Tag: history

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Leadership Uncategorized

The Sawed-Off Chair: Hyman Rickover’s Brutal Lesson in Accountability

It sounds like a legend, but it’s true.

If you wanted to command a nuclear submarine in the Cold War U.S. Navy, you first had to survive a personal interview with Admiral Hyman G. Rickover—the uncompromising “Father of the Nuclear Navy.”

In his office sat a notorious wooden chair. The front legs had been deliberately sawed short—several inches in some accounts—causing anyone who sat in it to slide inexorably forward. The seat was often polished slick as glass. While candidates fought to stay upright, Rickover unleashed a barrage of rapid-fire questions on engineering, history, philosophy, and their deepest personal failures. A weak or evasive answer might earn you banishment to a broom closet for hours “to think about it.” Other times, he’d deliberately provoke you just to see how you’d react under pressure.

Why would the man responsible for the most advanced, unforgiving technology of the era—nuclear reactors that could never be allowed to fail—rely on such seemingly petty tactics?

Because Rickover understood a hard truth: technology doesn’t prevent disasters. People do.

A nuclear reactor doesn’t care about your rank, your procedures, or your consensus. It obeys physics.

In an environment where a single mistake could mean catastrophe, Rickover demanded officers who took absolute, personal ownership of every outcome.

He put it best himself:

“Responsibility is a unique concept. It can only reside and inhere in a single individual. You may share it with others, but your portion is not diminished. You may delegate it, but it is still with you. You may disclaim it, but you cannot divest yourself of it… If responsibility is rightfully yours, no evasion, no ignorance, no passing the blame can shift the burden to someone else. Unless you can point your finger at the man who is responsible when something goes wrong, then you have never had anyone really responsible.”

That philosophy is why the sawed-off chair existed. It wasn’t hazing. It was a deliberate test: When your environment is uncomfortable, unfair, and literally working against you, do you complain? Do you slide off and give up? Or do you dig in, brace yourself, and maintain control while thinking clearly under stress?

Rickover wasn’t building bureaucrats. He was building leaders who could be trusted with the most dangerous machines ever created—men who wouldn’t hide behind systems, committees, or “shared accountability” when things went wrong.

Today, in our matrixed organizations, endless committees, and culture of diffused blame, this feels almost radical. We’ve grown comfortable with collective responsibility that conveniently means no one is truly responsible. Rickover called this kind of bureaucratic diffusion “systematic strangulation.”

We may not run nuclear reactors, but the principle applies everywhere that matters: in engineering, in business, in life.

True leadership isn’t about comfort or consensus. It’s about character forged in discomfort. It’s the lonely recognition that the buck doesn’t just stop with you—it starts with you, lives with you, and cannot be outsourced.

Categories
News Writing

A Tribute to John F. Burns

“The commitment to fairness and balance and to shunning conventional truths when our reporting leads us in unexpected directions has been our gold standard.” — John F. Burns

As I’ve gotten older I pay closer attention to the obituary section of the New York Times. It frequently teaches me and brings back unusual memories that surprise me. Today it was my memory of years of reading the writings of John Burns brought back to life as I read his obituary.

Burns retired over ten years ago. I now remember thinking at the time just what a loss that would be for the paper. Reading Alan Cowell’s obituary of John F. Burns this morning, I felt that absence acutely.

For years, Burns was my first read—a “fireman” of the foreign desk who didn’t just report on the heat; he translated the embers.

Burns belonged to an era of journalism that felt more like a literary calling than a content cycle. He was a man who could find the “sweep of history” in the “telling detail of the present.”

Who else would think to frame the harrowing siege of Sarajevo through the haunting notes of a cellist playing Albinoni’s Adagio amidst the rubble? He understood that to explain a war, you must first explain the soul of the city being broken by it.

His career was a map of the 20th and 21st centuries’ most jagged edges—from the “wasteland of blasted mosques” in Bosnia to the “harrowing regime” of the Taliban in 1990s Afghanistan.

