Category Archives: Parenting

Hurst University

For as long as my children can remember, I have told them that they are students at Hurst University.

It has no campus, no accreditation, and no admissions process. Joining the family is enough to get you enrolled. Graduation is another matter.

There is only one requirement. By the time you leave the house, you should be capable of building a future for yourself.

I don’t mean that you should know what you are going to do for the rest of your life. That is one of the stranger questions we ask young people. Most adults I know have changed direction enough times that pretending an eighteen-year-old is making a permanent occupational choice is unserious.

What I mean is more fundamental. You should know how to learn, and you should know how to work. When you encounter something you don’t understand, you should have some idea how to get from ignorance to competence. You should know how to take a hit without deciding the hit defines you, how to recognize when your assumptions were wrong, and how to change direction without treating everything behind you as wasted.

Most importantly, you should increasingly understand that the responsibility for what happens next belongs to you.

That is Hurst University.

There is nothing new about the idea. If anything, it is an old model of education with a family name attached to it.

For most of human history, becoming educated and becoming useful were close to the same thing. Children watched adults do real work. Then they helped. Then they were trusted with some small part of it. They made mistakes where mistakes were survivable, were corrected by somebody who knew more than they did, and tried again. As competence increased, the work got harder and supervision got lighter. Eventually the person who had been taught became somebody who could be trusted to act without waiting to be told what to do next.

That transfer is the part I care about most. The student eventually has to become responsible for the education.

I learned that the hard way, and then I had to watch one of my children learn it too.

He was not slow. He was seeking out serious material for pleasure at an age when adults found it surprising. But the way he took in information did not match the way the school delivered it, and the instruments the school trusted returned the wrong answer about him. The system then acted on the wrong answer. He began to notice he was being handled as a different kind of student, and he could not work out why.

The part I remember is not the meetings. It is that he started to wonder whether the school knew something about him that he didn’t.

I eventually moved him somewhere more willing to respond to an individual child, which helped. The more important intervention was a conversation.

I told him what I believed to be true. No institution was ever going to be able to take full responsibility for his education. Some things were going to be harder for him than for other people, and that was not going to change.

Then I told him that his challenges were his superpower.

I meant it literally, and I explained why. Most people are never forced to learn how they learn. They get through on the method they were handed, and they only discover its limits much later, if ever. He was going to have to build his own method starting now, at nine, because the handed one did not work for him. That is a brutal assignment and it is also an enormous head start. The world keeps rewarding people who can acquire what they need without being given it, and he was going to be practicing that while everyone else was still being taught.

Then I told him he was a student at Hurst University, and that when he graduated from this house he was going to be fine.

He is grown now. In his twenties he built a business and sold it to one of the largest financial services companies in the world, which is a thing you cannot do without becoming a fast and relentless student of whatever is in front of you. Entrepreneurs are not people who already know how. They are people who find out in time.

He is one of the sharpest people I know and one of the most prepared, and the second of those is the one he built. He worked out early that being the smartest person in the room and being ready for the room are different things, and that only one of them was under his control. He stopped waiting to find out which he would get. That habit came out of the friction, and I am not sure anything else would have produced it.

I was a student at Hurst University long before I had a name for it.

I was dyslexic and dysgraphic and did not present well to the educational system. At one point my parents were told they should prepare themselves for the possibility that I would never be capable of supporting myself.

That prediction did not age well.

I went to college young and moved out at sixteen. Some of that was rebellion, but mostly I wanted independence in the literal sense. I wanted control over my own life, including the economic responsibility that came with it.

A lot of my education after that happened without anybody designing it. Computers gave me problems I cared enough about to solve. Programming led into systems. Systems led into networking. Networking led into security. Security eventually required understanding companies, incentives, law, economics, organizations, and people. I kept encountering things I did not know and learning enough to get through the next door.

One capability created a reason to acquire another.

None of it started with me.

My father grew up on a subsistence farm, where you fix what breaks with what is in the barn because the alternative is doing without. He taught himself rocket chemistry as a kid off that same principle and was working on satellite hardware by his early twenties. I have written about him before, so I will not tell it twice.

What matters here is that he never called any of it an education. It was just what you did when you needed to know something.

So I did not invent this. I inherited it, gave it a name, and made the handoff deliberate. That last part is the part that matters, because none of it moves on its own. It has to be handed over on purpose, by somebody who decides to bother.

Years ago, when I wrote about apprenticeship, I described four things that had mattered enormously in my own development: access, direction, challenges, and support. I still think that framework is right, but I now see something underneath it. Those are not merely the ingredients of a good apprenticeship. They are the ingredients of an environment that gradually teaches someone to direct themselves.

Give somebody access to things worth learning. Put people around them who know more than they do. Give them problems slightly beyond their current ability. Support them enough that failure remains recoverable. Then, slowly, stop telling them what to do next.

That last part is the actual transfer.

Having three children made this clearer, because the same philosophy looks completely different depending on the student.

My second child has always worked. At four he was doing long division and by 6 or 7 he could recite the major bone and muscle groups. As a teenager he became a nationally ranked fencer, which is not something that happens to a person for being quick. It happens through years of drilling the same movement badly until it is good, losing in front of people, and going back the next day.

So the story I used to tell myself about him, that things came easily and he had therefore never built the habit, does not survive the evidence. He built it early. He built it in a domain nobody assigned him.

What was true is that school rarely asked him for it. He was admitted to several of the best aerospace programs in the world and ultimately chose computer engineering, and he is now somewhere the standard is set by people who are also very good and where thinking fast is the baseline rather than the edge.

So what I am watching is not a young man learning to work. It is someone moving a work ethic he already owns out of the place he first built it and into the place he intends to live. That transfer is the entire point of this essay, and he is doing it in front of me.

My third is different again. Nearly all of her identity is currently organized around a single competitive sport, roughly twenty-six hours a week of it. That is not a complaint. Twenty-six hours a week of anything difficult teaches repetition, correction, pain tolerance, delayed gratification, and performing while people watch. The work ethic already exists.

The problem is that she believes the work ethic belongs to the sport. Her brother has already shown that it doesn’t, which is the most useful thing an older sibling can do.

Activities end. The machinery that produced the excellence should travel.

Aptitude changes the educational problem. It does not remove the educational problem.

A child who struggles may need to learn that difficulty is not the same as inability. A child who rarely struggles may need enough friction to discover the value of preparation. A child who becomes excellent in one domain may need to discover that excellence is partly a process that can be carried elsewhere.

The curriculum changes because the student changes. The goal does not.

The goal is agency. By agency I mean the ability to encounter something unfamiliar and say, with some credibility, “I don’t know how to do this yet, but I know how to begin.”

