This chapter explores the successful adoption of a tool in an organization and discusses the potential impact of AI on developer productivity. It highlights how AI can save developers time and mental effort by handling complex tasks and systems. The chapter also touches on the implications of AI on measuring productivity and suggests alternative metrics and the use of AI tools in conjunction with coding.
This week's guest is Eirini Kalliamvakou, a staff researcher at GitHub focused on AI and developer experience. Eirini sits at the forefront of research into GitHub Copilot. Abi and Eirini discuss recent research on how AI coding assistance impacts developer productivity. They talk about how leaders should build business cases for AI tools. They also preview what's to come with AI tools and implications for how developer productivity is measured.
Discussion points:
- (1:49) Overview of GitHub’s research on AI
- (2:59) The research study on Copilot
- (4:48) Defining and measuring productivity for this study
- (7:44) Exact measures and factors studied
- (8:16) Key findings from the study
- (9:45) How the study was conducted
- (11:17) Most surprising findings for the researchers
- (14:01) The motivation for conducting a follow-up study
- (15:34) How the follow-up study was conducted
- (18:42) Findings from the follow-up study
- (21:13) Is AI just hype?
- (26:34) How to begin advocating for AI tools
- (34:44) How to translate data into dollars
- (37:06) How to roll out AI tools to an organization
- (38:47) The impact of AI on developer experience
- (43:24) Implications of AI on how we measure productivity
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