AI Redefines Development: Towards a New Mastery

Artificial intelligence is profoundly transforming the role of developers, shifting their focus towards management, governance, and mastery. The main challenge is no longer just to code faster, but to maintain control over the development process.

For decades, computing has revolved around writing code. The value lay in the ability to convert a need into instructions, then those instructions into applications, and finally those applications into systems. However, this foundational act is undergoing a significant transformation. In the most advanced teams, we observe not just an acceleration of software development, but a redefinition of what it means to "develop." An article from the New York Times on March 12, 2026, encapsulates this change with a striking phrase: in the age of AI agents, many programmers in Silicon Valley "barely program" in the traditional sense.

This observation should not be seen as mere journalistic provocation. It reflects a structural mutation. AI tools no longer just suggest a line of code or complete a function. They scan a repository, interpret a context, propose an architecture, write code, run tests, correct, iterate, and then return with a solution to arbitrate. The competition between Anthropic and OpenAI around Claude Code and Codex precisely illustrates this shift from assistance to semi-autonomous execution. WIRED describes this battle as a race towards agents capable of taking over entire segments of development work.

The most significant consequence of this evolution is not technical, but managerial. When machines start producing, the human role shifts towards problem formulation, context quality, constraint relevance, result verification, and risk acceptance. The critical skill is no longer just the ability to write correctly; it is the ability to ensure correct production. In other words, scarcity no longer resides solely in syntax or individual execution speed. It shifts towards discernment, structuring, steering, and accountability.

Many organizations make a misreading by viewing development AI as merely a local productivity lever, while it represents a change in the operational model. Coding agents do not simply reduce the time for certain tasks; they alter the entire software creation chain. They lower the marginal cost of experimentation, accelerate implementation, compress certain phases of the development cycle, and enable more abundant software production. AP highlights that coding has become one of the major use cases for generative AI in business, with a rapid rise of tools capable not just of assisting but of acting.

However, this new abundance creates another tension: the easier it becomes to produce code, the more vital it is to govern what is produced. Software is never just an accumulation of functions. It is a potential debt, a surface of exposure, an operating cost, a consistency issue, a compliance challenge, a critical asset for the business. When AI accelerates production, it also potentially accelerates the creation of complexity. The real question is therefore 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 gains new value. Not because they will always write faster than the machine, but because they will know where the machine goes wrong, where it oversimplifies, where it introduces invisible fragility, and where it optimizes locally at the expense of the global picture. As agents gain capability, human arbitration becomes strategic. Expertise shifts from action to judgment. The senior developer is no longer just someone who knows how to do; they are someone who knows how to decide what deserves to be done, preserved, corrected, industrialized, or discarded.

This evolution also has a social and economic dimension. The debate about employment is no longer theoretical. The Washington Post reported in March 2025 that in the United States, jobs classified under "computer programming" had declined by more than a quarter over two years, noting that AI was not the sole explanation for this drop, which was also part of a broader reversal in the tech market following the previous boom.

It would therefore be excessive to announce the disappearance of the profession. However, it would be equally unwise to deny that the most standardized, easily specifiable, and repetitive tasks are now highly exposed.

The most sensitive point likely concerns junior profiles. Historically, part of the learning process involved low-prestige but highly formative tasks of building, correcting, reading, and maintaining. Yet these are precisely the activities that agents absorb most easily. If the machine takes over entry-level cognitive tasks, how do we train future experts? How do we develop architectural intuition, attention to detail, and deep system understanding if we delegate too early the effort that allowed us to build the foundations? This is where the question shifts from technological to educational and organizational.

For leaders, the issue is therefore much broader than a debate about developer productivity. It touches on the very structure of the engineering function. It requires rethinking work modes, onboarding, platform governance, the security of generated code, the quality of reviews, decision traceability, application asset management, and value measurement. It also necessitates revisiting indicators: in a world where part of the code is produced by agents, counting lines written or even certain velocity metrics becomes 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.

That is why the correct reading of this revolution is neither anxious nor naively euphoric. Developers are not becoming obsolete. They are becoming more central in other ways. Their responsibility expands. Their scope rises in the value chain. They are less unitary code producers and more orchestrators of software intelligence. Their role is akin to that of an operational architect: someone who knows how to engage with agents, frame the system, control quality, and assume consequences.

The question is therefore not whether AI will replace developers. The real question is which companies will successfully redefine the profession, organization, and governance that go with it in time. Because when the cost of software production drops sharply, the competitive advantage no longer comes solely from the ability to build. It comes from the ability to build correctly, govern quickly, and industrialize without losing control.

This is why the current moment is strategic. We are not witnessing the end of software development. We are witnessing the end of a certain definition of software development. And in this new regime, code remains important, but it is no longer the center. The center, now, is mastery.