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Technically Legal - A Legal Technology and Innovation PodcastAuthor: Percipient - Chad Main
Technically Legal is a legal tech podcast exploring how technology is transforming the legal landscape. Each episode features insightful interviews with legal innovators, tech pioneers, and forward-thinking educators who are leading this change. Our guests share their experiences and insights on how technology is reshaping legal operations, revolutionizing law firm practice, and driving the growth of innovative legal tech companies. We also explore the broader implication of technology on everyone involved in the legal system, from practitioners to clients. The podcast is hosted by Chad Main, an attorney and founder of Percipient, a tech-enabled legal services provider. Chad launched Percipient on the belief that when technology is leveraged correctly, it makes legal teams more effective. Technically Legal Podcast is an ABA Web 100 Best Law Podcasts Honoree. Language: en Genres: News, Tech News, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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When Machines Have the Answers, Build Better Humans With AI Augmentation Not Automation (Vivienne Ming, Scientist, Professor, Author)
Thursday, 23 July, 2026
In this episode, theoretical neuroscientist Vivienne Ming discusses her new book, Robot Proof - When Machines Have All the Answers, Build Better People. The conversation examines the intersection of artificial intelligence and knowledge work, exploring how professionals can adapt to an increasingly automated economy. The discussion contrasts the limitations of basic AI task automation with the advantages of human-AI collaboration—the "cyborg" model—for solving complex, ill-posed problems. Ming highlights her research on forecasting market outcomes, the critical role of endogenous motivation, and how the legal industry and other elite professions must rethink entry-level training. Key Topics Discussed Theoretical Neuroscience and Early AI: Ming's background and the evolution of machine learning models from early academic research to modern agentic AI. The Polymarket Experiment: An analysis comparing the forecasting accuracy of standalone AI, unassisted humans, and human-AI collaborators, revealing the superiority of deep human-machine integration. Automation vs. Augmentation: The pitfalls of the traditional "human-in-the-loop" model and why replacing menial tasks often neglects essential human problem-solving skills. Well-Posed vs. Ill-Posed Problems: Identifying the specific areas where AI excels (algorithmic, factual answers) and where human intelligence remains superior (navigating uncertainty and undefined parameters). Labor Disruption and Economic Shifts: Examining historical technological revolutions, the Jevons paradox, and the future demand for specific, highly adaptable human skill sets. Endogenous Motivation: How internal drivers like curiosity, resilience, and perspective-taking predict professional success more accurately than standard extrinsic incentives. Practical AI Strategies: Actionable methods for professionals to refine their skills, including using AI as a critical "nemesis" to challenge assumptions and encourage deep, effortful processing. The Future of Elite Professions: The macro-level challenges facing organizations in developing junior talent—such as associate attorneys—when the entry-level tasks traditionally used for training are automated. Things We Talk About in this Episode Socos: Vivienne Ming's philanthropy and research newsletter (socos.org). Thinking, Fast and Slow: Authored by Daniel Kahneman. Anthropic Research: A study published in Science detailing the productivity of AI-assisted programmers. BCG AI Study: Research analyzing AI integration and performance among management consultants. Raj Chetty: Economic research regarding peer role modeling, education, and socioeconomic mobility.
