GitHub Copilot

“They've done some studies, and it's, they, studies suggest that it saves about 40% for new code for developers to write new code.”

“So far the most widely deployed application of the large language models is probably Copilot.”

“GitHub Copilot when they moved away from the waitlist to the paid version, they just had like hundreds of thousands of people paying from the first day.”

“And we had a lot of the things running things that today that look like copilot where you could, you know, get code suggestions for based on natural language instructions.”

“We measured that there's 40%, ah, performance efficiency improvements for our engineers.”

“Then we deployed to all, all of our engineering. So everybody's using this daily in their interactions.”

“And our acceptance rate for code suggestions was around 25%, actually over 25%, which is interesting in line with the recent research that GitHub published.”

“So we built the first sort of co-pilot alternative, essentially.”

“AI coding is very clearly in the pragmatists. You know, get up co-pilot now, I don't know, three years old cursor's flying off your shelves, wind service flying off the shelf, you know, replit, all these guys are kind of being, you know, used everywhere. So yo…”

“And so this continuous spectrum of, I can assign tasks, test generation, bug fixes, you know, security vulnerabilities bootstrapping a new feature to the coding agent to, I can take that code base into my local IDE, or I can keep working just as I'm used to al…”

“Many of these AI code generation companies that compete with GitHub Copilot are running their inference on Azure AI Foundry.”

“And so Copilot does that autocompletion that continues to be the most used features because it is always there.”

“GitHub Copilot, which I know you just had Thomas on the podcast not too long ago. Four hundred million dollars of ARR, fifteen million developers, very much the steward of this category first mover.”