This one was hiding under a rug.

We had recently moved into a new home, a 1982 house that had been flipped before we bought it. Among the updates was a new wood floor installed over the old slab. Off the main living area were sliding glass doors leading to the pool deck, and we had put a couple of rubber-backed runners inside the sliders. Nothing fancy. Come in from the pool barefoot, wipe your feet, protect the wood floor. Problem solved before there was even a problem.

Except there was a problem underneath the solution.

One day I lifted one of the runners and found mold stuck to the wood floor beneath it. It was heaviest immediately inside the slider and faded as it moved farther into the room, almost to the end of the rug.

Fuck.

I’m not a contractor, but it wasn’t difficult to construct a plausible theory. This was a roughly 45-year-old concrete slab with a brand-new floor sitting on top of it. Maybe the original vapor barrier wasn’t exactly performing like something installed yesterday. Maybe moisture was migrating through the slab. Maybe the rubber backing on the runner had simply created the perfect place to trap it. Whatever combination of physics and history was responsible, something clearly wasn’t right.

My first instinct was probably the same one most people have when something unexpectedly breaks.

How do I put it back the way it was?

The Tyranny of “As Good as New”

Fortunately, I knew the flooring was still available. That should have been reassuring, but instead my mind immediately began working through all the ways the repair could go wrong.

Would the replacement boards blend perfectly with what was already there? Could they be installed at exactly the same level? Would the seams disappear? Would there be tiny gaps? Would the repaired section become one of those things nobody else notices but the homeowner sees from across the room for the next fifteen years?

And then there was the disruption. We had just moved in. The last thing I wanted was another project involving workers, dust, furniture moving, schedules and the peculiar gravitational field contractors seem to create around a house.

I was annoyed because I had mentally defined success as restoration. The floor had looked one way yesterday. Something had gone wrong. Therefore the objective was to make it look exactly that way again tomorrow.

That sounds perfectly rational.

It is also an incredibly limiting way to think.

Less than a day later, I remembered something about myself that I somehow manage to forget every time I initially need it. One of the most useful superpowers I’ve developed in life is the ability to eventually look at painful moments as opportunities for a 10x upgrade.

I’ve done it with big things. Divorce. Business deals that blew up. Partnerships that went sideways. Moving from one state to another and rebuilding much of the machinery of everyday life along the way.

I’ve also done it with stupid little things that break around the house.

The scale of the problem changes. The question doesn’t.

If I’m going to pay the price anyway, what can I buy with the pain?

Shoot for the Star

I had a business partner who used to say, “It is better to shoot for a star and hit an eagle than shoot for an eagle and hit a rock.”

Not literally, of course. The eagle has enough problems.

His point was about ambition. Where you aim changes the universe of outcomes you are willing to consider. If your objective is simply to eliminate a problem, your imagination becomes constrained by whatever existed immediately before the problem appeared.

The broken thing becomes the specification for the repaired thing.

That is strange when you think about it. Why should yesterday automatically be the design brief for tomorrow?

The idea of a “10x upgrade” deliberately breaks that constraint. I don’t mean that literally. I’m not measuring the new floor with a ten-times-better floorometer. The exaggeration is the point. Asking how to make something 10 percent better produces incremental ideas. Asking how to make it twice as good might produce ambitious ones. Asking how to make it ten times better forces you to question assumptions that otherwise remain invisible.

Of course, there are limits.

The ultimate solution to our moisture question might involve ripping into the slab, figuring out exactly what was happening underneath the house and rebuilding everything from first principles.

The slab of the house we had just moved into.

Yeah, no.

Sometimes the pain really isn’t worth it.

But that didn’t mean the only alternative was replacing a few damaged boards and hoping the problem never returned. Once I stopped asking how to repair the floor and started asking what I actually wanted from that part of the house, the answer became almost embarrassingly obvious.

The whole reason the rug was there in the first place was because I wanted a transition zone between the pool deck and the wood floor.

So why not build one?

Make the Scar the Feature

Instead of trying to make the damaged area disappear, we removed a larger section of the wood flooring and replaced it with tile.

Essentially, we built a permanent tile runner into the floor.

Then we went a little farther. We sized the tile section slightly larger than the rug we wanted to put on top of it, so the rug would fit naturally within the new area. The wood floor remained everywhere I wanted wood. The entrance from the pool gained a surface far better suited to wet feet. And if moisture ever did work its way through that old slab again, it would no longer be trapped underneath a moisture-sensitive wood floor precisely where water and humidity were most likely to be part of everyday life.

The repair didn’t hide what happened.

It made what happened irrelevant.

That distinction turned out to matter more than I expected.

Because now, every time I walk across that spot, I genuinely enjoy it. I like how it looks. I like how it works. I like that it feels intentional, as though somebody thought carefully about how a house with a pool should transition from outside to inside.

And perhaps most importantly, I don’t look at it and remember mold.

I don’t remember lifting the runner. I don’t particularly remember the damaged flooring or the mood I was in when I discovered it. I don’t see a repaired problem.

I see something better.

That may be the most interesting part of the 10x idea. We usually think of improvement in terms of the thing itself: a better floor, a better business, a better relationship, a better life after some unwanted disruption. But there is another kind of improvement available. Sometimes you can transform the thing enough that you transform the memory attached to it.

The scar becomes the feature.

That doesn’t mean every catastrophe contains a hidden gift. Some things are simply terrible, and pretending otherwise is motivational-poster nonsense. Nor does every broken dishwasher require a spiritual awakening and a kitchen renovation.

But there is a useful question sitting somewhere between pessimism and forced optimism.

When life has already decided that you are going to pay a price, do you have to spend all that pain merely getting back to even?

Pain is expensive. If you have already been forced to pay for it, perhaps restoration is too small a return on the investment. We spend a lot of life trying to erase evidence that something went wrong. We repair the crack, replace the board, reconstruct the plan and congratulate ourselves when everything looks the way it did before. Sometimes that is exactly the right answer. Sometimes restoring what was lost is victory enough.

But sometimes the surprise has already created the opening. The floor is already coming up. The deal is already dead. The relationship is already over. The move is already happening. The old plan is already gone.

At that point, “How do I fix this?” may no longer be the most interesting question.

Try this one instead:

How good could the thing on the other side of this be?

🌱 Seed Thought: The next time something unexpectedly breaks, don’t immediately ask how to get back to where you were. Ask whether where you were is still worth going back to.

Share

I was in Hawaii on September 11, 2001, attending a Life Mastery event with Tony Robbins. I remember getting a page from a coworker back in New Jersey. If you remember pagers, there is a decent chance you were alive when 9/11 happened.

The message was something to the effect of: A plane crashed into the World Trade Center. Probably terrorism.

It was still dark in Hawaii. I grabbed the television remote and started pressing buttons. Nothing. I pressed harder. Tried again. Nothing. For a few confused seconds, I wondered why the television wasn’t responding. I wasn’t awake enough to imagine that whatever was happening on the mainland had somehow reached Hawaii. I was not even awake enough to notice something much more obvious.

The television cabinet was closed.

The remote couldn’t see the television, and I couldn’t see the television.

Once I figured that out, the story began coming together. By the time I got the television on, much of what would turn September 11 into 9/11 had already happened.

Later, walking outside into a crisp Hawaiian morning, I passed some people sitting at a table on the way to the convention center. I told them what had happened. They barely responded. I still wonder whether they thought I was some crazy guy wandering past their breakfast table talking to himself about airplanes and collapsing buildings.

At the convention center, history took over the event. Tony Robbins has since shared footage from that day, and it captures what happened there far better than I ever could. As for what that day was like for me, YouTube probably tells the story better than anything I could reconstruct twenty-five years later.

I don’t remember every detail anymore. I remember the feeling.

And there is an important difference between the two.

When Memory Stops Being Shared

When something enormous happens during your lifetime, you unconsciously assume everyone else lives with the same reference point. They may disagree about what it meant. They may disagree about what should have happened afterward. They may have experienced it from New York, California, Hawaii, Europe, or somewhere else entirely.

But at least they were there.

Twenty-five years changes that. A surprisingly large percentage of the adults walking around today were either children on September 11, 2001, or hadn’t been born yet. Of course they can learn what happened. They can read about it, watch the television broadcasts, see the photographs, listen to recordings, visit the memorial, or follow one of the accounts on X that recreated the events in real time.

But knowing what happened is not the same as having lived while it happened.

There is a kind of second-order information loss that occurs as time passes. First we lose the event itself. Then we begin losing the people who remember the world on both sides of it. That second loss is harder to notice because nothing disappears all at once. The memory just becomes a little less shared every year.

George Santayana famously wrote, “Those who cannot remember the past are condemned to repeat it.” The difficulty is that eventually remembering becomes impossible. You can learn the past. You can study it. You can even understand it extraordinarily well. But you cannot remember something you never experienced.

As time marches forward, more and more of history moves across that boundary. What was once common memory becomes inherited knowledge.

That distinction matters.

Until They Stay

Which brings me to something Palmer Luckey posted on X on the 25th anniversary of 9/11:

I don’t know whether he meant it literally. But the moment I saw it, I remembered something I had almost forgotten myself.

On February 18, 2002, barely five months after the attacks, I wrote a letter to President George W. Bush. I have no idea whether anyone meaningful ever read it, much less the President. This is what I wrote, lightly edited here only to correct typos and punctuation:

Mr. President,

When something we value is destroyed, we rebuild it. If it’s destroyed again, we rebuild it again. And again, and again, and again. Until it stays.

That, as our poet Tennyson once said, is the goal: “To strive, to seek, to find, and not to yield.”

In this time of change for America, I believe we must be diligent in ensuring the symbols of greatness never sway. The terrorists in New York took away more than life and a sense of security with their actions; they also took away one of America’s emblems of greatness, and indeed this was no accident.

The action you have directed our nation to take in Afghanistan, combined with the uniting of the world around a cause of intolerance of terrorism, is on a scale I am sure none of the “Evil Doers” contemplated. The patriotism of America in the face of adversity is something my heart is glad to be a part of. Our power and the influence of good shall smoke out the cowards wherever they might try to hide. More adversity will only strengthen the spirit of America.

Yet, I believe there is still one piece missing: the Evil Doers still think they can destroy our material symbols of greatness.

At a time when tax dollars are spent to protect us, I believe we must also spend to show those who use terror as a weapon that nothing can ever be taken away.

Let us, as a nation, rebuild the towers as they were. Again, and again, and again, if needed.

Until they stay.

—John Raymonds

Would I write exactly the same letter today?

If 9/11 happened today, I think I probably would.

That is different from saying I think we should tear into what exists at Ground Zero now and rebuild the Twin Towers in 2026. History has already forked. The memorial exists. A generation has grown up with the site as it is. The absence of the towers has itself accumulated meaning, grief, and memory. You cannot rewind twenty-five years and pretend none of that happened.

Palmer Luckey’s post made me think about the branch we didn’t take.

In the months immediately after September 11, there was still a choice to be made about what destruction was allowed to accomplish. My answer then was simple: rebuild the towers as they were.

Not approximately.

Not something inspired by them.

Put them back.

Until They Stay.

When I imagined rebuilding the towers in 2002, I did not imagine replicas that simply reproduced what had been destroyed. From the outside, I wanted them to look essentially the same. That was the point. Someone had attempted to permanently remove those shapes from the New York skyline. My response was that they should eventually look up and see them standing there again.

But underneath the skin, everything should have been stronger.

Stronger structure. Stronger fire protection. Stronger evacuation systems. Stronger communications. Stronger redundancy. Whatever engineers could learn from what happened should have been incorporated into buildings designed with the knowledge purchased at an unimaginably high price.

Yet even that isn’t quite what I mean by stronger.

I would have wanted what existed inside those buildings to have a stronger presence as well. The replacement towers should have mattered more than the towers that were destroyed. They should have been filled with institutions, businesses, public spaces, technology, culture, and activity that made them impossible to dismiss as merely two very large office buildings.

The ultimate test would have been almost emotional rather than architectural. Twenty-five years later, people should have been able to look at those towers and think:

That may have been the best money the United States government ever spent.

And yes, in the version I imagined, it would have been the United States government spending it.

A Stronger Owner

That part matters.

I wasn’t imagining that the private owner of the World Trade Center should simply absorb the cost of turning a national symbol into a national response. The owner would have been fairly compensated. The property would have changed hands.

A stronger owner would have stepped in.

The United States.

Not because I think governments generally make better landlords or real-estate developers. That would be an entirely different argument. In this particular case, the point was precisely that these buildings would no longer have been ordinary real estate.

September 11 had already changed that.

Once the towers were selected for destruction because of what they represented, rebuilding them became capable of representing something too. The rebuilding itself could have been a statement: you succeeded in destroying the buildings, but you failed to determine what would occupy this ground, what would appear on this skyline, or what this country would choose to preserve.

That is a very different response from pretending the attack never happened. The new towers would have carried the attack with them forever. Their very existence would have depended upon it.

They would have looked the same because forgetting wasn’t the goal.

They would have been stronger because learning was.

The Counterfactual

Of course, this is the luxury of a path not taken. I can imagine the towers completed without having to fight through the politics, engineering, cost overruns, competing visions, security concerns, families’ wishes, or thousands of other complications that would have accompanied the actual decision.

Counterfactual history is wonderfully cooperative that way.

But the question that Palmer Luckey’s post brought back wasn’t whether my 2002 proposal could have survived all those realities. It was whether there was something important in the instinct behind it.

We usually talk about resilience as recovery. Something terrible happens, we survive it, and eventually we construct a new normal around the damage.

