Tag: progress

Categories
AI Technology

The Bathwater Problem

Gary Kamiya was writing about the Tenderloin when he said it, but the line has been following me around: “The problem is that by saving the baby, you also save the bathwater.”

The pattern is remarkably consistent across every major information technology. Each one arrives promising to liberate the deserving — the faithful, the learned, the civic-minded — and each one immediately, inevitably, arms everyone else too. Gutenberg’s press was understood by its champions as a device for spreading the true Word; within decades it was the primary infrastructure for Protestant schism, Catholic counter-propaganda, astrological almanacs, and pornography. The reformers got their Bible. They also got their pamphlet wars.

The telegraph was greeted as a force for peace — shared information would make war irrational, commerce would bind nations. It also became the nervous system of commodity speculation, financial manipulation, and the first truly industrial-scale news hoaxes. The telephone: connection and the crank call, the crisis line and the threatening voice in the dark. Radio: FDR’s fireside chats and Father Coughlin. Television: Murrow taking down McCarthy, and also fifty years of manufactured consent. The internet: the largest library ever assembled and the largest sewer.

The pattern isn’t coincidental. It’s structural. Each technology expands what’s possible for human expression and coordination — and human expression and coordination contain both the noblest and the worst of us in roughly fixed proportion. The tool doesn’t change the ratio. It scales both sides of it.

What’s interesting historically is how each generation believes their technology will be different — that this time the architecture can be designed to select for the good. The internet era produced the most elaborate version of this belief: algorithmic curation would surface truth, network effects would reward quality, the wisdom of crowds would outcompete misinformation. Instead it turned out that engagement was the attractor, and outrage was the highest-engagement content. The bath got hotter.

The AI moment is the same belief system, restated with more technical sophistication. But the Kamiya line stands. You are saving a baby, and you are saving bathwater, and no one has yet designed a tub that can tell the difference.

The question isn’t whether the bathwater comes with the baby. It always does. The question is whether you turn on the tap.

Categories
Business Creativity Space SpaceX

Test like you fly!

There’s a phrase in the SpaceX documentary that keeps coming back to me: “Test like you fly.” It sounds like a slogan. The kind of thing that gets painted on a factory wall and eventually stops meaning anything. But the more I sit with it, the more I think it’s actually a philosophy that reaches well beyond rocket engineering.

The video — a 25-minute documentary SpaceX released last week — is ostensibly about Starship Version 3. New ship, new booster, new engines, new pad, new test site. Everything rebuilt. And they’re not shy about framing it as a reset, not an upgrade. One description I read called it “a quiet violence in progress.” That phrase stopped me cold, because it’s exactly right. Progress that looks violent from the outside — all that fire and metal — but is somehow quiet in its inevitability.

What moved me watching it wasn’t the engines. It was the engineers. SpaceX put the people on camera: the ones running cryogenic pressure tests at 80 Kelvin, stress-testing tank structures at 70% proof, explaining their failures and their data with the flat affect of people who have made peace with how long hard things take. There’s something almost monastic about it. You choose a problem that will not yield easily. You accept that the work will outlast any individual sprint of enthusiasm. You go back to it anyway.

I keep thinking about that in the context of what we’re doing with AI — the other enormous, fast-moving project that I spend so much of my mental energy on. The development arc is different: iterative releases, weeks not years between jumps, demos that blur into deployment. But the same principle is buried in there somewhere. The best AI teams I read about aren’t the ones shipping the most polished demos. They’re the ones building infrastructure for failure — evals, red-teaming, structured feedback loops. Test like you fly.

The Raptor 3 engines now produce 280 metric tons of thrust each. Thirty-three of them on a Super Heavy booster means over 17 million pounds of liftoff force. I have no intuitive frame for that number. What I do have a frame for is what those numbers represent: three years of iteration on top of five years before that, on top of a theoretical foundation laid by people who didn’t live to see any of this. There’s a compounding in that which I find genuinely moving. Nobody built the Raptor 3 in isolation. It came from everything that broke before it.

The hardest part of the documentary isn’t the engineering. It’s the implicit acknowledgment of how much remains undone. No Starship has yet achieved full orbital velocity with both stages intact. In-space refueling is still untested. The thermal protection systems need more work. And yet — SpaceX talks about unmanned cargo missions to Mars before the end of this year like it’s on the roadmap, not the wish list. That sentence used to sound like marketing. Watching the footage, it doesn’t anymore.

I’m not sure what to do with that feeling exactly. It’s something between awe and vertigo. We’re living in a moment when the audacious has started to have quarterly milestones. When the impossible keeps showing up on timelines and then — bewilderingly, uncomfortably — meeting them.

