Categories
AI Anthropic Economics Stanford

Weak Links, Powerful Ideas

I’ve been thinking about bottlenecks. Not the frustrating kind you encounter in traffic or while debugging code, but the deeper structural constraints that determine how progress unfolds in our lives, organizations, and economies. A single slow step can limit an entire system, regardless of how rapidly everything else improves.

It’s an idea that feels especially relevant today. While AI capabilities continue to advance at a remarkable pace, real-world productivity gains often appear far more gradual.

Enter Chad Jones—the Stanford economist whose work has become increasingly important for anyone trying to understand AI’s long-term economic impact. This week he announced that he will join the Anthropic Institute on leave from Stanford beginning June 30.

The move is noteworthy not simply because of who Jones is, but because of the ideas he brings with him.

The Economist Who Sees Growth Through Tasks and Bottlenecks

Chad Jones (Charles I. Jones) is the STANCO 25 Professor of Economics at Stanford Graduate School of Business. He has been one of the leading scholars studying long-run economic growth: how ideas accumulate, why innovation matters, and why growth rates have remained relatively stable even as the number of researchers worldwide has expanded dramatically.

His influential work helped explain a central paradox of modern economics: adding more researchers does not automatically produce ever-faster growth because, over time, new ideas become increasingly difficult to discover.

More recently, Jones has turned his attention to artificial intelligence. Papers such as A.I. and Our Economic Future and his 2026 collaboration with Chris Tonetti, Past Automation and Future A.I.: How Weak Links Tame the Growth Explosion, examine how advances in automation may reshape economic growth in the decades ahead.

The central insight is deceptively simple:

Economic output is ultimately constrained by its weakest components.

Weak Links: The Economic Version of Amdahl’s Law

Anyone with a background in computing will recognize a familiar pattern.

Amdahl’s Law tells us that even if part of a program becomes infinitely fast, overall performance remains constrained by the portion that cannot be parallelized. Accelerating 90 percent of a workload by a factor of a million still leaves the remaining 10 percent as a hard limit on total speedup.

Jones’ “weak links” framework applies a similar logic to the broader economy.

In task-based models where tasks are complements rather than easy substitutes, every task matters. Extraordinary progress in a handful of areas does not automatically translate into extraordinary gains for the system as a whole if critical bottlenecks remain.

Historically, a large share of productivity growth has come from automation—the transfer of tasks from human labor to rapidly improving machines and capital. Jones and Tonetti argue that much of past productivity growth can be understood through this lens. The breakthrough is not merely building better machines; it is expanding the range of tasks that machines can perform.

The AI Timeline Paradox

Looking ahead, the same logic applies to AI.

Even as advanced models automate larger portions of cognitive and physical work, growth may continue to be constrained by:

  • Tasks that still require human judgment or participation
  • Regulatory and institutional frictions
  • Physical-world coordination challenges

As a result, Jones’ modeling suggests that economic growth may accelerate substantially while still unfolding more gradually than either enthusiasts or skeptics expect.

This perspective offers a useful middle ground between two popular extremes: the belief that transformative AI-driven abundance is imminent and the belief that AI’s impact will prove largely illusory. Progress can be both real and constrained. The chain remains only as strong as its weakest link.

From Theory to Practice

One reason Jones’ work resonates with me is that it extends beyond economics.

Many successful builders and leaders instinctively operate according to a weak-links philosophy. Whether in engineering, manufacturing, logistics, or organizational design, the greatest gains often come from identifying the single constraint that limits the system and focusing disproportionate effort on removing it.

Consider how Elon Musk has approached challenges at Tesla, SpaceX, and xAI. Across very different domains, a recurring pattern emerges: identify the binding constraint, concentrate resources there, remove it, and then move to the next bottleneck.

Jones’ framework provides an economic explanation for why this approach can be so effective. In systems composed of complementary tasks, relieving a key constraint can create benefits that ripple throughout the entire system.

Why This Resonates

What I find most compelling about Jones’ work is its intellectual balance.

It neither dismisses the remarkable capabilities emerging from frontier AI systems nor assumes that technological progress automatically translates into social or economic transformation. Instead, it directs attention toward the frictions, constraints, and complementarities that determine how change actually unfolds.

At a time when conversations about AI often oscillate between utopian abundance and existential catastrophe, this framework offers something rarer: a disciplined way of thinking about progress.

The weak-links perspective reminds us that the future may be shaped less by spectacular breakthroughs than by our ability to identify and address the constraints that prevent those breakthroughs from creating widespread value.

A Chain and a Compass

There is a quiet power in recognizing weak links—whether in economies, organizations, projects, or our own lives. The places where progress feels slow or frustrating are often where the greatest leverage resides.

Jones’ research provides a language for understanding those constraints, and his move to the Anthropic Institute suggests that some of the most important conversations about AI’s future may increasingly take place at the intersection of research, policy, and real-world deployment.

For that reason alone, this is a development worth watching.

If you’re interested in exploring the underlying ideas, I recommend starting with Jones’ recent papers on his Stanford faculty site, along with Anthropic’s announcement of the Institute and its mission.

