Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Co-Founder & Leader & Chair of Silicon Valley AI Nexus (AI Nexus), Advisor @ Korean American Semiconductor Professional Alliance (KASPA), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, Director of AI Semiconductor @ K-BioX, Visiting Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, and Network Expert @ Gerson Lehrman Group

Abstract

Artificial Intelligence (AI) has crossed a threshold. What began as pattern-matching systems that classified images and completed sentences has evolved into agentic intelligence — AI that plans, reasons across multiple steps, uses tools, and executes complex tasks with minimal human supervision. This talk examines what makes the current moment a genuine inflection point rather than another hype cycle: the convergence of frontier model capabilities, plummeting inference costs, and the emergence of AI agents that don’t just answer questions but complete work. Drawing on firsthand experience across Silicon Valley and Korea’s technology ecosystems — from semiconductor simulation at Samsung to applied science at Amazon to founding AI-driven ventures — I will trace how the underlying mathematics of optimization and physics-based modeling has quietly powered this transformation, and why the leap to agentic systems changes the equation entirely.

The economic implications are arriving faster than institutions can adapt. Agentic AI is reshaping the production function itself: tasks once bundled into jobs are being unbundled, priced, and reallocated between humans and machines, while entire industries — from drug discovery to software engineering — are discovering that their core workflows can be compressed by orders of magnitude. This talk will explore the emerging economics of AI, including how scaling laws behave like production functions, why the value is migrating from models to the measurement and data layers beneath them, and what this means for the next generation of engineers deciding where to build their careers. Silicon Valley’s current transformation offers a live case study: which companies, skills, and business models are being rewarded, and which assumptions are being quietly abandoned.

Yet the deepest questions raised by this inflection point are not technical or economic but human. What happens to expertise when machines can reason? What does meaningful work look like when execution becomes cheap and judgment becomes the scarce resource? How should young engineers — standing at the beginning of careers that will unfold entirely within the age of agentic intelligence — position themselves not merely to survive the transformation but to shape it? Rather than offering predictions, this talk offers a framework: how to think about AI’s trajectory with the rigor of an engineer, the pragmatism of an entrepreneur, and the honesty to acknowledge what we don’t yet know. The goal is to leave the audience not with answers, but with better questions — the kind that will define the next decade of technology and the people who build it.