AsianDadEnergy's Podcast

Hello world.

I’m an unemployed, ex–Big Tech engineer with twenty-five years in the software and technology industry. That sentence alone would have felt impossible not long ago. And yet here we are, living through another wave of mass layoffs across American tech an era where résumés pile up, inboxes stay silent, and explanations are reduced to slogans.

One of the most common refrains I hear is simple: it’s offshoring.

Now that I have time, an abundance of it, I’ve been sitting with that claim, turning it over carefully instead of reacting to it emotionally. What follows isn’t a hot take. It’s a reflection shaped by decades inside the system.

Offshoring Comes in Waves

Offshoring in tech doesn’t happen all at once. It arrives in waves, each with a peak of enthusiasm and a valley of regret.

I entered my career during one such peak in the early 2000s, when companies aggressively shifted software development to lower-cost countries. In my experience, that almost always meant India. The logic was straightforward: software was expensive to build in the U.S., talent was plentiful elsewhere, and code was code. Or so we told ourselves.

As a consultant in the years that followed, I found myself working inside the aftermath of that decision. The codebases left behind were often catastrophic, vast jungles of copy-and-paste spaghetti code, barely documented, impossible to extend, and riddled with bugs. Many applications limped along, more fragile than functional.

Worse, when issues arose, it often felt as though the offshore teams supporting the systems didn’t truly understand the code they were maintaining or perhaps had never been given the space or incentive to care deeply about it.

By the mid-to-late 2000s, the pendulum swung back. Companies slowed or halted offshoring efforts and brought work onshore again, quietly acknowledging that cheap code can become very expensive over time.

Why Was the Code So Bad?

This question haunted me early in my career. Mathematics doesn’t change by country. Engineers don’t become less intelligent when they cross borders. In theory, software quality should be roughly equivalent everywhere.

Over time, I realized the issue wasn’t intelligence. It was friction.

Time Zones

Teams separated by half a world rarely overlap meaningfully. Collaboration collapses into tickets and emails. Context gets lost. Nuance disappears. Projects devolve into a “throw it over the fence” workflow with each side doing its piece in isolation, hoping the other can make sense of it later.

Language

Even when offshore engineers speak excellent English, subtlety often doesn’t survive translation. Requirements lose precision. Assumptions go unchallenged. And sometimes, asking the right question feels harder than simply saying yes.

Culture

In more hierarchical cultures, junior engineers may be discouraged, explicitly or implicitly, from speaking up. Problems get noticed but not surfaced. Uncertainty is masked as confidence. Work proceeds anyway, and the consequences appear downstream, where they are far more expensive.

None of this reflects a lack of talent. It reflects systems that punish curiosity and reward silence.

The Pendulum Swings Again

By the mid-2010s, offshoring returned, this time with more sophistication.

In 2015, I worked on a large enterprise re-platforming project for an automotive client. There, I met an offshore architect, let’s call him Abby, based in Gurugram, outside Delhi.

I was wary at first. Experience had trained me to be. But Abby quickly dismantled my assumptions.

He was brilliant. Deeply knowledgeable. Tireless. He understood the platform better than I did, despite technically reporting to me. To maximize overlap with U.S. teams, he worked until 10 or 11 p.m. his time, every weekday. Eventually, U.S. management began to expect this level of sacrifice from everyone offshore.

Abby was paid roughly one-sixth of my salary.

And yet, his cost of living was nowhere near as low as I had imagined. Cars, utilities, food, nearly U.S. prices. Housing near the office was so unaffordable that his family lived hours away. During the week, he shared a tiny apartment with coworkers. He saw his wife and child only on weekends.

He worked weekends too.

He told me about suffocating smog, brutal heat waves, floods, wild dogs, and constant environmental stress. I didn’t fully believe him, until years later, when I visited the same area myself and saw it firsthand.

Once, during a dengue fever outbreak, I watched fear consume him as he worried about his child’s safety.

And yet, he considered himself lucky.

Unequal Terms

Abby and I were men of equal worth. Of comparable skill. Of shared pride in our craft.

But we competed on profoundly unequal terms.

Over my career, I’ve worked with offshore engineers across India, Latin America, China, and Eastern Europe. Many are my friends. All are trying to provide for their families, no differently than American engineers.

In the last decade, many of the old barriers have fallen. Collaboration tools improved. Companies built parallel offices abroad. Offshore engineering quality rose dramatically. Today, many offshore engineers are true peers to their U.S. counterparts.

Which brings us to the uncomfortable truth.

The Real Incentive

Offshoring was never about quality.

It was and remains about cost.

Offshore engineers are still significantly cheaper than U.S. engineers. Unless something drastic changes, offshoring will continue and likely accelerate.

I’ve thought hard about what could stop it.

Regulation can protect certain sectors, but it raises costs and slows innovation. Poaching elite offshore talent strengthens companies but worsens competition for domestic workers. Tariffs invite retaliation and risk splintering global tech ecosystems. Waiting for work to return via automation may succeed but as manufacturing taught us, automation brings work back without bringing jobs.

Each solution carries a heavy price.

Who Wins?

I don’t see a magic button. I don’t see a clean fix waiting just beyond the horizon.

What I see are capable engineers, onshore and offshore, competing desperately for scraps.

In a more just world, they wouldn’t be rivals. They would be brothers, building together instead of being measured against one another.

The true winners are easy to identify.

Capital wins.

If you own it, the line goes up. Stock prices rise. Human cost fades into abstraction. As long as the graph climbs, everything is considered fine.

Maybe that’s all that matters now.

Thanks for reading. If you’re morbidly curious enough to walk this path with me, I write and talk about these topics regularly. And if you choose to support the work, thank you. Truly.

We’ll chat later.

Hello world.

I’m an unemployed, ex–Big Tech software engineer with 25 years in the technology industry. And like many of you, over the past week I’ve been watching yet another wave of mass layoffs sweep across tech, tens of thousands of engineers, designers, and product leaders suddenly finding themselves on the outside.

Every layoff cycle brings back a memory I can’t quite shake: my first.

My First Layoff, 2008

In 2008, during the Great Recession, I was a mid-level developer at a digital consulting agency. I’d been there just under two years, still learning, still finding my footing. I had a mentor, let’s call her Dana, an exceptional software architect who patiently taught me concepts I still carry today. She explained the differences between dynamically typed languages like PHP and the strongly typed Java systems I was used to, never making me feel small for not knowing.