That may be the most durable thing an education can produce.

My father was not the only one. My mother was doing the same thing in a different register, and I was even slower to see it.

She started as a hair stylist. Later she became a tool-and-die worker at Boeing. Then she moved into knowledge work and eventually retired as a business analyst.

Described by job title, those look like unrelated careers, the sort of résumé that gets read as drift. Described by behavior, they are the same story repeated several times. She kept becoming qualified to do things she had not previously known how to do.

Nothing about cutting hair prepares you to hold tolerances on a machined part. Nothing about machining prepares you to take apart how a business actually works and put it back together as a requirement. What carried across was not the content. It was the practice of arriving somewhere without the necessary knowledge and acquiring it in public, in front of people who already had it, while the work still had to get done.

She did that at least three times, each time later in life than the last, each time with more to lose. Watching it happen taught me more than any explanation of it would have.

I think we put far too much weight on occupational identity. We ask people what they “are” when what we mean is what they are being paid to do right now. Those are not the same thing.

A mechanic who becomes an engineer does not arrive empty-handed. A construction worker who moves into software already understands sequencing, dependencies, tolerances, physical constraints, customers, mistakes, and what happens when plans encounter reality. A salesperson who becomes a product leader knows things about incentives and people that do not appear in a product-management textbook.

Learning one serious domain teaches you more than the facts of that domain. It teaches you something about systems, and, if you are paying attention, something about how you yourself become competent.

That is why the question “What are you going to do with your life?” is not useful. Ask instead what you are going to do next.

You are not choosing forever. You are choosing next.

That does not make the decision unimportant. It changes what makes the decision good. A good next step should leave you with more than you had before. More competence, more judgment, more context, more relationships, more credibility, more capital, or simply more options.

Then you choose again.

This is also why I have never been comfortable with “follow your dreams” as career advice. Dreams are useful. They give us energy and they make us try things. But desire and strategy are not the same thing.

People have aptitudes whether we like that fact or not. The world has needs whether we like that fact or not. Some capabilities are scarce, some are common, and some interests map more naturally than others onto work that can support a life. If you expect something to support your life, understanding how it creates value is part of taking responsibility for the decision.

Years ago I was riding the gondola between the peaks at Whistler with three young women. One of them had recently graduated and was telling the others about an argument she had had with her boss. She had a degree now, and she thought she should be paid more. Her boss told her he could not pay her more simply because she had acquired the degree.

She was furious. Why had she bothered getting it?

Then, somewhere in the conversation, she mentioned that she worked at a sandwich shop.

I never learned what the degree was in. That is probably why the story stuck with me. The degree may have been enormously valuable, and may have opened an entirely different career a month later. But the credential itself had not changed the economic value of the work she was performing that afternoon.

The point is the distinction between learning something, possessing evidence that you learned something, and becoming capable of doing something the world values. Those things overlap, but they are not identical.

Eventually reality gets a vote.

That phrase matters to me because it applies well beyond credentials. You can believe you understand a system until you have to build one. You can believe you understand customers until you have to sell something to them. You can believe you understand leadership until somebody else’s livelihood depends on your judgment. You can believe you understand risk until the decision is yours and the consequences arrive with it.

Capability develops when knowledge begins colliding with consequence.

That is why work, projects, apprenticeship, competition, and responsibility matter so much. They are not simply places to apply learning. They are part of how learning becomes judgment.

And judgment is difficult to acquire without being wrong.

My father used to ask, “Do you know why God made young men so stupid?”

“So they could do the impossible.”

He usually said it about the early space program, and that was not a coincidence. The young men in the joke were him and the people he worked with. He was not describing a category of person. He was describing a room he had been in.

There is a limit to the principle. Ignorance can get you killed. But experience accumulates reasons a thing will not work, and there is a danger in becoming so sophisticated about risk that the sophistication becomes a reason never to take one.

If you are going to attempt difficult things, some of them will not work. What matters is what happens next.

Failure itself is not automatically useful. Failing repeatedly without changing anything is repetition. The useful part is the loop afterward. What happened, what assumption was wrong, what did I misunderstand, what was inside my control, and what should change next time?

The attempt failed. That is information.

“I am a failure” is something else.

Rejection works much the same way. Many worthwhile things require volunteering for outcomes somebody else partly controls. Apply for the job. Ask for the opportunity. Pitch the customer. Publish the idea. Start the company. Compete.

Somebody gets to say no.

If no is psychologically intolerable, you eventually begin designing your life so nobody ever gets the opportunity to say it. That feels safer, but it also shrinks the range of possible futures.

This is why resilience is less something you explain to a child than something you progressively load. An athlete does not begin with the maximum weight. A gymnast does not begin with the hardest skill. You give somebody difficulty at a scale they can survive, let reality push back, help them understand what happened, and then load a little more.

Over time they accumulate evidence about themselves. Discomfort ends. Embarrassment is survivable. Criticism can contain useful information. Preparation changes outcomes. Being wrong does not destroy you. Failure can be followed by another attempt.

This is what I mean when I tell my children that the hardest thing in life is managing your own psychology. Intelligence does not save you from fear. Talent does not save you from insecurity. Being right does not save you from ego. Knowing what you ought to do does not guarantee that you will do it.

A surprising amount of adulthood is remaining capable of acting while your own psychology is trying to convince you not to.

Which brings me to the message I worry about most, because it arrives sounding like sophistication.

Somewhere between the start of high school and the end of it, one of my children began telling me a story about his own future. Previous generations had taken the housing. Previous generations had taken the wages. The arithmetic of an ordinary adult life no longer worked, and there was not much point pretending otherwise.

I pushed back, and I want to be careful about what I was pushing back on. I was not arguing that housing is affordable or that the numbers are fine. Constraints are real, and a young person who cannot see them is not being educated, he is being flattered.

What I objected to was the shape of the conclusion. A structural fact had quietly become a personal verdict. He was not describing a difficult environment he would have to navigate. He was describing an outcome that had already been decided, which meant navigation was beside the point.

That is the mirror image of “follow your dreams,” and it fails for the same reason. One says the world will accommodate you. The other says the world will not permit you. Both remove the part where what you actually do makes a difference.

Narratives shape agency. Give young people tools, not verdicts.

The same idea applies to the people and environments we choose.

I have told my children for years that you cannot aspire to what you have not seen or experienced. We like to think our imagination is independent. It isn’t.

If you have never met anyone who built a company, building a company feels like something done by a different category of human being. Then you spend time around someone who has done it and the thing moves from abstract possibility into the set of things ordinary humans apparently do.

