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Thu, December 10AI-Native Software DevelopmentInfrastructure, Platforms & Scale
As software development scales from tens of thousands of human developers to millions of machine-speed developers, the assumptions that shaped traditional codebase management are being challenged. Practices that worked at one scale no longer behave the same way as development activity increases dramatically, and agents transform once familiar access patterns. The age old debate of mono- vs poly-repo is being challenged and reinvented as we grapple with the abilities of AI to hold global context but also make repo-wide errors.
This session explores the consequences and trade-offs of managing Google's multi-billion-line monorepo in this new environment. We will examine the advantages of a large shared codebase for global optimization, along with the scaling challenges created by years of organic growth and increasingly complex dependency relationships. The discussion also considers the scaling behaviours that emerge as codebases become exceptionally large.
Topics include the role of build and test caching in managing both scale and complexity, the challenges of measuring the impact of codebase cleanup efforts, approaches to dependency management, and techniques for reducing churn. The session also examines how the structure of a codebase can be used to reduce the blast radius of agentic development activity and addresses common misconceptions that make this more difficult than it needs to be.
Attendees will leave with practical insights into the engineering trade-offs involved in managing large codebases as software development increasingly operates at machine speed.
What You Will Learn:
Who Should Attend:
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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.
