Tag: learning

Categories
AI Bicycles History

The Bicycle Shop

Part 2 of 3…

It is eleven-thirty on a Tuesday night and she is arguing with a language model about a spreadsheet.

Not arguing, exactly. That’s not the right word. She is coaxing. She is debugging. She is reading error messages that tell her almost nothing and rewriting prompts that almost work, and she has been doing this for two hours, and the spreadsheet still isn’t right, and she is going to try one more thing before she gives up and does it by hand. She is a data analyst at a mid-sized logistics company in Columbus, Ohio. She is not a researcher. She is not a founder. Nobody is writing about her. She is just a person trying to get a machine to do something useful, and the machine keeps almost doing it, and she keeps learning, in the gap between almost and done, something she couldn’t have learned any other way.

She doesn’t know what she’s learning. That’s the important part.

In 1892, two brothers opened a bicycle repair shop on West Third Street in Dayton, Ohio. The bicycle craze was at its peak — the safety bicycle, with its two equal wheels and chain drive, had just replaced the penny-farthing, that absurd high-wheeler everybody called loose change and the riders, with complete seriousness, called the ordinary. The brothers fixed flats and adjusted brakes and built custom frames and ordered parts from Coventry and kept the books and swept the floor. It was ordinary work. Nobody was writing about them either. What they were doing was accumulating, without knowing they were accumulating, a physical understanding of how machines move through space — the gyroscopic principles, the weight distribution, the thousand small calibrations that kept a rider from falling. They were learning in their hands what no university taught and no book fully contained.

Eleven years later they flew.

We tell the Wright Brothers story as a story about flight. It makes sense — flight is the thing, the miracle, the moment the world changed. But the actual story, the one that explains how Kitty Hawk was possible, is a story about a bicycle shop. It is a story about unglamorous preparatory work, about the education that hides inside the constraint, about what you learn in the gap between the machine that exists and the machine that should exist. Orville and Wilbur didn’t go to Kitty Hawk despite the bicycle shop. They went because of it. The shop was the point. They just didn’t know it yet.

We are in the bicycle shop right now.

The people building with AI today — the prompt engineers, the fine-tuners, the agent builders, the data analysts in Columbus arguing with spreadsheets at midnight — are doing work that looks, from the outside, like mere tinkering. Unglamorous. Iterative. Full of failure. The tools are awkward. The models hallucinate. The context windows run out at the wrong moment. Every solution opens three new problems. It feels like the penny-farthing: powerful enough to be useful, constrained enough to be maddening, requiring a kind of practiced vault just to get started.

But that awkwardness is the education.

Every time a prompt fails, the person writing it learns something about how the model thinks — about what it responds to, what it resists, where it gets confused, where it surprises you. Every agent that breaks in production teaches its builder something about the gap between what a model can do in a demo and what it can do under load, with real data, with users who don’t behave the way you expected. Every context window that runs out forces a decision about what actually matters, what is essential, what can be cut. These are not just technical lessons. They are epistemic ones. They are lessons about the nature of intelligence, about how meaning gets encoded and retrieved, about what it means for a machine to understand something versus to pattern-match on the surface of understanding.

The people learning these lessons right now don’t have a name for what they know. They just know it in their hands.

This is how it always works. James Starley’s craftsmen in Coventry bent and brazed bicycle frames by feel and experience, knowing things in their hands they couldn’t fully explain on paper. That embodied knowledge — the tight tolerances, the interchangeable parts, the discipline of making things that had to work — migrated into every bicycle shop that followed, crossed the Atlantic, and ended up in a shed in Ohio. The Wright Brothers didn’t invent precision manufacturing. They inherited it, absorbed it, and applied it to a problem nobody else had solved because nobody else had brought those particular hands to that particular problem.

The chain drive was the hinge. Before it, the bicycle’s design was locked — bigger wheel for more speed, higher and higher off the ground, until the machine teetered at the edge of what a human could survive. The chain drive broke the constraint. It decoupled the pedals from the wheel, let the gearing do what only size had done before, brought the rider back to earth. What had been a machine for athletes became a machine for everyone. What had been the ordinary became, almost overnight, something new.

We are waiting for the chain drive.

