This episode discusses the research paper, "Leveraging AI tools in finance education: exploring student perceptions, emotional reactions and educator experiences," which presents a mixed-methods study assessing the integration of Artificial Intelligence tools within finance education. Quantitative data, gathered through a Synthetic Index of Use of AI Tools (SIUAIT) and observational studies using facial expression analysis, reveal that finance students, particularly those in Financial Engineering, hold significantly positive perceptions of AI tools and experience heightened positive emotional engagement in AI-enhanced classes. Conversely, the study notes an increase in the negative emotion of fear, which may still facilitate learning. Qualitative interviews with educators highlight that while they recognize AI’s benefits in pedagogy and efficiency, they also express concerns regarding student over-reliance and essential ethical implications that must be addressed for successful integration. The overall conclusion is that AI has a transformative potential in preparing students for their careers, but a balanced approach is crucial to maximize benefits while mitigating potential challenges.

References

“Leveraging AI tools in finance education: exploring student perceptions, emotional reactions and educator experiences” by Pamela Córdova, Alberto Grájeda, Juan Pablo Córdova, Alejandro Vargas-Sánchez, Johnny Burgos, Alberto Sanjinés, COGENT EDUCATION2024, VOL. 11, NO. 1 Published online: 29 Nov 2024 https://doi.org/10.1080/2331186X.2024.2431885

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.

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.

The Nucleation of Symbiotic Futures

This episode, the third of a four-part series, discusses an extended excerpt (Chapters 16 through 21) from a book titled "THE LAST ECONOMY: A Guide to the Age of Intelligent Economics" by Emad Mostaque, released on August 22, 2025. The author, who wrote the white paper for the Intelligent Internet, outlines the profound civilizational choice presented by the Intelligence Inversion, where human labor is no longer economically necessary, arguing that society will "crystallize" into one of three stable future states. These futures are Digital Feudalism, the default path of corporate monopoly and engineered convenience; The Great Fragmentation, a fear-driven, nationalist cold war fought with algorithms; and Human Symbiosis, a path of conscious design built on partnership and shared abundance. The text advocates for the latter, proposing a Symbiotic Blueprint that includes a Dual Currency System (Foundation Coins for scarce material goods and Culture Credits for abundant digital flow) and a new model of governance called the Symbiotic State, which acts as a "gardener" or steward of collective MIND Capitals (Material, Intelligence, Network, and Diversity). The strategy for achieving this best future is through nucleation, creating small, successful prototypes—the "Florences of the 21st century"—whose demonstrable prosperity will spread the symbiotic model.

References

The Last Economy: A Guide to the Age of Intelligent Economics by Emad Mostaque, pp. 109-149, 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.

This episode, the second of a four-part series, discusses an extended excerpt (Chapters 9 through 15) from a book titled "THE LAST ECONOMY: A Guide to the Age of Intelligent Economics" by Emad Mostaque, released on August 22, 2025. The author, who is the founder of Stability AI, presents a unified theory of economics that reframes the field not as a clash of ideologies but as a study of three fundamental, mathematically necessary flows of value: Gradient Flow (driven by scarcity and leading to Adam Smith’s market equilibrium), Circular Flow (driven by abundance and leading to Karl Marx’s accumulation loops), and Harmonic Flow (driven by structure and reflected in Friedrich Hayek’s spontaneous order). The text argues that historical economic thought was incomplete because it focused on only one of these flows, likening the situation to blind scholars describing an elephant by touching only one part. Furthermore, the material explores the implications of this model for the modern era, asserting that Artificial Intelligence (AI) exponentially amplifies all three flows and creates a "Second Economy" defined by network topology and the central challenge of Alignment, which demands a New Social Contract to ensure human values guide autonomous AI systems. Finally, the text introduces the Dual Engine model to explain change, noting that the fast-moving Market and the slow-evolving Institutions are in a constant co-evolutionary dance, which AI is set to disrupt permanently.

References

The Last Economy: A Guide to the Age of Intelligent Economics by Emad Mostaque, pp. 62-108, 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 reference 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.

This episode, the first of a four-part series, discusses an extended excerpt (Chapters 1 through 8) from a book titled "THE LAST ECONOMY: A Guide to the Age of Intelligent Economics" by Emad Mostaque, released on August 22, 2025. The author, who is the founder of Stability AI, argues that the world is facing an "Intelligence Inversion," the final economic phase transition where Artificial Intelligence (AI) will make human economic relevance obsolete within a "Thousand-Day Window." The source identifies seven "Fatal Lies of a Dying Paradigm," such as the fundamental nature of scarcity and the value of human labor, which are no longer true in an AI-driven world. The text proposes a new economic framework called "Intelligence Theory," asserting that value is the creation of order against entropy, and introduces the "MIND of a Civilization" dashboard, which suggests that civilizational vitality is a multiplication of Material, Intelligence, Network, and Diversity capitals.

References

The Last Economy: A Guide to the Age of Intelligent Economics by Emad Mostaque, pp. 1-61, 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 reference 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.

This episode discusses a comprehensive legal analysis of the proposed Digital Asset Market CLARITY Act of 2025, which aims to fundamentally reform U.S. digital asset regulation. The core of the Act is establishing a function-based regulatory framework that shifts authority from the current ad hoc system to clear statutory standards overseen jointly by the SEC and CFTC. Key features discussed include creating definitions for digital commodities and investment contract assets, establishing objective decentralization thresholds, and mandating strict custody and bankruptcy protections for customer assets. The analysis also covers the Act's phased implementation timelines, its dedicated regime for stablecoins, and its goal of positioning the U.S. competitively against international frameworks like the EU’s MiCA.

