Tag: lifelong learning

Categories
Music Radio San Francisco/California

Remembering San Francisco’s KKSF

KKSF FM on the radio overlooking San Francisco

At midnight on July 31, 1987, KKSF came alive at 103.7 FM.

The first thing anyone heard was Steve Winwood, “Back in the High Life Again,” which is either a coincidence or the most on-the-nose piece of programming in the history of San Francisco radio. Because that is more or less what the station promised everybody who found it in the weeks after: a higher life, a smoother one, arriving at 103.7 on your dial like a room you didn’t know you’d been missing.

I found it not long after, in my kitchen, which is where I found most things back then. The radio sat on the counter, tuned low enough that it lived beneath the sound of dishes and the refrigerator door and whatever else was happening in a life that was in the middle of being built.

This was after 8-tracks and before anyone had figured out you could carry a hundred thousand songs in your pocket, which meant the radio still had a kind of authority it doesn’t have now. You didn’t choose the next song. Somebody chose it for you, and you trusted them, the way you trust a bartender who’s been reading a room for twenty years.

On KKSF, the person doing the choosing had impeccable taste and a voice like a hand on your shoulder telling you it was going to be fine.

I think about how strange that arrangement sounds now, handing your afternoon over to a stranger’s judgment, and how completely I didn’t question it then. Roger Coryell had the mornings, or near enough to them, and he had that particular gift — rarer than people think — of sounding as though he was talking to exactly one person, and that the person happened to be you, standing at your own counter in your own kitchen a hundred miles from the studio.

Miranda Wilson took the middle of the day and kept it there for years, eventually becoming the last live voice on a station that had long since become an institution.

I didn’t know, listening in 1988 or 1991 or whenever it was, that I was living inside something that had an expiration date.

You never do.

You think the kitchen and the counter and the voice on the radio are just Tuesday, just the texture of an ordinary life. It takes twenty years and a different city outside the window before you understand that you were actually inside an era, capital E, and that eras, unlike Tuesdays, do not come back.

There was a name for what KKSF was doing: New Adult Contemporary, or NAC. But nobody listening in the kitchen cared about the name. The idea was simple enough — give the Bay Area something between the rock stations shouting at it and the classical stations lecturing it, light jazz easing into soft rock easing into something with a saxophone in it that nobody could quite name. Holding it together, day after day, song after song, was a program director named Steve Feinstein, chasing down lesser-known imports and out-of-print oddities that gave the station its particular ear, until he died in September of 1996, still on the job.

What they created was a kind of ambient permission.

You didn’t have to hurry.

You didn’t have to shout.

You didn’t even have to know what you were listening to.

The music simply arrived, and you let it stay.

There was a version of San Francisco in those years that felt, at least to me, as though it had room for that kind of unhurried, unbranded taste. This wasn’t necessarily a gentler city in any objective sense. San Francisco had plenty of its own troubles then, as it always has. But it was the city I was living in, and KKSF somehow belonged to its frequency.

Real estate hadn’t yet eaten everything. The tech money was already there, but it hadn’t yet rearranged the furniture of the entire region around itself. There were still corners of the Bay Area where nobody seemed to be optimizing anything.

KKSF didn’t optimize.

It curated.

And there is a difference.

The station lasted twenty-two years, which sounds like nothing until you try to think of anything else in your life that lasted twenty-two years without you noticing it was a relationship.

Then, in May 2009, it stopped being itself.

By then Clear Channel owned the station, and economics won. The format disappeared, the music changed, and Miranda’s voice — which had been finding its way into kitchens across the Bay Area for years — went quiet on that frequency for good.

I didn’t hear it happen.

I was somewhere else by then, doing something else, the way you are when the things that shaped you quietly stop existing without asking your permission first.

I only found out later, the way you find out most things now: long after the fact, scrolling, discovering that a piece of your own furniture had been sold at auction while you weren’t looking.

What I keep coming back to isn’t the music, exactly, though I could still hum half of it.

It’s the kitchen.

And the particular quality of trust required to let somebody else choose what you’d hear next — to be doing stuff and suddenly hear a song you’d never have chosen on your own, and follow it somewhere you wouldn’t have gone. I miss the feeling that the world could still surprise me without first asking what I wanted. Old world radio curation.

Sometimes I think about the city outside that kitchen window, too. It doesn’t sound like that anymore. It doesn’t have quite the same room, at least to my ears, for anything that isn’t trying to be the loudest thing in the room.

Sometimes when music is playing in the background and my mind is wandering I think of KKSF and how a radio station is just a number until somebody fills it with twenty-two years of somebody else’s kindness and wonderful taste.

Categories
Living Memories

The Depth of the Dimes

The lifeguard came out with a canvas bag, and that was the signal. Saturday morning, Kettering, Ohio, the municipal pool not yet warmed by the day, and forty kids arranging themselves along the edge like something had been rehearsed, though nobody had rehearsed it. We knew the shape of the thing without being told. He would walk to about the middle of the deck, and he would start throwing coins into the water.

Not pennies, mostly. I remember that distinctly, the way you remember the specifics of a thing you can’t explain the pull of. Nickels and dimes, a scattering of quarters, no half dollars that I ever saw hit the surface. Somebody in the recreation department had made a decision about denominations, a budget, maybe, or a sense of what a Saturday morning was worth to forty kids in Ohio in the summer, and none of us thought about that then. We watched the coins fall instead. That’s the part I keep returning to — not the diving, the falling. Coins don’t drop through water the way they drop through air. They wobble. They catch light and lose it. They take their time getting to the bottom, and you’d track one the way you’d track a hawk, losing it, finding it again against the pool floor, memorizing the spot before it disappeared into the general scatter of everything else he’d thrown.

We tried to angle ourselves near the shallow end, obviously. That was the whole strategy, such as it was — get positioned somewhere you could stand, somewhere your ears wouldn’t need to fight the pressure, somewhere the breath-holding would be a formality instead of a genuine risk. But the lifeguard wasn’t interested in making it easy. He’d throw a few coins shallow, just enough to keep the little kids occupied, and then he’d load up the deep end, and that’s where the real money went down. If you wanted more than a couple of nickels, you had to go where you couldn’t stand.

Then the countdown, and then all of us at once, forty kids hitting the water in the same three seconds, which meant the water itself turned against you before you’d even started looking — churned, clouded, chlorine stinging worse than usual because now there were elbows in it, knees, someone’s foot near your face. I wasn’t especially good at it. I want to say that plainly, because I don’t think I was; I’d come up with a handful, four or five coins, sometimes a quarter if I’d gotten lucky with the angle, and that felt like enough. Enough to matter. Enough to walk back to the locker room afterward with my trunks pocket sagging and clinking, enough to spend the rest of the morning transferring nickels and dimes into my actual pants pocket like a kid who’d earned something.

There was a boy down the street whose father worked at NCR, and some Saturdays he’d disappear to Old River instead — a real pool, he made sure we knew, an enormous one, with none of this scrounging-for-change business. I remember being aware of that difference without being able to name what it meant. He had the better pool. We had the lifeguard with the canvas bag.

I came across a piece online the other day, something about swimming and what it might mean, the kind of essay that circles a simple physical act until it turns into a meditation on something larger, and it knocked this memory loose after forty years of sitting untouched. I don’t know why that particular Saturday ritual is the one that stayed. Not the swimming lessons, not the diving board, not anything a person would call a memory on purpose. Just the coins going down, and all of us watching them go, already planning where we’d be standing when the countdown ended.

Categories
AI

The Encyclopedia and the Reasoner

I was standing in the cereal aisle a few weeks ago, doing the thing I always do — flipping the box over, scanning the fine print, comparing fiber grams like it mattered more than it probably does — when I thought about the model I’d been testing that morning. Sharp. Fast. Occasionally, confidently, wrong about something I could have looked up in ten seconds.

There was no label for that. No panel telling me what was inside, what it was good at, what it might get wrong, what it cost to run. Just a chat window and a kind of blind trust.

That’s the itch behind this post. What would it look like if AI models came with something like a Nutrition Facts label — the kind the FDA forced onto every box in your pantry back in 1994? Not as a gimmick, but as a real answer to a real problem: we are feeding these things into our decisions, our writing, our portfolios, our kids’ homework, largely on faith.

The IQ Number That Isn’t Quite an IQ Number

I keep running into a shorthand in investing circles — Jordi Visser and others talking about frontier models as “140 IQ” systems, reasoning at a level that outpaces most humans on the kinds of puzzles we associate with fluid intelligence. Pattern recognition. Logic chains. Novel deduction under pressure.

It’s a useful number. It’s also a bit of a trick.

Human IQ tests were built to measure something narrow and specific — not wisdom, not knowledge, not judgment, but the raw machinery of reasoning. When we borrow that language for AI, we inherit the same narrowness, which is fine as long as we remember it. A model that aces abstract reasoning benchmarks isn’t necessarily the model that knows the correct dosage, the right case law, or what actually happened in 1932. Reasoning and knowledge are cousins, not twins.

Two Kinds of Smart

Here’s an old-fashioned way to think about the split: Britannica versus World Book.

