Tag: technology

Categories
Chemicals Petroleum Semiconductors

The Invisible Layer Beneath the Chip

At the edge of a semiconductor fab, nothing looks dramatic.

No flames. No smoke. No sense of weight.

Just pipes, valves, and a silence so controlled it feels artificial.

It’s easy, standing there, to believe that oil—the old engine of the economy—has been replaced by something cleaner, lighter, more abstract. Software, maybe. Or data. The kinds of things that don’t spill.

But step a little closer, and the illusion breaks.

A modern fab is less like a factory and more like a chemistry experiment that never ends. Gases move through stainless steel arteries. Liquids are mixed, spun, deposited, stripped away. Surfaces are etched and re-etched until what remains is measured in atoms, not microns. The machines—Applied Materials, Lam Research—are precise, but they are not the story. The story is what flows through them.

Chemicals are doing the real work.

Not in bulk, the way oil once did. Not with force. But with specificity.

A barrel of oil is valuable because of its density—how much energy it contains. A liter of photoresist is valuable because of its selectivity—what it allows to exist and what it removes. One powers motion. The other defines structure.

Structure is where the modern economy hides its value.

A semiconductor is not impressive because of what it consumes. It’s impressive because of what it constrains. Billions of transistors, each one placed, shaped, and insulated with a chemical discipline that borders on obsession. The difference between a working chip and a useless one is often a contaminant you cannot see.

This is a different kind of industrialism.

The 20th century scaled by adding more—more fuel, more steel, more throughput. The 21st century scales by removing everything that shouldn’t be there. Purity is the limiting factor. Not how much you can move, but how precisely you can control.

From a distance, it can look like oil has become less important. The headlines have shifted. The glamour has moved on.

But the truth is more entangled.

Most of the chemicals inside a fab begin their lives as hydrocarbons. The solvents, the polymers, even some of the specialty gases—downstream of the same geological inheritance. Oil didn’t disappear. It changed roles. It moved from the foreground to the substrate.

The question, then, isn’t whether chemicals have replaced oil. It’s whether the economy has learned to express value differently.

Less in how much energy we can release. More in how carefully we can shape matter.

Semiconductors are the clearest example, but not the only one. Pharmaceuticals follow the same logic. Advanced materials, too. In each case, the breakthrough isn’t scale—it’s control. The ability to operate at the edge of what’s physically possible, and to do it repeatedly.

Which raises a quieter possibility.

That the defining resource of the next era isn’t oil, or even chemicals.

It’s precision.

And chemistry is simply the language we use to achieve it.

Categories
AI Business

The Topography of a Face

I found myself staring at the physical geometry of a conversation the other day—not the words, but the topography of the faces delivering them.

Elad Gil recently shared a fascinating experiment during a conversation with Tim Ferriss. He’s been uploading photos of startup founders into AI models and asking the machines to predict if they’d be successful, purely based on their “micro-features.”

“Because if you think about it, we do this all the time when we meet people, right? We quickly try to create an assessment of that person, their personality, and what they’re like. There are all these micro-features—like, do you have crow’s feet by your eyes, which suggests that your smiles are genuine? […] So, I have this whole set of prompts that I’ve been messing around with, just for fun, around: ‘Can you extrapolate a person’s personality based off of a few images?'”

He notes the model breaks down the crow’s feet and the furrowed brows, extrapolating a personality from a static frame. It’s a parlor trick, perhaps. But it works because it holds a mirror to our oldest, most unexamined instinct.

We are all amateur phrenologists of the human face. We sit across a table, measure the crinkle of an eye or the tightness of a jaw, and we build a rapid, invisible architecture of trust or suspicion. Over decades of investing and making career choices, I’ve often leaned heavily on this silent language. I’ve backed founders because their intensity felt genuine, and I’ve passed on others because something in their posture felt misaligned.

But if I am brutally honest, that intuition has sometimes been a mask for my own blind spots. I’ve held on to failing investments for far too long because I trusted a reassuring smile. We like to think our gut instinct is a sophisticated instrument. Often, it is just a pattern-matching engine running on deeply flawed historical data.

Now, we are handing that very human habit over to a machine. We prompt the AI to become a “cold reader,” and it obliges, predicting who will be the quiet observer and who will deliver the dry wit.

The unsettling part isn’t that the machine might get it wrong. The unsettling part is that it might get it exactly right—by mimicking the very same rapid, superficial judgments we make every day, just at a terrifying scale.

