The Innovation Show Yossi Sheffi - The Magic Conveyor Belt Part 3
In 2020, the World Economic Forum estimated that AI might destroy 85 million jobs by 2025. A 2022 study by the US Government Accountability Office (GAO) found that between 9 and 47% of jobs could be automated, particularly jobs requiring lower education levels or more routine tasks. Not surprisingly, workers, particularly low-skilled ones, perceive automation—specifically robots—as threatening their jobs. Fear and resentment toward job-stealing machines are not new. Throughout the series of overlapping industrial revolutions in history, many jobs were indeed diminished or replaced by machines. Yet, other jobs were created over time. Prof Yossi Sheffi shares his thoughts on the future of work and workers. We welcome back Professor Yossi Sheffi for the finale of our series on his book, "The Magic Conveyor Belt: Supply Chains, A.I., and The Future of Work."
00:00:00.000 The Impact of Automation on Jobs 00:02:39.488 The Fifth Industrial Revolution and Amplifying Human Creativity 00:03:46.708 Job Creation and Unemployment Rates 00:06:23.573 The Slow Pace of Job Displacement and Regulation 00:09:00.668 Technology Always Wins, Companies Must Invest in Workers' Skills 00:11:47.371 Negotiating for Skill Upgrades in the Advanced AI World 00:14:13.311 Regulation as a Short-Term Game, Reinvesting in Skills 00:16:12.024 The Impact of Automation on Logistics Jobs 00:17:26.473 The Rise of Autonomous Tracking and Workforce Preparation 00:19:36.312 The Impact of Model T on Job Creation 00:21:40.769 The Three Stages of Job Transformation: De-skilling, Scaling, Elimination 00:24:40.238 Job Changes and Creation in Automation 00:27:13.249 AI and Automation: Journalists' Fears and Future Job Opportunities 00:35:49.159 The Importance of Purpose After Retirement 00:36:50.790 The Importance of Investing in the Future 00:40:07.188 Staying Relevant in an Evolving Job Market 00:44:16.591 The Social Dimension of Automated Jobs 00:47:35.298 Embracing Change and Adapting to Working with Machines
That HBR article Yossi mentions: