GitHub CEO predicts the future of programming...(Full Interview)

What Are the Impacts of AI on Software Development?

Initial Reactions to GPT and Coding Agents

• The speaker initially doubted the effectiveness of GPT, believing it wouldn't work, but was amazed when it did, likening it to magic.

• There is a recognition that software development will change forever due to advancements in coding agents.

Development of GitHub Copilot

• The speaker recalls their experience with GitHub Copilot, which offered code completion by predicting lines of code as users typed.

• They were surprised by how well the model maintained syntax across different programming languages like Python, Ruby, and JavaScript.

Feedback and Impact Measurement

• Before public release, GitHub employees tested Copilot; feedback indicated it wrote 25% of the code in enabled files.

• The team verified this statistic through telemetry data and noted varying effectiveness across programming languages.

User Experience Insights

• High satisfaction scores (NPS around 72) from developers using Copilot indicated positive reception despite initial concerns about integration into personal setups.

• Over one million users adopted Copilot quickly after its public preview, many expressing surprise at its capabilities.

Evolution of Code Completion Features

• Tab completion became a primary interaction point for developers with AI coding assistants due to its simplicity and low learning curve.

Autocompletion and Programming Education

The Evolution of Autocompletion in Code Editors

• Autocompletion features, such as IntelliSense, enhance coding efficiency by predicting methods based on previously written code in editors like TextMate and Sublime.

• Autocompletion has been a learned behavior among developers for around 20 years, aiding them in recalling method signatures without needing to look them up constantly.

• Developers often rely on external resources (documentation, Stack Overflow, etc.) to solve problems, indicating a common practice of adapting code snippets from various sources into their projects.

• Many programmers learn through trial and error by modifying existing code examples (e.g., "Hello World" tutorials) to fit their specific needs.

• The integration of autocompletion with large language models (like CodeX from 2020) helps streamline the coding process despite occasional inaccuracies or "hallucinations."

Maintaining Flow State in Development

• Keeping developers in a flow state is crucial; it allows them to maintain focus and energy while building software efficiently within an IDE.

• Successful execution of code after modifications can lead to a sense of accomplishment for developers, reinforcing the value of hands-on programming experience.

The Importance of Teaching Programming Skills

• The speaker reflects on the significance of learning programming skills as essential for children today due to the pervasive role of software in everyday life.

• While there are benefits to teaching systems thinking, foundational programming knowledge remains vital for understanding technology's impact across various fields.

• Software is integral not only in tech but also influences sectors like transportation and agriculture; thus, computer science education should be prioritized alongside traditional subjects like math and physics.

• Understanding basic concepts such as binary logic is essential for everyone, regardless of whether they pursue careers directly related to computer science or technology.

Innovations in Coding Assistance Tools

• At Build 2023, GitHub Copilot introduced a coding agent that can interpret tasks based on existing codebases rather than starting from scratch.

Understanding the Role of AI in Software Development

The Engineer's Challenge with AI-Generated Code

• Engineers must verify changes made by AI agents through pull requests, raising concerns about understanding and alignment with business goals.

• There is a risk that AI-generated code may be insecure, potentially leading to security incidents and loss of customer trust.

• Businesses need to understand how to leverage AI agents effectively to maintain competitive advantages while ensuring code quality.

Evolving Skills in Software Development

• Learning AI-assisted development is now as crucial as mastering core programming languages; developers must evolve their craft continuously.

• The landscape of software development has changed significantly over the past 20 years, particularly regarding open-source adoption and security perceptions.

Changes in Developer Roles and Practices

• Modern developers often work across multiple disciplines (full-stack), unlike 20 years ago when roles were more specialized (e.g., front-end vs. back-end).

• Today's web developers typically do not engage deeply with hardware layers, relying instead on virtual machines for resource management.

Integrating Multiple Models in Development

• Developers are expected to integrate various models into their workflows, enhancing their skill set beyond traditional front-end and back-end development.

• The future will likely involve multiple models tailored for different use cases rather than a single model dominating the landscape.

Open Source Transition at Microsoft

• Microsoft has embraced open source under Satya Nadella's leadership, marking significant shifts in its approach over the last decade.