Yet, for all his Pulitzers and his debonair appearances in a Burberry raincoat on Red Square, there was a refreshing, stubborn humility to his craft.

He famously tilted against the “missionary complex” of modern reporting. He didn’t want to save the world; he wanted to see it—clearly, fairly, and without the blinding influence of ideology.

There is something deeply moving about his partnership with his wife, Jane Scott-Long which wasn’t familiar to me. While John was the “full force of talent” at the keyboard, Jane was the architect of safety, turning run-down Baghdad houses into fortified sanctuaries with “military-style blast walls” and, perhaps most essentially, a state-of-the-art coffee machine. They were a team that survived the “chaos of war” by creating a small, civilized center within it.

In his later years after she passed, Burns became more reclusive, a quiet departure for a man once known as a “raconteur with panache.” It’s a transition that mirrors the profession itself. He flourished in a pre-internet era, where time-zone differentials allowed for “considered writing.” Today, the “blue pencil” of the editor has been replaced by the instant, unvetted roar of the social feed.

His final story for the Times was about the reburial of King Richard III. It was a fitting end: a story about the “sweep of the centuries” propelling the news of the day.

As I reflect back on his work and my years of reading it, I realize that what I miss isn’t just the news he delivered. I miss the way he delivered it—with the patience of a historian and the heart of a poet. He kept the paper straight, and in doing so, he helped us keep our bearings in a world that so often feels lost. Especially today.

Categories
AI Farming History

The Harvest and the Algorithm: What 1990s Farms Teach Us About AI

There’s a strange kind of wisdom hiding in dusty old books about agriculture.

When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.

Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.

The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.

But here’s the part that should make your ears prick up.

The Seed That Was Supposed to Save Everyone

In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.

Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:

“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”

You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.

What Actually Happened

The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.

Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.

Now Listen to How We Talk About AI

We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.

Sound familiar?

Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.

She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.

If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.

Where the Analogy Might Break Down

Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.

You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.

The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.

Who Owns the Harvest?

Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.

The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.

If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.

Questions to Ponder

On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?

On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?

On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?

On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?

On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?

Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)

Categories
AI AI: Large Language Models

The Architecture of Unpredictability

There is a special understanding that comes from looking too closely at a map of a massive network or a large city. There is a point where the individual components vanish, and something else—something “other”—takes over.

Niall Ferguson captures this beautifully in The Square and the Tower:

“Large networks are complex systems which have ‘emergent properties’ – the tendency of novel structures, patterns and properties to manifest themselves in ‘phase transitions’ that are far from predictable.”

We like to believe we are the architects of our systems. We build platforms, we codify laws, and we design cities with the intent of order.

But Ferguson points out that once a network crosses a certain threshold of complexity, it enters a state of “phase transition.” It’s like water reaching 100°C; it doesn’t just get “hotter”—it becomes steam. It changes its fundamental nature.

We see this most vividly today in the trajectory of Artificial Intelligence. An LLM is, at its core, a gargantuan network of weights and probabilities. We understand the math of the individual neuron, yet we cannot fully explain how, at a certain scale, these systems begin to exhibit reasoning, humor, or theory of mind. These are not explicitly programmed “features”; they are emergent properties—the ghost that moves into the machine once the network becomes sufficiently dense.

Dario Amodei, CEO of Anthropic, describes this phenomenon through the lens of scaling:

“The thing that is so surprising about these models is that as you scale them up, they just keep getting better at things you didn’t explicitly train them to do… there’s this sense in which the model is ‘learning’ the structure of the world just by being forced to predict the next word.”

This is the “emergent property.” It is the intelligence of the beehive that no single bee possesses. It is the sudden, viral revolution that no single activist could have ignited. These properties are far from predictable because they don’t live in the nodes of the network; they live in the relationships between them.

The philosophical weight of this is humbling. It suggests that our world is governed by a structural momentum that defies linear logic.

When we find ourselves in these moments of societal or personal transition, perhaps the goal isn’t to control the outcome, but to understand the new physics of the system we’ve helped create.

