First previewed in partnership with OpenAI in 2021, GitHub Copilot is the world’s first at-scale AI developer tool. Sitting within the editor as a simple extension, GitHub Copilot draws context from a developer’s code to suggest new lines, entire functions, tests, and even complex algorithms. Since its release, GitHub Copilot has transformed developer productivity for more than one million people, helping developers code up to 55% faster.
The rise of generative AI models like GitHub Copilot has triggered widespread recognition that the age of AI has begun. But until now, generative AI has largely benefited the individual. That’s changing today.
GitHub Copilot builds the power of generative AI into an editor extension that suggests code in real-time. It works with code and natural language prompts to offer multiple suggestions that can quickly be accepted or rejected—and it learns alongside developers to adapt to individual coding styles and conventions. With GitHub Copilot, developers can use the editor of their choice from Visual Studio to Neovim, VS Code, or JetBrains IDEs.
Curious about how developers are using GitHub Copilot? People around the world are using GitHub Copilot to code faster, generate tests, and cut down on context switching. Check out our guide to learn more.
Back in June 2022, we reported that GitHub Copilot was already generating 27% of developers’ code. Today, we’re seeing this happen more and more with an average of 46% of code being built using GitHub Copilot across all programming languages, and 61% among developers using Java.
Moreover, our research shows that 90% of developers using GitHub Copilot report completing tasks faster, with 73% better able to stay in the flow and conserve mental energy. Our research has also found that up to 75% of developers who use GitHub Copilot feel more fulfilled and able to focus on more satisfying work.
A graphic showing top-level findings of research that finds GitHub Copilot helps developers code faster, feel more fulfilled at work, and generates up to 46% of all code.
This rate of success has enormous potential for developers and companies alike—just imagine the benefits of putting GitHub Copilot in the hands of 100-, 1,000-, or even 10,000-person development teams.
For an organization, developers fixing bugs faster means your product and supply chain is more secure. Developers manually writing less boilerplate over and over again means your teams are more productive and focused. Developers being able to learn and onboard new technologies means they can deliver better solutions for your customers.
Ultimately, Copilot for Business gives developers the ability to build for tomorrow instead of spending all their time bogged down with the problems of today.
Building AI into the full developer experience
Building software has never been more complex, and developers need new solutions. AI is bringing the most significant technological transformation in a generation. Organizations that harness AI to empower developers will win the future. And this is just the beginning of our vision to build an integrated, AI-powered GitHub.
In the coming years, we will integrate AI into every aspect of the developer experience—from coding to the pull request to code deployments—so developers can build their best in a world where all organizations will be more dependent on their success than ever. GitHub Copilot for Business is the first stride in this future, a future that will push the boundaries for all developers.
Fascinated by software development since his childhood in Germany, Thomas Dohmke has built a career building tools developers love and accelerating innovations that are changing software development. Currently, Thomas is Chief Executive Officer of GitHub, where he has overseen the launch of the world's first at-scale AI developer tool, GitHub Copilot -- and now, GitHub Copilot X. Before his time at GitHub, Thomas previously co-founded HockeyApp and led the company as CEO through its acquisition by Microsoft in 2014, and holds a PhD in mechanical engineering from University of Glasgow, UK.
An interview with economic researchers who are applying causal inference techniques to analyze the effect of generative AI tools on software development activity.
