AsianDadEnergy's Podcast

I didn’t expect to find it in my home office, finishing up a quick side project. But there it was, a realization that hit harder than any production outage I’ve ever debugged:

What used to take a startup, millions in funding, and months of work… now takes one person a few days.

And that changes everything.

The Assumption That Built an Empire

For the past 30 years, our economy has quietly revolved around a single idea:

Software is expensive.

That assumption shaped everything:

* Companies hired armies of engineers

* Venture capital poured in by the billions

* Entire industries formed around building and maintaining digital platforms

From payment systems to ad networks to logistics platforms, software wasn’t just useful, it was scarce. And scarcity created power.

That power turned a handful of tech companies into giants. It’s why so much of the market today is dominated by digital businesses. The moat was simple:

If it’s expensive and slow to build, few can compete.

AI Is Draining the Moat

Then AI showed up.

Not as true intelligence. Not as some omniscient brain.But as something far more disruptive:

A force multiplier.

Today, tools like Claude Opus allow small teams or even solo developers to:

* Build faster

* Ship cheaper

* Replicate complex systems

What once required coordination across dozens of engineers can now be done by a handful of people… sometimes just one.

The result?

Software is no longer scarce.

And when scarcity disappears, so does the moat.

The Quiet Death of the Interface

There’s a second shift happening, and it’s even more subtle.

For years, software companies made money by guiding human behavior through interfaces:

* Search boxes

* Dashboards

* Funnels

* Buttons and menus

Companies like Google and Meta built trillion-dollar ecosystems on this idea.

But AI agents don’t need interfaces.

They don’t click buttons.They don’t browse pages.They just… do the task.

That means the value isn’t in the interface anymore. It’s in the execution.

And if users stop interacting with software directly, entire business models start to unravel.

Where Does the Value Go?

If software is becoming cheap and invisible, the obvious question is:

Where does the money go?

The answer is surprisingly physical.

In the AI-driven world, the bottleneck isn’t code.It’s compute.

And compute isn’t abstract, it’s real, tangible, and expensive:

* Data centers packed with GPUs

* Advanced semiconductor manufacturing

* Massive energy infrastructure

* Global supply chains of rare materials

This is where the new moats are forming.

The New Winners

The industries positioned to win aren’t purely digital. They live in the real world:

* Data center construction and operations

* Semiconductor fabrication

* Networking infrastructure

* Energy production and management

* Materials like silicon, copper, and rare earth elements

And with them comes a different kind of workforce:

* Electricians

* Technicians

* Engineers working with physical systems

In a twist of history, the future of tech may look a lot less like code and a lot more like infrastructure.

The Ones at Risk

Not every company loses.

The largest players, those who own the infrastructure, will likely adapt.

But much of the software ecosystem built on top of expensive development?

* SaaS platforms

* Marketing tech stacks

* CRM systems

* Consumer apps

They’re facing a harsh reality:

If anyone can build it faster and cheaper… it’s no longer defensible.

A Shift Bigger Than Software

This isn’t an overnight collapse. It’s a slow, grinding transition.

But it’s already underway:

From digital to physicalFrom software to computeFrom scarcity to abundance

And like every major shift, it creates both risk and opportunity.

Final Thought

Standing there, finishing that small project, I realized something unsettling—and oddly exciting:

The industry I spent 25 years in is being rewritten in real time.

Not by some distant future.

But right now. Quietly. Efficiently. Inevitably.

And for those paying attention?

There are still fortunes to be made.

Just not where everyone is currently looking.

These days, in my unexpected post–Big Tech era, I spend a fair amount of time cooking for my family.

There are worse fates.

On this particular day, I was making stir-fried pork and turnips, which felt appropriate somehow: humble ingredients, a little heat, a little patience, and the faint possibility that if you look away for too long, everything burns. Which, now that I think about it, is also a decent summary of a lot of software projects.

And as I cooked, I found myself remembering one of my favorite old tech war stories, the kind that only becomes funny once enough years have passed and everyone involved has either recovered emotionally or changed LinkedIn jobs three times.

This one takes place in early 2010.

The tech industry was still dragging itself out of the wreckage of the Great Financial Crisis, with all the grace of a hungover college student swearing off alcohol forever. I had just crawled out of one disastrous project at a digital consulting agency when, as a reward for surviving it, I was promptly assigned to another.

The client was a major luxury car manufacturer. Let’s call them Clio.

Clio wanted a beautiful new brand website powered by an enterprise CMS. The sales pitch was irresistible: non-technical staff would be able to create and publish content without relying on developers. Elegant. Efficient. Empowering. In theory.

In practice, nobody on Clio’s internal IT team knew how to build a CMS-driven website at that scale. That gap, naturally, became our opportunity. Our agency sold them not just a delivery team, but also the comforting illusion that we would build it all together, shoulder to shoulder, on a schedule so aggressive it bordered on performance art.

And this was no ordinary website.

This was luxury automotive theater.

Thousands of pages dedicated to convincing visitors that spending six figures on a two-ton block of steel, leather, and ambition would elevate them into some higher, sleeker form of human existence. You weren’t buying transportation. You were buying aura.

At the center of it all was the vehicle configurator: the crown jewel of the experience. Prospective customers could choose paint, trim, interior finishes, accessories, and all the little details required to transform “car” into “my car.” That configuration data then had to be combined with inventory information and routed downstream to whichever dealership the customer selected, essentially generating a quote request wrapped in logistical reality.

In other words, it wasn’t enough to build something pretty. It had to function across a fractured dealership ecosystem where every regional network behaved like its own independent kingdom. Clio’s corporate IT had no consolidated enterprise middleware layer, so every dealership group had its own way of handling quotes and inventory: some used SOAP APIs, some used plain old XML, and a few still clung to late-90s CORBA integrations like they were family heirlooms.

Our site had to talk to all of them.

That should have been warning enough.

The Team

I joined as backend tech lead and quickly surveyed the battlefield.

There was Tyrone, a senior backend developer. Brian, a junior backend developer. Sudarshan, an internal Clio backend developer, exhausted and hollow-eyed, with a three-month-old baby at home and the unmistakable expression of a man running on caffeine, responsibility, and dread. On the frontend side there was Andre, the lead, sharp, capable, and blessed with a gift for devastating sarcasm — along with Sarah, a junior frontend developer, and Jiten, Clio’s internal frontend developer. Our project manager, Jake, was relentlessly cheerful, which in retrospect may have been its own kind of survival mechanism.

Above me sat Nigel, an enterprise architect with a thick working-class English accent and an admirably low tolerance for nonsense. Nigel reported to Alan, our technical director. Alan reported up to Haley, the account executive who managed the client relationship with an almost supernatural level of finesse.

I liked Nigel immediately. He was one of those rare architects who actually wrote code, especially the difficult parts he designed himself. He gave blunt feedback, helped quickly, and somehow made every crisis sound both more severe and more manageable by narrating it in an accent that made the whole project feel like a Dickensian factory.

