Gunnari Auvinen is a staff software engineer based in Cambridge, Massachusetts, with more than a decade of experience in software development and systems engineering. After earning a degree in electrical and computer engineering from Worcester Polytechnic Institute, Gunnari Auvinen built a career spanning system integration, full-stack development, and technical leadership roles. He currently works at Labviva, where he has led major initiatives including the design of a next-generation order processing system, infrastructure modernization efforts, and the development and maintenance of multiple production services and applications.
Earlier in his career, Gunnari Auvinen held senior engineering roles at Turo and Sonian, contributed to platform modernization projects, and taught full-stack JavaScript workshops at Hack Reactor. His professional interests include distributed systems, software architecture, microservices, and modern JavaScript and TypeScript ecosystems. This technical background provides a practical foundation for exploring artificial intelligence pair programming, an emerging approach that reshapes how developers collaborate with intelligent coding tools.
Understanding Artificial Intelligence Pair Programming
Artificial intelligence (AI) pair programming is a collaborative software development paradigm where a developer collaborates with an advanced coding agent to design, write, and debug code. The developer combines their cognitive and oversight capacities with those of the agent to improve code quality and security.
Before the introduction of AI, two developers would work together in software development. One professional, the driver, would write the code, and the other, the navigator, would review it and suggest design approaches and improvements. AI pair programming replaces one of these professionals with an advanced coding agent, redefining how humans and machines work together to create reliable software. GitHub Copilot, ChatGPT with code capabilities, and Cursor are popular examples of AI coding tools.
Adopting AI pair programming offers multiple benefits, including improved productivity. AI can promptly generate boilerplate code, the kind that is used repeatedly in various projects with little to no modification, and software documentation that guides users as they interact with a system. This saves time and reduces mundane work, allowing developers to focus on cognitively intensive tasks, such as design decisions.
Complex projects require extensive coding and attention to detail to ensure the software is functional and reliable. This complexity, notably, is vulnerable to bugs – errors that interfere with the software’s functioning. Working with an advanced coding agent helps in debugging complex projects, reducing the time and effort spent on this tedious activity. This resource also recommends patterns that can minimize bugs in the software development process, resulting in clean code that is easy to maintain in the future.
AI pair programming enables developers to learn on the job. Since this resource explains suggestions and recommendations in the development process, new developers can learn the best practices for developing robust software. Senior developers who interact with the coding agent also learn about new frameworks and models without having to read documentation.
Despite these benefits, adopting AI pair programming has a few challenges that developers should understand. First, it is tempting to use this tool to handle most development activities. Developers who constantly rely on this resource to improve or debug the original code may fail to understand how the new AI-suggested improvements work, and with time, they may find it challenging to solve issues. In some cases, developers may unknowingly introduce flawed suggestions to the base code, jeopardizing the project’s functionality and financial viability.
Optimizing this tool’s capacity requires significant professional experience. Developers who have worked with advanced coding agents on previous projects know when to trust suggestions and when to ignore them entirely. This insight is important for creating safe, high-quality code.
Bias is another significant concern when working with AI agents. This is particularly prevalent where the system relies on public code that is free to use. Some of these codebases may have inherent ethical biases, introducing prejudice to a software’s end users.
To maximize the benefits of AI pair programming while minimizing risks, developers can implement several best practices, such as treating AI suggestions as drafts. Developers should critically assess the suggestions to understand whether they solve a problem, fit their development style, and are testable and secure.
Developers should also provide adequate contextual information to the coding agents. An AI system requires access to the critical parts of a software’s codebase to provide accurate assistance when requested. Coding agents should also be clearly notified of the project’s requirements, constraints, and appropriate coding standards to improve focus and the quality of the software after development.
Lastly, after verifying that AI improvements are viable, developers should document these changes. It is imperative to indicate why the suggestion was accepted and any constraints involved to support interventions by future developers.
About Gunnari Auvinen
Gunnari Auvinen is a staff software engineer at Labviva with more than ten years of experience in software engineering, systems integration, and technical leadership. He has worked across industries including defense, data services, and mobility platforms, contributing to modern web architectures, infrastructure improvements, and large-scale production systems. A graduate of Worcester Polytechnic Institute, he specializes in distributed systems, microservices, and full-stack development, and is active in mentoring, code review, and architectural planning.