Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Leader of Silicon Valley Privacy Preserving AI Forum (K-PAI), 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, Network Expert @ Gerson Lehrman Group, and Chief Business Development Officer @ WeStory.ai

Abstract

This comprehensive exploration traces the remarkable evolution of Artificial Intelligence (AI) from humanity’s ancient aspirations to create intelligent machines through its current transformative impact across global industries. Long before John McCarthy coined the term “Artificial Intelligence” at the 1956 Dartmouth Conference, humans harbored deep psychological yearnings to breathe life into mechanical beings—from ancient Greek myths of Talos to medieval clockwork automata to Wolfgang von Kempelen’s famous Mechanical Turk chess-playing automaton. We’ll examine how these fundamental human desires to transcend our cognitive limitations and create companions that think like us have driven centuries of innovation, leading to the cyclical patterns of inflated hope and subsequent “AI winters” that followed major technological breakthroughs like the invention of electronic computers in the 1950s and the emergence of fuzzy logic in the 1980s. Drawing from extensive experience across semiconductor optimization at Samsung, large-scale AI systems at Amazon, and AI-powered biotech innovation at Erudio Bio, this lecture provides a unique Silicon Valley Entrepreneur, Scientist, Mathematician, Philosopher, and Connector’s perspective on AI’s modern trajectory—from the Big Data revolution (~2010) and the Deep Learning breakthrough (2012), through the Transformer architecture revolution (2017) and the explosive emergence of Large Language Models (LLMs) and Generative AI (genAI) (2022), to the current frontier of AI Agents (2024 and beyond). We’ll examine not just the technical milestones, but the underlying mathematical principles, computational breakthroughs, market forces, and the enduring human psychology that made each transition inevitable while revealing the patterns that will shape AI’s next evolutionary leaps.

The lecture delves deep into how these technological advances are fundamentally reshaping entire industry landscapes—from healthcare and pharmaceuticals to finance, manufacturing, and beyond. Through concrete case studies spanning biotech diagnostics, recommendation systems, semiconductor manufacturing, and enterprise automation, we’ll explore how AI is not merely automating existing processes but creating entirely new business models, market categories, and competitive dynamics. Special attention will be paid to the economic implications – how AI is redistributing value across supply chains, creating new forms of digital scarcity and abundance, and forcing organizations to reconceptualize their core value propositions in an AI-native world.

Beyond the technical and economic transformations, this lecture addresses the fundamental paradigm shift required in how we conceptualize human-AI collaboration. The prevailing discourse—whether AI will “replace” humans or merely serve as “assistants”—poses the wrong questions entirely. We need a dramatic reconceptualization – AI will be something entirely new, transcending both replacement and assistance paradigms. Rather than simply automating repetitive tasks while humans handle creativity, AI can actively stimulate and amplify human creative potential in unprecedented ways. Instead of drawing artificial boundaries about “where humans should stop and AI should begin,” we must develop entirely new models for co-existence and collaboration that leverage the unique strengths of both human and AI. These changes will unfold driven by irresistible market forces regardless of our preferences—but this doesn’t absolve us of responsibility. As we’ll explore, the future of AI is neither inherently pessimistic nor optimistic; it depends entirely on the choices we make and the frameworks we build to shape that future. Our challenge is not to resist these changes but to thoughtfully architect the paradigms that will govern human-AI collaboration in the coming decades.

Finally, we’ll decode the current investment landscape surrounding AI, examining both the unprecedented capital flows and the strategic imperatives driving them. From venture capital patterns and corporate R&D priorities to geopolitical competition and talent acquisition strategies, we’ll analyze how smart money is positioning itself for AI’s next phase while identifying the critical factors that separate sustainable AI businesses from speculative bubbles. The session concludes with actionable insights for students, researchers, and future industry leaders – how to build competitive expertise in your chosen domain while maximally leveraging AI capabilities, and why the winners in the AI era will be those who master the synthesis of deep domain knowledge with AI amplification rather than those who chase AI for its own sake.