“Right, lads, this is rubbish.”

Music to my ears.

Then there was Alan.

Alan may have been the strongest pure engineer I have ever worked with. He was deeply fluent across frontend and backend systems, moved through technical problems with terrifying confidence, and showed up to work in beach shorts while drinking wine throughout the day. He was brilliant, hilarious, chaotic, and occasionally shocking in a way that would probably trigger at least three HR workflows in a modern corporate environment.

At one point, while debugging a CORBA object hydration issue, he also found time to explain the logistics of juggling two girlfriends.

A true systems thinker.

Haley, meanwhile, was not technical, at least not in the conventional sense, but she possessed something arguably more powerful: an instinctive grasp of hierarchy, psychology, and influence. Watching her navigate conversations with client executives was like watching someone perform diplomatic sorcery. She could sense weak points in organizations the way experienced engineers sense flaky infrastructure. She always knew where the real power sat, where the insecurities were, and how to keep our agency embedded in the account.

I respected both Haley and Alan enormously.

They did not much care for each other.

Which, naturally, added a little extra spice to the atmosphere.

The Architecture from Hell

Then I learned what we were actually building.

Clio had already purchased an enterprise CMS, let’s call it Portrait, before fully defining the project requirements. This is the kind of sentence that, if you’ve worked in enterprise tech long enough, causes your soul to quietly leave your body.

Portrait, as it turned out, could only render web pages that looked like they had survived the 1990s by hiding in a filing cabinet. So instead of using the CMS to actually present finished web pages, content authors would enter data into Portrait’s grim administrative interface, essentially a joyless gray field factory and that content would then be published as massive XML feeds into a custom Spring MVC application we were building.

Our application would parse the XML, transform it, and then generate the sleek modern website the client actually wanted.

Which meant the CMS was functioning less as a publishing platform and more as a very expensive content mule.

In retrospect, it was one of those architectures that technically works while spiritually insulting everyone involved.

And then, on top of that already strange setup, we also had the vehicle configurator integrations to build.

Naturally, we got to work.

The Death March Begins

The project escalated quickly.

Within a matter of weeks, the vibe shifted from “challenging but manageable” to “everyone quietly lives here now.” We started doing long days. Then longer days. Then 12-hour weekdays. Then weekends. This was peak waterfall-era enterprise delivery, where every phase came attached to immovable dates and the preferred conflict resolution strategy was denial.

The code completion deadline approached like a freight train.

Then, about two weeks before it hit, the team began to fracture.

By 2010, the tech job market was finally starting to recover. Developers had options again. Unfortunately, our company was still managing people as though it were 2009 and everyone should be grateful for fluorescent lighting and a paycheck.

So one by one, people started leaving.

First Tyrone landed a higher-paying role and exited. Then Jiten left Clio for something better. Nigel, our architect, departed for a new opportunity. Jake, our project manager, left the project just three days before the delivery deadline.

And then, on the final day, Sarah came in visibly upset and told us she had learned she was being paid significantly less than other junior developers. She said she wasn’t in the mental state to work and went home.

At that point, our team had been reduced to Alan, Andre, Brian, Sudarshan, and me.

We were supposed to be code complete the next day.

We were not remotely code complete.

We needed at least another week.

Instead, we had one night.

Wings, Wine, and Collapse

So we did what doomed software teams have done since time immemorial: we convinced ourselves that force of will could substitute for time.

We coded like maniacs.

Code quality was no longer a meaningful concept. We were past elegance, past maintainability, past architecture. We were in the realm of tactical survival. The goal was not to build the right thing well. The goal was to get enough of the thing built that it could survive executive scrutiny for one meeting.

That evening, Alan ordered a huge tray of wings and brought in several large bottles of wine.

Nothing says “mission critical delivery” like poultry grease and alcohol.

We ate, we drank, we coded.

Sometime around 1 a.m., Sudarshan got a call from his wife. Their baby had a fever. He looked crushed and said he had to go home.

Brian, our junior engineer, snapped.

“How can you leave us hanging like this, man? It ain’t fair.”

He was furious, truly furious, red-faced, exhausted, unraveling. For a brief moment it looked like the night might end in an actual fistfight between two backend developers in an enterprise web project, which would have been the most honest possible metaphor for the whole experience.

I stepped between them, told Brian to calm down, and told Sudarshan to go home to his wife and child.

He left.

We kept coding.

At some point, Brian either wandered off or passed out. I honestly don’t remember. He simply ceased to be part of the system.

By 5 a.m., I hit my limit.

My head was pounding. My eyes could no longer focus on the lines of code in Eclipse. Alan and Andre were still at it, debugging the vehicle configurator with the stoic stamina of men either blessed by the gods or medically inadvisable.

I stumbled out of the war room and into a nearby meeting room called The Studio, where a futon couch sat waiting like an unwise but irresistible life choice.

I collapsed onto it and blacked out instantly.

The Intern Make-Out Couch Incident

I woke up to a strong musky smell and the sound of someone repeatedly saying my name.

I opened my eyes.

In my exhaustion, I had apparently drooled all over the futon. Standing over me was Haley, staring with a look of detached curiosity that made me immediately suspect this was going to be bad.

“What’s wrong?” I asked.

She said, “Oh, nothing. Just so you know, you were sleeping on the summer intern’s make-out couch.”

I have never gotten off a couch faster in my life.

I rushed to the bathroom, splashed water on my face, and stared into the mirror with the thousand-yard look of a man who had just realized the deadline had passed, the software was unfinished, and history might remember him primarily as a guy who desecrated a romance-adjacent piece of office furniture.

I was convinced we were cooked.

I thought I had failed the team. Failed the project. Possibly failed modern enterprise civilization.

And then Haley walked into the client demo.

The Demo

What happened next remains one of the great masterclasses in corporate storytelling I have ever witnessed.

Haley reviewed the state of the application, absorbed the reality in front of her, and then went into the executive presentation like a magician entering the final round of a boss fight.

She reframed everything.

The important parts were done.

The missing pieces were minor defects.

The project was succeeding.

And somehow, somehow, the client executives believed it.

Not only did they believe it, they were delighted. They praised the work. They felt good. They felt momentum. They saw progress, vision, and control where, only hours earlier, I had seen fatigue, technical debt, and a tragic quantity of drool.

That day taught me two things.

First: never underestimate the power of storytelling.

And second: never, ever sleep on the intern make-out couch.

Some mistakes stay with you.

Some, presumably, stay with the couch.

Why This Story Still Matters

I think about that project a lot now, especially as someone on the other side of a long career in tech.

Not because it was healthy. It wasn’t.

Not because it represented good management. It absolutely did not.

And not because I’m nostalgic for death marches, busted architectures, or enterprise middleware held together by stress and XML.

I remember it because it captured something timeless about this industry: behind every glossy launch, every triumphant executive presentation, every polished brand experience, there is often a small group of very tired human beings holding the whole thing together with grit, improvisation, and snacks.

Sometimes the systems are broken.

Sometimes the schedule is fantasy.

Sometimes the architecture is absurd.

Sometimes the people who save the project are not the ones writing the code, but the ones who know how to shape the narrative when reality shows up underdressed.

And sometimes, in the middle of all that chaos, you still get a story worth telling years later while stir-frying pork and turnips for your family.

Honestly? That’s not nothing.

The other day, I was standing at a gas pump, watching the numbers climb beside my aging Honda CR-V, when something felt… off.

Gas prices had jumped nearly 40% in a single week.

That kind of movement doesn’t happen in a vacuum. It’s not random. It’s not seasonal. It’s a signal.

And right now, that signal is pointing straight at a rapidly escalating conflict in the Persian Gulf.

War as a Terrible Investment

War is often discussed in terms of strategy, power, or necessity. But strip all that away, and it reveals itself as something far more primitive:

A high-stakes gamble with potentially unlimited downside.

Unlike building a business or writing software, where failure is contained, war has no natural ceiling on loss. It consumes capital, infrastructure, and human lives at a scale that defies rational calculation.

If you’re going to take on that kind of risk, the expected return had better be extraordinary.

And yet, in this case, the objectives feel… unclear.

Regime change? Weapons containment? Strategic deterrence? The goalposts seem to shift by the day. And when the purpose of a war is ambiguous, the cost becomes even harder to justify.

It begins to resemble something less like strategy and more like compulsion.

The Illusion of Overwhelming Power

On paper, the United States is vastly more powerful than Iran: militarily, technologically, economically.

But power, in modern conflict, is rarely symmetrical.

Iran’s advantages are not about matching strength, they’re about exploiting imbalance.

Geography, for one, is destiny. A vast, mountainous nation bordered by critical waterways is inherently difficult to invade and remarkably well-positioned to disrupt.

Then there’s the Strait of Hormuz, a narrow chokepoint through which a significant portion of the world’s oil, fertilizer, and trade flows. When that artery tightens, the entire global system feels it.

And then there’s the economics of warfare itself.

Cheap, mass-produced drones, costing tens of thousands of dollars, can damage or destroy assets worth millions or even billions. It’s not just warfare. It’s arbitrage.

When the cost of defense exceeds the cost of attack by orders of magnitude, the math stops working in your favor.

Information and Will

Technology has also reshaped the battlefield in another critical way: information.

Precise, real-time intelligence allows smaller actors to strike with disproportionate impact. A single well-timed attack can neutralize assets that took years to build.

But beyond geography and technology, there is one factor that consistently outweighs all others:

The will to fight.

History has shown, time and again, that when a population feels existentially threatened, when loss becomes personal, immediate, and irreversible, their resolve hardens.

And once that happens, wars stop being about objectives.

They become about endurance.

The Economic Fault Lines Beneath the Surface

What makes this conflict particularly unsettling is not just the military dimension, it’s the economic fragility beneath it.

Right now, much of the momentum in the U.S. economy is tied to a single narrative: the rise of artificial intelligence.

Massive investments in data centers, chips, and infrastructure are propping up growth. Remove that, and the underlying picture becomes far less stable.

This conflict threatens two critical pillars supporting that system:

1. Investment FlowsOil-exporting nations in the Persian Gulf have historically recycled their revenues into U.S. assets especially tech. If those revenues are disrupted, so too is that flow of capital.

2. Semiconductor Supply ChainsThe advanced chips powering AI are largely produced in East Asia, a region heavily dependent on energy flowing through the Persian Gulf. Disrupt that energy, and you risk disrupting the entire supply chain.

The result?

A localized conflict with the potential to trigger global economic consequences.

The Shape of Unintended Consequences

Wars rarely unfold according to plan. They ripple outward, touching systems far removed from the original battlefield.

Energy markets. Financial systems. Supply chains. Political stability.

What begins as a regional conflict can, under the right conditions, become something much larger, something systemic.

That’s the unsettling part.

Not just the war itself, but the second-order effects. The quiet, compounding consequences that don’t make headlines until they’re impossible to ignore.

Hoping for the Best, Preparing for the Worst

Standing at that gas pump, watching prices surge, it was hard not to feel that something bigger was shifting beneath the surface.

Maybe this resolves quickly. Maybe cooler heads prevail.

But maybe it doesn’t.

And if it doesn’t, the implications won’t be confined to a distant region or a single sector of the economy.

They’ll show up in everyday places like the price you pay to fill up your car.

Sometimes, the earliest signs of global change are also the most ordinary.

You just have to know where to look.

Hello world.

Not long ago, I was a senior software engineer in Big Tech, part of an industry that once felt like the unstoppable engine of the modern economy. For more than 25 years, my days were structured around product launches, sprint cycles, code reviews, and the comforting illusion that there would always be another project waiting on Monday morning.

Then came an unexpected change in employment status.

These days I describe myself, with only mild irony, as an involuntary early retiree.

And like many people who suddenly find themselves outside the machinery of corporate life, I’ve been experimenting with a new challenge: how to structure a day that feels productive, meaningful, and only slightly like I’m turning into a house cat.

The Morning Ritual

My day begins early, as all respectable adults’ days do with a heroic march into the bathroom followed by fifteen minutes of doom scrolling on the toilet.

Markets. Layoffs. Wars. The usual morning news.

Eventually my legs fall asleep, which is nature’s way of telling me it’s time to brush my teeth and attempt to resemble a functioning member of society.

Next comes coffee.

Not just drinking coffee, staring deeply into coffee, waiting for the caffeine to reinstall my operating system.

At some point, Leonard the cat appears demanding emotional support. His sister remains asleep somewhere in the house, contributing absolutely nothing to household productivity which, if we’re being honest, probably makes her the most successful member of the household right now.

Exercise and Existential Sunrises

After coffee comes thirty minutes of exercise: treadmill, weights, and the vague sense that unemployment is supposed to be the moment when one finally gets into the best shape of one’s life.

Out the window, the sun rises.

It’s beautiful, peaceful even.

There’s something oddly comforting about a sunrise when you’re unemployed. It’s a quiet reminder that the universe is vast, ancient, and largely indifferent to your LinkedIn profile.

The Morning Chaos

Soon the house transitions into its daily brief but intense period of chaos.

Kids running everywhere.My wife waking up.Cats weaving between legs like tiny furry assassins.

But eventually we achieve the objective: children successfully delivered to school.

With the house temporarily calm again, my wife and I sit down for breakfast while listening to the news, carefully calibrated information designed to keep us properly aligned with reality.

The Professional Part of Unemployment

Once nourished, I head to the computer to begin the day’s “work.”

First, editing short videos for my vlog.

Then writing essays for my newsletter where I attempt to sound wise about the tech industry despite having roughly the same information as everyone else.

Next comes my daily Chinese reading practice. My goal is modest but ambitious: eventually reaching the literacy level of a motivated middle school student.

After that, I respond to YouTube comments, which is important because the internet must always be answered.

Lunch, Stir-Fried

Around midday, I begin cooking.