Test like you fly. Fail with rigor. Build the thing you actually need, not the thing you could more easily explain.

I keep turning that over. There’s a post in there somewhere about writing, too — about the drafts nobody sees, the structural tests that fail, the versions that taught you the one that worked. But that’s for another day.

For now I’m just sitting with the footage of those 33 engines lighting up, and the quiet weight of how much went wrong before they could do that.

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
AI Work

Betting on Ourselves in the Age of AI

Every time tech takes a leap, we assume we’re finally obsolete. The current panic, which Greg Ip recently picked apart in the Wall Street Journal, is AI. We hear endless predictions of “economic pandemics”—server farms wiping out white-collar jobs overnight, leaving everyone broke and adrift.

It’s a terrifying story. It also completely ignores history.

Ip highlights the main flaw in the doomsday pitch: it misreads how markets work. We treat labor like a fixed pie. If a machine eats a slice, we assume that slice is gone forever.

“Technological advancements always cost some people their jobs—those whose skills can be easily substituted by tech. But their loss is more than offset through three other channels. The new technology enhances the skills of some survivors… it helps create new businesses and new jobs; and it makes some stuff cheaper…”

That cycle holds up. Take the 1980s spreadsheet panic, a perfect parallel. When Lotus 1-2-3 and Excel hit the market, bookkeepers freaked out. Then the number of accountants and financial analysts exploded. Software didn’t kill the need to understand money. It just did the math, letting people focus on strategy.

We’re seeing the exact same thing with software development. Coding isn’t dead. As AI makes writing basic code cheaper, demand for software just goes up. That requires more humans to architect systems and supervise the AI. The pie just gets bigger.

But my skepticism about the AI apocalypse goes beyond economics. It’s about why we pay people in the first place.

We don’t just buy services; we buy accountability. Ip notes that radiologists kept their jobs because patients want a real person explaining their scans. Google Translate has been around since 2006, yet the number of human translators has jumped 73%. When the stakes are high—a legal contract, a medical diagnosis—we want a human in the room. We want a real person on the hook.

The danger isn’t that AI will replace us. The danger is that we panic and forget our own adaptability. The transition will hurt, and specific jobs will disappear. We’ll need safety nets. But betting against human ingenuity has always been a losing wager.

Large language models are tools, not replacements. They handle the cognitive heavy lifting, much like tractors handled the physical heavy lifting. Tractors didn’t end farming; they just killed the plow.

Work will change. We’ll have to figure out which of our skills are actually “human.” But as long as we want the presence and accountability of other people, there will be jobs. We just have to evolve. And we do. It’s the human spirit. Or is this time “really different”?

Categories
AI Anthropic Future

Escaping the Gravity of the Present

I was watching a YouTube conversation with Dario Amodei recently, and the comments he shared at the end got me thinking about how remarkably bad we all are at imagining the future.

Whenever I try to picture what the world will look like in ten or twenty years, I usually end up picturing today—just slightly shinier. If a prediction sounds too weird or disruptive, my brain automatically rejects it. It just feels too unmoored from the reality I woke up in this morning. We all have this instinct to retreat to the safety of incremental change.

But as Amodei points out, that comfort zone is exactly what blinds us. He notes that we are constantly tempted to dismiss massive shifts simply because they feel like they “can’t happen.”

“However, by extrapolating simple curves or reasoning from first principles, one often arrives at counterintuitive conclusions that surprisingly few people believe.”

It’s a strange feeling to look at a simple data curve, follow the math, and realize the logical endpoint sounds completely unhinged. The truest maps of tomorrow often look like bad science fiction to us today.

But there is a catch here, and it’s a mental trap I know I’ve fallen into before. You can’t just sit in a room and logic your way into the future. Pure logic, stripped of real-world friction, usually just leads you confidently in the wrong direction. Amodei suggests a much more grounded formula:

“The right combination of a few empirical observations and thinking from first principles can allow one to predict the future in ways that are publicly available but rarely adopted.”

This struck a chord with me. It’s easy to get swept up in purely theoretical thinking. But the better approach is to start with what is actually happening on the ground—the messy, undeniable data. From there, you strip it down to its most basic truths and follow the thread, no matter how strange the destination looks.

It takes a certain kind of intellectual courage to trust the math when your gut is screaming that things are getting too weird. But learning to decouple what is true from what feels normal might be the only real way to prepare for what is coming.