We aren’t just parts of the network; we are the medium through which the unpredictable manifests.

Questions to Ponder

  • If your own consciousness is an emergent property of your neural network, where does “you” actually reside?
  • In the social networks we inhabit daily, what properties are emerging that we haven’t yet named?
  • As AI continues its phase transition, are we creating a tool, or are we witnessing the birth of a new kind of physics?
Categories
Living Serendipity

The Architecture of the Unexpected

We spend an incredible amount of energy trying to build a ceiling over our lives, a structure made of spreadsheets, five-year plans, and trend forecasts. We convince ourselves that if we just gather enough data, the future will become a navigable map. But Morgan Housel, in Same as Ever, cuts through this illusion with a quiet, devastating observation:

“We are very good at predicting the future, except for the surprises—which tend to be all that matter.”

It is a humbling thought. We can predict the mundane with startling accuracy—the seasons, the commute, the steady inflation of a currency. But the events that actually shift the trajectory of a life, a business, or a civilization are precisely the ones that no model accounted for. We are experts at forecasting the rain, yet we are consistently blindsided by the flood.

This reveals a profound tension in the human experience. We crave certainty because certainty feels like safety. We want to believe that the “tail events”—those low-probability, high-impact occurrences—are outliers we can ignore. In reality, history isn’t a steady climb; it’s a series of long plateaus punctuated by sudden, violent leaps.

The problem isn’t that our models are broken; it’s that we are looking at the wrong thing. Instead of seeking total foresight, we must prioritize serendipity and resilience. If the future is defined by surprises, then the most valuable asset isn’t a better crystal ball—it’s a wider margin of safety.

We must learn to live with the paradox: we must plan for a future that we know, deep down, will not go according to plan. The surprises aren’t just interruptions to the story; they are the story.

Looking back at the last decade of your life, what was the single ‘surprise’ event that defined your path more than any plan you ever made?

Categories
Authors Books History

The Devil’s Rope

We often mistake simplicity for innocence. When we look at a technological innovation, we tend to judge its weight by its complexity—the microchip, the steam engine, the nuclear reactor. But sometimes, history turns on the axis of something far more rudimentary. Sometimes, the world changes not with a bang, but with a sharp, metallic scratch.

I was recently reading Cattle Kingdom by Christopher Knowlton, and I stopped cold at a passage regarding the invention of barbed wire. It’s an object we pass by on highways or stumble over in overgrown fields without a second thought. Yet, Knowlton writes:

“None was more significant than the creation of barbed wire, which literally reshaped the landscape and set the stage for the era’s eventual destruction—at great personal cost to so many of its key players.”

It is a profound observation. We tend to romanticize the American West as a geography of endless horizons—a place defined by what it didn’t have: fences, borders, limits. It was the Open Range. But that openness was fragile. It existed only as long as the technology to close it was absent.

When Joseph Glidden and others patented their variations of “The Devil’s Rope” in the 1870s, they weren’t just selling steel fencing; they were selling a new concept of ownership. Before wire, a man owned what he could patrol. After wire, a man owned what he could enclose.

The quote strikes a melancholic chord because it highlights a paradox of human progress: the tool created to maximize the land ended up destroying the culture that relied on it. The cowboys, the cattle barons, and the drifters who defined the era were undone by the very efficiency they sought. The wire made the cattle industry profitable on a massive scale, but it also ended the cowboy’s way of life. It stopped the long drives. It turned the cowboy from a navigator of the plains into a gatekeeper.

And, as Knowlton notes, the “personal cost” was staggering. This reshaping of the landscape wasn’t just aesthetic; it was violent. The wire cut off migration routes for bison and the Indigenous tribes who followed them. It sparked the fence-cutting wars, neighbor turning against neighbor in the dark of night, snapping tension wires that represented their livelihood or their imprisonment, depending on which side of the post they stood.

