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.

This episode discusses a whitepaper that introduces the Intelligent Internet (II), a novel protocol designed to decentralize AI development and empower human agency. It proposes a "Third Path" by creating a "Bitcoin for the Intelligence Age," where Foundation Coins (FC) are minted only through Proof-of-Benefit (PoB), verifying societal good. The architecture includes three layers (Foundation, Culture, Personal) and is governed by principles such as Openness, Verifiable Public Benefit, and Human + Agent Dignity. The system aims to provide Universal AI (UAI) access to every individual via a sovereign II-Agent, with all knowledge anchored on auditable, open-licensed datasets through Anchor-Sets. The Intelligent Internet outlines a robust economic design, security model, and progressive governance structure, ensuring a transparent, auditable, and resilient public utility for the Intelligence Age.   References Intelligent Internet Whitepaper July 24, 2025 by Emad Mostaque https://webstatics.ii.inc/Intelligent-Internet-Whitepaper.pdf   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.

This episode explores and academic paper on the replication of hedge fund strategies using publicly available data and machine learning techniques, specifically autoencoders for dimension reduction and Generative Adversarial Networks (GANs) for synthesizing additional data. The author aims to demonstrate that such replicated portfolios can outperform traditional hedge fund returns after accounting for fees and transaction costs, thereby questioning the efficiency of current hedge fund performance. The research systematically evaluates different replication methodologies ultimately highlighting the superior performance and lower turnover achieved by the autoencoder-based strategies, especially when augmented with synthetically generated data. It presents a new way to benchmark hedge fund performance and potentially offers investors a more efficient alternative to direct hedge fund investment. References Shen, Kaiwen, Do You Really Need to Pay 2/20? Hedge Fund Strategy Replication via Machine Learning (October 10, 2022). Available at SSRN: https://ssrn.com/abstract=4243861 or http://dx.doi.org/10.2139/ssrn.4243861 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.

This episode explores how deposit-taking institutions, exemplified by Silicon Valley Bank (SVB), are transforming into "synthetic hedge funds". It examines SVB's hybrid business model, which combined on-balance-sheet "private equity-style banking" with off-balance-sheet "hedge fund-like trading strategies". The analysis highlights how SVB's reliance on "factor-based models" and "premature hedging exits" exposed it to significant interest rate and liquidity risks, ultimately leading to its collapse. The paper discussed argues that traditional regulatory frameworks are ill-equipped to address the complexities and systemic risks introduced by banks engaging in such "synthetic financial strategies," advocating for a reassessment of oversight to ensure financial stability in this evolving landscape. Reference Saeidinezhad, Elham, Banks as Synthetic Hedge Funds  (December 02, 2024). Available at SSRN: https://ssrn.com/abstract=5041554 or http://dx.doi.org/10.2139/ssrn.5041554 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.

In this episode, we review two academic papers investigating various aspects of hedge fund performance and investment strategies. One relatively new study primarily examines how macroeconomic factors and the inclusion of hedge fund strategies impact portfolio performance for risk-averse investors, particularly focusing on out-of-sample predictability and risk-adjusted returns. It highlights that integrating hedge funds and considering macro-driven patterns can significantly enhance economic value, even though traditional measures like Sharpe ratios may not always reflect this fully due to higher-order moments like skewness and kurtosis. The other paper provides more background with a comprehensive survey of literature on hedge fund performance up until 2004, detailing various biases in hedge fund databases (e.g., survivorship, instant history, selection) and discussing different performance measurement methodologies, including traditional and adjusted Sharpe ratios, and multi-factor models that account for their unique non-linear exposures and time-varying risk profiles. References Magnani, Monia, Does Macroeconomic Predictability Enhance the Economic Value of Hedge Funds to Risk-Averse Investors?  (October 15, 2024). BAFFI Centre Research Paper No. 232, Available at SSRN: https://ssrn.com/abstract=4988114 or http://dx.doi.org/10.2139/ssrn.4988114 Géhin, Walter, A Survey of the Literature on Hedge Fund Performance (October 2004). Available at SSRN: https://ssrn.com/abstract=626441 or http://dx.doi.org/10.2139/ssrn.626441 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.  