Categories
AI

What the Lessor Keeps

Two airlines can fly the same airplane. Not airplanes of the same type — the same airplane, serial number and all, handed back at the end of a lease and reassigned, sometimes within weeks, to a competitor on another continent. AerCap owns more commercial aircraft than any airline on earth, and it leases them to airlines that spend their advertising budgets convincing passengers that flying them is a distinctive experience. The 737 MAX that wears Ryanair’s livery this year might wear Lion Air’s the next, repainted, recertified, its avionics untouched, its airframe indifferent to the change of ownership. The lessor does not care who is flying its asset. It cares that the asset comes back in airworthy condition and that the lease payments clear.

What the airline owns, in the sense that matters, is never the aircraft. It is the route network built up over decades of slot negotiations at constrained airports. It is the maintenance log — every inspection, every part swapped, every anomaly a mechanic in Singapore flagged in 2019 that turned out to predict a fatigue crack nobody else had seen yet. None of that travels with the airplane when the lease ends. It stays behind, compounding, in systems the airline built and the lessor never touches.

Karl Mehta, who has spent a career inside enterprise software watching this kind of asymmetry repeat itself, put a version of it plainly: a model is a brain you rent, and you and your competitor rent the same one. The formulation has the compression of something that has been tested in a few dozen meetings before it found that sentence. It is also, structurally, the airplane story. Anthropic and OpenAI and Google are AerCap. They retain residual value on enormous capital assets — clusters of GPUs depreciating on a schedule, weights trained at a cost that only a handful of balance sheets in the world can absorb — and they lease access to those assets by the token, to anyone who can pay, including, in the same afternoon, two companies trying to put each other out of business. The model does not know whose prompt it is answering. It has no loyalty file. It has, in fact, no memory at all, in the ordinary sense of the word — each call begins exactly where the last one ended for everybody, which is nowhere.

The asymmetry that airlines exploit is the one available here too, and it sits one layer up from the engine. Call it the embedding store, the vector database, the fine-tuning corpus, the retrieval index — the terminology varies by vendor, but the function is constant. It is the accumulated, indexed residue of every customer interaction a company has had, structured so that the rented brain can be handed the relevant fragment of it at the moment of each new call. A bank’s fraud model and a competing bank’s fraud model can call the identical foundation model, route through the identical API, and arrive at entirely different verdicts on the identical transaction, because one of them is retrieving against eleven years of labeled chargebacks specific to its own card portfolio and the other is retrieving against four. The intelligence rented by the hour is, for practical purposes, a commodity, priced down toward marginal cost the way jet fuel is priced — everyone pays close to the same number per unit. The memory is not a commodity. It cannot be, because it is not for sale; it is the institutional record of what has already happened to you, and no amount of capital lets a competitor buy a copy of your chargeback history any more than it lets them buy your maintenance logs.

This produces a particular kind of corporate vertigo, which Mehta’s sentence is really addressing. For three or four years the industry conversation about artificial intelligence has been a conversation about models — which lab’s was larger, which benchmark moved, which release cycle a company should anchor its roadmap to. That conversation rewards being an early and aggressive lessee. But a lessee relationship, however aggressive, does not compound into anything a competitor cannot eventually also lease. The compounding, when it happens, happens in the layer below the API call: in how cleanly a company has structured the record of its own customers, its own failures, its own edge cases, so that the rented brain, plugged in fresh every morning with no memory of yesterday, can be handed exactly the right fragment of yesterday and made to look, for a few hundred milliseconds, like it has been there all along.

A hospital chart has two kinds of entries. There is the vital-signs strip clipped to the bed rail — temperature, pulse, blood pressure, checked every four hours and replaced every four hours, because a reading from yesterday tells the night nurse nothing about the patient in front of her right now. And there is the permanent record in the file downstairs: the allergy that nearly killed him in 2019, the surgery, the medication history going back a decade, written once and never overwritten, because that record is exactly as valuable ten years from now as it is today. Nobody confuses the two charts. Nobody staples last Tuesday’s blood pressure into the permanent file. The hospital figured out, long before anyone digitized it, that memory is not one problem. It is two, and they fail in opposite directions if you run them through the same system.

Most teams building the layer Mehta is describing make exactly that mistake — they staple everything to the same chart. The shorthand for it is dumping everything into a vector database and praying, and it is worth asking why that particular error is so popular. The answer is that it feels like progress: embeddings go in, something resembling memory comes out, and the team moves on to the next sprint without confronting the harder question, which is what kind of memory it just built.

Short-term memory is the vital-signs strip — everything the model needs to finish the task in front of it and nothing it needs after. A customer-service exchange in progress, the order number already mentioned, the fact that this is the second call today, belongs here. So does the scratchpad of a multi-step agent: the search results just pulled, the file just opened, the partial answer being assembled before it commits. The test is not how important the information is but how long it stays true. A customer’s mood this minute is real and gone in twenty minutes; storing it permanently is like stapling yesterday’s temperature reading into the permanent file, undated, until the chart tells you nothing about fever and everything about clutter. Short-term memory should live in the context window itself, or a session-scoped cache, and it should be allowed to die when the session ends. The sin is not forgetting it. The sin is remembering it forever.

Long-term memory is the file downstairs, and it does not come in one shape any more than that file does. The first shape is semantic memory — facts. A customer’s account tier. The chargeback history that decides, in fractions of a second, whether this morning’s transaction clears. Facts belong in a database with a schema, not a vector store, because a fact has a right answer and a vector store gives you an approximate neighbor. Ask a vector index what tier a customer is on and it hands you the five most semantically similar sentences in the corpus — one correct, four merely correct-sounding. Ask a schema the same question and it tells you, because that is what the schema is for.

