Tag: productivity

Categories
AI Stanford

The Unit of Production Just Collapsed

The lecture was a Stanford CS session, AI-native companies, Garry Tan walking through what it now takes to build something. He’d rebuilt his old startup, Posterous, in five days on a modest Claude plan. A thing that once required a team and a runway. He said it matter-of-factly, the way you describe something that’s already obvious to you and hasn’t yet reached everyone else.

The argument Tan and his colleague Diana Hu were making wasn’t really about AI. It was about the economics of effort — specifically, what breaks when the cost of turning an idea into a working thing falls by an order of magnitude.

Their framing: AI-native organizations running as closed-loop systems, agents with access to the real artifacts of work, able to iterate without the error-accumulation that comes from handoffs and headcount. Revenue-per-employee ratios of a million dollars or more, with live examples already in the YC portfolio. Document processing, logistics, voice agents for specialized workflows.

What I kept hearing underneath all of it was a quieter claim: the mental model of what a startup requires is wrong.

Or rather, it’s right about the past and increasingly wrong about the present.

The assumptions embedded in “I can’t do this alone” or “we’d need to hire for that” or “we don’t have the bandwidth” — those are load-bearing assumptions, and the load is shifting.

I have some small version of this — not as a founder, but as someone who retired into curiosity. The blog, the reading, the daily effort to keep up with what’s moving: each one is a practice in staying oriented while the map keeps changing.

What I notice is that the constraint has shifted. It’s not information anymore. It’s not even tools. It’s the capacity to ask better questions of the abundance, to know what matters when everything is accelerating.

That’s the thing I find unsettling, yet also genuinely interesting: the skills that remain irreplaceable are the hardest ones to teach, and the hardest to evaluate in yourself. Knowing what matters. Recognizing when an output is almost right and almost wrong. Setting direction in ambiguous conditions and being willing to be wrong about it. These were always the valuable things. They were just obscured by all the coordination overhead that surrounded them.

The students in that Stanford course were asked to build something called a One-Person Frontier Lab — use the best available tools to extend your own reach over ten weeks. It’s framed as an academic exercise. It doesn’t feel like one.

But I’m not building. I’m mostly watching, and thinking about what this radical new fermentation does to everything downstream — to labor markets, to what a company even is, to how we’ll organize work and meaning when the old unit of production no longer applies. Those are slower questions. But they’re the ones that feel urgent to me.

The old excuses are getting lighter. Not that everything is possible — but that the weight of the usual constraints has changed.

What you choose to build, and whether you choose to build it at all, is more purely a decision than it used to be. That’s either clarifying or terrifying, depending on the day and my mood.

Categories
AI Living

The Threshold

There is a specific feeling. You are trying to understand something — a medical term in a lab report, a clause in a contract, how a particular piece of software actually works under the hood — and you hit the edge of what you know. The territory beyond is unfamiliar and the path is unclear, and something in you decides, quietly and almost without announcement: I don’t know how to figure this out.

And then you move on.

Marc Andreessen, talking to Joe Rogan recently, buried something important inside a longer riff about AI prompting tricks. Most of his list was the kind of thing you’d read in a productivity newsletter — ask it to steelman both sides, pretend it’s a panel of experts. Useful, not revelatory. But one observation was different: pay attention to the exact moment you think “I don’t know how to figure this out.” That’s the moment you should open the AI.

He said it almost offhandedly. I haven’t been able to stop thinking about it.

What he’s really describing isn’t a technique. It’s a behavioral pattern that most of us developed so gradually we don’t recognize it as a choice. The feeling of epistemic overreach — of arriving at the edge of one’s competence — became, over decades, a stopping condition. We learned to treat not-knowing as a wall rather than a door because, most of the time, it functionally was one. The library was closed. The expert was unavailable. The research was paywalled. You moved on.

The habit calcified. Now it persists even when the conditions that produced it no longer apply.

I notice it in myself, and I’m someone who is genuinely curious — who likes knowing how things work, who will follow a thread further than most people bother to. That’s not modesty; it’s relevant context. Because even with that disposition, I still hit the wall. I’ll be reading something and encounter a concept I only vaguely follow — some nuance in immunology, some historical episode I’ve only half absorbed — and I feel the familiar slight contraction, the small withdrawal. I read past it. The curiosity was there. The friction was higher.

