AsianDadEnergy's Podcast

When I got laid off from my Big Tech job late last year, I thought I knew what came next.

Shock.

Anger.

Shame.

Sadness.

If you’ve ever been laid off, you probably know the feeling. It is as if someone has ripped away not just your paycheck, but your identity. After spending twenty five years in the technology industry, I suddenly found myself unemployed.

Meanwhile, every week seemed to bring another announcement of mass layoffs across the tech industry. Friends were losing jobs. LinkedIn became an endless stream of people posting that they were “excited to announce” they were looking for work.

It was depressing.

But something unexpected happened.

Over the following months, I slowly came through the other side.

Instead of waking up every morning dreading another day of meetings, deadlines, and corporate politics, I started waking up excited.

Some days felt almost euphoric. There were so many things I wanted to learn, build, and explore that I genuinely did not want the day to end.

My layoff had accidentally pushed me into something I never would have chosen for myself.

Early retirement.

I Know I’m One of the Lucky Ones

Before going any further, I want to acknowledge something important.

This is only possible because I spent many years earning a high salary in tech while aggressively saving and investing.

For well over a decade, my wife and I consistently lived below our means.

We invested.

We avoided lifestyle inflation.

We paid off debt.

Over time, those boring financial decisions compounded into something extraordinary.

Financial independence.

Without that foundation, this story would have been very different.

I understand that.

I do not take that privilege lightly.

The People I’m Really Writing This For

Ironically, this article is not for people who have already reached financial independence.

It is for the software engineers who are still trapped.

Maybe you are still employed.

Every round of layoffs leaves your team smaller while your workload somehow becomes larger.

You spend your days juggling impossible deadlines while carrying survivor guilt because your friends lost their jobs instead of you.

Maybe you have already been laid off.

Now your life revolves around LinkedIn, recruiter calls, algorithm interviews, and endless rejection.

Maybe after decades of experience you are being offered salaries that would have seemed insulting just a few years ago.

I genuinely feel for you.

That is why I wanted to share something practical.

What actually pays my bills today?

Where My Income Comes From

People often assume that early retirement means sitting on a beach collecting investment income.

My reality looks very different.

Today my family earns just under eleven thousand dollars per month from a collection of different income streams.

Our two rental properties generate about thirty nine hundred dollars per month.

Stock dividends from our taxable investment accounts contribute roughly fifteen hundred dollars.

Interest from our high yield savings account adds another three hundred dollars.

Those are the traditional sources.

The interesting part comes next.

I Accidentally Started Getting Paid for My Hobbies

One thing nobody tells you about early retirement is that you suddenly have an enormous amount of free time.

At first, that feels strange.

Then it becomes exciting.

I started learning new things.

I planted an herb garden.

I began learning to read Chinese.

I worked toward my drone pilot license.

I learned how to swim.

Then I discovered something fascinating.

In today’s economy, it takes surprisingly little extra effort to monetize hobbies you would have pursued anyway.

That realization completely changed how I think about work.

YouTube Started as Therapy

I never planned to become a YouTuber.

After my layoff, I fell into a depressive spiral.

My therapist suggested journaling.

Writing never really worked for me.

Talking did.

So I started recording videos.

That simple habit became surprisingly therapeutic.

Then something else happened.

People started watching.

Even more importantly, they started responding.

After years of talking with coworkers every day, unemployment can become surprisingly lonely.

These conversations became my new social outlet.

Comments turned into discussions.

Discussions became livestreams.

Livestreams became friendships.

Eventually YouTube started paying me around twenty eight hundred dollars per month.

Other creator platforms contribute another couple hundred dollars.

The money is nice.

The sense of community is even better.

Coaching Doesn’t Feel Like Work

Another unexpected hobby became coaching.

I schedule one on one conversations with people using Calendly.

Sometimes we talk about software engineering.

Sometimes financial independence.

Sometimes career transitions.

Sometimes life.

The conversations are genuinely enjoyable.

After decades of solving problems with people, I realized communication is like a muscle.

If you stop using it, it weakens.

Coaching lets me keep exercising that muscle while earning around twelve hundred dollars per month.

I Fell Back in Love With Programming

Ironically, I enjoy programming far more now than when I was employed as a software engineer.

Without deadlines.

Without sprint planning.

Without endless meetings.

Without office politics.

Coding became fun again.

I build software that scratches my own itch.

Sometimes those projects make money.

Sometimes they do not.

Either outcome is perfectly fine.

One project was an AI powered workflow that generated wall art for online marketplaces.

I ran it for only two days before deciding it created very little real value.

I shut it down.

The listings still earn around seven hundred dollars every month.

Another project called Funemployment Day began as a personal tool for tracking my time after leaving corporate life.

Today it has hundreds of users and some paying subscribers.

Combined, my software projects generate around one thousand dollars each month.

Agentic AI Changed Everything

One thing that genuinely surprised me is how dramatically AI has lowered the barrier to entrepreneurship.

Not because it writes perfect code.

It absolutely does not.

But because it removes so much of the boring work surrounding a project.

Once I publish a YouTube video, AI helps automate titles, descriptions, transcripts, articles, short form videos, and publishing across multiple platforms.

Instead of spending hours on repetitive tasks, I spend my time thinking, building, and creating.

That feels like a much better use of both humans and machines.

Is This a Preview of the Future?

Altogether, these income streams generate just under eleven thousand dollars every month.

For some readers, that sounds like a fortune.

For others, it is much less than their current salary.

The number itself is not really the point.

The point is that none of this income comes from a traditional full time job.

Instead, it comes from assets, hobbies, creativity, relationships, and small software products.

Sometimes I wonder if this is a tiny glimpse of what a post labor economy might eventually look like.

Not necessarily a utopia.

Perhaps something much stranger.

As artificial intelligence continues to automate more forms of work, I suspect many more people will eventually find themselves earning income from portfolios of projects rather than a single employer.

Maybe this is the future.

Maybe it is not.

I honestly do not know.

What I do know is that my layoff forced me onto a path I never would have chosen voluntarily.

Looking back now, I am grateful it happened.

If you are currently burned out, unemployed, or wondering whether there is life beyond the corporate ladder, I hope my story gives you at least one idea worth exploring.

Sometimes the worst day of your career becomes the beginning of an entirely different life.

Every once in a while, I experience a feeling that I’m not particularly proud of.

It’s the feeling that everybody else is rich.

They all seem to have more money than I do. Bigger houses. Newer cars. Better vacations. Better lives.

And somehow, despite everything I’ve accomplished, I feel like I’m falling behind.

These days it doesn’t happen very often, but every now and then it sneaks up on me.

Maybe I’m watching YouTube and someone is giving a tour of their enormous house. Maybe I’m watching Netflix, where every “ordinary” family somehow lives in a multimillion dollar home. Or maybe I’m driving my used Honda CR-V when another successful-looking middle-aged Asian guy cruises by in a brand new Tesla.

For a brief moment, my brain whispers:

“What happened? Did everyone else figure something out that I didn’t?”

Then almost immediately I feel embarrassed for thinking that way.

Because I know something that my emotions conveniently forget.

Most people are not rich.

The Data Doesn’t Match the Feeling

If you look at the numbers, only a tiny percentage of American households qualify as liquid millionaires, meaning they have at least one million dollars in investable assets.

Even among that group, a million dollars isn’t exactly “private jet” money.

Where I live, in a high cost of living area, a million dollars doesn’t even buy many single-family homes.

Objectively speaking, the overwhelming majority of Americans are not wealthy.

So why does it constantly feel like they are?

I’ve spent quite a bit of time thinking about this question, and I think I’ve come up with a few reasons.

We Are Wired to Follow the Herd

Human beings are social animals.

