Tag: friction

Categories
Stanford

Those Empty Switchbacks

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

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

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

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

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

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

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

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

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

Categories
AI Living

The Threshold

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.

Categories
Business Living Retirement Trading

The Whetstone and the Hammock

We spend the first half of our lives trying to build a fortress of comfort, operating under the assumption that the ultimate reward for a lifetime of labor is the sudden, permanent cessation of it. We dream of the hammock. We dream of the empty calendar. But an empty calendar is really just a blank canvas with no paint.

Patrick O’Shaughnessy recently sat down with Paul Tudor Jones, and their conversation inevitably drifted toward the later chapters of life. Jones shared a story about fulfilling a promise to his wife to move to Palm Beach after their youngest child went to college. Upon arriving, she sent him to a local general practitioner—an 83-year-old doctor still seeing patients. Jones asked the man for the secret to longevity in a town (Palm Beach) he bluntly described as the “land of the walking dead.” The doctor’s response was a swift hammer blow:

“It’s real simple. You retire, you die.”

It’s a jarring diagnosis, but it cuts right to the bone.

We are biological machines designed for friction. Take away the resistance, and the gears don’t just stop; they rust.

Jones took the lesson to heart, noting that if you don’t use it, you lose it. He works out two hours a day and continues to trade, deliberately keeping his mind pressed against the whetstone of the markets.

I’ve watched this play out in my own circles over the years. I’ve seen brilliant, energetic colleagues hand over their keys, step out of the arena, and within months, seemingly deflate. The sudden absence of daily problems to solve doesn’t bring peace; it brings a creeping atrophy.

I’ve found myself deliberately holding onto certain complex projects and investments not because they are financially necessary, but because they demand my attention. They force me to wake up and solve a puzzle. They provide the necessary gravity to keep my feet on the ground.

But Jones offered a second, perhaps more profound reason for staying in the game. He wants to make “an absolute pot of money” specifically to give it away. He views his daily work not as a grind, but as the pursuit of nobility. He found a way to bridge the gap between the selfish need to keep his own mind sharp and the selfless desire to fuel the causes he cares about. The work becomes an engine for something larger than himself.

The hammock is a trap. The mind requires weight to bear, a horizon to move toward. The goal is not to finally lay down our tools, but to choose precisely what we want to build with them until the very end.

Stay hungry, stay foolish – and stay busy!

Categories
Authors Books Business

The Whetstone of the Box

Give a team an unlimited budget and no deadline, and you almost guarantee their project will never ship. We spend our careers fighting for more runway, more resources, and a completely clear calendar, convinced that absolute freedom is the prerequisite for great work. Yet, when the walls finally fall away, we usually just freeze.

David Epstein’s upcoming book, Inside the Box, circles this exact paradox. His premise, arriving in early May, is that constraints do not diminish our capabilities; they forge them. We spend so much of our lives trying to escape boundaries, failing to recognize that those very boundaries are what give our efforts shape.

I think about the early days of writing code. We were working with severe memory limits—kilobytes, not gigabytes. Every line had to justify its existence. There was no room for bloat, no excess capacity to mask sloppy logic. It felt restrictive at the time, like trying to build a ship inside a bottle.

But that unforgiving physical boundary forced a ruthless elegance. You had to understand exactly what you were trying to accomplish. The constraint wasn’t an obstacle to the work; it was the whetstone that sharpened the blade.

We see this everywhere, once we learn to look for it. A photographer framing a shot with a fixed prime lens cannot rely on a zoom ring to find the picture; they have to physically move their feet. The limitation forces engagement with the physical world. Without the walls of a canyon, a river is just a swamp. It is the restriction that creates the momentum.

Epstein’s focus on how constraints make us better feels like a necessary corrective right now. We live in an era of infinite leverage and boundless digital canvases. The friction has been removed from almost everything we do.

But friction is where the traction lives. When we strip away all our limits, we don’t gain wings; we just lose our footing. We need the edges of the box to know exactly where we stand.

Categories
AI

The Ghost of Edison in the AI Data Center

For over a century, the story of modern electricity has been framed by the “War of the Currents.” Thomas Edison championed Direct Current (DC)—a stable, continuous flow of energy—while Nikola Tesla and George Westinghouse backed Alternating Current (AC), which could be easily stepped up in voltage to travel long distances across the grid.

Tesla won. AC became the lifeblood of the global power grid. But history has a funny way of looping back on itself. Today, as we stand on the precipice of the largest infrastructure build-out in human history—the artificial intelligence data center—Edison’s DC power is making a quiet, monumental comeback.

The catalyst? The sheer, unyielding physics of energy consumption.

The AI boom, driven by massive GPU clusters from companies like NVIDIA, is extraordinarily power-hungry. We are no longer measuring data center power in megawatts; we are measuring it in gigawatts. And when you are dealing with power at that scale, the friction of legacy architecture becomes a multi-billion-dollar bottleneck.

On X Ben Bajarin cited a recent conference discussion by an executive from power management supplier Eaton that highlighted a massive architectural shift happening right now behind the scenes:

“800-volt DC to the rack is probably one of the biggest architectural changes that are starting to be designed into data centers, and a lot of those designs are taking place right now. You know, honestly, when look at Eaton, I think that’s one of the untold stories here, is that DC power is probably one of the biggest transformational things that are going to hit the electrical industry since, quite frankly, AC electricity was around in the Edison days.”

To understand why this is revolutionary, you have to look at how a traditional data center gets its power. Power arrives from the utility grid as medium-voltage AC. It is then stepped down to low-voltage AC, sent to the server floor, converted into DC, stepped down again, and finally fed into the server rack at 54 volts.

Every time power is converted from AC to DC, or stepped down through a transformer, there is a penalty. It generates heat, and it loses energy.

“We estimate that there’s roughly about 5% electrical loss during that transition. If you could just go from DC, directly from the utility feed, all the way through the data center into the rack, that’s 5% efficiency gain that you could get.”

In the abstract, 5% sounds like a rounding error. But scale changes everything. Eaton projects that the upcoming data center build-out to support AI will require somewhere between 50 and 100 gigawatts of power.

“So on 50 gigawatts or 100 gigawatts of power generation that’s needed, that’s 5 gigawatts of power that all of a sudden just appears from the existing infrastructure. And that is really, that is really exciting.”

Five gigawatts is not a rounding error. Five gigawatts is the equivalent output of five standard nuclear reactors. It is enough energy to power millions of homes. And in this new 800-volt DC architecture, those five gigawatts aren’t created by burning more coal, building more solar panels, or splitting more atoms.

They are created purely by the removal of friction. By subtracting the unnecessary steps.

There is a profound philosophical metaphor hidden in this electrical engineering triumph. In our own lives, and in our organizations, we are obsessed with generation. When we face a deficit—a lack of time, a lack of output, a lack of revenue—our default instinct is to generate more. We try to work longer hours, hire more people, or drink more coffee.

But how much of our daily energy is lost to “conversion friction”? How much mental power evaporates when we constantly context-switch between tasks, essentially converting our mental state from AC to DC and back again? How much organizational momentum is lost translating an idea through five different layers of middle management before it reaches the “rack” where the actual work is done?

Often, the most elegant and impactful solution isn’t to generate more power. It is to look at the existing architecture of your life or business, identify the transition points that are bleeding energy as heat, and rewire the system to flow directly to the source.

The invisible architecture that shapes our digital lives is shifting. In the race to build the future of artificial intelligence, the biggest breakthrough wasn’t a new way to create energy, but a century-old method of preserving it.