A decade ago, programming was a craft. You designed systems, solved complex problems, made architectural decisions. You were paid for your judgment, your experience, your ability to navigate constraints and imagine original solutions.

Today, you are an executor. A code producer. A number in a team’s velocity.

And the AI arriving now simply makes obvious what the industry has silently decided: since you are already just a machine for producing code, why not use a real machine?

The Silent Shift: 2010 to 2020

In the early 2010s, the profession was still fragmented. There were the “real” developers: those who understood operating systems, architects who designed entire infrastructure, domain specialists who mastered databases or embedded systems. Each had an authentic zone of expertise.

Compensation reflected this reality. A senior developer with ten years of experience commanded respect. You were consulted before strategic decisions were made. You had a say in the overall architecture, even if you didn’t write it entirely yourself.

Then Agile arrived. Not a thoughtful version of Agile, but its corporate version: an obsession with sprints, story points, velocity. Suddenly, the profession was no longer measured by “Can they solve this difficult problem?” but by “How many points do they complete per sprint?”

Managers discovered something interesting: if you broke work into small enough pieces, those pieces became arbitrary. Nearly interchangeable. An 8-point story could be assigned indifferently to a junior or a senior. This had a cost: quality, architectural consistency, long-term maintainability. But those costs didn’t show up on a sprint dashboard.

Industrialization: 2015-2023

Between 2015 and 2023, the transformation accelerates and becomes systematic.

Companies discover they can break software into tasks small enough to become commodities. REST API to code? That’s a task. Add form validation? That’s a task. Integrate a third-party service? That’s a task.

Suddenly, whether you have 3 years or 15 years of experience no longer matters. If the task is small enough, you are interchangeable.

Teams fill with juniors. Seniors are given “tech lead” roles that don’t actually free them from coding, but load them with meetings. Architects are marginalized. Structural decisions, once the domain of experts, are made by product managers who ignore implementation but know the roadmap.

Specialization becomes a luxury you can no longer afford. You’re a developer? You must be full-stack, able to touch frontend, backend, DevOps, databases. Actually mastering one of these domains? Inefficient. Bad for velocity.

The language of that era says it clearly: “scaling up” teams, “moving fast and breaking things”, “done is better than perfect”. In other words: produce code quickly, never mind the quality. We’ll clean it up later. (We never clean it up.)

Compensation also reflects this shift. In 2010, a senior could negotiate a raise based on expertise and strategic impact. In 2020, raises came from the scarcity of competitive candidates, not the value of knowledge. When thousands of “developers” graduate from twelve-week bootcamps every month, your ten years of expertise loses its market value. You can code the same thing, so you get paid the same.

The Producing Machine: 2020-2026

By 2020, the picture was locked in. The developer was no longer a professional. They were a resource. A story point producer. Performance was measured by “output”, not by the architectural quality of what was produced.

JavaScript frameworks changed every two years. You had to learn React, then Vue, then Svelte. Not because each had real advantages over the others. But because the industry loved noise, movement, the feeling of progress. And each new framework essentially reset your experience to zero. Ten years of programming gave you no advantage over a junior who learned the latest framework six months ago.

The consolidation of big tech companies (GAFAM) accelerated the process. When Google, Meta, Apple, Microsoft, and Amazon become the gatekeepers of tech employment, they impose their criteria. And those criteria favor coding speed over deep understanding. Ridiculous technical interviews based on random algorithms. An obsession with “leetcode”. A fetish for fast code rather than thoughtful code.

Professional experience becomes almost irrelevant. You see senior job postings at “8+ years of experience” with no distinction from junior roles at “3-5 years”. It’s just a title that increases the price. The responsibilities are identical: code features.

And the explosive burnout of the profession? It’s the expected consequence of being treated like a machine. A machine that must produce constantly, under pressure, in urgency, with the feeling that it is completely replaceable.

The AI: The Logical Conclusion

Now that AI arrives, there is nothing surprising about it. It is even logical.

If you have structurally devalued developers to the point of reducing them to executors of elementary tasks, why would it be unnatural to replace them with an AI that does exactly that?

ChatGPT cannot design a system architecture. It doesn’t understand real trade-offs. It cannot make judgments about long-term complexity. But for 80% of the work assigned to junior and mid-level developers today, ChatGPT produces code that is “good enough”.

And “good enough”? That was already the standard. You had already accepted that the code would be thrown away in two years. That maintainability was not a priority. That technical debt would accumulate because there was no time to address it. Why expect better from a machine than from a human who already accepts “good enough”?

Companies now see the real logic: pay an AI a third of a junior developer’s salary to produce code of the same quality. (Or worse, but no one really measures it.) Bugs? Already there. Technical debt? Already unmanageable. 30% yearly turnover? Eliminated by automation.

It is the perfect conclusion to a decade of devaluation. You chose to transform the developer into an interchangeable worker. Don’t be surprised if the market decided that a real machine makes a better worker.

The Real Cost of This Transformation

Notice what was lost:

Responsibility. A craftsperson is responsible for their work. An executor of tasks much less so. Bugs, not my fault, it was just a poorly estimated story. The architecture that collapses after two years? I’m not even there anymore.

Knowledge transmission. Craftsmanship is transmitted. A junior working with a senior internalizes intuitions, patterns, ways of thinking. When you scatter teams with interchangeable juniors managed from afar by a tech lead without time for mentoring, that knowledge dies. Each generation starts from zero.

Personal investment. You don’t build something lasting when you know your decisions will be overturned in a year. You do the job. It’s less emotionally taxing. It pays worse, certainly, but it asks less of you. And that is exactly what the industry communicated: “We don’t want you to give your all. We just want you to produce code fast.”

Technical excellence. How do you aim for excellence when you are judged by velocity? How do you take time to do things well when you are in a two-week sprint? How do you develop deep expertise when you must be polyvalent across twelve different technologies?

The industry chose convenience over excellence. And now it is surprised that AI rivals its developers.

What Should Have Been

A different trajectory was possible.

Value specialization. Let developers deepen expertise in one area. Pay based on complexity solved, not story points completed.

Invest in mentoring. Seniors should spend 30% of their time developing juniors, not coding features.

Structure teams around expertise, not around commodity. Small stable groups with real ownership of code and architecture.

Reward maintainability. Measure code quality. Give time to repay technical debt. Accept that some sprints will be less productive because the team is refactoring.

Stop the management bullshit. Scrum never had scientific evidence. Story points are an illusion of control. Velocity charts are theater. Trust experts to organize their own work.

But all of this costs. Costs in short-term predictability. In control. In ability to shift people between projects without friction.

The industry chose the easier path. Treat developers as interchangeable resources. And now it reaps the rewards: a devalued profession, a career without prestige, a defensive and bitter community, and an AI that can, for most cases, do the job just as well.

Conclusion: You Created Your Own Replacement

By transforming the developer from craftsperson to simple executor, the tech industry created the perfect conditions for its own replacement by AI.

This is not a technological tragedy. It is a consequence of management choices. The same choices that destroyed craftsmanship in nearly every industry: automotive assembly, construction, manufacturing. When you reduce work to atomic, interchangeable tasks, you make it automatable. It is normal. It is predictable.

What could have been different is valuing expertise, investing in knowledge transmission, building stable teams around real responsibility. Paying based on impact, not throughput. Accepting that certain forms of excellence require time.

But it costs. It demands management courage. It demands accepting that you cannot “scale” a team of fifteen exceptional people. It demands saying no to shareholders demanding more growth.

The industry made its choice. It chose growth over quality. Convenience over excellence. Machines over craftspeople.

And now the machines are coming.