Memorial Day 2026 – a reminder from Andy Rooney: “If you think the world is selfish and rotten, go to the [American] cemetery at Colleville-sur-Mer overlooking Omaha Beach—see what one group of men did for another on D-Day, June 6th, 1944.” (Garrett M. Graff, When the Sea Came Alive)
Carl J. Loftesness (1921-2010) Master Sergeant – US Army
Curtis H. Loftesness (1923-1975) Lieutenant Colonel – US Army
When the plane started circling, I needed to disappear.
I was just goofing around on my own with an army surplus signaling mirror in our treeless backyard in Kettering. A thick piece of bright rectangular glass with an opening in the middle where light could shine through a cross. To signal you had to line up the light shining through with the opening.
Living close to Wright-Patterson, C-119 Flying Boxcars were a common sight. I could hear one coming before I could see it. I turned and scanned the sky. There it was.
Maybe this was worth a try. What did it even mean?
I held it up, aimed and hoped.
Then I saw it. The plane started a turn to the left. Uh oh.
I didn’t want to be reported. I ran into the woods behind our house. I watched and waited.
The circle completed. The boxcar flew on.
I walked back into the house and put the mirror back on the chest of drawers in my bedroom.
I never told anyone. Not even you.
There is a moment, right before surgery, when the anesthesiologist asks you to count backward from ten. You get to seven, maybe six, and then the world goes clean and white. Scientists have a word for the material responsible for that transition: borosilicate. The same compound in the syringe barrel is in the telescope mirror trained on the Andromeda galaxy, in the fiber strand carrying the surgeon’s consultation with a colleague three thousand miles away, in the smartphone screen the patient’s wife is staring at in the waiting room, hands shaking, refreshing nothing.
Glass is everywhere and we have made it invisible, which is the oldest trick civilization knows.
Vaclav Smil argues in Making the Modern World that the most consequential material of the last two centuries is not steel or silicon or oil. It is float glass — invented by Alastair Pilkington in 1959, when he watched dishwater spread across his kitchen sink and understood something that had eluded glassmakers for four hundred years. Pour molten glass onto a bath of molten tin and it finds its own level. It becomes, on its own, perfectly flat. Every window, phone screen, solar panel, and architectural facade descends from a man watching his wife do dishes.
What Smil doesn’t quite say — though you feel it accumulating across his pages — is that glass is the one material that consistently mediates between the inner and the outer. Not metaphorically. Literally. It stands at the boundary and says: you may look, but you may not touch.
The fiber optic cable looks like nothing. Pull back the orange jacket and you find strands thinner than a human hair, each one pure silica glass so precisely drawn that a photon launched into one end will emerge after sixty miles having lost less than five percent of its energy. That number seems impossible. It is a kind of miracle achieved through obsessive purity: any contaminant at the molecular level, any stress in the crystal lattice, any deviation in the core diameter, and the light scatters and dies. Underneath every ocean, through every mountain, connecting data centers in Virginia to servers in Singapore, there are hundreds of millions of kilometers of this material, laid in darkness, carrying light.
I think about that sometimes when I hit send. The electrons leave my keyboard, convert to photons at some local junction, and then travel — genuinely travel, as light through glass — to wherever they are going. There is something devotional about it, though I can’t quite say why. Maybe it’s the invisibility. Maybe it’s the faith required — that the thing you release will arrive, intact, somewhere it has never been.
Glass is in the MRI machine and the X-ray plate and the laboratory flask where the drug was first synthesized and the vial where it is stored and the syringe through which it enters the body. Glass does not react. It does not corrode. It does not leach. This chemical inertness, which seems like absence, is actually the whole point. Medicine needed a container that would hold the thing without becoming it.
There is also glass in the eye reading the label on that vial. The human lens is, optically speaking, a soft glass. It focuses, ages, clouds — cataracts are the eye’s glass going milky — and the surgeon replaces it with an intraocular lens engineered to behave like glass. We have spent considerable effort making fake versions of something the body was already doing.
For most of human history, clear glass was expensive, fragile, and small. Window glass in medieval Europe admitted light hazily, like looking through ice. Clear vision was for churches, which is perhaps why we came to associate light with the sacred — it literally arrived, in those buildings, in a way it did not arrive anywhere else. Then Pilkington’s tin bath made clarity cheap, and the world changed in ways nobody fully catalogued because the change was so pervasive: big windows, watched experiments, extended growing seasons, telescopes reaching farther, microscopes going smaller. Each a story of glass making a distance crossable that was not crossable before.
