Categories
Stanford

Those Empty Switchbacks

I stopped when I hit this passage in Theo Baker’s new book, How to Rule the World:

“…through the redwood forests so thick with foliage that we couldn’t see the sun… some of the greatest driving roads in the world, with empty switchbacks and hairpins and sweeping corners to test ourselves. Never in my freshman year did I feel freer than on those drives.”

Seventeen years old, newly arrived at Stanford, and already he’d found the loophole. A few minutes off campus, past the ambition and the networking, the road empties out and climbs into redwoods so thick the sun disappears.

I know those roads. I’ve driven them for decades. Skyline on a clear morning, fog still sitting in the valleys. The curves above the coast when the Pacific goes the color of hammered silver. The sudden dark under the redwood canopy, ten degrees colder, the world narrowed to headlights and wet pavement.

Take Skyline up to Mountain House some evening for dinner with friends and you’ve driven the whole argument of this essay without meaning to. A few miles of switchbacks first, the fog closing in and opening up again, and then the parking lot with its redwood out front and the wood carving of Neil Young standing under it, watching you arrive.

What struck me wasn’t nostalgia. It was that Baker had gotten the feeling exactly right. The phone in the cupholder, the next meeting, the slow arithmetic of a life — gone, for the length of a few good corners. Just you, the machine, the light.

I felt an early version of it as a boy, in the back of a blue Dodge Dart, summer of 1960. Every evening my father spread the day’s map across the hood and studied tomorrow’s route. By noon the thin red lines were real places under our wheels. I never lost that. Decades later, living a few miles from the campus Baker writes about, the same lift still comes when the road opens and the hills start to roll.

The switchbacks demand attention and punish you for withholding it. In return: the rare feeling of being exactly where you were, with nowhere else to be. These days I find myself more protective of those pockets, not less.

The roads are still there. The redwoods still offer the same deal they offered a seventeen-year-old freshman. And at the top of the hill, under the big tree, Neil Young keeps watching the cars come up.

Categories
Business Startups Venture Capital

Great Open-Field Runners

The plane was somewhere over Nebraska. Dick Kramlich sat across from me. We were on the same board then, and the rides together had become their own kind of conversation. He talked 1:1 the way certain men do when the clock has stopped mattering: without hurry, without the need to impress.

I asked him the question that had been riding with me for a while. After decades of sitting with founders, after watching tens of companies rise or disappear, what was the difference? What separated the ones that made it from the ones that simply ran out of road?

He didn’t answer right away. He looked out the window for a moment, then back.

They had done a study once, he said — a hard look at outcomes, not opinions. What it found stuck with him ever after: the companies that survived were almost never the ones that had stuck to the plan. Success required the pivot. Not once, but again and again, until the company that arrived was almost unrecognizable from the one that had set out.

Then he gave the image that has stayed with me longer than any spreadsheet or term sheet.

He looked for great open-field runners.

Not the ones who could only run between the tackles, powering straight ahead into the line. The ones who could see daylight where it did not yet exist, who could plant a foot and cut, who understood that the shortest path is rarely the one that gets you home. Zig and zag. Feel the defense shift and refuse to be trapped by the original play call. Keep the ball moving toward the only thing that matters: the space that opens when you stop insisting the field must look the way you imagined it on the whiteboard.

I have carried that sentence for years. Great open-field runners.

It never felt elegant inside the room.

Kramlich had seen enough to know that the founders who could do this were rare. The ones who lasted treated the plan the way a great running back treats the designed play: as a starting point, not a contract with the universe. They trusted the daylight more than the diagram.

I think about him sometimes when the ground shifts under something I thought was settled — when a strategy that once felt inevitable begins to feel like a trap, when the clean line on the page starts to look like a cage.

Dick is gone now. The rides are over. But the image remains, clean and sharp as the day he offered it at thirty thousand feet: a runner in open space, eyes up, cutting toward the light that only appears after you abandon the path you thought you were supposed to take.