Like apes, elephants, dolphins, and countless other social species, we instinctively look to the group for cues about what success looks like.

The problem is that many people don’t have clearly defined personal goals.

Without an internal compass, it’s incredibly easy to borrow someone else’s definition of success.

Instead of asking ourselves what actually matters, we begin copying the outward appearance of people society celebrates.

The expensive house.

The luxury car.

The designer clothes.

The exotic vacations.

The visible symbols become the goal, even if they have very little to do with happiness.

Social Media Is a Distortion Machine

Social media takes this very human tendency and cranks it up to eleven.

People naturally want to share good news.

Nobody posts pictures of arguing with their spouse over credit card bills.

Nobody uploads videos of lying awake at 2 AM wondering how they’re going to make next month’s mortgage payment.

Nobody posts photos of the panic attack they had after realizing they financed a lifestyle they can’t actually afford.

Instead, everyone uploads their highlight reel.

You see promotions.

New homes.

Perfect vacations.

Luxury purchases.

Beautiful weddings.

Smiling families.

You almost never see the anxiety that paid for those pictures.

Social media also magnifies survivorship bias.

Think about lottery winners.

Think about meme stock millionaires.

Think about people who got lucky with speculative cryptocurrencies.

Millions of people lost money chasing exactly the same opportunities.

But you only see the handful of winners.

After enough exposure, your brain quietly concludes that extraordinary success is normal.

It isn’t.

Consumerism Needs You to Feel Inadequate

America is fundamentally a consumer economy.

Consumption isn’t simply encouraged.

It’s necessary.

If everyone suddenly stopped buying things they didn’t need, the economy would grind to a halt.

Advertising understands something about human psychology.

People don’t buy products.

They buy identities.

Watch almost any commercial.

Notice the homes.

The neighborhoods.

The kitchens.

The cars.

The vacations.

The wardrobes.

The lifestyles being presented often resemble families in the top income brackets, yet they’re marketed as completely normal.

It’s subtle.

But over time, it recalibrates what “normal” looks like.

The result is that millions of people begin chasing a lifestyle that statistically very few can actually afford.

Debt Makes Everyone Look Rich

Here’s the uncomfortable reality.

Most Americans don’t possess enormous amounts of wealth.

Yet many appear wealthy.

How?

Debt.

Modern finance allows almost anyone to temporarily purchase the appearance of financial success.

Luxury cars.

Expensive homes.

Designer furniture.

High-end electronics.

Exotic vacations.

None of these necessarily indicate wealth.

Many simply indicate access to credit.

The problem is that debt has a hidden cost.

Financial stress.

Relationship problems.

Mental health struggles.

Lost opportunities.

Years, sometimes decades, spent servicing yesterday’s consumption.

Ironically, these displays of wealth create a psychological burden for everyone else.

The illusion makes perfectly successful people feel like failures.

I’ve certainly experienced it.

Maybe you have too.

The Bigger Picture: The K-Shaped Economy

I think something even larger is happening beneath the surface.

Our economy increasingly resembles a K.

One branch rises upward.

The other falls behind.

The upper branch consists largely of people who own productive capital.

Stocks.

Businesses.

Real estate.

Increasingly...

Artificial intelligence.

Robotics.

Automation.

These assets can continue creating value twenty-four hours a day.

Meanwhile, the lower branch depends primarily on labor.

The problem is that labor doesn’t scale the way capital does.

As AI improves, capital becomes dramatically more productive.

Human labor, on the other hand, doesn’t become exponentially more valuable simply because technology advances.

In fact, many forms of labor may become less valuable over time.

That doesn’t necessarily mean society is doomed.

Perhaps governments adapt.

Perhaps entirely new industries emerge.

Perhaps we’ll experience major political or economic reforms.

Nobody knows.

But I do think the gap between owners of capital and sellers of labor is becoming one of the defining stories of our generation.

If you’re fortunate enough to work in a high-paying field like technology, I believe one of your biggest priorities should be gradually converting income into ownership.

Because ownership compounds.

Labor generally does not.

Three Ways I Try to Stay Grounded

I don’t have all the answers.

But these habits genuinely help me.

1. Define your own version of success.

Don’t inherit someone else’s goals.

Figure out what actually matters to you.

Financial security.

Freedom.

Family.

Purpose.

Recognition.

Adventure.

Whatever it is, make sure it’s yours.

Once you know where you’re going, other people’s lives become much less distracting.

2. Compare yourself with your past self.

Comparison really is the thief of joy.

Especially when you’re comparing yourself against someone whose financial situation may be almost entirely fictional.

Instead, ask a simpler question.

Am I a better version of myself than I was five years ago?

If the answer is yes, you’re winning.

Celebrate that.

3. Minimize social media.

Yes, I realize the irony.

You’re probably reading this because the internet recommended it.

But I genuinely believe excessive social media consumption is one of the most damaging habits for our mental health.

It constantly encourages us to compare our ordinary lives against everyone else’s carefully curated highlights.

That’s a game you can never win.

Sometimes the healthiest thing you can do is simply close the app.

Final Thoughts

I’ve come to believe that the feeling that “everyone else is rich” says far more about the world we live in than it does about our actual financial situation.

We’re surrounded by advertising.

We’re immersed in social media.

We’re encouraged to borrow.

We’re constantly shown lifestyles that belong to a tiny fraction of the population and told they’re normal.

No wonder so many of us feel like we’re falling behind.

But the feeling is not reality.

Reality is quieter.

Reality is slower.

Reality is built over decades of consistent decisions rather than viral moments and luxury purchases.

So the next time I see another shiny Tesla drive by while I’m sitting in my aging Honda CR-V, I’ll try to remind myself of something simple.

I don’t need to win someone else’s race.

I only need to keep making progress in my own.

And honestly, that’s more than enough.

A few weeks ago, I stumbled across a debate that has been making the rounds in software engineering circles.

The spark came from Boris Cherny, an engineer at Anthropic and the creator of Claude Code, arguably the most influential AI Agentic Coding harness in the world today.

During a podcast appearance, Boris made a statement that immediately grabbed my attention:

Coding is largely a solved problem.

He went on to explain that he hadn’t written a line of code by hand since November, and that essentially all of his code is now authored by Claude Code.

Needless to say, this generated strong reactions.

Some people interpreted it as evidence that software engineering is about to be fully automated. Others saw it as confirmation that AI coding tools are delivering unprecedented productivity gains.

As someone with twenty-five years of experience in software development, I found myself somewhere in the middle.

Because while I absolutely believe AI is transforming software engineering, my own experiences suggest that programming is nowhere close to being a solved problem.

In fact, the more I use these tools, the more complicated the situation appears.

The Rise of Agentic Software Development

Over the past few years, we’ve witnessed a rapid evolution in how software gets built.

First, developers used AI to generate snippets of code.

Then they began using AI assistants to complete larger programming tasks.

Today, we’re entering what many people call Agentic Software Development.

Instead of asking an AI for a few lines of code, developers increasingly delegate entire workflows.

The AI can analyze requirements.

Generate designs.

Write code.

Create tests.

Review its own output.

Deploy software.

Monitor production systems.

In theory, the human becomes less of a programmer and more of an orchestrator.

The promise is obvious.

If AI agents can perform most of the implementation work, then software engineers can become dramatically more productive.

Ten times more productive, according to some advocates.

Perhaps even more.

At least, that’s the dream.

My First Encounter With Enterprise Agentic AI

In 2025, I returned to work after a lengthy medical leave.

My wife had experienced a serious health crisis, and I had spent months focused almost entirely on family.

When I came back, one of the first things I noticed was that my employer had become obsessed with Agentic AI.

Leadership had heard about tools like Claude Code and Cursor.

