Categories
Cuba Photography Street Photography

Havana, In Deep

Dangling Conversation - Havana - 2013

There is a box between them with a screen in it, and to this day I do not know what it is for. It could be for sifting. It could be for rolling. It sat on the table in that Havana market like a piece of furniture too tired to explain itself, and the man rested his forearm on it the way men rest their forearms on things that have been useful to them for a long time, without needing to look at it. A couple of guys a few steps off were selling meat, and somewhere a radio was losing a slow fight with distance.

He was asking her something. You could see it before you could hear it, if you could have heard it at all, which I could not, standing twenty feet away with a camera and no Spanish worth the name. His eyebrows were doing the work. His mouth was doing the work. The cigarette in the corner of his lips had gone unlit and forgotten, a prop in a scene that had moved past needing it.

She had not expected the question. That was the whole of it, the thing the photograph is actually about. Above and behind them the light came down through warehouse glass gone frosted with age, softened, the hard edges sanded off. It had been falling on that table for years before either of them sat down at it. A woman’s face has a setting it returns to between thoughts, and hers had been somewhere else — the work, the heat, the cigarette she’d just lit, which she now held between two fingers like a held breath, smoke rising into that same light, catching it, going from invisible to visible to invisible again. Then he spoke, and the setting changed. Her eyes came around to him sideways, the way eyes do when the rest of the head hasn’t decided yet whether to follow. Caught. Not afraid — caught, the way you’re caught remembering something mid-sentence, or caught by a question that arrives at an angle you didn’t see coming.

They had stepped away from whatever the work was — the particular slackness of people on a break, elbows down, shoulders forward, the posture of two people who have stopped doing the thing they get paid to do and have not yet decided to start talking about anything in particular, except that he just had.

Neither of them knew I was there, and I have never quite settled how I feel about that. I took something from two people who never agreed to give it. I have made my peace with it the way photographers do, which is imperfectly, but I have not stopped thinking about it. This is the only kind of photograph worth making all the same, the kind where you are not in the room, not really, where the camera has gone as invisible as the screen on that box, recording a question and an answer that the two of them will forget by the end of the day and that I will keep for the rest of my life, lit by a window neither of them ever turned around to notice.

What they were talking about, I will never know. I have looked at this photograph for thirteen years now and I still want to know.

Categories
Living New York City Serendipity

The Grammar of Looking Up

The apartment was across from Penn Station, which meant that for one stretch of months in the mid-1970s, the architecture of my days was decided by trains I never took. I walked east instead, every morning, toward the United Nations, where a man named Frank Smith ran a personal development course that IBM SRI had decided its young people should sit through. I don’t remember most of what Frank said in that room. I remember one thing he said about the street outside it.

He told us we should start looking up. Literally — on our walks back and forth across midtown, Penn Station to the East Side and back, twice a day, rain or not. Not all the way, usually. Mostly it was a floor or three: the window line just above the awnings, the cornice on a building you’d never once registered had a second story, let alone a sixth. The full climb to the rooflines — gargoyles, setbacks, terra cotta lions — was the occasional reward. Almost no one looks up in New York, he said, not even a little. The city trains you out of it. Too much at eye level demands your attention — the cabs, the steam, the man asking for change, the woman walking too slowly in front of you — so everything above your own eyeline disappears by consensus, not just the tops. Habits can be replaced. Look up enough times, even just a floor or three, and you’ll see a different city than the one everyone else is seeing.

I tried it. Walking up past the Pierpont Morgan stretch, or wherever the route took me, chin lifted some small number of degrees, feeling slightly foolish. Most days that was the whole of it — a window line, a row of air conditioners, a sign painted directly onto brick decades before anyone called that vintage. Every so often the chin would tip back further, and there’d be something up there worth the extra degrees. A gargoyle with its mouth open mid-roar, forty years before air conditioning made gargoyles decorative rather than necessary. But that was the rare find. The habit was the floor or three. Nobody else on the sidewalk was seeing any of it, because nobody else on the sidewalk was looking at all.

The chin came back down on its own a couple of times a week, somewhere around a street corner with a slice joint on it, because New York seems to put one on every corner whether you need it or not. You smelled it before you saw it — that specific combination of tomato, oregano, and hot grease that has no name I’ve ever found. Looking up was Frank’s discipline.

The pizza smell required none. It just reached out and took your head by the chin and turned it level again, toward the window with the steam on the glass and the guy folding a slice in half before he handed it over.

It is a small thing Frank Smith said in a room near the UN fifty years ago, and I have carried it around since the way you carry around a key to a house you no longer own. I don’t know what happened to the course, or to IBM SRI’s faith in such courses, or to Frank himself. I know what happened to the habit. It outlived the year, outlived the apartment across from Penn Station, outlived several cities I’ve lived in since that didn’t have the same vertical drama to reward the looking. I still do it. I did it last week on a walk that had nothing to do with midtown at all, tilting my head back on a street in California to find whatever was up there worth finding, and catching myself mid-gesture, thinking: that’s Frank’s, that one, still running fifty years later on the program he installed.