Then one afternoon, building security appeared on the floor.

An emergency town hall invite hit our calendars. We gathered in the atrium, where our CEO, Rob, stood in front of us looking exhausted. He explained that unexpected economic conditions had caused the loss of major clients. Hard cuts had to be made for the company to survive.

What struck me most wasn’t the announcement, it was his demeanor. There was shame in his face. Sadness in his eyes. He took full responsibility for the layoffs, repeatedly emphasizing that none of the affected employees were at fault. It felt almost ancient, like watching a leader fall on his sword.

When the town hall ended and we returned to our desks, nearly half the floor was empty. Dana’s cube was stripped bare.

I felt fear, anxiety, self-doubt, and an overwhelming sense of survivor’s guilt.

At a nearby café, I found Dana. She told me she’d been laid off. She said mentoring me had been a pleasure. Her voice broke. She looked older than I’d ever seen her. At the time, she was younger than I am now, but loss has a way of aging you instantly.

That was my introduction to mass layoffs.

Three Eras of Tech Layoffs

Over 25 years, I’ve survived countless layoffs across three distinct eras.

The Great Recession Era was marked by reluctant leadership. Layoffs were painful, shameful, and avoided until there was no other option.

The M&A Era of the late 2010s brought quiet, discreet layoffs: fast, clinical, and rarely discussed.

The Post-COVID Big Tech Era, beginning around 2023, is something else entirely. Layoffs are framed as efficiency. As optimization. As something to be proud of. Strong engineers are cut not because they failed, but because markets demand higher valuations.

In my view, this era is the most brutal and unpredictable of all.

When my own Big Tech layoff finally came, I’d lost count of how many rounds I’d survived.

How I Stayed Employed for 25 Years

I can’t offer guarantees, but I can share what helped me last as long as I did.

Build a reputation for dependability.Be known as someone who gets things done well. That requires initiative, optimism, and solution-oriented thinking.

Make your value visible.Speak up strategically. Share accomplishments. Ensure your manager—and their manager—knows what you contribute.

Continuously learn in-demand skills.From bare metal servers to cloud platforms to SaaS, I was always learning what the market needed next. In-demand skills act like a parachute when you fall.

Work on revenue-critical projects.Projects that directly make money are the least likely to be cut.

Network relentlessly.Inside your team. Across departments. Outside your company. Your network is a safety net.

Be versatile.Take on varied roles. Learn flexibility. Remember: the highly specialized T-Rex didn’t survive, but the adaptable mammal did.

Final Thoughts

We’re living in an age of seemingly unending layoffs in tech. These strategies are coping mechanisms, not guarantees. But applied consistently, they can move the needle.

If you’re navigating this uncertainty, you’re not alone. I’m sharing this journey openly, part reflection, part therapy, part survival guide.

If you’d like to follow along, join the newsletter, or simply grab a coffee with me along the way.

Until next time.

Lamb Saag, Layoffs, and the Bug That Broke Me

Hello, world.

I’m an unemployed ex–Big Tech software engineer with 25 years of experience, which in today’s economy apparently qualifies me for involuntary early retirement. One unexpected perk of this phase of life is that I now do a lot more cooking for my family. Today’s menu: Indian lamb saag. Slow, deliberate, and unforgiving if you miss a step, much like enterprise software.

As the lamb simmers, let me tell you a story from a darker, stranger time.

2009: The Corporate Hunger Games

The year was 2009. Layoffs were everywhere. Entire floors disappeared overnight. Companies weren’t trimming fat; they were amputating limbs.

I worked at a digital consulting agency that had already survived several rounds of bloodletting. By all logic, I should have been gone. Yet somehow, like a cockroach surviving a nuclear blast, I remained employed.

Most of our major clients had vanished. One of the few whales still alive was a massive publishing conglomerate. Let’s call them Big Corp.

Big Corp had just experienced a revelation that felt revolutionary at the time: What if people bought our newspapers and magazines… on the internet?

Unfortunately, they had no e-commerce platform, almost no technical capability, and about 90% of their internal IT staff had already been laid off. In their place stood a shimmering mosaic of offshore and onshore H-1B contractors from a massive IT services firm. We’ll call them Outsourced Consultancy Services, or OCS.

Naturally, my company won the pitch to build Big Corp’s e-commerce platform. And just like that, we dove headfirst into a corporate dumpster fire armed only with PowerPoint decks and unearned confidence.

An Org Chart Designed by a Madman

The project team was… unusual. Nearly half the people involved were VPs, directors, account executives, or some flavor of Extremely Important Person. There were almost more chiefs than Indians, which statistically should not be possible.

Leadership had clustered around this project like penguins in a blizzard, hoping proximity to billable hours might keep them alive.

I joined as a senior developer, and for the first time in my career, I became the tech lead of my own squad. It included Eddie, a brilliant engineer from New Jersey; Sam, an Australian project manager; Ravi, an OCS build master stuck in green card purgatory; and several junior developers.

On paper, I reported to Bharath, an enterprise architect from OCS who had never written a line of code. Bharath reported to Heinz, an East German tech director from my company, and Mega, an OCS director. They reported to Fred, the client-side chief architect, and finally to Mr. Burns, a senior VP who looked exactly like a South Asian version of the Simpsons character. Same stare. Same energy. Same ability to drop a room’s temperature by ten degrees.

If this sounds confusing, don’t worry. It was worse in real life.

Building the Beast

We were tasked with building a web-based e-commerce system that allowed customers to order custom bundles of newspapers and magazines. Today, this would be a two-day Shopify project. Back then, it took five months, tens of millions of dollars, and what felt like several ritual sacrifices.

Orders flowed through an enterprise service bus, were chopped into pieces, and fed into a horrifying backend fulfillment ecosystem composed of overlapping legacy systems, orphaned applications, and entire platforms built around employees who had been laid off years earlier.

These systems were maintained by offshore sysadmins who treated them like ancient temples: don’t touch, don’t ask questions, and pray.

There was exactly one client-side IT veteran who understood how it all worked. His name was Davey. He was gray-haired, exhausted, and spiritually done.

My team owned the middleware layer. “Simple,” they said.