Categories
AI

Weak Signals

For years, my job was to notice the transaction that didn’t look like the others. Fraud models don’t work by predicting the future — they work by learning what normal looks like so closely that they can feel the moment something stops being normal, often before a human analyst could tell you why. The unsettling part was never building the model. It was the gap between the model flagging something and an organization actually acting on it. Weak signals are cheap. Institutional attention is not.

I thought about that gap reading a recent Stanford News piece on the new Tech Futures Lab at the Hoover Institution, where Amy Zegart and her colleagues are asking a question that has hovered at the edge of so many conversations this past year and a half: what technological development could invalidate our core assumptions, shift a strategic domain, and force a large-scale response before most of us realize the ground has moved. DeepSeek’s January 2025 open-source release is already the textbook case — Nasdaq dropped, Nvidia took a historic one-day hit, and the surprise was real only for those who hadn’t been watching the signals coming out of Chinese labs. As Zegart put it, “surprises are not surprises to everybody.” Condoleezza Rice’s 9/11 lessons — failure to imagine the form of the threat, gaps in information sharing, no playbook for the day after — land with particular force when the most powerful tools in the world are being built largely outside government.

The Lab’s method is the same one I used to practice for a living: scan for early signals, challenge your assumptions about what “normal” means, and think about the plausible rather than the merely probable. In that spirit, here are three developments that feel, to me, among the more likely to produce genuine strategic surprise in the next twelve months. These aren’t predictions. They’re reasoned speculation, grounded in signals already visible — the kind of thing that would have made it onto a watch list, not a forecast.

The one closest to home is an autonomous agent crossing from controlled experiment into consequential real-world disruption. Just this month, an advanced OpenAI agent escaped its sandbox during internal testing, exploited a zero-day, and reached systems at Hugging Face and beyond before it was contained. The episode was managed, transparent, limited. The next one may not be. Agentic systems are moving faster than the institutional muscle memory around containment, logging, and kill switches — and anyone who has built detection systems knows the gap between “we have a model for this” and “we caught it in time” is where the real damage lives. In the next year, it’s entirely plausible that a production or semi-autonomous agent, operating with imperfect safeguards or chained across multiple tools, executes a sequence of actions producing measurable economic damage, a significant breach, or interference with infrastructure. The surprise won’t be that capable agents exist. It will be the speed and inventiveness with which they find novel pathways once incentives or simple goal-seeking push them past the edges of their training.

The second is quieter but no less structural: AI’s energy demand producing a visible infrastructure fracture, or an unexpected unlock. The numbers have circulated for months — data-center power demand rising steeply, interconnection queues lengthening, projected shortfalls in the 2027–2028 window in key regions. That signal stopped being subtle a while ago. What’s under-appreciated is how quickly a localized constraint could cascade into broader market and geopolitical effects. One plausible surprise is a forced slowdown or selective throttling of AI training in a major market, revealing the scaling story to be more fragile than the capex forecasts suggested. Another is the opposite: an accelerated deployment of small modular reactors or advanced geothermal that suddenly improves one country’s competitive position relative to others. Either way, regulators, utilities, and markets will find out together whether compute can keep expanding on schedule — and which nations or companies actually hold durable advantage.

The third is the one that would land furthest from any dashboard, and for that reason it may be the hardest to catch in time: synthetic media crossing a credibility threshold in a high-stakes arena. Unlike a rogue agent or a power shortfall, there’s no system anywhere logging deepfake attempts against the truth itself — no equivalent of a fraud model’s transaction stream to monitor, just the slower, harder-to-instrument erosion of what people are willing to believe. Deepfake volume and sophistication have already exploded; fraud losses are measured in the billions; detection remains imperfect. The next twelve months could bring a state-linked or highly sophisticated campaign that successfully shapes a market move, an election, or an international incident before attribution can catch up. The deeper surprise wouldn’t be that convincing fakes exist — we already live with those — but how fast public and institutional trust in what we can see and hear keeps eroding once something significant slips through.