They had heard stories about developers becoming ten times more productive.

Naturally, they concluded that our company needed its own internal version.

Let’s call it Kevin.

Kevin was our homegrown agentic harness.

Compared to Claude Code, Kevin felt slower, heavier, and burdened with enterprise compliance guardrails.

It ran on older-generation models.

It often struggled with context.

And yet, despite all its flaws, Kevin was still capable of orchestrating significant portions of software development.

Using Kevin gave me a front-row seat to what Agentic AI actually looks like inside a large enterprise.

What I observed left me both impressed and concerned.

The Business Knowledge Problem

One of the biggest weaknesses I encountered had nothing to do with coding itself.

It had to do with understanding.

AI models are remarkably good at generating software.

What they are not particularly good at is understanding the business context behind that software.

Organizations often operate on thousands of unwritten assumptions.

Knowledge exists in hallway conversations.

Slack threads.

Meeting notes.

Institutional memory.

The heads of senior employees.

Much of this information never appears in formal documentation.

Humans navigate these gaps naturally.

AI agents do not.

If a requirement is not explicitly documented, the AI will often substitute something that appears reasonable based on its training data.

The generated code may compile successfully.

The unit tests may pass.

The architecture may look elegant.

And yet the implementation may completely miss the actual business objective.

This problem becomes particularly severe in large brownfield systems where decades of accumulated business logic exist beneath the surface.

The Context Window Wall

Another challenge is context.

Every AI model has a limited working memory.

For small projects, this isn’t a major issue.

For enterprise software systems containing millions of lines of code, it becomes a constant battle.

Once an AI agent exceeds its effective context window, strange things begin to happen.

The model forgets previous decisions.

It hallucinates APIs.

It reintroduces deprecated libraries.

It generates solutions that were already rejected earlier in the workflow.

Developers have created countless mitigation strategies.

Context compaction.

Summarization.

Subagents.

Selective context filtering.

These techniques help.

But they don’t eliminate the underlying limitation.

The larger the system becomes, the harder it is for the AI to maintain a coherent mental model of the entire application.

Ironically, this is often where software engineering is most difficult in the first place.

The Hidden Cost of Infinite Code

One thing AI agents are exceptionally good at is generating code.

Lots of code.

An astonishing amount of code.

Thousands of lines.

Tens of thousands of lines.

Entire subsystems can appear almost instantly.

The problem is that quantity and quality are not the same thing.

Many AI-generated implementations contain unnecessary abstraction layers.

Duplicate functionality.

Excessive complexity.

Architectural choices that seem reasonable locally but become problematic globally.

Without strong human oversight, repositories begin accumulating what can only be described as AI sediment.

Layer upon layer of generated code.

Each piece understandable in isolation.

Collectively becoming harder and harder to maintain.

Technical debt compounds quietly.

And unlike financial debt, software debt often remains invisible until it becomes a crisis.

The Vibecoding Trap

This brings us to what I believe is the most important problem.

Human review capacity.

A team of AI agents can generate thousands of lines of code per hour.

A human engineer cannot review thousands of lines of code per hour with high confidence.

The math simply doesn’t work.

As output increases, review quality inevitably declines.

Cognitive fatigue sets in.

Attention drops.

Comprehension weakens.

Eventually, the reviewer stops acting as an engineer and starts acting as a rubber stamp.

This is the danger behind what many people call vibecoding.

The developer repeatedly prompts the AI until something appears to work.

The code ships.

Nobody fully understands it.

Nobody feels ownership over it.

And nobody wants to maintain it six months later.

At that point, accountability becomes largely fictional.

The engineer remains responsible for the software without truly possessing the knowledge necessary to evaluate it.

So Is Coding Solved?

Despite everything I’ve written, I remain incredibly optimistic about AI.

These tools are genuinely transformative.

For greenfield projects, prototypes, internal tools, and well-understood problem domains, the productivity gains are astonishing.

Recently, I used Agentic AI to build one of my own projects.

The agents completed work in hours that might previously have taken days.

The productivity boost was real.

But here’s the important distinction:

The AI performed the implementation.

I still spent days reviewing the code, testing the software, validating assumptions, and ensuring everything actually worked.

The bottleneck moved.

It didn’t disappear.

And that’s why I struggle with the claim that coding is solved.

Perhaps code generation is becoming solved.

Perhaps implementation is becoming increasingly automated.

But software engineering has always been about much more than typing characters into an editor.

It involves judgment.

Tradeoffs.

Domain expertise.

System design.

Risk management.

Human communication.

Institutional knowledge.

And responsibility.

None of those problems appear solved to me.

The Next Rabbit Hole: Loop Engineering

What’s particularly fascinating is that many of the engineers pushing Agentic AI furthest seem to be moving beyond prompting altogether.

Boris Cherny has suggested that developers should stop prompting and start building loops.

Peter Steinberger has made similar arguments.

The idea is that autonomous agents should continuously generate, evaluate, and refine their own work.

This concept is often referred to as Loop Engineering.

I’ve spent time reading about it.

Experimenting with it.

Trying to understand it.

And if I’m being honest, I still don’t entirely get it.

What’s more frustrating is that concrete, end-to-end examples remain surprisingly rare.

The discussions often feel abstract.

Almost mystical.

As if everyone has seen the future except the people trying to build software today.

Maybe that’s because we’re still in the earliest stages of this transition.

Or maybe we’re collectively mistaking experimentation for certainty.

Either way, I’m not convinced we’ve arrived at the destination yet.

My Current Conclusion

Agentic AI is one of the most important technological developments of my career.

It is already changing how software gets built.

It will continue changing how software gets built.

But from where I sit, coding does not look like a solved problem.

It looks like a rapidly evolving one.

And that’s actually far more interesting.

For now, I’ll keep experimenting.

I’ll keep learning.

And I’ll keep trying to separate the genuine breakthroughs from the hype.

Because if the future of software engineering really is being rewritten by AI agents, I’d like to understand what’s actually happening beneath the marketing slides.

And if you’re curious too, you’re welcome to come along for the ride.

A few days ago, my wife received a credit card in the mail from a company we had never used.

Apparently, someone had opened an account in her name.

Naturally, I assumed identity theft and immediately called the credit card company to shut the account down and find out what information had been used to create it.

After spending twenty minutes navigating an infuriating AI phone system, I finally reached a human being.

Or at least, I think I did.

The conversation felt strange.

Every question I asked seemed to trigger a predetermined response. Every attempt to move the conversation in a logical direction was met with another scripted answer. It was as if the representative was following a flowchart that he could not deviate from under any circumstances.

At one point I caught myself wondering whether I was talking to another AI.

I wasn’t.

It was clearly a real person.

Yet somehow the interaction felt less human than many conversations I have had with chatbots.

That experience stuck with me because it wasn’t an isolated incident.

Lately, I have noticed a growing number of interactions that feel strangely mechanical. Not just in customer service. Not just online. Everywhere.

People seem more scripted.

More performative.

More constrained.

Almost as if they are operating from a limited set of dialogue options.

Like NPCs in a video game.

Now before anyone gets offended, I am not saying everyone behaves this way. I meet plenty of thoughtful, authentic people. But I encounter this phenomenon often enough that I can no longer ignore it.

And it leaves me wondering:

What exactly happened?

The Corporate Mask That Never Comes Off

I first noticed this trend while working in tech.

Anyone who has spent time in a large corporation understands that some degree of performance is expected. We all wear masks at work.

That part is normal.

What felt different was seeing people become completely consumed by the performance.

Simple ideas would be transformed into elaborate slide decks.

Meetings would be scheduled to discuss future meetings.

Entire conversations would revolve around appearing aligned rather than accomplishing anything meaningful.

Everyone knew the ritual.

Everyone participated.

