Discover how top-performing teams achieve 100-150% productivity gains with AI by strategically restructuring workflows, automating wisely, prioritizing design, fostering deep work, reducing cognitive load, investing in growth, and optimizing tools. This goes beyond mere AI adoption, focusing on human-centric practices and intelligent measurement to drive real impact and retention.
May Walter, CTO of Hud, details a real-world case study on integrating AI agents into the SDLC for continuous performance optimization. The talk covers Hud's runtime intelligence layer, which uses production context to identify and fix high-ROI performance issues like N+1 queries and missing database indexes. It highlights the technical approach, challenges, and the development of a human-friendly reporting system that delivers measurable P90 latency improvements, enabling proactive optimization in mature codebases.
Joseph Krause, CEO of Radical AI, details how his company uses Self-Driving Labs (SDLs) and AI scientists to overcome the experimental bottleneck in materials science. By automating the full loop of hypothesis generation, synthesis, characterization, and testing, Radical AI is accelerating the discovery of novel alloys for aerospace, defense, and semiconductor applications, achieving 10x the pace of traditional methods. Krause explains why materials science is uniquely challenging for AI, how human intuition trains the AI, and why experimental data, not models, forms the core competitive advantage in this rapidly evolving, geopolitically significant field.
OpenAI's finance team showcases how they've transformed operations using AI tools like ChatGPT, ChatGPT for Excel, and custom agents built with Codex. They highlight principles of AI-native design, headcount leverage, rapid iteration, and specific applications that significantly boost individual productivity and organizational efficiency, including investor relations, LBO modeling, marketing analytics, sales insights, financial reporting automation, and agent-driven procurement, credit checks, contract review, and vendor risk management.
Liam Hampton from Microsoft presents a practical framework for using AI agents effectively by categorizing them into three types: local, background, and cloud. He demonstrates how to run all three simultaneously from a single VS Code interface to solve separate problems in one codebase, showcasing a powerful, integrated developer workflow.
Onur Solmaz from OpenClaw discusses the challenge of managing 300-500 daily, often AI-generated, pull requests. He introduces ACPX, a headless CLI for the Agent Client Protocol (ACP), designed to automate PR triage through a node-based workflow. The talk culminates in a vision for on-demand, disposable agent pods on Kubernetes, managed by a Go operator that provisions and tears down full compute environments per task, wiring them into chat platforms like Slack.