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So we’ve bought a Copilot license for our teams….AI in software solved, right?!
Well, in the Developer Success Lab – an empirical research lab studying the social science of developers and their wellbeing — we went on a journey in 2023 to understand something a little different about AI and software work: how developers are being impacted by changing expectations happening across the world of software development, and what leaders can do to help them.
In this talk, I want to share the research story of our large-scale study into the social science of genAI adoption as we fought to bring a human-centered approach to pressing questions that engineering organizations are facing about the rapidly-changing possibilities of AI-assisted coding.
In this talk, I’ll share our original empirical research findings from 3000+ software engineers and developers across 12+ industries engaged in the transition to generative AI-assisted software work. How are developers impacted by changing demands on their roles? Where might there be emerging equity & opportunity gaps in who has access to these new development capabilities? What are the risks to the quality of technical work, and the developer productivity, thriving, and motivation which drive that technical work? Along with rich data and important insights about the current state of genAI adoption, we learned that what developers are grappling with goes far deeper than flipping the switch on a new tool: by measuring key beliefs about where programming ability comes from and who is seen as “brilliant,” we gathered empirical evidence about how to explain why teams thrive and others flail during this transition.
Along the way, I’ll share the adventure of doing science on an innovative topic under high scrutiny – the choices we made as scientists about what to study and what to measure, including how using community-based methods to amplify diverse, underrepresented voices on tech gives us access to remarkable, incisive insights about how we use AI. Our research has found important risks and emerging equity gaps in how developers are experiencing the transition to AI, but also highlights the teams already creating resilience in this transition.
Despite the uncertainty of technological transition, moments of change are also a transformational opportunity to examine our shared definitions of success, productivity, and knowledge work. Despite the upheaval of changing technical work, developers’ core skills of lifelong learning and collaboration are key strengths that engineering managers can leverage to sustain innovative teams.