Not waiting passively — it is being built right now, in a hundred places at once, by people who mostly don’t know they’re building it. It might be the interface that finally makes AI genuinely accessible to people who can’t do the running vault. It might be the memory architecture that lets a model carry context the way a human carries context, not in a window but in something more like experience. It might be something nobody has named yet, something that will seem obvious afterward, the way all elegant solutions seem obvious after the fact.

What it will not be is the product of people who stayed away from the bicycle shop.

The analyst in Columbus closes her laptop at midnight. The spreadsheet is still not right. She has learned three things about how the model handles date formatting, two things about how it interprets ambiguous column headers, and one thing about her own assumptions that she didn’t know she was making. Tomorrow she will try again. She will get closer. At some point — not tomorrow, maybe not this year — she will get it right, and the thing she learned in the gap will be available to her for the next problem, and the one after that, and she will carry it forward without knowing she’s carrying it, the way craft always travels, in hands that have done the work.

She doesn’t know what she’s riding toward.

That’s the ordinary part. That’s always been the ordinary part.

Categories
Writing

Still Learning

I never thought about rhythm in my writing. Not once.

A lifetime of writing. More essays than I can count. One book. And the sonic quality of my sentences — the way they moved, or failed to move, through a reader’s mind — simply wasn’t something I considered. I was too busy trying to say something true. I thought that was enough.

What changed was reading differently. Not for pleasure anymore, or not only for pleasure. David Perell conducts long interviews with writers about how they actually work — not what they believe about writing, but what they do, physically, at the desk, in the dark, before anyone sees it. He asks the same structural questions of very different writers and the patterns emerge slowly, the way patterns always emerge: first you see it once and think nothing of it, then you see it again, then you can’t stop seeing it. Rhythm came up constantly. Always in different language. Pacing. Breath. Music. Momentum. Always pointing at the same thing.

Then I found this from Susan Orlean:

My new preoccupation was on the sonic quality of my writing — the rhythm and tone of the sentences. I began reading all my work out loud, listening for places that lagged and dragged, that didn’t sparkle. I knew it was unlikely that anyone else was reading my stories out loud, but I was convinced that you do “hear” writing in your head as you read, and this pushes you (or stalls you) through the piece. I wanted the music — that is, this subconscious tonal effect — to match the subject.

I stopped. Read it again.

Because she was describing something real — something I had been doing wrong for twenty-five years without knowing it was wrong. You don’t know what you can’t hear. That’s the whole problem. The silence where the knowledge should be is itself silent.

I don’t read my work out loud. There’s something strange about it, something that breaks the spell — you stop being a writer and become an actor, hearing your own sentences hanging in the air, too exposed. But I do read and read again, more carefully now, looking for the wobble. Orlean’s point holds regardless of method: you hear writing in your head as you read it, and that hearing either carries you forward or it doesn’t. The ear that matters is the one inside.

George Saunders has a practice he describes in A Swim in a Pond in the Rain: he reads from the beginning of a piece over and over, and the moment something feels off — a word, a rhythm, a single syllable landing wrong — he stops and fixes it before moving forward. Never skips the trouble spot. Never tells himself he’ll come back. His opening pages accumulate dozens of passes before he ever reaches the end. What he’s really doing, underneath the technique, is training himself to feel the exact microsecond when a reader’s attention would start to drift. To catch the loss before it happens.

That’s not craft instruction. That’s building a new sensitivity where there wasn’t one before.

John McPhee works from the other direction entirely. His famous boxes — index cards, sorted into piles, piles arranged into sequences, nothing drafted until the structure is known — are about architecture before a single word is written. He’s deciding which rooms exist, and in what order, before he furnishes any of them. Where Saunders builds outward from one true sentence, McPhee builds downward from a blueprint.

But they’re asking the same question. McPhee: is this section in the right place? Saunders: is this word in the right place? Both listening for the moment the piece loses its hold on the reader. Both doing triage on something most writers never even examine.

What I’m still learning — slowly, and late — is that rhythm isn’t decoration. It isn’t the thing you tend to after the real work is done. It’s structural. A sentence moving at the wrong speed for what it’s carrying fails the thought itself, not just the ear.

There’s something else I’ve been thinking about. If rhythm is the thing that’s hardest to hear in your own work — if the ear takes years to develop — then maybe the most useful writing tool isn’t a grammar checker. Those are solved. What isn’t solved is the rhythm problem. An editor who listens for the wobble, explains what’s failing and why, and works through the fix with you rather than just patching it. Not a red pen. A teacher.

I’ve been experimenting with exactly that. An AI editor I call Clark. His job isn’t correctness. It’s the sonic quality of prose — the rhythm — the same thing Orlean was describing, the same sensitivity Saunders spent years training. Clark finds what’s working as hard as what isn’t. And when something fails, he explains what the reader’s inner ear is hitting and why. Helpful.

I didn’t know much about rhythm in writing when I was fifty. Didn’t know it at sixty.

I’m not entirely sure I know it now. But I know it more than I did, which might be the only kind of knowing that’s real.

A lifetime of writing. Still learning how to listen.

Categories
Reading Writing

The Starting Five I Keep

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.

Categories
AI

The Shape of the Question

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.

Categories
AI Living

The Threshold

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.

Categories
AI Thinking Tools

Outsourcing Thinking but not Understanding

There’s a line mentioned in a recent discussion by Andrej Karpathy that I keep turning over: You can outsource your thinking but you can’t outsource your understanding.

It sounds like a warning. Maybe it is. But the more I sit with it, the more it feels like something older — a distinction philosophers have been trying to draw for centuries, suddenly made urgent by the fact that we now have a tool that makes outsourcing thinking almost frictionless.

Here’s what I notice when I use AI well: I get the answer, and I feel satisfied. There’s a small dopamine tick. Task closed. But if someone asks me an hour later to explain the reasoning, I often can’t. The thinking happened — somewhere — but not in me. I was a conduit. A confident one, too, which is the dangerous part.

This is different from looking something up. When I Google a fact and paste it into a document, I know I’m borrowing. The seam is visible. But when I ask an AI to reason through a problem with me, the output arrives in first person, in fluent prose that matches my own register, and something in my brain says I worked this out. The seam disappears. That’s new. That’s the thing we don’t yet have good instincts for.

Karpathy’s deeper point is about construction. He’s a builder by temperament — his mantra, which he traces to Feynman, is that if you can’t build it, you don’t understand it. What you can’t yet construct, you merely think you understand. There are always micro-gaps in your knowledge, invisible until you try to arrange the pieces yourself and find they don’t quite fit. The AI doesn’t change that equation. It just makes it easier to mistake the map for the territory — and to feel strangely proud of a map you didn’t draw.

Hesse understood this, in a different century and a different idiom. In Siddhartha, the young seeker travels to meet the Buddha himself — the most perfectly articulated wisdom in the world, delivered by the man who actually found it. Siddhartha listens, acknowledges that the teaching is flawless, internally consistent, the most complete account of liberation ever assembled. And then walks away. Not from arrogance, but from recognition: even the Illustrious One cannot hand you his liberation. The path was his. He walked it. That walking is not transferable, no matter how perfect the description of the destination. Received knowledge, however exquisite, is not the same as earned knowledge. The gap between them is exactly the size of your own unlived experience.

That’s the same argument, made across two and a half millennia. Feynman says you have to build it. Hesse says you have to live it. Karpathy says the AI can do neither for you.

He’s also made a related observation about educational video — that a lot of content on YouTube gives the appearance of learning but is really just entertainment, convenient for everyone involved. Nobody has to do the hard part. AI-assisted thinking has the same shape, just more intimate. You’re not passively watching — you’re actively typing, prompting, engaging. It feels like cognition. But engagement isn’t understanding. Typing a question is not the same as wrestling with it.

I don’t think the answer is to use AI less. That’s not Karpathy’s argument either — he’s spent the last year building a school premised on AI tutors expanding what people can learn. The lesson is about custody. When I hand a problem to an AI, I need to stay in the loop as a learner, not just as a reviewer. There’s a real difference between asking give me an answer and asking help me build the reasoning. The first outsources thinking. The second — if you insist on it, if you refuse to be a passenger — can still leave the understanding in you, where it belongs.

But insisting is the work. And the work is now easier to skip than it has ever been.

Understanding isn’t a product you receive. It’s a residue — what settles in you after genuine struggle, after the confusion and the dead ends and the small hard-won moments of clarity. Siddhartha couldn’t get it from the Buddha. You can’t get it from the AI. Karpathy’s line is a custody argument: the thinking can travel, but the understanding has to stay home.

