Jodie Burchell and Michelle Frost of JetBrains offer a measured, research-grounded perspective on the state of generative AI. They discuss the shifting definitions of AI, the enduring importance of foundational machine learning principles, historical parallels to previous 'AI summers,' the measurement problem of AGI, and what the evidence actually says about AI's impact on developer productivity.
A panel of experts from AWS, G-P, and AntStack discuss the practical impact of Generative AI on software development. They explore how AI is used as an accelerant for productivity, the challenges of applying it to large-scale system design, its role in modernization, and the future implications for developer careers and safety-critical systems.
Euro Beinat (Prosus Group) and Mert Öztekin (Just Eat Takeaway.com) discuss the practical challenges of scaling AI, focusing on developer productivity, the role of AI agents in automating the 'long tail' of tasks, and the critical importance of change management and governance to foster an AI-native culture without stifling innovation.
Sherwin Wu, head of engineering for OpenAI’s API platform, discusses the radical transformation of software engineering. He shares how 95% of OpenAI engineers use Codex to manage fleets of AI agents, cutting code review times from 15 to 3 minutes. Wu explores the widening productivity gap, the changing role of managers in an AI-first world, and why "models will eat your scaffolding for breakfast", urging developers to build for where AI is going, not where it is today.
Roman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development, discussing its impact on developer workflows, code quality, security, and the future of developer roles. They emphasize that while AI tools are powerful amplifiers, human oversight remains essential for quality, security, and legal compliance.
Nathen Harvey, leader of Google's DORA research team, discusses the surprising findings from their latest research on AI's impact on software development. While initial AI adoption correlated with decreased stability and throughput, the latest data shows a reversal for throughput. The conversation explores why this happens, presenting AI as an amplifier of existing systems and introducing DORA's seven essential capabilities for successful AI adoption, including the critical roles of documentation, trust, and expertise.