Category: Business

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AI Business Software

Ice Rinks

Nobody in the Valley thinks about ice rinks.

That line sat with me longer than the rest of a conversation between Patrick Collison and Amjad Masad. Collison runs Stripe. Masad runs Replit. Someone asked, more or less, where Collison would look if he were starting over. He did not name an AI lab. He named the software nobody fashionable wants to touch.

I have heard versions of this advice for years. Vertical SaaS. Boring industries. Schlep blindness. What felt new was the timing. The distance from an idea to the first dollar has roughly halved. On Stripe’s numbers, the time to reach a million, ten million, even a hundred million in recurring revenue is about half what it was in the last SaaS boom. Twenty percent of new startups now charge a customer inside thirty days, up from eight percent in 2020. New business creation on the platform nearly doubled in a year, a bigger jump than the pandemic spike.

When building the thing was the scarce skill, the programmer won. When a working product can be stood up quickly, the scarce skill is knowing why the old thing is still tolerated. The domain expert gets the edge. A teacher who understood schools built MagicSchool on Replit and rode it toward a company later described as worth half a billion dollars. The insight was the teacher’s. The tools closed the gap.

Collison’s map has three drawers.

Continue reading “Ice Rinks”
Categories
AI Business Technology

The Diffusion of Ordinary Work

A recent O’Reilly Radar piece has stayed with me longer than most: Jeff Ding’s diffusion theory of great-power competition applies just as well to AI adoption, and it suggests that companies chasing the frontier might be optimizing for the wrong thing.

Ding, a political scientist at George Washington University, pushes back on the standard story of technological power — that the country or company which first invents or dominates a glamorous new sector locks in lasting advantage. The historical record says otherwise. General-purpose technologies like steam, electricity, and computing produced durable national advantage not through invention but through diffusion: the slow, unglamorous work of embedding a technology into ordinary productive work across an entire economy. The infrastructure that mattered was never the breakthrough lab. It was the education and training systems that produced large numbers of competent, ordinary engineers who could put the technology to work. Ordinary engineers, in Ding’s framing, matter more than heroic inventors.

The same logic holds inside a company. Frontier models turn over every few months. Organizational know-how compounds.

Palantir makes the abstraction concrete. The company doesn’t train frontier models — it builds the layer underneath them: a live, machine-readable model of how a specific organization actually works, a data integration fabric, and a platform that connects whatever model a customer chooses to real operational decisions. It is deliberately model-agnostic. The value proposition is governance, context, and the accumulation of reusable logic rather than access to the newest weights. Practitioners embed with the customer, learn the domain, and configure the system against the customer’s own data and processes — diffusion as a job description.

Leadership has been unusually blunt about what this implies: frontier labs, they argue, are optimizing for benchmarks while under-delivering on what enterprises actually need. The clearest evidence for the argument is also the most citable one — there have been production cases where an unmodified open-weight model, running inside Palantir’s platform with customer-specific context, outperformed frontier models on the actual task. If true, and it appears to be, the implication is uncomfortable for anyone selling model quality as the whole story: the ground underneath the model — the ontology, the data, the accumulated rules — often determines outcomes more than the model itself.

Electrification is the closest historical analogue. Factories didn’t get more productive the day they installed electric motors. The gains showed up years later, once entire production systems had been redesigned around decentralized power. The lag was organizational, not technical. AI diffusion looks likely to follow the same shape — the bottleneck was never going to be model capability, it was going to be the patient, unglamorous work of redesigning how people actually work.

I don’t know who’s training the ordinary engineers right now — the ones who will spend the next decade doing the diffusion work rather than the invention work. I don’t think anyone’s tracking their names.

Categories
Business Startups

They Love Having Meetings

Paul Graham posted on X this morning:

The danger of selling to big companies, if you’re a startup, is that they don’t say no outright. They have months of meetings with you first. Since you hate meetings, that seems to you a sign of commitment. But it’s not. They love having meetings! It’s almost all they do.