The more sophisticated shops are already building the seam between the two, rather than picking one and living with its blind spot. A knowledge graph keeps the relationships a schema is good at — this customer, that account, this chargeback, in fixed and queryable connection to one another — while still letting a retrieval layer search across it by meaning rather than by exact key. The approach has a name now, GraphRAG, and the name matters less than what it concedes: that facts and resemblance are different operations, and the honest fix is to run both and let each one answer the kind of question it’s actually suited for, not to force a single index to pretend it can do both jobs at once.

The second shape is episodic memory — what actually happened. The specific conversation last March in which the customer explained, at length, why the previous fix didn’t work. The exact sequence of an agent’s failed attempt at a task, preserved so the next attempt doesn’t repeat it. This is where the vector store finally earns its keep, because an episode isn’t an exact-match lookup, it’s a resemblance — has anything like this come up before — and a vector index, built to find the nearest thing to a fuzzy question, is the right tool for that question and almost no other. The error was never using a vector store. The error is using only a vector store, for facts as well as episodes, on the theory that one hammer with sufficient cosine similarity can stand in for the whole toolbox.

The third shape is the rarest, and the one teams forget to build at all: procedural memory, which is not a fact and not an episode but a skill — the model’s learned sense of how this company writes a refund email, escalates a complaint, formats an invoice. Style is the visible half of it. The other half is harder to see and matters more: the rails the model is forced to run on before it ever gets to choose a word. A refund above some threshold routes to a human, no exceptions, because the workflow says so, not because the model was persuaded to think so on this particular call. An agent that touches a production database does it through a reviewed function with a fixed set of permitted calls, not through whatever query it improvises in the moment. None of that lives in a prompt, and none of it lives in the model’s weights either. It lives in code — the orchestration layer, the permissioning, the state machine the agent is required to pass through — and it is procedural in the oldest sense of the word: not a memory of what to say but a memory of what is and isn’t allowed to happen, enforced whether or not the model that day feels like remembering it. It doesn’t live in a database at all. It lives in fine-tuning, in carefully maintained house-style examples, and in the surrounding scaffolding of guardrails and permitted actions, and it changes slower than the other two, the way a surgeon’s hands carry both technique and caution years after the specific patients are forgotten. A company that has built rich semantic and episodic memory but skipped this layer has a model that knows everything about its customers, writes in exactly the right voice, and is one well-crafted prompt away from doing something the company never agreed to.

The real argument here is not which database serves which layer — that part is plumbing, and plumbing changes every eighteen months. The argument is that memory has to be triaged the way the hospital triages it, with something deciding on purpose what survives the session and what doesn’t, rather than writing every token of every interaction into the same undifferentiated store and trusting retrieval to sort it out later. A vector database with no triage in front of it is not a memory system. It is a landfill with a search function, and it will retrieve the wrong eleven-month-old conversation with the same confidence it retrieves the right one, because nobody wrote the part of the system whose only job is deciding what belongs on which chart.

The lessor’s airplane, repainted, will fly for someone else next year. The route network will not. Neither will the schema that knows a customer’s tier on contact, nor the index that remembers the conversation from last March, nor the fine-tuned hand that knows, without being told twice, how this company writes a refund email. These are the things that do not come back at the end of the lease, because they were never on it.

Categories
Magicians

Misdirection

There is a trick magicians call misdirection, and the secret of it is that you can show someone exactly what is happening, in plain sight, and they will still look at the wrong hand. The eye goes where it’s told. The trick survives not because it’s hidden but because attention has been pointed somewhere else, gently, by a man who understands exactly where you’ll glance next.

Ricky Jay spent his whole life inside that idea, and he learned it, near as anyone can tell, before he understood what it was for.

He was ten years old in a New Jersey bathroom, standing in front of a medicine cabinet, looking at two tubes that sat a foot apart — his father’s toothpaste, his father’s Brylcreem — and he switched them. His father brushed his teeth with hair cream and combed Colgate into his scalp. Jay would tell that story for the rest of his life with the precise comic timing of a man who had told it ten thousand times.

There was a basketball hoop bolted above the garage of that house, and Jay loved to shoot baskets against the aluminum siding his mother begged him not to dent. There were music lessons — accordion lessons — that his parents made him quit, a detail he liked to deliver with a shrug, probably the only kid in history whose parents made him stop taking music lessons. There was a guinea pig that urinated on his father’s necktie during a television appearance when Jay was seven, and his father’s only comment, delivered with no apparent affection: Perfect. You get all the glory and I get all the piss.

He said, when pressed, that he could not remember when his family moved from Brooklyn to the suburbs. He could not remember what year he started college, or the year he left, or how many of the five colleges he attended he actually finished. He had, by his own account and the testimony of nearly everyone who loved him, one of the most extraordinary memories in America — a man who could recite a hundred-item list cold, a man who could quote his own carnival barker spiel from a quarter-century earlier without missing a word. None of that machinery held a single fact about his parents in place.

What he did remember, with total clarity, down to the address, was a magic shop on West Thirty-fourth Street. What he remembered was his grandfather.

Max Katz was an accountant, an amateur magician, a man who loved cards and chess and calligraphy and codes, and who believed the way to learn anything was to find the best person doing it and watch their hands. He took young Ricky to see Dai Vernon and called him the Professor and told the boy to study the naturalness of his movements. He introduced him to Slydini, to Francis Carlyle, to a whole demimonde of men in midtown cafeterias who could make a coin disappear with nothing but patience and forty years of practice. When Ricky did his very first trick in front of an audience, at four years old, multiplying paper coffee creamers at a backyard barbecue, it was his grandfather’s friends who were there to see it.