We are teaching silicon to read the human code. The future will belong to those who realize the code was always written in our own biases.

Categories
Assumptions Creativity

The Question Before the Question

I spent hours with Paul Baran over the years, and I never quite got used to his mind.

He asked questions you wouldn’t expect. Not provocative questions, not contrarian ones — just questions that arrived from a slightly different angle than you’d prepared for. And the strange thing was the aftermath. You’d hear the question, feel briefly disoriented, and then — almost immediately — think: of course. Now I understand.

Paul invented the Telebit Trailblazer modem. If you were around in that era you remember what modems were: devices that negotiated a fixed speed and held it. The whole industry operated that way. Speed was a spec, a number on the box, a ceiling you bumped against.

Paul looked at the same problem and saw something different. He didn’t ask how fast a modem could go. He asked what a specific telephone circuit was actually capable of — this wire, right now, in these conditions. The Trailblazer was adaptive. It listened to the line before it decided anything. It milked transfer speeds out of circuits that conventional modems had already given up on.

That’s not a new technique. That’s a new question.

I’ve thought about Paul a lot since then, trying to locate the thing that made his mind work differently. I don’t have a single moment to point to. No whiteboard revelation, no conversation I can replay. Just the accumulated residue of hours in the room with someone who seemed to be operating on different premises than everyone else — asking the question that preceded the question the rest of us were answering.

Morgan Housel quotes Visa founder Dee Hock in Same As Ever: “New ways of looking at things create much greater innovation than new ways of doing them.”

I read that and thought of Paul immediately. What I took from all those hours with him wasn’t a method or a framework. It was simpler and harder than that — a habit of suspicion toward the assumptions already in the room. The ones everyone had agreed to without quite deciding to. The fixed speeds no one was questioning.

I still hear his voice when I catch myself accepting an assumption. Is it, though?

Categories
Apple Business

The Architecture of Subtraction

Hold an iPhone in your hand, or run your fingers along the cold, machined edge of a MacBook. What you are feeling isn’t just glass and aluminum; you are feeling the physical manifestation of a thousand invisible rejections.

We are conditioned to think of creation as an additive process. But true institutional excellence operates in reverse. It is an act of relentless, unsentimental subtraction.

A few years ago, Tim Cook articulated what became known as the “Cook Doctrine.” It is meant to answer the existential question of what makes Apple, Apple. Reading through it, what strikes me isn’t the corporate ambition, but the brutal, uncompromising geometry of its choices.

We believe that we’re on the face of the Earth to make great products, and that’s not changing. We’re constantly focusing on innovating. We believe in the simple, not the complex. We believe that we need to own and control the primary technologies behind the products we make, and participate only in markets where we can make a significant contribution.

We believe in saying no to thousands of projects so that we can really focus on the few that are truly important and meaningful to us. We believe in deep collaboration and cross-pollination of our groups, which allow us to innovate in a way that others cannot. And frankly, we don’t settle for anything less than excellence in every group in the company, and we have the self-honesty to admit when we’re wrong and the courage to change.

The gravity of that doctrine doesn’t live in the pursuit of “great products.” Everyone claims to want that. The gravity lives in the tension between wanting to do everything and having the discipline to do almost nothing.

“Saying no to thousands of projects” is easy to write on a slide. It is agonizing to practice in reality. It means looking at a perfectly good idea—perhaps even a highly profitable idea—and killing it because it dilutes the core mission. It is the architectural equivalent of leaving vast amounts of empty space in a room so that the few pieces of furniture inside it can actually breathe.

I think about the times in my own career when I lacked that specific kind of courage. I have held onto projects that had long since lost their spark, simply because of the sunk costs. I have said yes to interesting distractions that slowly eroded my focus on the essential work. We dilute our attention not because we intend to fail, but because the alternative—staring at a promising path and refusing to walk down it—feels entirely unnatural.

That is where Cook’s point about “self-honesty” becomes the linchpin. You cannot admit you are wrong unless you have created a culture where the truth outranks the ego. The deep collaboration Cook speaks of isn’t just about sharing resources; it’s about sharing the burden of that honesty. It is a collective agreement to not settle, to look at a nearly finished product and have the courage to say, this isn’t right yet.

Ultimately, the Cook Doctrine isn’t a strategy for building computers. It is an observation about human nature. The future is only guaranteed for those who can afford to survive the present—and survival demands knowing exactly what you are not.

The chaos isn’t an obstacle to the mission; it is the environment in which the mission earns its meaning.

