In a fascinating conversation with Matthew Berman, the CEO of GitHub shared his insights on the transformative impact of AI in software development. From the early days of GPT-powered code completion to the future of AI-assisted coding agents, this interview offers a rare glimpse into how programming is evolving at a breakneck pace. Drawing from his leadership role in developing GitHub Copilot and the recent announcement of Copilot going open source, he unpacks the challenges, opportunities, and exciting possibilities ahead. This article synthesizes those insights, exploring everything from the user experience of tab completion to the future of software architecture and the role of AI in programming education.
Table of Contents
- 🤖 The First Encounter with GPT and the Dawn of AI in Coding
- 💻 How AI Changed Software Development Forever
- ⌨️ The Genius of Tab Completion: A Simple Yet Revolutionary UX
- 📚 Is Learning to Code Still Essential?
- 🛠️ GitHub Copilot Goes Open Source: Empowering the Developer Community
- 🚀 What’s Next? Features and Innovations from the Community
- 🧩 The Future of Software: Where Deterministic Code Meets AI
- 🖥️ AI-Powered Operating Systems and Just-in-Time Applications
- 🎶 Vibe Coding and Coding Agents: The New Paradigm
- 🔧 Improving Coding Agents: Challenges and Opportunities
- 🌐 AI-Assisted Coding Ecosystems: Fragmented or Unified?
- 💡 Addressing AI Anxiety: Jobs, Reskilling, and New Opportunities
- ❓ Frequently Asked Questions (FAQ)
- Conclusion
🤖 The First Encounter with GPT and the Dawn of AI in Coding
When GitHub’s CEO first encountered the early GPT models designed for code completion, his initial reaction was skepticism. Despite his background in compiler technology from his university days, he doubted that a language model could reliably produce syntactically correct code across languages like Python, Ruby, and JavaScript. He expected the AI to make errors such as misplaced parentheses or incorrect semicolons.
However, witnessing the evolution of OpenAI’s Codex and GPT-3 models changed his perspective dramatically. These models began generating entire methods with correct syntax from simple prompts like “prime number detection” or “sorting algorithm.” The realization that AI could produce usable code without traditional compiler checks felt almost magical.
“I thought it wouldn’t work. I thought it would never work… but when I first saw GPT-3 and Codex evolve to write proper code, it was like magic.”
This moment marked a turning point not only for him but for the entire software development community. The technology was no longer a distant dream but a practical tool that could augment developers’ workflows.
💻 How AI Changed Software Development Forever
The launch of GitHub Copilot in June 2021 was a watershed moment. Before its public release, GitHub employees tested Copilot internally, and the results were staggering. Telemetry data revealed that Copilot was writing approximately 25% of the code in files where it was enabled. Initially met with disbelief, repeated checks confirmed this figure, which has only grown since.
Different programming languages showed varying degrees of AI assistance effectiveness. For example, Python users benefited more than those working with C or C++, mainly due to the complexities around dependencies and header files in the latter. The user feedback was overwhelmingly positive, scoring a Net Promoter Score (NPS) around 72 — an exceptional rating for a preview feature.
The CEO reflected on the surprise at how well Copilot was accepted despite developers’ usual attachment to their personalized environments, such as color schemes and shortcut configurations. Integrating AI into the inner loop of coding was expected to be met with resistance but instead was embraced enthusiastically.
“The response was overwhelmingly positive. We had so many people say, ‘I doubted it, and then it worked, and it was like magic.’”
⌨️ The Genius of Tab Completion: A Simple Yet Revolutionary UX
Tab completion may seem like an obvious interaction now, but it was a breakthrough in integrating AI into the coding workflow. The idea built on decades of developer familiarity with IntelliSense and other autocomplete features found in IDEs like Visual Studio and editors such as TextMate and Sublime Text.
These traditional autocomplete systems relied on static analysis of the codebase and language libraries, while Copilot introduced a dynamic, AI-powered approach. Developers already had the habit of looking up code snippets online, copying them, and adapting them to their needs. Copilot’s tab completion streamlined this process by generating relevant code suggestions directly in the editor, keeping developers in their flow state.
The CEO emphasized the importance of flow state in software development: the precious window where a developer’s focus and energy align perfectly to create. Interruptions like context switching or manual searches break this flow. AI-assisted tab completion reduces friction, enabling developers to maintain momentum and creativity.
