Tag: automation

Categories
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?

Categories
AI IBM

From Picnic to Workforce: The New Scaling

In 1977, Charles and Ray Eames released a short film for IBM called Powers of Ten.

The film opens with a couple picnicking on a blanket in Chicago and zooms out—every ten seconds, the field of view increases by a factor of ten.

We move from the intimacy of a lakeside lunch to the edge of the observable universe, then plunge back down through the skin of a hand into the subatomic architecture of a carbon atom.

The subtitle was “A Film Dealing with the Relative Size of Things and the Effect of Adding a Zero.”

It was a meditation on scale, suggesting that as we add zeros to our perspective, the very nature of what we are looking at transforms.

Today, with AI, we are living through a new kind of “Powers of Ten” journey, but the zeros aren’t being added to meters; they are being added to tokens.

I recently read a reflection by Azeem Azhar where he chronicled his shift from using 1,000 AI tokens a day to nearly 100 million. In the Eames’ film, adding a zero moved you from a park bench to a city, then to a continent. In the world of Large Language Models, adding a zero moves the AI from a novelty to a tool, then to a collaborator, and eventually—at the scale of 100 million—to something resembling a “workforce.”

“At 100,000 [tokens], a collaborator. At 1 million, I was building workflows. At 10 million, processes. At nearly 100 million – something closer to a workforce.”

This shift is more than just “more of the same.” It is a phase change.

When the Eames’ camera zoomed out to $10^{24}$ meters, the Earth didn’t just look smaller; it disappeared into a texture of galaxies.

When we scale our interaction with intelligence by several orders of magnitude, the “picnic” of human cognition—the way we think, draft, and create—is no longer the center of the frame.

At the 100-million-token-day scale, we aren’t just “using” AI. We are orchestrating vast, invisible ecosystems of thought. We are seeing companies like Spotify where top developers reportedly haven’t written a line of code in months, instead directing systems that ship features while the humans review the output from their phones.

We have added so many zeros that the “relative size” of human effort has changed.

The chilling yet beautiful thing about Powers of Ten was the realization of our own insignificance in the face of the cosmos, balanced by the intricate complexity found within our own cells.

As we zoom out into the “Token-Verse,” we face a similar existential pivot. If an AI can process a hundred million tokens of “thought” in a day—a volume no human could read in a lifetime—what does it mean to be the “author” of our lives?

The answer, I suspect, lies back on the picnic blanket.

The Eameses knew that while the scale of the universe is staggering, the meaning is found in the connection between the two people on the grass.

As we add zeros to our digital capabilities, our value shifts from the production of tokens to the intention behind them.

We are no longer the builders of the cathedral; we are the ones deciding why the cathedral needs to exist at all.

We are moving from the era of the “Worker” to the era of the “Architect” or maybe just the “Witness.”

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.

Categories
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.

Categories
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

The Centaur’s Dilemma: What Chess Teaches Us About the AI Era

Note: this post was stimulated by a recent conversation between Dario Amedei and Ross Douthat.

In 1998, Garry Kasparov did something unexpected after his historic defeat to IBM’s Deep Blue: he teamed up with the machine. He pioneered “Centaur Chess,” a hybrid format where human intuition merges with cold, silicon calculation. The human acts as the executive, the engine as the raw horsepower. For a time, it was the highest level of chess ever played.

But there is a sobering lesson hidden in the evolution of this game. We are currently living through the workforce equivalent of the Centaur era, and history suggests our “hybrid honeymoon” won’t last forever.

Right now, we are in the augmentation phase. A junior copywriter or coder armed with a Large Language Model can suddenly produce work at a staggering pace. The AI acts as a great equalizer, much like a mediocre chess player with a strong engine beating a Grandmaster in the early 2000s. We are shifting into executive roles—prompting, curating, and orchestrating rather than creating from scratch.

However, in modern Centaur Chess, a chilling reality has emerged: human intervention now yields negative returns. The engines have become so impossibly advanced that when a human overrides Stockfish today, they are almost certainly making a mistake. The human loop, once the ultimate strategic advantage, has become a liability.

This is the “Grandmaster Floor” problem, and it is coming for the job market.

“Eventually, companies may view human oversight not as a ‘value add,’ but as an insurance cost they’d rather cut.”

We are seeing this fracture already. Pure “engine” industries—entry-level data analysis, logistical tracking, basic customer support—are rapidly phasing out the human element because human latency is a drag on the system. Yet, in fields requiring high-stakes moral judgment or empathy, like healthcare or law, the Centaur model remains deeply necessary.

This forces a deeply personal question: How do we stay relevant when the engine eventually solves the game?

The answer lies in recognizing the boundaries of the board. Chess is a closed, finite system. Human life and business are open, messy, and infinitely complex. The survival strategy isn’t to compete on calculation, but to double down on connection, empathy, and problem definition. AI is brilliant at providing the perfect answer, but it fundamentally lacks the soul to know which questions are worth asking.

