Tag: innovation

Categories
AI AI: Large Language Models China

Cranes on the Horizon

In 2005, during my first trip to Shanghai and Beijing, the most striking feature of the skyline wasn’t the architecture—it was the cranes. More than I could possibly count, perched atop half-finished skyscrapers like a mechanical forest. Entire districts seemed to be mid-construction simultaneously, as if someone had pressed a button and the whole country decided to build everything at once. Dan Wang in his book “Breakneck” described China as the “engineering state” that approaches national problems with physical solutions. Back in 2005, coming from Silicon Valley, I thought I understood what growth looked like. I didn’t.

I’ve been thinking about that trip while reading Nathan Lambert’s recent piece, “Notes from Inside China’s AI Labs.” Lambert — who runs the Interconnects newsletter and does serious work tracking the open-weight LLM ecosystem — just returned from visiting essentially every major AI lab in China. Moonshot, Zhipu, Meituan, Xiaomi, Qwen, Ant Ling, 01.ai. He went in with genuine curiosity and came back with humility. That combination is rarer than it should be.

What he found was the cranes. Different domain, same energy.

Lambert’s central observation is about culture, not capability. The Chinese labs aren’t winning on any single technical breakthrough — they’re winning on execution discipline. He describes researchers, many of them active students, who bring no ego to the work. They absorb context fast, drop assumptions faster, and seem genuinely unbothered by the philosophical debates that seem to swirl constantly in the American AI community. When he tried to engage Chinese researchers on the long-term social risks of models or the ethics of AI behavior, those questions “hung in the air with a simple confusion. It’s a category error to them.” Their role is to build the best model. Full stop. To them, an LLM isn’t a philosophical entity to be interrogated; it’s a piece of infrastructure to be optimized.

That description landed for me. Not as a criticism of American research culture, but as a real observation about what the moment demands. Building good LLMs today is, as Lambert puts it, meticulous work across the entire stack — “all points of the model can give some improvements, and fitting them in together is a complex process.”

The work that matters most right now isn’t the 0-to-1 creative leap; it’s the thousand unglamorous decisions executed without complaint. Students who haven’t yet learned to lobby for their own ideas turn out to be well-suited for exactly this.

Lambert ends on a note that’s hard to shake. Looking up from his laptop on a high-speed train, he keeps seeing cranes on the horizon. He draws the same connection I did, though from the inside: the construction everywhere fits the broader culture and energy around building. “When I look up from my laptop and always see bunches of cranes on the horizon, it obviously fits in with the broader culture and energy around building in China.”

Twenty years after my first visit, the cranes are still there. They’ve just moved indoors — into server rooms and training runs and model releases that land every few months with quiet confidence. In 2005, what China was building was obvious: you could see the steel frames going up. What’s being built now is harder to see, which may be exactly why it keeps surprising us.

Check out Lambert’s essay – it’s remarkable. If the 20th century was defined by who could move the most earth, the 21st will be defined by who can move the most tokens. And right now, the cranes are moving faster than we think.

Categories
Science Stanford

Bypassing the Leaf

For my entire life, I’ve understood the world through a simple, quiet equation: green plants take sunlight and air, and turn them into the stuff of life. It is a slow, terrestrial magic we all learn in grade school.

But lately, after listening to Professor Drew Endy at Stanford, I’ve been sitting with a curious yet exciting realization: that ancient equation is being rewritten.

Professor Endy champions a concept called electrobiosynthesis, or eBio. At its core, it represents the engineering of a parallel carbon cycle that operates independently of traditional photosynthesis.

The global industrial complex is approaching a transition point where our traditional reliance on extractive fossil fuels is being superseded by a regenerative, biological manufacturing paradigm.

For millennia, humanity has relied on the biological “middleman” of the plant to capture solar energy. But natural photosynthesis, for all its quiet beauty, is limited by severe biochemical constraints. Most commercial crops convert less than 1% of incident solar energy into usable biomass.

Electrobiosynthesis changes the math. By bypassing the plant entirely, we can utilize high-efficiency photovoltaics—which capture over 20% of the sun’s energy—to drive carbon fixation directly into the metabolic hubs of engineered microbes. This fixed carbon is transformed into organic molecules, serving as the feedstocks for high-value products like proteins and specialty chemicals.

In my own career, I’ve watched industries undergo profound, structural phase shifts. This really feels like another one of them. It seems that we are looking at a future where any molecule that can be encoded in DNA can be grown locally and on-demand. This fundamentally decouples manufacturing from centralized industrial nodes and fragile global supply chains.