Categories
AI History Work

Flash-Frozen Cognition: Birdseye, AI, and the Future of Work

I was listening recently to a conversation between Liz Thomas, Tom Lee, and Michael Lewis — the kind of wide-ranging dialogue where a single offhand story can suddenly anchor everything that’s been swirling loosely in your mind.

Tom’s story was about the 1930s, the weight of the Great Depression, and a man named Clarence Birdseye.

Birdseye had watched the Inuit fish in the brutal cold of Labrador and noticed something the rest of the world had missed: fish frozen instantly at sub-zero temperatures tasted perfectly fresh when thawed. The ice crystals formed too quickly to rupture the cellular walls of the flesh. He took that observation home, patented the process, and introduced the world to flash freezing.

On the surface, he had simply figured out a better way to keep peas green and fish edible. What he had actually done was detonate a quiet economic bomb.

Before Birdseye, entire ecosystems of seasonal labor existed to preserve, salt, can, and rush perishable goods to market before they rotted. When flash freezing arrived, those jobs didn’t evolve — they vanished. The ice harvesters, the seasonal canners, the local preservationists all felt the sudden, biting frost of obsolescence. The cold came fast, and it was indifferent.

Yet zoom out on the timeline, and a different picture emerges entirely. Flash freezing didn’t just kill jobs — it invented new ones that nobody could have anticipated. It necessitated refrigerated trucking. It transformed the grocery store, conjuring the frozen food aisle from nothing. It reshaped the home appliance industry, making the household freezer a fixture of modern life. Most profoundly, it decoupled humanity from the harsh dictates of the harvest season, democratizing access to nutrition across geographies and income levels that had never known that kind of abundance.

The destruction was visible and immediate. The creation was invisible and slow — and vastly larger.

Listening to Tom tell this story, I couldn’t help but see our own reflection in it.

Right now, we are all hyper-focused on the ice harvesters of the cognitive economy. We look at AI — large language models, generative tools, automated reasoning — and we see the rupture. We mourn the entry-level analyst, the copywriter, the junior coder. The anxiety is real. The displacement is real. The cold is real.

But what we are struggling to visualize is the refrigerated trucking of the mind.

“AI is flash-freezing cognition. It is taking tasks that used to rot if not attended to immediately by expensive, time-consuming human effort, and preserving them in a scalable, frictionless state.”

When intelligence and execution can be flash-frozen and shipped anywhere instantly — to a first-generation entrepreneur in rural India, to a solo founder with no budget for consultants, to a teacher in a school that can’t afford specialists — what new aisles get built in the supermarket of human endeavor?

The honest answer is that we don’t know. The Inuit fishermen of Labrador couldn’t have imagined the frozen pizza aisle. The ice harvesters of the 1930s couldn’t have pictured the cold chain logistics industry that employs millions today. We are standing in their moment, watching the ice form, mourning the harvest — and almost certainly underestimating what comes next.

The true impact of AI won’t be measured in the jobs it automates. It will be measured in the industries, creative liberties, and human possibilities that emerge because we no longer have to spend all our energy just keeping the ideas from spoiling.

Questions to Consider

  1. The Invisible Creation: Flash freezing’s job creation vastly outpaced its job destruction — but only over decades. How long are we willing to hold that faith with AI, and what do we owe the people displaced in the interim?
  2. The Democratization Dividend: Birdseye’s invention ultimately made fresh nutrition available to people who never had it. Who are the equivalent beneficiaries of flash-frozen cognition — and are we building the infrastructure to actually reach them?
  3. The Harvest Season Question: We’ve always structured education, careers, and institutions around the assumption that expertise is scarce and slow to develop. What breaks — and what gets liberated — when that assumption stops being true?
  4. The Indifference Problem: The cold that killed the ice harvesters’ livelihoods was indifferent to their suffering. Is there anything about AI disruption that is meaningfully different from previous waves of technological displacement — or are we simply the latest generation to stand in that frost?

Categories
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.

Categories
AI AI: Large Language Models Claude Creativity Writing

Co-Creation

Ethan Mollick, a professor at the Wharton School, has written a popular book on artificial intelligence titled “Co-Intelligence“. Like I do with most books I read, I’ve collected a series of highlights of various passages I found interesting when I read Mollick’s book – a book I enjoyed very much. This morning I came across one of those highlights which sent me off on this exploration.

But first let me share a perspective. A few friends of mine – when exploring how they might use artificial intelligence – reject the use of AI tools for helping them write. They feel strongly that the words an AI creates aren’t their own and strongly reject simply using an AI-generated output to create articles for their blogs, etc. I certainly appreciate their point of view – but I’m finding a more nuanced perspective on this question is more appropriate for me.

