The software industry is being pushed into a new paradigm, and as always happens in moments of disruption, the first reaction is polarization. On one side, apocalyptic narratives announcing the end of developers. On the other, skeptical developers clinging to the idea that using AI in development isn’t all that special and that they will always be indispensable.

Neither extreme holds up over time, but both, while they last, have real effects: they distort perception, delay adaptation, and block more lucid decision-making.

The only space where long-term relevance can still be built lies somewhere between delusion and denial.

The End of Software Engineering as We Know It

The idea that AI will replace developers comes from a narrow view of what it means to "develop software."

Those who see the role as merely writing code tend to overestimate the power of tools that automate just that step, ignoring the broader creation, decision-making, and articulation process that surrounds it.

Today, systems are designed with multiple trade-offs: cost, scale, security, legacy, time, governance, and even internal politics within companies. None of these factors are trivial.

None of them can be delegated to AI without oversight. The promise that all it takes is to describe an idea and let an AI implement it from scratch ignores the most important thing: building software is, above all, about choosing what not to do.

There’s no code that can make up for bad decisions. And AI is still far from understanding business context, real product constraints, and the subjectivity that makes a system usable, viable, and sustainable.

Denial as a Survival Strategy

While there’s exaggeration in the promises, there’s also rigid attachment to how things have always been done.

This denial isn’t just technical resistance. It’s an unconscious strategy of self-preservation in the face of a change that threatens long-standing professional identities. It’s the fear of losing relevance, of being outpaced, of having to relearn.

And that fear often hides behind skepticism: Tech leaders who avoid reviewing their processes, developers who deliberately ignore new tools, teams that retreat into known scopes and resist experimentation.

Denying the transformation that AI is bringing may be comforting, but it comes at a cost. While some try to preserve old models, others are already operating with new assumptions, new workflows, and new results.

Denial offers temporary safety, but it delays progress, isolates talent, and disconnects teams from the market’s new reality.

In a context where the pace of change is exponential, denial is the shortest path to obsolescence.

The True Role of the Developer in a World with AI

Meanwhile, professionals who understand AI’s potential, without naivety, but with intent, are already operating at a different speed.

They’re delivering more with less. Clearing backlogs faster. Rethinking what it means to be “senior.”

Contrary to what many fear, AI isn’t replacing, it’s reallocating value. And in this new map, isolated technical knowledge is worth less than the ability to compose complex solutions, navigate ambiguity, and make clear decisions.

The developer’s role is changing. What used to be a technical-operational profile is evolving into a technical-strategic one. This means deeply understanding the business. It means being able to frame problems before solving them. It means knowing when AI helps, and when it just adds noise.

This new landscape requires technical fluency, but also contextual mastery. Tools like copilots, code assistants, auto-reviewers, and documentation generators are already part of the modern workflow. But they don’t work alone. They need direction. And that direction is still human.

The productivity of the future won’t be measured in lines of code, but in problems solved with minimal friction. In this model, the developer who masters AI as a tool, not a fetish, becomes a multiplier of capability.

Culture, Product, and Process: Impacts Beyond the Code

AI’s presence in development is not just a technical issue. It forces a deep rethinking of work models. Agile methodologies based on cadence and predictability need to adjust to faster, nonlinear cycles.

Product teams need to learn to iterate on solutions that were not 100% human-written, and leaders need to stop measuring effort, and start measuring impact.

This shift affects everything: How a team is formed, how a delivery is defined, how a hypothesis is validated. What’s being demanded now is not just that devs know how to use AI, It’s that companies know how to operate at a new pace, where the differentiator lies less in technical execution and more in strategic adaptability.

Conclusion: Finding the Middle Ground

We’re not talking about the end of software developers. We’re talking about the end of the old version of what we understand a developer to be.

AI doesn’t erase the developer, but it forces developers, leaders, and companies to rethink their roles. Denying this change is dangerous. Romanticizing it is too. The only viable path lies in the middle ground: observe with clarity, adopt with discernment, evolve with purpose.

Those who do that first don’t just survive. They lead.