Tag: leadership

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
Living Productivity

The Architecture of Arete

In the modern landscape of productivity, we are drowning in “how-to” guides and “ten-step” frameworks. We treat our lives like machines that need oiling, rather than gardens that need tending. But David Sparks’ recent work on an updated productivity field guide brings back a much older, more grounded philosophy: the marriage of roles and arete. This is the third edition of his field guide with refinements that he’s made along the way.

To understand why this matters, we have to look at how we usually define ourselves. Most of us operate via a chaotic “to-do” list—a flat, untextured pile of tasks. “Buy milk” sits right next to “Finish the quarterly report,” which sits next to “Call Mom.” This flatness is where burnout lives. It lacks a sense of who we are being when we do those things.

“A role is not just a job title; it is a container for responsibility and relationship.”

This is where Roles come in. When we organize our lives by roles, we stop seeing tasks and start seeing stewardship. We aren’t just checking boxes; we are fulfilling a duty to the parts of our lives that actually matter. But roles alone can become burdensome—mere masks we wear—unless they are infused with arete.

The Greeks defined arete as “excellence” or “virtue,” but its deepest meaning is “acting up to one’s full potential.” It is the act of being the best version of a thing.

However, a warning from the 2026 guide: Do not treat Arete as a yardstick to beat yourself up with when you fall short. Instead, treat it as a compass bearing. You will never perfectly ‘reach’ North, but you can always check to ensure you are rowing in that direction . Success isn’t matching the ideal; it is simply making progress from who you were when you started .

When you combine a defined Role with the pursuit of arete, productivity shifts from a mechanical burden to a philosophical practice. You are no longer just “writing an email”; you are practicing the excellence of a “Clear Communicator.” You aren’t just “doing the dishes”; you are practicing the excellence of someone who “Values a Peaceful Environment.”

To keep these roles authentic, we must also identify their Shadow Roles. If your Arete is the ‘Present Father,’ you must recognize the Shadow Role of the ‘Distracted Dad’ who is physically in the room but mentally scrolling email. Identifying the shadow doesn’t make you a failure; it gives you the awareness to course-correct before you hit the rocks .

Implementing this requires what Sparks calls the Arete Radar. In a world demanding instant responses, we must cultivate a ‘meditative gap’—a pause between a request and our answer . In that gap, we ask a single question: ‘Does this commitment serve my Arete, or does it distract from it?‘. This turns the act of saying ‘no’ into a strategic ‘yes’ to your deeper purpose.

This framework rescues us from the “productivity for productivity’s sake” trap. It suggests that the goal isn’t to get more done, but to be more present and excellent in the specific seats we have chosen to occupy. In the end, we don’t need better apps. We need a better understanding of our station and the virtue required to fill it.

Finally, we must stop solving for speed and start solving for meaningfulness. Efficiency is the enemy of Arete internalization. Sparks suggests the ‘Blank Page Ritual’: rewriting your Arete statements from scratch every quarter rather than just editing an old file. This intentional slowness forces the values out of your computer’s storage and hard-codes them into your soul’s permanent memory .

Categories
Business

The Geometry of Focus: Finding the Limiting Factor

In the modern landscape of high-stakes management, there is a recurring temptation to solve everything at once. We are taught to optimize across the board—to improve efficiency by 2% here, 5% there—until the entire machine hums. But in a recent conversation with John Collison and Dwarkesh Patel, Elon Musk repeatedly returned to a single, almost obsessive mantra: the “limiting factor.”

It is a deceptively simple phrase. It suggests that at any given moment, there is one specific bottleneck that dictates the speed of the entire enterprise. If you aren’t working on that, you aren’t really moving the needle. You are merely polishing stuff.

“I think people are going to have real trouble turning on like the chip output will exceed the ability to turn chips on… the current limiting factor that I see… in the one-year time frame it’s energy power production.”

Musk’s management technique is not about broad oversight; it is about a radical, almost violent prioritization. He looks at the timeline—one year, three years, ten years—and asks: What is the wall we are about to hit? Right now, it might be the availability of GPUs. In twelve months, it might be the physical gigawatts of electricity required to plug them in. In thirty-six months, it might be the thermal constraints of Earth’s atmosphere, necessitating a move to space.

This approach requires a high “pain threshold.” To solve a limiting factor, you often have to lean into acute, short-term struggle to avoid the chronic, slow death of stagnation. John Collison noted this during the interview:

“Most people are willing to endure any amount of chronic pain to avoid acute pain… it feels like a lot of the cases we’re talking about are just leaning into the acute pain… to actually solve the bottleneck.”