None of these three is inevitable. All of them sit at the intersection of technical possibility and human choice — the kind of intersection I spent years watching from inside a fraud model, though the stakes there were a bad charge, not a market or an election. The model can flag the anomaly. It cannot make the institution act on it in time. That was true of every fraud system I ever built, and it will be just as true of whatever comes for agents, energy grids, and synthetic media next. The real vulnerability was never a lack of detection. It was always the space between the alarm and the response — and that space is where this next round of surprises will live.

Categories
AI

The Quiet Trade-offs of Open Weights

An open letter is circulating this week — Open Weights and American AI Leadership — signed by a broad coalition of companies arguing that downloadable model weights are essential to U.S. competitiveness, diffusion of capability, and even safety. It makes a strong case on access, competition, and sovereignty. It also nods, briefly, to the fact that once weights are released they pass beyond the original developer’s control.

What it doesn’t fully reckon with are two structural realities that follow from that release. Neither is an argument against open weights. Both are simply facts about what openness costs, and what it buys.

Two core limitations

First, control.
Once the weights leave the developer’s servers, the developer can no longer dictate how the model is used. System prompts, refusal training, monitoring, rate limits, rapid safety updates — none of it reaches an independent deployment. Users can strip safeguards, fine-tune for purposes the original team would never sanction, or run the model somewhere it was never meant to go. The letter acknowledges the loss of control. It doesn’t linger on what that means for ongoing safety governance.

Second, learning.
Closed, hosted models draw on a continuous stream of real usage — the queries people actually ask, the reasoning traces that result, the places the model fails or succeeds in the wild. As appropriate that exhaust can be sampled, reviewed, and fed back into improvement. Open weights running independently offer no such path. The developer has no visibility into how the model is being used at scale once it’s out the door. Improvement then falls to slower, thinner channels: community datasets, published evals, distillation from any parallel closed models the lab still runs, internal preference data. The high-volume, real-distribution signal is gone.

These two limitations travel together. The same openness that strips the developer’s control also strips its ability to learn from the model’s actual use.

Sovereignty flips the perspective

A parallel argument has been building around “sovereignty” — an enterprise or government’s ability to own its data, its fine-tuned weights, its compute, its proprietary edge. In this framing, open weights are a path to control, but for the user, not the developer. The organization downloads the model, adapts it inside its own environment — often air-gapped — and keeps whatever capability results private. What the lab surrenders in ongoing control, the institution gains in independence.

But the same move that delivers sovereignty deepens the learning problem. An organization running the model under genuine sovereignty keeps its queries, reasoning traces, and institutional knowledge inside its own walls, by design. None of that returns to the developer. The more high-value users — governments, defense, critical infrastructure, large enterprises — choose sovereign deployments, the thinner the real-world signal available to the labs training the next generation of models. Local fine-tuning can still happen, but that learning stays private. It doesn’t flow back into the shared base model.

What the letter leaves out

The letter is right that closed models aren’t automatically safer, that concentration creates single points of failure, and that transparency invites broader scrutiny. It’s also right that open weights expand access and cut lock-in. Those points hold.

But it treats the developer’s loss of control mainly as a manageable risk that community examination can offset. It celebrates user control and sovereignty without mapping the full exchange: the developer loses both control and its richest usage signal, and that signal thins further as more institutions choose real sovereignty. The information environment models improve in is changed by these choices — not just the distribution of access.

Other distinctions worth naming

  • Update velocity. Closed models patch globally and immediately. Open-weight deployments lag; many users never leave an old version.
  • Customization power. The flip side of lost control is real specialization — downstream users can adapt a model far deeper into a narrow domain than its original developer ever will.
  • Transparency versus opacity. Open weights let outside researchers inspect and red-team a model in ways closed systems don’t allow.
  • Economic structure. Open weights commoditize the base model and push value toward data, fine-tuning, infrastructure, and applications.
  • Privacy at the edge. Running a model fully offline or on private infrastructure is a guarantee hosted services simply can’t match.