The founder walks out of the third or fourth session feeling hopeful. The room was full. People took notes. Someone said “interesting” more than once. A follow-up got scheduled. In the founder’s world, that much calendar time is expensive. It feels like proof that something is moving.

It isn’t. And this is easier to see from the other side of the table than from the founder’s.

Inside the big company, the meeting isn’t a delay before the work — it is the work. It’s how progress gets demonstrated, how risk gets spread thin enough that no one owns the outcome alone. A series of meetings can continue for months without anyone deciding yes or no, and nobody in the room experiences this as failure. The process is functioning as designed. No one has to kill the idea, because no one was ever positioned to fully own it. The calendar keeps filling because filling the calendar was most of the job.

The founder, who hates meetings for good reason, reads the big company’s willingness to keep talking as commitment. It’s a natural misreading, because outside a large organization, sustained attention almost always signals intent. Inside one, it can just as easily signal the opposite: an idea comfortable enough to keep discussing precisely because no one has been asked to stake anything on it.

The cost isn’t only the founder’s calendar. Energy that could have gone into shipping for actual buyers goes instead into decks, talking points, and the ongoing work of interpreting vague enthusiasm — real work, spent guessing at a decision someone else was never going to make. When the process ends — a polite “not at this time,” or more often just silence — the damage isn’t only the lost months. It’s the false signal that kept the founder from spending those months elsewhere.

The people on the other side of the table who move faster are the rare ones still able to say yes without assembling a committee to say it with them. Those conversations are shorter. They produce actual outcomes instead of follow-ups. They’re rare precisely because they require someone willing to own a decision alone — and most large organizations are built, deliberately or not, to make that as uncomfortable as possible.

Graham’s observation is simple and sharp because it names a quiet trap. The absence of a no is not the presence of a yes. Sometimes the most expensive thing a large company can offer a startup isn’t money, or even time. It’s the appearance of being taken seriously.

Categories
Business Games Living

A Good Life Is an Infinite Game Played Well

I keep coming back to the eight CEOs in William Thorndike’s The Outsiders.

On paper they were capital allocators of unusual discipline. Looked at another way, they were people who had figured out how to play a long game inside systems designed to reward short ones. They treated the company as something that should still be healthy decades later, not as a vehicle for the next announcement or the next victory lap. Per-share value compounded quietly over time mattered more than size, applause, or the quarterly scoreboard. They were willing to look wrong for years. And most of them had little interest in the theater of the charismatic chief executive. Their authority accumulated the slow way — through decisions that held up after the noise moved on.

It is the same stance James Carse described in an infinite game: a game whose purpose is not to win but to keep the play going. You are never finished, only ahead or behind, and the position worth wanting is often the one that feels slightly behind — the one that still demands invention.

Micky Malka, describing the founders he most wants to back, named a similar cluster of qualities: the energy of a scientist, the conviction of a missionary, the heart of a partner, the dreams of an athlete, the obsession of an owner. People built that way don’t need the room to agree they’re winning. The Outsiders ran on a quieter version of the same current — their edge was steadiness more than brilliance, the nerve to ignore the wrong scoreboard for as long as it took.

A good life asks for the same thing. Not trophies lined up on a shelf, but the willingness to keep playing with attention — to keep learning, to keep showing up without needing anyone in the room to say you’re ahead. That’s lifelong learning.

Henry Singleton ran Teledyne for almost three decades and rarely bothered to explain any decision to Wall Street. He wasn’t playing to be believed. He was playing to still be at the table thirty plus years on.

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 Business

The Reverse Information Paradox We’ve Always Had

Satya Nadella wrote recently about what he calls the Reverse Information Paradox: enterprises pay for AI intelligence twice. Once in money. Again in the proprietary knowledge they surrender through every prompt, correction, and evaluation. The better they use the model, the more of their own institutional understanding leaks into someone else’s system. The vendor ends up knowing more about the buyer’s business than the buyer knows about what the vendor retained.

Replace “model” with “employee” (or “consultant”) and the paradox is not new at all.

