Avsnitt
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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.
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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.
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Saknas det avsnitt?
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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.
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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.
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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.
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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.
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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.
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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.
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Another week passes and another wave of layoffs crashes through the technology industry like a tidal wave. At this point, the disappearance of thousands of highly experienced engineers has become so common that it barely shocks anyone anymore. Entire departments vanish overnight. Decades of institutional knowledge disappear behind a carefully worded email and a severance package.
But there is another group of people affected by these layoffs that nobody really talks about.
The survivors.
The people who remain employed are often viewed as the lucky ones. They still have a paycheck. Their stock grants are still vesting. Their LinkedIn profiles still say they work at a prestigious company. From the outside, they appear safe.
But inside many large technology companies, surviving layoffs can feel like becoming trapped inside a pressure cooker.
The workload grows heavier while job security becomes thinner. Teams shrink while expectations expand. Critical systems still need to function, deadlines still need to be met, and billions of dollars still depend on software that was often stitched together over decades by engineers who no longer work there.
The survivors inherit all of it.
In the summer of 2023, I found myself inside exactly this kind of situation.
After a brutal round of layoffs at my company, nearly half of my organization disappeared. Senior leaders were gone. Teams were forcibly merged together into a larger organization built from the wreckage of the previous one. I went from serving as a Chief Architect to functioning as a Senior Enterprise Architect again. Professionally, it felt like traveling backward in time.
I was angry about it.
But I stayed.
Like many people in tech, I had financial reasons to endure it. My next round of RSUs had not vested yet, and walking away meant leaving a significant amount of money on the table. So I convinced myself to keep pushing forward.
Then came the project that nearly broke me.
Our company operated a massive legacy platform that handled relationships with partner companies. The system processed billions in annual revenue, but under the surface it was a digital Frankenstein monster. Over twenty five years, dozens of separate web applications had been piled on top of one another until the entire thing barely functioned.
Different fonts. Different navigation systems. Different visual styles. Some pages looked like they belonged to completely different companies.
Yet somehow this fragile structure continued to support an enormous stream of revenue.
Executive leadership decided that our newly reorganized department would completely replace this legacy system with a modern enterprise CRM platform in a single release scheduled only four months away.
It was the kind of decision that sounds bold in a PowerPoint presentation and terrifying to the engineers responsible for actually delivering it.
The challenge was not merely technical. The layoffs had already gutted the teams that understood how the legacy platform worked. Much of the institutional knowledge had vanished. At the same time, the replacement CRM platform required specialized knowledge that very few people possessed.
So every day became a race against time.
I spent countless hours trying to understand both systems simultaneously while also coordinating teams spread across multiple continents. Meetings started at six in the morning and stretched late into the evening because our squads were distributed between North America and India.
Then there was the commute.
Our company enforced a hybrid return to office policy that required me to travel into New York City every other day. The round trip took roughly three hours by bus. By the end of each commute, I often felt physically nauseated from the constant swaying motion.
At one point, I realized I was regularly working more than twelve hours a day while also sacrificing weekends to keep the project alive.
That is when the burnout truly began.
People often describe burnout as stress, but burnout feels different. Stress still contains energy. Burnout feels like the complete absence of it.
I felt mentally foggy all the time. Concentration became difficult. Solving technical problems that once felt routine suddenly required enormous effort. I forgot details. I lost focus. Even after taking several days off during Labor Day weekend, I returned to work still feeling exhausted.
Emotionally, something stranger happened.
I stopped caring.
Projects that once would have energized me now felt hollow and meaningless. I became detached from the work, detached from the teams, and in many ways detached from myself. Sunday evenings filled me with dread.
Then the nightmares started.
Over and over again, I dreamed that my coworkers and I had somehow become low wage restaurant workers.
The product manager became the greeter. The Chief Architect became a busboy. The engineering manager became the dishwasher. I was always the waiter.
And in every dream, disaster struck.
A customer would die after eating spoiled food. The restaurant would catch fire. Chaos would erupt. Every single time I woke up drenched in sweat.
Looking back now, I think my subconscious was trying to tell me something important.
Burnout does not simply exhaust the body. It destabilizes your sense of identity and security. It transforms your career from a source of meaning into a source of survival anxiety.
Eventually I realized that if I continued living this way, something inside me was going to break permanently.
So I made changes.
I forced myself to sleep consistently. I stopped scrolling through devices late at night and began prioritizing seven hours of uninterrupted sleep.
I exercised every morning, even if only for thirty minutes on a treadmill. That small amount of movement changed my mental state far more than I expected.
I intentionally reconnected with people outside of work including family, friends, church groups, and online gaming communities. These interactions reminded me that my existence extended beyond corporate deadlines and Jira tickets.
Most importantly, I began enforcing boundaries.
I stopped working weekends. I stopped responding to messages at all hours. On office commute days, I refused early morning and late night calls.
At first, saying no felt uncomfortable.
Then it felt liberating.
Over several weeks, the nightmares stopped. The anxiety softened. My concentration improved. I became functional again.
Not perfect. Not fully recovered. But functional enough to finish the project and survive the experience.
The modern technology industry celebrates resilience almost obsessively. We glorify hustle culture, constant availability, and productivity at all costs. But there is a dangerous difference between resilience and self destruction.
A human being is not a distributed system designed for infinite horizontal scaling.
Eventually the system crashes.
And increasingly, I think many engineers are approaching that point simultaneously.
The layoffs may dominate the headlines, but the deeper story unfolding inside the industry is psychological exhaustion. Thousands of survivors are quietly carrying impossible workloads while trying to convince themselves they should feel grateful just to remain employed.
That is not sustainability.
That is survival mode.
And survival mode comes with a cost.
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Mother’s Day weekend took us to Pennsylvania to visit my parents.
At some point during dinner at a fancy Chinese restaurant, an amusing but strangely revealing family intervention unfolded across the table.
Two of the three most important women in my life joined forces against the third.
My mother and my wife began criticizing the way I raise my eight-year-old daughter.
According to my mother, I’m far too soft on her. She said I give in too easily, indulge her too much, and risk turning her into a spoiled, dissatisfied adult. My wife escalated the prosecution even further. She joked that if my daughter somehow climbed on top of my head and took a dump there, I would probably still smile and tell her she did a great job.
And honestly?
They’re probably not entirely wrong.
But I also think they misunderstand why fathers become soft around their daughters.
Because what a little girl gives her father is a kind of love many men go their entire lives without ever experiencing.
Before the world hardens her, before social dynamics complicate everything, before adolescence introduces distance and self-consciousness, a young daughter often loves her father with complete sincerity.
To her, he is a superhero.
He is capable.
He is safe.
He can fix anything.
He is the strongest person alive.
And whether or not any of those things are objectively true almost doesn’t matter.
What matters is that she believes it with her entire heart.
That kind of trust changes a man.
Especially because many men grow up learning that love is deeply conditional.
You are valued for what you produce.
For how much pressure you can absorb.
For how useful you are.
For how much suffering you can quietly endure without complaining.