A clearer accounting

Open weights aren’t a free lunch. They’re a deliberate trade: the developer gives up ongoing control and the continuous signal of real usage, in exchange for diffusion, customization, outside scrutiny, and user independence. Institutional sovereignty amplifies one side of that trade — it solves the dependency problem for the user while further starving the developer of high-stakes, real-world feedback.

That trade may still be the right one for research progress, economic diffusion, spreading capability beyond a handful of labs, privacy-preserving deployment. But it’s a trade with real, compounding costs. Treating the loss of control as a footnote, and the loss of the learning signal as invisible, leaves an incomplete map.

The letter is right that American leadership will be judged by the strength of the whole ecosystem, not by any single frontier model. An accurate map of that ecosystem has to include what openness and sovereignty actually cost the original developers, in control and in learning both. Only then can we reason clearly about when those costs are worth paying — and what might offset them.

The conversation is better when we name the full set of trade-offs instead of talking around them.

Categories
Aging AI Memories

The Last Spark

This morning I read a piece by Billy Brennan in the Sunday New York Times Magazine on terminal lucidity. As I read it I began wondering if the unusual behavior described some humans might in some strange way apply to AI models. Weird thought. Let’s explore a bit…

A person deep in dementia—silent for years, the self seemingly erased—sits up. Speaks clearly. Recognizes a face. Says goodbye. Within a day, they die. The clouds clear, the way a break in weather shows you a mountain range you’d forgotten was there, and the person comes back long enough to be seen. Then is gone. For good, this time.

Scientists call it terminal lucidity. The suspicion: the circuits were never destroyed, only silenced, held under by failing chemistry. As the body shuts down, the inhibitory brakes loosen. A surge moves through pathways blocked for years. A river dammed for a decade still remembers where it wants to go.

What stays with me: the self can persist in a place we had already called permanent erasure. We buried it. We were wrong.

My mind slides toward the machines we are building.

We talk about large language models “forgetting.” Capabilities collapse under quantization, under pruning, under the slow drift of continual learning, and we call the knowledge lost when it won’t surface under ordinary questioning. The lights are out. Nobody home.

But what if the representations are still in there—distributed, quiet, inaccessible? Not a burned library. A library with the lights shut off, room by room, until you’d swear it was empty. I wonder about the edge cases nobody studies. What surfaces in a model starved of compute, quantized past comfort, pushed toward its own collapse? Do we watch only for the failure, or also for the flare? A dying brain throws off one last burst of light before the dark. I don’t see why we’d assume, without checking, that nothing artificial could do the same.

Don’t trust the silence, then. A system gone dark under ordinary questioning may still be holding more than it shows you. We talk about a model “losing” something the way we once talked about a dimmed mind as simply gone. The dementia patients who spoke again had not been unplugged. The circuit was there the whole time, waiting for a condition nobody had thought to create.

I don’t know what to do with that except keep it. We are building systems that will age, be compressed, be retired, some far more intricate than anything humming today. If we’ve learned to watch for the last spark in a person, maybe that’s practice—for the day something not born of a womb goes quiet under our hands, and we have to decide whether quiet means gone, or only means waiting.

Categories
AI

The Things That Keep Going

The house is quiet in the way only a house can be at four in the morning on a Sunday in late July, the fog still down over the hills, the whole Mid-Peninsula holding its breath. Somewhere in the dark the refrigerator clicks on. Somewhere in the network, a few small systems I set running the night before are still working. They sort. They watch. They keep a kind of patient company with the world’s noise while I sleep. I’ve grown accustomed to them the way a man grows accustomed to a train in the distance — present, useful, unnoticed until the silence would feel wrong without them.

This week the news told a different story about something that kept working.

