AAPL: Tribal Knowledge and PLM with Ye Wang (EverCurrent) and Rob Ferrone

The conversation delves into the challenges and implications of tribal knowledge in AI, particularly in the context of engineering and manufacturing. It explores the definition of tribal knowledge, its impact on development speed, integration with AI, complexity tax, agile methodology, and its role in fast-paced hardware startups. Additionally, it discusses real-time configuration management, learning by doing, and the relationship between intellectual property and tribal knowledge. The conversation delves into the impact of AI and tribal knowledge on hardware startups, the role of young engineers, and the future of work. It explores data security, IP protection, and the modernization of the engineering experience. Additionally, it discusses the balance of quality, schedule, and cost in hardware companies, as well as the potential for AI to accelerate hardware innovation. The future of tribal knowledge and AI is also considered, along with the impact on young people entering the industry.TakeawaysTribal knowledge in AIChallenges of tribal knowledge in engineering and manufacturing AI and tribal knowledge are transforming the way work is doneThe impact of AI on hardware startups and the role of young engineersChapters00:00 Introduction to Tribal Knowledge in AI06:05 Defining Tribal Knowledge11:59 AI and Tribal Knowledge Integration17:58 Tribal Knowledge and Agile Methodology23:55 Real-Time Configuration Management29:07 IP and Tribal Knowledge36:03 Differentiating Tribal Knowledge and System of Record41:19 Balancing Quality, Schedule, and Cost in Hardware Companies53:31 The Future of Tribal Knowledge and AI#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino