Tag: SJL Musing

Categories
Business History Memories Radio

Permissionless Airwaves: The Legacy of FCC Part 15

Right now, as you read this, the air around you is thick with invisible conversations. Your phone is whispering to your router, your wireless headphones are singing to your laptop, and the smartwatch on your wrist is syncing quietly in the background.

We take this symphonic digital ecosystem completely for granted. But this panoply of wireless magic wasn’t just an inevitable product of technological march. It exists because of a profound, remarkably philosophical decision made by a bureaucracy in 1985.

It traces back to a seemingly mundane piece of regulatory code: the Federal Communications Commission’s Part 15 rules.

Historically, the airwaves were treated like highly exclusive real estate. If you wanted to broadcast a signal, you needed a license, a specific frequency, and a strict, government-approved mandate for what you were doing.

But within the radio spectrum, there were segments known as the ISM bands (Industrial, Scientific, and Medical). These were essentially the “garbage bands” of the airwaves. Microwave ovens, for instance, operated here, blasting out radio noise at 2.4 GHz. The interference was so heavy that the spectrum was considered practically useless for traditional communications.

Enter an FCC engineer named Michael Marcus. Marcus possessed a visionary understanding of a World War II-era technology called “spread spectrum” (famously co-invented by actress Hedy Lamarr). Spread spectrum didn’t rely on a single, clean channel; instead, it scattered a signal across a wide swath of frequencies, easily dodging interference.

Marcus argued for something radical: what if we opened up these “junk” bands to the public, allowing anyone to use spread-spectrum devices without asking for a license, so long as they adhered to basic power limits and didn’t cause harmful interference to primary users?

Incumbents fought it bitterly. Broadcasters and traditional telecommunications companies warned of absolute chaos. But in 1985, the FCC adopted the new Part 15 rules.

“We often talk about the great technological breakthroughs of our time as hardware or software triumphs. But sometimes, the most important enabling technology is just a clearing in the woods.”

Think about the nature of most regulation. It usually prescribes behavior. It looks at the future and says, “You may do exactly X, under condition Y.” But the Part 15 ruling did the opposite. It created a sandbox. The FCC didn’t try to predict Wi-Fi, Bluetooth, cordless phones, baby monitors, or the Internet of Things. In fact, they couldn’t have. They simply set the structural ground rules for how devices should coexist without stepping on each other’s toes, and then they stepped back.

This is the beauty of permissionless innovation. When you don’t have to ask a gatekeeper for access, a massive, uncoordinated burst of creativity happens.

A small company in the Netherlands could start working on what would eventually become Wi-Fi. Ericsson could invent Bluetooth. Innovators didn’t need to petition the government to launch a new product; the space was already cleared for them to play.

Part 15 was an admission of humility by a regulatory body—an acknowledgment that the most profound inventions are the ones we cannot yet foresee.

The greatest legacy of Part 15 isn’t Wi-Fi or Bluetooth. It is the enduring lesson that when you give brilliant minds a blank canvas and the freedom to experiment without asking permission, they will build a world more connected than you ever dared to imagine.

Note: this post was triggered by my reading of David Pogue’s new book Apple: The First 50 Years in which he describes the development of the Apple III and how its design met the requirements of the FCC’s Part 15 in terms of reduced RF interference.

Categories
AI Creativity Programming Writing

We Are All Painters Now: The Era of Vibe Coding

For decades, the act of creating software was exactly that: writing. It was a distinctly left-brained, agonizingly precise discipline.

Programmers were typists of logic, translating human intent into a rigid, unforgiving syntax that a machine could understand. A single misplaced semicolon, an unclosed bracket, or a misspelled variable could bring an entire system crashing down.

Building software meant placing one brick after another, working meticulously from the ground up.

In this traditional paradigm, coders were the ultimate embodiment of Annie Dillard’s writer. As she noted in The Writing Life, “Writers… work from left to right. The discardable chapters are on the left.”

When you wrote code, your mistakes, your refactoring, and your discarded logic were all part of a linear, grueling journey. If a feature didn’t work, you had to physically wade back into the text, debugging, reading line by line, and rewriting the narrative of the application. The discarded chapters were the endless hours spent wrestling with a single broken dependency.

But recently, a profound paradigm shift has quietly taken over our screens. We are transitioning out of the era of writing software and into the era of “vibe coding.”

Vibe coding fundamentally changes our relationship with the machine. With the rise of advanced AI coding assistants, we are no longer placing the bricks ourselves; we have become the architects and the creative directors. You don’t write the loop or manually construct the database query. Instead, you describe the feeling, the function, and the outcome. You tell the AI, “Make this dashboard feel more modern,” or “The logic here is too clunky, make it flow faster and handle edge cases gracefully.” You are coding by intuition. You are steering by the “vibe” of the output rather than the mechanics of the input.

Suddenly, Dillard’s other metaphor takes center stage. In the age of vibe coding, we have become painters.