Today’s menu: pork and turnip stir-fry.

Because nothing says “retired tech worker” quite like aggressively stir-frying pork at 11:45 in the morning.

Lunch with my wife is honestly one of the highlights of the day: simple, quiet, and surprisingly satisfying.

Afterward comes Coffee Number Two, which is critical for preventing my transformation into a fully non-functional daytime zombie.

The Afternoon: Building Something

In the afternoon I usually get a couple of hours of focused time working on a mysterious and disruptive side project.

In Silicon Valley language, this means:

I’m building something that might make money… eventually.

Some days I also do coaching sessions, which I genuinely enjoy. It’s nice talking to real humans instead of APIs, code repositories, and comment threads.

Evening: The Second Shift

Soon enough, it’s time to pick up the kids.

Dinner is chaotic again, but everyone is eating the food I cooked, which counts as a major victory.

Later I help my son with homework, which mostly involves pretending I still remember algebra from the 1990s.

Eventually homework is done, and I tuck my daughter into bed.

These quiet moments, the ones that used to happen while I was still answering Slack messages now feel like unexpected gifts.

The Sacred Night Ritual

Finally, the day ends with the sacred evening ritual:

Consuming large amounts of completely mindless content on the internet.

Strictly for research purposes, of course.

And then, sleep.

The Strange Gift of Time

So that’s a typical day in my life.

And honestly?

Funemployment isn’t so bad.

It’s slower. Less structured. Occasionally existential.

But it also contains small moments that used to be invisible, sunrises, lunches with my wife, cooking in the middle of the day, helping with homework, putting my daughter to bed.

In an industry obsessed with optimization, productivity, and constant growth, it’s easy to forget that life itself is not a sprint cycle.

Sometimes it’s just a long day filled with coffee, cats, stir-fry, and the quiet possibility that something new might eventually emerge from all that free time.

And if you’re curious to follow along on this strange little journey through tech, layoffs, side projects, and the occasional existential sunrise, you’re always welcome to join me.

Hello world.

I’m an unemployed former Big Tech software engineer with 25 years in the technology industry. These days I joke that I’m in “involuntary early retirement.”

One unexpected side effect of that situation is time, a lot more time with my kids.

Recently, that time turned into a long and surprisingly heavy conversation with my teenage son.

It started casually.It ended with a question that kept me awake most of the night.

My son and his friends believe the future is going to be bad no matter what they do.

So why bother trying?

My First Reaction: Annoyance

If I’m honest, my first reaction wasn’t particularly noble.

I felt irritated.

My instinct was to tell him to toughen up, to get out there and compete, to fight for opportunities, to stop whining about hypothetical problems.

In my mind I was thinking:

You’ve been given so many advantages. Why wouldn’t you take them and run?

And then something uncomfortable hit me.

I had heard this tone before.

It was my parents’.

Becoming Your Parents

Growing up, whenever I struggled with something in life, the reaction I often received was blame.

If something went wrong, it meant I had been:

* Entitled

* Ungrateful

* Lazy

* A disappointment

Every major decision I made as an adult was judged as some level of mistake.

Even now, my layoff from Big Tech, something that happened amid mass industry layoffs is seen by them as my greatest personal failure.

I love my parents.

But I’ve always believed that style of parenting is deeply counterproductive.

And in that moment I realized I was about to repeat it.

So instead of lecturing my son, I asked him a simple question.

“Why do you think the future is hopeless?”

What he told me was… remarkable.

Fear #1: There Won’t Be Enough Good Jobs

According to my son and his friends, the future job market looks bleak.

They hear constantly from older siblings, from social media, from the news that:

* AI will automate jobs

* robots will replace workers

* good careers will become scarce

The belief is that by the time they enter the workforce, the number of high-paying opportunities may collapse.

Whether this turns out to be true or not, it’s not an irrational fear.

We are entering a world where automation is accelerating.

And young people are paying attention.

Fear #2: The Cost of Living Will Crush Them

My son also believes that basic necessities will become prohibitively expensive.

Food.Rent.Transportation.Utilities.

He joked that by the time he’s an adult, buying groceries might feel like a luxury purchase.

During the conversation we both remembered the dystopian 1973 film Soylent Green.

In that movie’s vision of the 2020s, a bag of groceries costs over $200.

We laughed and then realized something uncomfortable.

In some cities today, that joke isn’t far from reality.

For many young adults earning median incomes, nearly every dollar they make goes toward simply existing.

Saving feels impossible.

Building wealth feels unrealistic.

That’s not a fantasy dystopia.That’s already happening.

Fear #3: The Monopoly Board Is Already Full

The next topic my son raised surprised me.

Assets.

He said joining adulthood today feels like joining a game of Monopoly when the game is already almost over.

Every property is owned.

Every asset is expensive.

Real estate prices have risen so much that many young people believe homeownership may never be possible for them.

Again, whether this perception is fully accurate isn’t the point.

The point is that an entire generation believes the ladder is being pulled up behind them.

And that belief shapes behavior.

Fear #4: Institutions Can’t Be Trusted

When I was a teenager in the 1990s, I had a very different view of institutions.

In my mind, the United States government was something like a real-life version of Captain America.

If a comet was about to hit Earth, surely America would lead the effort to save humanity.

The president was someone to admire.Corporate leaders were role models.

Today’s younger generation sees things very differently.

Many of them view governments, corporations, and institutional leaders as fundamentally corrupt.

The internet has exposed a tremendous amount of misconduct and hypocrisy among people in power.

Perhaps those behaviors existed before, they were simply hidden.

But today the transparency is relentless.

And it has eroded trust in a way that may be permanently destabilizing.

Fear #5: Dating Is Broken

The last topic we talked about was relationships.

According to my son, many young men feel completely invisible to young women.

There are a few forces interacting here:

* Traditional markers of success, income, status, career stability are becoming harder for young men to achieve.

* Third places, physical social environments where young people naturally meet are disappearing.

* Social media and dating apps create the perception of an endless supply of highly desirable partners.

The result?

A strange mismatch.

Many young men feel unwanted.Many young women feel dissatisfied with their options.

And both sides feel confused about why things aren’t working.

It’s a complicated social puzzle with no easy fix.

The Risk of a “Low-Desire Society”

At the end of the conversation my son asked the central question again.

“If the odds are stacked against us… why bother trying?”

That question terrified me.

Because an entire generation deciding not to try leads to something economists call a low-desire society.

We can already see examples in parts of East Asia:

* declining birth rates

* declining ambition

* declining economic dynamism

Young people stop competing.

They stop dating.

They stop having children.

They stop dreaming.

Eventually, the society begins to quietly collapse.

That’s not a future I want for my kids.

The Hard Truth

That night I barely slept.

I kept turning these ideas over in my head.

And the uncomfortable reality is this:

There may not be a clean, systemic solution to these problems anytime soon.

Fixing housing affordability, economic mobility, institutional trust, and social fragmentation would require massive changes to our entire socioeconomic system.