What I’m finding is that when I invoke AI to help me explore a topic, an idea, etc. that the result will almost always open up my thinking by exposing me to things, ideas, etc. that I hadn’t considered. In so doing, I come away feeling “richer” in the sense that I’ve learned – and not simply written – in the process. How I use the work product created by the AI varies – sometimes it’s helpful in critiquing something I’ve written. Other times I use it to help me better understand something – or explore by stepping outside my own brain.

Here’s an example from some exploring I did this morning. During my morning review of my Kindle book highlights using Readwise, I came across a quotation from Mollick’s book that I highlighted:

“If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.” (Ethan Mollick, Co-Intelligence)

I love the idea of how creativity is influenced by the often seemingly random interconnections between ideas – and this quote concisely made that point. I wanted to explore this further so I asked Claude Sonnet to help me by writing an article in the style of author John McPhee that’s based on the quote. Here’s the exact prompt I used with Claude:

You are an expert writer in the style of John McPhee. You are helping me write interesting articles for my blog. Create a 750 word draft article for my blog based on the following quotation: “If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.” (Ethan Mollick, Co-Intelligence)

I’ve been finding that Claude often does a more creative form of writing as compared to other chatbots. The article that Claude returned in response to my prompt really surprised me – so much so that I thought I’d just share it directly here.

So here we go…the following wasn’t written by me – rather, it was co-created based on a prompt from me and written by my co-intelligent partner Claude.

The Art of Cross-Pollination: Cultivating Innovation in Unlikely Places

In the verdant fields of central New Jersey, not far from where I sit typing these words, a peculiar ritual unfolds each spring. Farmers, their hands calloused from generations of working the land, don white protective suits that make them look more like astronauts than agriculturists. They move deliberately through their orchards, carrying small paintbrushes and vials of pollen.

These are the pear farmers of Mercer County, and they’re engaged in a practice as old as agriculture itself: cross-pollination. But why, you might ask, are they doing by hand what bees have done for millennia? The answer lies in the delicate biology of pear trees and the fickle nature of their reproduction.

Pear trees, it turns out, are notoriously poor self-pollinators. To produce fruit, they need pollen from a different variety of pear tree. But pear blossoms produce relatively little nectar, making them unattractive to bees. So these farmers, in their space-age suits, play the role of bees, manually transferring pollen from one variety to another, ensuring a bountiful harvest come autumn.

As I watched this process unfold on a crisp April morning, I was struck by a quote I’d recently encountered from Ethan Mollick, a professor at the Wharton School: “If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.”

The pear farmers of Mercer County, in their own way, embody this principle. They’ve taken an idea from one field (entomology) and applied it to another (agriculture), adding their own creative twist (the paintbrushes and protective suits). The result? A thriving pear industry in a region where it might otherwise be impossible.

This concept of cross-pollination – of ideas rather than pollen – is not unique to agriculture. In fact, it’s a principle that underpins much of human innovation and creativity.

Consider, for a moment, the work of Frances Glessner Lee, often called the “mother of forensic science.” Lee was born into wealth in the late 19th century and, like many women of her social standing, was skilled in the domestic arts, particularly miniature-making. But Lee had a passion for criminal investigation, nurtured by her friendship with George Burgess Magrath, a medical examiner.

In the 1940s, Lee combined these seemingly disparate interests to create the Nutshell Studies of Unexplained Death – intricately detailed dioramas of crime scenes used to train detectives. These miniature crime scenes, built with the precision of a dollhouse maker but imbued with the gruesome details of a murder scene, revolutionized forensic training. Lee had taken skills from the traditionally feminine world of crafts and applied them to the male-dominated field of criminal investigation, creating something entirely new and profoundly useful in the process.

This principle of creative cross-pollination extends far beyond the realms of agriculture and crime scene investigation. The history of human progress is littered with examples of innovations born from the unlikely marriage of disparate fields.

Take, for instance, the development of GPS technology. The seeds of this now-ubiquitous system were planted when physicists William Guier and George Weiffenbach at the Johns Hopkins Applied Physics Laboratory realized they could track the Soviet Sputnik satellite by monitoring its radio transmissions. This was an impressive feat in itself, but it was their colleague Frank McClure who made the cognitive leap that would change the world. McClure realized that if the scientists could track a satellite from the ground, they could also use satellites to track positions on the ground.

This insight – born from the collision of rocket science, radio engineering, and a healthy dose of Cold War paranoia – led to the development of the Transit system, the precursor to modern GPS. Today, this technology guides everything from missile systems to the smartphone in your pocket, all because a group of scientists were able to flip their perspective and see their work from a new angle.

But perhaps the most striking example of creative cross-pollination comes from the field of medicine. In the early 1940s, a Scottish bacteriologist named Alexander Fleming was researching influenza. One day, he noticed that mold had contaminated one of his petri dishes – a common enough occurrence in a microbiology lab. But Fleming observed something unusual: the area around the mold was clear of bacteria.

This chance observation, combined with Fleming’s willingness to investigate rather than simply discard the contaminated sample, led to the discovery of penicillin. Fleming had linked the disparate fields of mycology and bacteriology, added a dash of serendipity, and created something that would revolutionize medicine and save countless lives.

As we navigate an increasingly complex and interconnected world, the ability to cross-pollinate ideas from different fields becomes ever more crucial. The challenges we face – from climate change to antibiotic resistance – are too complex to be solved within the confines of a single discipline.

So perhaps we should all take a cue from the pear farmers of Mercer County. Don our metaphorical protective suits, pick up our idea-paintbrushes, and venture into unfamiliar orchards of thought. For it’s in these unlikely places, where disparate ideas collide and combine, that the seeds of innovation are sown.

Categories
Creativity Living

In Praise of Ignorance: A Catalyst for Creativity

For many years, my career was based on being an “expert” – a go-to consultant who knew his subject area in great detail, who studied and later taught its history, and who specialized in being an expert specialist. Along the way, I became sensitive to the notion “tyranny of the expert” advocated by some who preferred to avoid involving specialist experts like me in projects that I felt would clearly benefit from my skills and expertise.

This morning, one of my Readwise highlights came from Rick Rubin’s recent book. Reading that highlight brought back to mind that notion of the “tyranny of the expert” – and result in me asking Claude 3 for some help composing a musing on this notion more broadly defined as “beginner’s mind”. Here’s the musing – lightly edited by me. Q. Where are you applying your ignorance today?

Rick Rubin invites us to challenge our preconceptions and consider the liberating potential of a beginner’s mind. In a world that often prizes expertise and specialized knowledge, the idea of embracing ignorance as a pathway to progress might seem counterintuitive.

At the core of Rubin’s statement lies the notion that knowledge, while invaluable, can sometimes become a barrier to innovation and growth. When we approach a task or challenge with a wealth of preexisting knowledge, we may inadvertently erect barricades of assumptions, biases, and preconceived notions that limit our ability to think outside the box. These barricades can be self-imposed, as we unconsciously filter new information through the lens of what we already know, or they can be imposed by the weight of conventional wisdom and established practices within a field.

In contrast, ignorance can be a potent force for creativity and progress. When we approach a task with a blank slate, unencumbered by the baggage of prior knowledge, we are more likely to approach it with a sense of curiosity and open-mindedness. We are free to ask questions that may seem naive to the initiated but can potentially lead to fresh perspectives and innovative solutions. Ignorance, in this sense, becomes a canvas upon which we can paint new ideas without the constraints of established paradigms.

This idea is not new; in fact, it echoes the concepts of beginner’s mind and shoshin, central tenets in Zen Buddhism. These principles encourage practitioners to approach each experience with a fresh, open mind, free from preconceptions and prejudices. By embracing a state of not-knowing, we become more receptive to the present moment, allowing us to perceive things as they truly are, rather than through the filter of our expectations and assumptions.

The power of ignorance can be observed in various fields, from science and technology to art and literature. Consider the case of outsiders who have revolutionized entire disciplines by approaching them with a fresh, unencumbered perspective. Albert Einstein, for instance, challenged the very foundations of physics with his groundbreaking theories, which emerged from his ability to question longstanding assumptions about the nature of space, time, and gravity.

In the realm of art, naïve artists, untrained in formal techniques and unburdened by the weight of traditional art education, have produced works that defy conventions and challenge our perceptions of what constitutes “art.” Their ignorance of the rules and norms of the art world has paradoxically allowed them to create works that are truly original and avant-garde.

Of course, ignorance alone is not a panacea for progress. It must be accompanied by a willingness to learn, a curiosity to explore, and a commitment to mastering the necessary skills and knowledge required to bring one’s ideas to fruition. Ignorance, in this context, is not a permanent state but rather a temporary suspension of preconceived notions, a stepping stone towards new understanding and growth.

In our fast-paced, information-saturated world, where knowledge is readily accessible and expertise is often valued above all else, Rubin’s quote serves as a timely reminder to embrace the power of not-knowing. By approaching tasks and challenges with a beginner’s mind, we may just find the key to unlocking the barricades of knowledge that have been holding us back, and discover new paths to progress and innovation.