The field appears to currently be in its “transistor moment,” moving from laboratory feasibility to industrial pilot plants. It signifies the ability to construct and sustain life-like processes without being restricted to the terrestrial lineage of photosynthesis.

Of course, with such foundational power comes the weight of unintended consequences. The ability to engineer life at this level brings severe biosecurity risks, and even the “Sputnik-like” strategic challenge of international competition in biotechnology. There are profound ethical dilemmas on the horizon, such as the creation of “mirror life”—organisms made from mirror-image biomolecules that might be invisible to natural ecosystems.

But the trajectory seems set. The vision described by Professor Endy—a world where we grow what we need, wherever we are, using only air and electricity—is no longer a distant science fiction. It is a nascent industrial reality. This future is being written not in sprawling factories, but in the microscopic architecture of the cell.

I’ve just now reading a deep research report on this whole area that I asked Google Gemini to create. It’s fascinating and I’ve discovered a whole new area (beyond AI) to explore further.

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
AI Anthropic Business Google

The Weight of the Bill

Technicians working among server racks with real-time performance graphs displayed on overhead screens
Technicians monitor server performance and network data in a modern data center

Jordi Visser has been making the case for months — in his weekly YouTube commentary and on his Substack — that we are living through an exponential transition that most people are measuring with the wrong instruments. I think he’s right. I found two data points this week that suggest why.

I was somewhere in the middle of an Invest Like the Best episode when Dylan Patel said it — almost as an aside, the kind of thing you drop to establish context before moving on to the point you actually came to make. His firm, SemiAnalysis, analyzes the semiconductor and AI industries for a living. And their usage of Claude, he noted, has been growing. The costs have been growing too.

Exponentially.

He moved on. I didn’t.

I think Patel’s API bill might be one of the more honest documents in the current AI moment — more honest than the analyst reports his firm produces, more honest than the earnings calls where every public company performs its AI fluency for shareholders.

Surveys bend. When you ask someone whether they’re using AI in their work, you’re asking them to self-report on a technology that has become a proxy for relevance, for not being left behind. The incentive to say yes is enormous. And even when the yes is genuine, it tells you nothing about depth — whether AI has become load-bearing in how someone actually works, or whether it’s an impressive thing they do occasionally.

Nobody pays exponentially growing API costs for show. Money is the honest witness.

What makes Patel’s situation quietly strange is the recursion in it. SemiAnalysis exists to help sophisticated investors and technologists understand this industry — and they cannot predict their own consumption curve. They are inside the exponential the same way everyone else is. They just happen to be watching their bill.

Then this morning, a different number arrived. Google announced it will invest up to $40 billion in Anthropic — $10 billion committed now, another $30 billion contingent on performance milestones. This follows a separate $5 billion from Amazon, part of a broader arrangement under which Anthropic is expected to spend up to $100 billion on compute over time.

The temptation with numbers like these is to treat them as spectacle. Forty billion dollars is so large it becomes almost aesthetic — a statement about ambition, about the kind of bets that define eras. You feel the weight of the zeros and move on.

But I keep coming back to Patel’s API bill.

Because Google’s $40 billion and SemiAnalysis’s compounding monthly costs are saying the same thing, expressed at scales so different they almost don’t seem related. One is a research firm noticing that their tool usage has quietly escaped prediction. The other is one of the most sophisticated capital allocators on earth making a bet that strains comprehension. But both are pointing at the same reality: that this technology, wherever it takes hold, does not plateau. It compounds.

We have been waiting, I think, for the moment when AI adoption becomes legibly real — some threshold event that separates the signal from the noise, the press release from the actual change. The surveys were supposed to mark that moment. The enterprise announcements. The benchmark numbers.

Patel’s aside suggests we’ve been waiting for the wrong thing. You don’t arrive at the exponential. You just eventually notice you’re already in it — in an aside on a podcast, before moving on to the point you actually came to make.

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
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
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
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
Apple

The MacBook Neo

Reading the overwhelmingly positive reviews of the new MacBook Neo I am reminded of this from the recent book Apple in China:

“Engineers said the pressure to put in the long hours was all but mandatory. Indeed, a decade later after Jobs created Apple University, a corporate institution meant to convey his values to a new generation of employees, Apple came close to codifying the principle that pushing employees to burnout was acceptable.

In a slide deck called Leadership Palette, Apple states: “Fighting for excellence is about resisting the gravitational pull of mediocrity. It involves being dead tired and still pushing yourself, and others, to get it right, every time.”” (Patrick McGee, Apple in China)