Daniel Nadasi
Principal Engineer
Lessons from Economics for the Human / Agent Software Workforce
Lessons from Economics for the Human / Agent Software Workforce
AI is reshaping software engineering, creating uncertainty about how engineering roles will change as increasingly capable systems take on work that was once performed by people. While no one can predict the future with certainty, history offers examples of how technical innovation has transformed work in other industries and provides useful ways to think about the current transition.
This session explores what economics can teach us about the future of software engineering and agent-assisted development. We will examine how similar periods of technological change have affected work, productivity, and workforce composition, and discuss which lessons are applicable to software engineering today. We'll also look at how engineering organizations can use insights from their own codebases to better understand the impact of AI on engineering work, rather than relying on assumptions or conclusions drawn from broader industry discussions.
No background in economics is required. We will focus on minimal theory and practical concepts that help engineers and leaders interpret ongoing changes and make informed decisions.
Daniel Nadasi
Daniel serves as a Principal Engineer for Google's developer infrastructure, focusing on enabling the company to scale from 100k human developers to millions of machine-speed agents, and serves as global co-chair of Google's Software Engineering Steering, which defines the role and craft of Saftware Engineering across Google. At Google prior to this Daniel 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 and was a founding lead for Google's office of Cross-Google Engineering. His experience traverses the technical spectrum and includes infrastructure, machine learning, mobile and web.