But while we may not be able to fix the system overnight…

We can help our children navigate it.

What Parents Can Actually Do

After thinking about it more, I came up with a few practical strategies parents can use to help their kids.

1. Provide Emotional Support

Talk to your kids.

Listen to their fears without immediately dismissing them.

Let them know it’s normal to feel overwhelmed by the world.

Resilience begins with feeling understood.

2. Teach Frugality

Consumer culture trains people to spend constantly.

Teach your kids that happiness doesn’t require endless consumption.

Learning to live well on modest resources is a powerful advantage.

3. Build Self-Sufficiency

Young adults should learn how to manage everyday life:

* budgeting

* responsibility

* planning

* managing obligations

These skills build confidence and independence.

4. Provide Strategic Financial Support

The early years of adulthood are incredibly fragile.

Helping with targeted support can make a massive difference:

* allowing them to live at home temporarily

* helping with education

* contributing to a first home down payment

The goal is not dependency.

The goal is launch velocity.

5. Model Healthy Relationships

Children learn about relationships primarily from watching their parents.

Show them what respect, kindness, patience, and empathy look like.

Demonstrate that meaningful partnerships are built on character, not just status or superficial traits.

Apps like Tinder may filter potential matches, but they cannot teach someone what a healthy relationship actually looks like.

Parents can.

A Generation That Must Not Give Up

My son’s concerns are real.

They reflect the anxieties of many young people today.

But an entire generation giving up cannot be an option.

Every generation faces its own storms.

The job of parents is not to pretend those storms don’t exist.

It’s to help our children learn how to sail through them.

And maybe, just maybe, build something better on the other side.

Many years ago, I was a mid-level developer at a consulting company. I remember staring up at the org chart and wondering how people became architects, then senior architects, then technical directors. It felt mysterious. Political. Reserved for the chosen few.

And yet, over roughly six years, I climbed that ladder:Developer → Architect → Senior Architect → Tech Director.

At the time, it felt brutal.

In hindsight, it was almost straightforward.

The uncomfortable truth? The path that worked for me may no longer be enough today.

Let me explain.

The Old Playbook

When I was a mid-level engineer, I did three things obsessively:

* I volunteered for very hard projects.

* I sought out strong mentors.

* I pushed myself into system design work.

That was it.

1. Volunteer for the Deathmarch

One of those projects was an enterprise service bus middleware component for a massive publishing company, let’s call it Big Corp.

It was a nightmare.

Months of weekend work. Endless integration problems. And on go-live night, an open-source bug nearly broke me. I remember standing in the parking lot after midnight, overwhelmed, exhausted, and crying in my car.

It was ugly.

But I shipped.

And when you survive something like that, two things happen:

* Your technical depth sharpens dramatically.

* Leadership notices.

Hard projects compress experience. They force you to grow up fast.

2. Find Mentors (Even If It Feels Awkward)

I actively sought mentorship from senior technical leaders.

At first, mentorship can feel transactional. Almost burdensome to the mentor. But over time, something shifts. If you show initiative and deliver results, many mentors start to care. They feel invested.

One of mine, let’s call him Heinz, was a seasoned tech director. He taught me small tactical things (like why knowing sed and awk gives you borderline superpowers) and big strategic things (like why system design is the real career unlock).

Mentors don’t just give advice.They give visibility.They give sponsorship.

When promotion cycles came around, some of the people advocating for me were former mentors.

That matters more than people admit.

3. Move From Coding to Designing

The biggest inflection point in my career wasn’t learning another framework.

It was system design.

System design is fundamentally different from implementing user stories. It’s about defining modules, boundaries, data flows, tradeoffs, scalability constraints, the blueprint before the building.

When you do system design:

* You sharpen architectural thinking.

* You gain cross-team visibility.

* You operate at a higher altitude.

By the time promotions came, I had:

* Credibility from delivering painful projects.

* Visibility from leading system designs.

* Advocacy from mentors invested in my growth.

That combination moved the needle. Repeatedly.

Why That Path Is Harder Now

Over the years, I’ve mentored many mid-level engineers.

And recently I’ve come to a sobering realization:

The journey I took is significantly easier than the one mid-level engineers face today.

Today’s environment looks like this:

* Saturated job markets.

* Wave after wave of layoffs.

* Leadership pressure to “do more with less.”

* AI amplifying productivity expectations.

* Constant fear of replacement.

The things that once propelled me upward: volunteering for hard work, gaining visibility, expanding scope are now table stakes just to survive.

That changes the game.

So what can a mid-career, mid-level engineer realistically do right now?

Not magical strategies. Not silver bullets.

Coping strategies.

Time-buying strategies.

Because even one or two extra years of employment can mean:

* More savings.

* More investments.

* Greater optionality.

* More leverage over your own life.

Strategy #1: Build a T-Shaped Skill Set

Specialization used to be a moat.

If you knew COBOL, banks would keep you forever.

That moat is shrinking.

AI tools can now generate competent code across obscure stacks. The defensibility of narrow expertise is eroding.

Instead, build a T-shaped profile:

* Deep expertise in one core stack (your vertical bar).

* Broad exposure to adjacent domains (your horizontal bar).

For example:

* Go full-stack instead of only front-end.

* Learn data engineering or QA.

* Understand product management.

* Gain exposure to UX and design thinking.

Small teams of versatile engineers are now building what once required multiple departments.

The disruptors are compact.

You want to be on that side of the equation.

Strategy #2: Use AI as a Force Multiplier (If You’re Mid-Level)

Here’s an interesting dynamic.

AI tools like Claude Code are not productivity-equalizers.

They disproportionately benefit experienced mid-level engineers.

Why?

* Junior engineers lack the depth to detect hallucinations, subtle bugs, security issues, or scalability flaws.

* Some senior leaders have let their hands-on skills atrophy.

Mid-level engineers who are still deeply technical, and still actively coding, are uniquely positioned to 10x their output.

This window may not last forever.

But right now? It’s leverage.

Use it.

Strategy #3: Master the Delegation Decision

AI is not a machine god.

It automates a large chunk of software work, but not all of it.

Your competitive advantage becomes knowing:

* When to fully delegate.

* When to collaborate iteratively.

* When to do it yourself.

Ask three questions:

* How long does it take me to do this manually?

* How long does it take AI?

* How likely is AI to get it right?

If AI can generate a well-documented module quickly and safely? Delegate.

If requirements are fuzzy and need iterative shaping? Collaborate.

If business context is subtle, political, or risky? Do it yourself.

Finding that boundary is a superpower.

Strategy #4: Develop Deep Domain Context

AI is strong at generalizable coding tasks.

It is weaker where domain context lives in messy, semi-analog reality:

* Tribal knowledge.

* Undocumented workflows.

* Historical business constraints.

* Political tradeoffs.

Mid-level engineers can carve out near-term defensibility by embedding themselves deeply in domain knowledge.

Become the person who understands not just the system but why it exists.

That context is not easily scraped and trained on.