“A painting covers its tracks. Painters work from the ground up. The latest version of a painting overlays earlier versions, and obliterates them.”

When we vibe code, we ask an AI for a functional prototype, and it gives us a canvas. We look at it, test it, and sense whether it aligns with our vision. If it doesn’t quite hit the mark, we don’t necessarily rewrite the code from scratch. We simply prompt the AI to try again, adding a new layer of instruction. The AI paints a new layer of code directly over the old one. The awkward, underlying iterations—the messy attempts at styling, the inefficient logic of the first draft—are obliterated by the newest prompt.

The machine covers our tracks for us. We don’t need to know exactly how the underlying pixels were rearranged or how the syntax was refactored. The final application emerges as a stunning obliteration of its own clumsy past.

As someone who has spent time wrestling with the rigid demands of syntax, there is a strange, quiet grief in letting go of that left-to-right process. There is a deeply earned, tactile satisfaction in building something manually, understanding the precise weight and placement of every line of code. Relinquishing that control can feel like a loss of craftsmanship.

Yet, there is also a breathtaking liberation in this new medium. We are moving from a world of manual construction to a world of artistic curation. The barrier to entry is no longer fluency in a specific, arcane language; it is simply the clarity of your imagination and your ability to articulate your intent.

The next time you sit down to build something digital, notice the shift in your own posture. You no longer have to carry the heavy burden of the writer, agonizing over every word and leaving your discardable chapters on the left. You can step back, look at the whole canvas, and trust your intuition. Let the AI cover the tracks. Embrace the obliteration of the early drafts.

We are all painters now, coaxing the future into existence one brushstroke at a time.

Categories
Creativity Writing

The Crucible of the Blank Page

There is a distinct, often uncomfortable silence that accompanies a blank page. It’s not a lack of noise, but rather an overwhelming cacophony of unformed ideas waiting to be given shape.

We often operate under the assumption that we must have our thoughts perfectly ordered before we sit down to express them. We believe writing is merely the act of transcribing a fully formed philosophy from mind to paper.

But the truth is far messier, and infinitely more profound.

Flannery O’Connor captured this beautifully when she admitted:

“I write because I don’t know what I think until I read what I say.”

I find myself returning to this admission constantly, deeply resonating with the reality of it. I’m the same way.

The human mind is a brilliant but chaotic place, a swirling ether of impressions, emotions, half-remembered conversations, and half-baked theories. Left to its own devices, it rarely settles on a singular, coherent truth. It requires the friction of articulation—the physical, deliberate act of putting words into a sequence—to force those nebulous clouds into something solid.

In an era increasingly defined by the allure of frictionless output, there is a profound temptation to skip this wrestling match.

We are surrounded by tools and shortcuts designed to hand us the finished essay, the polished insight, the perfectly packaged takeaway without us having to endure the messy, chaotic energy of the drafting process. It is easy to look at the blank page as a hurdle to be cleared rather than a necessary landscape to be traversed. But bypassing that struggle is a critical mistake.

You cannot skip the work of wrestling with ideas. That struggle is not a barrier to good writing; it is the core chaotic energy that underpins it. It is the crucible where conviction is forged.

When you wrestle with a sentence, striking it out, rewriting it, abandoning it entirely for a new thought, you are not just editing text on a screen. You are editing your own mind. You are testing the structural integrity of your beliefs.

The chaotic energy of a rough draft—the fragmented sentences, the sudden leaps of logic, the tangents that seem to lead nowhere—is evidence of a mind actively searching for meaning.

It is through this very friction that we discover what we actually believe.

An idea might feel profound when it is floating weightlessly in your head, but the moment you try to pin it down with language, its flaws and hollow points become glaringly obvious. Writing forces a confrontation with our own intellectual blind spots.

If we outsource this process, or if we try to circumvent the chaos by relying on templates or taking the path of least cognitive resistance, we lose the very mechanism by which we come to know ourselves. We might successfully produce text, but we will not produce insight.

The value of writing isn’t just in the final product meant for a reader’s eyes; it is in the transformation that occurs within the writer.

To write is to step into the unknown spaces of your own intellect. It is an act of revelation as much as communication.

So, the next time you find yourself staring at a blank page, feeling the chaotic energy of unformed thoughts, don’t retreat.

Lean into the mess. Let the words spill out, rough and unpolished, and trust that in the wreckage of your early drafts, you will finally read what you say, and in doing so, discover exactly what you think.

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 Work

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

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

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

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

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

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

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

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

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

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

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

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

A couple of considerations:

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

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

Categories
AI Anthropic Claude Cybersecurity

The End of Obscurity

There is a particular kind of silence that surrounds a zero-day vulnerability. It is the silence of something waiting—a flaw in the logic, a gap in the armor, sitting unnoticed in the codebase for years, perhaps decades. We have slept soundly while these digital fault lines ran beneath our feet, largely because we assumed that finding them required a brute force that no one possessed, or a level of human genius that is incredibly rare.