The screen I am writing this on is glass. The Corning Gorilla Glass on this display is an alkali-aluminosilicate sheet, chemically strengthened through ion exchange, harder than most knives, clear enough that the pixels look like they are sitting on the surface rather than behind it. Apple spends considerable engineering effort making the glass seem like it isn’t there. The ideal phone screen is invisible. A window to computation.
And yet the glass is the thing you actually touch. All day. More than you touch almost anyone. The glass is warm from your hands. It has learned, in a way, the pressure of your thumbs.
Glass is the material of thresholds — it makes the threshold visible, makes it possible to stand at a door and see all the way through before you decide whether to enter. We built the internet through it. We see our loved ones through it. We study cancer through it. We watch the news through glass that traveled to us through glass captured by cameras with glass sensors launched on satellites with glass lenses through a sky that is itself, technically, a lens — bending and filtering the light from everything that has ever been.
In the hospital waiting room, the wife is still holding her phone. The screen has gone dark. She taps it. It lights up. She looks at her own reflection for a moment — the screen a mirror now — before the notification arrives and the glass goes transparent again, the way it always does, showing her something other than herself.
That is what glass does. It waits. It holds. And then, when there is something to show, it gets out of the way.
Marc Andreessen made two claims recently that don’t quite fit together, and I haven’t been able to stop pulling at the seam.
The first: for almost any topic, the top AI systems now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone. This isn’t a casual observation from someone without access — it’s a meaningful data point about what AI is actually doing to the value of expertise.
The second: the only real skill left in using AI is knowing what to ask. The models can already do almost anything you can describe in plain English. The bottleneck lives in your own head.
Hold those two claims next to each other. If the AI beats the experts, then the quality of your question only has to clear a low bar — good enough to unlock what the system already knows. You don’t need to ask like a cardiologist to get a cardiologist-quality answer. You just need to ask.
Except that’s not how it works in practice. And the gap between the two claims is where something important lives.
The better the question, the better the answer — even from a system that already knows more than any human alive. Expert-level interrogation of a superhuman system produces something qualitatively different from naive interrogation of the same system. The gap between a good question and a bad one doesn’t shrink because the underlying capability grows. It may widen. A sharper instrument in an unskilled hand doesn’t close the distance — it just makes the skilled hand more lethal.
What the AI has done is commoditize answers. What it has not done — cannot do — is commoditize the ability to know which question to ask.
There is a concept from epistemology that keeps surfacing here: the unknown unknown. Donald Rumsfeld made the phrase famous and then spent years living down the mockery, which was unfair, because the underlying idea is genuinely important. There are things you know you don’t know — the gaps you can name, the questions you can form. And there are things you don’t know you don’t know — the territory you can’t even see the edge of. The naive user of AI operates almost entirely in the second category. They ask what they already suspect. They get answers that confirm the shape of what they already believe. The system is brilliant and they are using it as a mirror.
The sophisticated user has learned to ask the AI to challenge their assumptions. To find the holes. To steelman the opposing view. To identify what’s missing from the framing. That second posture requires a kind of intellectual self-awareness — an ability to stand outside your own thinking and interrogate it — that is neither common nor easily taught.
Here is the uncomfortable implication: that self-awareness is not randomly distributed. It correlates with education, with reading, with having thought carefully about hard things for a long time. The people best positioned to ask good questions are, largely, the people who already had access to good answers through the old system. The gate moved. It didn’t disappear.
There’s a democratic story told about AI and I believe parts of it. The kid in rural South Dakota with a good question now gets an answer that rivals what the partner at McKinsey gets.
But access to information was never really the binding constraint. The binding constraint was always the ability to know what information you need — to feel the shape of your own ignorance precisely enough to ask for what fills it. That skill wasn’t distributed by the old system and it won’t be distributed by the new one. It has to be built, slowly, through years of reading and thinking and being wrong and trying again.
What AI may actually be doing is widening the gap between people who ask well and people who don’t — making the former dramatically more capable while leaving the latter approximately where they were, just with a faster way to get answers to questions they already knew to ask.
Somewhere right now, someone is sitting with the most capable thinking tool in human history, asking it to write a cover letter. The tool will do it beautifully. And the gap will quietly widen.
There used to be a pier with a shack on top jutting out into Princeton Harbor in Half Moon Bay.
The fog still comes in low most mornings, softening the breakwater until the boats in the slips are only darker shapes. You hear the harbor before you see it—the wet cough of diesel, the slap of water against fiberglass and steel, the occasional metallic ring of a line pulled tight.
When the marine layer lifts, the place shows its modern face: rip-rap stone, poured concrete, the long gray L of Johnson Pier running into water that no longer knows how to rise up and test a wooden structure. The air carries salt and exhaust. Gulls work the edges. Trucks idle in the lot. It is orderly now, built to stay.