I was arguing for something more stubborn.

Don’t merely recover from the act.

Deny the act one of its intended consequences.

If someone destroys a bridge, build a better bridge. If someone destroys a monument, restore the monument. And if someone attacks a symbol precisely because they want the world to watch it disappear, there is something extraordinarily powerful about refusing to grant them permanence.

Not because rebuilding somehow undoes the dead. Nothing could.

Not because steel and concrete defeat terrorism. They don’t.

And not because the past can be restored exactly as it was. It can’t.

But because there is a difference between acknowledging what someone did and allowing them to decide what remains afterward.

Twenty-five years later, I don’t know whether rebuilding the Twin Towers would have changed anything beyond the southern tip of Manhattan. Perhaps nothing would have been different. Perhaps the same wars would have been fought, the same policies adopted, the same arguments had, and the same scars accumulated.

Or perhaps one thing would have been different.

Every photograph of the New York skyline for the next century would have contained the answer.

You knocked them down.

We put them back.

🌱 Seed Thought: Some acts cannot be undone. But that doesn’t mean the person who committed them should get the final word on what remains.

Share

Two years ago, I wrote a Seed Scapes piece called Redefining Search. The premise was that artificial intelligence was beginning to change what the word “search” meant.

For most of the internet era, searching meant finding something that already existed. A webpage. A fact. A restaurant. A product. An answer someone had already written down. Google became extraordinarily good at helping us find the needle in an ever-expanding digital haystack.

LLMs introduced something different. Instead of merely retrieving an existing answer, they could assemble facts, perspectives and ideas into an answer that perhaps no one had ever written before.

I described the distinction this way: traditional search was about finding a needle in a haystack. AI might let us create a needle that never existed.

To test the idea, naturally, I consulted a six-pound toy poodle.

His name is Snowflake.

Snowflake presented a mystery. During the day, he follows the wife almost everywhere. The husband can call him from ten feet away and receive roughly the same response he would get from calling the refrigerator. Strangely, if the wife gives Snowflake permission, he will sometimes go visit the husband. Apparently, the husband is not merely second choice; he requires management approval.

But nighttime changes everything. When the wife goes to bed, Snowflake races into the bedroom, digs at the husband’s blanket until admitted, crawls underneath and settles into his armpit for the night.

In 2024, I gave that behavior to GPT and asked it to analyze Snowflake while doing an impression of Cesar Millan. Not because Cesar Millan had actually analyzed Snowflake, obviously, but because the fictional expert gave the experiment a useful lens. GPT talked about pack structure, leadership, security and the different roles Snowflake assigned to each human. It even prescribed homework: more walks for him, more cuddles for her.

At the time, the answer felt extraordinary. It was thoughtful, entertaining and unlike anything I could have gotten by typing keywords into Google.

I recently reread it.

It wasn’t nearly as good as I remembered.

The answer was pleasant, but generic. Ancestral pack instincts. The wife as Alpha. The husband as secondary leader receiving his nocturnal due. Swap in another dog, another couple and perhaps another species with a pulse, and much of the answer could have survived intact. It was a little like a horoscope with a Spanish accent.

That may be one of the more interesting measures of technological progress: yesterday’s magic becoming today’s mediocrity.

The original piece ended by comparing the AI of 2024 to Pong and noting that it took 31 years for Pong to evolve into Call of Duty. Imagine, I wondered, what AI might look like after a similar period of development.

Apparently, Snowflake wasn’t willing to wait that long.

He gave us more data.

And the machine changed.

The Natural Experiment

Since then, Snowflake’s household handed the universe a much better experiment.

The husband and wife are in the middle of a large interstate move — the kind involving containers, staging, storage, movers and that peculiar period when nearly everything they own seems to be either in the wrong house or the wrong state.

At one point, the husband drove a car down to the new house, planning to fly back. The wife and their son stayed behind with Snowflake.

Snowflake fell apart.

For days, he produced horrible, foul-smelling, soft messes on and around his wee-wee pad. Nothing obvious had changed with his food. There was no apparent illness. It became bad enough that “time for the vet” entered the conversation.

The obvious explanation was stress.

Hardly a Sherlock Holmes moment. His home was slowly disappearing into boxes and containers. Furniture was moving. Routines were changing. Dogs don’t receive closing documents, so presumably all Snowflake knew was that someone was dismantling his universe one room at a time.

Then the husband came home.

Snowflake’s digestive problems stopped almost immediately.

Which would have been a nice confirmation of the stress-from-chaos theory except for one inconvenient detail: the husband immediately made the house more chaotic. Furniture moved. Rooms changed. Staging accelerated. He spent his time dragging things around in preparation for the movers. By almost any objective measure, the physical environment became less stable after he returned.

The dog got better anyway.

Something about the explanation was wrong.

So the experiment was rerun.

Same dog. Same humans. Same basic mystery. The fictional Cesar Millan even returned, not because anyone suddenly needed celebrity dog-training advice, but because keeping the same character preserved something resembling a control.

But this time, instead of going back to GPT, the expanded story was handed to Claude Fable.

There was a reason for that.

The point wasn’t to stage a GPT-versus-Claude cage match or determine which company deserved a trophy. The idea was almost the opposite: go overpowered. Take a small domestic mystery that would seem almost comically undeserving of frontier-level AI, give a much more capable model far more information than a traditional search query would ever tolerate, and see what it did with the excess.

Don’t optimize the prompt.

Don’t strip out the details.

Give it the whole damn story.

The 2026 model began with the old observation but reached it differently. Snowflake wasn’t necessarily ranking the humans by affection, it suggested. He was assigning roles. The wife represented daytime security. The husband was the doorman, outside-trip coordinator and treat dispenser. Then, when her shift ended at bedtime, Snowflake switched jobs.

As fictional Cesar put it, the husband became a “warm, breathing cave wall.”

Finally, a professional designation worthy of putting on a business card.

But the interesting part wasn’t the joke or even the conclusion. It was how the model got there. Instead of placing a generic pack hierarchy over Snowflake’s behavior, it used the strange little details of his actual life: the permission structure, the treat economy, the timing of his allegiance shift, the husband’s absence, the digestive episode and, critically, what happened when he returned.

The humans had framed the problem as chaos.

The model noticed that their own evidence contradicted them.

If household chaos caused Snowflake’s gastrointestinal problems, then increasing the chaos should have made him worse. Instead, the husband returned, increased the chaos and Snowflake immediately improved.

The machine offered another hypothesis: perhaps the important variable wasn’t environmental stability at all. Perhaps it was pack integrity.

The husband’s absence had changed the social environment. His return restored it.

Or, as fictional Cesar rather memorably put it:

“The dog’s intestines just told you otherwise.”

Was that the answer?

Maybe.

But that “maybe” turns out to be one of the most important parts of the story.

Tell Me the Whole Story

Claude Fable didn’t diagnose Snowflake. Nor should it have.

It pointed out that stress-related gastrointestinal problems are real in dogs and that Snowflake’s symptoms and their timing were consistent with that possibility. But recurring or worsening episodes still warranted a veterinarian. Parasites, infection, diet and other medical causes don’t disappear merely because an AI has constructed an elegant behavioral theory. For a six-pound dog, dehydration isn’t something to philosophize about for very long.

In other words, the machine generated a hypothesis from evidence the humans hadn’t realized was evidence, showed why the hypothesis fit, and then identified where its confidence should end.

That distinction matters.

The remarkable thing wasn’t that AI had discovered what was wrong with Snowflake.

It was that AI noticed something the humans had mislabeled inside their own question.

And that’s when I realized this wasn’t really a story about how much AI had improved in two years. It was another chapter in Redefining Search.

For most of the digital era, computers have trained us to compress our questions.

Google rewarded keywords. Remove the story. Strip away irrelevant detail. Identify the important nouns. We became amateur database-query designers because the machine required us to know what mattered before we asked it to help us discover what mattered.

Even the early days of LLMs carried some of that legacy. “Prompt engineering” became an entire cottage industry. Assign the model a role. Carefully structure the request. Specify the desired output. Remove ambiguity. Feed the machine exactly the information it needs.

There is nothing inherently wrong with that. Clear questions still tend to produce clear answers.

But increasingly capable reasoning models introduce a fascinating inversion.

What if the details we were trained to remove are becoming the most valuable part of the question?

Imagine telling a friend you’re thinking about selling your company. You mention that you received an offer three years ago and turned it down. Revenue is higher now, but you’re enjoying the business less. Your son recently graduated from college and doesn’t want anything to do with it. Your wife keeps talking about traveling while you’re still young enough to enjoy it. A competitor recently sold at nine times EBITDA while another couldn’t find a buyer. And, strangely, the division now producing the most profit is the one you almost shut down five years ago.

Traditional search hears noise.

A reasoning model can hear variables.

The son’s lack of interest may matter. The wife’s desire to travel may matter. The failed competitor sale may matter. The unwanted division may matter. Or none of them may matter. The important capability isn’t that the model automatically knows which detail is decisive.

It’s that the model can afford to consider all of them.

That is fundamentally different from a search box.

If I already know which five facts matter, traditional search is extraordinary.

If I don’t know which five facts matter, I have a different problem.

And most of life’s interesting questions look a lot more like the second kind.

Business decisions, relationships, career changes, investments, negotiations and even mysterious little dogs rarely arrive as neatly structured prompts. They arrive as stories filled with contradictions, chronology, emotions, false assumptions, half-remembered events and details that seem irrelevant until suddenly they aren’t.

Wisdom often consists of knowing which supposedly irrelevant detail isn’t irrelevant.

For decades, computers forced humans to learn how machines wanted questions asked.

We may now be watching machines learn how humans naturally tell stories.

The Detail You Would Have Deleted

That makes me rethink my Pong analogy from 2024.

I imagined a fairly conventional technological progression. Pong becomes Call of Duty. Crude capability becomes sophisticated capability. AI gets faster, smarter and more knowledgeable until the primitive thing we were playing with becomes almost unrecognizable.

Two years later, I’m increasingly convinced I made the analogy too conservatively — not because we’ve already reached Call of Duty, but because I assumed I understood what game we were building.

I thought better AI would give us better answers.

Increasingly, I think its stranger contribution may be helping us discover that we were leaving half the question out.

The old search paradigm punished context. Every unnecessary word increased the distance between the query and the result. We learned to delete details until only the searchable skeleton remained.

The emerging paradigm can work in the opposite direction. Give it chronology. Give it contradictions. Tell it what happened before the thing happened. Tell it what you assumed. Tell it what changed afterward. Include the odd fact that doesn’t seem to belong. Let the machine decide whether it matters instead of making that decision before the conversation begins.

That doesn’t make AI omniscient. In some ways, it makes skepticism even more important. A machine capable of constructing a compelling explanation is also capable of constructing a compelling explanation that happens to be wrong. Elegant narratives are seductive whether they come from humans or silicon. Hypotheses still need evidence. Medical questions still need doctors and veterinarians. Financial questions still contain risk. Reality retains veto power.

But there is an enormous difference between outsourcing a decision and expanding the number of variables we can think about while making one.

That may be where the real transformation is happening.

The answer to Snowflake’s mystery may have been hiding inside details that once would have been considered clutter: who gives the treats, who opens the door, who needs permission from whom, who sleeps where, who disappeared, when the symptoms began, when they stopped, and the seemingly contradictory fact that the house became more chaotic precisely when the dog became healthier.

A traditional query would have stripped most of that away.

“Toy poodle diarrhea during move.”

Search.

Useful information would undoubtedly appear.

But the interesting question wasn’t really about diarrhea. It wasn’t even necessarily about moving.

The interesting question was which part of the story was actually the question.

Snowflake, for the record, may have known the answer all along. He’d been announcing his attachment nightly, from the husband’s side of the bed, for years. The humans just hadn’t considered that information relevant to what was happening on his wee-wee pad.

The machine did.

In 2024, I argued that AI might let us create needles that never existed.

Two years later, I think something even stranger is happening.

We can increasingly hand it the whole haystack.

And sometimes the needle turns out to be the detail we would have deleted.

🌱 Seed Thought: The future of search may not be finding the right answer faster. It may be discovering which part of the question you didn’t know mattered.

Share

There was a controlled burn somewhere near my house the other day.

Controlled burns are something I have become much more familiar with since moving to Central Florida. For anyone raised on Smokey Bear and the seemingly uncomplicated lesson that forest fire equals bad, the whole idea initially feels a little backward. Fire can obviously destroy a forest. But used deliberately, under carefully controlled conditions, it can also protect one—reducing the accumulated fuel that can feed a catastrophic wildfire while clearing away vegetation that has begun choking out parts of the ecosystem.

I had actually gotten the chance to participate in a controlled burn before, and watching one done properly changed the way I thought about fire. There is a strange contradiction to standing there deliberately setting nature on fire because you are trying to take care of it.

So when I received notice that there would be a controlled burn in the area, I wasn’t particularly concerned.

Then I walked outside.

The smell of smoke was unmistakable. The sky had changed. Looking west toward a large preserved area, an enormous wall of smoke seemed to rise just beyond the trees. Even the sun had been reduced to a dim orange-white circle struggling through the haze.

Damn. This thing must be close.

I wasn’t frightened. I knew what was happening. But from where I stood, the obvious conclusion was that the burn was taking place somewhere immediately beyond me, probably right inside the preserved land to the west.

So I did what any reasonable person with a drone and insufficient respect for FAA paperwork might be tempted to do.

I went up.

A Few Hundred Feet Changed Everything

I expected the drone to confirm what my eyes had already told me.