There is a lesson here for us today, far removed from the dusty plains. We are constantly inventing our own versions of barbed wire—digital boundaries, algorithmic silos, tools designed to corral information or efficiency. We build these structures to create order, to claim our stake, and to protect what is ours. But every time we draw a line, we must ask: what era are we destroying? What open range are we closing off forever?

The landscape is always being reshaped. The question is whether we are building fences that protect us, or cages that trap us in.

Categories
News

Turning Out the Lights

[Note: see also The Murder of the Washington Post by Ashley Parker who writes: “Jeff Bezos, the billionaire owner of The Washington Post, and Will Lewis, the publisher he appointed at the end of 2023, are embarking on the latest step of their plan to kill everything that makes the paper special.”]

I was struck this morning by the brutal dismantling of the Washington Post’s international reporting capabilities. The list of bureaus being shuttered by the paper reads like a roll call of the 21st century’s geopolitical fault lines: New Delhi, Sydney, Cairo, the entire Middle East team, China, Iran, Turkey.

It is a stunning retreat.

But to view this merely as a corporate restructuring or a casualty of the dying business model of print journalism seems to miss a deeper, darker signal. This seems like an actual cultural symptom.

“The world is becoming less America-centric by the minute while the United States is becoming more America-centric than ever.”

At the exact moment technology has rendered the world indistinguishable from a single room—where a virus, a meme, or a financial crash in one corner sweeps across the floor to the other in seconds—we are choosing to partition off that room.

There is a tragic symmetry to it. As the center of gravity shifts away from the us, the we respond not by engaging harder, but by closing its eyes.

When a newspaper that has shaped history decides that “reporting on the world” is no longer of valuable enough, it is doing more than saving money – although clearly that’s the primary motivation. It seems to be a surrender to the idea that what happens “over there” doesn’t matter enough to us because the people who were supposed to tell us it was coming are gone.

We seem to be turning out the lights in the rooms we find too difficult, believing that if we cannot see the world, the world cannot touch us. Feels wrong.

The moves closing these bureaus are part of broader cuts at the paper:

  • Closing the Sports section
  • Closing the Books section.
  • Restructuring and shrinking the Metro desk.
  • Suspending the Post Reports podcast.
Categories
AI

The Second Fire: From Finding to Forming

There is a specific kind of vertigo that comes with a paradigm shift. It’s the feeling of standing on the edge of a map that has just been unrolled to reveal twice as much territory as you thought existed. Lately, as I navigate the vast, generative landscape of AI, that old vertigo has returned. It’s a hauntingly familiar resonance, a structural echo of the late nineties and early 2000s when we first encountered the Google search bar.

Back then, the world was a series of closed doors. Information was siloed in physical libraries, expensive encyclopedias, or the unreliable oral histories of our social circles. Then came that clean, white interface with a single blinking cursor. Suddenly, the friction of “not knowing” began to evaporate. We weren’t just browsing the web; we were suddenly endowed with a collective memory. It felt like a superpower—the ability to summon any fact from the digital ether in milliseconds.

“Google is not just a search engine; it is a way of life. It is the way we find out who we are, where we are going, and what we are doing.”

Today, the sensation is different in texture but identical in weight. If Google gave us the power to find, AI is giving us the power to form.

The “Aha!” moment of 2026 isn’t about locating a PDF or a Wikipedia entry; it’s the realization that the distance between a thought and its realization has shrunk to almost nothing. When I prompt a model to synthesize a complex theory or visualize a dream, I feel that same electric jolt I felt twenty years ago when I realized I’d never have to wonder about a trivia fact ever again.

But there is a philosophical weight to this new “awesome.” With Google, the challenge was discernment—filtering the flood of information to find the truth. With AI, the challenge is intent. When the “how” becomes effortless, the “why” becomes the only thing that matters. We are moving from the era of the Librarian to the era of the Architect.

We are once again holding a new kind of fire. It’s warm, it’s brilliant, and just like the first time we saw that search bar, we know that the world we lived in yesterday is gone, replaced by a version where our reach finally matches our imagination.