Britannica was the encyclopedia my father would have trusted — dense, expert-written, unapologetically deep, assuming you could keep up. World Book was the one actually sitting on the shelf in most houses I knew growing up, mine included: friendlier, broader, built for a general reader, a little shallower in exchange for being a little more useful on a Tuesday night with a homework assignment due.

Neither is wrong. They’re optimized for different things. And training data does the same kind of sorting. A model fed heavily on curated, scholarly, expert-vetted sources leans Britannica — deep, careful, occasionally slow to update. A model trained on the sprawl of the open web leans World Book — broad, current, occasionally sloppy, sometimes brilliant at the edges precisely because it’s seen everything.

Any honest label for a model needs a section on this. Call it “Knowledge Sourcing.” Not just how big the training set was, but what kind of encyclopedia it’s pretending to be.

Sketching the Label

If I could design the box myself, it might read something like this:

Serving Size: 1 query, ~500 tokens

Reasoning Score: 138 (fluid problem-solving, logic, abstraction) Knowledge Depth: Moderate–High (cutoff: [date]; strongest in [domains]; weakest in [domains])
Ingredients: Curated scholarly corpora, licensed news archives, public web crawl, synthetic reasoning data, human feedback Allergens: Confident hallucination under ambiguous prompts; recency gaps beyond training cutoff; known weakness in [specific domain]
Cost per Serving: $X per million tokens; Y watt-hours per query Best Paired With: Retrieval tools, human review for high-stakes decisions

It’s a little tongue-in-cheek written out like that. But underneath the joke is something I actually want — the same instinct that made me read cereal boxes as a kid. Not to be scared of what’s inside, just to know.

The Part That Actually Excites Me

Here’s where the scaling laws get interesting, and where I think the real opportunity sits.

World knowledge is expensive. It’s greedy for data and parameters — you need to have practically read the internet to know the boiling point of tungsten, the plot of a minor Victorian novel, and the org chart of a mid-cap company all at once. Reasoning, it turns out, is a different kind of animal. It can be distilled, compressed, taught through synthetic problems and careful post-training, and squeezed into something far smaller than you’d expect.

Which means a genuinely thrilling possibility is already taking shape: sharp, high-reasoning models small enough to run on a phone or a laptop, entirely offline, because they’ve shed the encyclopedia and kept the mind. Pair one of those with a personal index — your own notes, your own documents, a retrieval layer built around your actual life — and you get something closer to a personal thinking partner than a general-purpose oracle. Private. Fast. Always available. Tuned to you rather than to everyone. Apple may be on to something with this kind of strategy?

I think about this constantly in my own workflow — the daily scans, the little agents I’ve built to help sort signal from noise, the genealogy digging, the investment frameworks I keep refining. What I usually want isn’t more encyclopedia. It’s a clear-headed reasoner sitting next to my own carefully kept knowledge, not buried under someone else’s version of the whole internet.

Why the Label Matters More Than the Score

None of this works, though, without honesty about what’s inside the box. A 140 on a reasoning benchmark tells you almost nothing about whether a model will quietly misremember a fact it was never that confident about in the first place. And a model can be extraordinarily knowledgeable while being a mediocre reasoner — plenty capable of reciting the right ingredients and still getting the recipe wrong.

The nutrition label movement in food didn’t eliminate junk food. It just made it possible to choose junk food on purpose, with your eyes open, instead of by accident. I’d like the same deal with AI. Not a demand that every model be a genius generalist, but a demand that I get to know what I’m actually consuming — and choose the lean local thinker over the bloated encyclopedia when that’s what the moment calls for, or the other way around when it isn’t.

Curiosity got me into that cereal aisle habit decades ago, and it’s the same instinct pulling me toward this idea now — not suspicion of the box, just a wish to read it clearly before I decide how much of it to trust.

What would you want on your label?

Categories
AI

The Taste Beneath the Summary

The real work of staying informed has never been volume. It has been the quiet, repeated acts of judgment: does this matter, to whom, why now, what is the signal beneath the noise.

A recent piece from Bridgewater’s AIA Labs and Thinking Machines Lab, “Learning to Replicate Expert Judgment in Financial Tasks,” describes training models to do the triage investors actually do—filtering news, research, central bank documents, internal notes, for relevance. Frontier models struggled with judgments that looked simple and weren’t. The fix wasn’t a bigger model. It was Qwen, fine-tuned on labeled examples from practitioners, and it beat the frontier leaders while costing a fraction to run.

The bottleneck was never model size. It was taste. And taste, it turns out, can be taught to something small and cheap, if you’re precise enough about what you’re teaching it—a market’s worth of Mercors is already proving the same thing at scale.

