Tag: automation

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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?

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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?
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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?
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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.

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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.

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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.

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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.

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AI Robotics

Breaking the Glass: When Intelligence enters the Physical World

For the last forty years, our relationship with digital intelligence has been trapped behind glass. From the beige box of the personal computer to the sleek slab of the iPhone, we have accessed information through a window. We stare at intelligence; it stares back, passive and disembodied. We ask it questions, and it flashes text on a screen. But it has no hands. It has no agency. It cannot pour a glass of water or comfort a child.

As Phil Beisel astutely notes, we are standing on the precipice of a profound phase shift:

“Optimus marks the moment intelligence leaves the screen and enters the physical world at scale.”

This isn’t just about a “better robot.” It is the convergence of three exponential curves crashing into one another: AI software capability, custom silicon efficiency, and electromechanical dexterity. When you multiply these factors, you don’t just get a machine; you get a new category of being. We are moving from “compressed book learning”—the LLMs that can write poetry but can’t lift a pencil—to embodied intelligence that understands physics, gravity, and fragility.

The Pluribus Moment

The philosophical implication of this transition is staggering. We are building a “Pluribus” entity—a hive mind where individual learning becomes collective capability instantly.

In the human world, if I learn to play the violin, you do not. I must teach you, and you must struggle for years to master it. In the world of Optimus, if one unit learns to solder a circuit or perform a specific surgery, the entire fleet learns it overnight. The friction of skill transfer drops to zero.

The End of Scarcity

Elon Musk calls this the “infinite money glitch,” a sterile economic term for what is actually a humanitarian revolution: the decoupling of labor from human time. If the machine can replicate human movement and action 24/7, the cost of labor effectively trends toward zero. We often fear this as “replacement,” but looked at through a lens of abundance, it is the collapse of scarcity.

We are watching the birth of a world where the physical limitations that have defined the human condition—exhaustion, injury, the slow grind of mastering a craft—are solved by a proxy that we built. Intelligence is no longer a ghost in the machine; it is the machine itself, walking among us, ready to work.

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AI History Living

The Echo of the Roar

It is a strange sensation to look back exactly one century and see our own reflection staring back at us, sepia-toned but unmistakably familiar. We often think of the “Roaring Twenties” as a stylistic era—flapper dresses, Art Deco skyscrapers, and jazz. But beneath the aesthetic was a seismic technological shift that mirrors our current moment with an almost eerie precision.

In the 1920s, the world was shrinking. The radio was the “Great Disrupter” of the day. For the first time in human history, a voice could travel instantly from a studio in Pittsburgh to a farm in Nebraska. It was the democratization of information, a sudden collapse of distance that left society both thrilled and anxious.

“The radio brought the world into the living room; the algorithm brings the universe into our pockets.”

Today, we stand in the wash of a similar wave. If the radio brought the world into the living room, the internet—and specifically the generative AI of this decade—has brought the collective consciousness of humanity into our pockets.

The parallels in infrastructure are just as striking. One hundred years ago, the internal combustion engine was reshaping the physical landscape. The horse was yielding to the Model T; mud paths were being paved into highways. The very geography of how we lived was being rewritten by the automobile. In the 2020s, the “highway” is digital, built on cloud infrastructure and fiber optics, and the vehicle isn’t a Ford, but an algorithm. We are transitioning from physical labor to cognitive automation just as they transitioned from animal labor to mechanical muscle.

The Texture of Time

There is a specific texture to this kind of time. It is a mix of vertigo and acceleration. In 1925, the cultural critic might have worried that the “machine age” was stripping away our humanity, turning men into cogs on an assembly line. In 2025, we worry that the “algorithmic age” is stripping away our agency, turning creativity into a prompt.

But here is the insight that offers me comfort: The 1920s were chaotic, yes, but they were also a crucible of immense creativity. The pressure of that technological change forged modernism in literature, new forms of architecture, and entirely new ways of understanding the universe (quantum mechanics began finding its footing then).

We are not just passive observers of a repeating cycle. We are the navigators of the rhyme. The technology changes—from vacuum tubes to neural networks—but the human task remains the same: to find the signal in the static. To ensure that as the machines get faster, our souls do not merely get cheaper. We must decide, just as they had to a century ago, whether we will be consumed by the roar, or if we will learn to conduct the music.

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AI AI: Large Language Models Investing

The Ledger of Curiosity

We often romanticize the “back of the napkin” idea. It is the symbol of spontaneous genius—the startup mapped out in a coffee shop, the ticker symbol hurriedly scribbled during a dinner party. But we rarely talk about what happens to the napkin afterwards.

Usually, it gets thrown away. Or lost. Or stuffed into a drawer, becoming just another artifact of a fleeting thought that had momentum but no direction.

In the first two parts of this experiment, I used Gemini 3 Pro to solve the friction of entry (transcribing my messy handwriting) and the friction of analysis (stress-testing the ideas against 10-K realities). But there was one final gap: Permanence.

An analysis that lives and dies in a chat window is barely better than one that lives and dies in a notebook. It is still ephemeral. To truly build a “Second Brain” for investing, the data needs to leave the conversation and enter a system.

“The goal of technology should be to stop us from losing the work we’ve already done.”

I tweaked my workflow one last time. I asked the AI to not just judge the stocks, but to format its judgment into a raw CSV block.

With a simple copy-paste, my handwritten scribble wasn’t just digitized; it was database-ready. It went from a piece of paper to a row in Google Sheets with columns for “Market Cap,” “P/E Ratio,” and “Primary Risk.”

Suddenly, I wasn’t just looking at a list; I was building a ledger. I can now track these ideas over months. I can see if the “Red Flag” the AI identified actually played out. I can measure my own batting average.

The goal of technology shouldn’t just be to make us faster at doing work. It should be to stop us from losing the work we’ve already done. By turning ink into data, we stop treating our ideas as disposable. We give them the respect of memory.