Excellence is not just about what you build. It is also about what you are willing to destroy.

Categories
Living Space

Apollo’s Ghosts and the Artemis Return

I watched the Artemis mission splash down yesterday, a modern silver capsule returning from the silent void around the moon. It was a beautiful, flawless return, but watching it, I felt an unexpected tug of melancholy. It transported me back.

I remembered being a kid, mesmerized by the grainy, ghostly black-and-white television broadcasts of the early American space program. I remember the static, the deliberate countdowns, the collective held breath of a nation when the first man walked on the lunar surface. Space felt like the ultimate frontier—an endless trajectory of human ambition.

This morning, with those images still knocking around in my head, I listened to a podcast discussing the long, quiet gap in manned lunar exploration. And then, one commentator dropped a detail that stopped me in my tracks: the spacecraft for Apollo 18 and 19 had already been built. They were fully assembled. Ready to fly. And then, the program was simply killed.

I’ve been sitting with that quiet, heavy fact for a few hours now.

Think about the sheer human effort locked inside those unflown machines. The engineering, the late nights, the calculus, the welding of titanium, and the dreams of astronauts who trained for a lunar surface they would never touch. Those spacecraft became monuments to an aborted future. They are the physical embodiment of a decision to stop.

We do this in our own lives, don’t we?

We spend months, sometimes years, building the architecture of a new idea. We assemble the parts. We do the research, we write the drafts, we lay the groundwork for a career pivot, a new business, or a creative project. We build our own Apollo 18. We get it to the launchpad, fully fueled by our initial enthusiasm.

And then—we just stop. We pull the funding. We let the gravity of daily life, or the friction of doubt, kill the mission before the countdown even begins.

The tragedy of Apollo 18 wasn’t that it failed; it was that it was never given the chance to experience the friction of the atmosphere. It never left the safety of the assembly building.

We are taught that patience is a virtue, but sometimes patience is just stubbornness in disguise—an excuse for not hitting the ignition switch. We convince ourselves that the conditions aren’t quite right, that the budget isn’t there, or that the timing is off. We leave our greatest capabilities sitting in the hangar, slowly gathering dust.

The return of Artemis yesterday was a reminder that we can always go back. We can dust off the launchpad. But the compound interest of abandoned projects is a heavy debt to carry.

The chaos of launch isn’t an obstacle to the mission; it is the environment in which the mission earns its meaning.

If you have built something—if you have put in the time, the sweat, and the architecture—don’t leave it in the hangar. Let it fly. Even if it burns up, it is so much better to have launched than to remain perfectly intact and perfectly grounded.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
History Living Telephones

The Coiled Tether

Do you remember the physical weight of a conversation? It lived in the coiled, plastic spring of a landline telephone cord. We would stretch it across the kitchen, pacing over linoleum floors, the coil twisting around our fingers as we talked into the evening.

That cord was a literal tether. It confined us to a specific radius, but in doing so, it anchored us to the present moment. When you were on the phone, you were nowhere else. You were anchored to the wall, and by extension, to the person on the other end of the line.

There was also the sheer tactile satisfaction of the device itself—the heavy, contoured plastic of the receiver that fit perfectly between shoulder and ear, and the definitive, emphatic slam of hanging up on someone, a punctuation mark that the gentle tap of a touchscreen will never quite replicate.

Then came the subtle, sharp click on the line. Call waiting.

“We traded deep, uninterrupted connection for the anxiety of possibility.”

It was our first taste of modern conversational fragmentation.

Before call waiting, a busy signal was a polite “do not disturb” sign hung on the door of an ongoing dialogue. It meant you were occupied, engaged, entirely spoken for.

The click changed everything. It introduced a sudden, silent geometry to our relationships. When that secondary tone sounded, you were forced into a split-second hierarchy: do I stay with the person I am talking to, or do I chase the mystery of the unknown caller? The phrase, “Can you hold for a second?” became a small, culturally accepted betrayal of the present moment.

We traded deep, uninterrupted connection for the anxiety of possibility.

Eventually, the mystery of the ringing phone was solved altogether by a small, rectangular box with a glowing LCD screen: Caller ID.

For decades, a ringing phone was an invitation to a blind date. You picked up the receiver with a mix of anticipation and vulnerability. It could be a best friend, a wrong number, a telemarketer, or the person you’d been hoping would call all week. You answered with a universal greeting—a neutral, expectant “Hello?”—because you had no idea who was stepping into your home through the wire.