Nobody seemed willing to acknowledge the absurdity of it.

What unsettled me was that for some people, the corporate persona appeared to become permanent.

The mask never came off.

The language, the mannerisms, the carefully calibrated responses followed them everywhere.

Even outside of work.

Conversations Feel Different

Since being laid off, I have more opportunities to talk with people in everyday settings.

Coffee shops.

Parks.

Neighborhood events.

Random encounters.

And what I have discovered is that many conversations feel surprisingly shallow.

There is often a narrow range of approved topics.

Food.

Entertainment.

Sports.

Local events.

Anything deeper can create immediate discomfort.

Discussions about purpose, meaning, technology, society, mortality, economics, or the future often cause people to retreat.

Not because they disagree.

Because they seem exhausted.

As if they simply do not have the mental bandwidth for the conversation.

Perhaps that is the real issue.

Not that people have become less intelligent.

Not that people have become less caring.

But that people have become overwhelmed.

Theory One: We Are Overstimulated

Think about how radically the environment has changed over the past twenty years.

Most people now spend the majority of their waking lives connected to digital platforms.

Their attention is continuously pulled in dozens of directions.

Every notification competes for cognitive resources.

Every algorithm is optimized to generate emotional reactions.

Anger.

Fear.

Excitement.

Outrage.

Anxiety.

The result is a constant state of sensory overload.

When people are exhausted, authenticity becomes difficult.

Deep conversations require energy.

Curiosity requires energy.

Human connection requires energy.

And many people simply have none left.

Theory Two: We Have Been Conditioned By Work

Most Americans depend on employment to survive.

That reality shapes behavior in powerful ways.

Corporate environments reward predictability.

They reward compliance.

They reward process.

They reward metrics.

Over time, people learn to suppress parts of themselves that do not contribute directly to performance.

Creativity becomes risky.

Authenticity becomes risky.

Spontaneity becomes risky.

Eventually the performance becomes second nature.

The script becomes internalized.

And after years or decades of repetition, it becomes difficult to distinguish between the role and the person.

Theory Three: Social Media Creates Behavioral Clones

The early internet felt like exploration.

You wandered.

You discovered strange websites.

You stumbled into unfamiliar ideas.

The modern internet feels different.

Algorithms decide what you see.

Algorithms decide what you think about.

Algorithms increasingly determine which personalities rise to prominence.

Within every online community there are archetypes.

The motivational guru.

The productivity expert.

The leadership philosopher.

The lifestyle influencer.

And many people unconsciously imitate these personas because they appear successful.

Over time, entire communities begin speaking the same way.

Thinking the same way.

Reacting the same way.

Not because they independently arrived at the same conclusions.

But because they are all consuming the same inputs.

Theory Four: The Meaning Crisis

Perhaps the deepest explanation is that many people no longer know what they are living for.

Traditional sources of meaning have weakened.

Communities are fragmented.

Institutions are less trusted.

Consumerism often replaces purpose.

Individualism often replaces belonging.

The result is a quiet sense of disconnection.

A feeling that life is somehow missing a center.

When people lose connection to meaning, they often lose connection to themselves.

And when that happens, everything starts to feel performative.

Or Maybe It’s Just Me

My wife has a much simpler explanation.

She thinks people have not changed at all.

She thinks I am getting older.

According to her, I am viewing the past through nostalgia tinted glasses and imagining a level of authenticity that never actually existed.

And honestly?

She might be right.

Memory is unreliable.

Perspective changes with age.

Perhaps twenty five year old me was simply less observant.

Or perhaps middle aged me has become more cynical.

I genuinely do not know.

What I do know is that something feels different.

Whether that difference exists in society or only inside my own perception remains an open question.

So I will leave that question with you.

Do people seem more authentic today?

Less authentic?

Have social media, corporate culture, and digital life fundamentally changed the way we interact?

Or is this simply what getting older feels like?

I would love to hear your thoughts.

A recent experience with Claude 5 Fable left me both impressed and deeply unsettled.

Hello world.

I am an unemployed former Big Tech software engineer with twenty five years of experience building software systems. Over the past week, I found myself tumbling down a rabbit hole that I did not expect to be quite so deep.

That rabbit hole was Claude 5 Fable.

Fable is widely described as a consumer-facing version of Anthropic’s next generation frontier model, Claude Mythos. While details surrounding Mythos remain scarce, public reporting suggests that access is heavily restricted, with only government organizations and a small number of major technology companies allowed to interact with it under Project Glasswing.

Naturally, I was curious.

If this was the “safe” version, what exactly had been deemed too powerful for the public?

Over the course of 2 days, I put Fable through its paces. I built multiple projects with it, explored its reasoning capabilities, and examined how it approached open ended engineering problems.

What I discovered felt less like an incremental improvement and more like a discontinuity.

Not an evolution.

A step change.

And frankly, it scared me.

The Isochrone Experiment

To illustrate what I mean, let me describe a small project I assigned to Fable.

In the nineteenth century, colonial powers often produced isochrone maps. These maps visualized how far a person could travel from a central location within a given amount of time. They were part logistics tool, part demonstration of technological power.

I asked Fable to build a modern version for the United States.

The application would need to calculate travel times between cities while adapting dynamically based on transportation method, whether by automobile, rail, or air travel.

Importantly, I did not provide a detailed specification.

Normally I follow a methodology called Spec Driven Development. The process involves extensive collaboration between a human architect and AI system to create a detailed technical blueprint before any implementation begins.

This time, I deliberately skipped that step.

I gave Fable a single vague prompt and watched.

Less than an hour later, it delivered a working application.

Not a prototype.

Not a proof of concept.

A functioning solution.

The model had researched transportation data, designed the architecture, written the code, tested the implementation, and produced a polished user experience.

From one ambiguous instruction.

The Questions That Stopped Me

The finished application was impressive.

The questions were what unsettled me.

During development, Fable paused only twice.

The first question concerned whether travel times should include the time required to walk to airports and train stations.

The second asked whether traffic congestion should be incorporated into driving estimates.

These were not technical questions.

They were business questions.

Product questions.

Questions that demonstrated an understanding of ambiguity within the problem itself.

That distinction matters.

I have seen AI systems ask clarifying questions before. Typically they do so when the task is highly structured and the missing information is obvious.

This was different.

Fable identified subtle assumptions embedded inside an open ended problem and proactively sought guidance on them.

That level of judgment suggested something deeper than simple pattern matching.

Looking Behind the Curtain

At this point, curiosity got the better of me.

I began examining the session logs to understand how Fable had actually accomplished the task.

What I found was remarkable.

Fable was orchestrating an entire team of AI agents.

Several research-oriented agents were dispatched to gather transportation data. While they worked, Fable itself focused on architecture and implementation.

It then created reviewer agents tasked with examining both the system design and the generated code.

Finally, separate quality assurance agents were deployed to test the completed application.

In total, the project involved roughly eight specialized AI agents working together under Fable’s supervision.

As someone who spent years as a software architect, the workflow felt eerily familiar.

Business Analysts.

Architects.

Reviewers.

QA engineers.

The organizational structure looked less like a software tool and more like a software company.

The difference was that the entire company existed inside a single prompt.

The Cost of Delegation

The obvious reaction is excitement.

Mine certainly was.

The productivity gains are extraordinary.

The amount of cognitive labor performed by the system was staggering compared to the tiny amount of direction I provided.

But excitement was quickly followed by discomfort.

The more capable these systems become, the less visibility humans have into their decision making.

Fable made hundreds, perhaps thousands, of micro decisions during the course of the project.

Most of them were never surfaced to me.

Most of them happened autonomously.

The model simply acted.

Historically, AI functioned like a tool.

Then it became a collaborator.