Curiosity alone was never enough. What determined whether I pushed through wasn’t how much I wanted to understand — it was whether understanding felt retrievable at all. Most of the time, it didn’t. So I moved on, and the curiosity found something else to chase.

There’s a darker version of this worth sitting with. The people who never developed the quit reflex — who hit not-knowing and felt compelled rather than defeated — are, disproportionately, the ones who built things. The intellectual persistence wasn’t incidental to their contributions; it was probably constitutive of them. Curiosity as stubbornness. The refusal to accept the wall as final.

Elon Musk is the limit case. When he decided he wanted to go to Mars and found the rockets prohibitively expensive, he didn’t defer to the aerospace industry’s consensus about what was possible. He started reading propulsion manuals and cold-calling engineers. The quit signal either never fired or got overridden so fast it made no practical difference. The result was reusable orbital rockets, which the industry had largely decided weren’t worth pursuing. The dig reflex, taken to its extreme, rewrote what was considered feasible.

But the trait is undifferentiated. It doesn’t come with a calibration mechanism. The same refusal to accept expert consensus that produced SpaceX also produces a certain amount of confident wrongness — the Twitter decisions, the Covid takes, the occasional foray into geopolitics with the certainty of someone who has read a lot of Wikipedia. The dig reflex, unregulated, has no obvious stopping condition.

AI doesn’t change that underlying trait. What it changes is the access cost for everyone else.

For most of human history, the friction wasn’t random. It selected for people whose drive was strong enough to overcome it regardless of cost — the right connections, the right institution, the time to burn. Now that friction is lower for everyone, nearly to zero, for an enormous range of questions.

What I’m trying to build is the opposite of the quit reflex. Not the Musk version — boundless, uncalibrated, occasionally catastrophic. Something more modest: the habit of checking before giving up. Noticing the moment of not-knowing and treating it as a question rather than a verdict.

It requires noticing the moment. Which is harder than it sounds, because the reflex is fast and the moment is brief.

The contraction happens. You’ve already moved on. Somewhere behind you, the question is still there.

Categories
Haiku Living Reading

The Presence We Keep Deferring

I have so many unread articles saved to Instapaper that I’ve stopped checking the count. Each one felt, in the moment of saving it, like something I needed. A long piece on urban planning, a profile of someone interesting, a reported essay I fully intended to sit with.

The app is beautifully designed for exactly this — the frictionless capture, the clean reading interface waiting patiently on the other side.

What it can’t do is manufacture the attention I didn’t have when I saved it and still don’t have now. The articles aren’t the problem. The premise is: that presence is something you can bank.

There’s a haiku I keep returning to, from Natalie Goldberg’s Three Simple Lines. It’s by a poet named Fumiko Harada:

Morning chill
I savor this moment —
one meeting one lifetime

Eleven words. No verb in the third line, which makes it feel less like a thought and more like a verdict.

The Japanese concept underneath it is ichi-go ichi-e — loosely, “one time, one meeting.” It’s a Zen idea with origins in the tea ceremony, the understanding that each gathering is singular and therefore irreversible. You cannot archive it. You cannot search for it later. When it ends, it doesn’t go anywhere you can retrieve.

This is what the Instapaper queue is, at scale: an archive of moments I decided to experience later. The article about urban planning was written by someone who spent months reporting it, on a day when some editor thought it was ready, and landed in my feed on a morning when something about the headline caught me. That constellation doesn’t reassemble. Later is a different article.

The tools I use every day are getting astonishing. There are systems that can summarize, translate, recall, explain, anticipate. I use them. I find them genuinely useful.

But there’s a habit of mind they reward — a kind of perpetual deferral of full attention — that I haven’t fully reckoned with. The promise, always, is that you can engage more completely later, once the summary is ready, once the transcript exists, once the notes have been taken. Presence becomes a productivity tax you pay while waiting for a deliverable.

Harada’s haiku doesn’t moralize. The speaker isn’t lecturing herself into awareness. She’s just cold, and awake, and choosing to notice. I savor this moment. The word “savor” does a lot of work. It implies effort. You savor things that could be missed.

The pivot in the third line is what stays with me. One meeting one lifetime. Not “this meeting will last a lifetime” — that would be sentiment. It’s more like a mathematical statement: the cardinality of this encounter is one. There is exactly one of them. This morning, this particular chill, whatever conversation or solitude is happening inside it — that set has one element. By tomorrow it has zero. No amount of documentation changes that arithmetic.