Instead, it contradicted them.

Once the drone climbed above the trees, the smoke that had seemed almost on top of me suddenly had a location. The enormous wall dominating my view from the ground became a plume rising in the distance. In fact, it was far enough away that even from the air I couldn’t immediately tell exactly where the burn was taking place.

Same fire.

Same smoke.

Same afternoon.

The two photographs were taken only about thirty minutes apart, so this wasn’t a story about the fire moving or the smoke clearing. The important variable wasn’t time.

It was perspective.

From the ground, my field of vision was compressed. Trees obscured the horizon. Smoke filled the available sky. I could smell it. I could see it blocking the sun. Everything my senses were telling me suggested proximity.

From the air, the horizon opened. Suddenly there was scale. I could see the miles of green between me and the smoke. What had occupied most of my world from the ground occupied only a small part of a much larger landscape.

Neither perspective was fake. Neither photograph had been manipulated. Neither required someone to lie.

Yet they told completely different stories.

That may be the more unsettling lesson.

The Camera Isn’t Lying. Neither Are You.

We tend to think disagreements about what happened require someone to be wrong. Worse, we increasingly assume they require someone to be dishonest.

But imagine two people standing in different places during that burn. One says, “The smoke was so thick it blocked out the sun.” The other says, “The fire was way off in the distance.”

Which one is telling the truth?

Both.

We sometimes use the phrase different perspectives as a polite euphemism for different opinions. But perspective isn’t merely opinion. Sometimes it is literally geometry.

No two people occupy precisely the same point in space. Move ten feet and an object can disappear behind another object. Climb a few hundred feet and something enormous can become tiny. Stand downwind and smoke can overwhelm you; stand somewhere else and you may barely notice it.

Then add the human brain.

We don’t merely see from different physical locations. We see through different histories. Experience, fear, knowledge, expectation, incentives, ideology, memory and emotion all become part of the lens. Two people can stand shoulder to shoulder looking at the same event and still not really be looking from the same place.

My brain had evidence: smoke, smell, obscured sun, known preserve to the west. It assembled those facts into a perfectly coherent story.

The story just happened to be wrong.

That distinction matters. I hadn’t hallucinated the smoke. My observations were accurate. My interpretation of them wasn’t.

And that is a much more dangerous kind of mistake because accurate facts make incorrect stories feel especially convincing.

Now Put a Screen Between Us

Which brings me to the little rectangles through which we increasingly experience the world.

Think about how much certainty we manufacture from photographs and short video clips online.

We see twenty seconds of an altercation and know who started it. We see a photograph of a crowd and know how large it was. We watch someone say something outrageous and know what they meant. We see a police encounter, a protest, a war, an argument, a politician, a CEO, a celebrity—and within seconds we have reconstructed everything outside the frame.

Except we haven’t.

We don’t know what happened thirty seconds earlier. We don’t know what is fifty feet to the left. We don’t know why the camera was standing where it was standing. We may not know who selected the clip, who cropped the photograph, what was excluded, or whether the person presenting it has an incentive to make us see one interpretation rather than another.

Sometimes there is deliberate deception, of course. But my controlled burn suggests something more interesting: deception isn’t necessary.

A completely authentic image can create a profoundly misleading impression.

That should bother us.

My ground photograph isn’t fake. If I posted it by itself with the caption Controlled burn right next to the house today, most people looking at it would probably find the statement perfectly plausible.

Then I could post the drone photograph and create an entirely different impression.

Same fire. Same photographer. Same day.

I wouldn’t even need Photoshop.

I would only need to choose your perspective for you.

And increasingly, that is exactly what happens online. Someone else decides where we stand before showing us the world. Then an algorithm selects which of those perspectives we are most likely to stare at, share, argue about and return to tomorrow.

Perhaps the appropriate response isn’t cynicism. We don’t have to assume everything we see is false.

We should simply become a little less certain that what we see is everything.

Before deciding what a photograph proves, ask what might be outside the frame. Before deciding what a video means, wonder what happened before the recording began. Before declaring another person’s description absurd, consider the possibility that they are standing somewhere you aren’t.

And before becoming absolutely certain that the fire is right beside you...

maybe send up the drone.

🌱 Seed Thought: The world doesn’t necessarily change when we change our perspective. Sometimes we finally see how much of the world our old perspective was hiding.

Share

The title sounds like an insult until you realize the smart people in question include all of us.

Its central argument is deceptively simple: intelligence and rationality are not the same thing. Being exceptionally good at thinking does not guarantee that the thing you are thinking toward is true. Sometimes it merely makes you exceptionally good at constructing an elaborate defense of something that isn’t.

Smart people aren’t necessarily harder to fool.

Sometimes they are simply better equipped to fool themselves.

That idea was fascinating when applied only to humans. It becomes considerably more interesting now that we have spent hundreds of billions of dollars building machines whose defining characteristic is intelligence.

Because apparently smart AI can believe stupid things too.

Before the AI philosophers come after me, yes, I know. Whether an artificial intelligence actually believes anything is debatable. A large language model does not necessarily have beliefs in anything resembling the human sense of the word. For purposes of this conversation, however, I’m going to commit the philosophical misdemeanor and use the word anyway.

The interesting question isn’t whether AI believes.

It is why something so intelligent can be so spectacularly wrong.

The Lawyer Inside the Brain

One of the most provocative ideas in Why Smart People Believe Stupid Things is that intelligence may actually make certain kinds of irrationality worse.

Research discussed by Gurwinder found that people with stronger numerical reasoning abilities were better at interpreting data when the subject was emotionally neutral. Give them statistics about whether a treatment helped a skin rash and the mathematically gifted performed exactly as we would expect.

Change the subject to something politically charged, however, and something strange happened.

Their superior reasoning ability didn’t necessarily rescue them from bias. Sometimes it helped them defend it.

The brain had quietly changed jobs. Instead of asking, What does this evidence tell me?, it began asking, How can I use this evidence to defend what I already believe?

That distinction matters enormously.

We tend to imagine intelligence as a flashlight. Make the flashlight brighter and eventually reality becomes impossible to miss. But perhaps intelligence is closer to an engine. A larger engine doesn’t determine where the car goes. It simply gets you wherever it is pointed faster.

Point intelligence toward truth and you may get discovery. Point it toward ego and you get rationalization. Point it toward ideology and you get propaganda. Point it toward status and you get some wonderfully sophisticated explanations for why everyone at the cocktail party already happens to be correct.

This is what makes intelligent people particularly interesting. Someone without much reasoning ability may accept a bad idea because he cannot see through it. Someone with enormous reasoning ability can construct an intellectual fortress around the same bad idea.

The second person may be considerably harder to reach.

As Gurwinder argues, intelligent people can become particularly good at convincing themselves of things they want to believe. The phenomenon has a name—motivated reasoning—but the name almost undersells it.

The smartest mind in the room can still be working for the wrong client.

Which brings us to AI.

We Built a Bigger Engine

Think about what happens when you ask a modern AI model a question.

Within seconds it can synthesize information across disciplines, recognize patterns no individual person could hold simultaneously, generate counterarguments, write code, analyze documents and explain quantum mechanics using a golden retriever if that happens to be your preferred learning style.

Then, occasionally, it says something breathtakingly stupid.

Our instinct is to treat this as evidence that the machine isn’t actually intelligent.

But what if that conclusion repeats exactly the mistake the video warns us about?

Perhaps intelligence was never the missing ingredient.

The missing ingredient was what intelligence is serving.

An AI model doesn’t arrive naked before reality and simply ask, “What is true?” It arrives carrying baggage. Training data. Fine-tuning. Reinforcement. System prompts. Safety rules. Product decisions. Cultural assumptions. The user’s wording. The previous twenty messages in the conversation. Every one of those things potentially changes the destination toward which that enormous reasoning engine is driving.

Humans have parents, schools, religions, tribes, political parties, incentives, careers and dinner parties shaping what we are willing to believe.

AI has datasets, developers, reward functions, policies and prompts.

Different architecture. Familiar problem.

That is one reason I have been intrigued by xAI’s explicit positioning of Grok as a “truth-seeking” AI. That description is not something critics have attached to it; xAI itself prominently describes Grok that way.

It is an audacious objective.

It is also a fascinating experiment.

If Gurwinder’s argument is correct, then making an AI smarter isn’t necessarily enough. The crucial question becomes what objective all that intelligence has been instructed to pursue.

So I did the obvious thing.

I asked Grok.

More specifically, I asked whether xAI’s stated goal of building a truth-seeking AI might make Grok less vulnerable to the machine equivalent of motivated reasoning—the same phenomenon that allows intelligent humans to become exceptionally good at defending bad ideas.

I originally asked Grok how the ideas in Why Smart People Believe Stupid Things mapped onto xAI’s stated design goals, and later asked it to reconsider its answer. The following combines and lightly edits those two responses for length and readability while preserving Grok’s argument.

What interested me almost as much as Grok’s answer was the change in tone between its original response and the one it gave when I returned to the question later. The underlying argument barely changed. Its assessment of itself did.

The first answer was considerably more willing to contrast Grok favorably with other AI systems and declare that xAI had specifically engineered around their weaknesses. When asked to reconsider, Grok became less interested in explaining why it was better than other models and more interested in explaining why its own solution remained incomplete.

There is something wonderfully appropriate about that.

An AI explaining how intelligence can rationalize its own preferred conclusions initially sounded rather confident that itsintelligence had largely escaped the problem. Given another opportunity to examine the question, it became more cautious about making precisely that claim.

That doesn’t prove Grok is more truth-seeking than another model. In some ways, it demonstrates why proving such a thing is so difficult. A directive is an intention. An architecture is an attempt. A benchmark is a measurement. None of them gets to declare victory over reality.

Which leads to an important distinction.

Truth-seeking is not the same thing as truth-finding.

A compass pointing north doesn’t guarantee you won’t walk into a swamp.

But having a compass still matters.

Perhaps that is the more meaningful way to compare AI systems in the future. Benchmarks tell us how powerful the engine is. Maybe we should spend considerably more time asking where the steering wheel is pointed—and whether the driver is willing to admit when the map is wrong.

There is another uncomfortable layer to this, though.

We keep talking as though the AI is doing all of this by itself.

It isn’t.

Slop, Leverage, and the Shortest Learning Curve in History

The current insult for bad AI-generated material is slop.

AI slop. Workslop. Music slop. Image slop. Video slop.

The word has become useful because there really is an extraordinary amount of disposable machine-generated garbage flooding the world. Give someone the ability to produce 500 images, 100 songs or 50 articles before lunch and eventually the internet begins to resemble a landfill with excellent grammar.

But I wonder how much of what we are seeing is a permanent feature of AI and how much is simply the messy first generation of people learning what to do with it.

Some slop probably has an extraordinarily short half-life.

In fact, there is something almost useful about it. We are collectively performing a kind of adversarial training in public. Generate something mediocre. Put it into the world. Watch people recoil. Learn what looks artificial, sounds artificial, feels artificial or simply isn’t worth anyone’s attention. Then try again.

The bad AI image with seventeen fingers becomes harder to get. The syrupy AI prose becomes easier to recognize. The song that sounds like someone typed “make me a country hit” into a box and went to lunch gets ignored.

The slop becomes training data—not merely for the machines, but for us.

That distinction matters because AI is often discussed as though it represents a binary choice between human creation and machine creation. My experience has been almost exactly the opposite.

My relationship with AI is about leverage.

There is a saying commonly repeated in AI circles: your job probably won’t be replaced by AI; it will be replaced by someone using AI. Versions of that thought have been attributed to several people, including Peter Diamandis. Whether or not he deserves the original credit matters less to me than the idea behind it.

AI doesn’t have to replace the human to change what the human can do.

For me, the attraction isn’t primarily that AI allows me to create something I couldn’t create. It allows me to create at a speed I couldn’t.

Sometimes that literally means speed. The equivalent of replacing an old computer with a much faster one.

But the more interesting acceleration comes from eliminating learning curves.

Throughout most of human history, wanting to do something outside your expertise meant making a choice. Spend months or years learning the skill yourself, hire someone who already possesses it, convince a knowledgeable friend to help, or simply accept that the idea in your head is probably going to stay there.

AI introduces another option.

You can borrow the expertise.

That is a very different kind of leverage.

Consider the child of a musician who grows up to become a musician. We might wonder how much of that is genetic. But there is another possibility that seems at least as important: that child grew up inside a world most of us didn’t.

There were instruments lying around the house. There were musicians coming through the door. There were conversations about songs, chords, performances, producers and mistakes. Questions could be asked casually over breakfast that someone else might need years to know enough even to formulate.

The child inherited more than genes.

The child inherited a peer group.

We underestimate how powerful that is.

Much of what separates people who become good at something from those who never attempt it isn’t necessarily raw ability. It is proximity. Someone nearby knows how the thing works. Someone answers the stupid first question. Someone explains what matters and what doesn’t. Someone provides the vocabulary. Someone points toward the next question.

For most of history, those peer groups were accidents of geography, family, school, money and luck.

AI changes that.

Increasingly, you can carry the peer group around with you.

Not one omniscient oracle. That would recreate exactly the problem this article started with. Rather, a collection of machines and agents capable of taking different roles: researcher, critic, programmer, musician, designer, editor, devil’s advocate, analyst, teacher.

That doesn’t give you their judgment automatically. It certainly doesn’t give you mastery.

But it gives you access.

And access changes what you are willing to attempt.