For many leaders, the “limiting factor” is often something they aren’t even looking at because it lies outside their perceived domain. A software CEO might think their limit is talent, when it’s actually the speed of their internal decision-making. A manufacturer might think it’s raw materials, when it’s actually the morale of the factory floor.

To manage by the limiting factor is to admit that 90% of what you could be doing is a distraction. It is a philosophy of subtraction and focus. It demands that we stop asking “What can we improve?” and start asking “What is stopping us from being ten times larger?” Once you identify that wall, you throw every resource you have at it until it crumbles. And then—and this is the part that requires true stamina—you immediately go looking for the next wall.

By focusing on the one thing that matters, we stop being busy and start being effective. We stop managing the status quo and start engineering what may feel like the impossible.

Categories
Creativity Curiosity Living Work

The Human Router

There is a distinct difference between information and wisdom, and often, that difference is measured in velocity. We are accustomed to thinking that faster is better—fiber optic cables, 5G, real-time Slack notifications. We want knowledge to travel at the speed of light.

But Dan Wang, in his book Breakneck, captures a sociological truth about Silicon Valley that defies this obsession with speed:

“When I worked in Silicon Valley, people liked to say that knowledge travels at the speed of beer. Engineers like to talk to each other to solve technical problems, which is how knowledge diffuses.”

It is a charming, slightly irreverent metric, but it points to something profound about how humans solve difficult problems. There is “codified knowledge”—the explicit instructions found in textbooks, API documentation, and internal wikis. This travels instantly. It is frictionless. It is also, usually, insufficient for true innovation.

Then there is “tacit knowledge.” This is the intuition, the heuristic, the war story about why a specific architecture failed three years ago. This knowledge is heavy. It doesn’t travel through fiber optics; it travels through proximity. It requires the social friction of a shared table and the serendipitous collision of two engineers venting about a seemingly unrelated problem.

Crucially, this mechanism requires a specific type of operator: the Connector. These are the unsung heroes of the “speed of beer” economy. They aren’t always the 10x engineers on the leaderboard. They are the “human routers”—the people who instinctively know that the problem you are facing today is the same one Sarah from the Platform team solved last year. They are the ones who drag the introverted genius out to the pub, not to distract them, but to plug them into the grid. They curate the environment where the spark can jump the gap.

In our modern drive for remote efficiency, we are optimizing for the transfer of data. But we must be careful not to optimize away the people who pour the drinks, literal or metaphorical. That slow, liquid diffusion of ideas is often where the real breakthrough hides—steered by those special few who know exactly who needs to talk to whom.

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.

Categories
AI AI: Large Language Models AI: Prompting

Liquid Software and the Death of the “User”

There is a profound disconnect in how we talk about Artificial Intelligence right now. In the boardrooms of legacy corporations, AI is a “strategy” to be committee-reviewed—a tentative toe-dip into efficiency. But on the ground, among the “AI natives,” something entirely different is happening. AI isn’t just making the old work faster; it is fundamentally changing the texture of what we build and how we think.

In a recent conversation, Reid Hoffman and Parth Patil explored this shift, and the metaphor that struck me most was the idea of software becoming “liquid.”

The Era of Liquid Software

For decades, we have treated software like furniture. We buy a CRM, a project management tool, or an analytics dashboard. It is rigid, finished, and distinct from us. We are the users; it is the tool. But Patil demonstrates a different reality: one where he drops a folder of raw CSV files into an agent like Claude Code and asks it to “look at the data and build me a dashboard.”

Sixty seconds later, he has a fully functional, interactive HTML dashboard. He didn’t buy it. He didn’t spend three weeks coding it. He simply willed it into existence for that specific moment.

This is “vibe coding.” It’s a term that sounds almost dismissive, but it represents a radical democratization of creation. You no longer need to know the syntax of Python to build a tool. You just need to know the “vibe”—the outcome you want, the logic of the problem, and the willingness to dance with an intelligent agent until it manifests.

The philosophical implication here is staggering. We are moving from a world of scarcity of capability to a world of abundance of cognition. When you can spin up a custom tool for a single week-long project and then discard it, the friction of problem-solving evaporates. The “app” is no longer a product you buy; it’s a transient artifact you summon.

Applying the “Vibe Code” Mindset

But how do we, especially those of us who don’t identify as “technical,” bridge the gap between watching this magic and wielding it? The conversation offers a roadmap. It starts by shedding the identity of the “user” and adopting the identity of the “orchestrator.”