Spend time around engineers and engineering becomes concrete. Spend time around excellent tradespeople and craftsmanship becomes visible. See someone change careers later in life and reinvention becomes less frightening. Watch someone take a serious risk, fail, recover, and try again, and failure becomes less terminal.

This is part of what apprenticeship does extraordinarily well. The apprentice is not merely receiving instruction. The apprentice is watching what competent people consider normal.

And normal is contagious. So are standards. So are ambitions. So are fears.

Whether people like the implication or not, we become, in meaningful ways, like the people with whom we spend our time. Which is why I have come to think of the whole environment as three inputs worth choosing deliberately. People shape your standards. Problems shape your capabilities. Feedback shapes your calibration.

Those are decisions, and like any decision they are worth revisiting. I am not disciplined about this and I have stayed in lanes longer than I should have. But without some mechanism for reconsideration, inertia starts impersonating intention.

You do not need to predict your life. You do need to keep steering it.

All of this was already true before AI. AI makes one part of it harder to ignore.

Instruction is getting cheap and abundant. The judgment that used to accumulate as a byproduct of doing unimportant work is not. I have written about both of those elsewhere, the vanishing on-ramp and what turns scarce once reasoning is cheap, and will not argue them again here.

The piece that belongs in this essay is smaller and harder. Every one of those questions is a decision about what to do next, and there is no longer anyone obvious to hand it to. A model will tell you what is true. It will not decide what you are willing to own.

That does not require a new educational philosophy. It makes an old one newly relevant.

You need access. You need direction. You need problems that matter. You need people who know more than you do. You need enough consequence for reality to get a vote. And eventually you need responsibility for deciding what happens next.

Which brings me back to the thing Hurst University was actually built around.

Family.

When children are young, the family carries almost everything. Food, shelter, transportation, money, protection, opportunity, judgment, and most of the consequences of decisions all sit primarily with the parents.

Childhood is, in part, the gradual transfer of that weight. Not all at once. That would be abandonment. A little at a time, while mistakes are still cheap and there is somebody nearby who can help make sense of them.

At first we choose for them. Then we let them choose between things we have selected. Then they make decisions we would not have made. Then they live with some of the consequences. We advise more and decide less.

If the process works, responsibility moves almost imperceptibly from one side of the relationship to the other.

Eventually they leave the house. They do not leave the family.

That distinction is more important than I understood when I first started making the Hurst University joke. The goal was never independence in the sense of needing nobody. Families do not work that way, and neither does the rest of life. The goal was to change your position inside the family.

When you are small, the family carries you. For a long time that is its job. Then you begin carrying more of yourself. Your decisions become yours. Your mistakes become yours. Your work becomes yours. Your education becomes yours. Eventually the roof over your head becomes yours too.

And somewhere along the way, if things have gone well, another transition begins. You become capable of carrying some weight for the people who once carried all of yours.

That is much closer to what I mean by graduation.

Not that you know what you are going to do for the next forty years. Not that you have accumulated the correct credentials. Not that you have stopped needing your parents, your siblings, or anyone else.

You leave the house able to participate in the family as an adult. You can build enough of a life to carry yourself, and enough capability that when the people you love need something from you, you have something to give.

The graduate of Hurst University is not the person who has all the answers. It is the person who has become difficult to make helpless.

Thirty years ago I did not have all of this worked out. I had a joke about Hurst University and a conviction that my children needed to leave the house able to make a future for themselves.

I understand the joke a little better now.

The future was never the thing I could give them. The best I could do was help them become people capable of building one.

Teach to the Median, Punish the Variance

Factories exist to produce consistent, cost-effective products. That is the point. The relentless optimization of cost of goods sold is not a side effect of industrial production. It is the mandate. And it works, until it doesn’t. The reason products last so much less than they did twenty years ago is not that we forgot how to make durable things. It is that durability lost the cost argument. Quality is expensive. Variance is expensive. The system optimizes both out. What survives is the median product, built to a price, reliable enough to ship, and no more.

Modern schooling often behaves the same way. It batches children by age, sequences content for throughput, and optimizes for a predictable median. Sir Ken Robinson made this observation twenty years ago, and the metaphor stuck, not because it is clever but because it names incentives, not architecture. When a system must operate at scale under budget, policy, and staffing constraints, variance becomes expensive. The median becomes the target. Outliers become the problem.

That is how you get the loop so many families recognize.

A child with a spiky profile, gifted and struggling at the same time, or simply learning in a different sequence, is hard for a production line to interpret. The system cannot see internal state. It can only see outputs it knows how to count. Pacing, compliance, turn in rates, standardized measures, and classroom friction. When it cannot measure what is actually happening, it collapses complexity into a label. Lazy. Defiant. Behind. Broken. Sometimes worse. The misclassification is not incidental. It is structural. The factory cannot afford to treat every student as a special case, so it treats special cases as defects.

Twice exceptional programs were a serious attempt to address exactly this failure mode. 2e was not supposed to be a vibe. It was an operational category, a way to route support without denying capability.

Institutions rarely attack reforms head-on. They metabolize them. The common move is not to announce that everyone is 2e. It is more subtle. Fold 2e into the general program, justify the change as an opportunity for all, and quietly remove the differentiated pathways, expertise, and accountability that made 2e real. The label survives. The function does not. The specialist becomes a roaming consultant, the pull-out becomes a generic intervention block, and the documentation becomes a checkbox.

Spencer Silver at 3M spent years trying to make a strong adhesive and produced one that was too weak to hold permanently. By factory logic it was a failed batch. It sat in the lab for years because the system had no category for a glue that did not stick properly. A colleague with a different problem recognized the variance as the feature. The factory almost never found out what it had.

This pattern is familiar in M&A. Companies are often acquired to address a capability, culture, or talent gap. The acquirer gets what it wanted on paper, and then the organization takes over. Microsoft bought Hotmail to compete in web-based email. Hotmail ran on Linux. Microsoft ported it to Windows, the product degraded, and what had been acquired to solve a problem became an example of the problem. The engineers who built Hotmail watched what they had created get dismantled and left. The institution did not transform around the acquisition. The acquisition transformed into the institution, and the talent that made it valuable walked out with their badges.

The proof a program still exists is not whether the brochure mentions it. It is whether the supports remain distinct, staffed, and enforceable. When a category stops changing what adults do, the system reverts to default settings. Teach to the median, punish variance, treat the casualties as defects.