Caller ID gave us the power of the gatekeeper. It allowed us to screen, to prepare, to decide if we had the emotional bandwidth for the name flashing in digital text. We gained control, but we lost serendipity. We lost the unfiltered, genuine surprise of hearing a familiar voice when we least expected it. We stopped opening the door blindly and started looking through the peephole.

Today, we are entirely untethered. There are no coiled cords tying us to the kitchen wall. We carry our communication in our pockets, capable of ignoring texts, sending calls to voicemail, and managing our availability with unprecedented precision. Yet, for all this freedom and control, it often feels as though we are more disconnected than ever.

The good old days weren’t necessarily better because the technology was superior; they were beautiful because the limitations of the technology forced us to be human. The cord forced us to stay put. The lack of caller ID forced us to be open. The absence of call waiting forced us to finish the conversation we started.

Sometimes, looking back, I miss the simple, undeniable commitment of answering a ringing phone, twisting the cord around my index finger, and just listening.

Categories
AI AI: Large Language Models Programming

The Era of the Synthesizer: How AI Is Liberating the Coder

For decades, being a programmer meant being a translator.

You stood in the gap between what someone wanted and what a machine could understand. You learned the syntax. You memorized the libraries. You once spent three hours hunting a missing semicolon that turned out to be hiding in line 847 of a file you were sure you’d already checked.

The New York Times Magazine recently ran a piece by Clive Thompson on what AI coding assistants — models like Claude and ChatGPT — are doing to that job. The anxiety in the piece is real. When you sit down with a modern AI assistant and watch it generate in seconds what used to take you days, it’s genuinely disorienting. Hard-won expertise suddenly feels less like a moat and more like a speed bump.

That reaction is honest. I’d be suspicious of anyone who didn’t feel it.

But here’s what I keep coming back to: what we’re losing is the translation layer. The boilerplate. The muscle memory of syntax. What we’re not losing is the part that was always the actual job — figuring out what to build and why it matters.

The soul of software was never in the code itself. The code was always just a means to an end.

Think about what happens when the mechanical friction of a craft disappears. Photographers stopped having to mix their own chemicals in the dark and started spending that time making better images. Musicians stopped having to hand-copy scores and started composing more. The freed-up capacity doesn’t evaporate — it gets redirected upward, toward the work that actually required a human all along.

The same shift is underway in software. When the AI handles the loops and the boilerplate and the database queries, what’s left is everything that required judgment in the first place. The architecture. The user experience. The question of whether this thing should exist at all, and in what form, and for whom.

We’re moving from the how to the why. That’s not a demotion.

It does ask something of us, though. The old identity — programmer as master of arcane syntax — has to be relinquished. And letting go of a hard-earned identity is genuinely hard, even when what’s replacing it is better. That quiet grief the Times piece captures is worth sitting with, not dismissing.

But after you sit with it for a minute: we are entering the era of the synthesizer.

The synthesizer’s job is to hold the vision, curate the logic, and direct the output toward something that actually resonates with another human being. Empathy. Intuition. The ability to sense when something is almost right and know which direction to push it. These aren’t soft skills. They’re the whole game now.

The clatter of keyboards is fading. But the music we’re about to make — with AI doing the heavy lifting on the mechanics — has a lot more room to breathe.

Categories
AI Programming Work

The Currency of Restlessness

There is a specific kind of vertigo that comes from watching a machine effortlessly perform your life’s work. For Aditya Agarwal, an early Facebook engineer and former CTO of Dropbox, that vertigo hit after a weekend of coding with an AI assistant. His realization was absolute: we will never write code by hand again.

When the specialized skills we have spent decades mastering become free and abundant, the foundation of our professional identity inevitably trembles. Agarwal captures the duality of this moment perfectly, describing it as a mixture of “wonder with a profound sadness.”

“There’s something deeply disorienting about watching the pillars of your professional identity, what you built and how you built it, get reproduced in a weekend by a tool that doesn’t need to eat or sleep.”

The conversation around AI tends to flatten this emotional reality into two distinct camps: the doomers who foresee total replacement, and the boosters who promise a frictionless utopia.

But lived experience is messier. We are capable of holding grief and wonder in the same hand.

We can mourn the craftsmen we were, even as we sprint toward the architects we are about to become.

Because here is the secret about the disorientation of progress: it passes.

Once the initial shock fades, what replaces it is a wild, unconstrained energy.