I’m working on believing that.

Categories
AI Programming Software Work

The Scarcest Thing

Garry Tan woke up at 8 a.m. after sleeping at 4. Not because he had to. Because he wanted to see what his workers had done overnight.

The workers are AI agents. Ten of them, running in parallel across three projects. And something about that sentence — wanted to see what they’d done — keeps stopping me. That’s not the language of someone using a tool. That’s the language of someone managing a team.

Tan gave a name to the state this puts him in: “cyber psychosis.” He said it as a joke. But the joke has an insight in it. He’s not describing addiction to a productivity app. He’s describing a shift in what it means to do creative work — the strange vertigo of becoming a director when you’d always been a laborer.

I’m retired. I watch this from the outside now, which is its own kind of vantage point. For most of my career, the path from idea to working product ran through people — through hiring and managing and the slow accretion of execution capacity. You had the vision or you didn’t, but either way you needed the team. The idea and the means of making it real were, structurally, separate things. The gap between them was where companies lived.

What Tan is describing is that gap closing.

The thing he built — gstack, his open-sourced Claude Code configuration — got dismissed in some quarters as “just prompts.” And it is just prompts, in the same way that a conductor’s score is just notation. The abstraction is the invention. What he encoded is a model of how a startup team thinks: the CEO who interrogates the why before a line of code gets written, the engineer who builds, the paranoid staff reviewer who looks for what breaks. Each role blocks a different failure mode. Blurring them together produces, as his documentation puts it, “a mediocre blend of all four.”

That’s an organizational insight. It has nothing to do with code.

Tan described being a “time billionaire” — not because his biological clock had slowed, but because he can now purchase machine-consciousness-hours. The bottleneck of implementation, which has governed every creative project since the beginning of creative projects, is dissolving for those who know how to direct.

The scarcest thing is shifting. It’s no longer the hours of execution. It’s the clarity of intent — knowing what you want to build and why the journey matters, before any of the workers start moving. That’s harder than it sounds. For decades, most of us could muddle through in the making of it. The act of building taught you what you were building. Now the making is cheap, and that shortcut is gone.

For someone watching from retirement, that’s not a small thing to absorb. The model I internalized over a long career — that ideas become real through sustained organizational effort, through teams and timelines and the grinding work of execution — is being revised faster than I expected. Not invalidated. Revised. The judgment still matters. The taste still matters. The why matters more than ever.

It’s just that the how has found new hands. Many of them. More than any team I ever assembled, available the moment the intent is clear enough to direct them, gone when the work is done. The constraint was always the hands. It turns out it was always the knowing.

Categories
Authors Books Business

The Whetstone of the Box

Give a team an unlimited budget and no deadline, and you almost guarantee their project will never ship. We spend our careers fighting for more runway, more resources, and a completely clear calendar, convinced that absolute freedom is the prerequisite for great work. Yet, when the walls finally fall away, we usually just freeze.

David Epstein’s upcoming book, Inside the Box, circles this exact paradox. His premise, arriving in early May, is that constraints do not diminish our capabilities; they forge them. We spend so much of our lives trying to escape boundaries, failing to recognize that those very boundaries are what give our efforts shape.

I think about the early days of writing code. We were working with severe memory limits—kilobytes, not gigabytes. Every line had to justify its existence. There was no room for bloat, no excess capacity to mask sloppy logic. It felt restrictive at the time, like trying to build a ship inside a bottle.

But that unforgiving physical boundary forced a ruthless elegance. You had to understand exactly what you were trying to accomplish. The constraint wasn’t an obstacle to the work; it was the whetstone that sharpened the blade.

We see this everywhere, once we learn to look for it. A photographer framing a shot with a fixed prime lens cannot rely on a zoom ring to find the picture; they have to physically move their feet. The limitation forces engagement with the physical world. Without the walls of a canyon, a river is just a swamp. It is the restriction that creates the momentum.

Epstein’s focus on how constraints make us better feels like a necessary corrective right now. We live in an era of infinite leverage and boundless digital canvases. The friction has been removed from almost everything we do.

But friction is where the traction lives. When we strip away all our limits, we don’t gain wings; we just lose our footing. We need the edges of the box to know exactly where we stand.