Open Source Development and GitHub Copilot

Nurturing Open Source Contributions

• The aim is to foster open source development without requiring contributors to purchase a plan, such as a $20 subscription, for projects like VS Code.

• The initiative targets developers who have supported the VS Code project over the past decade, emphasizing community engagement.

Learning from Copilot's Integration

• The client code for Copilot in VS Code serves as a learning resource for others developing AI software, whether competitors or new IDE creators.

• By adopting the MIT license, developers can integrate Copilot into their systems using GitHub and Microsoft APIs, promoting innovation across various tools.

Transparency and Community Support

• Maintaining transparency about Copilot’s functionalities is prioritized over secrecy since many aspects are already known or reverse-engineered by the community.

• Documentation on how Copilot operates has been available since 2021, allowing users to understand its underlying mechanics.

Encouraging External Development

• Developers are encouraged to fork and enhance Copilot with Microsoft's support, fostering contributions back to the project.

• There is excitement around external developers potentially introducing features that Microsoft hasn't prioritized due to resource constraints.

Future Innovations in AI Models

• As of May 2025, nearly 100 change logs have been recorded for Copilot this year alone, indicating rapid development and feature integration.

• Developers can connect other models through "bring your own keys," enabling them to test different AI models within Copilot's framework.

Addressing Technology Debt

• There remains significant technology debt across programming languages that could be addressed by extending features like agent mode in Copilot.

Future of Software Architecture

Blurring Lines Between Deterministic and Non-Deterministic Code

• The distinction between deterministic code (traditional software) and non-deterministic, generated parts is becoming increasingly unclear.

• While code should ideally remain deterministic, the prompts used to generate code can lead to different outputs due to the inherent non-determinism of models.

Role of Software Engineers in Future Development

• Software engineers will need to navigate between abstraction layers, particularly when dealing with natural language prompts that may lead to misunderstandings among team members.

• In collaborative environments, differing interpretations of specifications can result in varied outcomes; this complexity is a natural part of human collaboration.

Interaction with AI Models

• When working with AI models, engineers must recognize that their descriptions may not always translate directly into functional features as envisioned.

• Engineers might first use AI tools to create specifications or outlines before diving into coding, breaking down complex issues into manageable components.

Complexity Management in Engineering

• Effective engineering involves simplifying complex problems into smaller tasks that can be tackled more easily by both humans and models.

• Engineers must discern when it’s appropriate to rely on AI for implementation versus when they should handle tasks themselves based on complexity levels.

Future Operating Systems and User Interfaces

• There is potential for future operating systems to be dynamically generated; however, some foundational elements like the kernel will likely remain constant.

• Users may become less concerned about the underlying operating system as interfaces evolve towards more user-friendly interactions facilitated by intelligent agents.

Intelligent Agents as Primary Interfaces

• The future could see intelligent agents acting as primary user interfaces, managing tasks without users needing deep technical knowledge about the operating system.

The Future of Personalized Software and Coding Agents

The Role of Chatbots in Daily Life

• The integration of chatbots like ChatGPT and VChat into daily life is increasing, organizing tasks through interactions with both humans and AI agents.

• These chatbots can generate mini-applications on-the-fly to solve specific problems without maintaining a persistent state, making them efficient for temporary needs.

Personalized Applications for Family Management

• An example discussed involves tracking children's allowances, allowing parents to create tailored micro-apps that manage finances without the complexity of traditional accounts.

• Parents can use tools like co-pilot to generate apps that cater specifically to their family's needs, simplifying management while enhancing engagement with children.

Learning Coding Through Practical Application

• Building personalized software with children serves as an educational exercise, teaching coding in a hands-on manner while keeping kids engaged.

• Modern resources allow children to explore coding independently using natural language queries, reducing frustration compared to past learning experiences.

The Concept of Coding Agents

• GitHub's announcement regarding coding agents raises questions about the future role of developers when working alongside AI tools for coding.

• The discussion emphasizes that using AI does not mean relinquishing control; rather, it’s about determining when to rely on automated assistance effectively.

Evolving Software Development Practices

• Current driver assistance systems serve as an analogy for how developers might interact with AI—maintaining oversight while benefiting from automation.

