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In this episode of The Geek in Review, Greg Lambert and Marlene Gebauer welcome back ⁠Joel Hron⁠, Chief Technology Officer at ⁠Thomson Reuters⁠, for a timely conversation about the shifting relationship among foundation models, legal content providers, legal tech platforms, and the lawyers trying to make sense of the mess. Recent moves by Anthropic, including Claude’s legal practice area tools and MCP connections into legal platforms, raise a larger question for the market. Is a model provider still sitting behind the scenes, or is it starting to become a legal work environment of its own?

Hron explains Thomson Reuters’ commitment to what it calls ⁠fiduciary-grade AI⁠, a standard built around trust, verification, transparency, and accountability. For TR, legal AI needs more than a fast answer. It needs systems lawyers trust enough to stand behind. Hron points to Westlaw, Practical Law, KeyCite validity signals, citation ledgers, and verification tools as core ingredients in building AI systems suited for high-stakes professional work. In his view, almost right is not good enough when clients, courts, regulators, and professional obligations sit on the other side of the output.

The conversation turns to how CoCounsel and Westlaw Deep Research use legal content across far more than traditional research tasks. Hron explains that when AI systems gain access to trusted legal content and verification tools, they begin researching throughout the workflow, even while revising contract language or analyzing provisions. He also describes Litigation Document Analyzer, internally nicknamed the BS Detector, a tool designed to review claims in a document and map them to supporting authority, weak support, or no support at all. For lawyers who spend as much time verifying AI output as generating it, tools like these aim to move verification from a manual scavenger hunt into a structured process.

Greg and Marlene also press Hron on Anthropic’s legal plugins, MCP, and the idea of headless legal technology. Hron argues that MCP changes access, not advantage. In his view, the application layer is shifting, but the real competitive value sits in trusted content, expert systems, governance, and domain-specific intelligence. CoCounsel’s user interface represents one expression of TR’s legal agent capabilities, while MCP opens other ways for those capabilities to appear inside broader work environments. Some work will still need a purpose-built legal interface; other work might happen through email, Word, Claude, or another agentic workflow with little visible interface at all.

The episode closes with a larger discussion about what happens when AI starts performing more of the work itself. Hron shares TR’s internal engineering OKR, where more than 50 percent of pull requests should be written by AI, and explains why 51 percent serves as a useful mental model. Once AI performs a controlling share of the work, the human role shifts from doing the task to governing the system. For legal professionals, the same transition is coming. The key question is no longer only whether AI produces useful work. It is whether lawyers have built the systems, context, safeguards, and verification layers needed to trust the work, defend the work, and remain accountable for the work.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Legal AI, Trust, and Agents: Joel Hron on Thomson Reuters, Anthropic, and the Future of CoCounsel

This week on The Geek in Review, we talk with Abdi Shayesteh, CEO of AltaClaro, and Jeanine Conley Daves, Littler’s New York office managing shareholder, about a different question in the legal AI conversation. Instead of asking whether AI will write the brief, summarize the contract, or replace the junior associate, they focus on whether AI might help lawyers learn how to practice law. Their recent work around AltaClaro’s DepoSim points toward a model of legal training built less on passive observation and more on structured repetition, feedback, and skill development.

Shayesteh traces the origin of AltaClaro back to his own early years at King & Spalding, where he benefited from proximity to a mentor willing to explain the work. That experience also showed him the unevenness of the old apprenticeship model. Access to assignments, feedback, and sponsorship often depended on luck, relationships, and office geography. For Shayesteh, the idea of a “flight simulator for lawyers” grew out of the realization that pilots, athletes, and musicians all practice in structured environments before performance, while lawyers too often learn in front of clients, courts, and opposing counsel.

DepoSim applies this flight simulator concept to one of litigation’s highest-pressure skills: taking and defending depositions. The platform gives attorneys a simulated witness, opposing counsel, court reporter, and feedback system, with options to vary the difficulty and personalities involved. Conley Daves explains why this kind of realism matters. In a real deposition, a lawyer might face an evasive witness, a hostile witness, an aggressive opposing counsel, or a combination of all three. The simulator lets lawyers practice those moments repeatedly, receive targeted feedback, and return to specific skills such as exhibit handling, follow-up questions, or managing objections.

