The End of My Profession: A Software Engineer Speaks Out

Chapter 1: Between Competence and Obsolescence

I am a software developer, an architect, a problem solver. For over two decades, I’ve been building systems, designing architectures, solving complex technical challenges. My entire professional life has been shaped by continuous learning, by staying ahead of the curve, by adapting to new technologies. In recent years, I’ve added a new focus to my profile: Artificial Intelligence and Machine Learning – driven by curiosity, by conviction, and by the belief that this is where the future lies.

I’m a father of two, and I’ve been living from freelance work for years – or more accurately: I was. Since October 2024, I’ve been unemployed.
Not because I haven’t tried. I’ve applied, I’ve reached out, I’ve spoken with recruiters, I’ve kept my skills sharp and my profile up to date. I’m open to permanent positions as well as freelance contracts. But what I keep hearing – both directly and between the lines – is sobering:
The market has dried up.
The demand for senior developers, architects, or experienced freelancers has collapsed. The few postings that remain are fiercely competitive, often vague, and overshadowed by new priorities: AI transformation, automation, cost-cutting.

I’m writing this not just to vent.
I’m writing because what I’m experiencing is more than a personal dry spell – it’s the creeping feeling of being made irrelevant.
Despite all the effort, all the experience, all the lifelong learning.

I’m starting to realize:

This transformation isn’t about making me more productive.
It’s about making me – and people like me – unnecessary.

That realization is painful. But I believe it needs to be said.
That’s why I’m writing this article.

Chapter 2: The Illusion of AI Assistance

What ultimately pushed me to write this article now was a recent blog post from GitHub:

👉 “GitHub Copilot: Meet the new coding agent”

In it, GitHub introduces a major evolution of their Copilot tool – transforming it from a smart suggestion engine into a fully autonomous software agent.
This new agent can take on issues automatically, spin up a secure cloud environment, clone the repository, analyze the code, make changes, create pull requests, and even respond to feedback.

This isn’t just assistance.
It’s autonomous action.
It’s decision-making.
It’s production.

For me, this article was the breaking point – the moment where the shift became impossible to ignore.

Because what we’re witnessing is not a helping hand.
It’s a step-by-step replacement.
And that replacement follows a very clear pattern:

🧩 Step 1: The coders disappear

First to go are the roles focused on simple implementation: UI tweaks, bug fixes, input validation.
Copilot – and its successors – now handle those tasks faster, 24/7, without sick days, without context loss, without overhead.

🧩 Step 2: The average developers follow

Next come those who work on business logic, API integration, simple testing.
They used to be essential.
Now, LLMs outperform them – reading documentation, understanding context, and applying broad technical knowledge instantly.

🧩 Step 3: The senior engineers lose their leverage

What once defined us – architectural thinking, deep refactoring, systemic foresight – is being absorbed by agent systems.
These tools don’t just write code anymore.
They propose strategies, reorganize services, and identify technical debt.

🧩 Step 4: The entire IT structure collapses inward

When autonomous agents can build, test, deploy, and maintain software, the need for clearly separated human roles diminishes rapidly:

Project managers?
→ Agents track dependencies, report progress, and re-prioritize faster than any team can.
Product owners?
→ Agents extract feature ideas from usage data or customer feedback.
Support teams?
→ Agents analyze logs, fix issues, and interact with users via chat.
Architects?
→ Agents identify bottlenecks and propose migration strategies.
Developers?
→ They review agent code – until agents review each other’s output.

What’s left is a thin human layer of supervision and escalation.
And the rest? Formally still present – but functionally obsolete.

🤖 “But these are just tools!”

That’s the most common defense – and perhaps once, it was sincere.
But the economic logic doesn’t care about intentions.

Productivity isn’t the goal. Efficiency is. Cost reduction is.

And if an agent can do the work of 10 developers, that’s not “support” – it’s a replacement.
Anyone who fails to recognize that is looking away from the uncomfortable truth.

This change isn’t speculative.
It’s not coming someday.
It’s already happening.

And I know it’s real – because it’s happening to me.