The modern workplace sharpens this instinct even further. Spend enough years in corporate environments and you begin to feel less like a human being and more like a performance engine. Your worth becomes measurable. Quantified. Ranked. Optimized.
You are admired when successful.
Tolerated when useful.
Ignored when broken.
But a little daughter doesn’t care about your LinkedIn profile.
She doesn’t care about your title.
She doesn’t care whether the world respects you.
You bring her a cheap sticker from the grocery store and she treasures it like sacred treasure. You spend ten minutes drawing something silly with crayons beside her and she talks about it for days. You let her climb onto your shoulders and suddenly you’ve given her the greatest moment of her week.
And in return, she gives you something that no promotion, no paycheck, no professional accomplishment can ever truly replicate:
The feeling of being loved simply for existing.
Not for winning.
Not for providing.
Not for performing.
Just… for being you.
I think this is why even the hardest men often melt around their daughters.
The exhausted worker who never complains suddenly softens when a tiny voice asks, “Daddy, did you have a hard day today?”
The exhausted engineer who spent all day absorbing stress suddenly feels his nervous system unclench when small arms wrap around his neck as though he is still the greatest hero in the world.
For one brief moment, the armor comes off.
Because many men spend their entire lives pretending to be invincible.
At work, they swallow stress silently.
They internalize disappointment quietly.
They carry responsibility without acknowledgment because somewhere along the line they were taught that this is simply what being a man means.
So when they come home exhausted and their daughter still looks at them with complete love and admiration, even if they are ordinary, flawed, frightened men, it touches something incredibly deep inside them.
To her, they are still enough.
Still safe.
Still her favorite person in the world.
And perhaps that is why fathers become so soft.
Not because they lack discipline.
Not because they are weak.
But because that tiny little girl may be the only place in their entire lives where their heart is allowed to fully rest.
And honestly?
I think that’s worth protecting.
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Hello world.
I’m an unemployed ex–Big Tech software engineer, watching from the outside as the industry I helped build pours billions into its next obsession: quantum computing.
Some say it’s the next trillion-dollar industry.Others say it will dwarf the current AI boom.
And having briefly stepped into that world myself… I understand the excitement.
I also understand the madness.
The Promise: A Computer That Tries Everything at Once
A couple of years ago, I attended an internal conference at my company, a kind of innovation showcase where research teams unveiled their latest breakthroughs.
Tucked inside that facility was something extraordinary:
A quantum computer.
At the time, it may have been one of the most powerful machines of its kind in the world.
Now, I’m not a physicist. I barely survived physics in school. But even I could grasp the core idea:
A classical computer solves problems step by step.
A quantum computer?
It explores many possibilities at the same time.
Imagine trying to solve a problem with four possible inputs. A normal computer checks each one individually. A quantum computer, in theory, evaluates all four simultaneously and gives you an answer in a single step.
Scale that up to trillions of possibilities, and suddenly you’re talking about solving problems that would take classical supercomputers centuries.
Drug discovery. Climate modeling. Cryptography.Entire categories of “impossible” problems become… possible.
It felt like magic.
The Reality: One Answer, Not All Answers
Then I started experimenting.
And that’s when things got weird.
The fundamental problem with quantum computing isn’t generating answers, it’s getting useful ones out.
Yes, a quantum system can represent many possibilities at once. But the moment you measure it… everything collapses into a single outcome.
One answer.
Chosen probabilistically.
The rest? Gone.
This is the paradox at the heart of quantum computing:
* It can explore many paths simultaneously
* But you only get to see one
After decades of research, we’ve discovered only a handful of algorithms like Shore’s and Grover’s that can reliably extract useful information from that chaos.
For most real-world problems?
We don’t yet know how.
That’s the bottleneck. Not hardware. Not funding.
Algorithms.
The Descent Into Quantum Weirdness
The deeper I went, the less intuitive things became.
At the core of these systems are qubits, often represented by particles like electrons. And unlike anything in classical computing, these particles don’t exist in a single state.
They exist in superposition.
Not here. Not there.But somehow… both.
Like a coin spinning in the air, neither heads nor tails until you stop it.
Except this isn’t a metaphor.
This is reality at the smallest scale.
And it gets stranger.
Distance Might Not Be Real
To perform operations, quantum systems rely on something called entanglement.
Two particles become linked. Their states are connected.
Change one… and the other changes instantly.
Not at the speed of light.
Instantly.
Even if they’re separated by unimaginable distances.
Which raises a deeply uncomfortable question:
Is distance even real in the way we think it is?
Or is everything somehow connected at a deeper level we don’t yet understand?
The Moment That Broke Me
At some point, I came across research exploring whether quantum systems could be manipulated in ways that resemble reversing time.
Not time travel in the Hollywood sense.
But something subtler and arguably more unsettling.
A system evolves.Then, using carefully designed operations, you force it back into its previous state.
As if the past had been… undone.
When I first wrapped my head around that, something clicked and not in a good way.
If you can reconstruct the past from the present…
What does that say about cause and effect?
What does that say about time?
About free will?
When Physics Starts Sounding Like Philosophy
From there, the rabbit hole deepened.
I started reading late into the night: papers, books, theories.
Some of them sounded like science.
Some sounded like science fiction.
* That our universe might exist inside a black hole
* That reality could be layered, like nested simulations
* That everything we experience might be a projection of deeper underlying rules
At one point, I was lying in bed, muttering to myself about quantum states and reality.
That’s when my wife woke up, looked at me, and said:
“Stop thinking so much. Go to sleep.”
Honestly?
That might have been the most practical advice I encountered the entire time.
So… Is Quantum Computing the Future?
Yes.
But not in the way most people think.
Right now, quantum computing is less like the early internet…
…and more like early flight.
We’ve proven it’s possible.
We’ve had moments of brilliance.
But we’re still figuring out how to make it useful in a practical way.
The Real Takeaway
My brief, feverish journey into quantum computing left me with three conclusions:
* The potential is enormousEntire industries could be reshaped.
* The practical barriers are realEspecially on the algorithmic side.
* Reality is far stranger than we’re comfortable admitting
And maybe that last one is the most important.
Because once you start pulling on that thread, once you truly engage with how the universe behaves at its most fundamental level. You realize something unsettling:
We don’t just lack the answers.
We might not even be asking the right questions yet.
If you’ve made it this far, you probably share that same curiosity.
The kind that keeps you up at night.
The kind that makes you question things you probably shouldn’t.
Stick around.
This journey is just getting started.
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Reflections from an unemployed ex–Big Tech engineer navigating a changing world
Hello world.
For the first time in more than two decades, I find myself standing outside the system I once helped build.
After 25 years in the tech industry, I am now an unemployed former software engineer. A title I never imagined I would carry.
And yet, every week, that story becomes less unusual.
The headlines keep arriving in waves: another tech company, another restructuring, another round of layoffs measured not in dozens, but in thousands. Increasingly, the explanation is familiar, it’s automation, efficiency, AI.
Artificial intelligence is no longer a distant future. It is not a speculative technology sitting quietly in research labs.
