Summary
AI innovation moves fast, but it often comes with hidden costs: rapidly changing models, fragile data pipelines, opaque decision-making, and the pitfalls of “vibe coding”. This roundtable will explore how to spot, prioritize, and address AI-driven technical debt while maintaining development velocity. Join peers to exchange lessons, tools, and best practices for building systems that stay robust and future-ready.
Speakers
Yongsheng Wu is a seasoned engineering leader with over 20 years of experience building high-performance teams and large-scale systems at companies including Pinterest, Twitter, Salesforce, and most recently Granica.AI and Circle. He has led organizations through hyper-growth, built advanced data and machine learning platforms, and driven infrastructure innovations that enable rapid product development at scale. Yongsheng holds an M.S. in Computer Science from Stanford University.
on ELC Annual 2025