Szczepan Faber

It’s amazing how we can now build working apps just by few-shot prompting LLMs. But try doing this with monorepos of 10s of MLOC, like the ones used for planet-scale apps that must be secure and compliant. Responsible agentic coding at scale, where 1000s of engineers materialize code changes by engaging in deep sessions with AI is really challenging. We want to share Airbnb’s journey, the trade-offs we picked, learnings, outcomes and productivity impact we observed.

Join a panel of trailblazing engineering leaders who are shaping how the industry thinks about scaling software development. From navigating technical debt to responsibly integrating generative AI, this conversation will cover the hard-won lessons and emerging strategies for managing massive codebases.

We’ll dive into:

• Impact of developer time spent on code refactoring.
• The influence of generative AI code creation on automated code refactoring.
• Techniques for ensuring genAI authored code is high quality, maintainable, and secure—and proving it meets standards.
• Organizational challenges of managing large-scale code changes, including PR overload, developer trust, and risk mitigation.
• Measuring the impact of technical debt across large software organizations and driving leadership action.
• Risk assessment and mitigation strategies when rolling out changes that affect thousands of developers.
• The role of auto-refactoring in developer portals and real-world use cases for integration.
• Challenges and lessons learned from using generative AI for testing and dead code removal at scale.

Come with questions—this panel is built to spark insight and honest discussion.