📚 Is Learning to Code Still Essential?
With AI transforming how code is written, a natural question arises: Should kids still learn to code? The CEO is unequivocal: yes. Understanding coding remains crucial because software permeates nearly every aspect of modern life—from cars and homes to farming and policing.
He likened coding education to learning math or physics—subjects that provide foundational understanding even if one doesn’t pursue a career directly in those fields. Coding teaches systems thinking and logical reasoning, skills valuable across disciplines.
However, the nature of coding education is evolving. It’s not just about mastering syntax but also about learning to collaborate effectively with AI assistants. The craft of software development now includes integrating AI tools responsibly and understanding the implications of AI-generated code on security, quality, and business goals.
“Coding is a skill everybody should learn, regardless of whether they become computer scientists or physicists. It helps you understand the world.”
🛠️ GitHub Copilot Goes Open Source: Empowering the Developer Community
In an exciting development, GitHub Copilot has been made open source within Visual Studio Code, which itself has a rich history as an open source editor. The decision reflects GitHub’s commitment to the developer ecosystem that has supported it for over a decade.
By open sourcing Copilot’s client code under the MIT license, developers worldwide can now fork, extend, and integrate Copilot into their own tools and workflows. This transparency fosters innovation, enabling external contributors to add features or improve usability in ways that Microsoft and GitHub might not have prioritized.
The CEO highlighted that many aspects of Copilot had already been reverse-engineered by the community, making the open source move a natural progression. This step invites collaboration and accelerates the evolution of AI-assisted coding tools.
🚀 What’s Next? Features and Innovations from the Community
The possibilities unleashed by open sourcing Copilot are vast. The CEO is particularly excited about:
- Integrating new AI models into Copilot, allowing developers to bring their own preferred models and customize the experience.
- Extending agent mode for a wider variety of programming languages and migration scenarios, such as converting legacy codebases from COBOL to modern languages.
- Enhancing user experience features like undoing or stepping back changes made by AI agents.
With nearly 100 changelogs in 2025 alone, Copilot’s rapid evolution is a testament to the vibrant innovation happening in AI-assisted software development.
🧩 The Future of Software: Where Deterministic Code Meets AI
The CEO envisions a future where the boundary between deterministic traditional code and nondeterministic AI-generated code becomes increasingly blurred. While code itself must remain deterministic to execute reliably on hardware, the process of generating that code often involves nondeterministic natural language prompts and AI interpretation.
Software engineers will need to master switching between these layers—using natural language to specify features and then refining the deterministic code generated by AI. This hybrid approach mirrors how human teams collaborate, negotiating misunderstandings and evolving ideas into working software.
He also foresees tools that help break down complex problems into smaller, manageable tasks that AI can handle more reliably, with engineers overseeing the process to ensure quality and alignment with business goals.
🖥️ AI-Powered Operating Systems and Just-in-Time Applications
Looking further ahead, the CEO imagines a world where traditional operating systems become invisible to users, replaced by AI agents that dynamically generate personalized applications on demand. These “just in time” apps would solve specific problems tailored to individual users without the need for persistent, pre-installed software.
For example, a parent could use AI to create a bespoke micro-app to track their children’s allowance, customized precisely to their family’s needs. Such personalized software would be cheaper to produce and maintain than traditional one-size-fits-all applications.
“You don’t care anymore what operating system you’re running. The primary interface is an agent, an assistant, like Iron Man’s Jarvis, helping you with everyday tasks.”
🎶 Vibe Coding and Coding Agents: The New Paradigm
Vibe Coding, powered by AI coding agents, represents a shift from manual coding to collaborative creation with AI. These agents can generate prototypes swiftly, allowing developers to focus on higher-level design and iteration. However, the CEO cautions that the role of human developers remains critical, especially for verifying, reviewing, and ensuring the security and quality of AI-generated code.
He draws an analogy to advanced driver-assistance systems in cars: you don’t take your hands off the wheel completely but delegate certain tasks to the AI while maintaining control and accountability.
In real-world software projects, agents help with tedious tasks like bug fixing, writing tests, or finding security vulnerabilities—areas that developers often find boring but essential. This collaboration frees engineers to concentrate on creative problem-solving and architecture.
🔧 Improving Coding Agents: Challenges and Opportunities
While AI has expanded the possibilities of low-code and no-code solutions, the CEO acknowledges that agents still face limitations, especially when tackling complex, scalable systems. Understanding system architecture, authentication, and compliance remains a human responsibility.