What unsettles me is that we’re building tools that make the borrowing invisible — that dress outsourced reasoning in the first person, that let us feel like we’ve understood something we’ve only processed. Siddhartha at least knew he was walking away from the teaching. He felt the gap. We might not even notice ours.

Categories
Authors Books Maintenance

The Maintenance of Everything

I’ve been listening to a conversation between Stewart Brand and Ezra Klein, recorded to accompany Brand’s new book on maintenance. At one point Brand reaches back to 1908, to a contrast so clean it feels almost constructed: the Ford Model T and the Rolls-Royce, both introduced in the same year, representing two entirely different philosophies about what a made thing is supposed to be.

The Rolls-Royce was an argument for resolution. Built to a standard so exacting that the implicit promise was permanence — here is a finished object, complete, requiring nothing further from you except appreciation. The Model T was something else. It was a platform. Incomplete by design. Ford’s bet wasn’t on perfection; it was on adaptability. The car would haul grain or pump water or pull stumps, depending on what you attached to it. It would break. You would fix it. That was the relationship.

I know which car I drove.

In the early seventies I owned a VW Bug, and somewhere in the orbit of the Whole Earth Catalog I found a book called How to Keep Your Volkswagen Alive. It was written by a man named John Muir — not the naturalist, a different one — and it was unlike any repair manual I’d encountered. It spoke to you directly. It assumed you were capable. It had illustrations that looked hand-drawn because they were. It said, in effect: this car will need tending, and you are the person who will tend it, and here is what you need to know.

I learned to do things with that book I had no business doing. I learned because the car kept asking. Every fix revealed the next thing that needed attention. There was no arrival point, no moment when the car was finished. There was only the ongoing conversation between me and the machine — me sitting on the ground back by the engine, the book open on the pavement beside me, both of us getting a little dirty.

Brand’s argument, as I understand it, is that we’ve lost something in our preference for the Rolls-Royce model. We’ve come to see maintenance as the shadow side of ownership — the tax, the obligation, the evidence that the thing wasn’t perfect to begin with. We want objects, relationships, careers, selves that hold their shape without further input from us. We want to arrive.

But the Model T knew something the Rolls-Royce didn’t. The tending is the thing. Not a concession to imperfection. Not an interruption of the good parts. The relationship between the owner and the maintained object is where the real ownership lives — in the calluses, the grease, the Saturday afternoons with the manual open on the ground.

I’ve been thinking about this in a different context lately. A lot of people are now building out personal AI systems — assembling suites of skills and automations that handle what someone recently called the “donkey work” of knowledge labor. The tools involved, Claude Cowork among them, don’t arrive finished. They arrive as platforms. You extend them, adapt them, maintain them. The person who builds out a working suite becomes a different kind of owner than someone who just uses a polished app — they understand the machine because they’ve had their hands in it. Every skill added reveals the next thing that needs attention. The conversation between owner and tool is where the real capability lives.

Brand would recognize the lineage. The Whole Earth Catalog was an early attempt to give people the tools and knowledge to build their own platforms — to opt out of the Rolls-Royce consumer relationship and into something more generative and self-reliant. There’s a strange digital echo of that impulse in what people are building now, fifty years later, with entirely different materials and the same underlying instinct.

I don’t still have the book. I wish I did. But I’m still the kind of person who’d rather sit on the ground with a manual than just ride in the back.

Categories
Living

The Compound Interest of Ignorance

There’s an emotional navigation system within all of us, an internal map of behavior and consequence. We navigate by way of kindness, curiosity, and empathy.

Most days, we manage to keep the car on the road. But there is a particular intersection on this map, one that rarely ends well for anyone who finds themselves there, either driving or just walking by.

It’s the intersection where Annabel Monaghan located a particularly difficult archetype in Nora Goes Off Script. She describes it, with a precision that feels like the pop of a bubble, as “the corner of arrogance and cluelessness.”

“At the corner of arrogance and cluelessness, you find the worst kind of person.” (Annabel Monaghan, Nora Goes Off Script)

Indeed.

It’s easy, and frankly quite satisfying, to point fingers. We can all summon the mental image of someone parked right at that corner.

Perhaps it was a micromanaging boss who had never performed the basic function of the department. Perhaps it was a self-styled intellectual whose confidence was inversely proportional to their subject-matter expertise. We’ve all felt that specific, teeth-gritting frustration when faced with the wall of certainty erected by the fundamentally uninformed.

Arrogance on its own is, of course, rarely endearing. But there’s a difference between earned arrogance—the abrasive confidence of someone who actually knows what they are doing—and this unholy alliance. Pure arrogance is often about results; it says, “I am the best, and here is my proof.” It’s difficult to live with, but it is at least based on a form of reality.

Cluelessness, too, has its own nuances. We are all clueless about something (a truth that keeps life interesting). There is an innocence to genuine ignorance, an implicit opening for growth. To be clueless and know it is a temporary state. It’s the raw material for humility and learning.

But Monaghan’s observation zeros in on the specific danger when these two states merge.

Arrogance and cluelessness don’t just coexist; they compound.

This isn’t just a simple mistake (cluelessness) or just a big ego (arrogance). This is a system where the arrogance actively prevents the realization of the cluelessness.

The arrogance acts as a sturdy shield, deflecting any data, any feedback, any reality-check that might reveal the cluelessness underneath. The clues are everywhere, screaming from the spreadsheets or the strained smiles of everyone around them, but the arrogance filters them all out. This person cannot learn because the primary tool for learning—admitting you don’t know—is precisely what the arrogance forbids.

When you find yourself arguing with a person at this intersection, you aren’t arguing about facts. You aren’t arguing about solutions. You are trying to breach a fortress that has decided that the external world must adapt to its inner perception.

The “worst” part of it, the thing that makes it so toxic, is the casual destruction it wreaks. The person at this corner is navigating with a map they have drawn themselves, one that ignores all existing roads, all traffic lights, and every standard convention of behavior. They crash through the lives and efforts of others, convinced all the while of their own perfect navigation.

The hardest truth to swallow, though, isn’t about them. It’s about us. Because if we find this so true of others, the final realization is that none of us are immune to the lure of that corner. It’s an easy intersection to drift into. Whenever our confidence outpaces our real-world competence, whenever we get a tiny bit of power and a tiny bit of success and we think we know, we are in danger.

We are all just a bad day, a stressful project, or a momentary inflation of ego away from parking right at that corner ourselves. The antidote to that specific, devastating brand of arrogance isn’t trying to become more right; it’s remembering how deeply, often, and completely we are wrong.

Stay humble, stay foolish.

Categories
AI

Beyond the Summary: Using AI to Find the “Friction” in Your Thinking

We’ve reached the “Summary Plateau.”

You see it everywhere. Every browser extension, every note-taking app, and every enterprise LLM now offers a “Summarize” button. It’s the ultimate promise of the efficiency era: Give us the 2,000-word essay, and we’ll give you the three bullet points. But there’s a hidden tax on this kind of efficiency. When we ask an AI to summarize, we are asking it to smooth out the edges. We are asking it to remove the “noise.” The problem is, in the world of ideas, the noise is often where the signal lives. The friction—the parts of an argument that make us uncomfortable or that we don’t quite understand—is where the actual learning happens.

If we only consume the summaries, we aren’t thinking; we’re just acknowledging.

The Mirror, Not the Maker

I’ve been experimenting with a different approach. Instead of asking the model to make the content shorter, I’ve been asking it to make my engagement with the content harder.

I don’t want a “Maker” to write my thoughts for me. I want a “Mirror” to show me where my thoughts are thin.

When I’m wrestling with a complex piece—perhaps a deep dive on the future of venture capital or a philosophical treatise on Arete—I’ve stopped clicking “summarize.” Instead, I feed the text into the LLM and use these “Friction Prompts” to find the sand in the gears:

The Essential Toolkit

  • The “Steel Man” Challenge: “I am inclined to agree with this author’s conclusion. Find the three strongest counter-arguments that this text ignores, and explain why a reasonable person would hold them.”
  • The “Recursive Logic” Audit: “Identify the three most critical ‘logical leaps’ the author makes—points where a conclusion is reached without sufficient evidence. If those leaps are wrong, how does the entire argument collapse?”
  • The “Blind Spot” Audit: “What are the underlying cultural or economic assumptions this author is making that they haven’t explicitly stated?”
  • The “Cross-Pollination” Filter: “Connect the central thesis of this article to a seemingly unrelated field (e.g., Stoic philosophy or biological ecosystems). How does the logic of this text hold up—or fail—when applied to that different domain?”
  • The “Analog Translation” Test: “If I had to explain the core mechanism of this abstract concept using only physical, analog metaphors (like plumbing or woodworking), how would I do it? Where does the metaphor break down?”
  • The “Socratic Sharpening”: “Don’t summarize this. Instead, ask me three probing questions that force me to apply the core logic of this essay to a completely different industry.”

Sharpening the Blade

Summary is about completion (getting it done). Friction is about cognition (getting it right).

When the AI points out a blind spot in an article I loved, it creates a moment of cognitive dissonance. That “click” of discomfort is the sound of a mental model being updated. It’s the digital equivalent of using a whetstone on a blade—you need the friction to get the edge.

As we move further into this age of “Flash-Frozen Cognition,” the temptation to automate our understanding will only grow. But discernment—that uniquely human trait we’ve discussed here before—cannot be outsourced to a bulleted list.

The next time you’re faced with a daunting PDF or a dense long-read, resist the “Summarize” button. Ask the machine to challenge you instead. You might find that the most valuable thing the AI can give you isn’t an answer, but a better version of your own question.

A Deep Dive (Further Reading from the Archive)

If you resonated with this piece on cultivating discernment, you might find these earlier synthesis experiments worth a revisit:

  • On Flash-Frozen Cognition: A foundational post discussing how LLMs are freezing the current consensus, and how we must resist it.
  • The Harvest and the Algorithm: Comparing 1920s ice harvesting to 2020s cognition—the critical shift from scarcity to abundance.
  • The Arete of Attention: A look at the Stoic concept of virtue as the intentional direction of our most scarce resource: focus.
  • Longhand Thinking: Why the physical act of writing is the ultimate antidote to digital velocity.
Categories
Business Living

From Know-It-All to Learn-It-All

Momentum is a strange phenomenon. In physics, it is simply mass times velocity. But in human organizations, it is tradition multiplied by ego. When a ship reaches a certain size, its sheer mass resists any change in direction. Microsoft, a little over a decade ago, was the ultimate corporate supertanker. It was massively successful, incredibly profitable, and dangerously stagnant.

When Satya Nadella took the helm, he inherited a culture defined by its own historic brilliance. They were the smartest people in the room, and they knew it. But in a world moving faster than anyone could comprehend, being the smartest person in the room quickly becomes a liability. It creates a defensive posture. You spend your energy protecting your status and proving your intelligence rather than exploring the horizon.

As the observation goes, Nadella had to turn this bigger ship. His mechanism for doing so wasn’t a massive restructuring or a ruthless wave of firings; it was beautifully, disarmingly simple. He told his organization that they were going to make a fundamental, psychological shift.

“We’re gonna go from being a know-it-all to a learn-it-all culture.”

This isn’t just a corporate soundbite; it’s a profound philosophical pivot. The “know-it-all” operates from a place of fragility and fear. If your identity is built on knowing everything, any new information that contradicts your worldview is a threat that must be neutralized. A “learn-it-all,” however, operates from a place of abundance and curiosity. Contradictions aren’t threats; they are invitations to expand.

Looking inward, it is striking how easily we slip into a “know-it-all” posture in our own lives. Competence is deeply comfortable. When we get good at our jobs, our daily routines, or navigating our relationships, we build a fortress of certainty around ourselves. We stop asking questions because we assume we’ve already mapped the territory. We begin to ossify.

To adopt a learn-it-all mindset requires something deeply uncomfortable: vulnerability. It means walking into a room and quietly accepting that you might be wrong. It means replacing the urge to provide a quick, authoritative answer with the patience to ask a better question. It means letting go of the ego’s demand to be the expert.

The turnaround of Microsoft wasn’t just about a pivot to cloud computing or new product pipelines. It was a quiet victory of humility over arrogance. It was the realization that in an ever-changing world, the ultimate advantage isn’t what you already know, but how fast—and how willingly—you are prepared to learn.

We are all steering our own ships through shifting waters. The moment we decide we have nothing left to learn is the exact moment we begin to sink.