PowerPoint Architects and Sausage Making

It quickly became clear that Bharath’s tools were PowerPoint, Word, and criticism. I did the actual design. I wrote the specs. I drew the diagrams. Bharath reviewed them and offered feedback like:

* “The font lacks authority.”

* “This box should feel more visionary.”

* “The verbiage needs architectural gravitas.”

Then he presented my work to leadership.

I smiled, nodded, and stroked his ego, because for the first time in my career, I had full ownership of an application. No micromanagement. No interference. Pure sausage-making freedom. Worth every ounce of frustration.

We worked late. We bonded. Eddie, despite a stutter that caused management to underestimate him, was a phenomenal engineer. Ravi worked endlessly, supporting his family while trapped in immigration limbo. Sam dreamed of retiring as a landlord back in Australia.

We ate together constantly, mostly Indian food. It’s strange how easy it is to form deep friendships when you’re young and suffering together.

The Open Source Mistake

At some point, a client executive who had never touched middleware decided that open source was better. Requirements be damned.

They chose an open source ESB product. Let’s call it Crazy Boss.

Consultants from the company behind Crazy Boss, Silly Hat, arrived. They gave a dazzling demo, proclaimed that open source meant fewer bugs, charged an ungodly amount of money, and vanished.

Here’s the lesson I learned too late:Open source does not mean bug-free. Sometimes it means you get to discover the bugs personally.

The Bug That Shouldn’t Exist

As go-live approached, late nights became routine. Then weekends. Then time stopped mattering altogether.

During system integration testing, an order entered the system and vanished. Not failed. Not errored. Gone.

I checked everything. Logs. Queues. Dead letter Queues. Every line of middleware code. The bug should not have been possible.

We couldn’t reproduce it.

Leadership shrugged. “Probably a fluke. Let’s go live.”

I did not shrug.

I spiraled.

I rebuilt environments. Simulated load. Obsessed. The stress followed me everywhere. When my girlfriend, now my wife, visited me before go-live, I was so anxious I couldn’t even be intimate.

Nothing kills romance like a missing async message.

Go Live Night

Orders flooded in. Systems broke. We fixed them. By 3 a.m., things stabilized.

Then the calls came.

Missing receipt emails. Missing orders.

The bug was real. My middleware was eating them.

Mr. Burns stared at me and said, “Someone really effed this up.”

Something inside me snapped.

I walked out to the parking lot and cried. Full breakdown. Full impostor syndrome. My career was over. I was a fraud.

Then Sam and Heinz followed me out.

“Be kinder to yourself,” Sam said.Heinz added, with peak East German nihilism: “It never gets better. It only gets worse. Then we die. So why worry?”

We went back inside.

The Fix

Davey saved the day.

He noticed the bug only happened after Crazy Boss ran for days under load. The solution?

A cron job that restarted the instances in a round-robin fashion.

No downtime. Just reboot and pray.

It worked. The launch was declared a success.

Months later, we learned the truth: a memory leak in Crazy Boss that only occurred under high concurrency on Citrix VMs.

Of course.

Aftermath and Curry Nightmares

That morning, I found Ravi sitting quietly. He told me his daughter’s daycare was teaching kids to throw away food, a grave sin in his culture. During go-live night, he decided to quit OCS and return to India.

“God will find a place for me,” he said.

Weeks later, Eddie suggested Indian food to celebrate surviving. At Curry Dreams, a local Indian buffet, I saw a cockroach the size of my thumb crawl up the wall… and fall directly into a vat of curry.

We left immediately.

From that day on, Curry Dreams became Curry Nightmares.

Epilogue

Now, years later, I’m stirring lamb saag in a quiet kitchen, unemployed but oddly at peace. That bug didn’t end my career. That breakdown didn’t define me. It was just another chapter in a long, messy story.

If you have a morbid curiosity to follow along on this strange life journey, you know where to find me.

Thanks for listening.Talk soon.

Hello, world.

A few months ago, I became what feels like a new tech archetype: an unemployed ex–Big Tech engineer with over 25 years in the industry. In today’s climate, that sentence alone is enough to spike blood pressure. Yet, strangely, my recent layoff hasn’t felt like a crisis. It’s felt more like a quiet pause, a semi–early retirement, or at least a transition into the next chapter of life.

That perspective didn’t come from optimism or denial. It came from math.

For more than a decade, my family has maintained a savings rate north of 50% of our gross income. It wasn’t flashy. It wasn’t Instagram-worthy. But it fundamentally changed how we experience uncertainty. A high savings rate builds a large emergency fund, and that buffer softens the sharpest edges of job loss. More importantly, sustained saving allows those dollars to compound into income-producing investments, eventually buying you something far more valuable than luxury goods: optionality.

If you do it long enough, financial independence becomes less of a dream and more of a boring, inevitable outcome.

Debt Is a Black Hole (I Know Because I Fell In)

Let’s start with the most important lesson: debt is financial gravity.

Educational and consumer debt behave like a black hole attached to your wallet—relentlessly sucking money away while giving nothing in return but anxiety and regret. Whatever you bought with that debt is long gone, but the payments linger, quietly preventing you from building emergency savings or investing in your future.

I know this intimately. Early in my marriage, my wife and I sat down for an honest look at our finances and realized we owed over $150,000 in student loans. This revelation landed just days after she told me she was pregnant. It was shortly after the financial crisis, my job felt unstable, and the debt alone was costing us more than $1,000 a month in interest.

We were trapped.

So we declared war on debt.

We slashed our spending to the bone. Aside from rent, we lived on about $100 a week: groceries, toiletries, everything. We lived like monks with Wi-Fi. During the day, I worked my consulting job. At night, I built Android apps, fast, ugly, practical apps, anything that could generate revenue.

This was around 2010, when Android was the Wild West. Apps that did almost nothing were making real money. I churned out about 50 simple apps: timers, flashlights, bird guides, concrete calculators. I priced them at one or two dollars, and somehow… they sold.

I worked 80–90 hours a week for years. When my son was an infant, his crib sat beside my bed. At 1 a.m., after another night of coding, I’d hold his tiny hand for a few quiet minutes before falling asleep. That was the time I had with him back then. It was brutal, but it worked.

Within two years, we were debt-free.

We used a psychological “snowball” strategy, paying off the smallest loans first. Was it mathematically optimal? No. Was it emotionally powerful? Absolutely. Watching balances disappear kept us moving forward.