You pay for a person once with salary. You pay again with something harder to price: the context, relationships, and judgment they must absorb to become useful to you. The better they perform, the deeper the immersion, the more of your particular way of doing things moves into their head. Every correction and late-night conversation is another trace of institutional memory changing hands. When they leave, some of that memory leaves with them. Not always through theft. Usually just through the ordinary residue of good work.

The visible cost is salary; the invisible cost is the slow transfer of what makes you distinctive. High performers get more access precisely because they’re high performers, which means the leakage accelerates exactly when you can least afford it. The exhaust is just harder to see with people than with tokens — it moves through conversation and mental models instead of logs.

The analogy has a limit, and the limit matters. Employees bring knowledge in, not just absorb it. They have judgment and relationships a model doesn’t. Models are purely absorptive, and once something is inside them, it’s infinitely reproducible — a person can only be in one place, working for one employer, at a time. We’ve had a few hundred years to build tools for the human version of this problem: contracts, culture, non-competes. The model equivalent is still being invented in real time, which is exactly why Nadella felt the need to name it.

Apple’s recent legal action against former employees who joined OpenAI is this pattern in its sharpest form. Whatever the specifics, the shape is familiar: people who spent years inside one of the most sophisticated organizations in the world, carrying out knowledge that never appeared on any balance sheet and was hard to contain. No one fully anticipates what a mind absorbs simply by being in the room long enough.

That’s the real difference between the silicon case and the human one. You can try to take action to wall off knowledge flowing to a model. You cannot wall off what someone has learned to notice.

Categories
AI Business

The Wage of Knowing

In 1973 the Los Angeles Public Library installed a telephone line that worked while the building was dark. Dial H-O-O-T-O-W-L on a rotary phone, nine at night until one in the morning, and a librarian would answer. Somebody wanted to know the boiling point of mercury, or who wrote a poem they half remembered, or how many wives Henry VIII actually had, and a person on the other end of a cord found out. This went on for years. Nobody thought of it as data collection. It was just a service, a courtesy, a woman at a desk with a card catalog in her head.

I worked, in another life, in the payments industry, back when a merchant who wanted to charge your card had to call in and ask permission. There were rooms for this. Banks of phones, a bulletin of stolen numbers updated by hand, a floor limit past which a supervisor had to be found. The people answering the phones were, more often than you would guess, college students. Twenty years old, minimum wage, deciding in real time whether a stranger’s card was good. Nobody trained them for six months first. They learned the bulletin, they learned to listen for something wrong in a voice, and they said yes or no.

I have been driven, recently, by a car with nobody driving it. I noticed the wheel turning on its own and I braced for the wrongness of it. Thirty seconds later I was not bracing. I was looking out the window. The data says I was right to relax: across two hundred and twenty million miles, the cars involved in this experiment cause a small fraction of the serious crashes a human would have caused over the same roads. I did not need the data. I needed thirty seconds.

None of these people knew what they were doing. That is the thing about the librarian and the college student and, for that matter, about me learning to trust a wheel that moves by itself. The librarian was not building a search engine. The clerk was not training a fraud model. He was making rent. Their competence was not evidence, to them. It was just Tuesday. It became evidence later, to someone else, in a room they never saw — the accident logs, the chargeback data, the accumulated record of a million correct guesses that turned out to be exactly the material a system needed to learn the job and take it.

This is the part that is easy to get wrong. It is not that the human failed and the machine succeeded. It is that the human succeeding, over and over, in full view, was the demonstration that the job could be learned. You do not automate a task nobody can do. You automate the one being done well enough, often enough, for long enough that the pattern becomes visible. Doing the job right was never neutral. It was the case being built.

Which brings me to a woman I will call the lawyer, because there are thousands of her and none of them are exactly her. She has a laptop open at her kitchen table. She logs into a dashboard belonging to a company that pairs credentialed people with the AI labs that need them — a doctor here, a banker there, a corporate attorney with fifteen years of contract law behind her. She reads a model’s draft of a merger agreement and marks where it reasons like a first-year associate instead of a partner. She rewrites a clause. She explains, in the margin, why the model’s version would get laughed out of a negotiation. She is paid well for this. More, some weeks, than she billed certain clients.