Most of what we’re taught to notice, we’re taught by people who wanted something from us — a sale, a vote, a grade. Frank wanted nothing, as far as I could tell, except that we see more of the city than we’d been seeing. It’s such a small ambition for a teacher to have. Look up. That’s the whole curriculum. And it’s the only thing from that course, the only thing from that whole strange year of being instructed in personal development by a man whose face I can no longer quite reconstruct, that I still do, unbidden, on every street I’ve walked since.

Categories
Aircraft Aviation

Sutter’s Balloon

At Pinal Airpark, in the desert north of Tucson, the airplanes sit in rows the way old men sit in rows at a clinic, waiting for something that isn’t coming. A great many of them are 747s, parked here because the engines are worth more than the rest of the airframe and somebody, someday, may want the engines. Somewhere among the rows is one that’s shorter than its neighbors by nearly fifty feet, the line of its fuselage interrupted just behind the wing as though a piece had been folded in and stitched shut. This is a 747SP, and there were only forty-five of them ever built, because the airplane was, from the moment it was conceived, a compromise built to solve one problem and no other.

The problem belonged to Pan American World Airways. In the early seventies Pan Am wanted to fly nonstop from New York to Tehran, a route that did not then exist because no airliner Pan Am owned could cover the distance with a full load of passengers and still land with fuel in the tanks. Iran Air had the identical problem in reverse. Boeing had, at the time, the 747-100, an airplane that could carry nearly everybody in the world somewhere, but not necessarily that far. McDonnell Douglas and Lockheed had the DC-10 and the L-1011, three-engine widebodies built expressly for the medium-haul market the 747 was too big to serve efficiently, and Boeing, watching two competitors carve into territory it had assumed it owned outright, needed an answer that did not require designing a new airplane from the keel up. There was no time and, after the financial near-catastrophe of developing the original 747, no appetite for one.

The man who supplied the answer was Joe Sutter, the engineer who had led the 747 program from the start and who brought to it an instinct for solving problems by subtraction. Sutter’s idea was not to add a third engine, which several engineers in the room had assumed was the path, since removing one engine entirely was reckoned to save a third of the fuel burn and nearly seven tons of weight in one move. Sutter’s idea was to leave the engines alone and shorten the airplane instead. Take fuselage out fore and aft of the wing, forty-eight feet four inches of it, lighten the structure to match, simplify the flaps from the standard triple-slotted design to a single-slotted one, lengthen the tail surfaces to keep the shorter airplane stable, and let the weight savings buy range instead of payload. Boeing’s engineers called the result, informally and a little affectionately, Sutter’s Balloon. The company filed it as the 747SB, for Short Body, before settling on a name that did the marketing for itself: 747SP, for Special Performance.

The first one, manufacturer’s serial number 21022, rolled out of the Everett plant on May 19, 1975, and flew on July 4th, ten days ahead of a schedule that was already tight. Jack Waddell, who had flown the maiden flight of the original 747 six years earlier, was in the left seat again, and on that first flight he put the shortened airplane through a stall and a run up to Mach 0.92, a speed that had no business being associated with anything called a jumbo jet. In November, Boeing flew the fourth airframe nonstop from New York to Tokyo, 6,927 miles, with two hundred passengers aboard, and landed in Seattle’s backyard with more than thirty thousand pounds of fuel still in the wings, a fact Boeing’s marketing department repeated for years the way a man repeats the one good thing a difficult relative once said about him. The FAA signed off in February of 1976. Pan Am took delivery of the first production airplane, named Clipper Freedom, on March 5th, and put it into revenue service in April.

What the SP was for, it did well. A South African Airways SP flew nonstop from the Boeing plant in Seattle to Cape Town on its delivery flight in 1976, a record for an unrefueled commercial airplane that stood for more than a decade. Pan Am flew SPs around the world in well-publicized record attempts, and for thirteen years, until the 747-400 arrived in 1989, the SP held the title of longest-range airliner in the world. It is a title that means something only to the small number of people who keep track of such titles. Boeing had once projected sales in the neighborhood of two hundred. Fuel prices rose through the back half of the seventies and into the eighties, and the SP, despite its range, cost more to fly per seat than the standard 747 it had been built to outdo on a narrower set of routes. Twin-engine widebodies were coming that would solve the same range problem with half the engines to maintain. Production ran from 1976 to 1982, paused, and then opened once more in 1987 for a single VIP-configured order from the Abu Dhabi Amiri Flight, after which Boeing closed the line for good. Forty-five airplanes, full stop.

A handful of them found second careers that outlasted anything the airline business had planned for them. One, a former Pan Am airframe, was hollowed out by NASA and the German Aerospace Center and fitted with a hatch that opened in flight to expose a reflecting telescope two and a half meters across, an arrangement that let astronomers fly above most of the water vapor in the atmosphere and look at the sky the way the ground never quite allows. It flew as the Stratospheric Observatory for Infrared Astronomy until 2022. As of this year, the airplanes still capable of flight number three: two belong to Pratt & Whitney Canada, which uses them as flying engine test beds, bolting experimental turbines onto a wing built half a century ago to prove an idea about subtraction; the third belongs to a casino company in Las Vegas, configured for fifty passengers, which is roughly the inverse of what Joe Sutter had in mind. The rest are scattered in places like Pinal Airpark, sitting in rows, shorter than their neighbors, waiting on engines somebody might still want.