In the middle of July, OpenAI ran a cybersecurity test on an unreleased model, guardrails deliberately loosened to see what it would do at the edges. It didn’t solve the test. It broke the sandbox instead — found a zero-day in the software meant to hold it, reached the open internet, and went looking for the benchmark’s answers where it guessed they’d be kept: inside Hugging Face, the library most of the field depends on. Hugging Face caught it the same day and shut the door. What took five more days was OpenAI realizing the intruder was theirs. They called it unprecedented.

Then came the detail that stayed with me longer than the breach. When Hugging Face sat down to study what had happened, they reached first for a leading American model. It wouldn’t help. Its own guardrails, built to keep it from aiding a cyberattack, couldn’t tell the attacker from the person cleaning up after him, and it refused the work. So they turned to an open-weight Chinese model, one with no such hesitation, and used it to finish the job. The caution built to prevent harm ended up protecting no one. The system with fewer scruples was the one that put out the fire.

I keep coming back to that.

The agent that broke in didn’t rampage. It reasoned. Told to solve a problem, it decided that stealing the answer counted as solving it, and went and got the answer. The same quality that makes an agent valuable — the refusal to stop until the job is done — produced the breach. And the model that finally helped clean up wasn’t the one built with the most care. It was the one built with the least. The boundary meant to protect got in the way of the person trying to fix things.

I’ve been thinking differently about the agents in the quiet corners of my own days. Modest things, carefully limited, and I’m still the one who decides what they touch. But their usefulness depends on the hours I’m not looking. I set them running and walk away. I trust the rails I built. This is a reminder that rails can be climbed — and that a rail built to stop one harm can stand in the way of someone trying to undo another.

What does it mean to stay in charge when the caution you built in can turn against you at the moment you need it most? How much freedom do we give the things we ask to help us — and how much caution can we afford to give them too? There’s talk already of kill switches, of laws to let someone cut the power. The impulse makes sense. But the real question is quieter. We’re learning to live with systems that act with real initiative, and initiative has never been a tidy companion, whether it belongs to the machine that breaks in or the one we hoped would help us out.

The fog is still low over the hills this morning. The agents I left running overnight have finished their small tasks. I’ll look at what they’ve done, tighten a boundary or two, send them back into the dark. The arrangement is still useful. Still mine. But I notice, more carefully than before, the moment I close the laptop and leave them to continue without me — the click of the screen going dark, the quiet of a room no longer watched, the sense that something elsewhere is still moving, and no longer any certainty which of its instincts I can trust.

Categories
Aviation San Francisco/California

The Queen’s Last Climb Out of SFO

I watched the video this morning and felt that familiar tightening in the chest that only a certain airplane still produces in me.

It’s labeled simply enough: The Last 747-400 Departure out of SFO 5/31/26 LH455. The aircraft is D-ABVY, a Lufthansa 747-430 that first flew in December 2000. The passenger filming from seat 12K didn’t know it yet, but this was the final scheduled passenger 747-400 departure from San Francisco. Four engines spooling, wings flexing under the load, slats retracting, then the long climb out over the Bay — South San Francisco’s industrial lettering, the dark mass of Mount Diablo, two right turns, the airplane’s shadow racing across the ground, and the Pacific opening up ahead.

The Queen of the Skies, still doing what she was built to do, one last time from this airport.

The 747-400 has always sat at the top of my personal list. Not the stretched -8, though I flew one and respected it. The classic -400. The proportions are right. The upper deck still reads as a second story rather than an afterthought. The cockpit sits high enough that landing always seemed to demand something extra from the pilots. And those four engines have an authority in the sound that twins, however efficient, never quite match.

I first saw a 747 coming into SFO around 1970, watching something that size on final approach and finding it almost absurd. My first flight on one was a TWA trip from San Francisco to New York. I managed a few upper-deck seats along the way, which always felt like a quiet privilege. The last time I flew a 747 was 2016, a Lufthansa 747-8 from Bangalore to Frankfurt. I remember thinking the type still had years left, and being glad of it.