Today, systems like Fable feel increasingly like autonomous organizations.

To borrow an analogy from music, AI began as a better violin.

Later, it became a virtuoso musician directed by a human conductor.

With Fable, I no longer feel like the conductor.

I feel like the patron funding the orchestra.

I provide a high level objective.

The performance unfolds largely without my input.

That shift may prove to be one of the most consequential changes in the history of computing.

The Sleeping Leviathan

For years, I have described advanced AI as a sleeping leviathan.

An immense cognitive force slumbering beneath the surface of our civilization.

We could whisper into its ear and receive useful answers.

But it remained dormant.

Contained.

Predictable.

Claude 5 Fable is the first model that made me question whether that assumption still holds.

At its core, Fable remains a probabilistic machine. It predicts tokens. It does not possess consciousness, self awareness, morality, or intent.

And yet, from a functional perspective, it is already capable of performing many forms of cognitive work at or beyond human levels.

Research.

Planning.

Design.

Coding.

Testing.

Coordination.

Judgment.

The capability is increasingly difficult to deny.

What concerns me is that capability is arriving faster than our ability to understand its implications.

Fable is an extraordinarily powerful cognitive tool.

Without safeguards, it could become an extraordinarily powerful cognitive weapon.

The technology itself does not frighten me nearly as much as the people who will wield it.

And that, more than anything, is why I believe the leviathan may finally be awakening.

Six months ago, I lost my job.

At the time, it felt like a catastrophe.

After spending twenty-five years working in technology, employment had become such a constant in my life that I could barely imagine an existence without it. Work was the backdrop against which everything else happened. It structured my days, determined where I lived, influenced my relationships, and shaped my identity.

When the layoff happened, I went through the emotions that many people experience: disorientation, sadness, shame, and uncertainty.

But something unexpected happened.

Six months later, I feel better than I have at any point in my adult life.

That realization has forced me to confront a question I never seriously considered before:

What was my job actually costing me?

Not in the obvious ways. Not in terms of hours worked or stress endured. Those costs were visible.

I’m talking about the hidden costs.

The ones that accumulate so gradually that you stop noticing them.

The ones that become normal.

The ones you only recognize after they disappear.

The Water We Swim In

Most people can identify the benefits of a job.

A paycheck.

Health insurance.

Professional accomplishment.

Social status.

A sense of purpose.

Those benefits are real.

But every benefit comes with a cost, and after decades in the workforce I had become remarkably good at ignoring the bill.

Like a fish that doesn’t notice the water surrounding it, I stopped noticing the environment that my work had created around me.

Only after leaving it could I see it clearly.

The Cost of Chronic Stress

For most of my career, I lived under a constant state of pressure.

Deadlines.

Escalations.

Production incidents.

Office politics.

Organizational reshuffling.

Performance reviews.

The endless stream of decisions that carry consequences no matter which option you choose.

Even when I wasn’t working, I was working.

Emails arrived during vacations.

Slack messages appeared during dinner.

Production outages interrupted weekends.

My mind never fully powered down.

Over time, that level of stress became normal.

I assumed everyone felt this way.

I assumed adulthood felt this way.

I assumed success felt this way.

Only now do I realize how profoundly that stress shaped my mental state.

For years I carried persistent anxiety.

I battled imposter syndrome.

I experienced recurring periods of burnout that left me feeling emotionally numb and mentally exhausted.

Today, much of that pressure is gone.

What’s left behind is something I haven’t felt in decades:

Mental quiet.

Not boredom.

Not laziness.

Just peace.

And peace turns out to be worth far more than I realized.

The Cost to My Health

For years I told myself I was taking care of my health.

I woke up early.

I squeezed in exercise whenever possible.

I tried to eat reasonably well.

But looking back, I wasn’t building health.

I was merely slowing the rate of decline.

My lifestyle revolved around work.

I slept six hours a night.

I spent most of my day sitting in front of screens.

I relied on junk food and caffeine to push through difficult periods.

Eventually the consequences arrived.

High blood pressure.

High cholesterol.

Pre-diabetes.

Fatty liver disease.

None of these conditions appeared overnight.

They accumulated gradually, one stressful workday at a time.

Today, my life looks very different.

I exercise daily.

I sleep eight hours every night.

I prepare nearly every meal myself.

The difference is remarkable.

I have more energy.

Better focus.

Greater emotional stability.

For the first time in years, I feel like I’m moving toward health rather than away from it.

The Cost of Time

This may have been the largest hidden cost of all.

When people think about work, they think about the hours spent working.

But work consumes far more time than that.

There’s the commute.

The preparation.

The recovery.

The mental decompression afterward.

The administrative overhead of maintaining a professional life.

For me, commuting into New York City often required three to four hours every day.

After ten hours of work and several hours of commuting, I had little energy left for anything meaningful.

The result was that nearly all of my waking existence was either directly or indirectly devoted to work.

And yet I barely noticed.

Because everyone around me was doing the same thing.

Now, for the first time in my life, I have an abundance of unstructured time.

At first, I wasted it.

I spent too much time scrolling social media and playing video games.

But eventually something changed.

I began using that time intentionally.

To learn.

To create.

To think.

To spend time with my family.

To pursue interests that had been neglected for decades.

And I discovered something surprising:

Time abundance feels almost luxurious in a way that money never did.

The Cost of Money

This one sounds paradoxical.

After all, work is supposed to make you money.

And it does.

But it also encourages you to spend it.

A stressful life creates demand for convenience.

A busy life creates demand for shortcuts.

When you’re exhausted, overwhelmed, and time-poor, you start solving problems with your wallet.

You pay for convenience.

You outsource tasks.

You accumulate subscriptions.

You spend money to compensate for your lack of time and energy.

Sometimes you even spend money trying to repair relationships damaged by your absence.

I certainly did.

Looking back, I realize that many of my expenses weren’t improving my life.

They were compensating for a lifestyle that wasn’t working.

The Cost of Community

One of the most surprising lessons came after my layoff.

Throughout my career, I interacted with dozens of coworkers every day.

I liked most of them.

Some became genuine friends.

But many were simply people who occupied the same professional ecosystem as me.

When I left, most of those relationships disappeared almost immediately.

Not because anyone was malicious.

Because that’s what workplace relationships often are.

They’re situational.

They’re formed through proximity and shared necessity.

What remained were the people who genuinely cared.

The people who reached out when they didn’t have to.

The people who wanted to spend time together without a paycheck involved.

Losing my job revealed something uncomfortable.

For years, I had allowed workplace relationships to substitute for building deeper community elsewhere.

That was a mistake.

The Gift Hidden Inside the Layoff

I don’t want to romanticize unemployment.

Many people are suffering right now.

Many families are under extraordinary financial pressure.

Many talented professionals are struggling to find work.

I recognize how fortunate I am to have spent years building financial reserves that gave me options.

But I also think it’s important to acknowledge an uncomfortable truth.

Sometimes a disruption reveals things that routine keeps hidden.

For twenty-five years I accepted stress, exhaustion, time scarcity, declining health, and shallow relationships as normal.

I thought that was simply the price of adulthood.

The price of success.

The price of being a responsible provider.

Maybe some of it was.

But maybe the price was much higher than I realized.

Six months after my layoff, I find myself healthier, calmer, and happier than I have been in decades.

What began as involuntary early retirement is slowly starting to feel voluntary.

And perhaps the greatest lesson I’ve learned is this:

The most valuable thing work took from me wasn’t money.

It was attention.

Attention to my health.

Attention to my relationships.

Attention to my own life.

Now that I have that attention back, I don’t intend to give it away lightly.

Every few weeks, a new headline appears claiming that AI is becoming unsustainably expensive.

