Abstract

AI is genuinely lowering the barriers to building software - but democratization is the surface effect. The deeper transformation is structural: AI shifts the primary bottleneck in software development from implementation to system design and reasoning. In agent-driven workflows, the constraint is no longer writing correct code across a large surface area. It is making good architectural decisions, managing system-level tradeoffs, and reasoning coherently across concerns - performance, modularity, data flow, hardware characteristics - that previously required deep specialization or large, coordinated teams. Individual developers and small groups can now operate fluidly across layers that were previously siloed not by technical necessity but by organizational scale. This shift raises fundamental questions for the software engineering research community. How do we evaluate design quality in systems that evolve continuously under agent-driven modification? What does meaningful modularity look like when the cost of cross-boundary reasoning approaches zero? How should practices like refactoring, testing, and performance validation be reconceived when the artifact being maintained is as much a set of agent instructions as a codebase? This talk draws on experience building AI-powered developer tools at AWS and working on AI acceleration across edge and cloud at Arm to ground these questions in observable shifts in how complex systems are actually being built today - and to argue that the SE community’s next productive frontier is not productivity measurement, but the theory and practice of design in the age of capable agents.

Speaker Bio

Thomas Cottenier is a Senior Principal Engineer at Arm, working on developer platforms and AI services across edge and cloud environments. He previously served as a Principal Engineer at AWS, where he contributed to AI-powered developer tools including CodeWhisperer, Amazon Q, and Kiro. He holds a PhD in Computer Science in programming languages and software engineering and has over 20 years of experience spanning code generation, model-driven engineering, automated refactoring, and large-scale code modernization. His current work focuses on AI-native development, system design, and AI acceleration.