Brian Houck
DX - Distinguished Scientist
Brian Houck is an applied scientist focused on understanding and improving the developer experience within engineering organizations. His work combines large-scale telemetry, experiments, surveys, and qualitative research to uncover the technical and human factors that shape developer productivity and wellbeing.
He co-authored the SPACE framework, now widely used across the industry to measure developer productivity, and his research has informed engineering practices and decision-making at scale. His recent work explores how AI is transforming software development, and how teams can adopt it effectively to improve real outcomes—not just activity.
Brian’s core belief is simple: improving productivity isn’t just a technical problem. It requires addressing both the constraints that slow systems down and the human factors that shape how work gets done.
Presentations
AI adoption is accelerating across engineering organizations, but translating that adoption into meaningful impact remains uneven. While many teams see immediate gains in speed, those gains often stall as new bottlenecks emerge around verification, coordination, and trust. This talk shares lessons learned from observing AI adoption at scale across large engineering systems. We will explore what actually drives effective usage, why increased output does not always translate into better outcomes, and how organizations can rethink workflows to fully realize the benefits of AI.
We will cover practical strategies for driving adoption beyond surface-level usage, approaches for handling verification in AI-assisted development, and ways to measure success that go beyond activity metrics. The goal is to provide a clear, experience-backed framework for turning AI from a promising tool into a reliable driver of engineering impact.
Technical debt is often treated as a code-level problem, addressed in the cracks of time between features. In practice, it behaves more like a hidden tax on innovation, quietly reducing the amount of time and energy teams can devote to new ideas.
In this session, we examine data on the prevalence of technical debt across modern engineering organizations, showing that debt is not an exception but the norm. We connect this debt to everyday forms of developer toil that result from existing debt and actively contribute to its continued accumulation.
Drawing on quantitative signals we show how unpaid technical debt progressively erodes innovation time, even when teams appear busy and delivery metrics remain stable. Rather than focusing on strategies for paying down debt after the fact, this talk reframes technical debt as a systemic outcome of incentives, planning assumptions, and work design.