Tag: Future of Work

Categories
AI Books Writing

The Tax We No Longer Have to Pay

When Carol Coye Benson and I sat down to write Payments Systems in the U.S., one of the first problems we had to solve wasn’t about payments. It was about history.

To understand why the ACH network works the way it does, or why checks persisted decades longer than anyone expected, you need the institutional sediment underneath — the regulatory decisions, the failed experiments, the path dependencies baked in by choices made in the 1970s that nobody thought would still matter in the 2000s. The history is the explanation. Strip it out and you have a description of current practice with no account of why it exists or what it cost to get there.

But history takes pages. And pages test a reader’s patience. So you compress. You make judgment calls about what survives the cut and what gets left behind, and you make those calls knowing that every omission is a bet — a bet that the reader can follow without it, that the thread holds without that particular knot.

Writing it taught me something. The act of compressing, of finding the minimum sufficient version of a complex thing, forces a clarity that living inside the complexity never quite delivers. You don’t fully know what you understand until you have to say it precisely enough for someone else to follow.

But compression is always a loss. You feel it as you write. The version in the book is thinner than the thing you know.

Garry Tan uses a term — “tokenmaxxing” — that initially sounds like jargon from a performance optimization thread. The idea is simple: don’t be stingy with context. Give the model everything. Every source document, every relevant article, every piece of background that a human reader would never sit still for. Let it synthesize rather than guess.

The instinct it runs against is deep. We have spent decades building information systems around compression — search engines that retrieve rather than ingest, executive summaries that stand in for reports, one-pagers that distill months of work into something a decision-maker can absorb in four minutes. All of it was a rational response to a real constraint: human attention is finite and expensive. You couldn’t afford to read everything, so you built filters. The whole architecture of how organizations manage information was designed around that limit.

Tokenmaxxing is a bet that the limit has moved.

The model can read everything. The cost of giving it full context — the uncompressed history, the original sources, the institutional sediment — is low enough now that filtering before the model sees it may introduce more error than it prevents. You’re potentially discarding signal when you summarize for the model the way you’d summarize for a human. The model doesn’t need the one-pager. It can handle the report.

This doesn’t dissolve the need for curation entirely. More context isn’t always better — models can lose the thread in noise the same way humans do, just differently. The skill shifts from summarizing to selecting: not what’s the minimum version of this but what’s actually worth including. Different judgment, still essential.

But the deeper change is upstream of any particular project. The compression we built into every research process, every briefing, every book — that was never the goal. It was the tax we paid for human cognitive limits. Part of the process doesn’t pay that tax anymore.

When I think about writing that payments book today, I don’t think the book itself would change much — it still has human readers with finite patience. But the map we drew before writing it, the synthesis work, the “what connects to what across fifty years of regulatory history” work — that could happen at a different depth now. The understanding you bring to the writing can be informed by everything, not just the subset you had time to read.

The payments book was written entirely for humans, with all the compression that implies. But Tyler Cowen just published what he calls a “generative book” — 40,000 words released free online, paired on the same screen with a Claude interface so readers can discuss, interrogate, and extend it in real time. He’s writing for both audiences simultaneously now. The human reader and the model that will help that reader go deeper. The text is optimized not just to be understood but to be used — as context, as a jumping-off point, as raw material for a conversation that the author won’t be in.

That’s a different kind of writing. Not better or worse. Different. The compression decisions change when one of your readers has no patience to protect.

Writing still clarifies thinking. That part hasn’t changed. But what you’re clarifying, and who you’re clarifying it for, is quietly expanding.

Categories
AI Programming Software Work

The Scarcest Thing

Garry Tan woke up at 8 a.m. after sleeping at 4. Not because he had to. Because he wanted to see what his workers had done overnight.

The workers are AI agents. Ten of them, running in parallel across three projects. And something about that sentence — wanted to see what they’d done — keeps stopping me. That’s not the language of someone using a tool. That’s the language of someone managing a team.

Tan gave a name to the state this puts him in: “cyber psychosis.” He said it as a joke. But the joke has an insight in it. He’s not describing addiction to a productivity app. He’s describing a shift in what it means to do creative work — the strange vertigo of becoming a director when you’d always been a laborer.

I’m retired. I watch this from the outside now, which is its own kind of vantage point. For most of my career, the path from idea to working product ran through people — through hiring and managing and the slow accretion of execution capacity. You had the vision or you didn’t, but either way you needed the team. The idea and the means of making it real were, structurally, separate things. The gap between them was where companies lived.

What Tan is describing is that gap closing.

The thing he built — gstack, his open-sourced Claude Code configuration — got dismissed in some quarters as “just prompts.” And it is just prompts, in the same way that a conductor’s score is just notation. The abstraction is the invention. What he encoded is a model of how a startup team thinks: the CEO who interrogates the why before a line of code gets written, the engineer who builds, the paranoid staff reviewer who looks for what breaks. Each role blocks a different failure mode. Blurring them together produces, as his documentation puts it, “a mediocre blend of all four.”

That’s an organizational insight. It has nothing to do with code.

Tan described being a “time billionaire” — not because his biological clock had slowed, but because he can now purchase machine-consciousness-hours. The bottleneck of implementation, which has governed every creative project since the beginning of creative projects, is dissolving for those who know how to direct.

The scarcest thing is shifting. It’s no longer the hours of execution. It’s the clarity of intent — knowing what you want to build and why the journey matters, before any of the workers start moving. That’s harder than it sounds. For decades, most of us could muddle through in the making of it. The act of building taught you what you were building. Now the making is cheap, and that shortcut is gone.

For someone watching from retirement, that’s not a small thing to absorb. The model I internalized over a long career — that ideas become real through sustained organizational effort, through teams and timelines and the grinding work of execution — is being revised faster than I expected. Not invalidated. Revised. The judgment still matters. The taste still matters. The why matters more than ever.

It’s just that the how has found new hands. Many of them. More than any team I ever assembled, available the moment the intent is clear enough to direct them, gone when the work is done. The constraint was always the hands. It turns out it was always the knowing.

Categories
AI AI: Large Language Models Anthropic

Breakout

Jack Clark doesn’t panic easily. He spent years at OpenAI watching capabilities inch upward, then left to co-found Anthropic, and has been writing his Import AI newsletter long enough to have developed — and been wrong about — many priors. So when he publishes an essay saying he has reluctantly arrived at a 60% probability that fully automated AI R&D happens by the end of 2028, the word “reluctantly” deserves some weight.

His essay, published last week and titled “Automating AI Research,” isn’t a press release or a fundraising pitch. It reads more like a man thinking out loud at the edge of something large. “I don’t know how to wrap my head around it,” he writes, which is a notable thing to say publicly when you are one of the architects of the thing you can’t wrap your head around.

The argument is built from benchmarks — not any single one, but a mosaic of them assembled to reveal a trend. SWE-Bench, the test that measures an AI’s ability to solve real GitHub issues, was at roughly 2% when it launched in late 2023. A recent Anthropic model sits at 93.9%, effectively saturating it. METR’s time-horizon plot tracks how long an AI can work independently before needing human recalibration: 30 seconds in 2022, 4 minutes in 2023, 40 minutes in 2024, 6 hours in 2025, 12 hours today. The trajectory, if it holds, suggests 100-hour autonomous work sessions by the end of this year.

Clark marshals similar progressions across AI fine-tuning, kernel design, scientific paper replication, and even alignment research itself. His throughline is the same in each: AI is now genuinely competent at the unglamorous scaffolding of AI development — the debugging, the experiment runs, the parameter sweeps, the code reviews. And crucially, it can now do these things not just faster than humans, but for longer, with less supervision.

There’s a Thomas Edison quote at the center of the essay: “Genius is 1% inspiration and 99% perspiration.” Clark’s claim is that AI has become very good at the perspiration. The question of whether it can supply the inspiration — the paradigm-shifting insight, the Move 37 — remains open. But he argues it may not need to. Most of what has moved the AI field forward has been sustained, methodical work, not lone flashes of genius. If you can automate the 99%, you have something that compounds.

There’s a data point that makes Clark’s argument feel less like forecast and more like dispatch. Last month Boris Cherny, who runs Anthropic’s Claude Code, disclosed that he hasn’t written a line of code by hand in more than two months. Every pull request — 22 one day, 27 the next — written entirely by Claude. Company-wide, roughly 70–90% of Anthropic’s code is now AI-generated. Anthropic’s stated position: “We build Claude with Claude.” The loop Clark is describing as a probability by 2028 is already running, at least partially, today.

The word Clark uses for the threshold he’s describing is not “singularity” or “AGI.” It’s quieter than that. He calls it “automated AI R&D” — the point at which a frontier model can autonomously train its own successor. It’s a specific, falsifiable thing. And he puts a number on it: 60% by end of 2028, 30% by end of 2027.

I’ve been writing about the dark software factory and the 3D printer that prints better printers, finding metaphors for what seems like an inexorable process. Clark’s essay is a different kind of writing about the same thing — the primary source document, the engineer’s log, the inventory of evidence. Reading it is a little like watching someone carefully pack boxes before a move. Each individual item seems manageable. But there are a lot of boxes.

What he’s describing — if the trend holds — is not a feature or a product launch. It’s a breakout. The moment the loop closes and the system starts building itself. He’s not certain it happens. He just thinks it’s more likely than not, and he thought you should know.

Categories
AI Business Work

The Tipping Point Was Last November

Matthew Prince, Cloudflare’s CEO, said something on today’s earnings call that I keep turning over. He didn’t bury it or soften it. He named a date.

“Internally, the tipping point was last November.”

That’s a specific thing to say. Not “we’ve been on a journey” or “AI has been transforming our industry.” A month. A moment. The thing changed, and he knows when.

What changed, by his account, is that Cloudflare’s teams began seeing productivity gains so dramatic they were hard to describe — people who were two times more productive, ten times, in some cases a hundred times. “It was like going from a manual to an electric screwdriver.” Usage of AI tools internally is up more than 600% in just the last three months. Every line of production code is now reviewed by an autonomous AI agent.

And then he said goodbye to 1,100 people — about 20% of the company.

Today wasn’t just Cloudflare. Earnings season has become something like a drumbeat. Meta is cutting 8,000 employees this month. Amazon cut 16,000 in Q1. Oracle eliminated roughly 30,000 to fund AI infrastructure. Block cut almost half its workforce. PayPal is reportedly planning to cut 20% of its staff over the next few years. Coinbase cut 14%. Snap cut 16%. As of this week, more than 92,000 tech workers have been laid off in 2026 alone.

The scale is striking. But what strikes me more is the framing — the specific language being used to describe what’s happening. These aren’t being announced as cost-cutting moves or post-pandemic corrections, the way they might have been in 2022. They’re being announced as architectural decisions. Structural adaptations. Evolution.

Prince was careful to be explicit: “This isn’t a cost-cutting exercise or an assessment of individuals’ performance. It’s about defining how a world-class, high-growth company operates and creates value in the agentic AI era.” That’s not empty corporate language, or at least not only empty corporate language. The distinction he’s drawing — between trimming fat and reimagining how a company is built — maps to something real about what AI agents can now actually do.

There’s a legitimate version of this argument and a convenient one, and they’re being delivered in the same sentence by the same people, which makes them hard to separate. Some analysts suspect companies are using AI as cover for cuts they wanted to make for other reasons — rightsizing from pandemic-era overhiring, funding massive infrastructure buildouts, chasing margin. Oxford Economics flagged this: maybe some firms are “dressing up layoffs as a good news story.” The cynicism is warranted.

But then there’s the Cloudflare number: 600% increase in AI usage in three months. That’s not a narrative. That’s a measurement.

What’s different about this moment — what makes Prince’s “tipping point” language feel accurate rather than convenient — is that the people making these decisions are themselves users of the tools. They’ve seen the productivity numbers internally before anyone else has. They’re not theorizing about what AI might do to their workforce; they’re describing what it already did.

That’s the thing that changed. For years, AI’s labor impact was a future tense conversation. Economists studied it, think pieces warned about it, conferences debated the timeline. Then, somewhere around last November apparently, a cohort of technology companies crossed from hypothetical to empirical. The future tense became past.

Whether you read that as tragedy, as transformation, or as both depends on where you’re standing. 1,100 people at Cloudflare today are standing somewhere very specific. Prince acknowledged this with what felt like genuine difficulty: “A number of friends will no longer be colleagues.” Whether that difficulty changes anything material for the people leaving is a fair question.

But the acceleration itself — the thing he named — is real. The tipping point was last November. And if it was last November for Cloudflare, it was some nearby month for Amazon, for Meta, for Block, for all of them. Whatever these companies learned that changed everything, they all seem to have learned it around the same time.

That’s what I find myself sitting with today: not just the scale of the disruption, but the synchrony of it. The realization arrived, and then the decisions followed. Quietly at first, then all at once.

Categories
AI AI: Large Language Models

The 3D Printer That Prints Better Printers

Imagine a 3D printer that looks at its own design and begins printing a better version of itself. The loop closes. What had always required an external human intelligence now happens inside the machine. All by itself.

Jack Clark — Anthropic co-founder, someone who has spent years closer to this technology than almost anyone — puts the odds of this happening by 2028 at better than even. I have been turning that number over ever since I heard it. Not the technical claim, exactly. The feeling of it.

We have grown used to AI accelerating our work. Coders watch models close GitHub issues at rates that would have seemed miraculous eighteen months ago. Researchers delegate experiment design, kernel optimization, even the fine-tuning of smaller models. The scaffolding of AI progress is already being built, in part, by the systems themselves. But the moment the system begins to redesign the scaffolding — that is something new.

What unsettles me is not the raw capability, though that is staggering. It is the loss of distance.

For most of technological history, the creator stood outside the creation. Even the most sophisticated tools remained tools. Now the distinction begins to blur. A model that can meaningfully improve its own training process, its own architecture, its own alignment constraints, is no longer merely reflecting human intent back at us. It is participating in the shaping of its own nature. And because each iteration can happen faster than the last, the curve steepens in ways our intuitions, tuned to linear progress, struggle to grasp.

Clark is careful, as he should be. He speaks of validation work that will still fall to humans, of the need to broaden the pipes through which abundance flows, of preparing defense-dominant postures against misuse. Yet the image that lingers for me is quieter: the silence after the handoff. What does it feel like when the thing you have been painstakingly teaching begins to teach itself — and then to teach its teachers?

I think about Leo Szilard at the traffic light, or the first controlled chain reaction under the stands at the University of Chicago. Moments when a new regime of possibility quietly announced itself. Recursive self-improvement carries that same charge — not a single event but a process, one that could accelerate the very pace of events themselves.

The more I sit with it, the more I return to an older tension in our relationship with tools. We build them to extend ourselves, and in doing so we are always, subtly, extending — or perhaps risking — what we are. The values I try to live by — generosity, curiosity, compassionate honesty — are not refined in specifications. They are refined in friction, in relationship, in the slow work of being human with other humans. If the machines begin to optimize their own lineage at speeds we cannot match, will we still have the bandwidth to tend the parts of ourselves that no algorithm can yet measure?

I don’t know. None of us do. That uncertainty feels honest.

What feels clearer is the invitation. Not to fear the printer that prints better printers, nor to worship it, but to remain awake inside the loop. To ask, as each new version arrives, what kind of world we are collectively printing — and whether the values we claim to hold are baked into the design or merely etched on the surface, likely to wear away under the heat of iteration.

The light is still yellow. We are still deciding whether to step off the curb. But the traffic is already moving faster than it was a moment ago.

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
AI Business Media News

The Lost-Wax Casting of Cable News

I remember the physical weight of a television remote in the late 1990s, clicking through a suddenly expanding universe of 24-hour cable news. It felt like stepping into a river that never stopped moving.

This morning, Andreessen Horowitz (a16z) announced a new 24/7 “news channel” streaming on X, named “MTS” (Monitor the Situation). It joins networks like TBPN and a growing army of individual creators, all vying to fill the endless void of the present moment with non-stop commentary.

It feels like a significant shift in how we consume the present. But I suspect it’s actually just scaffolding.

In the lost-wax process of bronze casting, an artist sculpts a form in wax, builds a heavy ceramic mold around it, and then pours in molten metal. The heat is absolute. The wax melts away, completely consumed and replaced by the final, permanent structure. The wax was never the destination; it was merely holding the shape until the real material was ready.

Right now, human creators are the wax.

We are building the molds for the 24/7, always-on broadcast of the internet age. Human hosts are sitting in chairs, monitoring the situation, talking into the void, exhausting themselves to maintain the stream. They are doing the grueling, manual labor of defining what a continuous social-first news network looks and feels like.

But human endurance is fragile. We need sleep. We need silence. We eventually run out of words.

The artificial intelligence models currently learning to synthesize news, clone voices, and generate video are the molten bronze. Eventually, the human hosts of these endless streams will melt away. The channel will remain—a fully AI-driven entity that never blinks, never tires, and never needs a coffee break.

I’ve held on to failing investments for far too long, convinced that if I just put more energy into them, they would eventually stabilize and turn around. We often make this mistake. We mistake the transitional phase for the final destination. We think the current iteration of “monitoring the situation” with exhausted human pundits is the future of media.

It isn’t. It’s just the awkward teenage years of a medium waiting for its true native technology.

The human commentators are doing the necessary work of teaching the system what a 24-hour news network on a social platform requires. Once the lesson is learned, the teachers will no longer be needed. The future is only guaranteed for those who can afford to survive the present.

Is it ironic that TBPN was just acquired by OpenAI?

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 Creativity Programming Writing

We Are All Painters Now: The Era of Vibe Coding

For decades, the act of creating software was exactly that: writing. It was a distinctly left-brained, agonizingly precise discipline.

Programmers were typists of logic, translating human intent into a rigid, unforgiving syntax that a machine could understand. A single misplaced semicolon, an unclosed bracket, or a misspelled variable could bring an entire system crashing down.

Building software meant placing one brick after another, working meticulously from the ground up.

In this traditional paradigm, coders were the ultimate embodiment of Annie Dillard’s writer. As she noted in The Writing Life, “Writers… work from left to right. The discardable chapters are on the left.”

When you wrote code, your mistakes, your refactoring, and your discarded logic were all part of a linear, grueling journey. If a feature didn’t work, you had to physically wade back into the text, debugging, reading line by line, and rewriting the narrative of the application. The discarded chapters were the endless hours spent wrestling with a single broken dependency.

But recently, a profound paradigm shift has quietly taken over our screens. We are transitioning out of the era of writing software and into the era of “vibe coding.”

Vibe coding fundamentally changes our relationship with the machine. With the rise of advanced AI coding assistants, we are no longer placing the bricks ourselves; we have become the architects and the creative directors. You don’t write the loop or manually construct the database query. Instead, you describe the feeling, the function, and the outcome. You tell the AI, “Make this dashboard feel more modern,” or “The logic here is too clunky, make it flow faster and handle edge cases gracefully.” You are coding by intuition. You are steering by the “vibe” of the output rather than the mechanics of the input.

Suddenly, Dillard’s other metaphor takes center stage. In the age of vibe coding, we have become painters.

“A painting covers its tracks. Painters work from the ground up. The latest version of a painting overlays earlier versions, and obliterates them.”

When we vibe code, we ask an AI for a functional prototype, and it gives us a canvas. We look at it, test it, and sense whether it aligns with our vision. If it doesn’t quite hit the mark, we don’t necessarily rewrite the code from scratch. We simply prompt the AI to try again, adding a new layer of instruction. The AI paints a new layer of code directly over the old one. The awkward, underlying iterations—the messy attempts at styling, the inefficient logic of the first draft—are obliterated by the newest prompt.

The machine covers our tracks for us. We don’t need to know exactly how the underlying pixels were rearranged or how the syntax was refactored. The final application emerges as a stunning obliteration of its own clumsy past.

As someone who has spent time wrestling with the rigid demands of syntax, there is a strange, quiet grief in letting go of that left-to-right process. There is a deeply earned, tactile satisfaction in building something manually, understanding the precise weight and placement of every line of code. Relinquishing that control can feel like a loss of craftsmanship.

Yet, there is also a breathtaking liberation in this new medium. We are moving from a world of manual construction to a world of artistic curation. The barrier to entry is no longer fluency in a specific, arcane language; it is simply the clarity of your imagination and your ability to articulate your intent.

The next time you sit down to build something digital, notice the shift in your own posture. You no longer have to carry the heavy burden of the writer, agonizing over every word and leaving your discardable chapters on the left. You can step back, look at the whole canvas, and trust your intuition. Let the AI cover the tracks. Embrace the obliteration of the early drafts.

We are all painters now, coaxing the future into existence one brushstroke at a time.

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.