This episode, the fourth of a four-part series, discusses the appendices from a book that introduces a new scientific framework called Intelligent Economics, which posits that complex, persistent systems like economies evolve to minimize their total computational cost, a principle termed the Sorter's Law. Appendix A meticulously details the formal foundations of this theory, deriving the Lagrangian—the instantaneous computational cost—from three irreducible components (Predictive Error, Model Complexity, and Update Cost) and establishing the emergence of the four MIND Capitals (Material, Intelligence, Network, and Diversity) as necessary assets for long-term persistence. Appendix B establishes a deep, structural isomorphism between Intelligent Economics and the architecture of modern Generative AI systems, translating core economic concepts into their direct counterparts in machine learning, such as equating the economic Loss Function with the AI training process. Finally, Appendix C functions as a practitioner’s guide, providing a detailed MIND Dashboard with specific, measurable indicators for assessing the vitality of a civilization, company, or individual by moving beyond traditional metrics like GDP.

References

The Last Economy: A Guide to the Age of Intelligent Economics by Emad Mostaque, pp. 150-176, available at: https://ii.inc/web/blog/post/tle

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and potentially other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.