When Ricky’s bar mitzvah came, and his parents asked what kind of celebration he wanted, he didn’t ask for a band or a hall. He asked for a magician named Al Flosso, the man who ran that shop on West Thirty-fourth Street. Jay would say, decades later, that this was the only warm memory he had of his parents.

Max Katz died when Ricky was a teenager, and at the funeral, Flosso did something magicians do for one of their own: he broke a wand, ceremonially, and placed it in the casket. Jay called it the single most frightening thing he ever saw. He also said that his grandfather’s death marked the end of whatever relationship remained with his parents.

He spent the rest of his life being trained by a chain of older men — Vernon at Canter’s Deli until five in the morning, Charlie Miller watching him run the same sleight for hours without blinking, men who would sit across a table from a kid and say do it again, do it slower, do it until it disappears.

He never got the toothpaste joke to land any other way. He didn’t need to. Some hands you watch your whole life and still can’t explain.

Motivated by learning of the passing of Mark Singer whose profile of Ricky Jay in the New Yorker provided my direction to learning more about him.

Categories
Cooking Cooking Tips Recipes

On Cooking Backward

I have been thinking about a steak, and about Charlie Munger.

Munger liked to say, “invert, always invert,” meaning that some problems give up more easily if you turn them around and look at them from the back.

I don’t know that he ever cooked a steak in his life, but the advice applies to one anyway. There is a method called reverse searing, and it works like this: you put the meat in a slow oven first, so it warms through gently and evenly, and only at the very end do you give it a hard, fast sear in a hot pan, just long enough for a crust.

This is backward from how most of us were taught — sear first, finish low — and it turns out the old way was mostly wrong, or at least less reliable. The middle of the meat and the outside of the meat have different jobs, and trying to make one method serve both has always been a small, forgivable mistake.

Tri-tip is a peculiar cut of beef — the grain runs one way across part of it and then quietly changes direction, so that if you slice straight through without noticing, you end up with something tougher than it needed to be. You have to find the place where it shifts and turn your knife with it. It isn’t hard, once you know to look. There’s usually a faint seam where the change happens, almost like a crease in fabric, and once you’ve found it you’ll find it every time after.

I don’t want to make too much of this. It does seem true, in cooking and in other things, that the order you do something in matters more than people let on, and that the patient, unglamorous part — the slow oven, the waiting — usually deserves more credit than it gets.

The sear is what you remember. The low heat is what made it possible.

Categories
Menlo Park Serendipity

Two Kinds of Efficiency

The fog hadn’t lifted yet over Sharon Park, the kind of gray that Menlo Park wears many June mornings like it’s embarrassed to admit the sun is up there somewhere, and I was on my usual loop around the pond when I noticed in the distance that the goats were back. And one more thing too. I stopped.

On one side: forty, maybe fifty goats, heads down, working a hillside of dry summer grass like a crew that had done this job a thousand times, because they had. The city brings them in every year around now, before fire season, to eat down the fuel load that nobody wants to mow. White ones, brown ones, a few with horns curling back like something out of a hieroglyph. They don’t look up much. A goat eating is a goat with one job and no curiosity about yours.

On the other side, maybe forty yards past them, through the wire: a Waymo. White, sensor pod spinning slow on the roof like a lighthouse that had wandered inland and gotten confused about its purpose, parked at the curb with nobody in it. Just sitting there. Idling, if a thing with no engine can idle. Waiting on a fare, or waiting on nothing, the way these cars do now, patient in a way that doesn’t read as patience because there’s no face attached to it.

I stood looking for longer than the moment deserved, the way you do when something hands you a thought before you’ve earned it. I remembered I should take a photograph.

Here is what struck me, eventually: both of them were efficient. That’s the word that kept showing up, uninvited. The goats are efficient in the oldest way there is — they convert a problem (too much dry brush, a fire waiting to happen) into a solved problem, using nothing but appetite and stomachs and several thousand years of being bred for exactly this. Nobody programmed a goat. A goat doesn’t have a model. A goat has a memory that goes back to whatever the last hillside tasted like, and an instinct that says eat that one next, and that’s the whole operating system.

The Waymo is efficient in the newest way there is. Lidar instead of appetite. A map instead of memory. It doesn’t get bred for the job, it gets trained for it, mile after simulated mile, until eventually you can park it at a curb in a quiet park and trust it not to do anything stupid. It was, in its way, doing the same thing the goats were doing — converting a hard, slightly dangerous task that used to require a person’s full attention into something that just sort of happens now, off to the side, while everyone gets on with their morning.

I’ve spent a fair amount of my working life around payments systems and fraud models, which is its own quiet machinery — systems built to notice the thing before the thing becomes a problem, the same job the goats were doing on that hillside, eating the grass before it becomes a fire. So maybe that’s why I stood looking longer than I meant to. I recognized the shape of it, even though one side of the fence had hooves and the other side had a sensor array worth more than my first house.