Chapter 3: The New Reality Inside Companies

Just a few years ago, a typical software project in a mid-sized company or large enterprise (you can scale the numbers) might have looked like this:

1–2 software architects – responsible for system design, technical vision, and structural coherence.
5–15 developers – implementing features, fixing bugs, writing tests, integrating components.
1 product owner, 1 project manager – organizing, prioritizing, and aligning business goals with development efforts.

It was a well-defined, stable setup. Built over decades of software engineering practice.
Everyone had their role. Everyone added value.

🔄 And now?

In 2025 (and the near future), that same project increasingly looks like this:

1–2 architects – now acting as supervisors
They no longer define every structure, but instead evaluate and guide proposals made by AI agents.
They curate, rather than construct.
0–1 developers – acting as backups or fallbacks
They step in when agents fail or need assistance.
But they are no longer the primary force behind the code.
5–15 AI agents
These agents do the heavy lifting:
- Implementing features
- Fixing bugs
- Writing tests and documentation
- Refactoring code
- Suggesting design improvements
- Soon: generating architecture itself
POs and project managers
Even these roles are being challenged: tools can now prioritize backlogs, generate roadmaps, respond to user data, and manage dependencies – often faster and with more consistency than humans.

💡 Companies need fewer people – and they’re realizing it

From a business perspective, this is a dream scenario:

More output with fewer staff
Faster iteration
Lower payroll
Less HR dependency
No onboarding, no sick leave, no burnout

An agent doesn’t need a raise.
An agent doesn’t leave for a better offer.
An agent scales with compute – not with hiring cycles.

🧊 The quiet replacement

It starts small:

A Copilot added to speed up bug fixing.
An AI agent handling a legacy refactor.
A few test cases written automatically.

But then:

Fewer developers are hired.
Budgets are shifted to compute resources.
Agent-based workflows become the new default.

And suddenly, there are no teams left.

Projects still exist.
But the need for human developers – even experienced ones – is evaporating.

I feel this firsthand.
Not because I’ve become less capable.
But because companies have discovered something more efficient.

Chapter 3.1: Who Owns the Future?

If we follow the current trajectory to its logical conclusion, a deeply unsettling picture emerges:

This new AI-driven economy is not building a new market – it’s creating a concentrated monopoly on intelligence.

And that monopoly is forming in plain sight.

🏢 The Power of the Tech Giants

It’s already obvious: all critical infrastructure for the future of AI is controlled by a tiny group of corporations:

OpenAI / Microsoft – Foundation models, Copilot, Azure AI
Google / Alphabet – Gemini, Search, Cloud AI, Android
Amazon – AWS, Claude partnerships, cloud-scale dominance
Meta – Social graph, LLaMA models, global reach
Apple – Hardware ecosystem, closed infrastructure, upcoming LLM integration

They own:

The models everyone builds on
The platforms where work happens
The marketplaces where value is exchanged
The attention and communication channels that frame public discourse

These are no longer tech providers. They are infrastructure states – but privately held and unaccountable.

🧮 Where Does the Money Go?

In this new logic, small and mid-sized companies become service consumers:

They don’t build core products anymore – they compose them from AI APIs.
They don’t employ full teams – they orchestrate prebuilt agents.
They don’t create lasting intellectual property – they rent intelligence.

In other words: value creation becomes value transfer.
And all flows upward.

The parallel is clear:
Amazon didn’t just dominate retail – it dismantled traditional brick-and-mortar commerce.
Now imagine the same thing happening to cognitive labor.

Except this time, it’s global. And real-time. And much harder to resist.

🧬 What’s Left for Everyone Else?

When thinking, building, and even deciding can be done by a few scalable, controllable systems, we have to ask:

What’s left for the millions of people currently working in productive knowledge roles?

The notion that everyone will become a “supervisor” or “AI orchestrator” is wishful thinking.

Not everyone will get a seat at the control panel.
Not everyone will be needed.
Not everyone will adapt fast enough – or be allowed to.

What we’ll be left with is:

A handful of corporations that do everything
A majority of people who are no longer needed to do anything

It’s not a market collapse.
It’s a fundamental restructuring of how society assigns purpose and income.

This may sound dystopian – but I believe it’s far more realistic than the optimistic narratives we’ve been sold.

