Category: Work

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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.

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AI Work

Betting on Ourselves in the Age of AI

Every time tech takes a leap, we assume we’re finally obsolete. The current panic, which Greg Ip recently picked apart in the Wall Street Journal, is AI. We hear endless predictions of “economic pandemics”—server farms wiping out white-collar jobs overnight, leaving everyone broke and adrift.

It’s a terrifying story. It also completely ignores history.

Ip highlights the main flaw in the doomsday pitch: it misreads how markets work. We treat labor like a fixed pie. If a machine eats a slice, we assume that slice is gone forever.

“Technological advancements always cost some people their jobs—those whose skills can be easily substituted by tech. But their loss is more than offset through three other channels. The new technology enhances the skills of some survivors… it helps create new businesses and new jobs; and it makes some stuff cheaper…”

That cycle holds up. Take the 1980s spreadsheet panic, a perfect parallel. When Lotus 1-2-3 and Excel hit the market, bookkeepers freaked out. Then the number of accountants and financial analysts exploded. Software didn’t kill the need to understand money. It just did the math, letting people focus on strategy.

We’re seeing the exact same thing with software development. Coding isn’t dead. As AI makes writing basic code cheaper, demand for software just goes up. That requires more humans to architect systems and supervise the AI. The pie just gets bigger.

But my skepticism about the AI apocalypse goes beyond economics. It’s about why we pay people in the first place.

We don’t just buy services; we buy accountability. Ip notes that radiologists kept their jobs because patients want a real person explaining their scans. Google Translate has been around since 2006, yet the number of human translators has jumped 73%. When the stakes are high—a legal contract, a medical diagnosis—we want a human in the room. We want a real person on the hook.

The danger isn’t that AI will replace us. The danger is that we panic and forget our own adaptability. The transition will hurt, and specific jobs will disappear. We’ll need safety nets. But betting against human ingenuity has always been a losing wager.

Large language models are tools, not replacements. They handle the cognitive heavy lifting, much like tractors handled the physical heavy lifting. Tractors didn’t end farming; they just killed the plow.

Work will change. We’ll have to figure out which of our skills are actually “human.” But as long as we want the presence and accountability of other people, there will be jobs. We just have to evolve. And we do. It’s the human spirit. Or is this time “really different”?

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AI Work

The Dealers of Intelligence

There’s a scene early in John Kenneth Galbraith’s The Affluent Society where he describes Americans of an earlier era regarding industrial output with something close to reverence — the sheer productive capacity of the nation seemed almost miraculous, a force that could reshape civilization. Within a generation, of course, that same output had become background noise. Factories hummed, goods appeared, and nobody paused to marvel.

The miraculous had become mundane, and the mundane had become infrastructure.

I found myself thinking about that arc recently while listening to Sam Lessin on the More or Less podcast.

Lessin made an observation that I haven’t been able to shake: we probably aren’t heading toward a single, triumphant AGI monopoly — some god-machine that one fortunate company builds first and then rents to the rest of us in perpetuity.

Instead, Lessin suggested, we are barreling toward something far more ordinary, and in its ordinariness, far more interesting.

“There will be lots of ‘dealers of intelligence’. No one company will corner the market, no one big winner of AGI.”

Dealers of intelligence. I keep turning that phrase over. Where do we end up? No rapture, no singularity, no chosen company ascending to the throne of cognition. Just suppliers, distribution channels, price competition — the unglamorous mechanics of any maturing market.

And historically, that’s exactly how this tends to go.

Salt was once precious enough to pay soldiers with. Spices rewrote the map of the world. Steel, oil, and computing power each arrived wrapped in mystique and guarded behind scarcity before the inevitable happened: extraction improved, distribution scaled, and the miracle became a utility. Nobody thinks about the engineering marvel of the electrical grid when they flip a light switch. They just expect the light to come on.

If Lessin is right — and the competitive landscape of the last two years does little to argue against him — intelligence will follow the same curve. Not a single oracle, but a market. Cognitive utilities. Price-per-token negotiations. The same forces that commoditized bandwidth will commoditize reasoning, and we’ll argue about our AI subscription tiers the way we currently argue about our data plans.

Which forces the interesting question: when genius is cheap, what exactly becomes valuable?

The professional moats of the last century were largely built on the ability to process specialized information and output reliable answers.

The doctor, the lawyer, the financial analyst, the programmer — each occupied a protected position because access to their domain of reasoning was genuinely scarce.

If I can buy a substantial fraction of that reasoning from a commodity supplier for fractions of a cent, the premium on raw cognitive horsepower doesn’t just shrink. It collapses.

What’s left, I think, is the un-commoditizable. Empathy. Physical presence. Judgment under conditions of genuine uncertainty and consequence. And above all — taste.

Taste is the thing that has always resisted systematization, because taste isn’t rational in any clean sense. It’s the residue of lived experience, of specific childhoods and particular failures and the accumulated weight of caring about things over time.

An algorithm can produce a structurally flawless piece of music; it takes a human to decide whether it matters, and why, and to whom.

That act of curation — of choosing what deserves to exist and what doesn’t — is going to become more consequential, not less, as the supply of technically competent output explodes.

There’s something almost liberating about this, if you let yourself sit with it.

A world of commoditized intelligence is, paradoxically, a profoundly human one. It removes the burden of raw computation from the center of what we do and pushes us toward the edges — toward the questions only we can ask, the connections only we can feel, the decisions only we can be held accountable for.

The dealers of intelligence will handle the materials. We’ll still have to decide what to build. Architects.

Questions to Consider

  1. If intelligence becomes a commodity like electricity or bandwidth, which industries or professions will be slowest to feel that pressure — and why?
  2. Lessin frames this as a market with many suppliers rather than a winner-take-all race. Does the competitive landscape today support that view, or does it still look like a sprint toward consolidation?
  3. What does “taste” actually mean when the person exercising it is doing so with AI-augmented perception and judgment? Is it still the same thing?
  4. Who gets to haggle with the dealers? If cognitive utilities are cheap in aggregate but not universally accessible, does commoditization risk deepening inequality rather than democratizing thought?
  5. If the value of answering questions falls and the value of asking them rises, what does education need to look like — and how far is it from what it looks like now?
Categories
Work YouTube

Zero to Sixty Million

In his speech earlier today at the India AI Impact Summit, Sundar Pichai noted:

Twenty years ago, the concept of a professional “YouTube Creator” didn’t exist; today, there are upwards of 60 million around the world.

One platform, one simple idea (share any video with anyone), quietly rewired how millions of people work, express themselves, build communities, and define success. Twenty years is nothing in historical time, but it’s everything in human opportunity.

What new profession will we look back on in 2046 and say, “Twenty years ago, that didn’t exist”?

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AI History Work

Flash-Frozen Cognition: Birdseye, AI, and the Future of Work

I was listening recently to a conversation between Liz Thomas, Tom Lee, and Michael Lewis — the kind of wide-ranging dialogue where a single offhand story can suddenly anchor everything that’s been swirling loosely in your mind.

Tom’s story was about the 1930s, the weight of the Great Depression, and a man named Clarence Birdseye.

Birdseye had watched the Inuit fish in the brutal cold of Labrador and noticed something the rest of the world had missed: fish frozen instantly at sub-zero temperatures tasted perfectly fresh when thawed. The ice crystals formed too quickly to rupture the cellular walls of the flesh. He took that observation home, patented the process, and introduced the world to flash freezing.

On the surface, he had simply figured out a better way to keep peas green and fish edible. What he had actually done was detonate a quiet economic bomb.

Before Birdseye, entire ecosystems of seasonal labor existed to preserve, salt, can, and rush perishable goods to market before they rotted. When flash freezing arrived, those jobs didn’t evolve — they vanished. The ice harvesters, the seasonal canners, the local preservationists all felt the sudden, biting frost of obsolescence. The cold came fast, and it was indifferent.