But the order has removed a sound. The old pilings no longer work against one another with that slow, heavy creak. The deck no longer gives a little underfoot after it was soaked with a firehose once a week. There is no longer the low groan of a chain hoist swinging a load of salmon or crab up from a boat in the dark, or the close, briny steam inside the shack where Vera kept coffee going and put food on whatever table or crate was free. The lights that once burned at the outer end—small and steady against the black water—are gone. Before the breakwater was finished, winter storms still reached that far end. Men braced the doors and listened to the ocean hammer while the whole structure trembled on its pilings like something that might, on any given night, decide to let go.
Joe Romeo drove those pilings in 1942. The shack he raised was built to be lived in as much as worked in. It smelled of diesel from the fuel tanks on the deck, of wet creosote warming in the sun, of fish blood drying on the planks, and of whatever Vera had on the stove. Fishermen came in with scales still stuck to their forearms. The wood stayed dark and heavy because it was never allowed to dry out completely. After the breakwater went in, the water inside grew calmer and the work changed with it, but the pier still took its beating every season. It held because the men who used it treated it like something that had to be argued with rather than replaced.
By the time the county harbor district took it over, the pilings had begun to rot and shed pieces into the channel. What had once been a place where men ate and slept and unloaded fish became, in the new language of the harbor, a navigational hazard. The district saw liability where others still saw memory.
In 2018 the cranes came and the pilings were cut and hauled away. The shack that had held the life of a fleet inside its four walls was dismantled and carted off like any other piece of condemned wood.
What remains is the absence of that sound and that light, and the particular way a working harbor can be made safe enough to forget what it once required of the men who kept it alive.
There is a tree on a terrace at Filoli that is roughly the size of a lamp. It sits in a shallow black bowl, its trunk leaning with the easy confidence of something that has been leaning for decades, its canopy splayed against the California sky like a fist slowly opening. Behind it, the estate’s formal garden dissolves into soft focus — roses, balustrades, the suggestion of abundance. The bonsai doesn’t compete with any of it. It simply occupies its few cubic feet with a completeness that makes everything else feel approximate.
I’ve been thinking about what that completeness costs.
The tree is probably a juniper — the fibrous, spiraling bark, the dense scale-like foliage, the way the branch structure seems to remember every decision ever made about it. Bonsai practitioners talk about nebari, the visible surface roots, and movement, the quality of dynamism frozen into wood. This one has both. The trunk doesn’t just lean; it goes somewhere, pulled by some invisible argument the grower made with it over years, or decades, or longer. The moss at its base is so even and green it looks curated, because it was.
What strikes me standing in front of it is that this is a technology — not in the semiconductor sense, but in the older one. A technique for shaping time. The grower didn’t make this tree. They made conditions, and maintained them, and made them again, and the tree is what happened. The distinction matters. There’s no shortcut to the trunk diameter. There’s no prompt that produces the movement in that wood.
I work in a medium where the gap between intention and output has collapsed to nearly nothing. I describe something and it appears. There’s tremendous utility in that, and I’m not romantic enough to pretend otherwise. But Filoli’s bonsai terrace is a useful corrective — a reminder that some forms of beauty are only legible as records of duration. The lean of that trunk is not a feature. It’s an argument made slowly, over a life, against gravity.
I don’t know who grew it. I don’t know if they’re still alive. The tree, characteristically, offers no information about this. It just stands there in its bowl, complete, patient, not particularly interested in being understood.
There is a specific feeling. You are trying to understand something — a medical term in a lab report, a clause in a contract, how a particular piece of software actually works under the hood — and you hit the edge of what you know. The territory beyond is unfamiliar and the path is unclear, and something in you decides, quietly and almost without announcement: I don’t know how to figure this out.
And then you move on.
Marc Andreessen, talking to Joe Rogan recently, buried something important inside a longer riff about AI prompting tricks. Most of his list was the kind of thing you’d read in a productivity newsletter — ask it to steelman both sides, pretend it’s a panel of experts. Useful, not revelatory. But one observation was different: pay attention to the exact moment you think “I don’t know how to figure this out.” That’s the moment you should open the AI.
He said it almost offhandedly. I haven’t been able to stop thinking about it.
What he’s really describing isn’t a technique. It’s a behavioral pattern that most of us developed so gradually we don’t recognize it as a choice. The feeling of epistemic overreach — of arriving at the edge of one’s competence — became, over decades, a stopping condition. We learned to treat not-knowing as a wall rather than a door because, most of the time, it functionally was one. The library was closed. The expert was unavailable. The research was paywalled. You moved on.