The researchers were clear that expert judgment doesn’t reduce to rules or prompts. It took high-quality, domain-specific labels from people doing the actual work. The most powerful systems will be built in partnership with practitioners who can say, and keep saying, what “good” looks like in their own context.

Which raises the question I haven’t answered yet: what would I actually put in the labels, if someone asked me to teach my own taste to a cheap model.

Categories
AI

The Quiet Setup: MacSparky’s Robot Assistant and the Unfair Advantage Still Available

A single X post caught my attention this week. It described something quietly happening among a small group of solo professionals. They aren’t working longer hours or grinding harder. Instead, they’ve built a particular kind of setup around AI that carries much of the load.

While most of us still treat powerful models as clever search bars—typing questions and copying answers—these folks have given the AI a rich folder of context, a briefing file that orients it to their world, connections to their tools, and routines that let it produce real work on its own. The result can look like the output of a small team. From the outside it reads as talent or luck. Up close, it’s mostly architecture.0

The post (from @zephyr_hg) emphasized that this advantage remains available because most people haven’t yet made the shift from one-off prompting to building persistent systems. It landed with me because it echoes so closely the practical territory David Sparks (MacSparky) has been mapping for months in his Robot Assistant Field Guide.

MacSparky’s Approach: From Chatbot to Persistent Colleague

David’s work centers on building a true personal assistant using Obsidian (for a local, plain-text knowledge base) and Claude (in its file-aware “Cowork” or project capabilities). The system isn’t a chatbot that forgets everything between conversations. It’s designed to remember your projects, preferences, and people; triage email in your voice; handle morning briefings; track tasks; process documents; and support weekly reviews—freeing you from what David calls the “donkey work.”

The key ingredients will sound familiar to anyone who read that X post:

  • A dedicated context layer (your Obsidian vault or structured folder) holding the details of how you work.
  • Briefing/instruction files that tell the model who you are and what good looks like.
  • Integrations that connect it to email, calendar, files, and other tools.
  • Skills and routines that turn one-time intentions into repeatable, low-friction action.

David has been refreshingly transparent about the journey. He experimented earlier with more fully autonomous agents and even shut one down after learning what felt reliable and aligned. The Robot Assistant Field Guide distills those lessons into videos, workshops, templates, and a starter kit that lets people build without needing to code.

Why This Matters Now

Both perspectives point to the same shift in stance: moving from “How do I prompt better today?” to “What kind of system do I want running alongside me every day?”

For me, at this stage of life, that question carries weight. I’m not chasing maximum output for its own sake. I want arrangements that protect attention and energy for what actually matters—deep reflection, family history work, thoughtful investing, writing that might be useful to others, and simply being present. A well-designed AI setup doesn’t just save minutes; it changes the texture of the day by reducing context-switching and repeated explanations.

It feels like finding a productive seam in the current moment of AI evolution—one of those hidden transitions where leverage quietly compounds if you’re willing to build the architecture.

The Door Remains Open

The encouraging message in both the X post and David’s teaching is that this isn’t locked behind rare talent or expensive infrastructure. The models are accessible. The patterns are becoming clearer. What’s required is the decision to treat AI less like a toy and more like a colleague you’re willing to orient and trust with real work.

I don’t have my own “robot assistant” fully built yet. I’ve been experimenting with custom agents, structured daily scans, and ideas like “The Observatory” for signal synthesis. Reading these sources side-by-side sharpened my sense of the next layer: giving the system a proper home, clear instructions, and meaningful recurring work.

If you’re a solo professional, creator, or lifelong learner feeling the press of too many small tasks, this is worth exploring. Start small. Build a modest context folder. Write a briefing file that captures how you think. Experiment with one routine. Iterate from there.

The setup that outworks the grind isn’t magic. It’s deliberate, learnable, and still wide open.

What setups are you experimenting with these days? I’d love to hear in the comments or on X.

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 Books Writing

The Tax We No Longer Have to Pay

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.

Categories
AI AI: Large Language Models

The 3D Printer That Prints Better Printers

Imagine a 3D printer that looks at its own design and begins printing a better version of itself. The loop closes. What had always required an external human intelligence now happens inside the machine. All by itself.

Jack Clark — Anthropic co-founder, someone who has spent years closer to this technology than almost anyone — puts the odds of this happening by 2028 at better than even. I have been turning that number over ever since I heard it. Not the technical claim, exactly. The feeling of it.

We have grown used to AI accelerating our work. Coders watch models close GitHub issues at rates that would have seemed miraculous eighteen months ago. Researchers delegate experiment design, kernel optimization, even the fine-tuning of smaller models. The scaffolding of AI progress is already being built, in part, by the systems themselves. But the moment the system begins to redesign the scaffolding — that is something new.