She knows exactly what she is doing. That is the difference between her and the other three. The librarian did not know she was leaving a trail. The clerk did not know his good judgment would become someone else’s weights. I did not know, thirty seconds into that ride, that I was participating in anything at all. The lawyer knows. She is being paid, by the hour, at a rate that respects her expertise, to make her expertise legible enough that it no longer requires her. The company she works for has a name for this. They call it the reinforcement learning economy, which is a tidy way of saying: teach it everything, and then it will not need to call you back.

She does the work anyway. The rate is good. The work is interesting, in the way that teaching is interesting — you learn what you know by trying to say it clearly enough for someone else to use. Nobody is lying to her. The dashboard does not pretend to be anything other than what it is. She logs off at the end of the session the way anyone logs off after a long day of being excellent at something, tired in the specific way that comes from careful work, and she does not, from what I understand, spend the evening thinking about what she has just fed into the machine.

I keep coming back to the rotary dial. Somebody dialing H-O-O-T-O-W-L at midnight in 1973 could not have imagined the lawyer at her kitchen table. But the shape is the same, if you look at it long enough. A person answers a question well. The answering becomes a record. The record becomes a system. The system answers next time. Nobody in the room ever decided this was the plan. It just turned out, every time, to be the plan.

Categories
Business Creativity Innovation Inspiration

The Idea That Won’t Let Go

“There are three elements to every great idea: 1. It solves for ‘why.’ Long before you figure out what a product will do, you need to understand why people will want it. The ‘why’ drives the ‘what.’ 2. It solves a problem that a lot of people have in their daily lives. 3. It follows you around. Even after you research and learn about it and try it out and realize how hard it’ll be to get it right, you can’t stop thinking about it.”
— Tony Fadell, Build

The third element is the only one that isn’t optional. You can fake the why — retrofit it, hire a consultant to write it on a slide in a font called Montserrat. You can borrow the what; most products are just other products in a better jacket. You cannot fake the third thing, because the whole test of it is that it happens without your permission.

Call it a visitation. It shows up uninvited at the stoplight, in the shower, at 2:40 a.m. when the ceiling becomes a screen for it. You know the unit economics don’t work. You know the regulatory path is nine years long. You know — you know — that fourteen better-funded people already tried this and left with nothing but a Delaware C-corp and a grudge. None of it helps. The idea has already filed its paperwork. It lives here now.

You cannot bullet-point a visitation. There is no OKR for thinking about landing gear hydraulics while your wife is telling you about her sister’s wedding. Everyone who’s built something that mattered will tell you this, late enough at a dinner and honest enough on the wine: not conviction, not passion — that laminated word — but the visitor, still standing in the doorway of every other thought you’re supposed to be having.

It doesn’t check your calendar. It doesn’t care that you’ve moved on to point two, the sensible one, the one venture capitalists nod along to with their flat whites going warm. It was never about the market. The market is the alibi you build afterward, so you don’t have to say the true thing: I didn’t choose it. It moved in while I was asleep, and my life rearranged itself around the shape of it, the way a house rearranges itself around a family it didn’t ask for.

Maybe the third element isn’t a filter for the idea. Maybe it’s a filter for you — a way of finding out, by accident, at 2:40 in the morning, staring at a ceiling that has decided to keep the light on.

Categories
Business History

The Architecture of Unseen Influence

We build our monuments over the wrong graves. It’s a bad habit of ours, this craving for the lone genius—the larger-than-life figure who supposedly commands the tides of progress by sheer force of will. But look beneath the surface of how things actually get built. The reality is messier. And a hell of a lot more interesting.

Take Thomas Edison. Secular saint of American ingenuity. The wizard who single-handedly lit up the dark. Except he didn’t. Edison wasn’t a solitary creator; he was a brilliant, ruthless aggregator of other people’s breakthroughs and a master of public relations. He invented the bulb, sure, but his real masterpiece was the myth of himself. In the process, he eclipsed the collective sweat of his own labs and the far more elegant alternating-current systems of his rivals. He’s our most overrated figure—not because he lacked talent, but because his shadow blinded us to how progress actually happens.

