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Tue, April 27AI-Assisted Software Engineering
AI is reshaping software engineering, creating uncertainty about how engineering roles will change as increasingly capable systems take on work previously performed by people. While the future cannot be predicted with certainty, earlier periods of technological change offer useful ways to understand how innovation can affect work, productivity, and workforce composition.
In this session, you will explore what economics can teach us about the changing software engineering workforce and agent-assisted development. You will examine lessons from previous periods of technological change, consider which of those lessons apply to software engineering today, and explore how data from your own codebase can help you understand the impact of AI on engineering work rather than relying on broader industry assumptions. Drawing on experience with Google’s engineering organisation, the session also examines how a large software engineering workforce can be structured and guided as development practices continue to change. No background in economics is required.
What You Will Learn
How economic perspectives can help explain changes in software engineering as AI adoption grows
How engineering data from your own codebase can help you understand workforce and development trends
How engineering organisations can prepare for changes in AI-assisted software development
Who Should Attend
Software Engineers, Technical Leads, Engineering Managers, Staff and Principal Engineers, Software Architects, Engineering Directors, and Technology Leaders planning for AI adoption.
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Daniel serves as a Principal Engineer for Google's Scaled Software Enablement organization, which tackles the critical problem of how to effectively build and scale our developer systems for humans and agents, and serves as the global co-chair of Google's SWE Steering Committee. At Google prior to this Daniel served as a principal with the office of Cross Google Engineering, tackling cross-company technical strategy, and has led cross-functional teams across the software stack including Google’s geographic data infrastructure, Google My Business Locations, Google Photos, and Google Tasks among others. His experience traverses the technical spectrum and includes infrastructure, machine learning, mobile and web.