You can see the same dynamic in curriculum fights. When a system cannot reliably lift the floor, the path of least resistance is to lower the ceiling and call it equity. This is not cynical in intent. It is cynical in effect. Acceleration does not disappear. It moves off the books. Tutoring, test prep, schedule hacking, summer programs, parent advocacy. The families who can afford those channels use them. The families who cannot are left with the official story that the ceiling was lowered for their benefit. The median experience is preserved. The gap widens. Official metrics improve because the ceiling has been redefined.

None of this is morally mysterious. It is operational. What makes it damning is that schooling runs this population optimization model without the measurement and accountability that would make it legitimate.

Medicine is honest about something uncomfortable. Treatments have side effects. They do not affect everyone equally. Approval assumes some negative outcomes are acceptable in exchange for a greater good. But medicine only earns the right to make that utilitarian bargain because it is paired with surveillance and accountability. Trials, defined endpoints, adverse event reporting, label changes, and sometimes recalls. When a drug underperforms or causes unacceptable harm, the system has mechanisms to withdraw it.

Schooling borrows the utilitarian posture and skips the legitimacy conditions. There is no adverse event tracking for predictable harms like anxiety spirals, learned helplessness, disengagement, or the systematic grinding down of nonstandard profiles. When you ask what the rollback criteria are, you get a blank stare, because the system does not think in rollback terms. It thinks in throughput terms.

Here is a small, concrete example. One of my children has an accommodation plan tied to a documented set of specific needs. A teacher recently told us the plan would not be needed anymore because the child does not show ADHD signs. There is no ADHD diagnosis, and the plan is not based on ADHD. The teacher was not acting maliciously. They were acting normally inside a system that treats supports as vibes. In a system with real measurement, you do not withdraw support based on a vibe. You tie withdrawal to documented criteria, with a rollback plan if the criteria are wrong. This is not exotic engineering. It is basic change management. Define the hypothesis, define success, define failure, and pre-commit to the revert.

Schooling routinely does the opposite, and the response when things go sideways is not to revisit the decision. It is to escalate.

More pressure. More compliance. More labeling. The system treats opt-out as a containment breach rather than a performance signal, because enrollment and funding are coupled together. The institution has no incentive to register failure. It has strong incentives to frame failure as the students’.

So why does this cycle finally have a credible exit?

Because AI breaks the monopoly on instruction.

For most of modern history, if you wanted a coherent explanation, feedback loops, sequenced practice, and the ability to revisit a concept from a different angle without embarrassment, you needed the institution or you needed money. Those are the same thing for most families. AI makes those pieces abundant. It makes it cheaper to learn in a different order. It makes it cheaper to revisit a concept from five angles without being punished for needing a sixth. It reduces the penalty for variance in a way that nothing else in the past century has.

This is why models like Alpha School are worth watching, whatever you think of their specific implementation. They are proof that you can architect learning around mastery and coaching rather than batching and seat time. They are not just a new school brand. They are evidence that instruction is no longer scarce, and that the existing system’s grip on the delivery layer is loosening.

The tradeoff is real and worth being honest about. The devil you know versus the one you do not.

The existing system’s harms are normalized, which means they are mostly invisible. The new world introduces different risks. Dependency on opaque tools, misinformation at scale, AI-driven learning environments that are even more coercive than human ones because they optimize metrics nobody agreed to, and a widening gap between families who can navigate the options and those who cannot.

The credential layer will be the next fight. Institutions that lose control of instruction will shift to defending legitimacy. Seat time requirements, accreditation barriers, and the bureaucratic right to define what counts for the purposes of the next gate. If instruction becomes abundant, the last monopoly is not learning. It is recognition.

But the direction of travel is hard to reverse. Bureaucracy protects the status quo long past the point where the quo has lost its status. AI accelerates the expiration date. The more schooling responds to exits with escalation rather than adaptation, the more it will be outcompeted by systems that treat variance as signal rather than a defect.

I keep coming back to the medicine analogy, but with a sharper edge. In medicine, adverse events are data. In schooling, adverse events become discipline referrals and bad grades. One system updates on failure. The other system records the failure as the student.

AI is not a magic cure. But it is the first credible exit from a century-old loop. A factory that mistakes difference for defect, and calls the casualties the cost of scale.

The Housing Affordability Crisis

Recently, I was talking to one of my kids, now in university, about why housing feels so out of reach here in Washington. He asked the simple question so many young people are asking: Why is it so expensive to just have a place to live?

There’s no single answer, but there is a clear outlier, especially in big cities, that drives up costs far more than most people realize: bureaucracy.

How broken is the math? Policymakers are now seriously debating 50-year mortgages just to make homeownership work. A 50-year loan lowers the monthly payment, but it also means you never build real equity. You spend most of your adult life paying interest and end up owing almost as much as you started with. You cannot use it to move up because you never escape the debt. It is not a bridge to ownership. It is a treadmill.

And the reason we need it is not interest rates or construction costs. It is the cost of permission.

The Price of Permission

According to the National Association of Home Builders, about 24 percent of the price of a new home in America is regulation: permits, zoning, fees, and delays.

In Washington, the burden is closer to 30 percent.

At Seattle’s median home price of about $853,000, roughly $250,000 is regulation: permits, fees, and delays. If Seattle carried Houston’s regulatory burden, that same house would cost much closer to $600,000.

The difference is not labor or lumber. It is paperwork. It is the cost of waiting, of hearings, of permission.

King County then takes about one percent a year in property taxes, around $8,400 annually, for the privilege of keeping what you already paid for. Combined, bureaucracy and taxes explain almost a third of the cost of shelter in one of America’s most expensive cities.

The Hidden Cost of Bureaucracy

The public conversation stops at the sticker price. It should not.

That regulatory cost does not disappear once you close. It gets financed.

If you borrow $250,000 in regulatory overhead at 6.5 percent, here is what that bureaucracy really costs:

Loan TermRegulatory PrincipalInterest PaidTotal Cost
30 years$250,000$307,000$557,000
50 years$250,000$564,000$814,000

A quarter-million dollars of regulation quietly becomes more than $800,000 over the life of a 50-year loan.

Bureaucracy does not just raise prices. It compounds them.

The System That Made Housing Expensive

Every rule had a reason. Fire safety. Drainage. Noise. Aesthetic harmony. Each one made sense on its own. Together they have made it almost impossible to build.

Seattle’s design review process can take years. The Growth Management Act limits where anything can go. In parts of Woodinville, just minutes from Seattle, zoning is RA-5: one home per five acres. The same land under typical urban zoning could hold forty homes. Under Seattle’s new fourplex rules, one hundred sixty units. The scarcity is not geographic. It is legal.

Fees pile up. Permits expire mid-project. Every safeguard adds cost and delay until affordability becomes a memory.

If you want to see the endgame of this logic, look at the California coast.

After the fires that swept through the Santa Monica Mountains and Malibu last year, more than four hundred homes were lost.

By early 2025, fewer than fifty rebuilding permits had been issued, and barely a dozen homes had been completed.

Each application moves through overlapping city, county, and coastal reviews that can take years even for an identical replacement on the same lot.

In Texas, the same house could be rebuilt in less than a year.

Here, the process outlived the purpose.

Rules written to preserve the landscape now keep people from returning to it.

The result is a coastline where the danger has passed, but the displacement never ends.

We built a system that rewards control instead of results. The outcome is exactly what the incentives predict: scarcity.

The Multi-Family Trap

Try to build multi-family housing and you will see how the system works in practice. In much of Seattle it is still illegal. Where it is technically allowed, the odds are against you.

You buy land. You design a project. You spend years and millions navigating variances, hearings, and neighborhood appeals. You pay lawyers, consultants, and taxes while you wait. And at the end, the city might still say no.

You are left holding land you cannot use and a balance sheet you cannot fix.

Seattle’s “One Home” four-unit reform was meant to solve this. It helps on paper. In practice, the same bureaucracy decides what counts as acceptable housing, and the same delays make it unaffordable to build. We did not fix the problem. We moved it.

This is where incentives collapse. If a small developer looks at that risk and realizes they might spend years fighting the city and still lose, they walk away. They put the money in the stock market instead. It is liquid, predictable, and far less likely to end with a worthless lot.

When housing policy makes real investment riskier than speculation, capital leaves. When capital leaves, supply dies.

The Death of the Small Home

It used to be possible to build small. Starter homes, bungalows, cottages. The foundation of the middle class. They are gone.

Codes now set minimum lot sizes, minimum square footage, and minimum parking. Each rule pushes builders toward large, expensive projects that can survive the regulatory drag. The system punishes simplicity.

Seattle’s accessory dwelling unit and backyard cottage rules are small steps in the right direction. They make small building legal again, but not easy. Permitting still takes months, and costs that once seemed modest are now out of reach.

Some assume builders would choose large homes anyway. The math says otherwise. A builder who can sell ten $400,000 homes makes more than one who sells three $900,000 homes on the same land and moves the capital faster. Builders follow returns, not square footage. They build large because the regulatory drag makes small uneconomical, not because they prefer it.

The result is predictable. Modest housing disappears. “Affordable” becomes a campaign word instead of a floorplan.

The Tax You Can Never Escape

Even if you beat the system and buy a home, the meter never stops.

Property taxes rise every year, often faster than wages. The rate barely changes, but assessed values jump. A $600,000 house becomes an $850,000 house on paper, and the tax bill rises with it.

Those assessments are often based on bad data.

Valuations can rise even when real prices fall. The appeal process is slow and opaque. Few succeed. My own home’s assessed value rose 28 percent last year while Zillow’s and Redfin’s estimates fell 10 percent over the prior year. As a result, the county now values it substantially above what the market says it’s worth. I appealed. No response.

For many families, especially retirees on fixed incomes, it means selling just to survive. People move not because they want to, but because the tax bill leaves them no choice.

In places like Seattle, it does not end there. When you finally sell, you face a city-level real-estate excise tax on top of the state’s version. The government takes a cut of the same inflated value it helped create.

The overhead gets buried in the mortgage, compounded by interest, and slowly eats whatever equity a family might have built. By the time you sell, the city takes another cut. The cycle repeats. Ownership becomes a lease under another name.

The Rent Illusion

Renters often think they are immune to this.

They are not.

That same regulatory overhead that a buyer finances into a mortgage gets built into the developer’s cost structure before the first tenant ever moves in.

If a building costs 30 percent more to complete, the rent must be 30 percent higher just to service the debt.

Developers confirm it in their pro formas. Roughly 25 to 35 percent of monthly rent in new Seattle buildings reflects regulatory costs: fees, permitting delays, compliance financing, and required “affordable” offsets that increase the baseline cost for everyone else.

For a $2,800 two-bedroom apartment, that is $700 to $980 every month paid for process. Over a ten-year tenancy, a renter pays between $84,000 and $118,000 in hidden bureaucracy, enough for a down payment on the very home they cannot afford to buy.

Because rents are based on the cost to build, not the cost to live, the renter never builds equity and never escapes the cycle.

The result is two generations trapped in the same system: owners financing bureaucracy with debt, and renters financing it with rent.

The only real difference is who holds the paperwork. One signs a mortgage, the other a lease, but both are paying interest on the same bureaucracy.

It Does Not Have to Be This Way

Other places made different choices.

Houston has no conventional zoning. It enforces safety codes but lets supply meet demand. Builders build. Prices stay roughly twenty to thirty percent lower than in cities with the same population and heavier regulation, according to the Turner & Townsend International Construction Market Survey 2024.

Japan and New Zealand show that efficiency does not require deregulation. In Japan, national safety codes replace local vetoes and permits clear in weeks, not years, keeping the regulatory share near ten percent of cost. New Zealand’s 2020 zoning reforms shortened reviews and boosted new-home starts without sacrificing safety. Both prove that when policy favors results over process, affordability follows.

These places did not get lucky. They decided housing should exist.

The Collapse of Ownership

Owning a home was once the reward for hard work. It meant security, independence, a stake in the future. Now it feels like a rigged game.

The barriers are not natural. They were built.

Rules, fees, and taxes add a third to the cost of every house, yet do little to make any of it safer or better. They make it slower, harder, and more expensive.

Greed exists. But greed did not write the zoning map or the permitting code. What drives this system is something quieter and more permanent.

Every form, review, and hearing creates a job that depends on keeping the process alive. As John Kenneth Galbraith observed, bureaucracy defends its existence long past the time when the need for it has passed.

Regulation has become a jobs program, one that pays salaries in delay and collects rent from scarcity.

The toll is not abstract. It shows up in the quiet math of people’s lives.

Families sell homes they planned to retire in because the taxes outpaced their pensions.

Young couples postpone children because saving for a down payment now takes a decade.

Teachers, nurses, and service workers move hours away from the cities they serve.

Neighborhoods lose their history one family at a time.

It is not a housing market anymore. It is a sorting machine.

When my son graduates, this is the world he will walk into, a market where hard work no longer guarantees a place to live.

Ten years behind him, his sister will face the same wall, built not from scarcity but from policy.

They are inheriting a system designed to sustain itself, not them.

We could change this. We could make it easier to build, to own, to stay. We could treat shelter as something worth enabling rather than something to control.

That would mean admitting the truth.

This crisis is not the result of greed, or interest rates, or some invisible market force.