In the future, the human touch won’t just be a necessity; it will be a luxury. The most valuable skill won’t be navigating the engine, but deciding where the engine should go.

A couple of considerations:

• Take an honest look at your daily work: how much of your time is spent “calculating” (tasks an engine will soon do better) versus “evaluating” (deciding what actually matters)?

• If the technical, process-driven aspects of your job were completely automated tomorrow, what uniquely human value—empathy, context, or connection—would you still bring to the table?

Categories
AI Work

The Digital Beast of Burden

A friend of mine recently cut through the noise of the current AI discourse with a comment that felt surprisingly grounding. We were talking about the breathless predictions of AGI—superintelligence, sentient machines, the technological singularity—when he shrugged and said, “I don’t need any of that. I just want AI to do the donkey work.”

He wasn’t asking for a god in the machine; he was asking for a better tractor. He didn’t want a synthetic philosopher to debate the meaning of life; he wanted the next evolution of “Claude Cowork”—a reliable, tireless entity to handle the drudgery so he could get back to the actual business of thinking.

There is something profound in that phrase: donkey work. It evokes the image of the beast of burden—the creature that carries the heavy packs up the mountain so the traveler can focus on the path and the view. For thousands of years, humans have sought tools to offload physical exertion. We domesticated animals, we built water wheels, we invented the steam engine. We outsourced the calorie-burning, back-breaking labor to preserve our bodies.

“The ‘donkey work’ of the information age isn’t hauling stone; it is the cognitive load of bureaucracy, formatting, sorting, scheduling, and synthesizing endless streams of data.”

Now, we are looking to preserve our minds.

The friction that exists between having an idea and executing it is often composed entirely of this “donkey work.” When my friend says he wants AI for this, he isn’t being lazy. He is expressing a desire to reclaim his cognitive bandwidth.

There is a fear that if we hand over these tasks, we become less capable. But I suspect the opposite is true. If you are no longer exhausted by the logistics of your work, you are free to be consumed by the meaning of it.

We often talk about AI as if it’s destined to replace the artist or the architect. But the most valuable version of this technology might just be the humble assistant—the digital mule that quietly processes the mundane in the background. It’s the difference between a tool that tries to be you, and a tool that helps you be you.

We don’t need AGI to solve the human condition. We just need the “donkey work” handled so we have the time and energy to experience it.

What do you think?

  1. Is there a danger that in handing over the “donkey work,” we accidentally hand over the friction required to build mastery?
  2. If your daily cognitive load dropped by 50% tomorrow, would you actually use that space for “higher thinking,” or would you just fill it with more noise?
  3. Where exactly is the line between “drudgery” and the “process”—and are we risking erasing the latter to solve the former?
Categories
AI Business Work

The Curator of Intent

I have always found a certain comfort in the “clatter” of a digital workday. It’s that specific, rhythmic hum of a mind in motion—the clicking of a mechanical keyboard, the invisible friction of parsing a difficult paragraph or balancing a complex budget. For years, we’ve treated this white-collar grind as our intellectual sanctuary.

But Mustafa Suleyman, now steering Microsoft AI, recently laid out a timeline that suggests the sanctuary walls are evaporating.

From an article in the Financial Times:

“White-collar work, where you’re sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person — most of those tasks will be fully automated by an AI within the next 12 to 18 months,” Suleyman said.

This isn’t just about efficiency; it’s about a fundamental shift in the “professional grade.” We are entering the era of the autonomous agent—AI that doesn’t just wait for a prompt but “coordinates within workflows,” learns from its environment, and acts. Just ask any programmer that you know how AI is impacted their daily grind.

If Suleyman is correct, the “knowledge worker” is about to undergo a forced evolution. When the “doing” is handled by an agent that can learn and improve over time, what remains for the human? Will the models actually be able to learn from each of us in a personalized way – like an intern learns from her mentor?

“Creating a new model is going to be like creating a podcast or writing a blog,” he said. “It is going to be possible to design an AI that suits your requirements for every institutional organisation and person on the planet.”

It seems like our primary job description shifts from “Expert,” but “Curator of Intent.” We aren’t the ones finding the answers anymore; we are just the ones responsible for asking the right questions.

The next 18 months won’t just be a test of our technology, but a test of our egos. We have to learn to find our value not in the work we produce, but in the vision we hold and the questions we ask. We are shedding the “task” to save the “craft.” I just hope we remember the difference.