Categories
AI Blogs/Weblogs

How to Refresh Dated Blog Articles

Woman typing on laptop at wooden table in cafe with coffee and pastry
A woman working on her laptop at a sunny cafe table with coffee and pastry

This afternoon I used the following prompt to help me assess posts I’d written a few years ago for updating:

I have a personal blog at https://sjl.us
Please review my blog posts that are between two and five years old and give me a prioritized list of which ones you think I should update.

I was curious how well it would do at this task. Turns out I was pleasantly surprised. Here’s the result:

Here’s my prioritized list of your blog posts (published roughly April 2021–April 2024, i.e., 2–5 years old as of April 2026) that I recommend updating, ranked from highest to lowest priority.

I reviewed the site structure, monthly archives, and indexed posts via searches. Your blog has dozens of posts in this window (mostly personal essays, photography, history, productivity/PKM, and heavy AI experimentation from the 2022–early 2023 wave). I prioritized based on:

  • How much the topic has aged (AI/prompting/tech tools change fastest; photography, personal reflections, and history are more timeless).
  • Potential value of an update (adding 2026-era insights, new examples, current tool versions, or follow-up reflections makes the post fresher and more useful to readers).
  • Thematic clustering (your AI/PKM series from 2022–2023 forms a natural arc that would benefit from a cohesive refresh).

I focused on the highest-impact ones rather than every post. Many others (e.g., pure street photography, freeway essays, family stories like the Tracy obituary, or 2021 COVID-era reflections) are still strong as-is and don’t need updates unless you want to add new photos or minor notes.

Top Priority (Strongly recommend updating soon — these are the most dated but highest-potential)

  1. Multi-step prompting for Claude (August 11, 2023)
    https://sjl.us/2023/08/11/multi-step-prompting-for-claude/
    Why update? Early Claude-specific prompting techniques; the entire field (Claude 3/4 families, GPT-4o-class models, Grok, etc.) has advanced dramatically. Easy win: expand with 2026 best practices, new multi-step examples, and comparisons.
  2. My Current PKM Stack as of September 2023 (September 20, 2023)
    https://sjl.us/2023/09/20/my-current-pkm-stack/
    Why update? Details your Obsidian/Instapaper/Feedly/Drafts workflow at the time. Tools and plugins have evolved a lot; readers love current “state of my stack” posts. A 2026 version would be highly engaging.
  3. Using AI to Help Write (November 15, 2022)
    https://sjl.us/2022/11/15/using-ai-to-help-write/
    Why update? Pure GPT-3-era experimentation. Perfect opportunity to contrast 2022 capabilities with today’s multimodal models and show how your own AI-assisted writing process has matured.
  4. LLMs = Dream Machines (December 11, 2023)
    https://sjl.us/2023/12/11/llms-dream-machines/
    Why update? References to early hallucination discussions and Andrej Karpathy’s tweet feel historic now. Update with current “dream machine” examples and how you use LLMs in 2026.
  5. Crafting Effective Prompts for Your AI Assistant (Claude, ChatGPT, Bard, etc.) (September 9, 2023)
    https://sjl.us/2023/09/09/crafting-effective-prompts-for-your-ai-assistant-claude-chatgpt-bard-etc/
    Why update? Another core prompting/PKM-adjacent piece from the explosion period. Pair it with the multi-step post for a nice “2023 vs 2026 Prompting Guide” refresh.

High Priority (Worth updating — clear value-add)

  1. An AI Thanksgiving (November 22, 2022)
    https://sjl.us/2022/11/22/an-ai-thanksgiving/
    Why update? Fun GPT-3 + DALL·E experiment. Readers would love a 2026 “then and now” version with newer models/image generators.
  2. Hacking Creativity with AI (August 22, 2023)
    https://sjl.us/2023/08/22/hacking-creativity-with-ai/
    Why update? Early chatbot creativity techniques; the creative workflow space has exploded since then.
  3. On the Minimum Viable Environment (MVE) and Apple’s Vision Pro (July 1, 2023)
    https://sjl.us/2023/07/01/on-the-minimum-viable-environment-mve-and-apples-vision-pro/
    Why update? Pre-launch speculation. Vision Pro shipped in 2024 — you can now add real-world usage, ecosystem updates, and whether MVE thinking held up.
  4. Drafts – a tool for idea capture (April 16, 2022)
    https://sjl.us/2022/04/16/drafts-a-tool-for-idea-capture/
    Why update? The app was celebrating its 10th anniversary then; it’s now 14+ years old with major feature/UI changes. Your long-term usage makes this a great “then vs now” post.