If you want to move from passive observation to active application, here are three specific ways to start:

1. The “Interview Me” Protocol

We often stare at the blinking cursor, unsure how to prompt the AI. Hoffman suggests a reversal: Make the AI the interviewer. When you face a complex leadership challenge or a strategic knot, open your frontier model (Claude, GPT-4o, etc.) and say:

“Interview me about this problem until you have enough information to propose a framework or solution.”

This forces you to articulate your tacit knowledge, which the AI then structures into something actionable. It turns the monologue into a Socratic dialogue.

2. Build “Throwaway” Internal Tools

Stop looking for the perfect SaaS product for every niche problem in your team. If you have a messy recurring task—like organizing client feedback or synthesizing weekly reports—try “vibe coding” a solution. Use a tool like Replit or Cursor. Upload your messy data (anonymized if needed) and tell the agent:

“Write a script to organize this into a table based on sentiment.”

Don’t worry if the code is ugly. Don’t worry if you throw it away next month. The value is in the immediacy of the solution, not the longevity of the code.

3. Transform Meetings into Data

Meetings are usually where knowledge goes to die. They are ephemeral. But if you transcribe them (with permission), they become data. Don’t just ask for a summary. Feed the transcript to an agent and ask:

“Who should we have consulted on this decision that wasn’t in the room?”
“Create a decision matrix based on the arguments presented.”

This turns a passive event into an active, queryable asset.

Conclusion

The danger, as Hoffman notes, is the “secret cyborg”—the employee who uses AI to do their job in two hours and spends the rest of the week hiding. But the real win comes from the amplified team, where we share these “vibe coded” tools and prompts openly.

We are entering an age where your imagination is the only true constraint. If you can describe it, you can increasingly build it. The question is no longer “is there an app for that?” but “can I describe the solution well enough to bring it to life?”

Categories
AI AI: Large Language Models

The Texture of Autonomy

There is a distinct texture to working with a truly capable person. It is a feeling of relief, specific and profound.

When you hand a project to a junior employee who “gets it,” the mental load doesn’t just decrease; it vanishes. You don’t have to map the territory for them. You don’t have to pre-visualize every stumble or correct every navigational error. You simply point to the destination, and they find their way.

I was thinking about this feeling—this specific brand of professional trust—when I read a recent observation from two partners at Sequoia regarding the current state of Artificial Intelligence:

“Generally intelligent people can work autonomously for hours at a time, making and fixing their mistakes and figuring out what to do next without being told. Generally intelligent agents can do the same thing. This is new.”

The phrase that sticks with me is “without being told.”

For the last forty years, our relationship with computers has been strictly transactional. The computer waits. We command. It executes. Even the most sophisticated algorithms have essentially been waiting for us to hit “Enter.” They are tools, no different in spirit than a very fast abacus or a hyper-efficient typewriter.

But we are crossing a threshold where the software stops waiting.

The definition of intelligence in a workspace isn’t just raw processing power; it is the ability to recover from failure without supervision. It is the capacity to run into a wall, realize you have hit a wall, back up, and look for a door—all while the manager is asleep or working on something else.

When Sequoia notes that “this is new,” they aren’t talking about a feature update. They are talking about a shift in the ontology of our tools. We are moving from an era of leverage (tools that make us faster) to an era of agency (tools that act on our behalf).

This changes the psychological contract between human and machine. If an agent can “figure out what to do next,” we are no longer operators; we are managers. And as anyone who has transitioned from individual contributor to management knows, that is a fundamentally different skill set. It requires clearer intent, better goal-setting, and the ability to trust a process you cannot entirely see.

We are about to find out what it feels like to have a digital colleague that doesn’t just listen, but actually thinks about the next step.

Categories
Current Affairs

Careless People

“They were careless people … — they smashed up things and creatures and then retreated back into their money or their vast carelessness, or whatever it was that kept them together, and let other people clean up the mess they had made…” (The Great Gatsby)

Why not just impose tariffs on every country, even our best friends? Why not require those countries to negotiate with us for only we can then decide how much we share and with whom? Why not risk throwing the U.S. economy into recession or worse?

Why not? For the perspective of one investor, read this from Howard Marks of Oaktree Capital.

The public can smell incompetence. It may be springtime in America but the smells of our springtime this year are just off. Breathe deeply and see what you pick up.

See also: What it feels like right now!