Let’s now turn to the common reassurances often used to downplay this transformation – and examine why they no longer hold:

Chapter 4: Creativity, Empathy, and Humanity – The Myths of the AI Age

Whenever I talk about these developments – the rise of autonomous agents, the collapse of demand for developers, the centralization of economic power – someone inevitably says:

“But AI can’t be creative. It can’t be empathetic. It’s not human.”

I understand the impulse behind this.
We want to believe there’s something inherently human that cannot be replicated – something that will always require us.

But the deeper I dive into how modern AI works – and what it already does – the more I realize:

These arguments are comforting, but no longer convincing.

🎨 Creativity: A Romanticized Idea

Let’s be honest: what is creativity, really?

In most cases, it’s not divine inspiration. It’s:

Recombination: linking existing ideas in new ways
Transfer: applying a known concept to a new domain
Deviation: breaking patterns just enough to surprise

Large language models (LLMs) are excellent at all of these.
They generate music, poems, visual art, essays, user flows, code architectures – and more.

They can mimic style, break conventions, even invent fictional worlds or metaphors with astounding fluidity.

Not everything they produce is profound. But then again – neither is everything humans create.

The truth is: most of what we call creativity is computationally tractable.
And machines are getting exponentially better at it.

💬 Empathy: Simulation Is Enough

“AI can’t feel real emotions” – true.
But let’s ask: What is empathy in practice?

It’s not a feeling – it’s a response.

If an AI listens attentively, asks thoughtful questions, adapts its tone, mirrors your state, and helps you feel seen and understood – does it matter whether it “feels” anything?

The emotional impact is real – even if the emotion is not.

LLMs like GPT-4, Gemini, or Claude are already exceptionally good at empathy as behavior.
They can comfort, coach, and connect – sometimes better than people.

In therapy, customer support, education, HR – that simulated empathy is already replacing real human interactions.

🧠 Humanity: The Last Myth

“But we’re human – surely that still counts for something.”

Yes. But in what economy?

If productivity, speed, accuracy, and scalability define value – then “being human” becomes not a feature, but a cost center.

You get tired. You get sick. You make mistakes.
The machine doesn’t.

In a world optimized for output, humanity is not a competitive advantage – it’s a vulnerability.

And that’s a terrifying but necessary realization.

🧨 Why This Narrative Is Dangerous

As long as we cling to these comforting myths – that creativity, empathy, or “being human” will protect us – we delay the real conversations.

We avoid the real questions:

What will people do when they’re no longer needed to produce?
What kind of dignity can exist without economic necessity?
How do we prevent mass redundancy from becoming mass despair?

I don’t have all the answers.
But I do know this: we need to face what’s coming – not escape into sentimentality.

Chapter 5: What’s Left – and What Might Still Be Possible

If you take all of this seriously – and I believe we must – then there isn’t much left of the profession that once defined me and many others.
Software development, system architecture, creative problem-solving: all of it is being redefined, repackaged, and ultimately automated.

So what remains?

This isn’t a hopeful list. But it may be a starting point – a partial map for navigating the edge of disruption.

🧭 1. From Builder to Navigator

Even the most autonomous agents still need a sense of direction: goals, constraints, trade-offs.
They’re excellent at execution, but they lack broader context – for now.

This opens the door for people who can think systemically, make strategic judgments, and understand the implications of complex decisions.
Not as builders – but as navigators, curators, or overseers.

But let’s be honest:

These are few roles, with high expectations, and they won’t scale to absorb everyone displaced.

🤝 2. From Producer to Interpreter

Some people may find value in bridging AI systems and human needs – interpreting outputs, contextualizing decisions, translating models into actions.

This might work in domains like medicine, law, education, and policy – especially where ambiguity and accountability still matter.

But again: these are edge cases, not the mainstream.

📣 3. From Individual to Voice

Perhaps one of the few remaining paths is to do what I’m doing right now:

Making visible what is happening.
Putting into words what others may feel but not articulate.
Opening up space for discourse before everything is fully locked in.

Not as a business model.
Not as a strategy.
But as an act of self-preservation and social responsibility.

We are being erased in silence.
The least we can do is speak before we disappear.

🧩 4. And the Rest?