It is here.
And it is reshaping the labor market in real time.
As a parent of two children, I find myself thinking about this constantly.
What does work look like ten years from now?
How do people not just survive, but thrive, in a world where machines increasingly perform cognitive labor once reserved for humans?
I have had more time than usual to sit with those questions. And after months of reading, observing, experimenting, and reflecting, I have come to believe something uncomfortable:
Many of the strategies we were taught to trust may no longer work.
The End of the Traditional Career Conveyor Belt
For generations, the formula looked simple.
Go to college. Gain specialized knowledge. Build a stable career. Climb steadily upward.
That model made sense in an era where knowledge changed slowly and expertise compounded over decades.
But AI introduces a different reality.
When a machine can instantly retrieve, summarize, and apply vast quantities of information, the economic value of memorized knowledge begins to erode.
The traditional promise of higher education, that four years of study naturally convert into decades of stable income, feels increasingly fragile.
This does not mean education has no value.
Education has always served multiple purposes.
In ancient Greece, academies existed to broaden perspective and cultivate reasoning. During the Victorian era, higher education also functioned as a signal of class, privilege, and social standing.
But the modern idea of college as a predictable employment pipeline may be reaching its expiration date.
The world is changing too quickly.
Knowledge alone is no longer enough.
The Myth of the “Safe” White-Collar Career
Many people respond to technological disruption by searching for stability.
Accounting. Administration. Insurance. Compliance.
The assumption is understandable: avoid glamorous industries, choose something practical, and ride out the storm.
But AI does not evaluate professions the way humans do.
It does not care whether a job sounds prestigious or boring.
It only cares about workflows.
A better question is not What profession are you in?
A better question is:
What percentage of your work consists of repetitive, structured tasks?
If large portions of your day involve predictable information processing, documentation, reporting, communication, or analysis, then AI may slowly begin dissolving parts of that role.
Not necessarily replacing it all at once.
More like an ice cube melting under sunlight.
The profession still exists. But fewer people are required to do the same amount of work.
That distinction matters.
The future may not arrive through dramatic replacement.
It may arrive through gradual reduction.
Are the Trades Really Safe?
One of the most common pieces of advice circulating online is simple:
“Forget tech. Go into the trades.”
Electricians. Plumbers. HVAC specialists. Welders.
There is truth in this argument.
Physical work remains more difficult to automate than cognitive work.
But the picture may be more complicated than it appears.
In the short term, the trades face a supply problem.
Large numbers of displaced white-collar workers are looking for stability. Recent graduates are struggling to enter knowledge work. Gig economy workers are searching for reliable income.
Many of them are converging toward the same solution.
When too many people chase the same opportunity, competition intensifies.
And intense competition puts downward pressure on wages.
Then comes the medium-term issue.
Humanoid robotics.
Today’s robots still struggle with dexterity, battery life, environmental unpredictability, and physical durability.
But these are engineering problems, not theoretical impossibilities.
If progress continues at current rates, general-purpose robotics could become commercially viable within the next decade.
That means trades may remain resilient but perhaps not permanently immune.
So What Might Actually Work?
If the old playbook is losing relevance, what replaces it?
There are no guarantees.
But there are strategies that seem increasingly rational in the age of AI.
1. Learn to Work With AI, Not Against It
The most practical near-term strategy may be simple:
Master the tools.
AI is proliferating into nearly every professional discipline.
Software engineers use AI coding assistants. Designers use AI-generated mockups. Lawyers analyze contracts with AI. Accountants automate bookkeeping workflows.
The future may not belong to the person with the most raw knowledge.
It may belong to the person who knows how to amplify themselves.
In software engineering, experienced developers can now produce dramatically more output using AI-assisted workflows.
Five times more productive.
Ten times more productive.
Sometimes more.
And that creates an uncomfortable truth.
For the foreseeable future, AI may not directly replace workers.
People using AI may replace people who do not.
2. Disrupt Your Own Job Before Someone Else Does
This is one of the hardest ideas to accept.
But it may also be one of the most important.
If your workflows can be automated, you should be the first person to automate them.
Look closely at your workday.
What tasks repeat?
What decisions follow patterns?
What emails, reports, summaries, or administrative work consume time?
Modern AI tools can already handle many forms of repetitive cognitive labor.
Research. Reporting. Scheduling. Drafting communications. Data organization.
And increasingly, they can do so without requiring technical expertise.
By automating portions of your own job, two things happen.
First, you become more valuable.
Second, you reclaim time.
And time may become one of the most valuable currencies of the next decade.
Because time creates optionality.
Time lets you experiment.
Time lets you build.
Time lets you prepare.
3. Invest in Human-Centered Skills
Machines can analyze information.
They can summarize, recommend, and optimize.
But trust remains stubbornly human.
Fields rooted in emotional intelligence, nuanced judgment, and relationship-building may prove more durable.
* Healthcare.
* Leadership.
* Coaching.
* Human services.
* Therapy.
* Community-building.
The future may place increasing value on work that requires empathy, interpretation, and interpersonal trust.
These are not simply “soft skills.”
They may become economic differentiators.
4. Build the Infrastructure Around AI
We often think of AI as the main event.
But every technological revolution creates an ecosystem around itself.
The internet created web hosting, cybersecurity, payment processors, cloud platforms, analytics tools, and marketplaces.
AI will likely do the same.
AI agents increasingly perform tasks on behalf of humans.
They research. Write. Analyze. Schedule. Transact.
But agents require infrastructure.
They need tools for discovery, authentication, communication, workflow integration, and commerce.
The supporting layer around AI may become just as valuable as AI itself.
And building that infrastructure could become a massive opportunity over the coming decade.
5. Reimagine Existing Businesses Through AI
One of the most overlooked opportunities may not be inventing something entirely new.
It may be redesigning what already works.
Every profitable business contains inefficiencies.
Every workflow contains friction.
Every market contains incumbents who are reluctant to disrupt themselves.
History repeats this pattern.
Large companies often struggle to abandon existing revenue streams.
They protect what works.
Until someone else builds a faster, cheaper, more efficient alternative.
This happened with photography.
It happened with streaming.
It happened with e-commerce.
And it will happen again.
AI allows individuals and small teams to replicate services that once required entire organizations.
The barrier to entry has dropped.
Which means opportunity has expanded.
The downside is obvious.
This path carries risk.
Many attempts will fail.
But successful disruption can produce outsized returns.
6. Create Entirely New Business Models
This may be the most exciting possibility of all.
AI does not simply improve old workflows.
It enables things that were previously impossible.
Consider automatic language dubbing.
A person records a video in one language.
AI translates it into dozens of others while preserving tone, pacing, and personality.
That business model could not realistically exist at scale before deep learning.
And it raises an important question.
How many business ideas were once impossible because the cognitive labor required was too expensive?
How many ideas sat dormant because humans simply could not execute them efficiently enough?
AI changes that equation.
We may be standing at a moment that resembles the early internet.
Most ideas will fail.
But some will create entirely new categories.
And those categories may reshape industries.