Medium Priority (Nice-to-have updates if you have time)

  1. Reading Books in 2022 (May 11, 2022)
    https://sjl.us/2022/05/11/how-im-reading-books/
    Why consider? Kindle/app habits and highlighting workflows have evolved; easy to refresh with current tools and your 2026 reading volume.

Other notable 2023 AI-adjacent posts (e.g., NotebookLM deep-dive from Dec 2023, Explorations of PKM from Oct 2023) would slot in here if you want to do a broader “AI/PKM 2023 Retrospective” series.

Lower Priority / Probably Leave As-Is

  • Photography series (e.g., “Capturing the Art in Everyday Scenes,” Paris posts, “Redwood Circle,” etc.) → Timeless and visual; they hold up well.
  • History/personal essays (e.g., “Ode to a Queen,” “The World’s Most Beautiful Freeway,” “Remembering Blue Chip Stamps,” “Old Man Walking,” 2021 book recs, “Stuck in Neutral”) → Evergreen or emotionally resonant; updating could dilute their original voice unless you have new material.
  • Holiday/personal posts (e.g., Thanksgiving 2023, Merry Christmas 2021) → Fine as snapshots in time.

Quick recommendation on approach: Start with the top 5 (they form a nice cluster around AI prompting/PKM). Updating them together would create a strong “2023 AI Reflections – Revisited in 2026” section or category on the blog. Many of these posts already reference each other, so cross-linking updated versions would be powerful.

If you’d like me to pull full content from any specific post, draft sample update language, suggest new sections, or help with images/AI-assisted rewrites, just let me know! Your blog has a great reflective voice — these updates would keep it feeling current without losing that personal touch.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
Living Productivity Serendipity

In Praise of the Interruption

We live in an era of the hyper-optimized schedule. Every waking minute is categorized, color-coded, and squeezed for its maximum potential output. We download applications to track our sleep cycles, our hydration, our daily habits, and our deep work intervals. We have collectively adopted the mindset of the factory floor, treating our own lives like well-oiled machines, and viewing any deviation from the master plan as a glitch that requires immediate patching.

But in our relentless pursuit of efficiency, we risk engineering the magic out of our own existence. We try to pave over the wilderness of our days with the concrete of predictable routines. In doing so, we forget a fundamental truth about human nature, a truth that author Jenny Odell captures perfectly:

“We still recognize that much of what gives one’s life meaning stems from accidents, interruptions, and serendipitous encounters: the ‘off time’ that a mechanistic view of experience seeks to eliminate.”

When we adopt this mechanistic view of our experience, an interruption is viewed as a systemic failure. A delayed train is a disaster. A wandering, off-topic conversation with a stranger is a sunk cost of our valuable time. Yet, when we look back on the broader timeline of our lives, the moments that stand out in the sharpest relief are almost never the ones we scheduled in thirty-minute increments on our digital calendars.

Think about the architecture of your own life. I often reflect on the most vital relationships I’ve formed, the sudden and necessary shifts in my career, or the quietest, most profound moments of personal clarity I’ve experienced. Practically none of them were planned. They were born from a wrong turn taken on a road trip that led to a breathtaking view. They emerged from a sudden downpour that forced me into a crowded, unfamiliar coffee shop. They sparked when a friend called out of the blue on a Tuesday afternoon when I was “supposed” to be doing highly focused work.

These accidents, these beautiful and unscripted interruptions, are the connective tissue of a life well-lived. They are the gentle reminders that we are not algorithms processing daily tasks, but fragile, curious humans experiencing a deeply unpredictable world. When we try to eliminate the “off time,” we are unknowingly trying to eliminate the very environments where serendipity is allowed to breathe.

We need to leave room for the friction. We need to stop seeing the blank spaces on our maps—and our schedules—as terrifying voids that must be filled with productive noise. Instead, we must begin to see them as the fertile soil from which the unexpected grows. Efficiency, routines, and optimization can certainly help build a very productive life. But only the accidents, the interruptions, and the quiet serendipity of “off time” can build a meaningful one.

Categories
Living

When Patience is Just Stubbornness in Disguise

We are taught from childhood that patience is the ultimate virtue. Good things come to those who wait. Rome wasn’t built in a day.

