AI-Era Leadership in CEOs of Leading Global Companies: Based on Case Studies of Chinese CEOs of Global AI Chip Companies

Artificial intelligence (AI) has recently emerged as an important driving force in the evolution of technology and an unstoppable trend in the modern era. The combination of AI chips, algorithms, data and computing power, in addition to the rapid development, manufacturing, and application thereof have accelerated the advent of the AI era resulting in a regrouping of the operating methods of enterprises and a reshuffling of the competitive landscape. These changes have brought to light the limitations of existing leadership theories, and this lack of AI leadership is coming under the spotlight and becoming an obstacle to corporate innovation and development. In order to face the advent of the AI era head-on, there is a need to examine the applicability of traditional leadership theories and seek inspiration for leadership in the AI era through case studies of successful leaders in the AI field. Academia has shown much less interest than industry in excellent Chinese entrepreneurs in the field of AI chips. Consequently, this study aims to explore the basic characteristics of leadership in the AI era through case studies of leadership by excellent Chinese entrepreneurs in the global AI chip field, focusing on the entrepreneurial journey and leadership practices of three key figures: Nvidia's Jensen Huang, AMD's Lisa Su, and TSMC's Morris Chang. By comparing and analyzing the excellent entrepreneurial leadership of CEOs of global AI chip companies and their transformation-oriented leadership style, common characteristics of leadership in the AI era, including humility, empathy, and service-oriented leadership came to light, and honesty, competence, inspiration and foresight were confirmed as the four most important qualities of excellent leaders. This provides further meaning and direction for the study of future leadership. However, this study also has limitations, that is, the cases in this study are limited to the AI chip industry and are not representative of the AI industry as a whole. Consequently, there is a need for further analysis of case studies and empirical research covering the entirety of the AI field in the future.