The conversation also connects AI training to equity in professional development. Conley Daves notes that access to high-quality assignments and sponsorship has not always been distributed evenly across firms. A standardized, rubric-based feedback system gives more lawyers a chance to build core skills without waiting to be selected by the right partner or assigned to the right matter. Shayesteh adds that firms seeing the strongest results are not treating training as an after-hours side quest. They are creating protected time for deliberate practice, pairing AI feedback with human mentorship, and using simulation as a bridge rather than a substitute for coaching.

Looking ahead, Shayesteh and Conley Daves see simulation moving well beyond depositions. Oral argument, cross-examination, meet-and-confer sessions, negotiations, client interviews, and even Supreme Court preparation all fit within this training model. The larger shift is not automation for its own sake. It is the use of AI to help lawyers build judgment before the stakes are real. For law firms, that means better preparation, more consistent training, stronger associate development, and a clearer path toward delivering value to clients. For the profession, it suggests a future where competence is practiced deliberately, measured thoughtfully, and taught more fairly.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading The Flight Simulator for Lawyers: Abdi Shayesteh and Jeanine Conley Daves on AI, Deliberate Practice, and the Future of Legal Training

This week on The Geek in Review, we talk with Alex Su and Andy Chagui of Latitude about the shifting economics of law firm talent, the rise of flexible legal staffing, and the pressure AI is placing on traditional leverage models. Su, known across legal circles for his sharp commentary and creative legal industry videos, brings his background as a former Sullivan & Cromwell litigator and federal clerk to his current work leading revenue strategy at Latitude. Chagui adds the perspective of a former Carlton Fields shareholder who spent 15 years handling high-stakes federal litigation before moving into the new law space. Together, they offer a practical view of where law firm staffing is headed as clients, firms, and legal departments all face rising expectations around speed, value, and technology adoption.

Latitude’s model centers on high-end, flexible legal talent, experienced attorneys with Big Law or in-house backgrounds who step into law firms and corporate legal departments for specific engagements. Chagui explains that these lawyers often support overflow work, leave coverage, secondment requests, internal projects, and interim needs across practices ranging from litigation to corporate, labor, and employment. Su adds that staffing itself is not new, yet Latitude focuses on a segment of talent that traditional hiring models often miss, experienced attorneys with strong credentials who prefer engagement-based work over the standard full-time track.

The conversation turns quickly to why this model is gaining traction now. Remote work, post-COVID hiring shifts, and the growing acceptance of distributed teams have made it easier for firms to bring in experienced attorneys without requiring long-term headcount commitments. Chagui notes that many Latitude attorneys have 10 or more years of experience, meaning they often need less supervision than junior lawyers and move quickly into productive work. This matters as firms face inconsistent demand, intense competition for talent, and hesitation around layoffs, which in law firms often signal weakness rather than discipline.

AI adds another layer to the staffing problem. Firms have invested in tools such as Harvey, CoCounsel, and other specialized platforms, yet many knowledge management and innovation teams lack enough subject matter experts to train users, review outputs, build use cases, and handle quality control. Chagui describes Latitude lawyers helping firms train internal AI tools, review AI-generated work, and support practice-specific rollout efforts. Su points out that while some firms offer associates credit for AI training or innovation work, associates under billable hour pressure often choose client work first. Flexible talent gives firms another way to support AI adoption without asking already-stretched associates to carry the full load.

Su also frames flexible talent as a new form of leverage. Clients still trust senior partners and often accept premium rates for high-value judgment, but they are increasingly skeptical of paying top-tier rates for junior-level work. In that middle layer of legal work, AI, technology, and experienced flexible attorneys give firms more options. Su calls this “outsourced leverage,” a way to support the partner-client relationship while rethinking who performs the work underneath. The discussion also highlights a career-path shift for attorneys who prefer specialized, project-based work, especially in areas like knowledge management, AI implementation, and innovation support.

Looking ahead, both guests see uncertainty as the defining feature of the next phase of legal services. Chagui expects the traditional model to keep changing as firms and legal departments seek more flexible options. Su predicts continued upheaval around staffing, AI capabilities, and outside counsel relationships, especially as foundational AI models move further into in-house legal workflows such as NDA review, contract review, and eventually parts of diligence. Yet Su also offers a reminder for law firm leaders: premium legal judgment still has value. The rates for top partners are unlikely to fall simply because AI improves. The pressure will land instead on how firms structure the work beneath them.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Alex Su and Andy Chagui on Flexible Legal Talent, AI Pressure, and the Future of Law Firm Leverage

This week on The Geek in Review, we talk with Lennie Nuara, co-founder of Flatiron Law Group, about what it means to build a talent-first, AI-powered legal practice. Nuara brings a rare mix of lawyer, technologist, operator, and systems thinker to the conversation, drawing from decades of experience using technology to improve legal work, from early portable computers and databases to today’s generative AI tools.