Yet zoom out on the timeline, and a different picture emerges entirely. Flash freezing didn’t just kill jobs — it invented new ones that nobody could have anticipated. It necessitated refrigerated trucking. It transformed the grocery store, conjuring the frozen food aisle from nothing. It reshaped the home appliance industry, making the household freezer a fixture of modern life. Most profoundly, it decoupled humanity from the harsh dictates of the harvest season, democratizing access to nutrition across geographies and income levels that had never known that kind of abundance.

The destruction was visible and immediate. The creation was invisible and slow — and vastly larger.

Listening to Tom tell this story, I couldn’t help but see our own reflection in it.

Right now, we are all hyper-focused on the ice harvesters of the cognitive economy. We look at AI — large language models, generative tools, automated reasoning — and we see the rupture. We mourn the entry-level analyst, the copywriter, the junior coder. The anxiety is real. The displacement is real. The cold is real.

But what we are struggling to visualize is the refrigerated trucking of the mind.

“AI is flash-freezing cognition. It is taking tasks that used to rot if not attended to immediately by expensive, time-consuming human effort, and preserving them in a scalable, frictionless state.”

When intelligence and execution can be flash-frozen and shipped anywhere instantly — to a first-generation entrepreneur in rural India, to a solo founder with no budget for consultants, to a teacher in a school that can’t afford specialists — what new aisles get built in the supermarket of human endeavor?

The honest answer is that we don’t know. The Inuit fishermen of Labrador couldn’t have imagined the frozen pizza aisle. The ice harvesters of the 1930s couldn’t have pictured the cold chain logistics industry that employs millions today. We are standing in their moment, watching the ice form, mourning the harvest — and almost certainly underestimating what comes next.

The true impact of AI won’t be measured in the jobs it automates. It will be measured in the industries, creative liberties, and human possibilities that emerge because we no longer have to spend all our energy just keeping the ideas from spoiling.

Questions to Consider

  1. The Invisible Creation: Flash freezing’s job creation vastly outpaced its job destruction — but only over decades. How long are we willing to hold that faith with AI, and what do we owe the people displaced in the interim?
  2. The Democratization Dividend: Birdseye’s invention ultimately made fresh nutrition available to people who never had it. Who are the equivalent beneficiaries of flash-frozen cognition — and are we building the infrastructure to actually reach them?
  3. The Harvest Season Question: We’ve always structured education, careers, and institutions around the assumption that expertise is scarce and slow to develop. What breaks — and what gets liberated — when that assumption stops being true?
  4. The Indifference Problem: The cold that killed the ice harvesters’ livelihoods was indifferent to their suffering. Is there anything about AI disruption that is meaningfully different from previous waves of technological displacement — or are we simply the latest generation to stand in that frost?

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AI New York City San Francisco/California Work

The Paradox of the Pulse

The skyline has always been a silhouette of our collective ambition. For a century, the steel and glass towers of our major cities functioned as the secular cathedrals of the modern age. But as Andrew Yang observes in his reflection on the shifting urban landscape, the pews are emptying. The “doom loop”—a self-reinforcing cycle of vacant offices, declining tax revenue, and diminishing services—is a mathematical ghost haunting our city planners.

Yet, if you walk the streets of Manhattan today, the sidewalks are often busier than ever. In San Francisco, the “Cerebral Valley” AI boom is sparking a gold rush of intellect that rivals the original tech explosion. We are witnessing a strange paradox: the Death of the Office occurring simultaneously with a Rebirth of the Urban Pulse.

The crisis Yang describes is real, but it may be a crisis of form rather than function. We tolerated the friction of urban life for the sake of career “flow.” Now that the flow is digital, the city is being forced to justify its existence through something more primal: energy.

“We are looking at a fundamental restructuring of the American city. The office was the sun around which everything else revolved. Now, that sun is dimming.”

The AI boom isn’t happening over Zoom; it’s happening in “hacker houses” and shared spaces where the speed of a conversation over coffee outpaces a fiber-optic connection. This suggests that the “doom loop” might only apply to the traditional, sterile corporate cubicle. The city is shedding its skin. It is moving away from being a place where we must be, toward a place where we want to be.