What I didn’t expect was how unbothered each side seemed by the other. The goats did not care that there was an expensive autonomous vehicle parked within sight of their breakfast. The Waymo, for its part, did not care about anything, which I suppose is the whole point of it — it isn’t built to care, only to notice, and the goats had registered exactly zero on whatever sensor suite decides what’s worth noticing. Two systems, separated by maybe forty yards and several thousand years of technological distance, each one going about its business with total indifference to the other’s existence.

I used to think the line between old world and new world would announce itself — some clean morning where you’d wake up and the future would have visibly arrived, banners out, the old thing retired with a gold watch. It doesn’t work that way, it turns out. It works like this: a fence, some goats, a car with nobody driving it, and a guy on his usual walk who happens to notice that both of them are quietly, competently doing a job that fire season requires somebody — or something — to do.

I kept walking. The goats kept eating. The Waymo, as far as I know, was dispatched somewhere, picked up whoever needs a ride, sensor pod turning over the same hill the goats had already half cleared. Two kinds of efficiency, on either side of an electrified wire fence, neither one impressed by the other, both of them right.

I don’t know what to do with that, exactly, except to write it down and remember it. Some mornings my walk gives me exercise. Some mornings it gives me a simple memory I didn’t ask for, standing there looking.

Categories
Design Technology

The Battery That Refused to Leave

A standard AA battery is 50.5 millimeters long and 14.5 millimeters in diameter. It produces 1.5 volts. It weighs roughly twenty-three grams, about as much as a sheet of paper folded twice. In a Costco bulk pack, forty-eight of them together weigh a little over a kilogram — the heft of a hardcover book, or a decent cantaloupe. Most people buy them without thinking much about it. They go in the cart the way paper towels go in the cart.

The size has been in continuous production since 1907, when the American Ever Ready Company first manufactured it for use in early penlights. For the first four decades of its existence, the AA battery was what might be called an informal standard — widely used, commonly understood, but not officially codified. That changed in 1947, when the American National Standards Institute fixed the dimensions and voltage in writing. The naming convention itself had come earlier, out of a series of meetings in the 1920s between government officials and battery manufacturers who were trying to bring order to a proliferating market. They began with A for the smallest practical cell, then moved outward — B, C, D — for larger sizes. When smaller cells were needed later, the alphabet doubled back on itself: AA, AAA, AAAA. Running out of letters in both directions is its own kind of history.

What the standards committee built, whether they thought of it this way or not, was a commons. The word is precise. A commons is something no one owns and everyone can use — a pasture, a fishery, a language. The AA battery became a commons of power. Any battery from any manufacturer, made to the specification, would work in any device built to receive it. The chemistry inside could vary — zinc-carbon, alkaline, lithium, nickel-metal hydride — but the housing stayed the same. No license was required. No negotiation. A manufacturer building a flashlight in 1965 did not need to solve the battery problem. A company making a remote control in 1985 did not need to negotiate with a power supplier. The relationship between a device and its energy source belonged to no one, which meant it was available to everyone.

In 1959, an Eveready scientist developed the first commercially available alkaline AA, which lasted five to eight times longer than the zinc-carbon version it was designed to replace. The devices followed the power. Transistor radios. Portable tape players. Handheld games. Cameras. Each decade brought a new category of device that found the AA battery waiting for it, already standardized, already available at every drugstore and grocery checkout lane in the country. The commons kept growing because the commons was free to enter.

Apple, eventually, decided the idea was wrong.

The iPhone, introduced in 2007, had no user-replaceable battery. Neither did any iPod before it, any iPad after it, any MacBook, any AirPod, any Apple Watch. The power source in an Apple product is sealed inside the device, charged through Apple’s own cables and connectors, managed by Apple’s own software. This is not a cost-cutting measure or an engineering compromise. Apple’s products cost more than their competitors’, not less, and the sealed battery is part of what justifies the price. The company’s founding argument — refined over decades, made explicit in every product announcement — is that hardware and software and power, designed together and optimized together, produce a better result than any open standard can achieve. The AA battery asks nothing of you except that you insert it correctly. Apple has decided that is insufficient.

Tesla arrived at a similar conclusion by a different route. Where Apple sealed the power source to improve the user experience, Tesla sealed it to own the energy relationship entirely. The Supercharger network — Tesla’s proprietary charging infrastructure, built out across highways and cities at enormous expense — is not interoperable with other electric vehicles, or was not for most of its history. A Tesla charges at a Tesla station. The battery chemistry, the cell format, the thermal management, the software that governs charging and discharge — all of it is developed in-house, at Tesla’s gigafactories, for Tesla’s vehicles. The company has spent more time and money thinking about batteries than almost any organization outside of a national laboratory. But the battery it produces is not a commodity. It belongs to the car. The car belongs to Tesla’s ecosystem. The customer belongs there too.

Both companies are making a version of the same argument: that the future of technology is integrated, that the best products are closed products, that power should be managed rather than swapped. They have built that future, or a version of it, for the customers who can afford to live inside it.

Warren Buffett, in 2014, bought the thing neither of them wanted.

Berkshire Hathaway’s acquisition of Duracell from Procter & Gamble was structured as a stock swap — Berkshire exchanged its $4.7 billion stake in P&G for full ownership of the battery company, recapitalized with $1.8 billion in cash. The tax advantages were real and significant; Berkshire had held the P&G shares since the company’s acquisition of Gillette in 2005, and the cost basis was $336 million. A cash sale would have produced a substantial capital gains bill. The swap avoided that. Buffett is attentive to such things.