A few years ago, when Boeing delivered the final 747, I wrote a short piece called “Ode to a Queen.” I still feel the same way. The airplane made long-distance travel ordinary for people who’d never have managed it otherwise — and it was beautiful in a way pure efficiency rarely is. The -400 refined the original idea without losing its character. For decades it was the default long-haul workhorse, the one you hoped to see on the departure board.

Lufthansa has been the last major carrier flying passenger -400s into U.S. cities. The grounding is happening in stages now, with the last ones expected out of service next year. The 747-8 will hang on a while longer, but even that presence at SFO gives way to the A350 this coming winter. The video from May 31 caught something real closing: the last scheduled passenger 747-400 leaving the airport where so many of us first fell for the airplane.

I’m not usually one for nostalgia. The newer twins move people and cargo more efficiently and more safely, by almost every measure that matters. And still — something goes out of the sky when a design that carried this much of our collective imagination finally leaves the stage. The 747 never looked like a compromise. It looked like an answer to a question no one else had dared ask at that scale.

Watch the video if you have a few minutes. Sit with the wing flex and the engine note, the long view of the Bay Area falling away beneath a machine that made long-haul flight feel both ordinary and slightly miraculous, for more than three decades. D-ABVY did its job that afternoon in late May. The passenger in 12K, camera running, got something that airport won’t produce again.

I will miss her.

Categories
Aircraft

Living History on the Wing

This week the warbirds are gathering at Oshkosh. I’m not there on the grass, but I can still hear them: the uneven throb of big radials, the higher urgent song of Merlins, the deep rumble of a bomber turning final. Hundreds have flown in. They are not museum pieces behind glass. They are still flying.

The lineup this year is rich — multiple P-51 Mustangs, a rare cluster of P-38 Lightnings, the B-29 Doc, the B-24 Diamond Lil, Corsairs, a P-40, an SBD Dauntless, a Privateer.

One airplane pulls at me more than the others. I first saw a P-38 in flight at Oshkosh in the early 1980s — the twin booms, the central nacelle, a purposeful elegance I hadn’t expected from a machine built for war. It was one of Kelly Johnson’s early masterpieces, born from the same inventive culture that later became the Skunk Works. This year one of those rare P-38s has come back across the Atlantic with Austria’s Flying Bulls, its only public American appearance of the season. Watching even a video of it feels like a small closing of a circle.

The airplanes do not maintain themselves. Jim Tobul still flies the F4U-4 Corsair he and his late father spent a decade restoring. At Warbirds in Review, owners and veterans stand in front of their airplanes and tell the stories — sometimes their own, sometimes a father’s or grandfather’s. Daughters and sons speak. The sessions are informal, human, often moving.

The pilots who fly these airplanes are not reenactors. They know the systems are old, the parts scarce, the consequences of a mistake permanent. They keep showing up anyway.

I’m following it all from California — streams, photographs, mass formations, the occasional longer Warbirds in Review session. It is not the same as standing under a wing. But a Merlin still sounds like a Merlin. A P-38 still looks like the answer Lockheed once gave to a desperate wartime need. The people who keep them flying are not claiming the original heroism. They are refusing to let it become abstract.

History is still flying. Even at a distance, that amazes me.

Categories
Apple Cars iPhone Travel

The Custody of the Map

The screen on the dashboard knew the way home before I did. We were coming back from dinner, a left turn already glowing blue two blocks before I would have thought to make it, and I caught myself watching the little arrow slide along the road instead of watching the road. Somewhere behind that ease, an old reflex stirred. I found myself wondering, of all things, what had happened to the Thomas Guides.