Strategy #5: Learn to Tell the Story

This one hurts to admit.

In the AI era, raw information and logic are commoditizing.

For engineers, that’s a body blow.

But synthesis and storytelling remain leverage.

If you can:

* Frame tradeoffs clearly.

* Translate technical decisions into business narratives.

* Connect architecture to revenue impact.

* Turn scattered data into a coherent strategy…

You influence:

* Scope.

* Budget.

* Roadmaps.

* Headcount.

Storytelling moves resources.

Resources move careers.

The Reality

We are living through instability.

Small teams are replacing large ones.AI tools are raising expectations.Career ladders feel less predictable.

The old playbook still works, but it’s no longer sufficient on its own.

Today, mid-level engineers must:

* Be versatile.

* Be AI-literate.

* Be strategically aware.

* Be narratively strong.

It’s harder.

But not hopeless.

If you’re in the middle of your career and feeling squeezed from both sides by hungry juniors and cost-cutting leadership, you’re not imagining it.

The pressure is real.

The environment has changed.

But so have the tools at your disposal.

Use them wisely.

And buy yourself time.

Sometimes survival and compounding is the most underrated career strategy of all.

Hello world.

I’m an unemployed ex–Big Tech software engineer with 25+ years in the trenches of the software industry. I’ve ridden the dot-com bubble, offshoring waves, mobile revolutions, cloud migrations, and whatever we’re calling the post-pandemic tech reckoning.

And now? I’m watching the AI gold rush from the outside.

Depending on which headline you read this morning, artificial intelligence is either:

* An economically unsustainable hype machine

* A glorified autocomplete that can’t boost productivity

* Or the final exponential surge before Artificial General Intelligence automates every white-collar job in the next 12–18 months

So which is it?

Is AI just a “probabilistic parrot” repeating patterns from the internet? Or is something structurally different happening this time?

Let’s walk through what’s actually changed over the past five years in plain English.

Phase 1: The Giant Autocomplete

Early large language models like OpenAI’s GPT-3 were, at their core, probability machines.

You type:“Mary had a little…”

It predicts:“lamb.”

Not because it understands nursery rhymes. Not because it has memories. But because “lamb” statistically follows that phrase in its training data.

That training data? A massive scrape of publicly available internet text.

Impressive? Yes.Intelligent? Not really.

On its own, this was more a clever toy than economic earthquake.

Phase 2: Teaching the Parrot Manners

The next breakthrough was supervised fine-tuning.

Humans created tens of thousands of carefully written question-and-answer examples. The model was retrained on these, nudging it toward responses that felt helpful, structured, and safe.

This is how the first version of ChatGPT launched in 2022.

It was better. But still limited.

The bottleneck? Humans are expensive. Slow. Finite.

So AI researchers did what engineers always do when constrained by humans: they automated the humans.

Phase 3: Reinforcement Learning at Scale

Instead of having humans generate both questions and answers, the model could now generate answers while human experts simply judged them as good or bad.

Good answer? Increase the probability of similar responses.Bad answer? Penalize it.

This is reinforcement learning.

But even that eventually hits scale limits.

So researchers trained AI models to become evaluators learning from human feedback and then taking over the judging process. Now AI models could train future AI models.

And once humans are mostly out of the loop?

You can scale to absurd levels.

Millions. Billions. Trillions of generated tasks.Evaluated in weeks using massive data centers.

Still probability math.But at industrial scale.

Phase 4: Chain-of-Thought — The “Reasoning” Illusion

Then came reasoning models.

Instead of predicting a single answer to:

“If you have 8 pizza slices and eat 3, how many remain?”

The model breaks the problem into steps.

8 slices.Minus 3 eaten.Equals 5 left.

This “chain-of-thought” decomposition dramatically improves accuracy. Not because the AI understands pizza but because smaller probability jumps are easier than giant ones.

It’s still math.

Just math with better scaffolding.

Phase 5: Tools — From Talking to Doing

This is where things get serious.

Language models began calling tools.

Need the current temperature? Call a weather API.Need to write to a file? Call a document function.Need to push code to a repository? Trigger a Git action.

Models like Gemini, Claude, and modern ChatGPT versions now interact with external systems.

This transforms AI from:

Answer machine → Action machine.

And action is where jobs live.

Phase 6: Re-Act Architecture Thought, Act, Observe

Complex work isn’t one step. It’s dozens. Hundreds.

The “Reason + Act” (ReAct) pattern works like this:

* Think through a plan.

* Execute one step using a tool.

* Observe the result.

* Update context.

* Repeat until done.

This loop enables multi-step cognitive labor.

But there’s a catch: memory.

AI models have context windows, short-term memory buffers. Overload them, and they hallucinate or forget key constraints.

Enter the next hack.

Phase 7: Agentic Frameworks — The Harness Around the Brain

Agent systems wrap AI models in orchestration layers.

Think of it as:

* A planner agent

* Worker sub-agents

* A QA agent

* External databases storing long-term context

Each agent only sees what it needs.

One plans the website.Another writes code.Another tests it.

Some systems even simulate 24/7 “proactive” assistants using timed loops that periodically re-prompt the model — giving the illusion of autonomy.

Is it consciousness? No.

It’s math wrapped in for-loops.

But here’s the uncomfortable part:

It works.

Are We Near AGI?

No.

There is no understanding. No awareness. No inner life. No self.

We are not at the singularity.

But that doesn’t mean we’re safe.

The Productivity Question

Recent industry studies suggest frontier models like ChatGPT 5.2 and Claude 4.6 can sustain deep, complex cognitive work for over an hour with success rates exceeding 80%.

For context:

The average white-collar worker can maintain true deep focus for 60–90 minutes at a time.Across an entire day? Maybe 2–3 hours of real high-quality output.

If AI focus time doubles every six months, as some data suggests, we may soon see models capable of four-hour sustained cognitive blocks.

And they don’t need sleep.Or weekends.Or health insurance.

So… Will AI Replace White-Collar Jobs?

Here’s the honest answer:

Any job that consists primarily of interacting with a computer interface is at risk.

* Software engineering

* Accounting

* Legal research

* Financial analysis

* Marketing copy

* Project management

* Operations

AI models are being fine-tuned for each of these domains right now.

We are not watching a toy evolve.

We are watching a new kind of digital labor force scale geometrically.

The Paradox

AI is still “just” probability.

It doesn’t understand.

It doesn’t reason in the human sense.

And yet through scale, reinforcement learning, tool use, reasoning scaffolds, and Agentic orchestration, it can now perform long-duration cognitive work that looks remarkably similar to what many professionals do daily.

That’s the tension of this moment.

It’s not conscious.

But it’s competent.

Something Big Is Happening

As someone who’s spent 25 years in software, and who is currently unemployed, I don’t have the luxury of dismissing this as hype.

I’ve seen enough technology waves to recognize when the ground is actually shifting.

This one feels different.

Not because machines woke up.But because the scaffolding around them did.

