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Today at Collision Conference we unveiled breaking new research on the economic and productivity impact of generative AI–powered developer tools. The research found that the increase in developer productivity due to AI could boost global GDP by over $1.5 trillion.
Almost one year ago, we launched GitHub Copilot to the world. At the time, it felt like a leap into the unknown: We were introducing the first at-scale AI pair programmer to the world’s developers, and were cautiously optimistic. But now I am overwhelmingly confident in the promise of this technology. One year later, we have witnessed a sea change in software development.
Today, GitHub Copilot has been activated by more than one million developers and adopted by over 20,000 organizations. It has generated over three billion accepted lines of code, and is the world’s most widely adopted AI developer tool.
From productivity analysis to developer happiness, we have been collecting research and data on GitHub Copilot that will help us continue to improve and adapt our models. As we have recently passed the one-year anniversary of GitHub Copilot’s launch, I’m sharing key insights we’ve discovered along the way, and breaking research released today that includes the economic impact of the AI-powered developer lifecycle. Let’s dive in.
GitHub Copilot is turbocharging developer productivity. Analysis on a large sample of GitHub Copilot users (n = 934,533) reveals a sizable productivity impact. On average, within the first year in the market, users accept nearly 30% of code suggestions from GitHub Copilot and report increased productivity from these acceptances. Furthermore, we found that over time, the acceptance rate steadily increased as developers became more familiar with the tool. This suggests that GitHub Copilot has a large runway to continue its impact on developer productivity, as users become more accustomed to developing software with it.
AI developer productivity benefits could boost global GDP by over $1.5 trillion. Using 30% productivity enhancement, with a projected number of 45 million professional developers in 2030, generative AI developer tools could add productivity gains of an additional 15 million “effective developers” to worldwide capacity by 2030. This could boost global GDP by over $1.5 trillion, a boon to economic activity generated by this one group of workers. We know that the demand for software and developers will likely increase—as it has throughout the history of developer tools–and these productivity gains will continue to trigger an enormous impact, as developers seize new opportunities to utilize AI for solutions design and accelerate digital transformation worldwide.
Less experienced developers benefit more from GitHub Copilot. Our study also found that less experienced developers have a greater advantage with tools like GitHub Copilot, which is corroborated by other studies, including our own previous experiments on the impact of AI on developer productivity. As developers use these tools to upskill, they will become more fluent in prompting and interacting with AI to power the development lifecycle. This will ultimately help democratize software development for more people, help close the labor gap, and establish AI pair programming tools as part of the standard developer education experience.
GitHub is the engineering system for the age of AI. We also observed an explosion of open source innovation on GitHub. According to our ecosystem analysis of AI repositories on GitHub, the landscape that makes up those working on generative AI is diverse, from big tech companies to individuals. And open source activity around generative AI projects, based on our analysis of GitHub repositories and commits, has increased exponentially. We expect open source developers on GitHub to drive the next wave of AI innovation.
Previous research examined not only the acceptance rate, but the speed at which developers completed tasks with GitHub Copilot. For example, we found in a quantitative research study that developers completed tasks 55% faster with GitHub Copilot. Moreover, our early research found that 46% of code was completed by GitHub Copilot in those files where it was enabled. These are impressive figures, but productivity for productivity’s sake means nothing—we built GitHub Copilot for the sake of developer happiness. This is GitHub Copilot’s inherent purpose.
And it’s been a success in that regard, too. In one survey, 75% of developers said they felt more fulfilled when using GitHub Copilot. In another survey, developers said the top benefit of AI coding tools was improving their coding language skills, which developers also said can lead to a more positive workday. More productive, more satisfied, and more capable developers—that’s exactly what GitHub Copilot is all about.
The economic impact of generative AI over the next decade will be profound—and we’re already seeing large-scale adoption of AI coding tools like GitHub Copilot by developers and companies. In a recent survey, 92% of developers said they use AI tools both in and outside of work, which underscores how quickly these tools are redefining the overall developer experience.
We launched GitHub Copilot for Business earlier this year to bring the power of generative AI to organizations of all sizes, regardless of whether they use GitHub to build software. Three months later, more than 10,000 companies were already using it—and today, more than 20,000 organizations are using GitHub Copilot for Business.
And companies that use GitHub Copilot are seeing real results. Engineering teams at Duolingo, for instance, have used GitHub Copilot for Business to achieve a 25% increase in developer velocity. “With GitHub Copilot, our developers stay in the flow state and keep momentum instead of clawing through code libraries or documentation,” says Johnathan Burket, a senior engineering manager at Duolingo.
Apart from this, we are increasingly seeing organizations require their applicants to test for software development jobs with GitHub Copilot, suggesting that AI pair programming will become a standard tool for testing applicants. This means that learning how to use generative AI tools will soon become a core competency of a software developer.
What we draw from all this is that generative AI is turbocharging developer productivity with gains that will ultimately drive a boom in GDP for the global economy and, in turn, a surge in demand for software developers. We’ve seen this throughout the history of developer tool innovations from compilers to open source, and we’re already seeing that again with GitHub Copilot and soon GitHub Copilot X. One year later, we’ve realized this collision of AI and the software developer will not lead to a decrease in developer jobs—it will lead to AI augmenting developer potential and accelerating human progress.
As more developers adopt generative AI tools and become fluent in the skill set of prompting with a copilot, it is clear that this new way of software development has created an inextricable link between humankind and artificial intelligence that could well define how the world’s software is built for generations to come.
And the world will be all the better for it.