Technically Legal - A Legal Technology and Innovation Podcast

Emily Logan Stedman, commercial litigator at Husch Blackwell, discusses moving beyond the billable hour not just to modernize legal billing but to also prioritize lawyer well-being. The conversation explores the practical steps required to transition traditional litigation models toward Alternative Fee Arrangements (AFAs). Emily details her methodical approach to utilizing secure legal AI tools to analyze historical time entries, allowing her to offer phased flat fees, strategic counsel subscriptions, and predictable contract review pricing. The discussion also covers strategies for maintaining mental health in demanding legal environments, taking control of chaotic schedules, and reframing the billable hour as a tool for career management rather than a burden.
Things We Talk About in this Episode
Emily's transition from bankruptcy law to general commercial litigation after moving to Wisconsin.

The motivation behind launching The Grace Period podcast and advocating for lawyer well-being in big law.

Strategies for managing the billable hour, shifting daily mindsets, and setting strict boundaries for deep work.

The genesis of applying Alternative Fee Arrangements (AFAs) to unpredictable, complex litigation matters.

Structuring strategic counsel subscriptions and granular phased flat fees to provide cost predictability for corporate clients.

Utilizing AI to analyze historical billing data and build accurate, profitable flat-fee pricing models that secure firm buy-in.

Leveraging secure legal AI agents like Legora and Prompt Composer to map out discrete litigation phases and draft modern engagement letters.

In this episode, Justin Wales, Chief Legal Officer for Crypto.com makes a second visit to the podcast. This time around, he discusses his new book, The AI Legal Handbook, the emerging regulatory landscape, liability frameworks, and practical applications of artificial intelligence. Justin outlines how attorneys and regulators should approach modern AI models, the mechanics behind AI, and the effect of generative AI on legal education. The discussion covers key regulatory principles, the balance between strict liability and negligence frameworks, copyright issues in machine learning training, and the governance challenges of artificial general intelligence (AGI).
Things We Talk About in this Episode
Transitioning from cryptocurrency regulation to developing a unified framework for artificial intelligence law

The importance of legal professionals understanding underlying model mechanics and token prediction to evaluate hallucination risks

Generative AI's disruption of law school assessments and the pedagogical shift from written assignments to oral examinations

The convergence of cryptocurrency rails, agentic AI systems, and decentralized compute infrastructure

Foundational principles for AI regulation, emphasizing proportional oversight, human-centered accountability, and transparency over outright bans

Managing the "black box problem" and balancing algorithm explainability with technical efficiency

Comparative analysis of sector-specific guidelines versus federal mandates and the adaptability of existing rules of professional conduct

Evaluating legal liability standards: applying negligence versus strict liability to autonomous vehicles, healthcare diagnostic tools, and AI chatbots

Intellectual property considerations, fair use, and style mimicry in generative model training data

Preparing legal, economic, and governance frameworks for artificial general intelligence (AGI)

Where to buy The AI Legal Handbook: JustinWales.com or Amazon.
Episode Credits
Editing and Production: Grant Blackstock
Theme Music: Home Base (Instrumental Version) by TA2MI

In this episode April Miller Boise, with stints as Chief Legal Officer at Intel and managing partner at Thompson Hine, shares insights from her career. The conversation explores the shifting dynamics between corporate legal departments and outside counsel and the necessity of embracing legal technology. April also provides advice for junior lawyers on developing long-term expertise and trusted relationships in an AI-driven environment.
Things we Talk About in this Episode
Career progression: From Wall Street M&A partner and law firm managing partner to Chief Legal Officer.

The fundamental differences between driving P&L at a law firm versus serving on an executive leadership team in-house.

Addressing the widening gap between corporate expectations and traditional law firm service delivery models.

Experience navigating global manufacturing, innovation, and supply chain transformations at Eaton and Intel.

Practical approaches to generative and agentic AI adoption in legal departments and outside counsel.

Key attributes for modern legal talent: Intellectual curiosity, agility, and comfort with calculated risks.

Career advice for young lawyers: Balancing AI efficiency tools with the 10,000-hour principle of skill-building.

Why historical legal tech struggled to scale and how current enterprise AI investments are fundamentally altering the legal landscape.

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.

This episode features a discussion between host Chad Main and Rebecca Anderson, Head of Legal EMEA, Associate General Counsel at Microsoft, regarding the integration of artificial intelligence in corporate legal departments. The conversation covers Rebecca's career progression from a law firm associate to an international in-house counsel role. The core of the discussion examines Microsoft's internal AI deployment strategies, including the shift from personal productivity to intent engineering. Additionally, the episode addresses changing expectations for outside counsel regarding AI adoption and outlines the necessary skills for future legal professionals.
Episode Highlights
AI Deployment Phases: AI integration in legal departments progresses through three stages: personal productivity (prompt engineering), context engineering (curating institutional knowledge), and intent engineering (autonomous agents operating with human guardrails).

Scaling Legal Support: Corporate legal teams can manage high volumes of client requests by utilizing AI-driven triage systems overseen by paralegals, which allows senior lawyers to focus on complex, high-value legal work.

Expectations for Outside Counsel: Corporate clients expect law firms to implement AI strategies to improve efficiency, which will likely shift the industry's economic focus from billable hours to value-based billing.

Future Legal Skills: While AI literacy and data curation are critical, foundational skills such as human judgment, emotional intelligence, and strategic business context remain essential for legal professionals.

Things we talk about:
Copilot for Legal