The habit calcified. Now it persists even when the conditions that produced it no longer apply.
I notice it in myself, and I’m someone who is genuinely curious — who likes knowing how things work, who will follow a thread further than most people bother to. That’s not modesty; it’s relevant context. Because even with that disposition, I still hit the wall. I’ll be reading something and encounter a concept I only vaguely follow — some nuance in immunology, some historical episode I’ve only half absorbed — and I feel the familiar slight contraction, the small withdrawal. I read past it. The curiosity was there. The friction was higher.
Curiosity alone was never enough. What determined whether I pushed through wasn’t how much I wanted to understand — it was whether understanding felt retrievable at all. Most of the time, it didn’t. So I moved on, and the curiosity found something else to chase.
There’s a darker version of this worth sitting with. The people who never developed the quit reflex — who hit not-knowing and felt compelled rather than defeated — are, disproportionately, the ones who built things. The intellectual persistence wasn’t incidental to their contributions; it was probably constitutive of them. Curiosity as stubbornness. The refusal to accept the wall as final.
Elon Musk is the limit case. When he decided he wanted to go to Mars and found the rockets prohibitively expensive, he didn’t defer to the aerospace industry’s consensus about what was possible. He started reading propulsion manuals and cold-calling engineers. The quit signal either never fired or got overridden so fast it made no practical difference. The result was reusable orbital rockets, which the industry had largely decided weren’t worth pursuing. The dig reflex, taken to its extreme, rewrote what was considered feasible.
But the trait is undifferentiated. It doesn’t come with a calibration mechanism. The same refusal to accept expert consensus that produced SpaceX also produces a certain amount of confident wrongness — the Twitter decisions, the Covid takes, the occasional foray into geopolitics with the certainty of someone who has read a lot of Wikipedia. The dig reflex, unregulated, has no obvious stopping condition.
AI doesn’t change that underlying trait. What it changes is the access cost for everyone else.
For most of human history, the friction wasn’t random. It selected for people whose drive was strong enough to overcome it regardless of cost — the right connections, the right institution, the time to burn. Now that friction is lower for everyone, nearly to zero, for an enormous range of questions.
What I’m trying to build is the opposite of the quit reflex. Not the Musk version — boundless, uncalibrated, occasionally catastrophic. Something more modest: the habit of checking before giving up. Noticing the moment of not-knowing and treating it as a question rather than a verdict.
It requires noticing the moment. Which is harder than it sounds, because the reflex is fast and the moment is brief.
The contraction happens. You’ve already moved on. Somewhere behind you, the question is still there.
For fifteen years of my life, I lived inside the fortress of information asymmetry. I was part of a payments consulting business, and our model was exactly what Andrew Feldman described on a recent Moonshots episode when he pointed a sharp finger at traditional professional services.
His observation was simple, cutting, and entirely true:
“Their role today is to stand between ordinary people and obscure knowledge. And the application of that obscure knowledge to everyday problems.”
When I heard him say that, it landed with a quiet thud of recognition.
For a decade and a half, my colleagues and I were the ones standing in that gap. The payments industry—with its labyrinth of interchange fees, compliance structures, clearing networks, and legacy tech stacks—is a monument to obscure knowledge. Clients didn’t come to us because we possessed some divine, unreplicable wisdom. They came to us because the map was locked in our heads, and navigating the terrain without us was a recipe for an expensive disaster.
We charged for our time, and we earned it. We untangled complexity and solved real, everyday business problems for people who just wanted to move money safely from point A to point B.
But looking back now, I can see the architectural flaw disguised as a premium service. The economic foundation of that entire era relied on friction. It relied on the fact that it took an immense amount of human energy to retrieve a piece of obscure data and map it onto a specific business dilemma. You weren’t just paying for strategic guidance; you were paying a premium on artificial scarcity.
We are living through a moment where the marginal cost of intelligence is rapidly trending toward zero. When the barrier of “obscure knowledge” evaporates, the traditional toll bridges begin to look absurd.
For anyone starting a consulting business today, the playbook would have to be entirely different. When an LLM can parse thousands of pages of network operating rules, interchange tables, and regulatory compliance frameworks in a handful of seconds, the gatekeeper’s standing ground liquefies.
If your value proposition is merely standing between a client and a hidden database, your business model isn’t just flawed—it’s obsolete.
Yet, this collapses into a fascinating paradox. You might assume that when you democratize expertise, you eliminate the need for the expert. But as Dan Shipper recently observed, the reality of AI is completely counterintuitive.