Yang’s warning serves as a necessary guardrail. We cannot ignore the fiscal cliff of empty high-rises. However, the vibrancy of NYC and the reinvigoration of SF suggest that the city isn’t dying—it’s just no longer a captive audience. We are standing in the ruins of an old habit, watching a new, more intentional way of living together take root in the cracks.

Five Questions to Ponder

  • The Pull of Proximity: If we no longer have to be in the city for a paycheck, what is the specific “energy” that keeps you coming back to the sidewalk?
  • The AI Renaissance: Is the AI boom in SF proof that high-innovation industries require physical density, or is it just the last gasp of the old model?
  • Form vs. Function: If a skyscraper can no longer be an office, what is the most radical thing it could become to serve a “busy” city?
  • The Captive Audience: For decades, cities were built for people who had to be there. How does a city change when it has to “woo” its citizens every single day?
  • Digital Nomads vs. Urban Anchors: Are we moving toward a world of “temporary density,” where cities are vibrant hubs for projects but no longer long-term homes?
Categories
AI Programming Prompt Engineering Software Work

The Great Inversion

For twenty years, the “Developer Experience” was a war against distraction. We treated the engineer’s focus like a fragile glass sculpture. The goal was simple: maximize the number of minutes a human spent with their fingers on a keyboard.

But as Michael Bloch (@michaelxbloch) recently pointed out, that playbook is officially obsolete.

Bloch shared a story of a startup that reached a breaking point. With the introduction of Claude Code, their old way of working broke. They realized that when the machine can write code faster than a human can think it, the bottleneck is no longer “typing speed.” The bottleneck is clarity of intent.

They called a war room and emerged with a radical new rule: No coding before 10 AM.

From Peer Programming to Peer Prompting

In the old world, this would be heresy. In the new world, it is the only way to survive. The morning is for what Bloch describes as the “Peer Prompt.” Engineers sit together, not to debug, but to define the objective function.

“Agents, not engineers, now do the work. Engineers make sure the agents can do the work well.” — Michael Bloch

Agent-First Engineering Playbook

What Bloch witnessed is the clearest version of the future of engineering. Here is the core of that “Agent-First” philosophy:

  • Agents Are the Primary User: Every system and naming convention is designed for an AI agent as the primary consumer.
  • Code is Context: We optimize for agent comprehensibility. Code itself is the documentation.
  • Data is the Interface: Clean data artifacts allow agents to compose systems without being told how.
  • Maximize Utilization: The most expensive thing in the system is an agent sitting idle while it waits for a human.

Spec the Outcome, Not the Process

When you shift to an agent-led workflow, you stop writing implementation plans and start writing objective functions.

“Review the output, not the code. Don’t read every line an agent writes. Test code against the objective. If it passes, ship it.” — Michael Bloch

The Six-Month Horizon

Six months from now, there will be two kinds of engineering teams: ones that rebuilt how they work from first principles, and ones still trying to make agents fit into their old playbook.

If you haven’t had your version of the Michael Bloch “war room” yet, have the meeting. Throw out the playbook. Write the new one.

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AI Work

Why IBM is Hiring Beginners

There is a pervasive anxiety humming beneath the surface of the modern workplace—a quiet, collective fear that the bottom rungs of the corporate ladder are being systematically sawed off by artificial intelligence.

The common wisdom, echoed in countless op-eds and boardroom whisperings, is that entry-level jobs are the natural prey of the Large Language Model.

The tasks of summarizing, drafting, formatting, and basic coding are easily consumed by algorithms. If a machine can execute the rote labor of a junior analyst in three seconds, why hire the junior analyst at all?

It is a seductive, mathematically appealing logic, especially in an era of tightening belts and efficiency mandates.

Consequently, we are witnessing a landscape where many tech companies are quietly, or sometimes loudly, slashing their junior roles to lean on AI.

But amidst this trend, an alternative approach emerges that feels almost rebellious in its long-term optimism.

IBM, a legacy titan that has weathered every technological revolution of the past century and where I started my career, is leaning entirely the other way. Rather than cutting, they are reportedly tripling their entry-level hiring.

Reflecting on this strategy, IBM’s chief HR officer noted:

“The companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment.”

This perspective is profound because it challenges the very premise of what an entry-level employee actually is.

The prevailing, perhaps cynical, view treats a junior worker merely as a unit of basic output. If you view a beginner only as a spreadsheet compiler or a draft-writer, then yes, they appear redundant in the face of AI.

But what if we view the entry-level role not as a terminal function, but as an apprenticeship?

When we hire a beginner, we aren’t just buying their immediate, unpolished labor. We are investing in a trajectory.

We are bringing them into the fold so they can absorb the tacit knowledge of the organization—the unwritten rules, the cultural nuances, the complex, human art of navigating institutional friction.

An AI cannot learn the subtle interpersonal dynamics of a specific team, nor can it develop the intuition that comes from failing, recovering, and being mentored by a seasoned veteran.

If we automate away the entry-level, we effectively destroy the incubator for our future mid-level and senior leaders. Where will the experienced managers of 2030 come from if no one is allowed to be a beginner in 2026? You cannot suddenly parachute someone into a senior role and expect them to possess the deep, intuitive judgment that is only forged in the crucible of early-career trial and error.

The institutional memory breaks down.

IBM’s strategy recognizes a crucial reality: AI shouldn’t replace the beginner; it should accelerate them.

Imagine a junior employee who isn’t bogged down by mindless grunt work, but instead is handed the tools to instantly bypass the mundane. They can spend their foundational years analyzing, questioning, and engaging in higher-order problem-solving alongside their mentors. They transition from data-gatherers to hyper-learners.

By doubling down on human potential in an age of artificial intelligence, companies are making a strategic bet on the one asset that cannot be replicated by a server farm: the evolving, adapting, and deeply creative human mind.

The most successful organizations of the near future won’t be the ones with the fewest employees and the most algorithms; they will be the ones that used algorithms to cultivate the most formidable, deeply experienced human talent.

The ladder hasn’t been dismantled. It has merely been redesigned.

The only question is whether we have the foresight to keep inviting people to climb it.

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AI Software Work

Lights Out in the Digital Factory

A quiet, modern unease haunts the vocabulary we use to describe invisible labor. Add “ghost” or “dark” to any industry, and suddenly a mundane logistical optimization takes on the sinister sheen of a cyberpunk dystopia.

Consider the “ghost kitchen.” Stripped of its spooky nomenclature, it is merely a commercial cooking facility with no dine-in area, optimized entirely for delivery apps. Yet, the term perfectly captures the eerie absence at its core: the removal of the restaurant as a gathering place, leaving behind only the pure, mechanized output of calories in cardboard boxes. It is a kitchen without a soul.

Now, we are witnessing the rise of the “dark software factory.”

“A dark factory is a fully automated production facility where manufacturing occurs without human intervention. The lights can literally be turned off.”

When applied to software, the concept is both fascinating and slightly chilling. A dark software factory is an automated, AI-driven environment where applications, features, and codebases are generated, tested, and deployed entirely by machine agents. There are no developers huddled around monitors, no stand-up meetings, no keyboards clicking into the night. It is “lights-out” development. You input a prompt or a business requirement, and the factory hums in the digital darkness, outputting a finished product.

Why are these invisible factories so important? Because they represent the ultimate abstraction of creation. Just as the ghost kitchen separates the meal from the dining experience, the dark software factory separates the software from the craft of coding. It optimizes for pure, unadulterated output and infinite scalability. In a world with an insatiable appetite for digital solutions, human bottlenecks—our need for sleep, our syntax errors, our slow typing speeds—are being engineered out of the equation.

But I can’t help but muse on what we lose when we turn out the lights. There is a certain melancholy to this ruthless efficiency. When we abstract away the human element, we lose the “front of house”—the serendipity of a developer finding a creative workaround, the quiet pride of elegant architecture, the human touch in a user interface.