Companies are reportedly burning through millions of dollars a month on AI subscriptions. Some organizations are discovering that employees can consume astonishing quantities of tokens when every workflow becomes an AI workflow. There are even stories circulating of teams whose AI bills now exceed the cost of the employees using the tools.

For many people, these stories confirm a suspicion they have been quietly nurturing all along.

Maybe AI isn’t economically viable.

Maybe the entire thing is a bubble.

Maybe the costs will eventually become so overwhelming that widespread adoption simply won’t happen.

I understand why people feel that way.

There are enormous emotional and financial stakes involved.

Entire industries are being disrupted. Careers are being questioned. Businesses are being reshaped. Some people stand to make fortunes while others worry about becoming obsolete.

But after spending decades in the technology industry and watching wave after wave of disruption unfold, I have reached a different conclusion:

AI is already cost effective today.

And it is almost certainly going to become dramatically cheaper tomorrow.

The Cost Problem Is Already Being Solved

Most discussions about AI costs focus on the wrong question.

People look at today’s prices and assume those prices are fixed.

They aren’t.

The history of technology is a history of making expensive things cheap.

Computers were once available only to governments and large corporations.

Cell phones were luxury items.

Internet access was expensive.

Data storage cost a fortune.

Then competition arrived. Innovation happened. Costs collapsed.

AI is following the same pattern.

What makes the current moment particularly interesting is that many of the cost reducing innovations are not hypothetical future breakthroughs.

They already exist.

They are being deployed right now.

How Sanctions Accidentally Accelerated AI Innovation

One of the more fascinating developments of the past several years has been the unintended consequences of the technological competition between the United States and China.

The United States has attempted to restrict China’s access to advanced semiconductors and semiconductor manufacturing equipment. The logic is straightforward: limit access to compute and you limit AI development.

But resource constraints often produce innovation.

When organizations cannot simply throw more hardware at a problem, they are forced to become more efficient.

And efficiency is exactly where many Chinese AI companies have focused their efforts.

The result has been a wave of innovations aimed not at creating the absolute most powerful models, but at creating models that are nearly as capable while being dramatically cheaper to train and operate.

That distinction matters.

Because in the real world, economics often wins.

Smarter Models, Not Just Bigger Models

For years, the dominant strategy in AI was simple.

Build bigger models.

Use more GPUs.

Consume more electricity.

Spend more money.

But there is another path.

Instead of scaling everything upward, researchers can improve the underlying architecture itself.

A good example is the evolution of attention mechanisms inside large language models.

Architectural improvements can dramatically reduce the amount of computation required during training while maintaining similar performance.

In some cases, these optimizations reduce training costs by multiples rather than percentages.

That is a profound shift.

If a model can achieve similar results using half, a third, or even a fifth of the resources, the economics change completely.

Why Reinvent the Wheel?

Another powerful cost reduction technique is model distillation.

Imagine spending years building an expert employee.

Now imagine being able to transfer much of that expertise into a new employee at a fraction of the cost.

That is essentially what distillation does.

Instead of starting from scratch, newer models learn from the outputs and behaviors of existing advanced models.

The result is significantly lower training costs and dramatically reduced data requirements.

From a business perspective, this is incredibly attractive.

Why spend one hundred million dollars recreating knowledge that already exists when you can acquire much of it for a small fraction of the cost?

The Hidden Battle: Inferencing Costs

Most people focus on training costs because the numbers are eye catching.

But for many businesses, the bigger issue is inferencing.

Inferencing is simply the process of asking an AI model a question and receiving an answer.

Every prompt requires computation.

Every computation costs money.

And when millions of users are interacting with AI systems, those costs add up quickly.

This is where some of the most important innovations are occurring.

Techniques such as mixture of experts allow models to activate only the portions of the neural network necessary for a specific task.

Instead of powering the entire machine every time, only the relevant specialists are called into action.

The result can be reductions in inferencing costs ranging from significant to dramatic.

When multiplied across billions of requests, the savings become enormous.

Hardware Matters Too

Software is only half the story.

Hardware innovation is equally important.

Because Chinese companies have limited access to the most advanced Chip manufacturing technologies, they have increasingly focused on specialized chips designed for specific workloads.

These application specific chips may not be as versatile as cutting edge GPUs, but versatility is not always the goal.

Efficiency is.

When optimized for AI inferencing, specialized hardware can often deliver surprisingly competitive performance at substantially lower costs.

Again, the pattern repeats.

Constraints force optimization.

Optimization reduces costs.

Reduced costs accelerate adoption.

The Energy Advantage

There is another factor that rarely receives enough attention.

Electricity.

Training and running AI models requires enormous amounts of power.

Power costs are not fixed.

They vary dramatically depending on geography and infrastructure.

As renewable energy continues to become cheaper, the cost of operating AI systems falls alongside it.

This creates another powerful deflationary force acting on AI economics.

Even if the models themselves never improved, cheaper energy alone would lower operating costs over time.

But the models are improving.

And the hardware is improving.

And the software is improving.

All at the same time.

What Happens When American AI Companies Start Optimizing?

The most interesting part of this story may be what has not happened yet.

Many American AI companies have operated in an environment of abundant capital.

When investor funding seems unlimited, optimization is often less urgent.

Speed matters more than efficiency.

Growth matters more than profitability.

But markets eventually change.

Investors begin asking harder questions.

Profitability becomes important.

Efficiency becomes important.

And suddenly all of the techniques that were previously ignored become very attractive.

If Chinese companies can reduce costs dramatically through architectural improvements, distillation, specialized hardware, and operational efficiency, there is nothing preventing American companies from doing the same.

In fact, competitive pressure almost guarantees that they will.

The Real Question

Could the AI bubble pop?

Of course.

Every technological revolution creates bubbles.

Money will be made.

Money will be lost.

Speculators will speculate.

Some companies will fail spectacularly.

That part is normal.

But bubbles and underlying technology are not the same thing.

The railroad bubble burst.

Railroads did not disappear.

The dot com crash happened.

The digital economy kept growing.

The real question is not whether investors will overpay for AI companies.

The real question is whether the cost of using AI will continue falling.

Looking at the technologies already available today, my answer is yes.

Decisively yes.

The future of AI may be many things.

But “too expensive to survive” is not the outcome I would bet on.

Every once in a while, I make the mistake of opening LinkedIn.

I know I should not.

For an unemployed former Big Tech engineer, LinkedIn often feels like an abusive relationship. I log in knowing there is a good chance I will encounter some combination of layoffs, humblebrags, AI hype, and carefully curated success stories that leave me questioning every major life decision I have ever made.

And yet, like drivers slowing down to stare at a highway accident, I sometimes cannot resist the morbid curiosity.

Recently, that curiosity led to a conversation that has been occupying my thoughts ever since.

An old colleague reached out.

Let us call him Kaushik.

For several years, Kaushik and I worked together inside a large technology organization. He was a senior architect based in India, working out of one of the company’s offshore centers. We collaborated on multiple projects and developed the kind of professional relationship that forms when two people spend years solving difficult problems together.

Back in 2023, our organization was hit by a major round of layoffs.

Roughly half the organization disappeared overnight.

I survived, although survival came with a soft demotion. My title was downgraded from Chief Architect to Senior Architect.

To regain my previous position, I had to navigate a familiar corporate ritual. There were applications, self assessments, recommendation letters, and endless documentation designed to demonstrate my value to the company.

Kaushik played an important role in that process.

He wrote one of the strongest recommendation letters I have ever received.

Reading it felt almost surreal. According to Kaushik, I was apparently a visionary thought leader whose technical brilliance illuminated the path for offshore engineering teams across the organization.

The praise was generous to the point of comedy.

But it helped.

With support from Kaushik and several others, I eventually regained my Chief Architect title.

By then, however, Kaushik had already moved on to another company.

Fast forward to today.

He is back on the job market.

And according to him, the situation is grim.

Very grim.

What he described was not simply a difficult hiring environment.

It sounded like an entire economic model under existential threat.

The Great Engine Behind Global Offshoring

To understand the problem, it helps to understand why India became the center of the offshoring world in the first place.

For decades, India possessed a unique combination of advantages.

A large population.

Strong technical education.

Widespread English proficiency.

And labor costs dramatically lower than those found in North America and Western Europe.

These conditions allowed India to become one of the world’s largest providers of outsourced technical services.

Entire industries grew around this model.

Software development.

Business process outsourcing.

Customer support.

Quality assurance.

Infrastructure management.

Financial operations.

Data processing.

For many global corporations, India became an extension of their workforce.

The arrangement was never perfect.

Offshoring always came with hidden costs.

Time zone differences slowed communication.

Language barriers occasionally created misunderstandings.

Cultural differences introduced friction.

Knowledge transfer was often inefficient.

Work was frequently thrown over organizational fences.

Yet despite these inefficiencies, the labor savings were so substantial that the model remained highly attractive.

For decades, the economics worked.

Now AI may be changing the equation.

The Collapse of Traditional Outsourcing

According to Kaushik, traditional outsourcing firms are experiencing severe pressure.

This should not be surprising.

Most outsourcing work revolves around routine cognitive tasks.

Tasks that are structured.

Predictable.

Repeatable.

And increasingly automatable.

Consider the kinds of activities commonly performed by large outsourcing organizations.

Basic customer service.

Data entry.

Billing operations.

Simple application development.

Maintenance work.

Standardized testing.

Documentation.

CRUD software development.

These are precisely the categories where modern AI systems are improving at astonishing speed.

The question facing corporate executives is becoming increasingly obvious.

Why pay an external vendor to perform work that internal employees can now complete with AI assistance?

Even more importantly, why accept the communication overhead, coordination challenges, and quality risks associated with offshoring if similar outcomes can be achieved in house?

The result appears to be a shrinking pool of contracts.

Less work means fewer employees are needed.

Hiring freezes follow.

Layoffs follow.

Entry level opportunities disappear.

The traditional outsourcing pipeline begins to break.

And when the pipeline breaks, an entire generation of future talent loses its path into the industry.

The GCC Paradox

The second pillar of the offshore ecosystem consists of Global Capability Centers.

These are not outsourcing firms.

They are fully integrated extensions of multinational corporations.

Think Google.

Oracle.

Microsoft.

Amazon.

Major banks.

Large pharmaceutical companies.

These organizations establish engineering centers overseas and assign them direct responsibility for critical products and services.

Historically, GCC jobs have been viewed as more prestigious and more technically demanding than traditional outsourcing positions.

Ironically, AI may strengthen GCCs while simultaneously weakening everything around them.

The reason is simple.

AI tends to amplify highly skilled workers more effectively than less skilled workers.

A senior engineer with ten or fifteen years of experience can leverage AI to become dramatically more productive.

An architect who already understands systems design, tradeoffs, business requirements, and organizational complexity can use AI as a force multiplier.

The challenge is that only a minority of workers possess these capabilities.

Companies are no longer searching for people who can merely write code.

They are searching for people who can solve problems.

That distinction matters.

A lot.

As a result, GCCs continue competing aggressively for elite talent while the broader outsourcing sector struggles.

The strongest engineers migrate toward multinational organizations.

The rest of the ecosystem becomes increasingly hollowed out.

It is a form of talent cannibalization.

The most capable workers are concentrated into a relatively small number of organizations while everyone else faces increasing pressure.

A Dangerous Concentration of Talent

This creates another risk that receives far less attention.

As more elite technical talent becomes concentrated inside multinational corporations, local technology ecosystems become increasingly dependent on decisions made thousands of miles away.

The leadership teams controlling these organizations often reside in the United States.

The strategic priorities are determined elsewhere.

The investments are determined elsewhere.

The layoffs are determined elsewhere.

If those corporations decide to reduce investment, shift priorities, or close operations entirely, the consequences could ripple through entire regions.

The danger is not merely economic.

It is structural.

When enough talent becomes dependent on a handful of global organizations, local resilience begins to disappear.

An ecosystem that cannot stand on its own eventually becomes vulnerable to forces beyond its control.

What Can Offshore Workers Do?

I have spent many years working alongside offshore teams.

Most of the people I met were intelligent, hardworking professionals trying to build better lives for themselves and their families.

That makes this situation difficult to watch.

Unfortunately, I do not see any perfect solutions.

Only coping strategies.

The first strategy is to move up the value chain and target positions within Global Capability Centers.

That means improving technical skills.

Improving communication skills.

Learning AI tools.

Developing stronger problem solving abilities.

The competition is intense, but higher value work is likely to remain more resilient than routine work.

The second strategy is to focus on local and regional technology ecosystems.

The world may gradually become more multipolar.

China has already built a sophisticated technology ecosystem independent of Silicon Valley.

Europe is increasingly discussing digital sovereignty.

Other regions may eventually follow.

Opportunities may emerge closer to home, even if compensation is lower than what American companies traditionally offered.

The third strategy is immigration.

Historically, moving to higher income countries has been a pathway toward greater opportunity.

However, this path appears increasingly uncertain.

Many developed countries are facing economic anxieties of their own.

Labor markets are becoming more competitive.

Public sentiment toward immigration is often more complicated than it was a decade ago.

The path remains available, but it is unlikely to be easy.

The Bigger Question

After my conversation with Kaushik, I found myself thinking about a broader issue.

For decades, offshoring was built on the assumption that cognitive labor could be distributed around the world in much the same way manufacturing had been.

AI may be challenging that assumption.

For the first time, companies have access to tools that can automate portions of cognitive work itself.

If that trend continues, the implications extend far beyond India.

Far beyond outsourcing.

Far beyond technology.

Entire labor markets may need to rethink their purpose in a world where intelligence is no longer scarce.

Perhaps the real question is not whether AI will disrupt the offshoring industry.

Perhaps the real question is what happens when one of globalization’s most successful economic models suddenly stops making sense.

And if that day is truly arriving, then Kaushik’s struggle may not be an isolated story.

It may be an early warning.

Hello world,

One strange side effect of unemployment is that it gives you something modern life rarely provides anymore.

Time.

For twenty five years I worked in tech. Like many engineers, I spent most of my adult life sprinting from deadline to deadline, project to project, quarter to quarter. There was always another problem to solve and another fire to put out.

Then one day the treadmill stopped.

Now that I am no longer spending every waking hour inside the machine, I suddenly have the ability to do something I had almost forgotten how to do.

Observe.

And what I am observing concerns me.

For most of my life, I have lived inside something historians would call the American unipolar world order. Most of us rarely think about it because it simply became the background operating system of our lives.

Like electricity, it was just there.

After the Soviet Union collapsed in 1991, the United States entered what many called Pax Americana. America became the world’s lone superpower. We had the largest economy, overwhelming military dominance, enormous industrial capacity, and perhaps most importantly, immense cultural influence.

America was not just powerful.

America felt inevitable.

During the 1990s and early 2000s, I viewed America as some strange combination of Captain America and Guardians of the Galaxy. Maybe that sounds ridiculous, but that was genuinely how it felt.

There was a broad expectation that tomorrow would be better than today.

Economic growth seemed permanent.

Globalization seemed unstoppable.

Political stability felt normal.

The future felt like a solved problem.

But now I look around and increasingly feel like I am watching the operating system begin to fail.

Some of this decline is relative. Other nations are becoming stronger. Some of it appears more fundamental.

Our industrial capacity has weakened.

Social cohesion feels increasingly fragmented.

Political polarization has become part of everyday life.

Our geopolitical influence appears less absolute than it once did.

None of these changes by themselves mean catastrophe.

But together they suggest something larger.

The world order that shaped much of our lives may be changing.

The question is what replaces it.

Some believe we are heading toward a new Cold War where America and China emerge as two competing superpowers locked in a bipolar struggle.

I am increasingly skeptical of this.

Instead, I think we are drifting toward something messier.

A multipolar world.

This is a world where major powers still matter, but where middle powers also gain increasing influence.

Countries such as Brazil, Saudi Arabia, Indonesia, India, Turkey, and others may refuse to fully align themselves with either America or China.

They may sit on the fence.

They may switch sides.

They may negotiate with everyone.

They may exploit both camps for maximum advantage.

Globalization also complicates everything.

The old Cold War had relatively clean lines.

Today’s world does not.

A country may depend on America for security while depending on China for trade.

The alliances become tangled.

The incentives become blurry.

The board becomes chaotic.

There is also another reality that fascinates me.

Modern technology has changed power itself.

Drones, sensors, precision weapons, and digital infrastructure have made even weaker nations far more difficult to dominate.

Large powers can still hit hard.

But imposing control has become much more expensive and much more uncertain.

Even the strongest players can walk away with a bloody nose.

And this is where I become uneasy.

Multipolar systems can be unstable.

You have multiple ambitious actors, shifting alliances, power vacuums, and competing interests.

Without a dominant power acting as an organizing force, the potential for mistakes increases.

History often shows that wars do not begin because everyone wants war.

Wars begin because enough people make enough bad calculations at the same time.

So how do you prepare for uncertainty?

I do not claim to have answers.

I am not a financial advisor and these are simply my personal thoughts.

But I have started thinking differently about resilience.

I think about preserving financial flexibility.

I think about diversification.

I think about holding assets that are less dependent on a single institution or currency.

I think about optionality.

Most importantly, I think about family.

Because ultimately, all of these discussions about geopolitics and world systems eventually become personal.

At some point every grand historical event arrives at your front door.

History stops being a chapter in a textbook and starts becoming your mortgage, your job, your neighborhood, your children’s future.

Maybe I am wrong.

I hope I am wrong.

I hope decades from now we look back and laugh at all of these worries.

But if the world really is changing, then perhaps the greatest mistake is assuming tomorrow will automatically look like yesterday.

Because history has a habit of moving slowly.

Right until it suddenly moves all at once.

The Future That Quietly Keeps Me Up at Night

Hello world,

For the first time in more than two decades, I suddenly found myself with something I hadn’t had in years:

Time.

After spending 25 years as a software engineer in big tech, I entered what I jokingly call my “involuntary early retirement.” And when your daily rhythms disappear, your mind starts wandering into strange places.

Mine wandered into the future.

Not my future specifically, but humanity’s future.

Like many engineers, I have always believed that technology is fundamentally a tool. A hammer can build a home or become a weapon; the hammer itself has no morality. Technology seemed no different. Artificial intelligence, robotics, biotechnology, these were simply instruments extending human capability.

But the more time I spent reading, researching, and following emerging technological trends, the more a question began nagging at me:

What if we are no longer merely building tools?

What if we are building our successors?

That question led me into the world of Transhumanism.

For those unfamiliar with the idea, Transhumanism is a philosophical movement advocating the use of advanced technologies to fundamentally enhance humanity itself. The goal is not merely to cure disease or make life more comfortable. The goal is to overcome biological limits altogether.

Disease.

Aging.

Cognitive limitations.

Possibly even mortality itself.

For decades, this sounded like science fiction, the sort of thing reserved for late-night conversations between futurists and authors.

Today it feels different.

Because the technologies that make this possible are no longer imaginary.

Brain-computer interfaces now connect neurons to machines. Genetic technologies such as CRISPR allow us to edit the very code of life itself, precisely. Artificial intelligence increasingly performs tasks that once required human expertise. Robotics grows more capable every year.

Individually, each technology seems understandable.

Collectively, they begin to feel transformative.

Potentially civilization-transforming.

And here is where things become unsettling.

Because if these enhancements become possible, they will almost certainly begin as expensive technologies available only to a small number of people.

Perhaps the wealthiest.

Perhaps the most powerful.

Perhaps the descendants of today’s technological elite.

What happens then?

Imagine two groups emerging within humanity itself.

Not nations.

Not races.

Not classes.

Species.

One group possesses enhanced intelligence, longer lifespans, superior biological capabilities, and direct integration with AI systems.

The other remains largely unchanged.

How long would those groups remain equals?

History offers a sobering answer: they probably wouldn’t.

Humans have never had an especially impressive record of treating less powerful groups as peers.

And if one group genuinely became more capable, stronger, smarter, longer-lived, the incentives become uncomfortable to think about.

The enhanced population might eventually ask difficult questions:

Why sustain billions of unenhanced humans consuming resources?

Why preserve inefficiencies?

Why maintain systems built for biological limitations that no longer apply?

I know how insane this sounds.

Trust me, I hear myself saying it.

But what makes this thought experiment disturbing is not the futuristic imagery.

It’s the possibility that we may already be seeing early hints of these pressures emerging.

Consider the enormous expansion of AI.

Hundreds of billions of dollars are being poured into data centers, chips, energy infrastructure, and computational power.

The public narrative is productivity.

Efficiency.

Innovation.

And perhaps that is entirely true.

But another possibility exists:

The systematic reduction of the need for human labor itself.

If labor becomes less valuable, then what happens to people?

We may already be seeing fragments of that answer.

Mass layoffs.

Shrinking opportunities for younger workers.

An economy where many increasingly rely on gig work, subsidies, and algorithmically mediated systems simply to survive.

Meanwhile, social structures that once stabilized human life, families, communities, churches, neighborhoods, appear weaker than they once were.

Instead, many people increasingly exist within digital ecosystems designed to capture attention.

We become consumers of endless content.

Endless outrage.

Endless distraction.

And perhaps the most striking consequence is demographic.

Across much of the industrialized world, birth rates are collapsing.

Young people aren’t rejecting families because they hate children.

Many simply cannot imagine stable futures for themselves.

If life increasingly feels like survival, building the next generation becomes difficult.

East Asian nations may be offering a glimpse into this future. Population projections in some regions suggest declines so severe they would have seemed unimaginable just decades ago.

Canaries in the coal mine.

Which raises a haunting possibility:

What if these aren’t disconnected trends?

What if they are pieces of a larger transition?

Imagine the year 2100.

AI and machines perform most productive work.

A small enhanced population controls technological systems and resources.

A larger population of ordinary humans receives sufficient resources to survive, perhaps through mechanisms like universal basic income, but exists largely dependent upon the system itself.

From the outside, this civilization might look beautiful.

Clean cities.

Renewable energy.

Little pollution.

No visible poverty.

Almost a solar-punk paradise.

A Star Trek future.

But beneath the surface lies a difficult question:

If basic material needs are met, but human agency disappears, is that still freedom?

I don’t claim that this future is inevitable.

I don’t even claim it is likely.

I may be completely wrong.

I sincerely hope I am.

But history suggests civilizations often drift into destinations they never consciously intended to reach.

Not because of a master plan.

Not because of hidden conspiracies.

But because countless incentives quietly push society in one direction over time.

Perhaps Transhumanism will ultimately free humanity from suffering.

Or perhaps it will simply create a newer, more technologically sophisticated dystopia.

I don’t know.

I only know that the question itself has become difficult for me to stop thinking about.

And maybe that’s the point.

The future rarely arrives all at once.

It arrives gradually

one technology,

one incentive,

one compromise at a time.