Shipper points out that AI effectively packages up “yesterday’s competence” and makes it cheap and ubiquitous.
Suddenly, anyone can generate a complex contract, a software pull request, or a payments flow strategy with the click of a button. But when cheap competence skyrockets, adoption explodes, resulting in an unprecedented glut of generic output—what the internet has collectively taken to calling “slop”. It’s the default, lazy answer that lacks soul, context, and nuance.
When everything begins to look and smell the same, a strange thing happens: the market’s demand for genuine difference sky-rockets.
The shift we are facing across all professional services—whether legal, financial, or consulting—isn’t about eliminating the expert. It is about changing the expert’s job from data-retriever to orchestrator and judge. The floor has been raised. Yesterday’s ceiling is today’s baseline.
What remains is the ability to read a room. To watch a client’s shoulders tighten when you present an option that’s technically correct but organizationally impossible. To notice the glance exchanged across the table before anyone speaks. No LLM parses that. The map is universal now; the guide still has to be in the room.
We don’t need fewer guides; we need fewer toll booths. The future of consulting doesn’t belong to those who hoard the map. It belongs to those who use a universally available map to help people actually walk the terrain.
Today I was watching the interview of Gavin Baker by Patrick O’Shaughnessy on his Invest Like the Best podcast. Like prior conversations this was another fascinating excursion into the mind of a sophisticated and very successful tech venture investor.
During the conversation, Patrick asked Gavin what agents he was using that were especially helpful and he mentioned one which summarizes YouTube podcasts and videos for him. Like most of us Baker just doesn’t have the time to watch or listen to them himself so good summaries are really helpful.
Turns out I’ve been working on a Google Gemini Gem that does this for me. When Baker mentioned his I fired up the new Gemini 3.5 Flash model and asked it to summarize the Baker interview.
Later in the conversation Baker used the term “battlefield AI” which caused me to go back to Gemini again to learn more about that. The results were so interesting that I asked Gemini to create a syllabus for a semester class on these subjects. After that I asked it to convert our whole conversation into a Markdown file so I could share it. You’ll find it below.
I found this whole experience pretty stunning. I came away very impressed with Gemini 3.5 Flash both for the quality of the responses but also the sheer speed. Wow!
Anyway I hope you enjoy the following!
I have so many unread articles saved to Instapaper that I’ve stopped checking the count. Each one felt, in the moment of saving it, like something I needed. A long piece on urban planning, a profile of someone interesting, a reported essay I fully intended to sit with.
The app is beautifully designed for exactly this — the frictionless capture, the clean reading interface waiting patiently on the other side.
What it can’t do is manufacture the attention I didn’t have when I saved it and still don’t have now. The articles aren’t the problem. The premise is: that presence is something you can bank.
There’s a haiku I keep returning to, from Natalie Goldberg’s Three Simple Lines. It’s by a poet named Fumiko Harada:
Morning chill
I savor this moment —
one meeting one lifetime
Eleven words. No verb in the third line, which makes it feel less like a thought and more like a verdict.
The Japanese concept underneath it is ichi-go ichi-e — loosely, “one time, one meeting.” It’s a Zen idea with origins in the tea ceremony, the understanding that each gathering is singular and therefore irreversible. You cannot archive it. You cannot search for it later. When it ends, it doesn’t go anywhere you can retrieve.
This is what the Instapaper queue is, at scale: an archive of moments I decided to experience later. The article about urban planning was written by someone who spent months reporting it, on a day when some editor thought it was ready, and landed in my feed on a morning when something about the headline caught me. That constellation doesn’t reassemble. Later is a different article.
The tools I use every day are getting astonishing. There are systems that can summarize, translate, recall, explain, anticipate. I use them. I find them genuinely useful.
But there’s a habit of mind they reward — a kind of perpetual deferral of full attention — that I haven’t fully reckoned with. The promise, always, is that you can engage more completely later, once the summary is ready, once the transcript exists, once the notes have been taken. Presence becomes a productivity tax you pay while waiting for a deliverable.
Harada’s haiku doesn’t moralize. The speaker isn’t lecturing herself into awareness. She’s just cold, and awake, and choosing to notice. I savor this moment. The word “savor” does a lot of work. It implies effort. You savor things that could be missed.
The pivot in the third line is what stays with me. One meeting one lifetime. Not “this meeting will last a lifetime” — that would be sentiment. It’s more like a mathematical statement: the cardinality of this encounter is one. There is exactly one of them. This morning, this particular chill, whatever conversation or solitude is happening inside it — that set has one element. By tomorrow it has zero. No amount of documentation changes that arithmetic.
I’m working on believing that.
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