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With all of the investment in and implementation of enterprise AI, one would think that AI should be transformative for so many aspects of work. AI will drive higher revenue, increase efficiency, and improve productivity while transforming the toil of labor into a more enjoyable existence for knowledge workers. However, as has become common knowledge at this point, despite the seeming panacea that AI transformation is touted to be, the majority of businesses continue to not see a return on their AI investments. ...

Note: diagrams for the economic model and the structural workflow for the Super App can be found in the Appendix. As covered in part 1 of this series, the jagged frontier for live sports exists for the following reasons: Economics: advertising only/advertising mostly or subscription only/subscription mostly business models are likely not the path to profitable longevity, Discovery: customer discovery remains a challenge, even when leagues are available on the largest TV platforms out there, including Roku and Samsung, Fragmentation: a customer pain point for every league, from the NFL and NBA down to college sports and below, Customer retention: for subscription-based services, churn is an ever-present challenge, that arguably becomes more and more challenging as the proliferation of customer options continues, and Engagement share: Competition for engagement and time spent, when you’re competing against behemoths like Netflix, YouTube, and Meta, has never been more challenging and will only get more competitive. Recent Industry Announcements Versant Sports Portfolio: Versant, the Comcast cable network spinoff that began trading independently in January 2026, announced in November 2025 that “USA Sports” would be the unified brand and division name for its sports portfolio across USA Network, Golf Channel, and CNBC. The portfolio includes NASCAR, PGA Tour, Premier League, WWE, WNBA, USGA, LPGA Tour, and League One Volleyball (LOVB), totaling more than 10,000 hours of sports programming in 2026, approximately 1,000 of which are dedicated to women’s sports. ...

One of my favorite generative AI/AGI writers is Professor Ethan Mollick, whose latest book is “Co-Intelligence: Living and Working with AI” and who is also the author of the substack One Useful Thing. As someone with early access to the latest frontier models from labs including Open AI and Anthropic, he’s one of my first reads when the latest models are released so that I can quickly be up to speed on the advancements and new capabilities. And while Professor Mollick finds AI remarkable and shares compelling use cases for the technology, he also talks about “the jagged frontier”. The jagged frontier is a concept that describes the uneven and unpredictable boundaries of AI capability. In essence, AI can perform complex, expert-level tasks, such as math and coding, with remarkable efficiency and efficacy, while failing at seemingly simpler tasks that lie outside the capability reach of the models. In his GPT-5.5 post, he mentions that “every few months a new model arrives…[and] the size of the leaps grows each new release cycle. The jagged frontier is still there. It is just much further out than it used to be”. ...

Evaluating home purchase scenarios and associated investment opportunity costs. Photo by Breno Assis on Unsplash. Part 1 of Home Purchase Scenario Model Analyses. Introduction. For those who have previously considered or are considering the purchase of a home, then this post is meant for you to help customize your scenarios and vet your available options. I hope that you find the included financial model, available here, informative as you evaluate potential home purchase scenarios. This post and model should help you understand the full costs associated with home ownership and the inherent opportunity costs of not investing downpayment and ongoing maintenance costs in other assets, e.g., passive market ETFs. ...

Extending Stock Portfolio Analyses and Dash by Plotly to track Robo Advisor-like Portfolios. Photo by Aditya Vyas on Unsplash. Part 3 of Leveraging Python for Stock Portfolio Analyses. Introduction. This post is the third installment in my series on leveraging Python for finance, specifically stock portfolio analyses. In part 1, I reviewed a Jupyter notebook with all of the code needed to extract financial time series data from the Yahoo Finance API and create a rich dataframe for analyzing portfolio performance across individual tickers. The code also included a review of some key portfolio metrics with several visualizations created using the Plotly library. In part 2, I extended Part 1’s analyses and visualizations by providing the code needed to take the data sets generated and visualize them in a Dash by Plotly (Dash) web app. ...