This is why I find the blanket dismissal of AI-assisted work as “slop” increasingly uninteresting. There absolutely is AI slop. There is also human slop. There always has been. Bad novels existed before ChatGPT. Terrible songs existed before Suno. Hideous graphic design existed before image generators. PowerPoint alone should have settled this argument sometime around 1997.

AI did not invent mediocrity.

It industrialized it.

But it industrialized experimentation too.

That is the other side of the equation we talk about much less.

The same technology that allows someone who doesn’t care to generate a thousand mediocre images allows someone who cares enormously to explore a thousand possibilities that previously would have required a studio, a staff, several specialists and a fairly uncomfortable invoice.

The important variable isn’t whether AI touched the work.

It is what the human brought to the relationship.

Taste still matters. Curiosity matters. Judgment matters. Knowing what to reject matters. Knowing when the machine has produced something technically impressive but emotionally dead matters enormously.

And perhaps most importantly, knowing what question to ask next matters.

If I use AI to make music, write, research or explore an idea, I don’t experience the machine as replacing me. I experience it as removing distance between an idea and my ability to interrogate it.

The machine accelerates the process.

I still decide where we’re going.

Slop?

For me, I think not.

Intelligence Needs a Boss

Perhaps the most important lesson from Why Smart People Believe Stupid Things isn’t really about intelligence.

It is about intention.

Intelligence is leverage.

That word turns out to connect both halves of this story.

Human intelligence gives us extraordinary leverage over the world, but also extraordinary leverage over ourselves. It allows us to discover reality and to rationalize our preferred version of it. Artificial intelligence simply increases the leverage.

A sufficiently capable AI may become brilliant at whatever objective we give it—including objectives that have little to do with truth.

And a human working with that AI receives the same bargain.

More capability.

Not necessarily more wisdom.

That may be the part of the AI revolution we’re underestimating. We talk endlessly about what happens when everyone has access to extraordinary intelligence. But giving everyone access to intelligence is not the same thing as giving everyone judgment.

Give a curious person AI and perhaps you amplify curiosity.

Give a creator AI and perhaps you amplify creativity.

Give an entrepreneur AI and perhaps you amplify experimentation.

Give a bureaucrat AI and I’m sure we’ll discover exciting new ways to create forms.

Give someone determined to prove himself right a vastly more capable reasoning engine and you may simply create the most formidable confirmation-bias machine in human history.

The technology is leverage.

Leverage doesn’t decide what deserves to be lifted.

The strange thing is that AI may eventually help us understand something about ourselves that psychology has been trying to explain for centuries, because we can see the machinery more clearly when it belongs to the machine.

When an AI gives a ridiculous answer because of its training, instructions or incentives, we immediately want to inspect the system. What was in the data? What was the prompt? What reward was it optimizing? What constraint pushed it toward that conclusion?

We rarely perform the same debugging operation on ourselves.

What trained me?

What am I optimizing for?

Who is my peer group?

Which tribe am I trying to please?

What evidence would I accept if it supported the other side?

What belief am I defending because changing it would cost me status, identity or pride?

Maybe the question shouldn’t be why smart AI believes stupid things.

Maybe the interesting question is why we expected intelligence alone to solve stupidity when it never did for us.

The great promise of AI is usually framed as making intelligence abundant. We are racing toward a world where extraordinary cognitive capability may become nearly free.

That sounds wonderful.

But if intelligence is leverage, making it abundant doesn’t automatically make truth abundant.

It makes whatever intelligence is pointed toward abundant.

More discovery. More creativity. More experimentation. More persuasion. More rationalization. More art. More slop. More truth.

And potentially much more bullshit.

We spent thousands of years discovering that being smart doesn’t make a person right. Then we built machines smarter than ourselves and somehow expected the rule to change. AI gives us something humans have always wanted: access to intelligence, expertise and creative capability beyond our own.

But leverage has never determined direction.

Which means the defining skill of the AI age may not be intelligence at all.

It may be knowing what deserves to command it.

🌱 Seed Thought: The smartest mind in the room can still be working for the wrong client. The question—whether that mind is made of neurons or silicon—is who hired it.

Share

They are the uncommitted.

Dante places them in the Vestibule of Hell, inside its gates but before the river Acheron and the circles of Hell proper. In life, they took no meaningful side between good and evil. In death, Heaven doesn’t want them, but neither does Hell. They spend eternity chasing a blank banner while wasps and hornets sting them. Their blood and tears fall to the ground, where worms consume them.

It is classic Dante: the punishment is an extension of the life that produced it. They stood beneath no banner, so now they chase one that means nothing. They wouldn’t willingly bleed for anything, so now their blood is shed for nothing.

The post ended with a modern distillation of Dante’s warning:

Stand for nothing, and nothing is what you’ll become.

Joe Lonsdale quote-posted it with an observation aimed specifically at people in business: friends who “avoid politics” rather than studying the issues and doing their civic duty to defend the principles of the civilization that allowed their lives and businesses to flourish.

That hit a nerve with me, although perhaps not exactly the one intended.

Because I’m not convinced everyone needs to become political. I’m certainly not convinced the world needs more people fighting with strangers on X. God knows we seem to have that market adequately supplied.

But I am increasingly convinced there is a difference between choosing to be quiet and choosing to hide.

And money makes that distinction particularly interesting.

When Freedom Becomes Something You Have to Protect

I had been talking with a friend in business about the extraordinary amount of hate directed toward Elon Musk. The latest version was triggered by discussions of his wealth and that he became the world’s first trillionaire, but the particulars almost don’t matter. Elon has become one of those cultural Rorschach tests where people often reveal more about themselves than they do about him.

My friend thought much of the criticism was insane.

I agreed, but pushed him a little further: “IMHO you should be more on the court in these battles.”

His response was understandable: “You want me to fight with people lol.”

Not really.

There is an enormous distance between fighting with people on the Internet and being willing to say what you actually believe. People like Bill Ackman don’t have to spend their days trading insults with anonymous avatars to make their positions known. You can be thoughtful. You can be civil. You can even be quiet most of the time.

But the conversation made me realize something about money that I hadn’t quite articulated before.

Having money should mean having freedom. If you need to watch what you say in order to keep it, that money is just prison bars.

That doesn’t mean blurting out every opinion that enters your head. Discretion isn’t cowardice. Privacy isn’t cowardice. Keeping your politics out of a business where they have no useful purpose can be wisdom rather than weakness. Civilization would probably improve considerably if more people rediscovered the ancient technology known as shutting the fuck up.

The question is why you’re shutting up.

There is a point where prudence becomes self-censorship, and another where self-censorship becomes self-erasure. The difficulty is that there is no bell that rings when you cross from one into the next.

It usually feels like good business.

Don’t upset the customers. Don’t upset the employees. Don’t upset the LPs. Don’t upset the board. Don’t say that. Don’t invest in that. Don’t associate with him. Don’t question this. Don’t defend them. Why create a problem when you don’t have to?

Each individual decision can be perfectly rational.

Eventually you may discover that you’ve rationally edited yourself out of your own life.

The Price of Standing Under a Banner

I thought about Lux Capital and Anduril.

Today, investing in defense technology is fashionable enough to have become its own venture category. Anduril is one of the industry’s defining companies. That was not the environment in which it began.

When Lux backed Anduril from its first financing round, defense technology carried enormous reputational baggage in Silicon Valley. Lux didn’t merely make the investment and hide it in the portfolio. Josh Wolfe publicly defended the reasoning behind it, arguing that protecting the country and the people who defend it was a legitimate mission for technological talent and venture capital.

Some people didn’t like it. Some LPs reportedly didn’t want exposure to that kind of investing. Fine.

That’s what a belief costs.

The important thing isn’t that Lux turned out to be financially right about Anduril, although being right certainly makes the story more fun. The danger of using successful investments as morality tales is that we can accidentally suggest conviction is virtuous because it eventually produces a gigantic valuation.

It isn’t.

Had Anduril failed, the underlying question would be exactly the same: Did you make the decision because you believed it was right, or did you make the decision that created the least social friction at the time?

We spend an enormous amount of time discussing the immoral pursuit of money. Greed. Fraud. Exploitation. Betrayal. The terrible things people will do to acquire wealth have supplied literature, religion, philosophy, and Hollywood with material for thousands of years.

But perhaps there is another moral hazard of wealth that gets much less attention.

What will you refuse to do because you’re afraid of losing it? Or not making more of it?

The first is about selling your principles to become rich. The second is about slowly surrendering them to remain rich.

I’m no longer sure which transaction is cheaper.

The Golf Invitation I Still Remember

Oddly enough, thinking about all of this brought me back roughly 35 years to something so insignificant that I can’t believe I still remember it.

I was never a graphic designer, but I arrived in business at exactly the moment when the Macintosh was beginning to democratize design. Suddenly normal people could make things that previously required specialists. Desktop publishing was new and magical. I was using Aldus PageMaker, which probably dates the story more effectively than anything else I could tell you.

A business I was involved with was holding a golf outing for customers and vendors, and someone needed to create the invitations. I could do it.

This was the era of clipart, and I found an illustration of a golfer swinging a club. It was black and white, hand-drawn, and not exactly a masterpiece of representational art. But the golfer appeared to be a Black woman.

Fine with me. It was a golfer.

I put the invitation together and showed the draft to the woman responsible for the event.

There was pushback.

A woman?

Couldn’t I make it a man?

Could I at least lighten the skin?

I wish I could tell you this is the point in the story where 35-years-younger me delivered a brilliant speech about race and sex, refused to change the artwork, and walked heroically into the sunset while inspirational music played.

I didn’t.

I couldn’t change the sex of the clipart, but I could lighten the skin.

So I did.

And went back to work.

What haunts me isn’t simply that I changed it. What bothers me even more is how little I understood the moment while I was standing inside it. Call it naïveté, inexperience, or just being oblivious, but I don’t remember processing the request as the little moral test I can so easily recognize today.

It was an invitation to a golf outing.

Why make a big deal about it?

Thirty-five years later, I don’t remember what the invitation looked like. I don’t remember who attended the outing. I don’t remember whether anyone complimented the design, and I certainly don’t remember whether the afternoon generated another dollar of business.

I remember lightening the golfer’s skin.

That’s an astonishing return on an insignificant decision.

The things we imagine will matter forever often disappear completely. Meanwhile, some tiny moment when nobody was watching—or when we didn’t even realize we were being tested—follows us for decades.

I’m not interested in retroactively beating up the younger version of myself. He didn’t understand something that the older version does. That’s one of the privileges, and occasional horrors, of getting older.

But I do wish he’d understood enough to say, politely:

No.

And maybe that is why the story of Dante’s uncommitted landed differently for me than another argument about whether businesspeople should get involved in politics.

This isn’t really about politics.

It’s about agency.

Enough Money to Say No

I still believe there is dignity in being quiet with money.

You don’t need to flex it. You don’t need to make wealth your identity. You don’t need to broadcast every opinion or turn your company into an extension of your political beliefs. There is something deeply unattractive about people who acquire money and then seem to believe it has also made them authorities on everything.

But there is a difference between not displaying your wealth and allowing your wealth to control you.

Money should expand the range of things you are free to say no to. It should let you survive the angry customer, the departing LP, the disapproving social circle, the fashionable consensus, or the person asking you to lighten the golfer’s skin.

Otherwise, what exactly have you accumulated?

The paradox is that the more successful we become, the easier it is to construct sophisticated reasons for protecting what we’ve built. Early in life, there isn’t much to lose. Later there are companies, portfolios, reputations, boards, partnerships, employees, relationships, invitations, memberships, and social circles. The cage becomes extraordinarily comfortable.

We may even call it freedom.

That is where Dante’s blank banner becomes interesting. His uncommitted weren’t necessarily monsters. Their failure was that when life demanded they attach themselves to something larger than their own preservation, they wouldn’t.

Perhaps that is the moral hazard of success nobody warns us about.

You can spend your life accumulating enough money to never again have to say something you don’t believe—and then become so afraid of losing it that you stop saying what you do.

That isn’t financial freedom.

It’s just a more expensive prison.

🌱 Seed Thought: We usually ask what someone was willing to do to become wealthy. Maybe the better question comes afterward: Now that you can afford to stand for something, what are you still afraid of losing?

Share

I’ve only done a handful in my life. There was Cambridge, Massachusetts, for college. Southern California for a couple of years early in my career, back when life was wonderfully simple and most of what I owned could fit into a relatively small number of boxes. Then there was the move back to New Jersey.

This one was different.

Moving from New Jersey to Florida wasn’t simply about geography. It was about downsizing, changing seasons of life, and relocating a home office into a room that doesn’t even exist yet. The addition in Florida is still waiting to be built, so this wasn’t a matter of moving from one finished home to another. It became an exercise in moving into the future in stages.

People hear you’re moving and inevitably offer the same advice.

“Just hire movers.”

I would gladly pay someone to carry heavy furniture.

What I can’t outsource is deciding what should be carried.

Every possession had to answer a question. Does it go directly to Florida? Does it spend months in storage until the addition is complete? Is it headed for Goodwill? The dumpster? Facebook Marketplace? Every item represented a decision, and every decision depended on a future version of life that hadn’t arrived yet.

And that was before you got to the things that don’t fit neatly into moving checklists.

A wine cellar sounds wonderfully romantic until you try moving it across state lines. I’m no wine snob, which made it even more ridiculous. Hiring specialists to lovingly transport bottles that, in many cases, cost less than the transportation itself never made much sense. It was enough wine to be complicated, but not precious enough to justify treating it like museum pieces.