Nuara explains why he resists the phrase “AI-first” in legal practice. For him, legal work begins with talent, judgment, and expertise. AI enters as a force multiplier, not the driver. At Flatiron, the firm’s model was already built around flat fees, lean staffing, process discipline, and structured data before generative AI entered the picture. AI now adds more horsepower to a system already designed to reduce waste, repeat touches, and unclear workflows.

Much of the discussion focuses on M&A due diligence, where Flatiron rethinks the deal life cycle from intake through closing. Instead of throwing documents into a massive repository and hoping AI sorts it out, Nuara describes breaking work into smaller pieces: diligence questions, responses, documents, clauses, topics, closing checklists, and reports. That structure lets lawyers use AI for deduplication, extraction, clause comparison, first-pass drafting, and issue spotting while keeping human judgment between higher-risk steps.

Nuara also warns against getting seduced by polished AI output. He describes generative AI as persuasive, fluent, and sometimes dangerously average. The bigger risk, in his view, is less hallucination and more “model monoculture,” where legal drafting drifts toward sameness because models train from overlapping bodies of public material. In complex private transactions, average language is often the wrong answer. Lawyers still need to understand leverage, client priorities, risk allocation, and where to push beyond market terms.

The episode closes with a look at pricing, training, and the future structure of law firms. Nuara argues that AI will pressure the billable hour, change junior lawyer training, and force firms to rethink the traditional pyramid. He also raises a practical concern from the early Westlaw and Lexis days: the cost of the tool matters. Flatiron tracks AI usage down to the clause level, treating tokens as part of matter economics. For legal professionals watching AI reshape transactions, this conversation offers a grounded reminder: better tools matter, but better process and better judgment still decide the outcome.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Flatiron Law Group’s Lennie Nuara on Talent-First AI, M&A Workflows, and the Future of Legal Practice

This week on The Geek in Review, we talk with Andrew Thompson, CTO of Orbital, about why legal AI built for a specific practice area has a strong claim in a market crowded by general-purpose models. Thompson explains how Orbital focuses on real estate law, using AI, spatial intelligence, and legal workflow design to support transactions involving property portfolios, title review, survey analysis, and complex documentation. With more than 200,000 property transactions processed and a major $60 million, Series B investment fueling its U.S. expansion, Orbital sits at the center of the debate over whether the future of legal AI belongs to broad model platforms or tools built for the messy details of actual legal work.

Thompson’s path into legal technology brings a practical operator’s mindset to the conversation. Before Orbital, he worked across software, fintech, proptech, and real estate marketplaces, where speed, accuracy, and operational friction shaped business outcomes. That background informs his view that successful legal AI starts with the work itself rather than the model alone. For Orbital, the key is teaching AI to think like a real estate lawyer at the right level of abstraction, then pairing the model with domain-specific tools, data, and workflows.

The conversation gets especially interesting when Thompson walks through Orbital’s use of spatial intelligence. Real estate law often turns written legal descriptions, old maps, title documents, surveys, and boundaries into high-stakes decisions about physical land. Thompson explains the challenge of moving from words on a page to points, lines, curves, and property boundaries on a map. This leads to a broader discussion of large language models, visual language models, OCR, and classical machine learning, with Thompson making clear that the best current systems still require a toolbox rather than blind faith in one model.

We also explore Thompson’s concept of the “prompt tax,” the hidden maintenance burden created when model behavior changes faster than product teams expect. Thompson describes Orbital’s mantra of “betting on the model,” which means building for where AI capabilities are heading while still delivering value today. He separates durable domain expertise from brittle prompt tricks, arguing that legal AI companies need reusable legal knowledge, strong evaluation habits, and a willingness to rebuild assumptions as models improve.

Looking ahead, Thompson sees the impact of AI arriving faster than the standard three-to-five-year forecast. He points to software engineering as an early signal for what legal work might experience next, with professionals increasingly orchestrating humans and AI agents together. The billable hour, client value, accountability, empathy, and judgment all come under pressure as AI handles more cognitive labor. For real estate lawyers and legal technologists, Thompson’s message is direct: the winners will be those who understand the work deeply, build with technical humility, and know when the map matters as much as the document.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Orbital CTO Andrew Thompson on Practice Area AI, Real Estate Law, and the Future of Legal Work

This week on The Geek in Review, we talk with Greg Mazares Sr., CEO of Purpose Legal, about what it takes to lead through one of the most important transition periods in legal services. Drawing on decades of experience across business, litigation support, and e-discovery, Mazares brings a steady, practical view to a market flooded with AI claims and rapid change. His message is clear from the start. The legal industry has faced major shifts before, from paper banker boxes to digital workflows, and this moment is another chapter in that longer story. Rather than treating AI as a threat, he sees it as a tool for adaptation, growth, and smarter client service.

A central theme in the conversation is Mazares’ belief that AI works best when paired with people and disciplined process. He argues that the future does not belong to technology alone, but to organizations that know how to combine tools, talent, and operational rigor. That philosophy sits behind Purpose Legal’s acquisition of Hire Counsel and its broader push to reunite technology and staffing under one roof. In Mazares’ view, clients do not simply want software. They want experienced professionals who know how to apply AI in defensible, repeatable ways that improve outcomes without sacrificing judgment.

The discussion also highlights Purpose Legal’s new offerings, including Purpose Xi and Case Optics, which aim to deliver early case insights in days rather than weeks. What makes Mazares’ framing stand out is his insistence that speed alone is not the point. Faster results matter only when paired with expert validation, tested workflows, and credible guardrails. He describes a legal market where clients once assumed AI would let them bring everything in-house, but now increasingly value outside experts who bring both technological fluency and hard-earned experience. That shift, he suggests, is raising the level of service providers from operational support teams to strategic partners embedded more deeply in legal work.

Greg and Marlene also press Mazares on data security, client trust, and the cultural pressures that come with rapid growth. Here again, his answers return to discipline and execution. He points to major investments in cloud security, around-the-clock protection teams, and tighter controls over on-site review environments. He also argues that many of the greatest risks still come from human behavior, which makes vetting, supervision, and protocol design as important as any technical control. On culture, Mazares emphasizes recognition, communication, and adaptability as the backbone of a company that wants to grow without losing its identity. For him, scaling a business is not only about revenue. It is about building a place where people feel seen, trusted, and prepared for change.

The episode closes on a thoughtful look at the next few years for litigation, junior associates, and the billable hour. Mazares predicts that junior lawyers will not disappear, but their role will shift toward becoming guides in the use of AI, both inside firms and in conversations with clients. As routine work becomes more compressed, he expects associates to provide higher-value service in fewer hours, with stronger technical fluency and a more consultative posture. It is a fitting end to an episode grounded in realism rather than hype. Mazares does not present AI as magic, and he does not dismiss its significance either. Instead, he offers a view of the future shaped by adaptability, experience, and the belief that in legal services, the winning formula still comes down to people, process, and sound judgment.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Greg Mazares Sr. on AI, E-Discovery, and the Future of Human-Led Legal Services

This week on The Geek in Review, we talk with Gregory Mostyn, CEO of Wexler.ai, about how his company is building a sharper form of legal AI for litigation. In a market crowded with broad platforms that aim to handle every legal task at once, Mostyn describes Wexler as a focused system built for one of the hardest problems in disputes, understanding the facts. He shares how the idea grew from watching his father, a judge, carry home stacks of ring binders and spend late nights reviewing case materials by hand. That early picture of legal work, heavy with paper and pressure, became the spark for a company aimed at helping lawyers work through massive records with more depth, speed, and precision.

A central idea in the conversation is Wexler’s view that the most useful unit of analysis in litigation is not the document, but the fact. Mostyn explains that lawyers are often handed a mountain of emails, messages, filings, and exhibits, yet what they need is a clear understanding of what happened, why it matters, and where the pressure points sit. Wexler is designed to pull out events, inconsistencies, and supporting details from that record so litigators are working from a factual map rather than a pile of files. That shift matters because disputes are rarely neat. Important evidence may be tucked inside an offhand message, a late footnote, or an exchange written in vague, coded language. Wexler’s aim is to turn that mess into something a trial team can use to shape strategy.