This episode examines different facets of the housing market and its interconnectedness with broader economic factors. It reviews three recent SSRN papers. One source explores how contractionary monetary policy can lead to higher homeowners’ insurance prices, particularly for financially constrained insurers with interest-rate-sensitive assets, subsequently impacting home prices and mortgage applications. The other source investigates the effects of affordable housing developments (specifically, those financed by Low-Income Housing Tax Credits) on local rental markets, finding no evidence of increased rents in nearby market-rate apartments, and in some cases, even downward pressure on rents. Both highlight the complex interplay of financial mechanisms, policy interventions, and their observable effects on residential real estate. References Anguche, Scovia, Homelessness in the United States and its effects on the Economy (November 08, 2024). Available at SSRN: https://ssrn.com/abstract=5092765 or http://dx.doi.org/10.2139/ssrn.5092765 Damast, Dominik and Kubitza, Christian and Sørensen, Jakob Ahm, Homeowners Insurance and the Transmission of Monetary Policy (January 31, 2025). Available at SSRN: https://ssrn.com/abstract=5119139 or http://dx.doi.org/10.2139/ssrn.5119139 An, Brian and Fitzpatrick, Caleb and Jakabovics, Andrew and Orlando, Anthony W. and Rodnyansky, Seva and Voith, Richard and Zielenbach, Sean, The Effects of Affordable Housing Development on Local Rental Markets (April 03, 2025). Available at SSRN: https://ssrn.com/abstract=5204026 or http://dx.doi.org/10.2139/ssrn.5204026 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.

This is the second Expanding Frontiers episode devoted to quantum computing for finance. It  explores the current (2024) state and future potential of Quantum Machine Learning (QML), specifically focusing on its applications within the financial services industry. We discuss various QML algorithms including Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks, and also touch upon quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks. The paper discussed identifies key financial applications for QML, such as risk management, credit scoring, fraud detection, and stock price prediction, while also outlining the promises and limitations of integrating QML into real-world financial operations. The review aims to serve as a practical guide for financial professionals and data scientists interested in understanding QML's relevance to their field.   References  A Brief Review of Quantum Machine Learning for Financial Services (July 2024) Mina Doosti, Petros Wallden, Conor Brian Hamill, Robert Hankache, Oliver Thomson Brown, Chris Heunen https://doi.org/10.48550/arXiv.2407.12618   Resources Medium article: Are You Ready to Learn About Quantum Computing? 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.  

This episode provides an overview of multimodal Financial Foundation Models (MFFMs), exploring their progress, potential applications, and associated challenges. It emphasizes the ubiquitous nature of multimodal financial data—including text, audio, images, and tabular information—in various financial applications like search, robo-advising, and trading. The paper review also addresses the development lifecycle of MFFMs, from pre-training to fine-tuning and alignment, while highlighting the need for robust benchmarks. Crucially, it discusses significant challenges such as data privacy, the risk of misinformation and hallucination, and the need for ethical AI readiness and governance within the financial sector.   References Liu, Xiao-Yang and Cao, Yupeng and Deng, Li, Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges (May 31, 2025). Available at SSRN: https://ssrn.com/abstract=5277657 or http://dx.doi.org/10.2139/ssrn.5277657   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 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 how smart finance—like social impact bonds and pay-for-success models—can help end homelessness. I share insights from my work with the Coalition to End Homelessness in South Florida and recent policy shifts nationwide, including the controversial Grants Pass ruling. We’ll look at data, dignity, and the human stories behind the numbers. This is about funding outcomes, not overhead. Reference Wilkens, K. (May 29, 2025.), Funding the Future: How Smart Finance Can End Homelessness  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.

This episode reviews the book, Quantum Computing for Finance by Oswaldo Zapata, outlining the foundational concepts of classical and quantum computing, including topics like Boolean logic, qubits, quantum gates, and error correction. It explores how quantum computing can potentially enhance classical financial methods such as portfolio optimization, Monte Carlo simulations, and machine learning algorithms used in areas like credit risk assessment and fraud detection. The discussion includes how the book surveys the current quantum computing landscape, detailing different hardware technologies, key companies and startups, and offering advice on how financial institutions can prepare for the integration of quantum technology, emphasizing the importance of talent development and hybrid approaches. References Zapata, O. Quantum Computing for Finance The book is available at: https://www.scribd.com/document/860542791/Quantum-Computing-for-Finance-Oswaldo-Zapata-PhD 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.