Morgan Housel nailed this structural blind spot by tracing the tangled ancestry of major turning points:

“Every current event – big or small – has parents, grandparents, great grandparents, siblings, and cousins. Ignoring that family tree can muddy your understanding of events, giving a false impression of why things happened… Viewing events in isolation, without an appreciation for their long roots, helps explain everything from why forecasting is hard to why politics is nasty.”

Look past the blinding light of the celebrity inventors and you find the long roots that actually remade our world. Take FCC Part 15. It’s an event almost no history textbook bothers to mention. In the early 1980s, a lone staff engineer named Dr. Michael Marcus looked at three chunks of the radio spectrum—stuff discarded as “garbage bands” reserved for industrial microwave ovens—and saw an opening. The entire telecom establishment thought he was chasing a recipe for chaotic interference.

Marcus didn’t blink. He spent years pushing through a dry, technical ruling in 1985 to open those garbage bands for unlicensed public use. A total footnote. Yet that single, unheralded bureaucratic open door laid the invisible foundation for Wi-Fi, Bluetooth, and the entire wireless ecosystem running your life today. Marcus didn’t get a ticker-tape parade; he got political friction and a quiet transfer to a back-office enforcement role. No hero on horseback. Just a guy in a cubicle who rewired the world.

We do the same thing with politics. We rank presidents by the volume of their rhetoric or the body count of their wars. Meanwhile, men like Chester A. Arthur get left in the dusty margins of trivia. Arthur was the ultimate product of the spoils system—a New York machine politician who climbed to power on institutional corruption. Then James A. Garfield was assassinated, and Arthur was thrust into the big chair. Something clicked. Instead of feeding the machine that birthed him, he turned inward, defied his old patrons, and signed the Pendleton Civil Service Act. He dismantled the very patronage system he’d mastered. It was a stunning act of quiet integrity that killed his political future but saved the republic’s administrative soul.

Or take Frances Perkins. Ask the average student who gave them the weekend, the forty-hour work week, unemployment insurance, and the abolition of child labor, and you’ll get a blank stare. Perkins was FDR’s Secretary of Labor. She wasn’t a regular on the campaign posters. She just stood in the back of the room, turning abstract economic suffering into concrete human safety nets.

I’ve been sitting with this for a few days, thinking about my own career—and the times I mistook the loudest person in the room for the smartest. I chased the visionary founders with the spellbinding pitches. I ignored the quiet engineers and the mundane infrastructure choices that actually determine whether an idea scales or snaps. It takes a few painful, expensive missteps to realize that the real compounding interest of progress is almost always generated in the dark.

History isn’t a solo act. It’s an intricate, mostly anonymous collaboration between accidental reformers, stubborn bureaucrats, and regulatory footnotes. If you want to understand where we’re going, stop staring at the stage lights.

Start looking at the wiring.

Categories
Aging AI Business Living

The Being Phase

There is a metric making the rounds in technology investing circles that is, on its face, about market share and revenue concentration. Alex Sacerdote of Whale Rock Capital calls it the New Rule of 40 for AI. The formula is simple: take the percentage of a company’s sales derived from AI, add its percentage market share in that AI category, and if the sum reaches 40, you have a winner. Celestica, a company most people have never heard of, scores extraordinarily well. It owns somewhere between half and sixty percent of the cloud Ethernet white-box switch market. NVIDIA doesn’t need a formula. It simply is what it is.

Sacerdote designed the metric to cut through a specific kind of noise — the companies claiming AI exposure they don’t actually have, the giants whose AI revenue hovers at one or two percent of their base while their press releases suggest otherwise. The framework is a detector. It finds the companies that have stopped becoming AI infrastructure and started simply being it.

I found myself less interested in the companies than in that distinction.