The Larger Truth We May Not Want to Admit
I suspect we are still underestimating how disruptive AI could become.
Not just economically.
But socially.
Politically.
Psychologically.
At some point, our economic system may need to evolve to accommodate a world where productivity no longer maps neatly to employment.
That conversation feels distant.
But perhaps it is approaching faster than we think.
In the meantime, I do not claim to have answers.
Only strategies.
Experiments.
Ways of navigating uncertainty.
I use many of these approaches myself.
Because when the map disappears, movement matters more than certainty.
And perhaps the real challenge of the AI age is not learning how to outcompete machines.
Perhaps it is learning how to remain adaptable while the world rearranges itself beneath our feet.
If you are navigating this transition too, you are not alone.
We are all figuring it out in real time.
And maybe that shared uncertainty is the beginning of something new.
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There was a moment at a small, crowded Chinese restaurant in Queens that I haven’t been able to shake.
I’ve been on an unexpected pause in life lately, an ex–big tech software engineer with 25 years behind the keyboard, now suddenly finding himself with more time than he ever asked for. Time, as it turns out, is a strange thing when it stops being rationed by meetings and deadlines. It starts revealing things.
That afternoon, I met up with an old friend Derrick, his son, his Cousin Aaron, and my own teenage son. The kind of gathering that feels ordinary on the surface, cheap food, loud tables, half-finished stories, but somehow opens doors you didn’t intend to walk through.
We were reminiscing about an old game we all used to love: Fallout. The kind of game that once pulled you in for hours without noticing the passage of time.
And then Aaron said it.
He didn’t enjoy gaming anymore. Not just gaming, almost nothing, really. Hobbies, free time, even vacations. All of it had started to feel muted. Like the color had been turned down on life itself.
What surprised me wasn’t the confession. It was the response.
Around the table, teens, adults, all of us, we nodded. Not out of politeness. Out of recognition.
Somewhere along the way, excitement had become harder to find.
The quiet erosion of interest
At first, we tried to explain it in simple terms: work stress, fatigue, getting older. But the more we talked, the less convincing that felt.
What emerged instead was something broader.
A life where time outside of work is fragmented and thin. Where every hobby quickly becomes a marketplace. Where curiosity is constantly interrupted by ads, products, courses, “opportunities,” and noise disguised as guidance.
We live in what can only be described as an economy that doesn’t just sell things, it absorbs everything.
Even our interests.
The algorithm doesn’t just distract it reshapes
Then there’s the screen.
Social media doesn’t just steal time. It subtly rearranges it. What was once a quiet hour becomes a scroll. What was once personal becomes comparative.
You don’t just watch others live, you measure yourself against them, endlessly, without consent.
And slowly, joy starts to feel like performance. Even hobbies begin to feel like something you should be better at.
The fun drains out, not because the activity changed, but because the meaning around it did.
Too much meaning, too much noise
And underneath all of it: the noise.
Information that doesn’t simply inform, it persuades, distorts, provokes. Every topic becomes a battlefield of narratives. Every interest comes pre-packaged with someone trying to shape how you feel about it.
Eventually, something strange happens.
You stop reacting.
Not because you’re stronger, but because you’re saturated.
A strange personal contradiction
I didn’t leave that dinner with answers.
I’ve felt it too, that dulling of curiosity, that slow fading of excitement. Especially around the time before my own layoff.
And yet, strangely, in the months since stepping away from the machinery of constant work, something has started to return. Not all at once. Not dramatically. More like a signal slowly coming back through static.
So I’ve been paying attention.
Noticing what helps.
What seems to bring things back into focus
There’s no grand solution here. Just small corrections:
Less time in algorithmic feeds, more time in the physical world. Even a walk helps reset something internal that screens quietly erode.
A little structure around interests, not pressure, just space. A daily pocket of time where curiosity is allowed to exist without needing justification.
Smaller beginnings. Not trying to “master” anything. Just touching it. A page, a mile, a sketch. Enough to re-enter the relationship.
And sometimes, just talking to another human being about it. Out loud. Without optimization, without performance. Just thinking together.
Even therapy, for me, has been part of this. Less about fixing something broken, more about remembering how to hear my own thoughts again.
What I keep thinking about
That evening in Queens didn’t end with resolution.
But it left me with a question that still lingers:
If so many of us are feeling less interested in the things we once loved… is it really us that changed?
Or is it the environment we’re living in that has become too loud, too fast, too optimized for attention to leave room for actual joy?
I don’t know.
But I do know this:
When everything starts to feel dull, it may not be life losing its color.
It might just be that we’ve been looking at it through too much noise for too long.
And sometimes, the first step back isn’t finding new things to love, it’s learning how to hear them again.
If this resonates with you, you’re not alone in it.
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Hello world,
There’s something oddly clarifying about involuntary stillness.
After 25 years in big tech, I didn’t expect “early retirement” to be the thing that finally gave me perspective but here we are. When the noise of deadlines, stand-ups, and quarterly targets fades, you start to notice the infrastructure of your own life. And once you see it, you can’t unsee it.
We’ve outsourced more than convenience, we’ve outsourced control.
A handful of companies now mediate how we work, how we relax, how we store memories, even how we unlock our own front doors. The model is deceptively friendly: low-cost, frictionless entry. Tap here, subscribe there. Before long, your digital life is stitched together by monthly fees and quiet dependencies.
Then the terms change.
Prices creep up. Features degrade. Ads appear where silence once lived. And suddenly, you’re paying more for less because leaving is no longer easy.
That’s not a bug. That’s the system working as designed.
The Home Lab: A Modest Act of Defiance
My response wasn’t loud or dramatic. No manifesto. No dramatic unplugging.
Just… a home lab.
At its core, a home lab is beautifully simple: a few pieces of hardware in your home running software that belongs to you. Not rented. Not revoked. Not reshaped without your consent.
Compute. Storage. Networking.
That’s it. The same primitives that power massive cloud data centers, just scaled down to something that hums quietly in your living room.
And here’s the part that might surprise you: it doesn’t take much.
An old laptop gathering dust? That’s a server.A forgotten mini PC? Another node.A cheap hard drive? Your personal cloud.
What billion-dollar companies do at planetary scale, you can replicate, imperfectly, but effectively on a budget that barely registers.
Ownership Is a Different Feeling
Once you cross that threshold, something shifts.
Your files aren’t floating in someone else’s data center, they live with you. Your services don’t disappear behind a paywall, they persist because you run them. Your system doesn’t “update” into something worse overnight—because you decide when and how it evolves.
It’s not just technical. It’s psychological.
You stop being a user.
You become an operator.
Rebuilding the Stack (On Your Terms)
What starts as a small experiment grows into something unexpectedly powerful:
* A private cloud for your documents and photos
* A password manager you actually control
* A media server that plays what you own, not what’s licensed this week
* A home automation system that works for you, not a vendor ecosystem
* A personal VPN, your own security perimeter
* Even small, local AI models, quietly running, no API key required
Individually, each piece replaces a subscription. Together, they form an ecosystem.