Mostyn also walks through the mechanics that separate Wexler from more general legal AI products. He describes a detailed fact extraction pipeline that processes unstructured material and turns it into structured data before the system reasons over it. That design helps Wexler deal with the disorder of litigation, where timelines blur, people contradict each other, and key details are easy to miss. He also points to the scale of the platform, noting that it handles large document sets and supports work such as deposition preparation, trial preparation, summary judgment briefing, and early case assessment. One of the more striking features is real-time fact checking during depositions, where the platform helps lawyers spot contradictions in testimony as the questioning unfolds. The effect is less like using a search box and more like working with a tireless junior team member who has read the whole file.

Trust, accuracy, and restraint are another major part of the discussion. Mostyn is careful not to oversell what AI can do. He openly states that no system is perfect, yet he argues that Wexler reduces risk by staying inside the record given to it. It does not search the internet, does not drift into outside material, and ties its outputs back to specific text in the source documents. That discipline is important in litigation, where a made-up citation or invented fact is more than embarrassing, it is dangerous. Mostyn presents Wexler as a tool that helps lawyers verify, question, and sharpen their understanding of the case. The result is less time spent slogging through repetitive review and more time spent thinking about how to use the facts in a meaningful way.

The conversation closes on a bigger question about where this kind of technology leads the profession. Mostyn believes that as AI takes on more of the burden of document review and fact development, the value of human lawyering rises in other areas. Strategy, advocacy, witness preparation, courtroom performance, and judgment all become more important when the groundwork is assembled faster and more thoroughly. He also suggests that clients are beginning to care less about how many hours were spent reviewing documents and more about whether their lawyers are prepared, informed, and effective. For listeners interested in litigation, legal AI, and the next stage of law firm economics, this episode offers a thoughtful look at a company betting that the future belongs to tools built for depth, discipline, and the hard realities of dispute work.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading From Document Review to Fact Intelligence, Gregory Mostyn on How Wexler.ai Is Reshaping Litigation

This week we welcome back Niki Black to unpack the findings from the newly released 2026 Legal Industry Report from 8am The conversation centers on a legal profession moving into a new phase of AI adoption, where individual lawyers are embracing general purpose AI tools at a striking pace, while many firms still lack even basic policies or training. Niki explains that this disconnect is especially visible among solo, small, and mid-sized firms, where limited resources often slow formal governance even as day-to-day use rises fast.

A major theme of the discussion is the widening gap between personal experimentation and institutional readiness. Niki notes that lawyers are not waiting for permission, and many are already relying on AI to support research, drafting, and routine work. At the same time, firms are struggling to provide guidance, training, and guardrails. The episode highlights the growing risk of shadow AI in legal practice, especially when lawyers and staff turn to unsanctioned tools to keep pace with client demands. For smaller firms, the answer is not elaborate bureaucracy, but practical direction, clear expectations, and a recognition that even a modest policy is better than none.

The conversation also turns to client expectations and the economic pressure AI is placing on the traditional law firm model. Greg and Marlene press Niki on whether firms are truly ready to move away from the billable hour as AI compresses the time needed to complete legal work. Niki argues that large firms face deep structural obstacles because compensation systems, staffing models, and internal economics remain tied to hourly billing. Still, she sees pressure building from in-house counsel, boutique competitors, and smaller firms that use technology to deliver comparable work at lower cost. The result is a market that may resist change, but not escape it.

Another standout part of the episode explores how AI is reshaping access to justice. Niki points to the promise of generative AI as a force multiplier for legal aid lawyers and public defenders, especially when paired with trusted tools and better funding. She rejects the idea that technology alone will solve the justice gap, but makes a strong case that AI, combined with stronger institutional support, helps lawyers serve more people with better results. At the same time, the hosts and Niki acknowledge the risks of a two-tiered system, where wealthier clients benefit from high quality tools while vulnerable users face lower quality, error-prone outputs.