The dark software factory sounds sinister not because it is inherently evil, but because it is utterly indifferent to us. It doesn’t care about craftsmanship; it cares about compilation. As we consume the outputs of these ghost kitchens and dark factories, we must ask ourselves: in our rush to automate the creation of our physical and digital worlds, what happens to the art of making?

The future of production is increasingly invisible. The dark factories are already humming. We just can’t see them.

Categories
AI Work

Surviving Our Own Success: The Existential Shift of the AI Era

We are standing on the precipice of a profound shift—not just in how we work, but in what work actually means to us. Sam Harris talks about it here. It’s disturbing in many ways!

Lately, the cultural conversation has been thick with a specific kind of anxiety. The rising tide of concern around artificial intelligence and job displacement isn’t merely an economic panic; it is an existential one. For a long time, we comforted ourselves with the idea that the timeline for artificial general intelligence (AGI) was measured in decades. It was a problem for our children, or perhaps our grandchildren, to solve. But as recent discussions among tech leaders highlight, that timeline is compressing rapidly. We are now hearing serious projections that within the next 12 to 18 months, “professional-grade AGI” could automate the vast majority of white-collar, cognitive tasks.

“For centuries, human beings have defined themselves by the friction of their labor.”

We introduce ourselves with our job titles at dinner parties. We measure our worth by our productivity, our outputs, and the unique skills we’ve honed over decades. We willingly incur hundreds of thousands of dollars in student debt to secure a spot on the bottom rung of the corporate ladder, believing that with enough effort, we can climb it.

But suddenly, we are faced with the reality that the ladder isn’t just missing a few rungs; it is evaporating entirely.

Here lies one of the great ironies of our modern age: we always assumed the robots would come for the physical labor first. We pictured automated plumbers, robotic janitors, and android mechanics. Instead, they are coming for the thinkers. They are coming for the lawyers drafting contracts, the accountants crunching tax codes, the marketers writing copy, and the software engineers writing the very code that powers them. The high-status cognitive work we prized so deeply—the work we built our entire educational infrastructure around—turns out to be the easiest to replicate in silicon.

When a machine arrives that can mimic, accelerate, or entirely replace that friction, the foundation of our identity begins to tremble. We are moving from a world where we are the engines of creation to a world where we are merely the editors of it. A single person might soon do the work of a thousand, spinning up autonomous AI agents to execute entire business strategies, architect software, and manage logistics in a single afternoon.

Yet, as terrifying as this sounds, the most startling realization isn’t a dystopian fear of rogue machines or cyber terrorism. It’s that this massive economic disruption is actually what success looks like. This isn’t the failure mode of AI; this is the technology working exactly as intended, ushering in an era of unprecedented productivity and, theoretically, boundless abundance.

The emergency we face is that our social and economic systems are entirely unprepared for a reality where human labor is optional. We are witnessing what some have described as a “Fall of Saigon” moment in the tech and corporate worlds—a frantic scramble where a few founders and final hires are grasping at the helicopter skids of stratospheric wealth before the need for human employees vanishes. If we are truly approaching a future where human labor is obsolete, how do we share the wealth generated by these ubiquitous systems?

Perhaps there is a quiet grace hidden within this disruption. If AI takes over the mechanical, the repetitive, and the cognitive synthesis, it leaves us with the deeply, undeniably human. It forces us to lean into the things an algorithm cannot compute: empathy, lived experience, moral judgment, and the beautiful, messy reality of physical presence.

The future of work might not be about competing with machines at all. It forces us to confront the terrifying, beautiful question: Who are we when we don’t have to work? It is an invitation to finally separate our human worth from our economic output, and to redesign a society that shares the wealth of our own invention. We are entering an era of abundance. The only question is whether we have the collective imagination to survive our own success.

Questions to Ponder

  1. If your job title was erased tomorrow, how would you define your value to the world?
  2. How do we build a society that rewards human existence rather than just economic output?
  3. What is one deeply human skill or passion you would cultivate if you no longer had to work for a living?