With the incorporation of artificial intelligence, this dynamic is beginning to change. Today, AI can take on part of this collaborative role and become a new member of the pair, capable of suggesting alternatives, detecting errors, and assisting throughout different stages of the process.
However, its contribution is not limited to accelerating code writing. AI can also generate unit tests, suggest refactoring opportunities, document developments, or identify potential vulnerabilities. As a result, some tasks that previously required more time or a later review can now be addressed from the earliest stages, incorporating controls and best practices more naturally into everyday workflows.
But there is one particularly interesting aspect: learning. For those who are just beginning their professional journey, AI can serve as a first resource for answering questions, understanding a function, exploring different alternatives, or becoming familiar with patterns and quality standards. From there, the process becomes more dynamic, as developers can move forward, experiment, and ask follow-up questions without every doubt necessarily requiring them to interrupt another team member.
At the same time, this dynamic can reshape the role of more experienced professionals. When some of the more operational questions can find an initial answer through these tools, senior developers can devote more of their time to conversations that require judgment, context, and business knowledge. In other words, discussions can move beyond simply focusing on how to solve a problem and go deeper into why one alternative may be better than another.
However, this support does not end once the code has been written. AI can also be incorporated as an additional review layer before a development reaches production. Analyzing the work completed, detecting inconsistencies, identifying vulnerabilities, or suggesting improvements can provide a second perspective and strengthen the controls carried out before implementation.
Of course, this does not mean transferring responsibility to the tool. The final decision on which alternative to adopt, how to interpret project requirements, or when a development is ready to move forward remains in the hands of the team. While artificial intelligence provides speed, consistency, and the ability to process large volumes of information, professional experience makes it possible to evaluate those suggestions based on the context, objectives, and specific characteristics of each project.
Ultimately, the value of pair programming with AI is not simply about writing code faster, but about expanding the way teams can work. It can support those who are learning, free up time for senior professionals to focus on more complex issues, and provide an additional review layer before production. In this way, technology does not replace collaboration between people, but introduces a new way to complement it.
Artificial intelligence, then, is transforming not only the way software is developed, but also how knowledge is learned, reviewed, and shared within teams.
By Alejandro Álvarez, Software Developer of Baufest.
