In advance of this year's RelFest in Chicago, Aron Ahmadia, Vice President of Applied Science at Relativity, joins the podcast to discuss AI evaluation, AI benchmarking and the transition from traditional Technology Assisted Review (TAR) to generative AI in legal document review and eDiscovery. The conversation covers the daily operations of a global AI team, the concept of "evaluation as a product," and the methodology behind testing large language models (LLMs) for legal tasks. Aron also discusses a recent study that compared the accuracy and efficiency of TAR and human document review against Relativity's generative AI tool, aiR for Review.

Aron talks about:

• A day in the life of the VP of Applied Science at Relativity.

• Navigating the rapid pace of AI and predicting what remains constant in legal tech.

• Aron's background: From early computer networking in a law firm to data science at DARPA and Capital One.

• Defining "evaluation as a product" and the importance of maintaining private benchmarks for LLMs.

• The trade-offs between human evaluations and automated AI evaluations.

• TAR (Predictive Coding) versus Generative AI: How prompt-based reasoning differs from label-based classification.

Editing and Production: Grant Blackstock

Theme Music: Home Base (Instrumental Version) by TA2MI

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.

• 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).

• 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.

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.

• 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.

• 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.

Copilot for Legal

In this episode of Technically Legal, veteran patent lawyer Laurence Brown and Partner at the UK-based intellectual property firm EIP discusses the evolving concept of the "AI-native" law firm and how EIP is actively navigating this transition through their hybrid intelligence service, Visser AI.

Laurence shares insights from his 25-year career, discussing how EIP uniquely integrates litigators and patent attorneys to provide comprehensive IP services. The core of the conversation centers on Visser AI, named after the renowned European patent attorney Derk Visser, which leverages specialist AI tools to accelerate patent drafting and prosecution. The discussion covers the shifting economics of legal billing, the transition from billable hours to fixed fees, and how AI is actively redesigning traditional, linear patent workflows. Laurence also believes AI not only delivers faster, more cost-effective results for clients without compromising quality, but also makes the day-to-day work of attorneys more engaging.

Defining AI-Native: A true AI-native firm goes beyond simply prompting a frontier model; it involves fundamentally redesigning workflows to fully leverage AI technology.

The Economics of AI: The efficiency gained through AI allows firms to offer competitive fixed fees, providing cost reductions for clients while maintaining law firm profitability.

Redesigning Workflows: AI tools enable parallel workflows in patent drafting, such as senior attorneys structuring claims while juniors draft the description, breaking the traditional linear process.

Specialist vs. Generalist AI: When it comes to complex patent law, purpose-built platforms like Patently significantly outperform general-purpose LLMs in quality and contextual understanding.

European AI Regulation: The European patent industry's conservative nature has led to risk-centered AI guidelines, but the conversation is shifting toward the potential disservice of not utilizing these tools to assist clients.

Tyler Foreman, the Vice President of AI at Rocket Lawyer, joins the show to discuss the intersection of artificial intelligence and the legal industry. Foreman shares his untraditional legal tech career path, spanning engineering at Intel, drone data analytics, and ultimately making the move to legal via contract lifecycle management (CLM) at DocuSign, before diving into his current work at Rocket Lawyer helping to provide legal resources for small-to-medium businesses and individuals.

The conversation focuses on how modern generative AI and Large Language Models (LLMs) act as a legal operating system to simplify contract reviews, document drafting, and client intake, while maintaining essential connections to human attorneys.

We catch up with legal tech entrepreneur Nathan Wenzel to discuss his journey from founding and exiting SimpleLegal to launching his newest venture, LegalOperator.ai (formerly Lexiomatic). Nathan shares insights on the evolution of private equity (PE) structures, the shifting economics of the enterprise software market, and how artificial intelligence is disrupting traditional software development and corporate legal operations.

• The Reality of Private Equity: Nathan breaks down the differences between growth PE, transition PE, and dividend-focused PE, highlighting his experience with operational scaling post-acquisition.

• The Death of Overpriced SaaS: With AI lowering the barrier to entry for building software, traditional $100,000+ enterprise software pricing models are facing immense pressure to become lean, affordable, and transparent.

• The "Vibe Coding" Phenomenon: While "vibe coding" (loosely describing an app to an AI) works for quick prototypes, building enterprise-grade software still requires meticulous specifications, security infrastructure (like SOC 2 compliance), and robust edge-case testing.

Jamie Tso and Ray Sun, the founders of LegalQuants, discuss a fundamental shift in the legal profession: the transition from legal engineering to "legal quant" work. The conversation explores how technically fluent lawyers are using vibe coding, frontier AI models, and first-principles thinking to move beyond mere efficiency and toward the complete redesign of legal services.

Jamie and Ray share their backgrounds in Big Law and their journeys into building custom legal tech tools. They delve into the origins of the LegalQuants community—an exclusive, invitation-only network of "builders"—and discuss the future of the billable hour in an era where AI can automate routine intellectual labor.

• Jamie and Ray's origin stories: From annotating mutual fund prospectuses to early experiments with machine learning and TensorFlow.

• Ray's background as one of the world's first Legal Engineers and the creation of his Global AI Regulation Tracker.

• Defining the Legal Quant: How they differ from traditional Legal Engineers by seeking "alpha" and redesigning legal workflows from first principles.

• The growth of the LegalQuants community: From a small WhatsApp group to a global network of elite lawyer-builders.

• The "Unicorn Talent" gap: Why the next generation of elite legal work is defined by the operator, not the tool.

• The death of the friction-based pricing model and the future of value-based billing.

• Stress-testing Claude and Anthropic's Legal Plugin: Why the "ceiling" of AI utility is set by the lawyer's ability to design custom skills.

• LegalQuants Substack: legalquants.substack.com – Weekly digests and deep dives into legal AI strategy.

• Global AI Regulation Tracker: techieray.com – Ray Sun's interactive map of worldwide AI laws and policies.