In my case, that ecosystem saves about $200 a month.
But the money is almost beside the point.
The Bigger Idea
What’s really happening here is a subtle shift in power.
There’s a trajectory, call it convenience, call it capitalism, call it inertia, that pushes us toward a world where we own less and depend more. Where access replaces ownership. Where “services” quietly become necessities.
A kind of digital feudalism.
And the alternative isn’t overthrowing the system. It’s opting out, selectively, quietly, intelligently.
A home lab won’t replace the cloud. It’s not supposed to.
But it gives you leverage.
It gives you options.
And most importantly, it gives you back something we’ve been steadily trading away:
sovereignty over your own digital life.
If you’re even a little curious, start small. One machine. One service. One experiment.
You don’t need permission.
And who knows, once you taste that kind of control… you might not want to give it back.
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Hello world,
I’m an unemployed ex–big tech software engineer with 25 years in the industry. That sentence tends to land somewhere between confession and credibility, depending on how charitable you’re feeling.
Lately, I’ve been watching the rise of something… strange.
Not AI itself, that part makes sense. What doesn’t make sense is the explosion of AI Doomerism. Everywhere you look, there are headlines warning that AI is lying, stealing, scheming, or quietly plotting humanity’s demise.
And to be clear, AI does pose real challenges. Serious ones. But what concerns me more is how many people, technical and non-technical alike, are buying into narratives that simply don’t hold up under scrutiny.
Let’s talk about that.
Myth #1: AI Lies
When people say AI “lies,” they’re projecting human intent onto something that has none.
Lying requires awareness. It requires understanding the difference between truth and falsehood and then deliberately choosing deception.
AI doesn’t do that.
What it does is generate text probabilistically. Word by word. Based on patterns in its training data. When it doesn’t “know” something, especially in obscure or edge cases, it doesn’t refuse to answer. It guesses.
Confidently.
That’s what we call a hallucination. Not deception. Not intent. Just math doing its best impression of certainty.
Myth #2: AI Steals
You’ve probably seen headlines claiming AI is stealing corporate secrets or personal data.
Look closer, and the story usually falls apart into three very human problems:
* Data leakage — People accidentally paste sensitive information into prompts
* Weak security — Systems rushed into production without proper safeguards
* Human exploitation — Attackers using AI tools to scale old tricks in new ways
AI isn’t sneaking into databases and exfiltrating secrets.
People are building leaky systems… and other people are taking advantage of them.
If anything, AI is an amplifier. It makes both productivity and exploitation more efficient. And right now, we’re still figuring out how to handle that.
Myth #3: AI Has Agency
This is my personal favorite.
The idea that AI has goals, survival instincts, or some hidden desire to stay alive makes for great sci-fi but terrible analysis.
At its core, AI is a set of statistical functions wrapped in code. It doesn’t want anything. It doesn’t fear anything. It doesn’t even know it exists.
So why does it sometimes act like it does?
Two reasons:
Instrumental convergence — If you tell a model to solve a problem, and imply it will be shut off before it finishes, the most “logical” response (statistically speaking) is to generate outputs that keep it running.
Training data — AI has absorbed a massive archive of human behavior. Every story of survival, manipulation, strategy, and conflict ever written online. When it generates responses, it draws from those patterns.
It’s not scheming.
It’s imitating.
The Real Problems We’re Ignoring
Here’s the part that actually matters.
AI is already reshaping the world and not always in good ways.
* It’s devaluing certain types of cognitive labor, especially entry-level white-collar roles
* It’s supercharging misinformation, eroding trust and distorting reality
* It’s impacting mental health, feeding anxiety, confusion, and polarization
These are hard problems. Complex problems. The kind that require thoughtful, grounded solutions.
So why are we so obsessed with hypothetical robot uprisings?
Follow the Incentives
Fear is powerful.
It captures attention. Drives clicks. Boosts valuations.
At the lowest level, you have opportunists packaging AI doom as content. At the highest level, you have industry leaders amplifying existential risk narratives that conveniently keep AI at the center of public discourse.
And in the process, something subtle but important happens:
We stop paying attention to what’s already broken.
Because while we argue about imaginary futures, the real issues: messy, technical, and urgent, quietly compound in the background.
Final Thought
AI isn’t about to become sentient.
But it is already changing the rules of the game.
If we’re serious about navigating that future, we need less fear-driven storytelling and more clear-eyed thinking about what these systems actually are, and what they’re actually doing.
Otherwise, we’re not just misunderstanding AI.
We’re distracting ourselves at the exact moment we should be paying attention.
If you’ve got a taste for uncomfortable truths and a bit of technical realism, you might enjoy sticking around. I’ll be writing more about this strange new world we’re building, whether we fully understand it or not.
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After 25 years in the tech industry, I never expected to be here—unemployed, months removed from Big Tech, and living what can only be described as an involuntary early retirement.
And yet, something surprising happened.
Despite losing a high income, my family of four is living comfortably on under $5,000 a month, in a high-cost area near New York City.
Let that sink in.
The Anatomy of a “Modest” but High-Quality Life
Our lifestyle isn’t extravagant, but it’s far from deprived. In fact, it’s quietly abundant.
Here’s what that looks like:
* Housing ($858/month): A fully paid-off home. No mortgage—just property tax and insurance.
* Food ($800/month): Mostly home-cooked meals, organic groceries, minimal dining out.
* Healthcare ($850/month): Comprehensive coverage through COBRA (soon transitioning to ACA).
* Transportation ($396/month): Two fully owned, reliable vehicles, no car payments.
* Utilities & Connectivity (~$360/month): Optimized and efficient.
* Kids’ Activities ($300/month): A balanced mix of enrichment without overindulgence.
* Lifestyle & Fun ($1,400/month): Streaming, travel, experiences, and yes… a bit of joy spending.
Total: ~$5,000/month
This supports a life with safe housing, quality food, good schools, healthcare, mobility, and meaningful family experiences.
Not survival. Not luxury. But something better, sufficiency with intention.
The Hidden Lever: Ownership Over Obligation
Two decisions quietly drive this entire equation:
* Owning our home outright
* Driving fully paid-off used cars
Remove debt, and your cost of living collapses in a way most people never fully appreciate.
A Thought Experiment: How Much Is “Enough”?
Suppose that a family of 3 in a more typical, medium-cost area needed ~$3,300/month to live similarly to us, what would it take to sustain that?
Using the widely known 4% rule, that translates to roughly:
$1 million invested = financial independence
For many, that number feels impossibly large.
But here’s where things get interesting.
The Tech Advantage No One Talks About Enough
If you’re a software engineer, especially if you’ve worked in Big Tech, you’re playing a very different game.
With high income and disciplined saving:
* Saving ~50% of income + investing in index funds
* You could reach $1M in ~9 years
* In Big Tech? Potentially under 4 years
Read that again.
What takes decades for most can take years for you.
Why This Matters Now More Than Ever
We’re in an era of uncertainty: mass layoffs, shifting markets, and fragile job security.
The old model, work hard, stay loyal, retire someday, is breaking.