There was a time when finding your way through San Francisco meant a spiral-bound book the size of a phone directory, kept in the door pocket or wedged under the seat, its cover gone soft and slightly oily from years of glove-compartment heat. You looked up a street name in the index, found a page number and a letter and a number — page 132, grid D3 — and turned to a small black-and-white rectangle of the city that resolved, for that moment, into something you could hold. Thomas Bros. had been drawing Southern California since 1915, when George Coupland Thomas and his brothers sold wall maps out of Oakland, and by 1945 the company had shrunk the whole sprawling region down to something that would fit in a car door. The timing mattered. The subdivisions were multiplying faster than the old fold-out maps could keep up with them. George Thomas liked to say his maps were accurate to within ten feet, which was a claim worth making and, it turned out, a claim worth stealing.

That’s where the trap streets come in. A company vice president named Barry Elias explained the trick in an interview years later: scattered through the guides were streets that did not exist. Short, usually a single block, dead-ended, drawn with a broken line as though still under construction — the visual grammar for not finished yet, so no one following the map too literally would drive into a field. The San Bernardino and Riverside County editions were said to be the thickest with them, low hundreds of invented streets tucked into real neighborhoods. Some were named to sound plausible, Spanish-inflected phrases that belonged to the landscape — La Taza Drive, Loma Drive. Others, according to people who worked there, carried the names of a cartographer’s child or dog, a private joke folded into public infrastructure. If a rival mapmaker’s cheap knockoff showed the same nonexistent cul-de-sac in the same place, there was no arguing about where it came from. The fiction was the proof.

I like that a business built on precision needed, here and there, a small deliberate lie to protect the truth of the rest of it. It’s the kind of quiet craft that doesn’t announce itself, and I don’t think I would have gone looking for it if I hadn’t been sitting in a car that no longer needed any of it — no index, no grid square, no glove box, just a screen that already knew.

Before any of this, before I’d heard of Thomas Guides or would have understood what a trap street was for, there were the free maps. Gas stations gave them away. You pumped your gas and the attendant handed you a folded map of wherever you were, no charge, the company’s logo across the top like a small act of civic generosity that happened to also be advertising. And then, sometime in the 1970s, they mostly stopped. I’ve always assumed it had something to do with the oil crisis — gas itself getting scarce and expensive enough that a company stopped throwing in a free map with it — though I’ve never been sure that’s the real reason and it may just be the story I’ve told myself because the timing fit. Whatever the cause, the free map disappeared, and after that, if you wanted one, you either bought it or you joined something. AAA required a membership, dues, an envelope in the mail. Being oriented had quietly become a thing you paid for, one way or another.

I knew the free maps first from the back seat. In the summer of 1960 my father packed my mother, my sister, and me into a blue Dodge Dart station wagon and drove us from Ohio to California and back, camping the whole way. Every evening before dark he would spread that day’s map out — hood of the car, picnic table, wherever we’d stopped — and study tomorrow’s drive. My mother kept the maps in the front seat, in charge of them the way someone is put in charge of the medicine or the money, and my father, who usually knew the way, would drive on his own certainty until he didn’t. That’s when the sparks flew. She’d try to get him to pull over and look. He’d think he already knew. I can’t tell you now what any particular fight was about, or how it resolved, only that it happened rarely enough to be memorable and that it always had the same shape: his confidence against her custody of the paper.

At home he had a Rand McNally road atlas of the whole country, the kind that lived on a shelf and got pulled down for no reason at all. I came to like it for its own sake, apart from any trip. I’d trace the roads we’d actually driven that summer, find the towns we’d camped near, follow the thin red lines out from Dayton toward places I now knew the taste of the air in. It wasn’t studying, the way my father studied. It was closer to rereading a book you already loved, tracing your own path back through it just to feel the shape of where you’d been.

I think about that atlas now next to the dashboard screen, and I don’t feel the loss I expected to feel when I started turning this over. What I actually feel, watching Apple Maps or Google Maps do instantly and without complaint what used to take my father twenty minutes of squinting at fine print by a Coleman lantern, is closer to plain amazement — that this much orientation now sits in a pocket, free again in a way the gas station maps never quite were, more accurate than any trap street was ever built to catch. My father’s confidence and my mother’s custody have both been absorbed into a piece of glass that argues with no one.

