AI does not replace developers: it shifts their role towards management, governance and control. The challenge is no longer just to code faster, but to maintain control.
For decades, computing has been organized around a founding gesture: writing code. The value was found in the ability to transform a need into instructions, then these instructions into applications, then these applications into systems. However, this gesture is changing in nature. What we see today in the most advanced teams is not simply an acceleration of software development, but a redefinition of what “develop” means. The New York Times article of March 12, 2026 summarizes this shift with a striking formula: in the era of AI agents, many Silicon Valley programmers “barely program” in the traditional sense of the term.
This observation should not be read as a journalistic provocation. It reflects a structural change. The tools are no longer limited to suggesting a line or completing a function. They browse a repository, interpret a context, propose an architecture, write code, run tests, fix, iterate, then come back with a solution to arbitrate. The competition between Anthropic and OpenAI around Claude Code and Codex illustrates precisely this shift from assistance to semi-autonomous execution, and WIRED describes this battle as a race towards agents capable of taking over entire sections of development work.
The most important consequence is therefore not technical. She is managerial. When the machine begins to produce, the role of the human shifts to the formulation of the problem, the quality of the context, the relevance of the constraints, the verification of the results and the acceptance of the risk. Critical competence is no longer just the ability to write accurately; it is the ability to produce justly. In other words, rarity no longer resides solely in syntax or individual execution speed. It moves towards discernment, structuring, management and responsibility.
This is where many organizations misread. They see development AI as a local productivity lever, whereas it is a change in operational model. Coding agents don’t just reduce the time of certain tasks; they modify the entire software creation chain. They reduce the marginal cost of experimentation, accelerate implementation, compress certain phases of the development cycle and make more abundant software production possible. AP also underlines that coding has become one of the major use cases of generative AI in business, with a rapid rise of tools capable not only of assisting, but of acting.
But this new abundance creates another tension: the easier it becomes to produce code, the more vital it becomes to govern what is produced. Because software is never just an accumulation of functions. It is a potential debt, an exposure area, an operating cost, a consistency problem, a compliance issue, a critical asset for the company. When AI accelerates production, it also potentially accelerates the creation of complexity. So the real question is not: “Can we produce more?” The real question is: “Can we absorb, control and sustain what we produce faster?”
In this context, the experienced developer takes on a new value. Not because he will always write faster than the machine, but because he will know where the machine is wrong, where it oversimplifies, where it introduces an invisible fragility, where it optimizes locally while degrading globally. The more agents gain in capacity, the more strategic human arbitration becomes. Expertise moves from gesture to judgment. The senior is no longer just the one who knows how to do things; he is the one who knows how to decide what deserves to be done, preserved, corrected, industrialized or rejected.
This development also has a social and economic dimension. The debate on employment is no longer theoretical. The Washington Post reported in March 2025 that in the United States, jobs classified in the “computer programming” category had declined by more than a quarter over two years, while specifying that AI did not alone explain this decline, which was also part of a broader turnaround in the technology market after the previous boom.
It would therefore be excessive to announce the disappearance of the profession. On the other hand, it would be equally imprudent to deny that the most standardized, most easily specifiable and most repetitive tasks are now highly exposed.
The most sensitive point undoubtedly concerns junior profiles.
Historically, part of the learning involved construction, correction, reading and maintenance tasks with low prestige but high educational value. However, it is precisely these activities that agents absorb most easily. If the machine supports the cognitive entry level, how do we train future experts? How do we develop architectural intuition, a sense of detail, a deep understanding of systems, if we delegate too early the effort that made it possible to build the foundations? This is where the question is no longer technological, but educational and organizational.
For managers, the subject is therefore much broader than a debate on developer productivity. It affects the very structure of the engineering function. It requires us to rethink working methods, onboarding, platform governance, the security of generated code, the quality of reviews, the traceability of decisions, the management of application assets and the measurement of value. It also requires revisiting indicators: in a world where part of the code is produced by agents, counting lines written or even certain velocity metrics is becoming less and less relevant. A team’s performance will be measured more by its ability to transform a business intention into a robust, observable, maintainable and governed system.
This is why a good reading of this revolution is neither anxious nor naively euphoric. Developers don’t become useless. They become more central on other levels. Their responsibility is expanding. Their scope moves up the value chain. They are less unitary producers of code than orchestrators of software intelligence. Their role is similar to that of an operational architect: someone who knows how to dialogue with agents, frame the system, control quality and assume the consequences.
So the question is not whether AI will replace developers. The real question is to know which companies will be able to redefine the profession, the organization and the governance that goes with it in time. Because when the cost of producing software drops suddenly, the competitive advantage no longer comes only from the ability to build. It comes from the ability to build fairly, to govern quickly, and to industrialize without losing control.
This is why the current moment is strategic. We are not seeing the end of software development. We are witnessing the end of a certain definition of software development. And in this new regime, the code remains important, but it is no longer the center. The center now is mastery.