In this episode we discuss the increasing integration of artificial intelligence (AI) and machine learning (ML) into asset management, focusing on their application in portfolio management, risk assessment, and trading strategies. AI, particularly ML, allows models to process vast, complex datasets and identify patterns beyond traditional methods, promising enhanced efficiency and predictive accuracy. However, these technologies introduce new challenges, including data quality issues, the risk of overfitting, and the potential for bias in models, necessitating robust governance frameworks and regulatory oversight. One source specifically examines the use of transformer models, similar to those in large language models, to improve asset pricing by enabling sophisticated cross-asset information sharing. References Chakrabarti, Fabozzi, Narain, and Sood (2025) Ethical AI in Asset Management: Frameworks for Transparency, Compliance and Trust, Journal of Financial Data Science, Winter 2025, pp. 18–35. https://www.DOI.org/10.3905/jfds.2025.7.1.018 Kelly, Bryan T. and Kuznetsov, Boris and Malamud, Semyon and Xu, Teng Andrea, Artificial Intelligence Asset Pricing Models (January 2025). NBER Working Paper No. w33351, Available at SSRN: https://ssrn.com/abstract=5103546   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.  

This podcast episode discusses state and local AI regulations impact investor risk, finding that such laws can decrease risk by incentivizing firms to adopt better AI governance and reduce misconduct. It also examines how financial analysts utilize AI and its impact on their behavior. Finally, we discuss a third academic research paper on AI in risk management and forecasting from a global perspective. It is based on these three references: Ciconte, Will and Rozario, Andrea and Urcan, Oktay, Artificial Intelligence Regulation and Investor Risk: Evidence from State and Local Artificial Intelligence Mandates (March 19, 2025). Available at SSRN: https://ssrn.com/abstract=5023685 or http://dx.doi.org/10.2139/ssrn.5023685   Shanthikumar, Devin M. and Yoo, Il Sun, Artificial Intelligence and Analyst Productivity (November 30, 2024). Available at SSRN: https://ssrn.com/abstract=5040339 or http://dx.doi.org/10.2139/ssrn.5040339   Vyas, Anshul, Revolutionizing Risk: The Role of Artificial Intelligence in Financial Risk Management, Forecasting, and Global Implementation (April 21, 2025). Available at SSRN: https://ssrn.com/abstract=5224657 or http://dx.doi.org/10.2139/ssrn.5224657       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.

This episode explores the evolving landscape of digital assets and their implications for institutional investors, highlighting the necessary learning process, expert engagement, and meticulous due diligence required due to unique risks and opportunities. It examines various valuation techniques for these intangible assets, acknowledging their speculative nature and rapid changes. A significant portion discusses tokenization, detailing its potential to enhance liquidity and access in various alternative investments like hedge funds, private equity, and real estate, while also outlining associated risks and constraints. Finally, the text touches upon the concept of financial democratization through fintech and digital assets, presenting both utopian possibilities and dystopian warnings, and emphasizing the need to consider underlying power dynamics beyond mere access to financial services. Let me know if you are interested in any of the references mentioned.   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 an appendix in my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), 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.  

This episode provides a comprehensive overview of private credit and distressed debt markets. It outlines different types of private credit, including leveraged loans, direct lending, mezzanine debt, and distressed debt, detailing their characteristics and the vehicles used for investment. The text explores fixed income analysis, emphasizing the distinctions between bonds and loans regarding liquidity, risk, and interest rates, and explains the significance of credit risk assessment and the bankruptcy process in these markets. Furthermore, it examines capital structure, recovery rates, and various investment strategies within the distressed debt sector. We also discuss the implications of regulatory changes and the growth of alternative lending sources following the 2008 financial crisis.    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 a chapter from my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), 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.   By listening, you acknowledge that the information provided is subject to change and should not be solely relied upon for decision-making.