tenderloin when he said it, but the line has been following me around: “The problem is that by saving the baby, you also save the bathwater.”
The pattern is remarkably consistent across every major information technology. Each one arrives promising to liberate the deserving — the faithful, the learned, the civic-minded — and each one immediately, inevitably, arms everyone else too. Gutenberg’s press was understood by its champions as a device for spreading the true Word; within decades it was the primary infrastructure for Protestant schism, Catholic counter-propaganda, astrological almanacs, and pornography. The reformers got their Bible. They also got their pamphlet wars.
The telegraph was greeted as a force for peace — shared information would make war irrational, commerce would bind nations. It also became the nervous system of commodity speculation, financial manipulation, and the first truly industrial-scale news hoaxes. The telephone: connection and the crank call, the crisis line and the threatening voice in the dark. Radio: FDR’s fireside chats and Father Coughlin. Television: Murrow taking down McCarthy, and also fifty years of manufactured consent. The internet: the largest library ever assembled and the largest sewer.
The pattern isn’t coincidental. It’s structural. Each technology expands what’s possible for human expression and coordination — and human expression and coordination contain both the noblest and the worst of us in roughly fixed proportion. The tool doesn’t change the ratio. It scales both sides of it.
What’s interesting historically is how each generation believes their technology will be different — that this time the architecture can be designed to select for the good. The internet era produced the most elaborate version of this belief: algorithmic curation would surface truth, network effects would reward quality, the wisdom of crowds would outcompete misinformation. Instead it turned out that engagement was the attractor, and outrage was the highest-engagement content. The bath got hotter.
The AI moment is the same belief system, restated with more technical sophistication. But the Kamiya line stands. You are saving a baby, and you are saving bathwater, and no one has yet designed a tub that can tell the difference.
The question isn’t whether the bathwater comes with the baby. It always does. The question is whether you turn on the tap.
When Carol Coye Benson and I sat down to write Payments Systems in the U.S., one of the first problems we had to solve wasn’t about payments. It was about history.
To understand why the ACH network works the way it does, or why checks persisted decades longer than anyone expected, you need the institutional sediment underneath — the regulatory decisions, the failed experiments, the path dependencies baked in by choices made in the 1970s that nobody thought would still matter in the 2000s. The history is the explanation. Strip it out and you have a description of current practice with no account of why it exists or what it cost to get there.
But history takes pages. And pages test a reader’s patience. So you compress. You make judgment calls about what survives the cut and what gets left behind, and you make those calls knowing that every omission is a bet — a bet that the reader can follow without it, that the thread holds without that particular knot.
Writing it taught me something. The act of compressing, of finding the minimum sufficient version of a complex thing, forces a clarity that living inside the complexity never quite delivers. You don’t fully know what you understand until you have to say it precisely enough for someone else to follow.
But compression is always a loss. You feel it as you write. The version in the book is thinner than the thing you know.
Garry Tan uses a term — “tokenmaxxing” — that initially sounds like jargon from a performance optimization thread. The idea is simple: don’t be stingy with context. Give the model everything. Every source document, every relevant article, every piece of background that a human reader would never sit still for. Let it synthesize rather than guess.
The instinct it runs against is deep. We have spent decades building information systems around compression — search engines that retrieve rather than ingest, executive summaries that stand in for reports, one-pagers that distill months of work into something a decision-maker can absorb in four minutes. All of it was a rational response to a real constraint: human attention is finite and expensive. You couldn’t afford to read everything, so you built filters. The whole architecture of how organizations manage information was designed around that limit.
Tokenmaxxing is a bet that the limit has moved.
The model can read everything. The cost of giving it full context — the uncompressed history, the original sources, the institutional sediment — is low enough now that filtering before the model sees it may introduce more error than it prevents. You’re potentially discarding signal when you summarize for the model the way you’d summarize for a human. The model doesn’t need the one-pager. It can handle the report.
This doesn’t dissolve the need for curation entirely. More context isn’t always better — models can lose the thread in noise the same way humans do, just differently. The skill shifts from summarizing to selecting: not what’s the minimum version of this but what’s actually worth including. Different judgment, still essential.
But the deeper change is upstream of any particular project. The compression we built into every research process, every briefing, every book — that was never the goal. It was the tax we paid for human cognitive limits. Part of the process doesn’t pay that tax anymore.
When I think about writing that payments book today, I don’t think the book itself would change much — it still has human readers with finite patience. But the map we drew before writing it, the synthesis work, the “what connects to what across fifty years of regulatory history” work — that could happen at a different depth now. The understanding you bring to the writing can be informed by everything, not just the subset you had time to read.
The payments book was written entirely for humans, with all the compression that implies. But Tyler Cowen just published what he calls a “generative book” — 40,000 words released free online, paired on the same screen with a Claude interface so readers can discuss, interrogate, and extend it in real time. He’s writing for both audiences simultaneously now. The human reader and the model that will help that reader go deeper. The text is optimized not just to be understood but to be used — as context, as a jumping-off point, as raw material for a conversation that the author won’t be in.
That’s a different kind of writing. Not better or worse. Different. The compression decisions change when one of your readers has no patience to protect.
Writing still clarifies thinking. That part hasn’t changed. But what you’re clarifying, and who you’re clarifying it for, is quietly expanding.
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