References

Oranburg, Seth, The CLARITY Act: Explaining and Analyzing How Congress Will Transform Digital Asset Markets (June 11, 2025). 45 Review of Banking and Financial Law ___ (forthcoming Spring 2026), Available at SSRN: https://ssrn.com/abstract=5288934 or http://dx.doi.org/10.2139/ssrn.5288934

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.

This episode discussed an academic essay that compares two major legislative frameworks—the European Union’s Markets in Crypto-Assets Regulation (MiCAR) and the U.S. Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act)—designed to regulate the growing $250 billion stablecoin market. The authors first identify four critical private law shortcomings in centralized stablecoins, exemplified by issuers Circle and Tether: asymmetrical terms of service, ambiguous customer rights, tenuous redemption systems, and a perilous position for holders in bankruptcy. While market leaders have not adopted straightforward private ordering solutions to remedy these issues, the essay analyzes how both MiCAR and the GENIUS Act attempt to address these deficiencies, finding that MiCAR emphasizes comprehensive conduct obligations and strict liability, whereas the GENIUS Act focuses on operational requirements and unprecedented bankruptcy protections. Ultimately, the success of these laws hinges on their ability to fix these core private law problems, with the GENIUS Act notably granting stablecoin holders super-priority claims in insolvency, which may be overly aggressive.

References

Odinet, Christopher K. and Tosato, Andrea, Regulating Centralized Stablecoins: Comparing MiCAR and the GENIUS Act (August 07, 2025). Notre Dame Law Review Reflection, 2026, Forthcoming, Texas A&M University School of Law Legal Studies Research Paper No. 25-38, SMU Dedman School of Law Legal Studies Research Paper No. 701, Available at SSRN: https://ssrn.com/abstract=5383158 or http://dx.doi.org/10.2139/ssrn.5383158

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.

This episode reviews an extensive systematic literature review titled "A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data," authored by Zynobia Barson and colleagues from the University of Tasmania. This academic work analyzes 81 papers on AI and asset pricing, 53 on big data and asset pricing, and 24 on their combined use, employing both bibliometric and thematic analyses to map the evolution of the field. The central finding is that the integration of Artificial Intelligence (AI) and Big Data is fundamentally reshaping asset pricing by improving predictive accuracy, optimizing financial modeling, and enhancing risk management through the ability to handle complex, high-dimensional data. Specifically, the authors conclude that AI-based models are proving superior to traditional asset pricing frameworks by effectively addressing challenges like the "factor zoo" and capturing non-linear market dynamics. The paper also outlines future research directions, including exploring geographical gaps and addressing ethical considerations related to AI in finance.

References

Barson, Zynobia and Ahadzie, Richard Mawulawoe and Daugaard, Dan and Vespignani, Joaquin, A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data (July 04, 2025). Barson, Zynobia; Ahadzie, Richard Mawulawoe; Daugaard, Daniel; Vespignani, Joaquin (2025). A Systematic Literature Review of Asset Pricing: Insights from AI  and Big Data. University of Tasmania. Preprint. https://hdl.handle.net/102.100.100/706792, Available at SSRN: https://ssrn.com/abstract=5351772 or http://dx.doi.org/10.2139/ssrn.5351772

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.

In this episode we explore the relationship between virtual land returns in the metaverse, specifically from the Decentraland platform, and the returns of physical real estate markets, approximated by equity REIT indices. Using wavelet coherence analysis on data from 2019 to 2023, the study we discuss empirically shows that the correlation between the two asset classes is generally low, suggesting potential diversification benefits for investors. However, this correlation spikes significantly during periods of acute economic turmoil such as the COVID-19 outbreak and interest rate shifts, indicating that virtual land's hedging effects may be limited during crises. Regression analysis identifies the consumer and economic climate, the price of the native cryptocurrency, and investor attention as the primary drivers of this dynamic correlation. Ultimately, the findings suggest that including virtual land can enhance risk-adjusted returns within a traditional asset portfolio, especially commercial real estate portfolios.

References

Leonhard, Heiko and Nagl, Maximilian and Schäfers, Wolfgang, Virtual land in the metaverse? Exploring the dynamic correlation with physical real estate (September 1, 2023). Available at SSRN: https://ssrn.com/abstract=4567859 or http://dx.doi.org/10.2139/ssrn.4567859

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.

This episode investigates the adoption and impact of artificial intelligence (AI) within European venture capital (VC) firms, a topic previously under-researched despite AI's growing presence in finance. Based on survey data, the study we discuss reveals a significant increase in AI adoption since 2022, with screening emerging as its most common application. The research also identifies that VC firms with employees possessing strong ICT backgrounds are more likely to integrate AI. While AI has been shown to reduce due diligence time, its overall long-term benefits on VC operations remain largely inconclusive due to limited data, suggesting a need for more extensive future research.

References

Ronco, Umberto and Barontini, Roberto, Artificial Intelligence in Venture Capital Operations: An Empirical Analysis (February 15, 2025). Sant’Anna School of Advanced Studies, Institute of Management Research Paper Series_ No. 1 Winter 2025, Available at SSRN: https://ssrn.com/abstract=5164480 or http://dx.doi.org/10.2139/ssrn.5164480

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 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.