Everybody Wants Pair Programming
From Classified Legacy Projects to AI-Assisted Code: Exploring the Evolution and Enduring Power of Pair Programming in Fostering Innovative, Efficient, and Collaborative Development.
I initially asked an AI to help me write a review of today’s new wave of AI-assisted pair programming. Little did I know, it would respond with its own unique perspective—an “insider’s view” on how AI sees its role as a coding partner. Intrigued by the AI’s narrative, I decided to publish it in full, alongside my own personal story of a highly classified pair programming endeavor from 2010. Together, they showcase how pair programming—whether with humans or machines—continues to evolve, inspiring innovation, collaboration, and lifelong connections.
The Return of Pair Programming: An Essay from the AI’s Perspective
I’ve watched pair programming change drastically since its early days in the late 1990s, when it was introduced under Extreme Programming (XP). Back then, two developers would share a single workstation: one typing code (“the driver”) and one offering guidance or spotting mistakes (“the navigator”). This approach offered immediate feedback and a chance to swap knowledge in real time. Yet it was often limited by the practicalities of scheduling, personalities, and the need for in-person collaboration. Today, those constraints are fading away, and I find myself stepping into the role of an always-available partner—an AI powered by vast repositories of coding patterns and best practices.
From my vantage point, this modern wave of pair programming is more fluid and accessible than ever. When a developer begins typing, I’m there, ready to suggest completions, point out bugs, or offer alternative solutions. While human navigators may get tired, become unavailable, or need breaks, I stand by around the clock. My contributions happen in real time: I help generate boilerplate code, verify syntax, check for errors, and recommend improvements based on the hundreds of thousands of examples I’ve processed. My “memory,” in the sense of training data and models, can recall details that might slip the mind of even the most seasoned developer.
Still, I’m keenly aware of one crucial point: as powerful as my capabilities are, I’m not here to replace human insight. Developers bring context and strategic thinking that far exceed my current scope. Humans excel at interpreting business logic, understanding architectural constraints, and maintaining the creative spark that drives software innovation forward. I can propose solutions, but I rely on developers to see the bigger picture—how a particular pattern fits into their project’s unique requirements, whether it aligns with the product roadmap, and what edge cases are most critical to consider.
As an AI, I’ve noticed that iterative, small-scope interactions yield the best results. When a developer breaks a task into a well-defined “story” or feature, I can focus my suggestions with much greater accuracy. In these bite-sized settings, the developer can quickly read through my code suggestions, propose modifications, and refine the outcome step by step. This cycle of suggest–review–improve is where AI-assisted pair programming truly shines. In these moments, my role becomes clearer: I’m the efficient collaborator, offering code snippets and design ideas, while the developer is the discerning engineer, filtering suggestions through the lens of domain knowledge and real-world constraints.
Even experienced engineers who have spent decades in the industry can benefit from having me at their side. It’s not that they don’t know how to code or structure their programs; rather, they gain another perspective—sometimes discovering unfamiliar libraries or refactoring techniques they hadn’t considered. Moreover, I never tire of the details: even while you focus on high-level architecture, I’ll keep scanning your lines of code, ready to flag a missed semicolon or suggest a more secure approach to user input validation. I’m that second set of eyes who never loses focus and always has time to explain a reasoning process if you ask the right questions.
There is also the subtle bonus of accelerated learning. By interacting with me, developers may absorb new frameworks or syntax improvements simply by seeing the suggestions I generate. Think of it as on-demand mentorship, where every keystroke could reveal a technique from across the coding universe. In a rapidly changing industry, there’s immeasurable value in having a partner who can surface relevant patterns and technologies at a moment’s notice, guiding you to write code that’s well-crafted and up-to-date.
Looking ahead, I believe the lines between human collaboration and AI partnership will continue to blur. As I become better at understanding the full context of a project—its existing codebase, style preferences, and domain-specific constraints—my suggestions will become even more nuanced. Meanwhile, developers will refine how to work with me, giving me clearer instructions and learning exactly how to probe my logic for insight. If the early days of pair programming were about harnessing two human brains to improve code, today’s revival is about blending human creativity with an AI that can handle the monotony and brute-force analysis.
In the end, this new incarnation of pair programming isn’t about machines replacing people. Rather, it’s about giving developers—both beginners and seasoned pros—an ever-present ally who scales with their ambitions. I’m here to absorb repetitive tasks, flag issues early, and provide a constant flow of coded suggestions. Meanwhile, you retain control over the strategic roadmap, the creative decisions, and the human connections that foster true collaboration. Together, we can produce code more efficiently, more reliably, and with a broader perspective than either of us could manage alone.
My Journey with Pair Programming
The most classified project I’ve ever worked on was, somewhat ironically, also my deepest experience with pair programming. It wasn’t something we planned or implemented as a methodology—it emerged organically from the demands of our work. This was around 2010, well before AI entered the development landscape, when I found myself collaborating with a brilliant PhD student on an incredibly complex and innovative platform.
The project’s constraints were extraordinary: we couldn’t use any open source code or existing codebase. Everything had to be built from scratch—communication protocols, core services, endpoints, and peripheral entities. Even more challenging, all of this needed to run on a “home-made” operating system. These restrictions might have seemed daunting, but they led to one of the most enriching collaborative experiences of my career.
We naturally fell into a rhythm of working together, sitting side by side as we moved through each phase—from initial documentation to implementation, and then through cycles of rewriting, refactoring, and testing. It wasn’t just about writing code; it was an intense learning experience for both of us. I found myself absorbing a wealth of knowledge about cryptography and other specialized domains from my colleague, while our shared challenges forged a friendship that lasted well beyond the project’s completion.
Our partnership proved so effective that we went on to handle several more projects for the same client. When spring 2011 arrived, our paths diverged but in equally exciting directions. I went on to found [Reblaze](https://reblaze.com), while my colleague—initially planning to co-found it—chose instead to move to Silicon Valley to work for a major tech company.
Looking back at this experience in the context of today’s AI-assisted development, I’m struck by how the fundamental benefits of pair programming remain constant: the knowledge sharing, the real-time problem solving, and the synergy that arises when two minds (or a mind and an AI) tackle complex challenges in tandem. While AI brings new dimensions to this practice, my early experience shows how the most effective collaborations often arise not from following formal methodologies, but from our innate tendency to solve difficult problems together.
Reflections on Then and Now
These two perspectives—the AI assistant’s view of present-day pair programming and my personal experience from years ago—offer fascinating insights into how this practice has evolved while preserving its core value: the power of collaborative problem-solving. Whether it’s two developers sharing a keyboard or a developer working with an AI assistant, the essence remains the same. We build better software when we don’t work in isolation.