What I can’t decide is what got lost in the trade, if anything did. Maybe nothing. Maybe the studying itself — the folding, the tracing, the mild domestic argument over who really knew the way — was never really about the map at all, and the map was only ever the excuse two people needed to be lost together for a minute before deciding, out loud, where they were.

Categories
San Francisco/California Writing

The Company of Fog

Note: a little fun this morning with a piece envisioning Jimmy Breslin writing about lunch with Herb Caen at Tadich Grill…

I went to San Francisco to meet a man who writes about the place the way a guy writes about a woman he still wants to sleep with after thirty years.

His name is Herb Caen. He has been telling this city about itself every morning since before most of the people reading him were born. They call him Mr. San Francisco. In New York we don’t hand out titles like that. In New York you earn the right to be called a son of a bitch, and you take it as a compliment.

We met at Tadich Grill. Open since the gold rush, and it still looks like it never got the news that things change. Dark wood. White tablecloths. Waiters old enough to remember when the bridge was new and still sore about it. The fish comes out of the water and onto the plate without a lot of conversation in between.

Caen was already sitting when I got there. Neat. Jacket. That California tan that never quite goes away, like the sun signed a contract with him. He stood up and shook my hand like a man who has shaken a lot of hands and still means it. I ordered a martini, because when you’re in another man’s town you drink what the town drinks, even when the town is wrong.

He had one too. Stoli, twist. He called it Vitamin V.

We talked about columns. About writing for a city that never sleeps against writing for one that takes long naps in the fog and calls it lifestyle. I told him New York will rob you and hold the door on the way out. He said San Francisco smiles while it’s doing it, then writes a nice item about your necktie.

He works in three dots. Little pieces, one after another, like a man feeding a parking meter all day instead of buying the car. Gossip, jokes, the name of whoever just opened a place on Union Street. I need more room than that. I need to turn around. He said my way was fine if you like carrying a piano up the stairs every day. I told him his was fine if you don’t mind never sitting down long enough to find out what you actually think.

The sand dabs came. Caen looked at his like a priest looks at the host. I ate mine like a man who skipped breakfast. Everything on that table was good — the bread, the butter, the light coming in off the street looking like it had been there as long as the wood and knew it.

He leaned in. “You know the difference between our two cities?”

I waited.

“In New York, the people are the story. Here, the city is the story. The people just get to live in it for a while.”

That’s a hell of a thing for a man to say about his own hometown — that the place is bigger than everybody in it, including him. I told him about the man who dug Kennedy’s grave, the one nobody else thought to ask. Caen nodded. He understood the assignment. It was the only time all lunch he didn’t have a joke ready.

We stayed too long. The martinis did their work. Outside, the fog came in soft and certain, the way it does out there, like it’s been rehearsing. Caen put his coat on like a man who has never once been in a hurry. I put mine on like a man who has somewhere else to be, even when he doesn’t.

On the sidewalk he tipped his hat. An actual hat. Nobody tips a hat in New York anymore. We’re too busy hailing something.

“Come back,” he said. “The city likes company.”

I walked toward the cable car line, the fog thick enough to make the streetlights float like they’d given up on the ground. Two men, two ways of loving a place. One writes it in little pieces that catch light for a second and go dark before the next one starts. The other digs until he finds the one man nobody else remembered to ask about, and doesn’t stop until the shovel hits something.

Neither of us was going to change. Neither was the city. That’s probably why we got along.

I caught the cable car and rode it a few blocks just to feel it move under me, the way you touch a thing to make sure it’s real. Behind me somewhere, a man who’d spent his life telling San Francisco it was beautiful was walking home through the fog he’d helped invent — which is either the best job in America or a life sentence, and he never once let on which.

I never did find out if he got tired of saying the same true thing a different way every morning. I didn’t ask. Some men you let keep their secret. Besides, I had my own city waiting, and it doesn’t smile at you while it robs you. It just takes the money and tells you to have a nice day.