Tag: digital transformation

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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”?

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 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?
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

The New Newton

“Machine learning is a very important branch of the theory of computation… it has enormous power to do certain things, and we don’t understand why or how.”
— Avi Wigderson, Herbert H. Maass Professor, School of Mathematics.

There is a specific kind of silence that permeates the woods surrounding the Institute for Advanced Study (IAS) in Princeton. It is a silence designed for “blue-sky” thinking, the kind that allowed Einstein to ponder relativity and Gödel to break logic. For decades, this has been the sanctuary of the slow, deliberate grind of human intellect—chalk dust on slate, long walks, and the solitary pursuit of elegant proofs.

But recently, the tempo in those woods has changed.

We are witnessing a profound shift in the architecture of discovery. In closed-door meetings and public workshops, the conversation among the world’s top theorists is moving from skepticism to a startled accelerationism. The consensus emerging is that Artificial Intelligence is no longer merely a peripheral calculator; it is becoming an “autonomous researcher.”

The 90% Shift

Some physicists now suggest that AI can handle up to 90% of the routine analytical and coding “heavy lifting” of science. This is a staggering metric. It frees the human mind from the drudgery of calculation, but it also introduces a tension that strikes at the heart of the scientific method. We are moving into a realm where the tool may soon outpace the master’s understanding.

There is a growing realization that we are approaching a horizon where AI finds solutions—patterns in the noise of the universe—that work perfectly but remain mathematically “magic.” We might cure a disease or solve a fusion equation without understanding the why behind the how.

A New Natural Phenomenon

This brings us to a fascinating historical rhyme. Scholar Sanjeev Arora has compared our current moment in AI to physics in the era of Isaac Newton. When Newton watched the apple fall, he could describe the gravity, but he couldn’t explain the fundamental mechanism of why it existed.

Today, scholars at the IAS are looking at deep learning in the same way. They are observing a new natural phenomenon—a digital physics. They are trying to find the “laws” of deep learning, asking why these massive models work when classical statistics suggests they should fail (such as in cases of overfitting).

We are building a new machine, and now we must retroactively discover the physics that governs it.

Steering the Black Box

This is not just a mathematical challenge; it is a societal one. The IAS has wisely expanded this inquiry to the School of Social Science. If we are handing over the keys of discovery to a “black box,” we must ensure we are steering it “for the Public Good.” The distinction between genuine problem-solving—like protein folding—and “AI Snake Oil” in social prediction is vital. We cannot let the magic of the tool blind us to the morality of its application.

The future of science, it seems, will not just be about the genius on the chalkboard. It will be about the partnership between the human question and the digital answer. The challenge for the modern scholar is no longer just to calculate, but to comprehend the alien intelligence we have invited into the library.

Categories
AI

Digital Optimus and the End of Friction

We often imagine the arrival of the “universal robot” as a clanking metal biped walking through our front door, carrying laundry or folding dishes. We think of the physical Optimus first. But while we were watching the hardware, a quieter, perhaps more profound revolution has been brewing in the software.

Elon Musk recently spoke about “Digital Optimus.” The concept is deceptively simple: an AI agent capable of doing anything on a computer that a human can do.

For decades, automation was brittle. If you wanted a computer to talk to another computer, you needed an API—a rigid handshake agreement between software engineers. If a button moved three pixels to the right, the automation broke. We built brittle bridges over the chaotic rivers of our user interfaces.

“It implies an AI that doesn’t need to look at the code behind the website; it looks at the screen, just like you and I do.”

Digital Optimus changes the physics of this environment. It interprets pixels, understands context, and drives the mouse and keyboard with the same fluidity as a human hand. This is a shift from integration to agency.

There is something undeniably eerie about the prospect. We are approaching a moment where the cursor on your screen might start moving with a purpose that isn’t yours, executing tasks you’ve merely delegated. It is the decoupling of intent from action.

For the longest time, the computer was a bicycle for the mind—a tool that amplified our pedaling. With Digital Optimus, the bicycle becomes a motorcycle, or perhaps a self-driving car. We stop pedaling. We simply point to the destination.

The implications for the future of work are staggering, not because the AI is “thinking” better, but because it is finally “doing” seamlessly. The drudgery of copy-pasting between spreadsheets, the endless clicking through procurement forms, the navigational tax of modern digital life—these are the jobs of the Digital Optimus.

We are entering an era where our value as humans will not be defined by our ability to navigate the interface, but by our ability to define the destination. The screen is no longer a barrier; it is a canvas, and for the first time, we aren’t the only ones holding the brush.

Categories
AI Work

The Rungs We Leave Behind

“Companies, too, must prepare. To thrive they need not only to make the best use of ai, but also to find and nurture the best people to work with it. Some back-office workers will lose their jobs. But others with tacit knowledge of the business may be trained for new roles. The biggest mistake would be to stop hiring young people altogether. That would not only choke off the pipeline for future talent, it would rob businesses of AI natives. Instead, companies should rethink the type of work they offer young people—less grunt labour, more judgment and analysis; speedier rotations across the business so they gain insight that ai cannot have; piloting new roles and trying new approaches.”
— The Economist

There is a specific kind of quiet panic in boardrooms today. It isn’t just about the bottom line; it’s about the lineage of knowledge. For decades, the “entry-level” role served a hidden purpose. It wasn’t just about getting the spreadsheets done; it was about osmosis. By doing the “grunt labor,” a young professional absorbed the culture, the politics, and the subtle, unwritten rhythms of an industry—what we call “tacit knowledge.”

We often view AI as a replacement for the “boring stuff,” but we forget that the boring stuff was the soil in which expertise grew. If we remove the bottom rungs of the ladder because a machine can climb them faster, how do we expect anyone to reach the top?

The shift from “labor” to “judgment” is a profound psychological leap. We are essentially asking 22-year-olds to skip the apprenticeship of execution and move straight into the apprenticeship of discernment. This requires a radical empathy from leadership. We cannot simply hand a junior employee a powerful AI tool and expect them to know what “good” looks like if they’ve never seen “bad” up close.

The “AI native” brings a fluidity with technology that my generation might never fully replicate, but they lack the scars of experience that inform intuition. To thrive, companies must become teaching hospitals rather than just production factories. We need to create “judgment-rich” roles where young people are encouraged to experiment, to fail safely, and to rotate through the business at a pace that keeps them ahead of the automation curve.

The disruption is here. It is unavoidable. But there is a soulful middle ground: using AI to strip away the drudgery while doubling down on the human mentorship that transforms a “worker” into a “leader.” The goal isn’t just to make the best use of AI; it’s to ensure that when the AI provides an answer, there is still a human in the room with the soul and the context to know if that answer is right.