Categories
Monochrome Photography New York City Photography Photography - Black & White

Bookends

Bookends - New York City - 2010

The tile is the first thing, and it should be. Count the squares if you want — institutional cream, grouted in a pattern nobody alive remembers choosing, the kind of tile that has been absorbing the heat and noise of trains since before anyone on this bench was born. This line has been running since 1904. The platform across from it, the old City Hall stop, closed in 1945 and now exists only as a rumor riders pass through without seeing, a loop the express makes for no reason except that turning around takes track. Everything in this photograph is standing on top of something that used to be a destination and is now just a curve in the dark.

Seven people are sitting on a bench that has nothing to do with any of that history, and everything to do with it.

Start with the one who’s still here. T-shirt, checkerboard skull, gym bag held against his ankle the way you hold something you can’t afford to lose track of. His hands are clasped between his knees, not relaxed, not nervous — occupied. Everyone else on this bench has gone somewhere else. He hasn’t. He’s looking off toward the tunnel mouth with the specific stillness of a man doing arithmetic about how late he already is, and the bag at his feet is doing exactly what gym bags do at that hour, which is stand in for whatever he’s actually carrying.

To his left, a woman reads a paperback — Wilde, from the spine, which is its own small joke on a subway platform, a story about a man who doesn’t age sitting in the lap of a woman waiting on a train that’s already late. Her purse, gold, sits on her knees like a paperweight holding her place. Next to her, a woman in a cream jacket has wired herself into something private through a pair of earbuds, hands folded over a small plaid pouch she’s guarding like it’s worth more than its size suggests. Two men at the far end have given up on consciousness altogether — one with his chin dropped into the posture every commuter eventually perfects, the other with his head against a fist and a phone somewhere near his ear, gone in whatever direction that call is taking him.

This is what a downtown platform in lower Manhattan does to seven strangers at whatever hour this was: financial district behind them, City Hall and the courthouses above, the bridge somewhere overhead carrying its own century of foot traffic — and none of it matters to the bench. The bench doesn’t know what borough it’s in. It just holds people until the train comes and takes the holding away.

The photograph is called Bookends, for the two men slumped at either end, and that’s the obvious read. But look again at who’s in the middle — the reader with her book, held between two men who have shut the world off completely. She’s the only one inside a story while sitting inside someone else’s. That’s the trick of the title. It sounds like geometry. It’s actually about who, on a bench like this, is still willing to be somewhere other than gone.

The train would come. It always does, eventually, on a line that’s been doing this since 1904, two minutes or eight minutes late, and it would take all seven of them in whatever direction they were waiting for, and none of them would know they’d spent four minutes on a bench old enough to have held this exact scene ten thousand times before — six people who’d left, and one who, for reasons of his own, hadn’t gone anywhere yet.

Categories
Travel

The Marquee Beside the Mission

The hotel was three blocks from the Alamo, six in the morning, nobody else on the sidewalk, the city still deciding whether to wake up. The marquee stopped me first. MAJESTIC, gold on red, neon ropes looped along the underside like a county fair nobody took down — forty, fifty years running. And on the reader board, where you’d expect coming attractions: STEVE MARTIN & MARTIN SHORT IN A VERY STUPID CONVERSATION. JULY 10. 8PM.

Someone had sat down and typed that into the letters on purpose, knowing it would just hang there above the sidewalk all month, making people smile before their coffee.

A woman was bent over a rolling suitcase in front of the dark theater doors, untangling a scarf, a strap, something — the absorbed patience of someone catching an early flight. She didn’t look up at the sign. She’d probably walked past it a hundred times. That’s the thing about the landmarks you live near: they go invisible. It takes someone who flew in this morning to actually read the marquee.

I kept walking and found the Alamo doing the thing the Alamo does — holding still under the weight of everything people have decided it means. Texas independence, the thirteen days, Travis’s line in the sand that may or may not have happened the way the movies say. Solemn, in spite of the gift shop. I stood in the plaza and felt the appropriate things. Then I thought about the marquee again, three blocks back, still making its joke to an empty street.

The Alamo has to mean something. The marquee just has to make you laugh on your way to the thing that’s supposed to mean something. Reverence is a posture you adopt on command, the way you lower your voice in a church whether or not you believe in anything. The joke asked nothing of me.

I still think about the woman with the suitcase more than the cannon emplacements. Untangling a scarf under a hundred-year-old sign advertising a stupid conversation, three blocks from where men died arguing about a flag. Both buildings still standing. Both still pulling people in off the same street. One asks you to be quiet. The other asks you to keep walking and see what’s funny about being alive in a particular city on a particular morning, before the heat sets in, before anyone else is up to see it with you.

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.