Then came the guns and ammo. Moving those isn’t difficult because they’re heavy. It’s difficult because every option seems to come with another page of regulations, restrictions, or unanswered questions. I even looked at the Auto Train for a while before eventually piecing together a different solution.

And then there were the pinball machines.

That turned out to be the final puzzle piece. When the “expert” company I contacted couldn’t even be bothered to return a phone call, it was time for Plan C.

Thankfully, by the time the three-week mission began, most of those specialty logistics were already behind me. The hardest individual problems had been solved. What remained wasn’t one impossible challenge. It was hundreds of ordinary ones waiting patiently to be executed, one after another.

If someone invents a magical Sorting Hat that can understand not only where everything belongs, but also where your life is headed, I’ll happily write the check.

Until then, the sorting remains personal.

When Time Runs Out

Originally, I thought I had plenty of it.

Then a buyer appeared for the New Jersey house, and suddenly the calendar compressed. The mission changed overnight. The house needed to be emptied. The closing needed to happen. Everything had to be ready far sooner than expected.

The outcome couldn’t have been clearer.

Sell the house.

Be completely moved out.

Finish on time—or better yet, ahead of schedule.

Standing in the middle of everything that remained, however, my brain did exactly what brains tend to do when confronted with overwhelming complexity.

It tried to solve the entire move all at once.

Every unfinished room demanded attention simultaneously. Every decision seemed connected to three others. Instead of carrying boxes, I found myself mentally carrying the entire project every waking moment. The logistics themselves weren’t impossible. Holding all of them in my head at once was.

Then, somewhere in the middle of that overload, I remembered something I had written about earlier.

Process over outcome.

It is one thing to write about an idea.

It is another thing entirely to need it.

So I stopped thinking about the closing date and gave myself a different mission.

Three weeks.

That’s all.

For three weeks my highest-value targets would be the heavy furniture, the items destined for storage, and everything that would eventually fill the portable moving containers. The smaller decisions would still happen naturally along the way, but they no longer owned my attention.

The outcome wasn’t today’s responsibility.

The process was.

Practice Before the Mission

Looking back, I now realize the move really began months earlier.

The Florida addition wasn’t even on paper yet. Nobody was looking at buying the New Jersey house. There was no deadline breathing down my neck. I simply wanted to make the Florida house comfortable while keeping the New Jersey house fully functional. Rather than hiring a traditional mover, I decided to try U-Pack for the first time.

Looking back, it was almost magical.

I spent a couple days carefully loading one of their moving cubes, closed the doors in New Jersey, and a few days later opened those same doors in Florida. Everything was exactly where I had left it. It felt as though Star Trek had finally invented the transporter. The only difference was that the commercial break between disappearing and reappearing happened to last three or four days.

I loved the process.

There was something deeply satisfying about knowing that an entire room could simply vanish from one house and quietly reappear in another. Better yet, I learned that my admittedly overprotective packing style actually worked. Nothing was broken. Nothing shifted. Nothing surprised me. Professional movers would almost certainly have packed faster than I did, but I wasn’t optimizing for speed. I was optimizing for confidence, and somewhere along the way I discovered that I genuinely enjoyed the puzzle. Turning a moving container into a three-dimensional game of Tetris, finding exactly the right place for every oddly shaped object so nothing could move during a thousand-mile journey, never felt like work to me.

At the time, I thought I was simply moving furniture.

What I was really doing was building experience.

That mattered later.

When the larger move suddenly accelerated, I wasn’t stepping into completely unfamiliar territory. I already knew what it felt like to pack carefully, stage efficiently, and trust portable containers with everything I owned. So when AI helped me discover 1-800-PACK-RAT while researching long-term storage options, it no longer felt like gambling.

It felt like the next logical step.

This move had different requirements. Some things needed to disappear into storage until the Florida addition was complete. I’d heard of PODS, like most people have, but AI surfaced 1-800-PACK-RAT because of features I didn’t even know to search for—steel containers, climate-controlled warehouse storage, and the ability to access my containers at the destination before taking final delivery.

Even so, trusting an entire cross-country move to a company I had never personally experienced was unnerving.

Then came the familiar reminder.

Not today’s problem.

Today’s problem was wrapping furniture.

One Day at a Time

Years ago, I rode my first century on a bicycle.

One hundred miles.

Up until then, the farthest I had ever ridden was fifty.

If I had spent the day thinking about mile one hundred while sitting on mile twelve, I probably never would have finished. Instead, I focused only on what I already knew I could do. Ride another comfortable segment. Stop. Recover. Eat something. Drink water. Repeat.

Breaking the ride into manageable pieces didn’t make it shorter.

It made it possible.

Without realizing it, I approached this move exactly the same way.

Every morning I got up, had breakfast, and went to work. Ten or twelve focused hours wrapping furniture, staging boxes, making decisions, preparing another section of the house. Then I stopped. Tomorrow would take care of tomorrow.

Something unexpected happened.

As the days passed, I wasn’t becoming more exhausted.

I was becoming calmer.

The process itself simplified life. Because today’s work was already defined, there was very little room left for distraction. I wasn’t wondering what to do next. I wasn’t negotiating with myself. I simply kept moving.

Even my Oura Ring noticed.

My recovery improved. My sleep improved. My readiness scores climbed. Instead of burning myself out trying to finish everything at once, I was somehow becoming healthier while working ten- and twelve-hour days.

Looking back, I think that’s one of the hidden gifts of focusing on process. It doesn’t simply organize your work.

It organizes your mind.

Halfway through those three weeks I had one of those wonderfully disorienting moments that only happen inside long projects. I walked past the main powder room and remembered removing the mirror during the very first days of the move. It felt like something that had happened months earlier.

That same feeling came rushing back from my century ride years ago.

I remember standing on a bridge somewhere near Six Flags in South Jersey, looking around and thinking, Wow... I’m really far from home.

Not because I had suddenly ridden twenty miles.

Because I had quietly ridden fifty.

Progress rarely announces itself.

You usually notice it only after you’ve been faithfully showing up for quite a while.

The Mission

Eventually, loading day arrived.

I hired movers to do the heavy lifting while I directed traffic. They supplied the muscle because I had already spent three weeks supplying the thought. Everything had been wrapped, staged, organized, and waiting.

I still couldn’t resist doing one last job myself.

The Tetris.

Watching every piece settle into exactly the right place made me happier than it probably should have. It wasn’t about saving space. It was about knowing nothing would move once those steel doors closed.

When the movers left, there were still a few loose ends to finish, but I could feel the mission winding down. Those final couple of days were the hardest, not because there was so much left to do, but because emotionally I had already crossed the finish line.

Then the container doors closed.

The locks snapped into place.

Mission accomplished.

Or so I thought.

A short time later the home sale that had accelerated the entire timeline fell apart. The buyer’s proof of funds suddenly became questionable. The deposit never materialized after attorney review. To this day I still don’t know what the end game was.

Oddly enough...

...it didn’t matter very much.

The mission hadn’t changed.

The house still needed to be emptied.

The move still needed to happen.

Florida was still waiting.

The outcome that had created all the urgency disappeared almost overnight. The process, however, had already done its work.

Months ago, I wrote about process over outcome as an idea.

Three weeks taught me what it actually feels like.

The process doesn’t simply get you to the destination.

It changes the person making the journey.

🌱 Seed Thought: We tend to think the finish line gives meaning to the work that came before it. More often, the work gives meaning to the finish line. The destination may change, disappear, or arrive later than expected. The person you become while steadily moving toward it is the only part that was never negotiable.

Share

Several years ago, Scott Adams often wrote that many things which appear irrational suddenly make perfect sense when viewed through the lens of persuasion. Whether you agreed with his conclusions or not, the exercise itself was fascinating because the facts hadn’t changed, the people hadn’t changed, and the events themselves hadn’t changed. Only the filter had changed, and suddenly the behavior looked different.

Good investors do this all the time, looking through the lens of incentives instead of headlines. Biologists might choose evolution, economists scarcity, and engineers systems. None of those perspectives tells the entire story, but each reveals something the others hide.

Recently, I found myself adding another lens to the collection.

Process versus Outcome.

It began while reading Abigail Shrier’s Bad Therapy: Why the Kids Aren’t Growing Up. Regardless of where you land on the book’s broader arguments, one observation stopped me in my tracks. She quotes the analogy that “Going to therapy is like learning to ski by focusing on the trees.”

Any ski instructor will tell you exactly why that’s a bad idea. The trees aren’t the destination—they’re obstacles. Beginners who stare at them almost invariably drift toward them. The goal isn’t to pretend the trees don’t exist; it’s to keep your attention on the open path where you’re actually trying to go.

That metaphor reaches far beyond therapy.

One of the central themes in Bad Therapy is the possibility of iatrogenesis—the uncomfortable idea that the treatment itself can sometimes contribute to the illness it was meant to solve. Medicine has long accepted this possibility. Every surgery carries risks. Every prescription lists side effects. Every physician understands that interventions can create unintended consequences.

Yet outside of medicine, we seem strangely reluctant to ask the same question. Can a school make learning harder? Can a company make innovation slower? Can a government program create greater dependency? Can therapy itself sometimes reinforce the very struggles it hopes to resolve?

These are uncomfortable questions because they force us to examine not simply whether a process exists, but whether it still serves the outcome it was created to achieve.

That thought stayed with me.

Then, a completely unrelated controversy brought it back into focus.

When the Means Become the Mission

The recent turmoil surrounding 60 Minutes became one of the year’s most public debates about journalism. Some argued the newsroom needed to modernize in order to remain culturally relevant. Others believed protecting traditional journalistic standards mattered more than adapting to changing media habits. Predictably, the conversation quickly dissolved into politics.

I found myself looking somewhere else.

A chart showing decades of declining viewership was far more interesting than the arguments surrounding it. Whether the decline was inevitable because of streaming, changing media habits, or simple demographics almost didn’t matter. The graph wasn’t making a political statement. It was simply asking a question.

If the mission of journalism is to inform the public, what happens when fewer and fewer members of the public are showing up?

That question doesn’t tell us what changes should be made.

It simply refuses to let process become immune from outcome.

The defenders of 60 Minutes often spoke passionately about editorial standards, institutional integrity, and journalistic process. Those things matter. They matter enormously. But process was never supposed to become the mission.

It was supposed to serve the mission.

The same question can be asked without a hint of politics.

If a school graduates students who know less, should we examine the curriculum?

If a business follows every internal procedure while steadily losing customers, should it examine the procedures?

If years of therapy leave someone less resilient instead of more resilient, should we ask whether the treatment deserves scrutiny?

These questions are not attacks.

They are accountability.

Why Institutions Drift

There’s a reason this happens so often.

Processes are comforting because they are teachable, repeatable, and defensible. They give organizations stability and individuals identity. You can point to a manual, a methodology, a certification, or a policy and confidently say, “We followed the process.”

Outcomes are much messier. They expose uncertainty, reveal failure, and force us to admit that faithfully executing a plan does not guarantee success.

Over time, many institutions quietly reverse the relationship. The process begins by serving the outcome. As years pass, however, the relationship can slowly invert until the outcome exists largely to justify the process.

The vocabulary rarely changes.

A school still talks about education.

A hospital still talks about healing.

A corporation still talks about customers.

A news organization still talks about informing the public.

A therapist still talks about helping people.

But the measure of success subtly shifts. Instead of asking whether people are healthier, wiser, better informed, or more resilient, the institution asks whether everyone faithfully followed the approved methodology.

The means quietly replace the end.

This is how institutional drift happens.

Not through malice.

Not through conspiracy.

Often through good intentions repeated so consistently that no one remembers which outcome those intentions were originally meant to produce.

Better Questions

Perhaps the greatest value of a Process versus Outcome lens is that it removes some of the emotion from the conversation.

Instead of asking whether a policy is compassionate, ask what outcome it produces.

Instead of arguing whether a methodology is elegant, ask whether it works.

Instead of defending tradition or celebrating disruption, ask what each is trying to accomplish.

Changing the lens doesn’t automatically produce better answers.

It produces better questions.

And better questions have a way of exposing assumptions that passionate arguments often leave untouched.

The next time you find yourself in the middle of one of today’s endless public debates, resist the temptation to immediately pick a side.

Try changing the lens instead.

You may discover that the disagreement isn’t about politics, education, business, therapy, or journalism at all.

It may simply be about whether everyone is still skiing toward the open slope, or whether we’ve become so fascinated by the trees that we’ve forgotten there was ever a mountain to ski down.

🌱 Seed Thought: Every process was invented to produce an outcome. The moment we become more committed to protecting the process than measuring the outcome, we haven’t preserved the mission—we’ve replaced it.

Share

One of the ideas that has gained enormous popularity over the last decade is the phrase process over outcome.

The concept appears everywhere. Athletes repeat it. Business leaders embrace it. Investors quote it. Variations of it can even be found in military training. The basic premise is straightforward: outcomes are often outside our control, while processes are not. If we focus on the actions we can control and execute them consistently, better results tend to follow.

There is a great deal of wisdom in that idea. In fact, one reason the concept is often associated with the Navy SEALs is because elite military organizations understand something that most people learn only after repeated failure. When conditions become difficult, obsessing over a distant outcome can become counterproductive. During the grueling selection process, candidates who focus exclusively on earning their Trident often struggle. The goal becomes too large, too distant, and too overwhelming. Those who survive tend to narrow their focus to the next task, the next hour, or the next challenge immediately in front of them.

The lesson appears obvious. Stop worrying about the finish line and focus on the next step.

For years, I accepted that lesson almost without qualification. Then I started thinking about one of the most difficult periods of my business career and realized there was an important piece missing from the story.

When Good Processes Produce Bad Results

In The Interview That Never Happened — Part VIII: The Role You Didn’t Know You Were Playing, I wrote about a period when our family business was facing challenges that were not immediately obvious to those inside the company. Looking back, what makes that experience interesting is that we were not suffering from a lack of effort, discipline, or process. In many respects, we had an abundance of all three.

The company had processes everywhere. Meetings were held. Reports were produced. Orders were entered. Machines were scheduled. Quality systems were followed. Customers were serviced. From the inside, the organization looked busy, responsible, and hardworking. If effort alone determined success, we should have been thriving.

Yet the financial results were telling a different story. Margins were under pressure. Growth was becoming harder to sustain. The capabilities of the organization were no longer aligned with the challenges ahead. The deeper problem was not that people were failing to follow the process. The problem was that we had stopped asking whether our processes could still produce the outcomes the business required.

That distinction sounds subtle, but it changes everything.

Most organizations assume that process and outcome naturally stay connected. They do not. Over time, the connection can weaken without anyone noticing. Processes become familiar. Familiarity becomes comfort. Comfort reduces scrutiny. Eventually, people spend more time discussing whether the process is being followed than whether the process itself is still relevant.

That was the trap we had fallen into. We were measuring our adherence to the system while losing sight of whether the system was producing the future we needed.

The Factory Floor Doesn’t Lie

The irony is that our industry should have taught us this lesson better than most.

Rigid plastics manufacturing is a business built around process. A modern injection molding operation requires enormous capital investments. Machines cost hundreds of thousands or even millions of dollars. Tooling can easily cost hundreds of thousands more. All of that infrastructure exists to manufacture products that may ultimately be sold for only a few cents per piece. The economics leave very little room for error.

Success depends on consistency. Machines must run around the clock. Scrap rates must remain low. Downtime must be minimized. Variability must be controlled. Every unnecessary second in a production cycle eventually appears somewhere on an income statement. In a business like ours, process is not a management philosophy. It is the difference between profitability and failure.

Manufacturing teaches respect for process because process creates repeatability. A machine that behaves differently every day is not an asset; it is a liability. Customers depend on consistency. Financial performance depends on consistency. In many ways, the entire industry is built on reducing drift and eliminating unnecessary variation.

But manufacturing also teaches a second lesson that is easier to miss.

Before a process can be optimized, it must first be validated.

There is no value in becoming extraordinarily efficient at producing the wrong result. A factory can achieve world-class consistency while manufacturing a product nobody wants. A sales organization can develop a highly refined process for acquiring customers who never become profitable. A management team can create sophisticated reporting systems that perfectly explain a business in decline. In every case, the process may be functioning exactly as designed while the outcome steadily moves in the wrong direction.

The process itself is not the goal. The process is merely a tool. Its value comes entirely from its ability to produce a desired outcome.

The Missing Step

This is where I think many discussions about process miss something important.

The Navy SEAL lesson is often interpreted as “focus on the process and ignore the outcome.” That isn’t actually what is happening. Before a team ever begins training, rehearsing, or executing, the mission has already been established. The objective is clear. The desired outcome is known. Only then do leaders begin developing the processes, procedures, disciplines, and contingencies required to achieve it.

In other words, the process is never the starting point. The process is the response.

That distinction matters because organizations have a tendency to reverse the order. Processes that were once created to solve a specific problem often outlive the conditions that made them useful. Markets change, competitors evolve, technologies improve, and customer expectations shift. Yet companies frequently continue operating under assumptions that may no longer be true.

What begins as a useful process can slowly transform into a tradition. Over time, traditions harden into assumptions, and assumptions become invisible. People stop asking why something is done because they can no longer imagine doing it differently. The process itself begins to feel permanent even though the circumstances that justified it may have disappeared years earlier.

This is one of the reasons turnarounds are so difficult. The challenge is rarely convincing people to work harder. Most organizations in trouble are already full of hardworking people. The challenge is helping people see that a process which once contributed to success may now be standing in its way.

The leadership changes we made in our own business were not really about introducing process. We already had process. What we lacked was a clear connection between our processes and the outcomes the business needed. The new leaders brought different skills, different perspectives, and different ways of looking at the company. They could start with the outcome and work backward. They could look at financial performance, operational performance, quality performance, and customer performance, then ask a simple but powerful question:

What processes would reliably produce those results?

That question changed everything.

The Feedback Loop

The older I get, the less interested I become in choosing sides between process and outcome. The two are not opponents. They are part of the same feedback loop.

Outcomes tell us whether our processes are working. Processes determine whether our outcomes can be repeated. One without the other eventually breaks down. An outcome without a process is often luck. A process without a successful outcome eventually becomes ritual.

The challenge for leaders is not deciding whether process or outcome matters more. The challenge is continuously testing one against the other. Every process should ultimately answer a simple question: does this still move us toward the outcome we need?

That question never goes away. Markets change. Customers change. Technology changes. Competition changes. What worked yesterday can quietly stop working tomorrow. Organizations that survive understand that process is not something to be worshipped. It is something to be questioned, refined, and occasionally abandoned.

Looking back, I no longer see a contradiction between the lessons of the Navy SEALs and the lessons of our plastics factory. The SEALs were right to focus on the process. We were right to focus on the process. The mistake is believing that process alone is enough.

Before we commit ourselves to discipline, consistency, and execution, we must first ensure that the process we are following is capable of producing the outcome we seek.

Without outcomes, processes drift into ritual. Without processes, outcomes drift into luck.

And reality, unlike management consultants and motivational posters, always keeps score.

🌱 Seed Thought: Discipline without direction is just a more efficient way to get lost.

Share

If you were around the early Macintosh days, you might remember when software occasionally came with source code—printed, meant to be typed, meant to be understood. Megamax C did exactly that. In my dorm room at Massachusetts Institute of Technology, I typed Megaroids in line by line. No shortcuts, no safety net—just patience and curiosity. When it finally ran, something shifted. Not because the game was remarkable—though, to be fair, for its time it absolutely was—but because it worked at all. That was over forty years ago, and like most things from that era, the hardware, the disks, and the paper itself have long since disappeared.

The Internet Remembers Poorly

We like to think the internet never forgets. What’s more surprising is that it remembers things that happened before it even existed—and even then, only partially.

Megaroids resurfaced in Programming C on the Macintosh by Terry A. Ward, which should have been the end of the search. Instead, it became the beginning of a different kind of problem. The source wasn’t clean. The hex data had holes. OCR, which performs reasonably well on prose, quietly falls apart when faced with dense hexadecimal blocks where a single incorrect digit breaks everything.

So I went looking sideways. I found a later binary, a version ported to Lightspeed C, and ran it through Mini vMac. Boot, load, wait—and there it was. The sound, the motion, the feel. Not memory or approximation, but something much closer to the original experience.

The reaction wasn’t dramatic. It didn’t need to be.

Thank God for the people who keep this stuff alive.

Not a Translation

At that point, the question shifted from curiosity to possibility: could this run again here, outside of emulation? Not preserved behind glass, but rebuilt in a way that made sense today.

The first problem was trust. The book wasn’t reliable, the scans weren’t reliable, and even the binary was just an artifact rather than a blueprint. So I created a single anchor—Megaroids.c—a reconstructed source of truth assembled from fragments, cross-checks, and a fair amount of trial and error. It’s worth noting that this file was never meant to compile. Megamax C is long gone, and that wasn’t the goal. The goal was something far more pragmatic: to create a version of the code that was complete enough for an AI to read, reason about, and work through—even if pieces were still missing.

From there, I brought in Claude—not as a novelty or a shortcut, but as something closer to an expert with alien patience.

What followed wasn’t a clean translation from C to Swift. Some parts mapped directly, but large portions did not. It reminded me of something I wrote in Odd Books, about a novel written entirely without the letter “e,” and how its translators didn’t simply convert the language—they preserved the constraint. English without “e” became Spanish without “a,” Russian without “o.” The story survived, but only because each version honored its own impossible rule set.

That’s what this felt like. Some parts of Megaroids translated cleanly, like English to Spanish. Others required something more interpretive—reconstructing intent across entirely different constraints. Sound routines tied to hardware that no longer exists had to be reimagined. Timing assumptions built for a completely different machine had to be approximated. Even the user interface had to be negotiated, because what made sense on a compact Mac in 1985 doesn’t carry forward without adjustment.

This wasn’t about asking what the code did. It was about asking what the experience allowed, and then finding a way to express that again in a world where the original rules no longer apply.

The Slow Part

Ironically, the port itself didn’t take that long compared to everything surrounding it. Sorting out the hex data took longer than the actual translation, and the overall process stretched across about a month. But even that description is misleading, because most of that time wasn’t continuous work. It was pauses—waiting for Claude limits to reset, stepping away, and then returning days later with two small fixes and one more idea.

Progress didn’t come in large, visible steps. It came in returns. A week later, a small tweak. Another week, a sound behaving more like it should. Another, a subtle adjustment that made everything feel just a little closer to right. The work itself wasn’t heavy, but it was persistent—and strangely enjoyable in a way that only this kind of slow polish can be.

The Moment

The defining moment didn’t come when the app compiled or even when the game launched. It came with the debug screen. Every sprite was laid out in front of me—imperfect, visible, and completely honest. For the first time, it was obvious which ones needed work, and more importantly, it was possible to fix them systematically.

Seeing them move correctly in the game after that—that part never gets old.

What This Was

It would be easy to call this a port or a translation, but both descriptions miss something essential. What actually happened here was a reconstruction of intent across four decades—across different hardware, different assumptions, and a completely different computing environment.

The more interesting part is that this wasn’t the goal. This idea didn’t start the project; it emerged from it. After writing the Vibe Coding: When the Machine Writes Back series, there was a kind of residual effect—new ideas appearing without much prompting. This was one of them. Something from the past, made accessible again not because it was planned, but because it was suddenly possible to attempt it.

That’s the real shift. It’s not just that we can build faster. It’s that we begin to consider building things we would have dismissed outright before.

The source is here. There’s also a binary there if you just want to run it. Whether you remember Megaroids—or just remember that era—it runs again.

🌱 Seed Thought: Sometimes progress isn’t about creating something new. It’s about finally having the tools to recover what was almost lost—and realizing it was never just the code you were after

.

Share

At some point, the building stops. Not because there’s nothing left to do—there always is—but because you reach a version that feels complete enough to stand on its own. Version 1.0. A line in the sand that says: this exists now.

That’s where this story could have ended. An idea turned into something real—built differently than expected, faster than expected, with friction in places I anticipated and ease in places I didn’t.

But that wouldn’t have been the whole story.

The Part I Was Sure About

If there was one thing I felt confident in, it was this: the App Store process was still going to be painful. Too many steps. Too many requirements. Too many places for something small to derail everything. I had been through it before. I knew the terrain. AI could help fill in blanks, draft policies, translate intent—but it couldn’t change the system itself.

That part, I assumed, hadn’t moved.

The Quiet Reversal

And then… it did.

The app went through review.

First attempt.

No rejections. No back-and-forth. No unexpected blockers.

Just approved.

The App

For something that started as a question—and turned into a conversation—the outcome is now something you can actually touch:

Galvanix

Not a prototype. Not a concept. A real app. Live. Working.

The Punchline (And Then Another One)

So the story folds back on itself.

All that friction I expected at the end? Didn’t show up.

All that skepticism about the App Store? Didn’t hold.

Which leaves a slightly uncomfortable conclusion: even the parts I was sure hadn’t changed… might have.

What This Was Really About

This was never just about coding. Or SNMP. Or Swift. Or building an app.

It was about what happens when the cost of turning an idea into reality drops low enough that hesitation becomes the only real barrier left.

I didn’t do this the optimal way. I didn’t prepare. I didn’t follow a system.

I just stayed in the conversation long enough to reach the other side.

What Happens Next

That’s the part still unwritten.

Because if this is what a first pass looks like—unstructured, inefficient, done in real time—then the ceiling is nowhere in sight. Not just for me, but for anyone willing to engage with it.

But something else happened along the way. Something I didn’t expect—and don’t fully understand.

I didn’t just finish with an app.

I finished with more ideas.

Not extensions of Galvanix. Not obvious next steps. Entirely separate directions—one for iOS, one for macOS—that have nothing to do with what I just built. The strange part is they didn’t require any new knowledge. I’ve been coding for 40 years. There’s nothing in those ideas that I couldn’t have thought of before.

But I didn’t.

And I don’t think I ever would have—without doing this.

There’s something about diving in—engaging directly, moving through the friction instead of thinking about it—that unlocks something on the human side. Not AI. Not tooling. Just us.

A part we don’t access as often as we get older. As we get more efficient. More fixed. More certain about how things are “supposed” to work.

This didn’t just lower the barrier to building.

It lowered the barrier to thinking differently.

🌱 Final Seed Thought: The biggest change isn’t that we can build faster. It’s that the distance between thinking about something and holding it in your hand has quietly disappeared.

Epilogue (One More Thing)

If you made it this far, you’ve walked through the messy version of this idea—the unoptimized path, the blind starts, the moments where things worked before they made sense.

There’s another way to see it.

Cleaner. More distilled. Almost inevitable in hindsight.

Someone who has been thinking about leverage, code, and creation far longer than most puts it into a different light—less about the experiment, more about the principle underneath it.

If this series felt like discovery, this will feel like clarity.

Naval Ravikant captures that perspective here.

It’s the same story.

Just told from higher ground.

