By Daniel Jardí (ERNI Spain)
Artificial intelligence has emerged stronger than anyone ever would have expected in every area of human knowledge. From medicine to education, from scientific research to art and engineering, AI is changing the way that we understand, create, and solve problems. This is not simply a technological change; it is also a conceptual shift since it is altering the very scales with which we used to measure complexity, creativity and expertise.
Evolution of the programmer
Software development is one field where you can frequently see this disruption to skilled knowledge workers. AI-based tools – generating code, providing code suggestions or detecting bugs in real time – are fundamentally changing the life of the programmer. Tasks that could previously take hours of effort (e.g. concentration, searching and testing) can now be accomplished immediately using an AI assistant. However, although AI can speed up the coding process, it is essential to engage in validation and correction of the generated results. This requires multiple interactions between AI and the human developer to reach the optimal solution. The more complex the solution, the more important this AI/human pair becomes in terms of creation, validation and correction. The entry curve has been drastically reduced, which democratises access but also raises expectations.
New paradigm of difficulty
We are therefore facing a new paradigm of difficulty: what was once considered easy will now be immediate; what was normal will now be easy; and what was once thought impossible will become the new difficult. This evolution forces us to rethink both technical training and how we value work in software.
In this new paradigm, the skill to write code in a specific language loses stature to the more important ability to simply describe what should be built, how the system should behave, what customer needs it must satisfy, and what qualities the solution must possess to be robust, scalable and maintainable. It is no longer just about typing lines of code, but about modelling technical intentions with precision, logic and systemic vision, and iteratively refining AI proposals. What will be essential is the ability to communicate effectively with AI tools to turn complex ideas into solid implementations.
The rise of the software designer
Artificial intelligence is closing a longstanding gap in the software development lifecycle. Until now, different roles – architects, developers, testers, DevOps experts, database or scripting specialists – had to collaborate closely, and friction or limitations often arose due to a lack of cross-experience. Today, AI allows any professional to tackle tasks outside their comfort zone, with the confidence that they will receive contextual technical support, practical examples and automated solutions. A developer can generate a CI/CD pipeline without being a DevOps expert; a tester is able to create mocks and automated tests without deep knowledge of the framework; an architect can explore technical decisions by seeing how they translate into real code.
This does not eliminate the need for experts, but it does raise the base level of all participants. However, in this context, it is very important to maintain knowledge within the human team and continue to promote the growth of junior profiles. This way, control over what is happening is not lost, and the ability to supervise and deeply understand the developed solutions is ensured. What once required a chain of specialists can now be addressed in a more integrated and agile way, thanks to AI functioning as a bridge between disciplines – always under appropriate human supervision.
Not a threat – an opportunity
In short, the traditional programmer gives way to the software designer: someone who understands the problem, structures the solution, communicates the requirements with precision, and collaborates critically with intelligent tools to bring it to life, validating and correcting when necessary. Far from being a threat, this evolution represents a unique opportunity: to free ourselves from mechanical complexity and focus on the strategic, the creative, and the truly human – without neglecting continuous learning and knowledge transfer.
By the way, this text was written by a Software Engineer with no experience in communication or marketing, simply with the help of artificial intelligence. Of course, this is just a glimpse of the new paradigm of difficulty.
For more insights about the influence of AI on the various industries, read our previous article: Embracing the AI megacycle: Transformative impacts on industries and everyday life or explore the latest .experience magazine edition on Our journey through Tech and AI.