Categories
Living Writing

The Loop and the Pixel

There is a distinct muscle memory associated with the 1950s classroom. It smells of chalk dust and floor wax, but mostly, it feels like the cramping of a small hand wrapped around a pencil. We didn’t just learn to write; we were initiated into the discipline of the loop. The Palmer Method or Zaner-Bloser weren’t suggestions—they were rigorous architectures of communication. We made endless rows of O’s and l’s, tilting the paper just so, learning that language required flow, connectivity, and a certain deliberate grace.

Then, the world sped up.

By the 1990s, the loops began to unravel. As keyboards clattered their way into dominance, the efficiency of the printed letter—and eventually the typed pixel—took precedence over the artistry of the connected script. By 2010, the erasure was formalized; cursive was dropped from federal education standards (Common Core) to make room for “electronic literacy.” We traded the unique signature for the standardized font. We gained speed, certainly, but I often wonder what we lost in the translation.

“New Jersey this week joined a list of more than 20 states slanting in favor of bringing cursive instruction back to classrooms. Lessons on the looping letters were dropped from federal education standards in 2010, part of a shift toward focusing on electronic literacy.” — The New York Times

It seems the pendulum is swinging back. Proponents argue for its utility—the ability to read historical texts or a grandmother’s birthday card—but I believe the resurgence touches on something deeper.

In an increasingly digital world, cursive is an act of resistance. Typing is percussion; it is staccato and disconnected. Cursive is string; it is continuous and fluid. When we write in cursive, we are physically connecting thoughts, linking one letter to the next without lifting the pen. It forces the brain to slow down and the hand to dance.

As we stare into screens that demand our instant reaction, perhaps we are realizing that we crave the friction of pen on paper. We are bringing the loops back not because they are faster, but because they are human.

Categories
AI History Living

The Echo of the Roar

It is a strange sensation to look back exactly one century and see our own reflection staring back at us, sepia-toned but unmistakably familiar. We often think of the “Roaring Twenties” as a stylistic era—flapper dresses, Art Deco skyscrapers, and jazz. But beneath the aesthetic was a seismic technological shift that mirrors our current moment with an almost eerie precision.

In the 1920s, the world was shrinking. The radio was the “Great Disrupter” of the day. For the first time in human history, a voice could travel instantly from a studio in Pittsburgh to a farm in Nebraska. It was the democratization of information, a sudden collapse of distance that left society both thrilled and anxious.

“The radio brought the world into the living room; the algorithm brings the universe into our pockets.”

Today, we stand in the wash of a similar wave. If the radio brought the world into the living room, the internet—and specifically the generative AI of this decade—has brought the collective consciousness of humanity into our pockets.

The parallels in infrastructure are just as striking. One hundred years ago, the internal combustion engine was reshaping the physical landscape. The horse was yielding to the Model T; mud paths were being paved into highways. The very geography of how we lived was being rewritten by the automobile. In the 2020s, the “highway” is digital, built on cloud infrastructure and fiber optics, and the vehicle isn’t a Ford, but an algorithm. We are transitioning from physical labor to cognitive automation just as they transitioned from animal labor to mechanical muscle.

The Texture of Time

There is a specific texture to this kind of time. It is a mix of vertigo and acceleration. In 1925, the cultural critic might have worried that the “machine age” was stripping away our humanity, turning men into cogs on an assembly line. In 2025, we worry that the “algorithmic age” is stripping away our agency, turning creativity into a prompt.

But here is the insight that offers me comfort: The 1920s were chaotic, yes, but they were also a crucible of immense creativity. The pressure of that technological change forged modernism in literature, new forms of architecture, and entirely new ways of understanding the universe (quantum mechanics began finding its footing then).

We are not just passive observers of a repeating cycle. We are the navigators of the rhyme. The technology changes—from vacuum tubes to neural networks—but the human task remains the same: to find the signal in the static. To ensure that as the machines get faster, our souls do not merely get cheaper. We must decide, just as they had to a century ago, whether we will be consumed by the roar, or if we will learn to conduct the music.