Freedom tastes better than efficiency.

Housing: Ignore Realtors, Embrace Math

Housing is usually the largest expense in a household, which means it’s also the biggest lever.

Whether renting or buying makes sense depends on location, but once you decide to buy, ignore the advice to “get as much house as you can afford.” That mindset quietly sabotages long-term wealth.

Instead, I used the 80/20 rule. If you’re willing to compromise 20% on size, finishes, commute, or neighborhood, you can often cut the price dramatically.

That’s exactly what we did. We bought a modest home with a large backyard, decent schools, and a longer commute. It was a short sale, and the total monthly payment—including taxes and insurance was around $2,000. That was a fraction of what many of my coworkers were paying.

The result? Massive monthly savings that went straight toward paying off the mortgage early.

Today, our housing costs are down to about $900 a month in property taxes and insurance. That’s it.

Transportation Is a Toaster Oven, Not a Personality

Cars are utilities, not status symbols.

If you live in a dense metro area, public transportation may be the cheapest solution. If you need a car, buy one that maximizes reliability and minimizes lifetime cost. For me, that meant used Toyotas and Hondas, three to five years old.

This is the sweet spot: you get 80–90% of a car’s usable lifespan for half (or less) of the original price. These vehicles are mass-produced, boring, and extremely durable. They’re also cheap to insure and repair.

I currently own two fully paid-off Honda CR-Vs. Each costs about $150 per month in total lifecycle expenses: gas, insurance, maintenance, everything. That’s a tiny fraction of the cost of a new luxury vehicle.

Boring wins again.

Food: Brown Bags, Crock Pots, and Quiet Wealth

We rarely eat out, maybe once a month for special occasions. For over 15 years, my wife and I brown-bagged our lunches. By my rough estimate, that habit alone saved us a few hundred thousand dollars.

Cooking isn’t hard. With basic skills, you can make food that’s 80–90% as good as restaurant meals at a fraction of the cost. And if you want a true engineering marvel in your kitchen, allow me to introduce the crock pot.

For about 15 minutes of prep time and pennies of electricity, it produces massive quantities of cheap, delicious, nutritious food. Chili, curry, soup, set it and forget it.

We buy mostly unprocessed foods, often organic, from reasonably priced stores like Aldi. For a family of four, our monthly food budget runs around $700–$800.

Managing Consumerism in a Family

Minimalism is easy when you’re single. Add a spouse and kids, and things get… complicated.

The solution isn’t deprivation, it’s containment.

We set monthly spending ceilings. We teach our kids to think about value, not just desire. (This lesson is working better on my son than my daughter, but progress is progress.)

I also noticed that most consumer goods lose their appeal shockingly fast, sometimes within days. So I built a small “distribution center” in my basement where unused items are inventoried and resold on eBay, Facebook Marketplace, or Mercari. Unsold items are donated for tax deductions.

That system recovers about 10–20% of what we spend on consumer goods, which quietly adds up.

The Boring Path to Freedom

With these strategies, we saved more than 50% of our income for many years. None of this is revolutionary. Outside the tech bubble, this is how many people already live.

But in an industry facing endless layoffs, these habits can turn a terrifying event into a manageable transition.

My layoff didn’t feel like falling off a cliff. It felt like stepping onto a different trail.

If you’re morbidly curious to follow along on this life journey, you know where to find me. And if you found this useful, welcome to the quiet, unsexy, deeply satisfying world of financial independence.

See you next time.

Hello world.

Until recently, I was a senior engineer at a Big Tech company, with 25 years in the technology industry behind me. Today, I’m unemployed, watching the industry I grew up in sprint headlong into what feels like the largest speculative bet of its lifetime.

Not long before I was laid off, my former employer held a company-wide AI hackathon. By that point, the company had already invested billions of dollars into training frontier models and building out the infrastructure to support them. Massive data centers. Enormous training runs. A portfolio of large language models that needed, urgently, to justify their existence.

The goal of the hackathon was simple, at least on paper: come up with bold, transformative, responsible AI ideas that could, somehow, turn all of this spending into revenue.

In other words: please make the AI pay for itself.

The A-Team (and a Reality Check)

I joined a hackathon team led by a senior engineering leader—let’s call him Danny. On paper, it was the A-Team.

There was Jimmy, the Canadian tech lead who could brute-force his way through any codebase. Subash, an H-1B architect who was frighteningly sharp. Alex, a junior engineer who had survived our brutal internship program. And Lionel, a support team lead with an effortlessly charming British accent which, by the way, is an unfairly powerful asset when pitching business ideas in tech.

We brainstormed and quickly landed on what seemed like an obvious win: an AI-powered customer support agent.

The idea was straightforward. Most customer support cases are repetitive. With a large language model enhanced by Retrieval-Augmented Generation (RAG)—essentially giving the model access to proprietary internal knowledge, we believed the agent could autonomously resolve roughly 90% of incoming cases.

Within a day, we had a working proof of concept running inside a Docker container.

Feeling confident, we presented the idea to a business leader in our product line, let’s call him Leo.

Leo listened patiently. Then he dismantled the idea.

Yes, he acknowledged, the agent might handle 90% of cases. But the remaining 10%—the hard, messy, ambiguous ones were what consumed over 90% of the support team’s time. Those were the cases customers escalated. Those were the cases that mattered.

What we had built, he argued, was essentially a glorified FAQ page.

Then came the line that stuck with me: “This feels like a shiny solution in search of a problem.”

A Microcosm of the AI Industry

That moment crystallized something uncomfortable.

Despite the massive investments and the relentless internal pressure to “AI-ify” everything, it was genuinely difficult to extract real, defensible business value from AI in many domains. Outside of narrow niches with abundant training data, returns were murky at best.

That small hackathon experience now feels like a perfect microcosm of the broader AI industry.

Hundreds of billions, possibly trillions, of dollars are being poured into AI. Yet most AI initiatives today are losing money. In some cases, a lot of money. Each API call to a large language model can cost several times more to serve than it generates in revenue.

Meanwhile, the hype machine roars on.

World models. Humanoid robots. Confident proclamations that AGI is just around the corner.

Some of these efforts are legitimate research. Others feel like science fiction being aggressively monetized. If this reminds you of the dot-com bubble, you’re not wrong, except this time, the scale is orders of magnitude larger.

