About the Episode

In this eye-opening episode, we are joined by Serf, a leading expert in Transition Studies, to unpack the methodologies and tools that drive societal shifts. Starting with the fundamental differences between foresight and transition studies, we delve into tangible use-cases, measuring success, and the balance between short-term and long-term thinking. Using the transition from horses to cars as a historic lens, we critique the current Electric Vehicle (EV) movement and speculate on what’s beyond cars. The latter half of the episode shifts gears to discuss the AI arms race, touching on generative AI, FOMO, regulation risks, and how to rethink the AI control problem. A must-listen for anyone intrigued by how transitions in technology and policy shape our world.

Drift Website: https://drift.eur.nl/ 

About the Guest

Serf is all about realising transitions, and his specific contribution is raising awareness. As coordinator & facilitator eduction he knows how to get people moving and to set up and support transformative education! By organising workshops, developing courses and facilitating events, he himself is constantly on the move for the Transition Academy – to collect the knowledge of DRIFT and other changemakers and share this with future transition champions.

Serf at Drift: https://drift.eur.nl/people/serf-doesborgh/ 

Serf’s LinkedIn: https://www.linkedin.com/in/serf-doesborgh/?originalSubdomain=nl 

TimeCodes: 

00:06:09:04 Interview begins

00:06:59 Who is Serf?

00:10:13 What is Transition Studies?

00:14:37 Tangible Use case of Transition Studies

00:18:10 Difference between foresight and transition studies

00:20:11 How is success measured in Transition studies?

00:23:27 What are the tools of transition studies?

00:30:20 Short term thinking vs long term thinking

00:30:59 The transition from horses to cars

00:38:07 Critique of EV’s

00:41:41 What is beyond cars?

00:39:28 How is the generative AI revolution seen via transition management?

00:50:35 Ai and Fomo

00:56:24 Regulation and Risk of AI

01:02:11 How to rethink the AI control problem

01:06:39 Doom thinking about AI

01:07:53 Conclusion