If you’re watching this space with a mix of curiosity and existential dread, you’re not alone.

We’re all trying to figure out what comes next.

And whether we’ll be coding it…

Or competing with it.

Hello world.

After more than 25 years in the software industry, and an unexpected exit from Big Tech, I found myself in an unfamiliar situation: time. Lots of it. The kind of time engineers rarely have, the kind that invites curiosity, experimentation, and occasionally, questionable ideas.

This is the story of one such idea.

A Simple (and Slightly Dangerous) Thought Experiment

What if you could build a system that:

* Discovered profitable market niches automatically

* Generated digital wall art tailored to those niches

* Published the results directly to online marketplaces

* And quietly earned income in the background

In other words: could software design, create, and sell creative products without human involvement?

It sounded like the perfect retirement-side experiment—equal parts engineering challenge and philosophical mischief.

Designing With an AI Co-Architect

To move from idea to implementation, I worked alongside OpenAI’s ChatGPT-5, using it less like a tool and more like a collaborator.

The planning phase felt like an architectural dialogue:

* The AI proposed system designs

* I interrogated assumptions

* We refined constraints together

* Repeat… until the structure made sense

It didn’t feel like architecting. It felt like conducting an orchestra, one where every musician already knew every instrument ever invented.

At times, it was like whispering into the ear of a sleeping leviathan.

The Architecture: Automation All the Way Down

The final system ran as an automated workflow in n8n, orchestrating a sequence of Node.js tasks:

* Identify promising keywords

* Analyze successful products

* Generate new creative concepts

* Produce artwork using generative models

* Build marketing assets

* Syndicate listings through Printify

For the initial MVP, distribution targeted Etsy and Amazon.

From there, I generated a full Product Requirements Document using AI and handed implementation to a coding agent powered by Anthropic’s Claude Haiku model.

Then I did something that would have been unthinkable earlier in my career.

I went for coffee.

When I came back, most of the system was written.

The Surreal New Development Loop

My role shifted dramatically:

* Review AI-generated code

* Run integration tests

* Fix edge cases

* Deploy

No sprint planning.No backlog grooming.No weeks of incremental implementation.

By the end of Day One, the pipeline was live in my home cloud.

Naturally, I assumed I had built a fully automated passive-income machine.

Naturally, I was wrong.

Reality: The Humans Are Still Needed

The very first production run exposed the messy parts of the real world.

1. AI Image Imperfections

Roughly 1 in 15 generated images contained subtle visual defects, things only a human eye could catch. There is still no reliable automated test for “this looks weird.”

2. Intellectual Property Landmines

Some outputs drifted dangerously close to recognizable styles or copyrighted material. Text can be screened. Visual similarity? Much harder.

3. Marketplace Integration Chaos

The Print-on-Demand pipeline was far from seamless:

* Listings failed unpredictably

* Variants split into separate products

* Metadata required manual correction

Instead of passive income, I found myself babysitting automation.

After a full day of supervision, I had published about 100 products.

Automation, it turns out, still needs adult supervision.

The Moment of Existential Debugging

By Day Two, I stepped back and asked a different kind of question:

Was this even a good idea?

If perfectly automated, the system could flood marketplaces with thousands of derivative works. But that doesn’t create new value, it just redistributes attention within an existing market.

In economic terms, it wasn’t innovation.It was acceleration without direction.

And that led to one of the great luxuries of early retirement:

If you’re working on something pointless, you can just stop.

So I walked away.

The Twist: It Made Money Anyway

A month later, curiosity got the better of me. I checked the accounts.

Those ~200 listings I had published before quitting?

They had quietly generated revenue:

* About $600 on Etsy

* Around $70 on Amazon

After costs, fees, and fulfillment, the profit was roughly $200.

Not life-changing.But not zero either.

An abandoned experiment had paid for a nice dinner and perhaps a bottle of soju.

What This Experiment Really Taught Me

This project wasn’t about wall art. It was about understanding how AI is reshaping the act of building.

We are entering a phase where:

* Designing systems may matter more than coding them

* Iteration happens conversationally, not procedurally

* Engineers supervise intelligence rather than implement logic

* The bottleneck is no longer syntax, it’s judgment

AI can build astonishing things quickly.

But deciding what is worth building remains stubbornly human.

What’s Next?

There are far more interesting problems to explore than teaching machines to sell wall décor. So I’ve moved on to projects that feel less like automation experiments and more like genuine discovery.

Still, this strange little system remains one of the most educational things I’ve built.

Not because it succeeded.

But because it showed me where the real questions now live.

If you enjoy thoughtful experiments at the intersection of software, AI, and life after Big Tech, feel free to follow along. There’s plenty more tinkering ahead.

Hello world.

After more than twenty-five years in the software industry, I now find myself in an unfamiliar position: an ex–Big Tech engineer with time to think. And when you suddenly have time, real, unstructured time, you start asking questions that never quite fit into sprint planning or quarterly OKRs. Questions about where all of this is going. About what kind of civilization we may be constructing, almost accidentally, through code, platforms, and incentives.

One possible answer is something historians would recognize immediately, even if we insist on calling it innovation: techno-feudalism.

A System That Refuses to Stay in the Past

To understand the idea, it helps to revisit classical feudalism—not as a medieval curiosity, but as one of the most stable socioeconomic systems humanity has ever produced. For centuries across Eurasia, feudal societies organized themselves around a simple structure:

* A vast majority, over 90%, were serfs, bound to land they did not own.

* A narrow middle layer, perhaps 5–10%, were specialists, tradespeople, clergy, or small landholders.

* At the very top sat the lords, fewer than 1%, who owned nearly everything.

Serfs possessed little beyond their labor. Land, tools, housing, and even access to basic resources were controlled by their lords and leased back in exchange for rent, often paid through work rather than money. Social mobility was rare, ownership rarer still.

The system was extractive, unequal, and often brutal. Yet it was astonishingly resilient. Feudalism endured not because it was just, but because it was economically self-reinforcing.

How Feudal Systems Sustained Themselves

When productivity plateaued or populations grew too large, lords adapted through three recurring strategies:

* Expansion – acquiring new land or resources to maintain surplus value.

* Monetization – leasing labor elsewhere, converting obligation into currency.

* Control of Dependency – ensuring those at the bottom remained structurally tied to the system.

Feudalism did not collapse on its own. It weakened only when demographic shocks, wars, and new frontiers shifted bargaining power back toward labor. Scarcity of workers forced change.

In other words, feudalism ended when people once again became economically valuable.

The Digital Echo of an Old Order

Now consider the present.

Today, ownership of infrastructure, platforms, and capital is increasingly concentrated. Many people do not own their homes outright, their transportation, or even the software tools required to function professionally. Instead, they subscribe. Monthly payments replace property. Access replaces ownership.

Housing is rented. Software is licensed. Entertainment is streamed. Mobility is leased. Even productivity increasingly depends on platforms we do not control.

This is not medieval agriculture. But structurally, it rhymes.

A small group builds and owns the digital “land.” A technical middle class maintains it. The majority participates through continuous payment for access.

The Accelerant: Automation of Cognitive Labor

Artificial intelligence introduces a new dynamic. Unlike past mechanization, which primarily displaced physical labor, modern systems can perform economically useful cognitive tasks, analysis, generation, coordination at dramatically lower cost.

If large segments of human work lose scarcity, the historical mechanism that dismantled feudal systems, valuable labor, may not reappear this time.

Without surplus income, fewer people accumulate capital. Without capital, ownership concentrates further. Dependency deepens, even if living standards remain materially adequate.

A society can become comfortable and constrained at the same time.

A Narrow Path Forward

This trajectory is not inevitable. History never repeats exactly. But it often presents familiar shapes under new names.

We may be approaching a fork between two futures:

* One leads toward a highly centralized, subscription-mediated existence: efficient, stable, and quietly stratified.

* The other toward a world where technological abundance broadens ownership rather than concentrates it.

The difference will not be determined by technology alone, but by how societies choose to distribute its gains.

Somewhere between dystopia and utopia lies a narrow path. Whether we recognize it and choose to walk it remains an open question.

For now, with time to think, it is a question worth asking.

Hello, World.

I’m an unemployed, ex–Big Tech software engineer with 25 years in the industry.

That sentence would have terrified me a decade ago. Today, it feels strangely calm.

We’re living through another brutal cycle of tech layoffs. Thousands of engineers: smart, capable people are refreshing LinkedIn feeds and grinding through interviews that feel increasingly like Squid Game with better lighting. Many are hurting. Some are in real financial distress.

By a combination of discipline, luck, and a few painful lessons, I’m not.

I’m financially independent, though not in the glamorous influencer sense. It’s more like involuntary early retirement. I’m not drawing a paycheck, but our living expenses are covered by returns from a pool of investments built slowly over time. It is an enormous privilege, and I don’t take it lightly.

What follows is not advice. I’m not a financial advisor. I’m simply a middle-aged engineer showing his work, the wins, the mistakes, the weird detours and how they compounded into stability.

Index Funds: The Boring Backbone

In 2008, during the wreckage of the financial crisis, I fell down a personal finance rabbit hole. Your Money or Your Life shifted my thinking. John Bogle’s The Little Book of Common Sense Investing sealed it.

The idea was simple: buy low-cost index funds that mirror the broader market, like the S&P 500, and let time do the heavy lifting. Most active managers fail to beat the market long term. If professionals with teams of analysts struggle, what chance did I have after a day of debugging Java?

So in early 2009, while markets were still bruised, I began investing heavily in S&P 500 index funds with a smaller allocation to total market bond funds, roughly 90/10.

Funding came from:

* 401(k) contributions (eventually raised to 8%)

* Employer matching (free money, always take it)

* Post-tax brokerage investments, which ramped up after we paid off student loans

I also funneled nearly every cash bonus into index funds.

Starting in 2009 was luck. The S&P 500 was near historic lows. Over 17 years, those funds averaged roughly 13% annually. Exponential compounding quietly made index funds our largest asset class. Today, the dividends from our taxable accounts form a meaningful portion of our income.

Boring worked.

Real Estate: Tangible Wealth

My father never trusted fiat currency. He believed real wealth is something you can touch.

So we bought it.

We own our primary residence and two rental properties, all mortgage free. We purchased during the softer post-crisis 2010s and paid them off through aggressive saving.

Some argue a primary home isn’t an investment. I see it as capital that appreciates and reduces our housing cost dramatically compared to renting. The rentals, modest homes within commuting distance of major cities, produce consistent income.

Property values have grown around 7% annually. Rental yields run 7–8% per year for us. Combined, real estate is our second-largest asset class and a powerful stabilizer.

The Side Hustle That Should Have Been Sold

In 2010, I built Android apps to pay off debt. It was the Wild West. Multiple app stores. Low competition. In 2011, I cleared over $100,000.

Then the gold rush ended.

Competition intensified. Earnings halved in 2012. A buyer offered $53,000 for the entire business. I declined, insulted.

I should have taken it.

By 2013, new apps earned less and less. Eventually I stopped building. The old apps trickled income for a decade until Google deprecated the SDK and delisted them in 2023.

Over ten years, the passive income didn’t even reach half the buyout offer.

Lesson: Know when the peak is behind you.

HYSA: Sleep Insurance

A high-yield savings account isn’t exciting. It’s insurance.

We built it from three months of expenses to one year as layoffs at work intensified. After my lay off, the severance added another year. At 3.75%, FDIC-insured, it’s less about growth and more about peace.

Peace compounds too.

Big Tech Stock: Concentration Risk

Joining Big Tech 7 years ago brought RSUs and an ESPP program with a 15% discount, effectively instant upside.

When shares vested in 2020, pandemic growth sent the stock soaring. I held. From 2020–2024, it averaged roughly 22% annual growth for me.

But concentrated positions make me nervous. I’ve been gradually converting shares into diversified index funds to manage capital gains taxes.

Concentration builds wealth. Diversification keeps it.

Precious Metals: Hedge or Warning Sign?

A doomer friend convinced me in 2009 to buy silver. I did, modestly, consistently, every Christmas.

Silver averaged roughly 12% annual growth over that period.

Precious metals produce no cash flow. They simply sit there, shiny and indifferent. Whether that performance represents savvy hedging or currency erosion is an uncomfortable question.

FOMO Stocks: Tuition Paid

In 2014, I invested $20,000 into hype-driven “visionary” tech stocks.

Two years later: $7,000 gone. Roughly –17% annually.

That was my tuition for ignoring fundamentals.

Soviet Ammo: The Accidental Asset

As a teenager, I bought surplus Cold War ammunition for pennies per round.

It sat in a basement for decades.

Two years ago, I sold one can for $700.

Roughly 10% annualized growth. The market is strange.

Crypto: The Best Return I Don’t Understand

In 2017, skeptical but curious, I mined a small amount of Monero on an old laptop. It earned $17 and destroyed a CPU fan.

I converted it to Bitcoin and forgot about it.

Today, it’s worth around $200, about 27% annualized growth.

It’s my best-performing asset.

I still don’t understand it.

The Big Picture

We made mistakes. Missed peaks. Held too long. Sold too soon. Chased hype. Ignored offers. Got lucky.

But over 17 years, consistent investing in productive assets, especially low-cost index funds and real estate, did most of the heavy lifting.

Financial independence wasn’t achieved through genius. It was built through:

* High savings rate

* Boring diversification

* Employer benefits

* Controlled lifestyle inflation

* And a lot of time

In uncertain times, stability is a gift.

If you’re navigating layoffs, know this: markets move in cycles. Careers do too. The key isn’t predicting the future, it’s surviving long enough for compounding to matter.

If you’re curious to follow this ongoing experiment in middle-aged reinvention, engineering, investing, existential reflection, I write monthly and share updates.

Thanks for reading.

Until next time.