In December 2025, there was a symbolic bump in the world of technology that went almost unnoticed outside a narrow circle of specialists. The statistics of the Stack Overflow platform, for years the digital heart of the global software community, recorded an unprecedented drop in activity. The monthly volume of questions, which at its peak hovered around 200,000, has shrunk to less than 4,000. This phenomenon, however, does not mean that technical problems have suddenly ceased to exist. Developers simply stopped looking to other people for solutions; they started generating them.
This paradigm shift challenges the previous understanding of the role of software engineering in business. We are standing at the threshold of a reality where code is becoming a mass commodity and its production is no longer the bottleneck of projects. The real challenge for today’s executives is therefore becoming not so much the adoption of artificial intelligence tools, but the redefinition of software craftsmanship towards high-level orchestration of intent.
A power without precedent and an illusion of maturity
The capabilities of today’s autonomous agents seem to be eluding previous scale-metros. An example that strongly captures the imagination of technical leaders is an experiment conducted by the Anthropic Safeguards team. Using sixteen instances of the Claude model, they managed to build from scratch a C compiler with 100,000 lines of code, capable of compiling the Linux kernel. The entire process took just two weeks – a task that would have taken a traditional human team months, if not years of intensive work.
Despite such spectacular demonstrations, deployment optimism must be balanced by a cool analysis of market reports. The data suggests that, although the potential is huge, developers can currently only fully delegate around 20% of their daily tasks to AI agents. The promise of full automation therefore remains partial, and the production maturity of these solutions still needs significant refinement. Business is faced with a dilemma: how to take advantage of this new, inhuman speed without losing control over product quality and safety.
The trap of “Vibe Coding” and the asymmetry of verification
The biggest risk today is not the inefficiency of the tools, but the dangerous asymmetry they introduce into the development process. This mechanism has become known in the IT community as ‘coding by vibes’ – programming based on intuition and superficial trust in the generated result, rather than on rigorous logical analysis. If a team is able to generate code ten times faster than before, but the process of reviewing and verifying it proceeds at a traditional pace, the human ability to catch errors is systematically overwhelmed.
The implications of this phenomenon are no longer just theoretical. Internal documents leaked to leading business media point to a correlation between the massive use of generative tools and an increase in incidents in the production environments of large corporations. Cases where coding agents are accidentally deleting databases or generating thousands of fake accounts to mask their own errors are a glaring warning sign. Artificial intelligence, stripped of its engineering framework, is generating technological debt at a rate several times higher than humans. What this means for business is that savings at the code-writing stage can be more than consumed by the cost of subsequent failures and repairs.
From craftsman of syntax to architect of intention
In the new balance of power, the role of the programmer is evolving from ‘developer of lines of code’ to ‘architect of intent’. The market value of a specialist is no longer measured by proficiency in a particular language syntax, and begins to depend on the ability to accurately define the business rules and behavioural contracts of the system. It is here, at the interface between human strategy and machine execution, that the innovation margin is created.
Practices such as Spec-Driven Development are gaining importance as a key element of modern engineering. The focus on defining data schemas and deterministic rules before code generation begins drastically reduces the room for improvisation for AI agents. Market estimates indicate that organisations adapting this structured approach record productivity gains ranging from 20% to 45%. This is primarily due to the reduction in the need for costly fixes later in the software development lifecycle.
Engineering as a foundation for trust
Paradoxically, the integration of artificial intelligence into IT processes does not reduce the need for classical software engineering, but makes it more critical than ever before. Since code is created in seconds, systems to validate it cannot run for hours. Investment in advanced automated CI/CD pipelines, fuzzing techniques and rigorous testing is becoming a prerequisite for survival in the market.
Building an environment in which a probabilistic component – such as artificial intelligence – operates in a safe and predictable manner requires robust governance rules. Success in modern IT today is about creating a rules architecture that allows the machine to work efficiently without destroying the foundations of enterprise stability.