Share

Everything so far might sound more intentional than it actually was. It wasn’t. This wasn’t a system, a workflow, or something refined through best practices. It was a first impression—raw, unoptimized, and deliberately unprepared. That turns out to matter more than anything else.

Flying Blind (On Purpose)

There’s a temptation to rewrite this as something more structured than it was—to imply a method. There wasn’t one. No YouTube deep dives, no prep phase, no “best way” to do any of it. That wasn’t an oversight—it was intentional. If this only works when you prepare perfectly, then it doesn’t really change anything. What matters is that it worked anyway.

Not the Optimal Path (And That’s the Point)

There are already people building with AI in far more structured ways—multi-agent systems, coordinated “teams,” emerging frameworks. I didn’t do any of that. I used my own baseline knowledge, Claude as a thinking partner, and Claude Code as an execution layer. That’s it. No orchestration, no optimization—and it still worked. Which raises the real question: if this is what happens at the low end of the curve, what happens when people get good at it?

The Parts AI Didn’t Fix

For all the focus on AI writing code, the friction that remained was revealing. The App Store is still a maze—privacy policies, terms of service, content declarations, age ratings, screenshots, compliance layers. I had been through it before, which helped, but it’s still a trudge. AI made it easier to fill in the blanks and translate intent into the required language, but it didn’t remove the process.

Where the Real World Pushed Back

There were also edges where the abstraction gave way to reality. Shakebugs needed a backend fix before the “contact developer” feature would work. iPad screenshots exposed a layout issue I hadn’t considered. For privacy, I used BlurData (to anonymize App Store screenshots), which had its own issues that required back-and-forth with the developer. This wasn’t a closed system—it was a chain of systems, each with its own constraints. AI didn’t eliminate that. It just helped me move through it faster.

The One Time It Slipped

There was one moment where Claude Code stalled—looping on a problem, trying variations, not quite landing the fix. For the only time in the entire process, I stepped in. Not to write code, but to notice. A lowercase “l” where an uppercase “L” belonged. I asked: shouldn’t this be LogLevelDebug instead of logLevelDebug? That was it. I didn’t change a line myself. I just pointed at the mismatch. It corrected, and everything moved again.

The Crash That Made It Better

There was one real crash—the multithreaded issue inside the Swift package. That wasn’t a failure of the app so much as a limitation of the underlying library. Fixing it required changes to the package itself, adding proper thread handling. That moment didn’t break the system—it strengthened it. The result was a more robust package than what I started with, not just a working workaround.

The Part That’s Easy to Miss

What stands out most isn’t what broke—it’s what didn’t. No hangs, no cascading failures, no instability inside the app itself. For something involving networking, cryptography, UI, and external integrations, that’s unusual. There were bugs and iterations, but the system held. The process felt… smooth. Almost eerily so.

What This Actually Was

It’s easy to frame this as success. It’s more accurate to call it a first pass—unoptimized, unstructured, and done without preparation. And it still produced something real. That’s the signal. Not that everything worked perfectly—but that it worked at all under those conditions.

🌱 Seed Thought: You don’t have to take the optimal path anymore. You just have to start—and stay in it long enough to reach the other side.

Share

Up to now, this has felt like a personal experiment—an app, a switch, a series of problems solved faster than expected. Part IV is where that framing breaks. Because once you step back, the question isn’t what you built. It’s what it means that you could.

The Number That Changes the Story

At the end of all of this, there’s a number that doesn’t quite sit right: $5. Not exact, not worth auditing—but real enough. A prorated subscription, a few bursts of usage, some pauses waiting for limits to reset. That was the cost.

And if that doesn’t register immediately, it should.

Because five dollars used to be forgettable. Now it’s enough to cross the threshold into building something real.

What $5 Used to Mean

Five dollars gets you very little. Not a gallon of gas in California. Maybe a coffee, depending on how honest the menu is. A throwaway app you’ll delete in two days. Half a sandwich at an airport. A tip you don’t think about.

It’s frictionless money—the kind you spend without reflection.

What $5 Became

Now compare that to what just happened. For roughly the same cost, you explored SNMP without knowing SNMP, modified a cryptographic library without understanding the math, built an iOS app without writing code, debugged concurrency issues, navigated GitHub and distribution pipelines, and shipped something that works.

Not theoretically. Actually.

The Collapse of the First Barrier

This is the shift: not that AI makes things easier, but that it collapses the cost of trying. Historically, the gating question wasn’t “is this possible?” It was “is this worth the time, money, and coordination?” That question just changed.

And when the cost of attempting something approaches zero, behavior changes with it.

The Dangerous Middle

There’s a category of work that lives in the middle—not deeply creative, not deeply technical, just movement. Coordinating, relaying, managing the space between things. That layer used to be protected by complexity.

It isn’t anymore.

The New Divide

The line isn’t technical versus non-technical. It’s simpler: people who create value, and people who move it around. AI doesn’t remove humans. It removes the need for humans whose role is purely translation without transformation.

The Shift in Leverage

You didn’t just build an app. You changed your relationship with leverage. Before, skills determined possibility, teams determined scale, and money determined speed. Now, curiosity determines entry, clarity determines output, and persistence determines completion.

Everything else is negotiable.

The Part That Should Make You Pause

This isn’t rare. It’s not even particularly advanced anymore. Which means if you did it, others can—and they may not stop where you did.

The question isn’t “can AI do this?” That’s answered.

The question is: what are you doing that AI can’t amplify?

🌱 Seed Thought: When the cost of building drops to almost nothing, the only thing left that matters… is whether what you build is worth anything at all.

Share

Up to this point, the experience has been accelerating—ideas turning into working systems faster than they should. But speed has a cost. Not failure exactly… but fragility. Part III is where that shows up. Eventually something breaks in a way that can’t be ignored, and the only way forward is to engage a layer you never intended to understand. Not fully—just enough.

When Momentum Meets Reality

Up to now, progress had a rhythm—fast, iterative, forgiving. But systems don’t stay forgiving. Eventually they reveal where the abstraction thins. The crash was the signal. Not dramatic, just enough to interrupt the flow and force a harder question: where is this actually breaking? For the first time, the answer wasn’t obvious.

Crossing the Boundary

When the issue pointed to the package, the choice became simple: work around it or go deeper. Earlier, modifying a library felt optional. Now it felt necessary. Concurrency, thread safety, shared state—concepts you recognize but don’t fully command. And yet you stepped in. Not to master them—just to move through them.

The Role That Didn’t Exist Before

This is where the experience becomes difficult to categorize. You’re not writing the code, but you’re not passive either. You’re identifying where the problem lives, asking the right questions, interpreting just enough to act. Somewhere between operator and engineer—a role that didn’t quite exist before.

The Line That Explained Everything

Then came the moment—the one every builder eventually meets, but rarely understands in full.

Nothing obvious was broken. The code compiled. The logic flowed. The system behaved… almost correctly. But authentication failed just enough to make it clear something deeper was off—the kind of problem that doesn’t announce itself, it whispers.

And then this surfaced:

One line. Not a typo. Not bad code. The implementation was structurally sound—clean, even. And yet it violated something invisible: the protocol itself. The padding was random, which meant the message being signed was not the same as the message being sent. The HMAC—computed correctly—was validating the wrong thing.

Nothing was “broken.” Everything was just… slightly misaligned. And in cryptography, slightly is the same as completely.

Why That Matters

This is the part that’s hard to fully appreciate unless you’ve lived inside systems like this. To find that bug manually, you would need to understand the SNMP v3 message structure, the role of scopedPduAsn, how HMAC validation is computed, the difference between deterministic and non-deterministic padding, and the expectations defined in RFC 3414—not conceptually, but precisely, across execution paths.

That’s not one layer of knowledge—it’s a stack: protocol, cryptography, implementation detail, execution order. And the failure didn’t exist in any one layer. It existed between them.

The code did what it said. It just didn’t do what the system required.

That kind of gap usually takes hours—if not days—of tracing, logging, reading specs, and second-guessing assumptions. Here, it resolved to a single line—not because the problem was simple, but because the system helping solve it could hold the entire context at once, something no single human typically does.

The Limit That Wasn’t

There’s always a moment where you expect to hit the edge—where abstraction fails and progress stops. This should have been it. It wasn’t. Because the system didn’t require full understanding. It required recognition, direction, and the willingness to stay in the loop.

🌱 Seed Thought: You don’t need to understand every layer. You just need to know when something doesn’t make sense—and stay with it long enough to find the line that fixes it.

Share

m

If Part I was about proving something was possible, Part II is about something more unsettling: what happens when things start working before you fully understand them. This is where most people slow down—double-check, rebuild, study. That instinct doesn’t go away, but it gets challenged. Because the feedback loop tightens, and the question quietly shifts from “is this correct?” to “is this useful?”—and those are not the same thing.

The Fork in the Road (Literally)

Once SHA-512 became the blocker, the problem stopped being about switches and became about infrastructure—libraries, dependencies, code you didn’t write and weren’t planning to. In another era, this is where things slow down: documentation, tracing implementations, maybe even reconsidering the approach. Instead, you forked the repository. Not because you were certain—but because it was the next move. That distinction matters.

Coding Without Coding

This is where things start to feel slightly unreal. No Xcode, no breakpoints, no stepping through logic—just clone the repo, point AI at it, describe what needs to change, wait… and then: “Done.” Was it done? You didn’t know. And that uncertainty didn’t stop you—it changed how you moved forward. Because the real friction wasn’t the code. It was everything around it.

The Unexpected Boss Fight: GitHub

Commits, pushes, pull requests, working directories—none of it technically hard, but unfamiliar enough to slow momentum. Ironically, the most “modern” part of the process was the least assisted. AI helped rewrite cryptographic logic, but it didn’t help much when your working directory behaved in ways you didn’t expect. That contrast is telling.

Skipping the Line

Instead of isolating and testing the package properly, you moved forward anyway—built the app, added the package, tested it live. Not how engineers are trained, but exactly how momentum behaves when iteration costs drop to near zero.

The Moment It Shouldn’t Have Worked

And then it did. Not perfectly, not cleanly—but undeniably. An app talking to a switch, powered by a modified package you didn’t write. That moment doesn’t feel like success. It feels like disbelief.

Where the Friction Moved

If Part I hinted at it, Part II makes it obvious: AI doesn’t remove friction—it relocates it. Away from writing code and understanding protocols, toward tooling ecosystems, distribution pipelines, naming conflicts, provisioning profiles. You didn’t write code for hours, but you spent hours navigating everything required to ship it.

The New Loop

By now, a rhythm had formed: build quickly, observe, adjust, repeat. The cycles tightened—not because the problems got smaller, but because the distance between idea and feedback collapsed.

🌱 Seed Thought: AI doesn’t eliminate the work. It changes where the work lives—and how fast you can move through it.

Share

This is a story about coding—but not in the way you think.

If you’ve never written a line of code, you’re not behind—you might actually be closer to the point. And if you have coded before, this may feel even stranger, because it challenges something you’ve probably believed for decades: that building something requires understanding how it works.

It used to.

This story isn’t about learning SNMP, or Swift, or encryption protocols. Those show up—but only as characters passing through the scene. The real subject is something quieter and far more disruptive: what happens when you can build things without carrying the full weight of how they’re built?

What follows is not a tutorial. It’s an experience—one that starts with a simple question, drifts into unfamiliar territory, and slowly reveals that the rules underneath it all may have changed more than we realize.

If you’re expecting step-by-step instructions, this won’t give you that.

If you’re curious about where the edges are moving—and what that might mean for how we all work, build, and create—then it’s worth staying with.

Because somewhere along the way, this stops being a story about coding…

…and starts becoming a story about leverage.

Before we step into the story, it helps to know what world we’re walking into. Not in detail—just enough to recognize the landmarks as they pass by.

• Switch: Think of it as a highly organized traffic controller for data. Every device on a network—computers, cameras, access points—plugs into it. Its job is to make sure information gets where it’s supposed to go, efficiently and quietly.

• PoE (Power over Ethernet): One cable. Two jobs. It carries data and electricity. That means things like cameras or Wi-Fi access points don’t need separate power—they come alive through the same wire that connects them.

• SNMP (Simple Network Management Protocol): A long-standing, slightly awkward, but incredibly useful way to ask devices questions and sometimes tell them what to do. It’s less like a modern app and more like speaking in precise, numbered phrases that machines understand.

• LLDP (Link Layer Discovery Protocol): Devices quietly introducing themselves to each other. “Here’s who I am, here’s what I’m connected to.” It’s how a network builds a kind of awareness of its own structure.

• SSH / CLI: The command line version of knocking on a device’s door and saying, “Let me in, I know what I’m doing.” Powerful, direct… and not particularly forgiving.

• REST API: A more modern, structured way for software to communicate. Clean in theory. In practice, only as good as whoever built it.

• Encryption (SHA, authentication, etc.): The part that makes sure when you’re talking to a device, it’s really you—and that no one else is quietly listening in or pretending to be you.

The Idea That Didn’t Feel Like One

It didn’t begin as a prompt—it began as friction. The switch worked, the cloud worked, everything technically worked. But something about routing control through a remote layer for hardware sitting ten feet away felt off. Not broken—just indirect. The kind of inefficiency that doesn’t justify action… until it suddenly does.

That’s when the thought surfaced: Could I just talk to it directly? And almost immediately after: Could I build something to do that from my phone? Not a plan. Not even a goal. Just a question that refused to go away.

Familiar Words, Unfamiliar Terrain

When SNMP entered the conversation, it wasn’t new—it was remembered. Like pulling an old tool out of a drawer and realizing you still recognize it, but you’re no longer sure how to use it. BASIC. Assembly. C. There was a time when this was native ground. Now it was more like a map you could read, but not navigate instinctively.

And that’s where this could have ended. Not because it was impossible—but because it sat in that dangerous middle ground: possible, but annoying. Instead, something else happened. Instead of solving, you started asking. And the answers kept coming—not as lectures, but as directions.

Proving It’s Real

Before apps, before Swift, before anything ambitious, there was a single binary question: Can I talk to the switch?Terminal became the testing ground. SNMP v2 fell apart quickly—static IP requirements killed the mobile idea. SNMP v3, though, had promise. Authentication, encryption, real control.

Then came the first wall: SHA-512. Supported by the switch. Not supported by macOS.

That kind of mismatch used to stall everything. Not because it was unsolvable—but because it required just enough digging to drain momentum. This time, instead of digging, you delegated. Upgrade via Homebrew. Try again. Success.

And just like that, something subtle shifted: you weren’t solving problems—you were moving through them.

A Different Kind of Build

Once the switch responded, the path forward wasn’t fully clear—but it was undeniable. If communication was possible, then everything else was just layers. And that’s when the real question emerged, sharper than the first: Could I build the entire thing without writing code?

Not as a shortcut—but as an experiment.

Because underneath the technical steps, a deeper shift was already underway. The question had changed from how do I build this? to how do I describe this well enough for something else to build it?

That’s not an incremental improvement.

That’s a different paradigm entirely.

🌱 Seed Thought: You don’t need to understand everything anymore. But you do need to recognize what should be possible

Share

There are a lot of ways to answer that.

Most of them involve money.

That’s not where it gets interesting.

The Illusion of “When”

I remember being at Namale in Fiji for a Date with Destiny event. The local Fijians would hear people say, “When I make enough money, I want to move here.” That confused them. Why not just move now?

There’s something in that question that sticks. Not because everyone should move to Fiji, but because it exposes how often we build lives around things we don’t actually want. If you’re always wishing for something you don’t truly want, you’ll never get to the things you do. And on the other side, some of the things you do want are closer than you think—you just make excuses for why they’re not possible.

Wealth as Freedom

For me, wealth became a much simpler idea: how much do you owe others?

Before selling the business, I carried what most operators carry—personal guarantees, mortgages, revolvers, leases. A long list of obligations tied to people and institutions that don’t know you and don’t care about you beyond repayment.

That’s pressure.

When that’s gone, something changes. You sleep differently.

That wasn’t the goal.

But it was the result.

The Money You Don’t See

There’s a saying that a business is only worth what’s in your bank account after the check clears.

Even that’s incomplete.

The tax man still has his hand out. If you’re not thinking about value on an after-tax basis, you’re not thinking about value at all. The number you negotiate is not the number you keep, and if you don’t understand that before the sale, you’ll feel it after.

Don’t Get Lost in the Wrong Identity

I was lucky in one way. I got involved in a business I loved fixing and improving. The industry itself? Plastic packaging? Not so much.

That distinction matters.

Too many people tie their identity to the industry they’re in instead of the value they create. At some point, that industry will change, decline, or disappear. When it does, if your identity is tied to it, you’re lost. If your identity is tied to what you actually do—solve problems, build systems, create value—you’re portable.

That’s a different kind of security.

Success Isn’t Easier the Second Time

There’s a belief that once you’ve done it, the next one is easier.

Maybe.

Maybe not.

Success is hard. And while skill matters, so do timing and context. It’s easy to believe your own story about why it worked. It’s harder to accept that not all of those conditions will exist again.

The Part People Don’t See

There was a point, before everything worked, where I reduced my salary to $1,000 a year just to keep health insurance. At the same time, I was asked to pay for a ticket to the company picnic.

That was my “go fuck yourself” moment.

Not a proud one. But a real one.

It’s easy, after the fact, for people to underestimate what was actually at risk. Even when you’re transparent, most people don’t fully see it.

There’s no clean solution to that.

It just comes with the territory.

🌱 Seed Thought: Wealth isn’t the number you reach. It’s the position you’re in when you no longer have to answer to it

.

Share

The Moment It Clicked

There wasn’t a single dramatic moment, but there was a shift I could feel. I came from an engineering mindset—measure everything, optimize everything. That concept worked—until it didn’t.

When I hired the COO who would ultimately help scale the business, he approached things very differently. More first principles. Less optimization for its own sake. The question became simple: could both approaches achieve the same outcome? The answer was yes.

That was the click.

I didn’t need to be the operator anymore. I needed to trust someone aligned with the outcome and let them execute differently than I would. That meant letting go of my version of how things should be done and putting on a different hat.

Curiosity Over Control

People like to frame this as ego or control.

That wasn’t it.

Curiosity has always been a core value for me. And real curiosity means accepting that other people can do certain things far better than you ever will. You can learn from them. You can understand their process. But trying to out-execute them in areas where you’re weak is a losing game.

The job becomes alignment, not replication.

Process vs. Outcome

I learned this the hard way.

Before hiring the right COO, I fired a President. The financials told the real story—we were heading straight into a wall. But if you looked at the org chart, the reports, the visible processes, nothing clearly explained why. Everything looked like a functioning business.

It wasn’t.

The focus was on process—titles, structure, how things were done—instead of outcomes. There was no path to alignment that way. When the focus is on process, you can always debate. When the focus is on outcomes, reality shows up fast.

The Hidden Trap in Growth

There’s a pattern that kills a lot of growing businesses: they don’t change how they manage as they grow. The assumption is simple—if we just work harder at what got us here, it will carry us forward.

It won’t.

I’ve written about this more directly in a previous piece (see: Stages of Business Growth), but the short version is this: the systems, expectations, and people that got you to one level are not guaranteed to get you to the next.

The turnaround didn’t come from pushing harder. It came from bringing in people the business could grow into—people who set clearer expectations, stronger accountability, and higher standards. And the most important part: we did it with buy-in. Existing management saw the path, and most came to the same conclusion—we were heading in the right direction, but they didn’t have the skill sets to take us there.

That’s a hard realization.

But it’s an honest one.

The $100M Question

Joe’s final question in this thread was framed like a punchline: “What haven’t I asked that would be worth $1M, $10M, or $100M to someone listening?”

The answer is straightforward:

Are you building a business that can grow beyond you?

Because if the answer is no, you’ve already capped the outcome.

🌱 Seed Thought: You don’t scale a business by doing more. You scale it by becoming something different inside of it

.

Share

They are related.

But they are not the same.

One is about how you see.
The other is about how you are seen.

And most people treat both as events instead of ongoing practices.

The Rembrandts Are Already There

There’s a concept often referred to as “Rembrandts in the attic”—hidden value inside a business that the owner either doesn’t see or doesn’t present well. The instinct is to think you need to create those Rembrandts.

You don’t.

They’re already there, or they’re not.

The real question is whether you—and others—can see them.

How do you do that?

You talk to people. Not when you’re ready to sell. Not when you need capital. As part of running the business. You talk to companies that could acquire you. You talk to companies you might want to acquire. You listen. You ask questions. You pay attention to what they notice, what they ignore, what they value, and what they discount.

That process doesn’t just inform a future transaction.

It trains your brain.

It’s like learning to draw. The skill isn’t in how you hold the pencil. It’s in how you see the world before the pencil ever touches the paper. Once you see it differently, everything you do reflects that shift—even if you can’t point to a single moment where it changed.

That’s how you uncover the Rembrandts.

The Conversation Before the Transaction

One of the most valuable things you can do is build relationships long before they are needed.

I remember a company we were interested in acquiring. Getting them on the phone was difficult. “We’re not for sale,” click. Eventually the conversation shifted—not because we pushed harder, but because we showed up differently. “Oh, you’re not an investment banker… let me tell you about those.” Then it became, “let’s go golfing.”

Years later, we bought that business for a little over $100M.

What changed?

Timing.

But more than that, familiarity. Trust. Relevance.

Every business will eventually be sold—whether by decision or by circumstance. If you’re not already part of that owner’s world as a peer, a sounding board, or a known quantity, you won’t be the first call when that moment comes.

And first calls matter.

The “Why” That Holds Up

Now shift to the other side of the table.

When I look at a business as an investor, the first hurdle is simple:

Tell me your why.
Do I understand it?
And does it still make sense if the world shifts?

The “why” goes beyond numbers. Financial projections don’t impress me. They look good on paper for everyone. Good ideas can be just as dangerous. I remember someone pitching a business around aggregating remote feeds for high school sports. Interesting idea. But why would someone use that instead of just going live on YouTube?

If I can’t see the behavior behind the business, I’m out.

There’s another version of this that’s just as important: areas where I don’t have knowledge. If you’re building something in a space where I don’t understand the pain points—say, a next-generation HR system—I can’t properly evaluate whether what you’re doing matters.

And you teaching me doesn’t count.

Why’s need to be resilient. They need to survive shifts in behavior, changes in technology, and moments no one saw coming. After 9/11, parts of our business disappeared overnight while others grew. If your “why” depends on a static world, it’s fragile.

Buying vs. Investing

There’s also a distinction that doesn’t get talked about enough: buying a business is not the same as investing in one.

Buying is commitment.

You are allocating time, energy, and responsibility. If you have control, you are the operator whether you intended to be or not. The success or failure of that business sits closer to you than you might expect.

Investing is different. It’s more passive, at least by design. You’re supporting, advising, and providing capital, but you are fundamentally betting on management to execute.

And if they don’t, your money goes to zero.

It’s not like you get to hire better people and “try again.”

The two require very different mindsets.

I learned that the hard way. After selling the family business, I spent time doing direct investments. What I found was that it pulled me back into operating more than I expected. Not formally, but practically. You get involved. You care. You spend time. And over enough investments, that becomes something else entirely.

Now, I look to place capital with people who are leading those investments—private equity funds, venture firms, individual operators running SPVs—people who are already in the seat I would otherwise drift back into.

It’s not about avoiding involvement.

It’s about knowing where you’re most effective.

🌱 Seed Thought: Attractiveness isn’t something you create at the moment of sale. It’s something the market recognizes long before you ever ask the question.

Share

The Industry Forced the Game

We were in rigid plastic packaging—bottles and caps. On the surface, not particularly glamorous, but the economics underneath it created a very specific game. You’re converting resin into containers, which puts you between large multinational resin suppliers and customers who were themselves becoming multinational. The old “mom and pop” packaging companies couldn’t operate at that level. The industry was consolidating whether you liked it or not.

You had three choices: grow, sell, or die.

Growth, however, wasn’t just about getting bigger. It required doing something most operators in that space didn’t fully understand.

The Business Became a Chessboard

The real advantage we built was not just in manufacturing. That was table stakes. What mattered was how we integrated what we acquired and how we positioned the entire system.

Shipping plastic is expensive in a very specific way. Once a bottle reaches a certain size and travels a certain distance, you’re essentially shipping air—and the cost of that air can exceed the value of the container itself. That means proximity to where the product is filled becomes critical. As we grew through acquisition, we grew in locations. That allowed us to move machines, shift production, and constantly reposition ourselves closer to demand.

The entire company became a chessboard.

We were always rearranging it to be in the strongest position possible.

Purchasing was the second major lever. Someone—either us or the company we acquired—was always doing it better. Better pricing, better terms, better scale. When you improve purchasing in a business like this, it doesn’t trickle down to the bottom line. It drops straight to it. There were smaller operational improvements as well, but these were the big moves: where you produce and how you buy.

Integration, People, and the Discipline to Choose

A lot of companies acquire. Very few actually integrate. Our ability to absorb a business within six months—so completely that you couldn’t tell it hadn’t always been part of us—was what made us different. But integration didn’t mean forcing everything into our way of doing things.

If we were better, we pushed our processes into their operations. If they were better, we changed.

That requires humility and discipline. Not everything different is better, and knowing the difference is a skill. This also became one of our strongest filters. If a company relied heavily on temporary labor to compensate for poor processes or inconsistent production, that wasn’t just an operational issue—it was a cultural one. Different standards. Different expectations. Different reality.

We passed, no matter what the price was.

At the same time, value was always grounded in fundamentals. Growth without profitability is just motion. Value only happens when you are making money. Make money, sell a great product, and keep reducing cost while maintaining quality. We had a cost savings team dedicated to finding ways to spend money intelligently to lower cost. If the return justified the investment, we made it. We were always investing in becoming better.

Revenue is what shows up. Value is what makes it inevitable.

None of this works without the right people, and this is where things get uncomfortable. As expectations rose, some long-time employees realized they couldn’t meet them. A few shifted into smaller roles, but most moved on. That’s reality. When the business grows, not everyone grows with it.

At the same time, the general labor force became happier. Friction decreased. Success became clearer. People could see progress and be rewarded for it. The system worked better, and that improved everything.

Our hiring philosophy became sharper:

Most people hire for the present.

We hired for the future and forced the present to catch up.

We didn’t look for people who could just do the job as it existed. We looked for people the business could grow into.

The Fuel and the Alignment

Going from $90M to $300M in four years doesn’t happen without fuel. For us, that fuel was private equity. There was no other way to move that fast at that scale. Capital made the strategy executable, but capital alone doesn’t create value—it amplifies it.

Which brings it back to the real driver behind everything: alignment. We all knew what the challenges were. We all knew what the opportunities were. We all knew what success would look like—and what it would be worth.

That alignment made speed possible.

🌱 Seed Thought: Growth at that level isn’t about doing more. It’s about knowing exactly what matters—and aligning everything around it.

Share