What unsettles me is not the raw capability, though that is staggering. It is the loss of distance.

For most of technological history, the creator stood outside the creation. Even the most sophisticated tools remained tools. Now the distinction begins to blur. A model that can meaningfully improve its own training process, its own architecture, its own alignment constraints, is no longer merely reflecting human intent back at us. It is participating in the shaping of its own nature. And because each iteration can happen faster than the last, the curve steepens in ways our intuitions, tuned to linear progress, struggle to grasp.

Clark is careful, as he should be. He speaks of validation work that will still fall to humans, of the need to broaden the pipes through which abundance flows, of preparing defense-dominant postures against misuse. Yet the image that lingers for me is quieter: the silence after the handoff. What does it feel like when the thing you have been painstakingly teaching begins to teach itself — and then to teach its teachers?

I think about Leo Szilard at the traffic light, or the first controlled chain reaction under the stands at the University of Chicago. Moments when a new regime of possibility quietly announced itself. Recursive self-improvement carries that same charge — not a single event but a process, one that could accelerate the very pace of events themselves.

The more I sit with it, the more I return to an older tension in our relationship with tools. We build them to extend ourselves, and in doing so we are always, subtly, extending — or perhaps risking — what we are. The values I try to live by — generosity, curiosity, compassionate honesty — are not refined in specifications. They are refined in friction, in relationship, in the slow work of being human with other humans. If the machines begin to optimize their own lineage at speeds we cannot match, will we still have the bandwidth to tend the parts of ourselves that no algorithm can yet measure?

I don’t know. None of us do. That uncertainty feels honest.

What feels clearer is the invitation. Not to fear the printer that prints better printers, nor to worship it, but to remain awake inside the loop. To ask, as each new version arrives, what kind of world we are collectively printing — and whether the values we claim to hold are baked into the design or merely etched on the surface, likely to wear away under the heat of iteration.

The light is still yellow. We are still deciding whether to step off the curb. But the traffic is already moving faster than it was a moment ago.

Categories
Living Serendipity

Why Comfort Zones Block Serendipity and Growth

Barista preparing coffee with customers sitting and chatting in a cozy café
Customers enjoy coffee and pastries in a warm, inviting café

Serendipity used to be the default setting of my days, but recently I find myself having a quiet, losing negotiation with the front doorknob every time I try to step outside. There is a specific, invisible weight to the handle on a quiet evening—a subtle, undeniable gravitational pull that recommends I simply stay inside. My favorite reading chair feels less like comfort these days and more like an anchor.

I have been writing in this space since 2001. If you look back through the archives of my life—both the digital ones and the memories filed away in my head—you will find a younger version of myself who frequently and willingly threw himself into the unknown. Back then, I assumed serendipity would always just be there, waiting for me to stumble into it on a diverted commute or during a late, unplanned dinner.

Lately, I’ve noticed a subtle shift. As I’ve gotten older, my comfort zone has hardened from a permeable boundary into a brick wall. The things that once sparked a quiet thrill of spontaneity—a sudden change of travel plans, an unfamiliar route home, saying yes to an event where I know absolutely no one—now often trigger a low-grade exhaustion before they even begin. I find myself pre-calculating the energy cost of every deviation from the routine. I weigh the known comfort of my home against the unpredictable variables of the outside world, and the home usually wins.

But I have been sitting with a growing realization lately: when we meticulously optimize our lives for comfort, we inadvertently foreclose on serendipity.

Serendipity requires a loose grip. It demands a willingness to be occasionally inconvenienced. You cannot schedule a chance encounter, and you cannot algorithmically generate a moment of sudden, blinding clarity. Those things only happen in the messy, unmapped spaces between our planned destinations. They live in the friction of the unexpected.

I often think about the writers and thinkers who deliver sentences with such compression and weight. Their most profound insights didn’t arrive because they stayed perfectly insulated from the world. They arrived because they allowed themselves to be interrupted by it.

I am trying to learn how to open the door again. It doesn’t mean manufacturing chaos or pretending I have the boundless, restless energy of my thirties. Acknowledging my own changing capacity (especially physically) is necessary, but using it as an excuse to stop exploring is a mistake.

Overcoming this gravity means making a conscious, deliberate choice to leave the itinerary blank for an afternoon. It means taking the long way home, even when the usual route is faster. It means accepting that the discomfort of stepping outside the routine is the unlock to open a new experience.

The architecture of a well-lived life isn’t built out of safety. The most interesting rooms are the ones we never intended to enter but just happened into.