But the silence is breaking.

I was reading Anthropic’s Red Team report from earlier this week (triggered by reading Bruce Schneier’s amazement), specifically their findings on the new Opus 4.6 model. The technical details are impressive, but the philosophical implication is what stopped me, like Bruce, cold.

For years, digital security has relied on “fuzzers”—programs that throw millions of random inputs at a system, banging on the doors to see if one accidentally opens. It is a noisy, chaotic, brute-force approach.

The new reality is different. As the report notes:

“Opus 4.6 reads and reasons about code the way a human researcher would—looking at past fixes to find similar bugs that weren’t addressed, spotting patterns that tend to cause problems.”

This is a fundamental phase shift. We are moving from the era of the Battering Ram to the era of the Jeweler’s Loupe. The machine is no longer guessing; it is understanding.

There is something deeply humbling, and slightly terrifying, about this. We have spent the last half-century building a digital civilization on top of code that we believed was “secure enough” because it had survived the test of time. We trusted the friction of complexity and the visibility of open source to keep us safe. We assumed that if a bug had existed in a core library for twenty years, surely it would have been found by now.

But the AI doesn’t care about time. It doesn’t get tired. It doesn’t have “developer bias” that assumes a certain function is safe because “that’s how we’ve always done it.” It simply looks at the structure, reasons through the logic, and points out the crack in the foundation that we’ve been walking over every day.

We are entering a period of forced transparency. The “security by obscurity” that held the internet together is evaporating. When intelligence becomes commoditized, vulnerabilities become commodities too. The question is no longer “is my code secure?” but rather, “what happens when the machine sees the flaws I cannot?”

It’s a reminder that complexity is a loan we take out against the future. Eventually, the bill comes due. We are just lucky that, for now, the entity collecting the debt is one we built ourselves, designed to tell us where the cracks are before the ceiling collapses. Let’s hope that we are out far enough in front of it.

Categories
Living Music

The Strangest of Places

There is a particular kind of silence that fills the room when you read the obituary of a contemporary. It isn’t just the news of a celebrity passing; it is a check engine light on your own dashboard. Bob Weir is gone. He was 78. I am 78.

I have good memories of seeing him playing with Jerry Garcia, Phil Lesh, et al at the Fillmore in San Francisco. Such a different time the 60’s were and the Dead’s music was a big part of that.

When you share a birth year with someone, you share a timeline. You walked through the same decades, witnessed the same wars, the same shifts in culture, albeit from different vantage points. For Weir, it was from the stage of the Fillmore or Winterland Ballrooms and stadiums across the world. For me, it was a different path. But arriving at this specific mile marker—seventy-eight years of age—feels like we both pulled into the same station at the same time, only for him to disembark while I stay on the train a little longer.

I was reminded of a line from “Scarlet Begonias,” quoted recently by Alyssa Mastromonaco:

“Once in a while you get shown the light in the strangest of places if you look at it right.”

In our youth, those “strangest places” were literal—backstage hallways, late-night diners, or the chaotic joy of a festival crowd. We looked for the light in the noise. But at 78, the definition of strange changes. The strangest place to find the light now is often in the mirror, observing a face that has weathered nearly eight decades. Or it is found in the quiet of an early morning, realizing that the absence of pain is its own kind of euphoria.

Weir spent a lifetime improvising, trusting that the music would find its way back to the tonic note. There is a lesson in that for those of us left here. The “light” isn’t always a flash of brilliance or a grand finale. Sometimes, if you look at it right, the light is simply the grace of being here, right now, able to listen to the song one more time.

The music never really stops, does it? It just changes players.

Categories
Books

The Observer Observed

I first encountered Susan Orlean not in person, but in the ashes. Specifically, the ashes of the Los Angeles Central Library. Reading The Library Book was a masterclass in how to weave a forensic investigation with a love letter to a public institution. It was reportage, but it possessed a beating heart. She has spent decades at The New Yorker perfecting the art of the “curious observer”—the person standing just to the side of the frame, noticing the detail everyone else missed.

That is why picking up Joyride felt different.

In a memoir, the observer must finally step in front of the lens. The transition from The Library Book—which is about the preservation of collective memory—to Joyride—which is about the fluidity of personal memory—is a fascinating shift. When a journalist writes a memoir, there is often a tension. They are used to looking outward, hunting for the story in orchids or arsonists. Turning that gaze inward requires a different kind of bravery.

“A commute has a destination; a joyride has only a duration.”

The title itself suggests a specific philosophy of living. It implies that the movement itself is the point. As I read, I found myself thinking about the difference between navigating a life and simply driving through it. Orlean captures that distinct feeling of the wind in your hair, the blur of the scenery, and the realization that the “plot” of our lives is often just the things that happen while we are busy steering.

We read writers like Orlean not just for what they saw, but for how they saw it. In Joyride, she reminds us that the most interesting routes are rarely the most direct ones. A great read!