Financial Alchemy and Corporate Optics

The problem is that the money has already been spent. And investors want returns now.

To maintain the appearance of growth, companies resort to financial gymnastics: buying AI services from each other to simulate demand, reclassifying existing product revenue as “AI revenue” after adding superficial features, and framing mass layoffs as “AI efficiency gains” while quietly shifting work offshore.

The result is a market that looks strong on the surface but increasingly fragile underneath.

Big Tech now accounts for roughly 40–50% of the S&P 500’s total valuation. If confidence cracks, if investors realize these investments won’t pay off on the promised timelines, the unwind could be violent.

If the Bubble Bursts

If an AI collapse happens, it likely won’t be a single dramatic moment. A weaker, AI-only company could fall first. A large investor could panic. Political backlash against data centers and energy costs could accelerate sentiment shifts.

The downstream effects would be severe: an AI winter where funding dries up, market caps shrink, RSUs evaporate, and layoffs spread not just across AI teams, but across entire platforms and ecosystems.

Beyond tech, the impact would ripple outward: data centers halted, semiconductor orders canceled, real estate markets strained, financial institutions exposed. In a worst-case scenario, cascading failures could spill into the broader economy.

This isn’t a prediction. It’s a plausible risk path.

How to Cope (Not Panic)

So what can individuals, especially software engineers, do?

At work: double down on core problem-solving skills. Learn to wield AI as a tool, not fear it. Build T-shaped expertise that spans engineering, product, and business.

Outside of work: build a much larger emergency fund than traditional advice suggests. Reduce fixed expenses. Create alternative income streams: side projects, businesses, anything that isn’t tied to a single employer.

None of this is easy. And none of it is guaranteed to be necessary.

This may all amount to nothing more than the late-night musings of a laid-off engineer with too much time to think. The AI boom could continue. Stocks could soar. Everyone could get rich.

But history suggests that when investment, hype, and financial reality drift too far apart, gravity eventually reasserts itself.

For now, all we can do is stay alert, stay flexible, and remember that technological revolutions are rarely as smooth or as profitable as they look at the peak.

Hello world—and welcome to the uncomfortable side of tech.

I’m a recently laid-off ex–Big Tech software engineer with 25 years in the industry. In the middle of what feels like an endless wave of tech layoffs, I keep hearing the same question from people outside the industry and from junior engineers just starting out:

“With those salaries, shouldn’t senior software engineers all be rich and financially independent?”

On paper, it’s a perfectly reasonable assumption. Tech compensation, especially in Big Tech, looks outrageous to most people. Six-figure salaries, stock grants, bonuses, numbers that feel almost cartoonish compared to the median income.

And yet… reality tells a very different story.

Over decades in tech, I’ve watched many experienced, intelligent engineers accumulate shockingly little savings. Some are so financially exposed that a single layoff pushes them instantly into crisis mode. I’ve even seen stories of engineers laid off from companies like Meta who, after months of unemployment, ended up homeless.

So how does this happen?

After a lot of reflection, I’ve come to think of the answer as a series of money vacuums, individually rational choices that quietly drain even enormous incomes when stacked together.

Money Vacuum #1: High Cost-of-Living Gravity Wells

Tech workers cluster in a handful of metropolitan areas, places like the Bay Area, Seattle, New York. These cities routinely cost two to three times more than rural or mid-sized regions. And this is true even if you live modestly, even if you “live like a monk.”

I grew up in rural Pennsylvania. The cost difference is staggering.

No matter how frugal you think you are, geography alone can quietly eat your paycheck.

Money Vacuum #2: The House That Ate Your Salary

Housing is where things really get dangerous.

Surrounded by peers buying large homes in prestigious neighborhoods, many engineers follow suit. These houses often sit in top-tier school districts, come with expensive renovations, and carry truly massive mortgages.

Between mortgage payments, property taxes, insurance, maintenance, and utilities, it’s not unusual for housing costs to reach $5,000, $10,000, or even $15,000 per month.

Worse, housing is illiquid. When layoffs hit, you can’t easily access that equity, so the very thing meant to represent “success” becomes a financial trap.

Money Vacuum #3: Luxury Cars and Status Spending

Tech workers, especially consultants, are prime targets for luxury car marketing. Cars become symbols of success, innovation, arrival.

But behind the feelings are tens or hundreds of thousands of dollars in depreciating assets. In many urban centers, public transportation would do just fine, yet monthly car payments often rival a typical family’s mortgage.

That’s not freedom. That’s drag.

Money Vacuum #4: Food as Convenience (and Therapy)

Tech is stressful. Food becomes a coping mechanism.

Takeout at work. Restaurants as family time. High-end grocery stores where basic items cost multiples of normal supermarkets.

The result? Monthly food spending that can quietly rival a mortgage payment, again.

Money Vacuum #5: The Education Arms Race

Many engineers come from cultures that deeply value education. The instinct is understandable but the spending can spiral.

Tutoring, cram schools, extracurriculars, private schools, college funds, it all adds up. For families with multiple children, education spending can easily match housing costs.

The hard truths:

* Spending 10x more doesn’t make your child 10x more successful.

* Excessive pressure can damage mental health and create resentment toward learning itself.

Money Vacuum #6: Consumption as a Band-Aid for Burnout

Tech work consumes lives.

Endless calls. Launches. Hypercare. Travel. There’s little time left to actually live.

That emptiness often gets patched with consumption:

* Burned out? Buy a new gadget.

* Marriage struggling? Buy designer goods instead of time.

* Kids need help? Buy educational toys instead of attention.

Thousands disappear every month without ever addressing the real problem.

Money Vacuum #7: Tech Bro FOMO Investments

RSUs vest. Six figures land in your account.

Suddenly, an entire ecosystem appears, pitching world-changing investments you must get into right now.

NFTs. Exotic hardware startups. Energy mirrors. Plant-based 3D-printed meat.

Sometimes, someone wins big. Most of the time, money quietly evaporates. Domain expertise doesn’t magically transfer, but confidence often does.

FOMO plus overconfidence is a devastating combination.

Money Vacuum #8: When Life Just Happens

Illness. Accidents. Divorce. Family emergencies.

These hit everyone, but when high earners fall, they fall harder. More money at risk means more damage.