Software development teams will always have extensive backlogs filled with new features, tech debt, and evolving standards. AI agents can accelerate delivery but cannot fully replace the nuanced decisions humans make regarding product direction and prioritization.
Ultimately, AI agents are tools to expand human creativity and productivity, not substitutes for human engineers.
🌐 AI-Assisted Coding Ecosystems: Fragmented or Unified?
Regarding the variety of AI coding tools—ranging from tab completion and plugins to fully autonomous agents—the CEO predicts a future where multiple specialized agents coexist rather than converge into a single monolithic system.
He envisions an interconnected ecosystem of agents tailored to different domains: personal assistants, work-specific agents with institutional knowledge, travel booking bots, and more. These agents will communicate via protocols enabling seamless integration, reducing context switching for users.
Such a landscape mirrors today’s software ecosystem, where diverse applications collaborate through APIs and integrations, but now powered by intelligent agents speaking a common language.
💡 Addressing AI Anxiety: Jobs, Reskilling, and New Opportunities
Many people worry about AI replacing jobs, especially in knowledge work and software development. The CEO offers reassuring perspective, pointing to historical precedents like the industrial revolution and the rise of personal computers, which transformed jobs but did not eliminate the need for human creativity and adaptability.
While some roles, such as human translators, may diminish due to AI advances, new jobs will emerge that leverage AI to enable more creative, fulfilling work. AI tools like GitHub Copilot democratize programming, allowing anyone on the planet to become a software developer regardless of background or language barriers.
The key to thriving in this new era lies in continuous learning, reskilling, and embracing AI as a collaborator rather than a competitor.
“Those in jobs that may be replaced by AI will find new, exciting opportunities through reskilling with AI’s help.”
❓ Frequently Asked Questions (FAQ)
What was the initial reaction to AI-generated code like GitHub Copilot?
Initially, there was skepticism about whether AI could generate syntactically correct and useful code. However, after witnessing the capabilities of GPT-3 and Codex, many were amazed at how well AI could assist in writing code, leading to widespread adoption.
How does tab completion enhance the coding experience?
Tab completion builds on familiar autocomplete features but uses AI to generate contextually relevant code snippets, reducing the need for developers to leave their IDE to look up code, thus maintaining flow and productivity.
Should children still learn traditional programming languages?
Yes, understanding programming fundamentals remains essential as software is deeply integrated into daily life. Learning to code also develops critical thinking and problem-solving skills that are valuable across disciplines.
Why did GitHub decide to open source Copilot?
Open sourcing Copilot aligns with Visual Studio Code’s open source heritage, fosters community innovation, and allows developers to customize and extend AI coding tools freely.
Will AI replace software engineers?
No, AI is a powerful tool that enhances developers’ productivity. While AI can automate certain tasks, human oversight, creativity, and system-level thinking remain indispensable.
What is the future of software architecture with AI?
The future involves a blend of deterministic code and nondeterministic AI-generated components, where engineers guide AI in breaking down complex problems and verify AI outputs to build reliable systems.
How will AI agents coexist in the software ecosystem?
Multiple specialized AI agents will coexist and interoperate, serving different roles in personal, professional, and specialized domains, connected through protocols and integrations to provide seamless experiences.
What advice is there for those anxious about AI’s impact on jobs?
Embrace lifelong learning and reskilling. AI opens new opportunities for creativity and innovation. History shows that technological shifts create new roles even as they transform existing ones.
Conclusion
The future of programming is undeniably intertwined with AI. GitHub’s CEO paints a hopeful and pragmatic picture of how AI-powered tools like Copilot are revolutionizing software development—making it faster, more accessible, and more collaborative. From early skepticism to embracing open source collaboration, the journey reflects a maturing ecosystem adapting to new possibilities.
As AI continues to blur the lines between deterministic code and generative models, developers will evolve their craft, mastering the art of working alongside intelligent agents. Education remains foundational, but now with an added layer of AI fluency.
Ultimately, this transformation promises not to replace human ingenuity but to amplify it, ushering in an era where software engineers can focus on solving bigger problems, innovating faster, and creating personalized experiences never before possible.
For anyone passionate about the future of software, AI, and coding, the insights shared here provide both inspiration and a roadmap for navigating the exciting years ahead.