• Most software projects involve building upon existing codebases rather than starting anew; thus, the craft of software development will evolve but not be replaced by AI tools.

Accelerating Idea Realization in Software Development

• A key motivation behind the rise of coding agents is the desire to transform ideas into products quickly without getting bogged down by setup complexities or boilerplate code.

Vibe Coding and the Future of Software Development

The Role of Vibe Coding in Prototyping

• Vibe coding allows for rapid prototyping, enabling developers to quickly translate ideas into functional code that often surpasses their initial capabilities.

• It emphasizes a creative process where developers can focus on building prototypes while offloading tedious tasks to coding agents.

Challenges in Software Development

• In real software projects, attention must be given to security vulnerabilities, code quality, and team standards due to budget constraints and efficiency goals.

• Developers often find themselves burdened with non-creative tasks like bug fixing and writing test cases, which detracts from their ability to innovate.

The Evolution of Coding Agents

• Many individuals without prior coding experience are successfully creating applications using AI agents, although there is a limit to the complexity they can handle.

• Low-code/no-code solutions have existed before AI but were limited; AI has significantly expanded what users can achieve without deep technical knowledge.

Limitations and Future Potential

• As projects scale from hundreds to thousands of users, understanding system architecture becomes crucial for effective implementation beyond basic functionalities.

• There exists an endless backlog in software companies due to ongoing technical debt and evolving compliance requirements; this backlog presents both challenges and opportunities for improvement.

Optimism for AI in Software Engineering

• The integration of intelligent agents could revolutionize software development by allowing teams to implement more ideas efficiently while managing complex backlogs.

The Future of AI-Assisted Coding and Knowledge Work

Integration of AI Agents in Development

• The discussion begins with the various forms of AI-assisted coding tools, including tab completion, VS Code forks, and plugins. The question arises whether these will converge into a single interaction or remain fragmented.

• It is acknowledged that while developers hope for a unified system, the reality involves multiple systems due to diverse companies and business incentives.

• The speaker emphasizes that having a single agent performing all tasks may not be feasible; instead, multiple specialized agents will exist.

Connecting Different AI Agents

• There are existing tools that allow connection between different AI agents (e.g., GitHub integration with other platforms like JFK Artifactory).

• A vision is presented where personalized agents can manage both personal and work-related tasks seamlessly, retaining knowledge relevant to each context.

• Personal agents could assist with everyday decisions (e.g., dinner recommendations), while work agents would have access to institutional knowledge necessary for job functions.

Managing Knowledge Transfer Between Personal and Work Agents

• When transitioning from one job to another, it’s proposed that knowledge specific to the company remains with the work agent while personal information stays with the individual’s agent.

• There will be challenges in distinguishing between personal and professional conversations, necessitating smart agents capable of managing intellectual property concerns.

Ecosystem of Specialized Agents

• The potential for an ecosystem of interconnected agents is discussed, which could include travel or task-based agents feeding information back into personal or work contexts.

• Ideally, these systems would communicate effectively at an operating system level without requiring users to switch contexts frequently.

Concerns About Job Replacement Due to AI

• Acknowledgment of anxiety surrounding job security in light of advancing AI technologies; questions arise about potential job displacement in fields like translation.

• While some jobs may diminish due to automation (e.g., translators), new roles are expected to emerge as a result of AI advancements.

Opportunities Created by AI Tools

• GitHub Copilot exemplifies how AI can democratize software development by enabling individuals without traditional programming backgrounds to create applications easily.

• This shift allows more people globally to engage in software development without needing extensive resources or language proficiency.

AI and the Future of Work

The Impact of AI on Job Roles

• AI is transforming job roles in ways that were unimaginable two decades ago, instilling confidence that those displaced by AI can reskill into new, potentially more fulfilling positions.

• Historical precedents, such as during the industrial revolution and the advent of personal computers, show that while some jobs are automated, new opportunities arise for workers to transition into different roles.

• An example from the software industry illustrates this shift: Microsoft eliminated dedicated tester roles due to automation. Many former testers successfully transitioned into engineering or product management positions.

• This historical context provides reassurance that technological advancements lead to job evolution rather than outright loss; individuals adapt and find new paths in their careers.