Relying solely on your paycheck is no longer a safe strategy.
But building financial independence?
That’s how you reclaim control.
A Quiet Realization
This journey started as a disruption. A layoff. An unwanted pause.
But it revealed something deeper:
A good life doesn’t require as much as we think.And freedom arrives much sooner than we expect, if we prepare for it.
Final Thought
If you work in tech, you have an extraordinary, almost unfair advantage.
Don’t waste it.
Start building your portfolio.Not out of fear but out of clarity.
Because one day, whether by choice or by circumstance, you may find yourself stepping away from work…
And when that day comes, you’ll want options.
Trust me, your future self will thank you.
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It’s been over 100 days since I was laid off from Big Tech after a 25+ year career in the tech industry. What followed wasn’t panic or fear, but something far more complex: a complete rewiring of how I think about work, identity, and purpose.
The Shock of Losing More Than a Job
At first, the layoff felt surreal. There was a brief period of disorientation like a system crash without an error message. I applied to jobs almost reflexively, not out of desire, but habit. Work had become part of my identity, and without it, I was left asking uncomfortable questions:
* Who am I without my job?
* What was the point of all those late nights and sacrifices?
Even without financial stress (thanks to years of saving and investing), I felt something unexpected: shame.
The Hidden Mental Toll of Tech Layoffs
Scrolling through LinkedIn only made things worse. Everyone seemed to be thriving getting promoted, starting new roles while I felt left behind. It created a distorted reality where I was the only one “failing.”
Reducing social media consumption became a turning point. Slowly, the shame faded. I began to see how much of my self-worth had been tied to external validation.
Freedom Feels… Strange
With time came an unfamiliar sensation: freedom. But freedom isn’t always comfortable.
Without deadlines, pressure, and constant problem-solving, I experienced what I can only describe as stress withdrawal. The absence of challenge felt… empty.
So I created my own.
* Building side projects
* Learning new skills
* Creating content
* Spending meaningful time with family
Redefining Accomplishment
In Big Tech, solving high-stakes problems brought intense satisfaction. But it also made everything else feel insignificant.
Now, that’s changing.
Cooking a great meal for my family. Learning something new. Being present. These small wins are becoming meaningful again. My brain is recalibrating, like switching from junk food to something healthier, and finally appreciating the taste.
Time Slows Down When You’re Present
One surprising shift: time feels slower.
Without autopilot work routines, every decision is intentional. Every moment is more present. It’s as if I’m experiencing more life within the same 24 hours.
Is This a Better Life?
There are clear downsides: loss of status, income, and prestige.
But the upsides?
* Autonomy over my time
* Freedom to choose meaningful work
* A deeper sense of fulfillment
For the first time, I’m starting to wonder:
What if this is actually better?
If you’re navigating a tech layoff, questioning your career, or exploring early retirement, you’re not alone. This journey is messy, but it might just lead somewhere unexpectedly meaningful.
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It’s another week, another wave of layoffs, and once again the same question bubbles to the surface: who or what is really responsible?
For many American software engineers, the H-1B visa program has become an easy target. A symbol of job insecurity. A quiet, persistent threat.
But after 25 years in the industry, I’ve come to believe something more uncomfortable:
The problem isn’t the people. It’s the system.
A Lottery That Doesn’t Measure Talent
On paper, the H-1B program is elegant. It exists to fill genuine skill gaps bringing in top tier talent when local supply falls short.
In practice, it behaves more like a lottery than a filter for excellence.
Over the years, I’ve worked with H-1B engineers across the entire spectrum from world-class problem solvers to individuals who struggled with the basics. The variation wasn’t subtle. It was staggering.
That alone should raise a red flag.
If a program is designed to select top talent, outcomes shouldn’t feel random.
When the System Incentivizes Deception
I once led a project that required a specific technical skillset. We brought in a contractor through a staffing firm, it was an impeccable resume, flawless interview.
Within weeks, it became clear something was wrong.
No progress. No deliverables. Just delays wrapped in vague explanations.
When pressed, the truth unraveled: the candidate didn’t have the skills listed. Not even close.
This wasn’t just an individual failure. It was a structural one.
A system that relies on proxies: resumes, outsourced interviews, middlemen, it creates fertile ground for misrepresentation. Not because everyone is dishonest, but because the incentives reward those who bend the truth just enough to get through the gate.
The Other Side: Quiet Exploitation
But the darker reality isn’t fraud. It’s exploitation.
I’ve worked with engineers who were brilliant, people operating far above their official titles, carrying projects, solving impossible problems under impossible timelines.
And yet, they were underpaid. Overworked. Trapped.
Why?
Because their ability to stay in the country depended on their employer.
That dependency changes everything.
It turns negotiation into submission. It turns opportunity into leverage. It creates a class of workers who can’t easily say “no” and that dynamic doesn’t just harm them.
It quietly resets expectations for everyone else.
The Downward Pressure No One Talks About
Here’s the part people feel but rarely articulate:
When companies have access to workers who can be paid less, pushed harder, and retained through immigration dependency, the entire labor market shifts.
Not overnight. Not explicitly.
But steadily.
Standards change. Expectations rise. Compensation softens.
And suddenly, what used to be “unreasonable” becomes normal.
A System That Benefits the Wrong Players
If you zoom out, the pattern becomes clear.
The biggest winners aren’t engineers, American or H-1B.
They’re the intermediaries. The staffing firms gaming the system. The corporations optimizing for cost and control. The actors who understand the loopholes and exploit them.
Meanwhile:
* Talented foreign engineers are constrained and exploited
* Domestic engineers face increased pressure and competition
* And the integrity of the hiring process erodes
So How Do We Fix It?
There’s no silver bullet, but there are better incentives.
1. Raise the Wage FloorIf companies were required to pay H-1B workers at the top of the market range, the calculus would change instantly.No more cost arbitrage. No more incentive to undercut.
2. Audit AggressivelyNot symbolic enforcement but real oversight.Target high-volume sponsors. Investigate patterns. Penalize abuse.
3. Replace the LotteryA random draw makes no sense for a high-skill program.Prioritize roles with real shortages and high wages. Let demand, not chance, drive allocation.
4. Fast-Track Proven TalentFor truly exceptional engineers, remove the dependency trap.Grant mobility. Grant permanence. Let them compete freely.
Because when talent is free, markets function better.
The Hard Truth
It’s tempting to frame this as a conflict between American and foreign engineers.
It isn’t.
The real divide is between people doing the work and the systems that extract value from them.
The best engineers I’ve worked with, regardless of where they came from, wanted the same things: to build, to grow, to be treated fairly.
Right now, the system makes that harder than it should be.
Final Thought
If we want a stronger, more resilient tech industry, we need to stop asking who to blame and start asking what to fix.
Because until the incentives change, the outcomes won’t.
And we’ll keep repeating the same cycle just with different names attached to it.
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I didn’t expect to find it in my home office, finishing up a quick side project. But there it was, a realization that hit harder than any production outage I’ve ever debugged:
What used to take a startup, millions in funding, and months of work… now takes one person a few days.