But the more durable rationale was simpler. Buffett has spent sixty years looking for businesses that are easy to understand, that generate predictable cash, that sell something people buy out of habit. See’s Candy. GEICO. Coca-Cola. The common thread is not glamour but persistence — products whose value proposition does not need to be reinvented, whose customers return not because they have been excited but because they have been satisfied, reliably, for a long time. Duracell has twenty-five percent of the global battery market. It has been the category leader for decades. The people who buy it at Costco are not making a considered choice between competing technologies. They are buying what they have always bought.

The Costco pack of forty-eight is, in Buffett’s framework, infrastructure. Not the infrastructure of data centers or power grids — the quiet infrastructure of daily life, the kind that gets restocked when the supply runs low and otherwise goes unnoticed. Smoke detectors. Remote controls. Children’s toys. Wireless computer mice. Clocks on kitchen walls. The devices that run on AA batteries are not going away, and the economics of replacing them — not just the devices but the habits, the muscle memory, the universal availability of the standard — are formidable. Buffett is not betting that the AA battery will conquer the future. He is betting that it will remain in the present for a very long time.

Two different visions of where technology is going, then, expressed in the form of capital allocation. Apple and Tesla have built sealed ecosystems and asked their customers to enter. Buffett bought the battery for the people who haven’t. The AA cell, fifty millimeters long and fourteen and a half millimeters wide, 1.5 volts, unchanged in its dimensions since a group of manufacturers met in the 1920s to agree on something everyone could use — it sits at the back of a kitchen drawer in most houses in America, waiting for the smoke detector to chirp.

Categories
AI AI: Large Language Models AI: Transformers Authors Podcasts Writing

The Billboard

The fog was still sitting on the hills when I put in my earbuds and headed out.

Sebastian Mallaby was talking about billboards.

Tim Ferriss had asked him the question he asks everyone: if you could put anything up there, for millions of people to see, what would it be? Mallaby has spent years inside the minds of the people who shaped modern finance — the hedge fund managers, the venture capitalists, the builders of things that changed how the world moves money. He has more material than most people accumulate in a lifetime. He could have said anything.

He said: Prepare your mind.

I kept walking. The houses were quiet in the particular way they get when school lets out for summer — no buses, no car doors, no kids at the corner. Somebody’s sprinklers were running.

The phrase comes originally from Louis Pasteur, who understood something that most people don’t: that chance is not democratic. It does not distribute itself evenly among those who wait. It finds the people who are ready. Chance favors the prepared mind. Pasteur said it, and then he proved it, and then the rest of us spent a century and a half learning it was true.

What struck me about Mallaby’s answer wasn’t the phrase itself. It was the way he said it had kept appearing in his research, surfacing in different decades and different worlds, like a message the material kept trying to send him.

He told the story of Arthur Patterson at Accel Capital. Before a new technology arrived, Accel would work through the implications — what company needs to be built, what founder fits the moment, what the right pitch looks like. So when an entrepreneur finally walked in, when the situation was live and competitive, they already knew ninety percent of what they were hearing. They could move fast because they had already moved slow.

That’s preparation as institutional practice. But Mallaby found the phrase again in a different register entirely, embedded in a single human moment that has always seemed to me like one of the hinge points of our era.

He was interviewing Ilya Sutskever, asking him why he had seen it so quickly.

In 2017, a paper called Attention Is All You Need appeared online. It described a new architecture for neural networks — the transformer — that would eventually rewrite the terms of what artificial intelligence could do. On the day the paper went up, Sutskever read it. And then he ran. He went down the corridor to find his collaborator Alex Radford and told him to stop what he was doing. Everything. Stop. We are going to build a language model on this architecture.

Not someday. Now.

Mallaby asked him how he had seen it so clearly, so fast. And Sutskever’s answer, in its essence, was the same two words: prepared mind.

He had been thinking about the problem of modeling sequential data since his PhD in Canada. For years he had been carrying a question the field hadn’t answered yet. And when the answer appeared — when the transformer showed up on a website one ordinary day — he didn’t have to reason his way toward it. He recognized it. The solution arrived and found a mind that had been waiting for it, that had already cleared space for it, that was already arranged around the shape of exactly this kind of answer.

This is what preparation actually is. Not the accumulation of facts. Not readiness in the generic sense, the vague self-improvement sense. It is the long, patient cultivation of a specific question, held close and kept alive until the answer has somewhere to land.

Mallaby chose that phrase for his billboard because it kept finding him — in the venture capital world, in the AI world, across decades and disciplines and very different kinds of genius. The prepared mind is not a personality trait. It is a practice. It is the work you do before the work arrives.

The sprinklers had clicked off by the time I turned back toward home. The fog was starting to lift off the hills. I was thinking about what I had been preparing for, whether I even knew.

Categories
AI: Diffusion Models Art and Artists

An Algorithm by Hand

He didn’t know what he was looking for the first time he walked into the Alhambra. You come in from the heat and the light hits the walls and for a moment you just stand there, your mind doing something it doesn’t have words for yet.

That was 1922. Escher was twenty-four years old, recently graduated. The Moorish artists who made these walls had been dead for six centuries. They had left no notes. No theory. Just the walls.

He came back in 1936. Some things you have to see twice.

What the artists in the Alhambra had discovered — without algebra, without proof, working in a tradition that forbade them from drawing a single living creature — was that there were exactly seventeen ways to tile an infinite plane with a repeating pattern. The Russian mathematician Fedorov would articulate this formally in 1891, centuries after the fact, the way mathematics sometimes chases art home and explains what it already knew.