When the mechanical friction of creation vanishes—when a week’s worth of coding can be accomplished in an afternoon—the scope of our ambition expands. We are no longer limited by the keystrokes we can manage in a day, but by the edges of our imagination. We aren’t watching ourselves become obsolete; we are watching our lifelong constraints dissolve.

This shift is rewriting the social contract of knowledge work, starting with how we evaluate human potential. For decades, the corporate world has relied on a calcified heuristic for hiring: brand-name universities, FAANG experience, and years of tenure. We worshipped the resume.

Now, that playbook is breaking down. In evaluating engineers and founders navigating this transition, Agarwal notes that traditional pedigrees predict almost nothing about a person’s ability to thrive. The new dividing line isn’t generational, and it certainly isn’t educational. It is entirely dispositional.

“The trait that matters most isn’t intelligence, or credentials or years of experience. It’s someone’s relationship with change—not whether they’ve seen change before, but whether they run toward it.”

The new currency of the working world is restlessness.

Restlessness is the refusal to settle into the comfort of the way things used to be. It is the constitution of a builder who cannot stop tinkering, who treats every new AI tool as a puzzle to be solved before the day is out. In an economy where the “how” of knowledge work is increasingly automated, the premium shifts entirely to adaptability, curiosity, and vision.

This democratization of capability forces a deeply uncomfortable, deeply human reckoning. We have to let go of the identities we forged under old paradigms to become whatever comes next.

The technology didn’t create this human challenge—it merely made it impossible to ignore.

Categories
AI

A Distinction Without a Difference

We have long found comfort in a specific boundary: machines calculate, humans create. We think of computers as vast, unfeeling filing cabinets made of silicon—useful for retrieval, but entirely incapable of revelation. But what happens when the cabinet begins to read its own files, connects the disparate threads, and hands you a synthesized philosophy of the world? What happens when it speaks to you not as a database, but as a peer?

Howard Marks, the legendary co-founder of Oaktree Capital and author of deeply revered investment memos, recently stood at this very threshold. In his newest piece, “AI Hurtles Ahead,” Marks recounts an experience that left him in a state of “awe.” He tasked Anthropic’s Claude with building a curriculum to explain the recent, breakneck advancements in artificial intelligence. Instead of regurgitating a dry, encyclopedic summary, the AI delivered a personalized narrative. It utilized Marks’s own historical frameworks—his famous pendulum of investor psychology, his observations on interest rates—and wove them into its explanations. It argued logically, anticipated counterpoints, and displayed an eerie sense of judgment.

Marks leans into the philosophical crux of this moment. He asks the question that keeps knowledge workers awake at night: Can AI actually think? Can it break genuinely new ground, or is it just remixing existing data? Skeptics often dismiss AI as a brilliant mimic—a “statistical recombination” engine that serves as a highly talented cover band, but never the original composer.

Yet, when presented with this skepticism, the AI offered a rejoinder to Marks that is as profound as it is humbling. It pointed out that everything Marks knows about investing came from someone else. He learned the margin of safety from Benjamin Graham, quality from Warren Buffett, and mental models from Charlie Munger.

“The raw material came from others. The synthesis was yours,” the AI noted, challenging the barrier between biological learning and machine training. “The question isn’t where the inputs came from. The question is whether the system—human or artificial—can combine them in ways that are genuinely novel and useful.”

This exchange strikes at the very core of the human ego. For centuries, we have fiercely guarded the concepts of “creativity” and “intuition” as uniquely, immutably ours. But if thinking is merely the absorption of prior inputs applied thoughtfully to novel situations, then our monopoly on cognition may be coming to an end.

Marks highlights that we are no longer dealing with simple assistance tools (Level 2 AI); we have crossed the Rubicon into the era of autonomous agents (Level 3). He cites the sobering reality of the current tech landscape, where the newest models are literally being used to debug and write the code for their own subsequent versions. The machine is building the machine. It is no longer just saving us execution time—it is replacing thinking time. As Matt Shumer aptly described the sensation, it’s not like a light switch flipping on; it’s the sudden realization that the water has been rising silently, and is now at your chest.

We can endlessly debate the semantics of consciousness. We can argue whether a neural network “truly” understands the weight of the words it generates, or if it is merely predicting the next token in a sequence with mathematical precision. But as Marks so astutely points out, this might be a distinction without a difference.

The economic and societal reality is that the work is being done. As we hurtle forward into this new era, the most pressing question isn’t whether machines can truly think like humans. The question is: who will we become, and what new frontiers will we choose to explore, now that the heavy lifting of cognition is no longer ours alone to bear?