We elevate patience to a saintly status, conditioned to believe that if we simply hold on long enough, the universe will inevitably reward our suffering with success.

In his book Same as Ever, Morgan Housel offers a piercing observation that shatters our romanticized view of waiting:

“Patience is often stubbornness in disguise.”

That single sentence is a quiet earthquake. It forces us to examine the things we are holding onto and the real reasons why we refuse to let them go.

We like to tell ourselves we are being patient—with a stagnant career, a fractured relationship, or a creative project that refuses to take flight. The label of “patience” feels noble. It feels righteous. It protects our ego from the sharp, uncomfortable sting of failure.

But if we strip away the noble veneer, what remains is often simple, unyielding stubbornness. It is the refusal to adapt, the refusal to admit defeat, and the refusal to accept that the world has changed while we were standing still. “I’m staying the course” is much easier to say than “I’m terrified to admit I made a mistake.”

I think about the seasons in my own life where I mistook one for the other.

I held onto projects that had lost their spark, telling myself that the breakthrough was just around the corner, just one more iteration away. I’ve held on to failing investments for far too long.

In hindsight, I wasn’t practicing patience. I was practicing avoidance. I was avoiding the grief of letting go and the daunting reality of starting over from scratch.

So, how do we distinguish between the two? How do we know when we are nurturing a slow-growing seed, and when we are merely digging our heels into the dirt and being stubborn?

The difference lies in our relationship with reality. True patience involves a quiet confidence and an active engagement with the present. It requires us to make incremental progress, to observe the feedback the world gives us, and to adjust accordingly. Patience is flexible yet realistic.

Stubbornness, on the other hand, is rigid. It ignores feedback. It closes its eyes to the changing environment and insists that reality bend to its will.

It takes vulnerability to look at something you’ve poured your heart and time into and say, “This isn’t working, and I am choosing to walk away.” It is not a weakness to change your mind when the evidence suggests you should. Often, it is the ultimate act of self-awareness. Annie Duke wrote a whole book about quitting being an underutilized choice.

Sometimes, the most productive thing we can do with our time is to stop waiting, let go, and walk in an entirely new direction.

Categories
AI AI: Large Language Models Programming

The Era of the Synthesizer: How AI Is Liberating the Coder

For decades, being a programmer meant being a translator.

You stood in the gap between what someone wanted and what a machine could understand. You learned the syntax. You memorized the libraries. You once spent three hours hunting a missing semicolon that turned out to be hiding in line 847 of a file you were sure you’d already checked.

The New York Times Magazine recently ran a piece by Clive Thompson on what AI coding assistants — models like Claude and ChatGPT — are doing to that job. The anxiety in the piece is real. When you sit down with a modern AI assistant and watch it generate in seconds what used to take you days, it’s genuinely disorienting. Hard-won expertise suddenly feels less like a moat and more like a speed bump.

That reaction is honest. I’d be suspicious of anyone who didn’t feel it.

But here’s what I keep coming back to: what we’re losing is the translation layer. The boilerplate. The muscle memory of syntax. What we’re not losing is the part that was always the actual job — figuring out what to build and why it matters.

The soul of software was never in the code itself. The code was always just a means to an end.

Think about what happens when the mechanical friction of a craft disappears. Photographers stopped having to mix their own chemicals in the dark and started spending that time making better images. Musicians stopped having to hand-copy scores and started composing more. The freed-up capacity doesn’t evaporate — it gets redirected upward, toward the work that actually required a human all along.

The same shift is underway in software. When the AI handles the loops and the boilerplate and the database queries, what’s left is everything that required judgment in the first place. The architecture. The user experience. The question of whether this thing should exist at all, and in what form, and for whom.

We’re moving from the how to the why. That’s not a demotion.

It does ask something of us, though. The old identity — programmer as master of arcane syntax — has to be relinquished. And letting go of a hard-earned identity is genuinely hard, even when what’s replacing it is better. That quiet grief the Times piece captures is worth sitting with, not dismissing.

But after you sit with it for a minute: we are entering the era of the synthesizer.

The synthesizer’s job is to hold the vision, curate the logic, and direct the output toward something that actually resonates with another human being. Empathy. Intuition. The ability to sense when something is almost right and know which direction to push it. These aren’t soft skills. They’re the whole game now.

The clatter of keyboards is fading. But the music we’re about to make — with AI doing the heavy lifting on the mechanics — has a lot more room to breathe.