Escher took the problem and made it harder. He asked: what if the edge between two tiles is also the outline of a fish? What if the sky is made of birds and the birds are made of sky? He would move a single line and the whole system would tremble. He did this for years. Revision after revision, in small notebooks, by hand.

There is a word for what he was doing. We just didn’t have it yet.

The word is algorithm.

An algorithm is a set of rules, followed in sequence, to solve a problem. We think of them as things that live in machines, in data centers drawing enough power to light a city. We think of them as fast. Escher’s algorithm was not fast.

He would begin with a grid. Hexagons, maybe, or the interlocking diamonds of a pattern he had traced from the Alhambra walls. Then he would ask the question that made everything hard: what lives here? Not what shape — what creature? What thing with a spine and a purpose and an outline that a human eye would recognize before the brain caught up?

The constraint was absolute. Every point on every edge had to satisfy two animals simultaneously. Change one line and you changed everything downstream, the way a single altered fact in a long investigation suddenly makes you reread everything you thought you knew.

He failed constantly. The notebooks are full of it. Half a lizard becoming nothing. A bird whose wing destroyed the fish below it. He would back up and try again, the way you back up on a road that has stopped being a road.

He was doing, neuron by neuron, what a diffusion model now does in milliseconds.

But here is the thing about milliseconds. They don’t leave notebooks.

Categories
Aging Atmosphere

The Atmosphere Business

There are some rooms you just want to be a part of. A restaurant critic wrote that recently in the Financial Times, and I’ve been turning it over ever since. Not because it’s surprising — anyone who loves restaurants already knows it’s true — but because it named something I’ve been experiencing without quite having the words for it.

I’ve been paying more attention to rooms lately. Not to what’s happening in them, but to the rooms themselves. The light temperature. The materials. The way a space settles around you when you walk in.

There’s a Greek restaurant in Palo Alto called Evvia that I’ve been going to for years. It has no modern feel whatsoever — no reclaimed industrial aesthetic, no Edison bulbs performing nostalgia, no carefully curated emptiness. Instead: a wall of jars and bottles filled with colored liquids, honey-blonde wood, light that feels like it was chosen by someone who understood that warmth is not a design choice but a form of hospitality. I couldn’t tell you what era it conjures. I just know that when I walk in, something in me slows down. And the food is superb. That matters too — not as the reason you came, but as the room’s final kept promise.

I don’t think I would have noticed any of that at 35.

At 35 you move through rooms. You’re pointed forward — toward the person across the table, the evening’s agenda, whatever brought you there. The room is backdrop. At 35 I was probably thinking about the wine list before I’d finished reading the menu, planning the next thing while the current thing was still happening.

Something shifts. I can’t name the moment it happened, because there wasn’t one. Just a gradual noticing — that I was paying attention differently. That the room had become as interesting as the reason I came.

The word that comes to mind is savor. Which has an interesting relationship with time. You’d think that with less of it ahead, you’d move faster, extract more, optimize. Instead the opposite happens. Each thing becomes more worth inhabiting fully. The scarcity makes you slower, not faster. More permeable.

The FT writer talks about wanting to be held by a room. That’s a passive construction — something done to you, not by you. I think that capacity to be held requires a surrender that younger people can’t quite manage. Too much forward momentum. Too much else to get to.

What comes with the savor, I’ve found, is peacefulness. Not contentment exactly — contentment can be a kind of settling, a lowering of expectations. This is different. Peacefulness has knowledge in it. You’ve seen enough to know what a good room is worth. You’ve been in enough bad ones — too loud, too bright, too eager to impress — to recognize when a room is simply, quietly doing its job.

Evvia does its job. The honey-blonde wood absorbs the evening. The jars catch the light. The atmosphere, as an old restaurateur once put it, is what they’re actually selling.

Later in life, I know how to buy it.

Categories
Authors Books

More John McPhee

Yesterday I shared a list of John McPhee paperbacks that are in our home library. Using Google Gemini I had it prepare a list of the books from a photo I had taken of the stack of books.

Today as I was on my morning walk listening to an Economist podcast about de Tocqueville in America, it dawned on me that I should come back to Gemini and ask it for a ranked list of McPhee books that I should consider adding to our home library. Here is the prompt I used:

Now give me a list of his books that I don’t own. Rank the list based on recommendations. Include the same info as before.

Here is that list – useful the next time I’m at a used bookstore!

Recommended John McPhee Books (Not in Your Stack)

This curated list ranks the best books by John McPhee that are missing from your current home library. It prioritizes his most historically significant, critically acclaimed, and widely loved masterpieces.

1. Annals of the Former World

Year Published: 1998

Summary: This monumental, 700-page epic is the culmination of twenty years of research and travel alongside geological experts. It traces the deep geological history of the North American continent along the Interstate 80 corridor. The book is actually a compilation of five smaller works (Basin and Range, In Suspect Terrain, Rising from the Plains, Assembling California, and Crossing the Craton), tracking plate tectonics, mountain building, glaciers, and deep time.

How Reviewed: Widely considered McPhee’s magnum opus, it won the 1999 Pulitzer Prize for General Nonfiction. Critics universally praised it for taking what is traditionally considered a “dry” science and turning it into a gripping, human, and philosophically profound narrative about the Earth’s violent history.

An Interesting Story: McPhee originally estimated the geology project would take him about one to two years. It ended up consuming two decades of his life. Because geologists make notoriously distracted drivers—constantly swerving across highway lanes to examine exposed rock formations on roadcuts—McPhee had to do most of the driving during their cross-country road trips just to keep them safe.

2. Coming into the Country

Year Published: 1977

Summary: An extraordinary three-part portrait of Alaska during a chaotic, pivotal era in the 1970s. The first part covers a dangerous, pristine wilderness canoe trip down the Salmon River in the Brooks Range. The second details the political gridlock of trying to establish a new state capital. The third and longest section is an intimate look at the rugged, eccentric, and fiercely independent settlers of the remote gold-rush town of Eagle near the Yukon border.

How Reviewed: A massive bestseller and critical triumph. It is universally regarded as one of the greatest books ever written about Alaska, capturing both the staggering scale of the wilderness and the complex, headstrong psychology of the people who flee to it.

An Interesting Story: While documenting the remote lives of wilderness settlers, McPhee met a man living in a cabin who had survived a brutal sub-zero winter with almost no food. At one point, the man’s entire remaining winter rations consisted of a single, frozen head of cabbage. McPhee’s meticulous fact-checkers at The New Yorker tracked down the source to verify the exact status and size of the cabbage before they would let him publish the story.

3. The Pine Barrens

Year Published: 1968

Summary: An exploration of a sprawling, million-acre wilderness of pitch pines, cedar swamps, and sandy aquifers hidden in the middle of heavily urbanized New Jersey. McPhee describes the unique ecology of the area and profiles the isolated, self-reliant residents—traditionally called “Pineys”—who lived off the land by gathering cranberries, digging bog iron, and hunting.

How Reviewed: A classic of regional literature and early environmental journalism. It was praised for exposing a secret, beautiful world right in the backyard of the busy East Coast, and it is widely credited with helping to spark the political movement that ultimately federally protected the region.

An Interesting Story: At the time McPhee wrote the book, there were major state plans to pave over the Pine Barrens to build a massive, multi-runway international jetport and a brand new city of 250,000 people. McPhee’s beautifully written, highly sympathetic portrait of the area’s quiet wilderness and historic communities turned public opinion so heavily against the developers that the entire project was permanently scrapped.

4. Levels of the Game

Year Published: 1969

Summary: A masterclass in narrative structure. The book is framed entirely around a single semi-final tennis match played at the 1968 US Open at Forest Hills between Arthur Ashe and Clark Graebner. As the play-by-play of the match unfolds stroke-by-stroke, McPhee weaves in the biographies of the two young men, demonstrating how their family backgrounds, races, and political worldviews directly dictate the way they play tennis.

How Reviewed: Frequently cited by sportswriters as one of the greatest sports books ever written. Critics were mesmerized by how McPhee transformed a simple, brief tennis match into a brilliant, microscopic psychological study of two contrasting Americas in the late 1960s.

An Interesting Story: The book’s structure is incredibly tight—there are no chapters or headers; it reads as one continuous, unbroken volley of text from the first serve to the final match point. Because the manuscript was so structurally dependent on the exact sequence of tennis play, McPhee had to build a massive physical storyboard using index cards on his dining room table to track the score of the match alongside his biographical flashbacks.

5. Encounters with the Archdruid

Year Published: 1971

Summary: A brilliant structural experiment in narrative journalism. The book profiles David Brower, the passionately uncompromising executive director of the Sierra Club (whom his adversaries mockingly called “the Archdruid”). McPhee takes Brower on three separate wilderness expeditions, pairing him on each trip with one of his primary ideological enemies: a mineral engineer in the North Cascades, a resort developer on a pristine Georgia barrier island, and a dam-building commissioner in the depths of the Grand Canyon.

How Reviewed: Highly praised for its absolute neutrality. Instead of taking a side, McPhee steps back and allows both sides of the environmental debate to articulate their values in real-time as they hike, raft, and camp together.

An Interesting Story: During the Grand Canyon rafting trip, David Brower and his fierce ideological opponent, Floyd Dominy (the commissioner responsible for constructing major western dams), were forced to share a tiny rubber raft through dangerous, churning whitewater rapids. Dominy, a tough-talking Westerner, was terrified of the water. Brower, despite hating Dominy’s dams, quietly guided the raft safely through the rapids, forging a brief, surreal moment of mutual respect between the two bitter enemies.

6. Draft No. 4: On the Writing Process

Year Published: 2017

Summary: A masterclass handbook on the art and craft of nonfiction writing. Pulling from his decades of writing for The New Yorker and his legendary writing seminars at Princeton University, McPhee reveals his highly structured, sometimes eccentric methodology for conducting interviews, organizing mountains of research, drawing structural diagrams, and editing drafts.

How Reviewed: Celebrated as an instant classic for writers, students, and journalists. Reviewers loved the book’s warm, humble, and practical advice, combined with hilarious behind-the-scenes anecdotes of the editing world.

An Interesting Story: McPhee reveals that during his early career, he suffered from such severe writer’s block that he would literally tie himself to his office chair with a bathrobe sash to force himself to stay at his desk and type. He also details his “search-and-replace” editing method: on his fourth draft, he reads his work with a dictionary, finding any word that feels slightly lazy or uninspired, circling it, and listing dozens of synonyms beneath it until he finds the perfect match.