It is the outcome of decades of good intentions hardened into bad incentives.

When a system that claims to protect people starts protecting itself, everyone pays, whether they own or rent.

It was not the market that failed. It was the process.


Sources and Further Reading

Intuition Comes Last

Early in my career, I was often told some version of the same advice: stop overthinking, trust your intuition, move faster.

The advice was usually well-intentioned. It also described a cognitive sequence I don’t actually experience.

For me, intuition does not arrive first. It arrives last.

When I am confident about a decision, that confidence is not a gut feeling. It is the residue of having already explored the space. I need to understand the constraints, see how the system behaves under stress, identify where the edges are, and reconcile the tradeoffs. Only after that does something that feels like intuition appear.

If I skip that process, I don’t get faster. I’m guessing instead of deciding.

This took me a long time to understand, in part because the people giving me that advice were not wrong about their experience. What differs is not the presence of intuition, but when it becomes available.

For some people, much of the work happens early and invisibly. The intuition surfaces first; the structure that produced it is only exposed when something breaks. For others, the work happens up front and in the open. The structure is built explicitly, then compressed.

In both cases, the same work gets done. What differs is when it shows and who sees it.

This is why that advice was most common early in my career, before the outcomes produced by my process were visible to others. At that stage, the reasoning looked like delay. The caution looked like uncertainty.

Over time, that feedback largely disappeared. As experience accumulated, the patterns I had built explicitly began to compress and transfer. I could recognize familiar structures across different domains and apply what I had learned without rebuilding everything from scratch.

That accumulation allowed me to move faster and produce answers that looked like immediate intuition. From the outside, it appeared indistinguishable from how others described their own experience. Internally, nothing had changed—the intuition was still downstream of the work. The work had simply become fast enough to disappear.

This is where people mistake convergence of outcomes for convergence of process.

This is where large language models change something real for people who process the way I do.

Large language models do not remove the need for exploration. They remove the time penalty for doing it explicitly.

The reasoning is still mine. The tool accelerates the exploration, not the judgment.

They make it possible to traverse unfamiliar terrain, test assumptions, surface counterexamples, and build a working model fast enough that the intuition arrives before impatience sets in. The process is unchanged. What changes is the latency.

This is why the tool does not feel like a shortcut. It doesn’t ask me to act without coherence. It allows coherence to form quickly enough to meet the pace others already assume.

For the first time, people who reason this way can move at a pace that looks like decisiveness without abandoning how their judgment actually forms.

For some people, intuition is a starting point.
For others, it is an output.

Confusing the two leads us to give bad advice, misread rigor as hesitation, and filter out capable minds before their judgment has had time to become visible.

AI doesn’t change how intuition works.
It changes how long it takes to earn it.

And for people who process this way, that difference finally matters.

The Vanishing On-Ramp

This past week I spent more concentrated time with the newest generation of AI models than I have in months. What struck me was not just that they are better, but where they are better. They now handle routine engineering tasks with a competence that would have seemed impossible a year ago. The more I watched them work, the more obvious it became that the tasks they excel at are the same tasks that used to form the on-ramp for new engineers. This is the visible surface layer of software development, the part above the waterline in MIT’s Iceberg Index.

What these systems still cannot reach is everything beneath that waterline. That submerged world contains the tacit knowledge, constraint navigation, history, intention, and human forces that quietly shape every real system. It holds the scars and the institutional memory that never appear in documentation but govern how things actually work.

Developers have always been described mostly by skills. You could point to languages, frameworks, and tools and build an easy mental model of who someone was. These signals were simple to compare, which is why the industry relied on them. But skills alone do not explain why certain developers become the ones the entire organization depends on. The difference has always been context.

What the models can and cannot do

The models thrive in environments that are routine, self-contained, and free of history. They write small functions. They assemble glue code. They clean up configuration. They do the kind of work that once filled the first two years of an engineering career. In this territory they operate like a competent junior developer with perfect memory.

The challenges begin where real systems live. The deeper you go, the more you find decision spaces shaped by old outages, partial migrations, forgotten constraints, shifting incentives, and compromises that were never recorded. Production systems contain interactions and path dependencies that have evolved for years. These patterns are not present in training data. They exist only in the experiences of the people who worked in the system long enough to understand it.

There is also a human operating layer that quietly directs everything. Customers influence it. Compliance obligations shape it. Old political negotiations echo through it. Even incidents from years ago leave marks in code and behavior that no documentation captures. None of this is visible to a model.

The vanishing on-ramp

As AI absorbs more of the low-context work, the early career pathway narrows. New engineers still need time inside real systems to build judgment, but the tasks that once provided this exposure are being completed before a human ever sees them. The set of small, safe tasks that helped beginners form a mental map of how systems behave is slowly disappearing.

This creates a subtle but significant problem. AI takes on the easy work. Humans are asked to handle the hard work. Yet new humans have fewer opportunities to learn the hard work, because the simple tasks that once served as scaffolding are no longer available. The distance from beginner to meaningful contributor grows longer just as the ladder is being pulled up.

AI can help with simulated practice. A motivated learner can now ask a model to recreate plausible outages, messy migrations, ambiguous requirements, or conflicting constraints. These simulations resemble real scenarios closely enough to be useful. For people with curiosity and drive, this is a powerful supplement to traditional experience.

But a simulation is not the same as lived exposure. It does not restore the proving ground. It does not give someone the slow accumulation of judgment that comes from touching a system over time. The skill curve can accelerate, yet the opportunities to prove mastery shrink. We will need more developers, not fewer, but the pathway into the profession is becoming more difficult to follow.

What remains human

As skills become easier to acquire and easier to automate, the importance of context grows. Contextual judgment allows someone to understand why an architecture looks the way it does, how decisions ripple through a system, where the hidden dependencies live, and how history explains the odd behaviors that would otherwise be dismissed as bugs. These insights develop slowly. They come from exposure to the real thing.

There is also a form of entrepreneurial capability that stands out among strong engineers. It is the ability to make decisions that span technical concerns, organizational dynamics, customer needs, and long-term consequences, often without complete information. It is the ability to reason across constraints and understand how tradeoffs echo through time. This capability is uniquely high-context and uniquely human.

At the more granular level, some work is inherently easier to automate. Common patterns with clear boundaries are natural territory for models. Rare or historically shaped tasks are not. Anything requiring whole-system awareness remains stubbornly human. This aligns with predictions from economic and AI research: visible tasks are automated first, while invisible tasks persist.

The vanishing on-ramp sits directly at this intersection. AI is consuming the visible work while the invisible work becomes more important and harder for new engineers to access.