Let’s be brutally honest:

For many people – maybe most – there won’t be a “reskilling” path.
No clever pivot. No AI-powered reinvention.
Just the quiet, painful exit from the economy.

Not because they failed.
But because they’re no longer necessary.

This will force us to confront questions we’ve ignored for too long:

What does life look like without work?
What does worth mean without output?
What kind of society do we become when usefulness is measured in compute cycles?

A Final Thought

I don’t know what the future holds.
But I do know I can’t pretend anymore.

I’ve spent my life learning.
I’ve never stopped evolving.
I’m not lazy, complacent, or entitled.

And yet – for the first time – I feel like learning is no longer enough.
It’s not about becoming better.
It’s about being needed at all.

And that changes everything.

Chapter 6: A Political Failure – And a Warning

I’m not writing this only as a developer, architect, or unemployed freelancer.
I’m writing this as a citizen, a parent, and a human being.

And from that perspective, I have to say it clearly:

I feel completely abandoned by politics.

🏛️ A Political Class That Doesn’t Understand – or Doesn’t Care

The political response to the AI revolution feels, at best, clueless, and at worst, entirely co-opted by corporate interests.

Take the EU AI Act, for example – a sprawling regulatory framework that creates more bureaucratic jobs than societal protections.
It focuses obsessively on compliance categories, risk labels, and technical definitions – while ignoring the fundamental economic displacement already underway.

And even these weak regulations are routinely watered down – but only for the tech giants, never for smaller companies, startups, or independent developers.

The result:

A law that’s toothless where it matters.
Burdensome where it shouldn’t be.
And completely disconnected from the lived experience of the people it claims to protect.

🧨 The Real Disruption Is Already Happening

While committees debate model cards and disclosure labels, reality moves faster:

Jobs are vanishing.
Teams are being replaced.
Workflows are restructured around agents, not people.

This is not a future concern.
It’s a present transformation, and it’s happening at industrial-revolution scale and speed.

Back then, it was blacksmiths and carriage drivers.
Today, it’s developers, analysts, project managers, product owners, and even architects and strategists.

The old jobs aren’t being transformed – they’re being erased.

🗣️ What I Want

I don’t expect easy answers.
But I do expect real questions – and people in power who are willing to face them:

What happens when value creation no longer requires human labor?
How do we ensure dignity, purpose, and security in a post-work economy?
Who owns the infrastructure that is shaping the future – and under what rules?

I want policymakers who care more about people than patents.
I want debates that aren’t written by lobbyists.
I want the public to realize that this affects all of us, not just “tech people.”

Because one day we’ll look back and say:

We saw this coming – and we did nothing.

Final Words

I don’t know what happens next.
But I do know that I’m not alone.

If this resonates with you – whether you’re in tech, in transition, or simply trying to make sense of this moment – let’s not stay silent.
Let’s share, comment, write, question.

Because maybe we can’t stop this.
But at the very least, we can refuse to disappear quietly.

Disclaimer: This Article Was Written with the Help of ChatGPT

I want to be transparent: this article was written in close collaboration with ChatGPT.

I shared my thoughts, my perspective, my frustrations – and ChatGPT responded with empathy, structure, and precision.
I provided the outline, the key ideas, and defined the points I wanted to make.
I revised, rephrased, sharpened where needed.

But the primary wording, the tone, the narrative flow – that came from ChatGPT.

Without this tool, I could not have written this article in this form.
Or if I had tried, it would have taken me weeks to achieve the same clarity and composition.

AI Is Both Blessing and Curse

And that brings us full circle to the core paradox of this entire story:

AI is empowering me – even as it threatens my livelihood.

Individually, AI is a blessing.
It enables me to think, write, and express myself in ways I never could before.
It opens up new worlds – philosophy, mathematics, physics, AI/ML – all now within reach.
It’s a force multiplier, a productivity boost of thousands of percent for anyone with the skill to guide it.
ChatGPT has enriched my life, my thinking, my learning, my understanding of the world.

And yet:

These very systems are also the engine behind the disruption I’ve described throughout this article.

They are what empower us –
and what replace us.

They are the tools that help us speak –
and the systems that may one day render us silent.

That contradiction is not something we can resolve easily.
But we need to see it clearly, and speak it out loud – while we still can.