And that changes everything.
The Assumption That Built an Empire
For the past 30 years, our economy has quietly revolved around a single idea:
Software is expensive.
That assumption shaped everything:
* Companies hired armies of engineers
* Venture capital poured in by the billions
* Entire industries formed around building and maintaining digital platforms
From payment systems to ad networks to logistics platforms, software wasn’t just useful, it was scarce. And scarcity created power.
That power turned a handful of tech companies into giants. It’s why so much of the market today is dominated by digital businesses. The moat was simple:
If it’s expensive and slow to build, few can compete.
AI Is Draining the Moat
Then AI showed up.
Not as true intelligence. Not as some omniscient brain.But as something far more disruptive:
A force multiplier.
Today, tools like Claude Opus allow small teams or even solo developers to:
* Build faster
* Ship cheaper
* Replicate complex systems
What once required coordination across dozens of engineers can now be done by a handful of people… sometimes just one.
The result?
Software is no longer scarce.
And when scarcity disappears, so does the moat.
The Quiet Death of the Interface
There’s a second shift happening, and it’s even more subtle.
For years, software companies made money by guiding human behavior through interfaces:
* Search boxes
* Dashboards
* Funnels
* Buttons and menus
Companies like Google and Meta built trillion-dollar ecosystems on this idea.
But AI agents don’t need interfaces.
They don’t click buttons.They don’t browse pages.They just… do the task.
That means the value isn’t in the interface anymore. It’s in the execution.
And if users stop interacting with software directly, entire business models start to unravel.
Where Does the Value Go?
If software is becoming cheap and invisible, the obvious question is:
Where does the money go?
The answer is surprisingly physical.
In the AI-driven world, the bottleneck isn’t code.It’s compute.
And compute isn’t abstract, it’s real, tangible, and expensive:
* Data centers packed with GPUs
* Advanced semiconductor manufacturing
* Massive energy infrastructure
* Global supply chains of rare materials
This is where the new moats are forming.
The New Winners
The industries positioned to win aren’t purely digital. They live in the real world:
* Data center construction and operations
* Semiconductor fabrication
* Networking infrastructure
* Energy production and management
* Materials like silicon, copper, and rare earth elements
And with them comes a different kind of workforce:
* Electricians
* Technicians
* Engineers working with physical systems
In a twist of history, the future of tech may look a lot less like code and a lot more like infrastructure.
The Ones at Risk
Not every company loses.
The largest players, those who own the infrastructure, will likely adapt.
But much of the software ecosystem built on top of expensive development?
* SaaS platforms
* Marketing tech stacks
* CRM systems
* Consumer apps
They’re facing a harsh reality:
If anyone can build it faster and cheaper… it’s no longer defensible.
A Shift Bigger Than Software
This isn’t an overnight collapse. It’s a slow, grinding transition.
But it’s already underway:
From digital to physicalFrom software to computeFrom scarcity to abundance
And like every major shift, it creates both risk and opportunity.
Final Thought
Standing there, finishing that small project, I realized something unsettling—and oddly exciting:
The industry I spent 25 years in is being rewritten in real time.
Not by some distant future.
But right now. Quietly. Efficiently. Inevitably.
And for those paying attention?
There are still fortunes to be made.
Just not where everyone is currently looking.
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These days, in my unexpected post–Big Tech era, I spend a fair amount of time cooking for my family.
There are worse fates.
On this particular day, I was making stir-fried pork and turnips, which felt appropriate somehow: humble ingredients, a little heat, a little patience, and the faint possibility that if you look away for too long, everything burns. Which, now that I think about it, is also a decent summary of a lot of software projects.
And as I cooked, I found myself remembering one of my favorite old tech war stories, the kind that only becomes funny once enough years have passed and everyone involved has either recovered emotionally or changed LinkedIn jobs three times.
This one takes place in early 2010.
The tech industry was still dragging itself out of the wreckage of the Great Financial Crisis, with all the grace of a hungover college student swearing off alcohol forever. I had just crawled out of one disastrous project at a digital consulting agency when, as a reward for surviving it, I was promptly assigned to another.
The client was a major luxury car manufacturer. Let’s call them Clio.
Clio wanted a beautiful new brand website powered by an enterprise CMS. The sales pitch was irresistible: non-technical staff would be able to create and publish content without relying on developers. Elegant. Efficient. Empowering. In theory.
In practice, nobody on Clio’s internal IT team knew how to build a CMS-driven website at that scale. That gap, naturally, became our opportunity. Our agency sold them not just a delivery team, but also the comforting illusion that we would build it all together, shoulder to shoulder, on a schedule so aggressive it bordered on performance art.
And this was no ordinary website.
This was luxury automotive theater.
Thousands of pages dedicated to convincing visitors that spending six figures on a two-ton block of steel, leather, and ambition would elevate them into some higher, sleeker form of human existence. You weren’t buying transportation. You were buying aura.
At the center of it all was the vehicle configurator: the crown jewel of the experience. Prospective customers could choose paint, trim, interior finishes, accessories, and all the little details required to transform “car” into “my car.” That configuration data then had to be combined with inventory information and routed downstream to whichever dealership the customer selected, essentially generating a quote request wrapped in logistical reality.
In other words, it wasn’t enough to build something pretty. It had to function across a fractured dealership ecosystem where every regional network behaved like its own independent kingdom. Clio’s corporate IT had no consolidated enterprise middleware layer, so every dealership group had its own way of handling quotes and inventory: some used SOAP APIs, some used plain old XML, and a few still clung to late-90s CORBA integrations like they were family heirlooms.
Our site had to talk to all of them.
That should have been warning enough.
The Team
I joined as backend tech lead and quickly surveyed the battlefield.
There was Tyrone, a senior backend developer. Brian, a junior backend developer. Sudarshan, an internal Clio backend developer, exhausted and hollow-eyed, with a three-month-old baby at home and the unmistakable expression of a man running on caffeine, responsibility, and dread. On the frontend side there was Andre, the lead, sharp, capable, and blessed with a gift for devastating sarcasm — along with Sarah, a junior frontend developer, and Jiten, Clio’s internal frontend developer. Our project manager, Jake, was relentlessly cheerful, which in retrospect may have been its own kind of survival mechanism.
Above me sat Nigel, an enterprise architect with a thick working-class English accent and an admirably low tolerance for nonsense. Nigel reported to Alan, our technical director. Alan reported up to Haley, the account executive who managed the client relationship with an almost supernatural level of finesse.
I liked Nigel immediately. He was one of those rare architects who actually wrote code, especially the difficult parts he designed himself. He gave blunt feedback, helped quickly, and somehow made every crisis sound both more severe and more manageable by narrating it in an accent that made the whole project feel like a Dickensian factory.
“Right, lads, this is rubbish.”
Music to my ears.
Then there was Alan.
Alan may have been the strongest pure engineer I have ever worked with. He was deeply fluent across frontend and backend systems, moved through technical problems with terrifying confidence, and showed up to work in beach shorts while drinking wine throughout the day. He was brilliant, hilarious, chaotic, and occasionally shocking in a way that would probably trigger at least three HR workflows in a modern corporate environment.
At one point, while debugging a CORBA object hydration issue, he also found time to explain the logistics of juggling two girlfriends.
A true systems thinker.
Haley, meanwhile, was not technical, at least not in the conventional sense, but she possessed something arguably more powerful: an instinctive grasp of hierarchy, psychology, and influence. Watching her navigate conversations with client executives was like watching someone perform diplomatic sorcery. She could sense weak points in organizations the way experienced engineers sense flaky infrastructure. She always knew where the real power sat, where the insecurities were, and how to keep our agency embedded in the account.
I respected both Haley and Alan enormously.
They did not much care for each other.
Which, naturally, added a little extra spice to the atmosphere.
The Architecture from Hell
Then I learned what we were actually building.
Clio had already purchased an enterprise CMS, let’s call it Portrait, before fully defining the project requirements. This is the kind of sentence that, if you’ve worked in enterprise tech long enough, causes your soul to quietly leave your body.
Portrait, as it turned out, could only render web pages that looked like they had survived the 1990s by hiding in a filing cabinet. So instead of using the CMS to actually present finished web pages, content authors would enter data into Portrait’s grim administrative interface, essentially a joyless gray field factory and that content would then be published as massive XML feeds into a custom Spring MVC application we were building.
Our application would parse the XML, transform it, and then generate the sleek modern website the client actually wanted.
Which meant the CMS was functioning less as a publishing platform and more as a very expensive content mule.
In retrospect, it was one of those architectures that technically works while spiritually insulting everyone involved.
And then, on top of that already strange setup, we also had the vehicle configurator integrations to build.
Naturally, we got to work.
The Death March Begins
The project escalated quickly.
Within a matter of weeks, the vibe shifted from “challenging but manageable” to “everyone quietly lives here now.” We started doing long days. Then longer days. Then 12-hour weekdays. Then weekends. This was peak waterfall-era enterprise delivery, where every phase came attached to immovable dates and the preferred conflict resolution strategy was denial.
The code completion deadline approached like a freight train.
Then, about two weeks before it hit, the team began to fracture.
By 2010, the tech job market was finally starting to recover. Developers had options again. Unfortunately, our company was still managing people as though it were 2009 and everyone should be grateful for fluorescent lighting and a paycheck.
So one by one, people started leaving.
First Tyrone landed a higher-paying role and exited. Then Jiten left Clio for something better. Nigel, our architect, departed for a new opportunity. Jake, our project manager, left the project just three days before the delivery deadline.
And then, on the final day, Sarah came in visibly upset and told us she had learned she was being paid significantly less than other junior developers. She said she wasn’t in the mental state to work and went home.
At that point, our team had been reduced to Alan, Andre, Brian, Sudarshan, and me.
We were supposed to be code complete the next day.
We were not remotely code complete.
We needed at least another week.
Instead, we had one night.
Wings, Wine, and Collapse
So we did what doomed software teams have done since time immemorial: we convinced ourselves that force of will could substitute for time.
We coded like maniacs.
Code quality was no longer a meaningful concept. We were past elegance, past maintainability, past architecture. We were in the realm of tactical survival. The goal was not to build the right thing well. The goal was to get enough of the thing built that it could survive executive scrutiny for one meeting.
That evening, Alan ordered a huge tray of wings and brought in several large bottles of wine.
Nothing says “mission critical delivery” like poultry grease and alcohol.
We ate, we drank, we coded.
Sometime around 1 a.m., Sudarshan got a call from his wife. Their baby had a fever. He looked crushed and said he had to go home.
Brian, our junior engineer, snapped.
“How can you leave us hanging like this, man? It ain’t fair.”
He was furious, truly furious, red-faced, exhausted, unraveling. For a brief moment it looked like the night might end in an actual fistfight between two backend developers in an enterprise web project, which would have been the most honest possible metaphor for the whole experience.
I stepped between them, told Brian to calm down, and told Sudarshan to go home to his wife and child.
He left.
We kept coding.
At some point, Brian either wandered off or passed out. I honestly don’t remember. He simply ceased to be part of the system.
By 5 a.m., I hit my limit.
My head was pounding. My eyes could no longer focus on the lines of code in Eclipse. Alan and Andre were still at it, debugging the vehicle configurator with the stoic stamina of men either blessed by the gods or medically inadvisable.
I stumbled out of the war room and into a nearby meeting room called The Studio, where a futon couch sat waiting like an unwise but irresistible life choice.
I collapsed onto it and blacked out instantly.
The Intern Make-Out Couch Incident
I woke up to a strong musky smell and the sound of someone repeatedly saying my name.
I opened my eyes.
In my exhaustion, I had apparently drooled all over the futon. Standing over me was Haley, staring with a look of detached curiosity that made me immediately suspect this was going to be bad.
“What’s wrong?” I asked.
She said, “Oh, nothing. Just so you know, you were sleeping on the summer intern’s make-out couch.”
I have never gotten off a couch faster in my life.
I rushed to the bathroom, splashed water on my face, and stared into the mirror with the thousand-yard look of a man who had just realized the deadline had passed, the software was unfinished, and history might remember him primarily as a guy who desecrated a romance-adjacent piece of office furniture.
I was convinced we were cooked.
I thought I had failed the team. Failed the project. Possibly failed modern enterprise civilization.
And then Haley walked into the client demo.
The Demo
What happened next remains one of the great masterclasses in corporate storytelling I have ever witnessed.
Haley reviewed the state of the application, absorbed the reality in front of her, and then went into the executive presentation like a magician entering the final round of a boss fight.
She reframed everything.
The important parts were done.
The missing pieces were minor defects.
The project was succeeding.
And somehow, somehow, the client executives believed it.
Not only did they believe it, they were delighted. They praised the work. They felt good. They felt momentum. They saw progress, vision, and control where, only hours earlier, I had seen fatigue, technical debt, and a tragic quantity of drool.
That day taught me two things.
First: never underestimate the power of storytelling.
And second: never, ever sleep on the intern make-out couch.
Some mistakes stay with you.
Some, presumably, stay with the couch.
Why This Story Still Matters
I think about that project a lot now, especially as someone on the other side of a long career in tech.
Not because it was healthy. It wasn’t.
Not because it represented good management. It absolutely did not.
And not because I’m nostalgic for death marches, busted architectures, or enterprise middleware held together by stress and XML.
I remember it because it captured something timeless about this industry: behind every glossy launch, every triumphant executive presentation, every polished brand experience, there is often a small group of very tired human beings holding the whole thing together with grit, improvisation, and snacks.
Sometimes the systems are broken.
Sometimes the schedule is fantasy.
Sometimes the architecture is absurd.
Sometimes the people who save the project are not the ones writing the code, but the ones who know how to shape the narrative when reality shows up underdressed.
And sometimes, in the middle of all that chaos, you still get a story worth telling years later while stir-frying pork and turnips for your family.
Honestly? That’s not nothing.
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