What we must build next

If the future is going to function, we need new mechanisms for developing context. That may mean rethinking apprenticeships, creating ways for beginners to interact with real systems earlier, or designing workflows that preserve learning opportunities rather than eliminating them. Senior engineers will not only need to solve difficult problems but will also need to create the conditions for others to eventually do the same.

AI is changing the shape of engineering. It is not eliminating developers, but it is transforming how people become developers. It removes the visible tasks and leaves behind the invisible ones. The work that remains is the work that depends on context, judgment, and the slow accumulation of lived understanding.

Those qualities have always been the real source of engineering wisdom. The difference now is that we can no longer pretend otherwise.

This shift requires us to change how we evaluate talent. We can no longer define engineers by the visible stack they use. We must define them by the invisible context they carry.

I have been working on a framework to map this shift]. It attempts to distinguish between the skills AI can replicate today (common domains, low complexity) and the judgment it cannot (entrepreneurial capability, systems awareness).

Beyond Gutenberg: How AI Is Teaching Us to Think About Thinking

At breakfast the other day, I was thinking about those old analogy questions: “Hot is to cold as light is to ___?” My kids would roll their eyes. They feel like relics from standardized tests.

But those questions were really metacognitive exercises. You had to recognize the relationship between the first pair (opposites) and apply that pattern to find the answer (dark). You had to think about how you were thinking.

I was thinking about what changes when reasoning becomes abundant and cheap. It hit me that this skill, thinking about how you think, becomes the scarcest resource.

Learning From Nature

A few years ago, we moved near a lake. Once we moved in we noticed deer visiting an empty lot next to us that had turned into a field of wildflowers. A doe would bring her fawn and, with patient movements, teach it where to find clover, when to freeze at a scent, and where to drink. It was wordless instruction: demonstration and imitation. Watch, try, fail, try again. The air would still, the morning light just breaking over the field. Over time, that fawn grew up and brought its own young to the same spot. The cycle continued until the lot was finally developed and they stopped coming.

That made me think about how humans externalized learning in ways no other species has. The deer’s knowledge would die with her or pass only to her offspring. Humans figured out how to make knowledge persist and spread beyond direct contact and beyond a single lifetime.

We started with opposable thumbs. That physical adaptation let us manipulate tools precisely enough to mark surfaces, to write. Writing captured thought outside of memory. For the first time, an idea could outlive the person who had it. Knowledge became persistent across time and transferable without physical proximity. But writing had limits. Each copy required a scribe and hours of work, so knowledge stayed localized.

Then came printing. Gutenberg’s press changed the economics. What took months by hand took hours on a press. The cost of reproducing knowledge collapsed, and books became locally abundant. Shipping and trade moved that knowledge farther, and the internet eventually collapsed distance altogether. Local knowledge became globally accessible.

Now we have LLMs. They do not just expose knowledge. They translate it across levels of understanding. The same information can meet a five-year-old asking about photosynthesis, a graduate student studying chlorophyll, and a biochemist examining reaction pathways. Each explanation is tuned to the learner’s mental model. They also make knowledge discoverable in new ways, so you can ask questions you did not know how to ask and build bridges from what you understand to what you want to learn.

Each step in this progression unlocked something new. Each one looked dangerous at first. The fear is familiar. It repeats with every new medium.

The Pattern of Panic

Socrates worried that writing would erode memory and shallow thinking (Plato’s Phaedrus). He was partly right about trade-offs. We lost some oral tradition, but gained ideas that traveled beyond the people who thought them.

Centuries later, monks who spent lifetimes hand-copying texts saw printing as a threat. Mass production, they feared, would cheapen reading and unleash dangerous ideas. They were right about the chaos. The press spread science and superstition alike, fueled religious conflict, and disrupted authority. It took centuries to build institutions of trust: printers’ guilds, editors, publishers, peer review, and universities.

But the press did not make people stupid. It democratized access to knowledge. It expanded who could participate in learning and debate.

We hear the same fears about AI. LLMs will kill reasoning. Students will stop writing. Professionals will outsource thinking. I understand the worry. I have felt it.

History suggests something more nuanced.

AI as Our New Gutenberg

Gutenberg collapsed the cost of copying. AI collapses the cost of reasoning.

The press did not replace reading. It changed who could read and how widely ideas spread. It forced literacy at scale because there were finally enough books to warrant it.

AI does not replace thinking. It changes the economics of cognitive work the same way printing changed knowledge reproduction. Both lower barriers, expand access, and demand new norms of verification. Both spread misinformation before society learns to regulate them. The press forced literacy. AI forces metacognitive literacy: the ability to evaluate reasoning, not just consume conclusions.

We are in the messy adjustment period. We lack stable institutions around AI and settled norms about what counts as trustworthy machine-generated information. We do not yet teach universal AI fluency. The equivalents of editors and peer review for synthetic reasoning are still forming. It will take time, and we will figure it out.

What This Expansion Means

I have three kids: 30, 20, and 10. Each is entering a different world.

My 30-year-old launched before AI accelerated and built a foundation in the old knowledge economy.

My 20-year-old is in university, learning to work with these tools while developing core skills. He stands at the inflection point: old enough to have formed critical thinking without AI, young enough to fully leverage it.

My 10-year-old will not remember a time before you could converse with a machine that reasons. AI will be ambient for her. It is different, and it changes the skills she needs.

This is not just about instant answers. It is about who gets to participate in knowledge work. Traditional systems reward verbal fluency, math reasoning, quick recall, and social confidence. They undervalue spatial intuition, pattern recognition across domains, emotional insight, and systems thinking. Many brilliant minds do not fit the template.

Used well, AI can correct that imbalance. It acts as a cognitive prosthesis that extends abilities that once limited participation. Someone who struggles with structure can collaborate with a system that scaffolds it while preserving original insight. Someone with dyslexia can translate thoughts to text fluidly. Visual thinkers can generate diagrams that communicate what words cannot.

Barriers to entry drop and the diversity of participants increases. This is equity of potential, not equality of outcome.

But access without reflection is noise.

We are not producing too many answers. We are producing too few people who know how to evaluate them. The danger is not that AI makes thinking obsolete. It is that we fail to teach people to think about their thinking while using powerful tools.

When plausible explanations are cheap and fast, the premium shifts to discernment. Can you tell when something sounds right but is not? Can you evaluate the trustworthiness of a source? Can you recognize when to dig deeper versus when a surface answer suffices? Can you catch yourself when you are being intellectually lazy?

This is metacognitive literacy: awareness and regulation of your own thought process. Psychologist John Flavell first defined metacognition in the 1970s as knowing about and managing one’s own thinking, planning, monitoring, and evaluating how we learn. In the AI age, that skill becomes civic rather than academic.

The question is not whether to adopt AI. That is already happening. The question is how to adapt. How to pair acceleration with reflection so that access becomes understanding.

What I Am Doing About This

This brings me back to watching my 10-year-old think out loud and wondering what kind of world she will build with these tools.

I have been looking at how we teach gifted and twice-exceptional learners. These are kids who are intellectually advanced but may also face learning challenges like ADHD or dyslexia. Their teachers could not rely on memorization or single-path instruction. They built multimodal learning, taught metacognition explicitly, and developed evaluation skills because these kids question everything.

Those strategies are not just for gifted kids anymore. They are what all kids need when information is abundant and understanding is scarce. When AI can answer almost any factual question, value shifts to higher-order skills.

I wrote more detail here: Beyond Memorization: Preparing Kids to Thrive in a World of Endless Information

The short version: question sources rather than absorb them. Learn through multiple modes. Build something, draw how it works, explain it in your own words. Reflect on how you solved a problem, not only whether you got it right. See connections across subjects instead of treating knowledge as isolated silos. Build emotional resilience and comfort with uncertainty alongside technical skill.

We practice simple things at home. At dinner when we discuss a news article: How do we know this claim is accurate? What makes this source trustworthy? What would we need to verify it? When my 10-year-old draws, writes or builds things: I ask what worked? What did not? What will you try differently next time, and why?

It is not about protecting her from AI. That is impossible and counterproductive. It is about preparing her to work with it, question it, and shape it. To be an active participant rather than a passive consumer.

I am optimistic. This is another expansion in how humans share and build knowledge. We have been here before with writing, printing, and the internet. Each time brought anxiety and trade-offs. Each time we adapted and expanded who could participate.

This time is similar, only faster. My 20-year-old gets to help harness it. My 10-year-old grows up native to it.

They will not need to memorize facts like living libraries. They will need to judge trustworthiness, connect disparate ideas, adapt as tools change, and recognize when they are thinking clearly versus fooling themselves. These are metacognitive skills, and they are learnable.

If we teach people to think about their thinking as carefully as we once taught them to read, and if we pair acceleration with reflection, this could become the most inclusive knowledge revolution in history.

That is the work. That is why I am optimistic.


For more on this thinking: AI as the New Gutenberg

Beyond Memorization: Preparing Kids to Thrive in a World of Endless Information

What does it take to prepare our children for a tomorrow where AI shapes how they get information, robots change traditional jobs, and careers transform faster than ever—a time when what they can memorize matters far less than how quickly they can think, adapt, and create? As a parent with children aged 29, 18, and 9, I can’t help wondering how to best prepare each of them. My oldest may have already found his way, but how do I ensure my younger two can succeed in a world so different from the one their brother entered just a few years before?

We’ve faced big changes like this before—moments that completely changed how we work and what opportunities exist. A century ago, Ford’s assembly line wasn’t just about making cars faster; it changed what skills workers needed and how companies treated employees. Decades later, Japan’s quality movement showed us that constant improvement and efficient thinking could transform entire industries. Each era required us to learn not just new facts, but new ways of thinking.

Today’s change, driven by artificial intelligence and robotics, is similar. AI will handle basic knowledge tasks at scale, and robots will take care of repetitive physical work. This means humans need to focus on higher-level skills: making sense of complex situations, evaluating information critically, combining ideas creatively, and breaking down big problems into solvable pieces. Instead of memorizing facts like a living library, our children need to know how to judge if information is trustworthy and connect ideas that might not seem related at first glance. They need to see knowledge not as something you collect and keep, but as something that grows and changes through questioning, discussion, and discovery.

Where can we find a guide for developing these new thinking skills? Interestingly, one already exists in our schools: the teaching strategies developed for gifted and twice-exceptional (2e) learners—students who are intellectually gifted but may also face learning challenges.

Gifted and 2e children think and learn in ways that are often intense, complex, and different from traditional methods. Teachers who work with these learners have refined approaches that develop multimodal thinking (using different ways to learn and understand), metacognition (thinking about how we think), and critical evaluation—exactly the skills all young people need in a future filled with smart machines and endless information.

Shift from Memorization to Meaning Instead of drilling facts, encourage your child to question sources. If you’re discussing a news article at dinner, ask: “How do we know this claim is accurate? What makes the source trustworthy?” Now they’re not just absorbing information; they’re actively working to understand it.

Foster Multimodal Exploration Make learning richer by using different approaches. Let them build a simple robot kit, draw a diagram of how it works, and then explain it in their own words. By connecting hands-on activity (tactile learning), visual learning, and verbal explanation, they develop deeper understanding.

Encourage Metacognition After solving a puzzle or coding a simple project, have them reflect: “What worked best? What would you try differently next time?” By understanding their own thought processes, they become better at adapting their approach to new challenges.

Highlight Interdisciplinary Connections and Global Outlook Show them that knowledge doesn’t exist in separate boxes. A math concept might connect beautifully with a musical pattern, or a historical event might be understood better through science. Help them see that good ideas and innovation come from everywhere in the world, not just one place or tradition.

Emphasize Emotional and Social Intelligence In a world where machines handle routine tasks, human qualities like empathy, communication, and teamwork become even more important. Encourage them to be comfortable with uncertainty, to see setbacks as chances to learn, and to develop resilience (the ability to bounce back from difficulties). These people skills will matter just as much as any technical knowledge.

Deep Learning and Entrepreneurial Thinking Like classical scholars who focused deeply on fewer subjects rather than skimming many, children benefit from spending more time thinking deeply about carefully chosen topics rather than rushing through lots of surface-level information. Consider teaching basic business and problem-solving skills early—like how to budget for a project or spot problems in their community that need solving—so they learn to create opportunities rather than just wait for them.

Finally, we’re raising children in an age where AI is becoming a constant helper and resource. While information is everywhere, the ability to understand it in context and make good judgments is rare and valuable. By using teaching techniques once reserved for gifted or 2e learners—multiple ways of learning, thinking about thinking, careful evaluation, global awareness, and creative combination of ideas—we prepare all children to be confident guides of their own learning. Instead of being overwhelmed by technology, they’ll learn to work with it, shape it, and use it to build meaningful futures.

This won’t happen overnight. But just as we adapted to big changes in the past, we can evolve again. We can model skepticism, curiosity, and flexible thinking at home. In doing so, we make sure that no matter how the world changes—no matter what new tools or systems appear—our children can stand on their own, resilient, resourceful, and ready to thrive in whatever tomorrow brings.