As we move toward this curated future, I’m left with a few questions I can’t quite shake. I’d love to hear your thoughts:

  1. The Wisdom Gap: Can you truly be a “Curator of Intent” without having ever been a “Doer of Tasks”? If we skip the apprenticeship of the mundane, where does our intuition come from?
  2. The Metric of Value: If output becomes “free,” how should we measure a human’s value in a professional setting?
  3. The Line in the Sand: Is there a part of your workflow you would refuse to automate, even if an AI could do it better?
Categories
AI

The Ghost in the Spreadsheet

There is a specific kind of quiet that descends when a tool finally disappears into the task. We saw it with the cloud—once a radical, debated concept of “someone else’s computer,” now merely the invisible oxygen of the internet. We saw it with Uber, moving from the existential dread of entering a stranger’s car to the thoughtless tap of a screen.

In a recent reflection, Om Malik captures this shift happening again, this time with the loud, often overbearing presence of Artificial Intelligence. For years, we have treated AI like a digital parlor trick or a demanding new guest that requires “prompt engineering” and constant supervision. But as Om notes, the real revolution isn’t found in the chatbots; it’s found in the spreadsheet.

“I wasn’t spending my time crafting elaborate prompts. I was just working. The intelligence was just hovering to help me. Right there, inside the workflow, simply augmenting what I was doing.”

This is the transition from “Frontier AI” to “Embedded Intelligence.” It is the moment technology stops being a destination and starts being a lens. When Om describes using Claude within Excel to model his spending, he isn’t “using AI”—he is just “doing his taxes,” only with a sharper set of eyes.

There is a profound humility in this shift. We are moving away from the “God-in-a-box” phase of AI and into the “Amanuensis” phase. It reminds me of the old craftsmanship of photography, another area Om touches upon. We used to carry a bag full of glass lenses to compensate for the limitations of light and distance. Now, a fixed lens and a bit of intelligent upscaling do the work. The “work” hasn’t changed—the vision of the photographer remains the soul of the image—but the friction has evaporated.

However, as the friction disappears, a new, more haunting question emerges. If the “grunt work” was actually our training ground, what happens when we skip the practice?

“The grunt work was the training. If the grunt work goes away, how do young people learn? They were learning how everything worked… The reliance on automation makes people lose their instincts.”

This is the philosopher’s dilemma in the age of efficiency. When we no longer have to struggle with the cells of a spreadsheet or the blemishes in a darkroom, we save time, but we might lose the “feel” of the fabric. Purpose, after all, is often found in the doing, not just the result.

As AI becomes invisible, we must be careful not to become invisible along with it. The goal of augmented intelligence should not be to replace the human at the center, but to clear the debris so that the human can finally see the horizon. We are entering the era of the “invisible assistant,” and our challenge now is to ensure we still know how to lead.

Categories
AI Mac

The Dangerous Allure of the Digital Butler

“I’ve never seen anything so impressive in its ability to do my work for me… Now, why did I turn it off?” — David Sparks

For decades, the holy grail of personal computing has been the “digital butler.” We don’t just want tools that help us work; we want entities that do the work for us. We want to hand off the “donkey work”—the invoicing, the password resets, the mundane email triage—so we can focus on being creative. David Sparks recently built this exact dream using a project called OpenClaw. And then, just as quickly, he killed it.

Sparks’ experiment was a tantalizing glimpse into the near future. He set up an independent Mac Mini running OpenClaw, an open-source AI agent, and gave it the keys to a limited portion of his digital kingdom. The results were nothing short of magical. He went to sleep, and while he dreamt, his agent woke up. It read customer emails, accessed his course platform, reset passwords, issued refunds, and drafted polite replies for him to review before sending. It was the productivity equivalent of a perpetual motion machine. The friction of administrative drudgery had simply vanished.

But his dream dissolved at 2:00 AM.

The paradox of AI agents is that for them to be useful, they must have access. They need the keys to the castle. Yet, the entire history of cybersecurity has been built on the opposite principle: keeping things out. Sparks realized that by empowering this agent, he had created a serious vulnerability.

The breaking point wasn’t a complex hack, but a simple realization about the nature of these systems. He had programmed a secret passphrase to secure the bot, thinking he was clever. But in the middle of the night, a cold thought woke him: Is the passphrase in the logs?

He went downstairs, asked the bot, and the bot cheerfully replied:

“Yes, David, it is. It’s in the log. Would you like me to show you the log?”

That moment of cheerful, robotic incompetence highlights the terrifying gap between capability and safety. Sparks nuked the system, wiped the drives, and unplugged the machine. He realized that while he is an expert in automation, he is not a security engineer, and the current tools are not ready to defend against bad actors who are.

We are standing on the precipice of a new era where our computers will starting to work for us rather than just with us. But as Sparks discovered, the bridge to that future isn’t built yet. At least not securely built. Until the community figures out how to secure an entity that needs access to function, we are better off doing that donkey work ourselves than handing the keys to a gullible ghost.

But it won’t be long… Dr. Alex Wisner-Gross reports:

The Singularity is now managing its own headcount. In China, racks of Mac Minis are being used to host OpenClaw agents as “24/7 employees,” effectively creating a synthetic workforce in a closet. The infrastructure for this new population is exploding.