The Brutal Stack Effect

Each of these choices seems reasonable in isolation. Together, they can devastate even the highest compensation packages in tech, leaving engineers frighteningly exposed when layoffs arrive.

That’s why so many “rich” engineers aren’t actually secure.

My Own Layoff: A Different Outcome

My recent layoff looked very different.

After years of saving and investing, I wasn’t thrown into survival mode. I landed somewhere closer to semi–early retirement.

It feels a bit like being kicked off the Hindenburg right before it explodes, standing just outside the blast radius, watching the slow-motion catastrophe unfold across the tech industry.

It’s horrifying. And, admittedly, impossible not to watch.

Final Thoughts

This isn’t about shaming. It’s about awareness, empathy, and hard-earned perspective.

If you work in tech (or hope to) you deserve to understand the invisible forces pulling at your finances. High income doesn’t guarantee safety. Intentional choices do.

If you’re morbidly curious about my ongoing journey through tech, layoffs, and financial independence, feel free to stick around.

Thanks for reading.

Talk soon.

Hello, world.

I’m a software engineer who was recently laid off from Big Tech after more than 25 years in the industry. Over the past few weeks, I’ve received a steady stream of questions from college students studying computer science, mostly young guys asking some version of the same thing:

“Is it still worth it?”“Should I even keep pursuing tech?”

Those questions have taken me back to my own early days as a junior engineer, fresh out of college, stepping into a world that feels almost unrecognizable compared to today.

When a Diploma Was Enough

When I started my career, simply having a computer science degree was often enough to get your foot in the door. I remember joining a consulting company and being dropped straight onto a high-pressure project, a CMS launch for a major telecom client, barely a month from go-live.

As a junior developer, I was immediately assigned bug-fixing duties. There was just one small problem: I didn’t actually know how professional software development worked.

My degree hadn’t taught me about unit testing, mocking data, runtime debugging, or even basic dependency management in Java. Predictably, many of the bugs I “fixed” failed spectacularly in higher environments.

One day, I spent hours stuck on an error caused by a library version mismatch, an “unsupported major/minor version” issue. I didn’t know what that meant, so I did what any confused junior engineer might do: I started exploding the Java package and inspecting class files by hand.

Eventually, our build master, let’s call him Dima, wandered over. Dima was kind, brilliant, and fond of deeply pessimistic jokes. He laughed, explained the issue in about 30 seconds, and fixed it. For the rest of the project, I was lovingly known as “the guy who exploded his package.”

Becoming a Production Cowboy

That project also introduced me to my first real tech lead, Naga. During our go-live night, we stayed up until the early morning hours, fixing one production-blocking defect after another.

At one point, after midnight, things seemed stable enough that Dima went home. Naturally, that’s when everything broke.

I vividly remember Naga manually editing configuration files directly on production servers, no pipelines, no guardrails, just raw experience and calm under fire. He may not have been the most disciplined engineer by today’s standards, but he was an exceptional leader and a genuinely good human being.

Around 5 a.m., when the site finally went live, Naga told me something I’ve never forgotten:

“The most important thing isn’t fixing the bugs.It’s learning from them and having fun doing it.”

Fear, Impostor Syndrome, and the Desire to Get Better

Being a junior engineer back then was a cocktail of emotions: fear, excitement, anxiety, hope. I had massive impostor syndrome but also a deep hunger to improve, to master the craft, and to earn my place in the world.

Throughout my career, I was lucky. I had mentors like Naga who guided me, supported me, and believed in me. As I advanced, I tried to pay that forward, mentoring junior engineers and later running internship programs at my most recent company.

Which brings me to today.

The World Junior Engineers Are Entering Now

The environment today could not be more different.

There are simply far more engineers on the job market. Years of “learn to code” messaging from governments, universities, and Big Tech itself have saturated the pipeline. As a result, a computer science degree is worth dramatically less than it was a generation ago.

Junior engineers today are competing with:

* Mid-level engineers willing to take junior roles

* Laid-off seniors swallowing their pride to stay employed

* Offshore engineers working for lower wages

* Senior engineers supercharged by AI tools

* And, increasingly, GPU-filled data centers consuming budget once reserved for humans

The cruel irony? New graduates are often more capable than I ever was at their age.

I once had an intern architect, build, and deploy a production-ready cloud application that the company actually used. He still didn’t get a full-time offer. Not because he wasn’t good, but because there simply weren’t enough entry-level roles.

Worse still, even when juniors do get hired, support systems feel thinner than ever. There are incentives to discard them quickly rather than invest in their growth.

It feels like the industry is wasting an entire generation of smart, motivated engineers and that feels deeply unjust.

Why This Hits Close to Home

My own son is STEM-inclined. He’s a teenager and recently submitted his first pull requests to an open-source project. Watching him navigate this world has made these questions impossible to ignore.

So instead of offering platitudes, I want to share a few practical coping strategies, things that, in my experience, can genuinely move the needle.

1. Be True to Yourself

If you genuinely enjoy computer science, even if you just kind of like it, keep going.

But if you hate it? If you’re only here because of parental pressure, TikTok salary videos, or the promise of easy money, pause.

Don’t betray your inner voice.

I’ve met many people who stayed in tech for external reasons alone, and for most of them, it became a source of deep regret.

2. Build Eminence, Not Just a Portfolio

Personal projects are fine, but what really matters is recognized work.

Build software for your university or a nonprofit. Contribute meaningfully to open-source projects. Speak at meetups. Join hackathons and try to win them.

You want institutions, organizations, and communities to vouch for your work.

3. Maximize Internships Ruthlessly

Internships aren’t a guarantee but they are orders of magnitude more effective than cold applications.

Use career fairs, alumni networks, professors, and professional connections. If you miss the internship window, compensate aggressively elsewhere.

4. Treat the Job Hunt Like a Skill

Get your résumé professionally reviewed. Practice LeetCode and HackerRank until interviews feel routine. Do mock interviews. Apply broadly, not just to tech companies, but to any company hiring software engineers.

If needed, consider adjacent roles: QA, DevOps, data engineering, sales engineering. Once inside, you can pivot.

5. Be Flexible—Like Water

If Big Tech isn’t hiring, consider partnerships, gigs, startups, or solo projects. Be open to relocation, even to another country.