I spent years at Visa watching a network that had long since crossed that threshold. By the time I arrived, Visa wasn’t becoming the global payments infrastructure. It was the global payments infrastructure. The work was real — fraud detection, modeling, the daily labor of keeping something enormous running — but the existential question had been settled before I got there. The network existed. Merchants accepted it because cardholders carried it. Cardholders carried it because merchants accepted it. That loop had been closing for decades. We were custodians of a fait accompli.

There’s a particular feeling to working inside something that has already won. It’s not complacency exactly. The problems are genuine and the stakes are high. But the uncertainty has a different quality — it’s operational uncertainty, not existential uncertainty. You’re not asking whether the thing will survive. You’re asking how to run it well.

I didn’t have language for that distinction then. Sacerdote’s metric gives me some. The companies that score highest on his New Rule of 40 have resolved their existential question. They’re not fighting for position. They’re administering a position already held.

The question that has followed me out of that career, and out of several decades of watching technology cycles turn, is simpler and more personal than any investment framework.

When did I cross that line myself?

I have been writing at sjl.us since 2001. That’s not a boast — it’s a data point. Twenty-five years of thinking out loud, of ideas arriving rather than being argued, of the specific memory as structural anchor. The blog is not becoming anything. It is what it is: a record of a mind moving through time, accumulated into something that has its own weight and shape.

The book on payments systems exists. The career at Visa exists. The photographs exist. The train journeys exist. The years in Dayton exist, and the years on the Peninsula, and the particular way the light falls on the California coast at Pescadero in the late afternoon — when the fog is still offshore and the hills are improbably green and everything goes briefly, completely quiet, as if the world is deciding whether to continue.

These are not things I am building toward. They are things I am.

Sacerdote would say I have high market share in a specific category. The category is small — one person, one particular configuration of experience and attention and accumulated knowing — but the share is essentially total. There is no competitor for the position of having lived this particular life. The moat is absolute. The switching costs are infinite.

I used to find that thought melancholy. The narrowing as loss. The aperture closing on what remains.

I’m not sure I find it melancholy anymore.

The L-Curve, Sacerdote says, is a long flatline followed by a vertical explosion. The tinkering phase, then the moment of lift. He means it as a description of demand curves for technology infrastructure. But I recognize the shape from somewhere closer. The long middle of a life, building and becoming, and then the morning you wake up and realize the building is substantially done. What remains is the being.

That’s not an ending. It’s a different kind of beginning.

Sacerdote’s metric will eventually stop working. All frameworks do. The AI infrastructure cycle will mature, the L-Curves will flatten, and some new measure will emerge to find the next thing that is just beginning to become what it will be. That’s the nature of markets. The detector has to change as the signal changes.

But there’s a complication worth naming. Analysts at Citadel Securities published a note recently observing that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints, and marginal returns. Token bills are arriving unexpectedly. Compute is scarce. The vision of AI as ubiquitous, frictionless, and immediate is colliding with physical reality. Their conclusion: asset prices will periodically be forced to reconcile ambition with physical constraint.

That’s not a refutation of Sacerdote. It’s a reminder that feeling like you’ve arrived and having actually arrived are different things. The being phase has to be load-tested. The position has to hold under pressure.

I think about the fiber optics Corning is laying into the massive data center clusters — ultra-thin, bendable, carrying more light than anything that came before. The cable doesn’t know it’s infrastructure. It just carries what it’s given, at the speed it’s capable of, across whatever distance is required. It doesn’t matter what the cable believes about itself. What matters is whether the light actually moves.

That seems right to me. You become what you are over a long time, largely without noticing. And then one day someone builds a metric that accidentally describes your life, and you recognize yourself in it, and you think: yes. That’s the shape of it. High concentration. High share. A moat that deepened while you were looking elsewhere.

But the moat still has to hold.

The being phase, it turns out, is not the end of something. It’s the proof that something was built. And the daily question — for companies, for infrastructure, for a person in his late seventies still writing, still paying attention — is whether what was built is actually load-bearing.

You don’t get to stop finding out.