Big Risks, Engineering Bottlenecks, and AI

Engineering organizations often shy away from ambitious, high-risk projects like migrations or rearchitectures, even when the status quo hurts like hell. At the same time, the headlong rush to AI-coding is itself ambitious and high-risk, even if we don’t think of it as such!
This session shows how these issues are tied together, and how to tackle both at once. You’ll learn how to take bigger swings and tackle projects that deliver real impact – projects like integrating AI tools into your development workflow.
Expect real talk, lessons from the field, and practical strategies you can apply in your own team.

Key Takeaways:

1. Understand why engineering teams default to safety and why AI alone doesn’t speed you up.

2. Learn how to build foundations for bold projects like AI adoption.

3. Learn how to integrate AI into your develpment safely and effectively.

• 1 Minute Survey
• What is Antithesis?
• What does AI testing done right look like?
• Your computer can test better than you (and that's a good thing)
• Why Google Stores Billions of Lines of Code in a Single Repository
• State of the software engineering job market in 2025: what the data says
• Measuring the Impact of Early-2025 AI on Experienced Open-Source
• An Empirical Evaluation of Property-Based Testing in Python.pdf
• Ship boldly, break nothing
• Measuring the impact of AI on software engineering – with Laura Tacho