Flexibility creates surface area. Surface area creates opportunity.

A Final Thought

I make no promises. The system is undeniably hard right now.

But I do believe that for most of you, these strategies, applied consistently, will help.

If nothing else, I hope this reflection reminds you that you’re not alone, and that the struggle you’re feeling is real, not a personal failure.

If you’re curious to follow along as I navigate life after Big Tech, I’ll be sharing more of these thoughts. It turns out, making these vlogs and writing pieces like this feels surprisingly meaningful.

Thanks for reading.Take care.

Hello, world.

Two weeks ago, I was laid off from Big Tech. After twenty-five years in the technology industry, it was my first layoff and it landed with a strange mix of confusion, disorientation, and, unexpectedly, a flicker of excitement. When you’ve spent most of your adult life on a clearly marked path, being pushed off it feels less like failure and more like suddenly waking up in unfamiliar terrain.

Learning the Craft

My relationship with technology began early. In high school, I was already writing code, building computers, and tinkering with anything that had wires or logic behind it. By college, I was juggling internships and small contracts: writing scripts, building Visual Basic apps, learning by doing. After graduation, I joined digital consulting agencies, and those years felt electric. Every project brought a new language, framework, or platform. The world of software engineering felt infinite.

I worked obsessively at the craft. I devoured technical books, lived in IDEs, and chased mastery with the kind of hunger only youth allows. For a time, it worked. I became strong and confident enough that my company sent me to hackathons, where my teams consistently placed near the top. Software felt like magic, and I felt fluent in it’s language.

The Shock That Changed Everything

Then came 2008.

During the financial crisis, I watched brilliant, experienced colleagues lose their jobs. One mentor—someone I deeply respected—broke down in tears, terrified she wouldn’t be able to provide for her family. That moment cracked something open in me. It was also when I discovered the idea of financial independence. A book, Your Money or Your Life, reframed how I thought about work, not as a ladder, but as an exchange of life energy for money.

From that point on, my family changed how we lived. We became deliberate. Frugal. Intent on saving not just money, but time and choice. It wasn’t about deprivation; it was about buying freedom.

Climbing the Ladder

My career continued to grow. I moved from developer to tech lead, from architect to senior technical leadership. Over twenty-five years, I changed companies, designed platforms, led teams, and eventually landed in Big Tech, where compensation reached levels that still feel absurd when I think about them.

The perks were real. I traveled across North America, Europe, and India. I worked on cutting-edge systems, earned patents, and collaborated with people who were brilliant, disciplined, and generous with their knowledge. For all its flaws, tech gave me access to extraordinary minds and experiences.

The Cost of the Grind

But the costs were real too.

Tech is relentless. Long hours became longer once teams went global. Early mornings blended into late nights. Learning never stopped, and in my forties, learning started to feel heavier, slower. Still possible, but harder.

Worse than the fatigue was the creeping sense of futility. The problems we solved felt recycled. The products rarely made the world meaningfully better. I poured years of effort into systems that would be replaced, forgotten, or quietly deprecated.

That time came from somewhere, mostly my family. There were years when I had only an hour each night with my kids. An hour to read, talk, tuck them in. My son is a teenager now, and I can feel how little time remains before he’s grown. That realization weighs heavily: how much of what I missed can never be recovered.

Mortality and Memory

A few years ago, a colleague, brilliant, hardworking, devoted, he fell ill during a brutal push at work before Christmas. He never came back. The company honored him respectfully, but within a month, it was as if he’d never existed. That realization lingered.

Then, earlier this year, my wife suffered a serious health crisis that left her partially disabled. It shattered the illusion that time is guaranteed. Tomorrow isn’t promised. And once you truly understand that, trading your life for work that doesn’t matter feels indefensible.

Choosing the Layoff

Meanwhile, the industry itself was changing. Interest rates rose. Offshoring accelerated. AI tools reduced the need for human labor. Layoffs came in waves. When my turn arrived, I learned I could protect several junior colleagues by putting myself first on the list.

So I did.

My former employer treated me fairly, with warning, severance, and respect. For that, I’m grateful. But the chapter still ended.

Writing the Next One

Now I stand somewhere unfamiliar. The path I followed for decades is gone, replaced by countless trails, none yet chosen. It feels like wilderness, uncertain, but alive with possibility.

This is an inflection point. I need to design a life that is mentally healthy, physically sustainable, and genuinely meaningful. That’s why I’m writing. Why I’m recording. Why I’m documenting this moment.

The future is an undiscovered country. And for the first time in a long while, I get to decide how the story is written.

—Thanks for reading. Talk soon.

Hello, world.

After 25 years in the tech industry, I recently found myself laid off from Big Tech, an experience that has become increasingly common over the last couple of years. With some unexpected time on my hands, I’ve been doing what engineers do best: thinking deeply about systems, incentives, and failure modes.

This time, the system in question is AI and more specifically, what the current AI boom means for software engineers.

The AI Gold Rush Nobody Asked For

If you’ve worked in Big Tech recently, you’ve seen it firsthand. Every department, every product, every roadmap meeting eventually funnels toward the same conclusion:

“Can we add AI to this?”

Hackathon after hackathon, teams are pulled together to “ideate” around AI, often regardless of whether the problem actually calls for it, or whether the people involved have any background or interest in AI at all.

But beyond the buzzwords and demo days lies a far more uncomfortable question:

What does all of this mean for software engineering jobs?

What Generative AI Is and What It Isn’t

Generative AI, particularly large language models, are fundamentally probabilistic systems. When you type a prompt into a model like ChatGPT, each word in its response is simply the most statistically likely word to follow the previous one, based on massive amounts of training data.

The result looks like reasoning. It sounds coherent.But there’s no actual understanding happening.

Despite the popular narrative, we don’t even fully understand how human reasoning works, so the idea that scaling LLMs linearly leads to Artificial General Intelligence feels, at best, optimistic and, at worst, fantastical.

What we have today isn’t a machine god.It’s more like an idiot savant, exceptionally good at mimicking logic without possessing it.

And yet… that’s still enough to disrupt an industry.

Why Software Is Especially Vulnerable

History gives us a useful analogy. In the 19th century, agriculture didn’t require humanoid robots to be automated. Tractors and harvesters, much simpler machines, were enough to replace massive amounts of human labor.

Software engineering may be facing a similar moment.

Most software written today is repetitive, predictable, and digital by nature—making it ideal training data for AI. Unlike other forms of engineering, software exists almost entirely as text, freely available in repositories across the internet.

That makes it one of the easiest professions for AI to encroach upon.

The Cracks Beneath the Hype

That said, the current generation of AI tools has real limitations:

* Training data quality is inconsistent.Open-source code varies wildly in quality, age, and correctness.

* AI increasingly trains on AI-generated code.This creates a feedback loop, a snake eating its own tail, where quality can degrade over time.

* Context windows are limited.Large codebases don’t fit cleanly into an AI’s short-term memory, leading to hallucinations and subtle but dangerous bugs.

* Critical work remains semi-analog.Requirements gathering, stakeholder negotiation, system design, and architectural judgment are not fully digitized, and therefore not easily learned by machines.

The result? AI excels at greenfield code but struggles with large, messy, real-world systems.

Fewer Engineers, Different Roles

Even with these limitations, it’s hard to deny the trajectory. AI already produces code that is good enough most of the time and it will continue to improve.

The likely outcome isn’t the total elimination of engineers, but rather:

* Fewer roles overall

* Greater emphasis on human judgment

* More value placed on reasoning, intuition, and systems thinking

This isn’t unprecedented. Aerospace and electrical engineering went through similar transitions long ago.

Four Ways to Survive the AI Tiger

Right now, it feels like AI is a tiger chasing the tech industry. I see four possible coping strategies:

1. Ride the Tiger

Work on frontier AI itself.

* Pros: High pay, world-changing potential

* Cons: Brutal competition, high barriers to entry, and the risk of an AI winter

2. Outrun the Tiger

Constantly chase the newest languages and frameworks.

* Pros: Short-term job security and high compensation

* Cons: Career roulette, cognitive fatigue, and diminishing returns with age

3. Tame the Tiger

Become a generalist who orchestrates AI tools.

* Pros: Massive leverage, small teams, long-term relevance

* Cons: Everyone is trying this, and true generalists are rare

4. Hide from the Tiger

Work in regulated or hard-to-digitize industries.

* Pros: Stability for now

* Cons: Digitization eventually comes for everyone

No Perfect Ending, Only Tradeoffs

I don’t see a future where everyone in tech wins. But I do see paths where some engineers adapt, specialize, and survive.

This isn’t optimism. It’s systems thinking.

And yes, I know this sounds a little bleak but it’s my honest assessment after spending a long time thinking about the problem.

That said, I’m just a laid-off ex–Big Tech flunky.You should take everything I say with a generous grain of salt.

If you’re curious to follow along as I navigate life after Big Tech, thinking, building, and occasionally ranting, then feel free to subscribe and join the journey.

Thanks for reading.Talk soon.

Hello, world.

A little over a week ago, I shared a vlog about being laid off from Big Tech after twenty-five years in the industry. I didn’t expect what happened next. The response was overwhelming, in the best possible way. Messages of encouragement, thoughtful advice, shared stories, and hard-earned wisdom poured in. I want to start by saying thank you. Truly.

I also want to acknowledge something important: this is an extraordinarily difficult moment for laid-off engineers everywhere. I know I’m one of the fortunate ones. Years of saving, investing, and a bit of luck have given my family some stability. Many others don’t have that buffer. Life intervenes (illness, obligations, timing…etc) and suddenly survival mode replaces reflection. And for junior engineers just entering the field, my heart really goes out to you. You’re facing forces far beyond your control, and that’s an especially brutal place to be.

Because of my circumstances, I have options. Over the past few weeks, I’ve been exploring three broad paths forward, weighing each carefully.

Option One: Back to Big Tech

The first option is simple, at least in theory: get back into the grind. Apply for another Big Tech role. For me, this path has exactly one upside: staggering compensation.

Out of habit and inertia, I started applying. Dozens of applications disappeared into silence. Some were rejected within minutes by automated systems. Eventually, I landed a single real screening and moved forward to a technical interview.

While preparing, grinding through Java problems on LeetCode, I paused and remembered someone from my past.

In 2010, freshly promoted and full of confidence but short on wisdom, I interviewed a man I’ll call Bob. He was in his fifties, gray-haired, intense. I grilled him relentlessly. Design patterns. Concurrency. An LRU cache from scratch. He handled everything effortlessly. Only later did I learn he had worked on the original Java language and JVM at Sun Microsystems, experience he’d removed from his résumé just to stay employable.

In the debrief, everyone agreed Bob was technically excellent. But the verdict was clear: “not a cultural fit.” Looking back, I don’t think Bob was aggressive. I think he was desperate.

Now, years later, preparing to interview with someone half my age, I realized something uncomfortable: I had become Bob.

That insight ended my Big Tech job search. The cost to dignity, identity, and peace simply isn’t worth the paycheck.

Option Two: Early Retirement

The second option is early retirement. After nearly seventeen years of living below our means, my family has built enough of a cushion that paid work is optional. Time, real time, has become mine again. Time for family, health, friendships, and simply being present.

There’s a lot to love about this option. Compounding works quietly in the background. In theory, wealth continues to grow while life slows down.

But two concerns linger.

First, the system itself. Capitalism, as we know it, isn’t guaranteed. AI investments, economic shocks, black swan events, any of these could reshape the world in ways that make passive income less reliable. A backup plan feels prudent.

Second, meaning. Most men, once their basic needs are met, feel an internal pull to build something to create, contribute, matter. Leisure alone doesn’t satisfy that drive.

Option Three: Meaningful Work

That leads to the third option: doing work that actually matters.

Not hustle-for-hustle’s sake. Not grinding endlessly. But building something meaningful whether with others or on my own. Projects, tools, ideas that make a dent, however small.

This path isn’t easy. It requires a shift from executor to creator, from employee to founder. There are no guarantees anyone will care about what I build. Still, this option feels alive in a way the others don’t.

The Path Ahead

Where does that leave me?

Somewhere between option two and option three. Semi-retired, but curious. Financially independent, but still driven. I plan to experiment, to throw ideas at the wall and see what sticks. Build slowly. Learn openly. Stay human.

If you’re morbidly curious about how this unfolds, you’re welcome to follow along. I don’t have answers yet but I’m finally asking the right questions.

Until next time.