By the end of the episode, the conversation expands from AI tools to a broader structural shift across firms, clients, and law schools. Niki sees the next three to five years as a period of deep change, where pricing, training, competition, and professional expectations all evolve at once. She also shares her own methods for keeping up, including RSS feeds, trusted blogs, and LinkedIn, with a few playful complaints about Substack making life more complicated. The episode leaves listeners with a clear message: the biggest issue is no longer whether AI will affect legal practice. It already is. The real question is whether the profession can adapt fast enough to manage the consequences wisely.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Niki Black on AI Adoption, Billing Pressure, and the Governance Gap in Legal

Anastasia Boyko joins us this week for a wide-angle conversation about AI adoption, leadership, and the uncomfortable truth behind “we are watching what peer firms do.” A Yale-trained tax lawyer with experience spanning Axiom, legal education, and innovation leadership, Boyko argues that precedent-driven instincts are turning into a liability when the underlying rules of the market are shifting in real time.

The episode opens with lessons from the Women + AI 2.0 Summit at Vanderbilt and the “AI competence penalty” narrative. Boyko’s central principle for law firm leaders is simple, stop copying the competition and start operating with intention. Strategic planning matters more than tool shopping, especially when uncertainty makes leaders freeze, over-index on fear, or chase noise instead of outcomes.

From there, the conversation sharpens into client reality. Boyko shares what she is hearing from in-house leaders, and it is not comforting for firms. Legal departments are working to reduce dependence on outside counsel, business partners inside companies often accept “good enough,” and the models keep improving. The risk is not losing to a peer firm; it is losing the client relationship because the work stops feeling necessary.

A major theme is talent and the apprenticeship gap. Boyko argues firms underinvest in people, even as they spend aggressively on software stacks. AI can help junior lawyers with coaching and confidence, but it does not replace mentorship, judgment-building, or context. The skills that matter now include client advisory, operational thinking, critical judgment, and the ability to solve problems across a complex system, not only perform discrete tasks in a vacuum.

The episode closes on legal education and the future value of the JD. Boyko urges students to be selfish about learning AI, especially when faculty guidance comes from avoidance or philosophy rather than experimentation. Looking ahead, she predicts the JD’s value shifts upward, away from rote production and toward proactive advisory work, relationships, anticipatory counsel, and wisdom-driven judgment. In other words, fewer fire drills, more looking around corners.

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Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Anastasia Boyko on Advisor Mode, Training Lawyers for the Post-Pyramid Firm

Ray Brescia joins The Geek in Review this week to unpack a role with peak academia vibes, Associate Dean for Research and Intellectual Life at Albany Law School. Greg frames the title as “Chief Curator of Smart People Ideas,” and Ray embraces a “player-coach” approach, coaching faculty scholarship, unblocking stalled projects, and connecting peers across disciplines. The throughline is community, research momentum, and a practical view of how ideas move from draft to impact.

The conversation then pivots to the core thesis of Ray’s book, Lawyer 3.0. Ray maps the legal profession across three eras: Lawyer 1.0 as a low-barrier “amorphous bar,” Lawyer 2.0 as the institutional buildout of law schools, bar exams, ethics codes, and modern law firms, and Lawyer 3.0 as the next inflection point driven by technology. Ray ties prior shifts to urbanization, immigration, and industrial-scale commerce, then parallels those forces with today’s generative AI and analytics reshaping research, drafting, discovery, and service delivery.

Ray retells the famous milkshake study, then translates the idea into legal services: clients are not shopping for “a lawyer,” clients are shopping for problem resolution. This reframing pushes law firms to examine intake, scoping, and service design through the lens of client outcomes, business problems, and life problems, not internal practice labels. The milkshake becomes a metaphor for product-market fit in law, with fewer crumbs on the steering wheel.

Ray contrasts “bespoke services” with productized pathways, including a Model T style offering that meets most client needs at lower cost, plus higher-cost custom work when risk or complexity demands. Ray highlights expert-system style workflows such as Citizenshipworks, describing a TurboTax-like experience for straightforward matters, with “red flags” triggering referral to a lawyer. The same logic extends to limited scope representation and “lawyer for the day” programs in high-volume courts, where informed consent, reasonable scope, and “first, do no harm” reduce the chance of clients feeling abandoned midstream.

The final stretch tackles law firm AI adoption, hallucination risk, and professional responsibility. Ray stresses minimum competence: verify cases, verify quotations, verify sources, and treat generative outputs as drafts or starting points, not final work product. The panel discusses guardrails, education, and workflow design for large firms, plus the rising reality of clients arriving with AI-generated “research.” Ray’s crystal ball points toward more commoditized legal services at scale, a latent market of underserved people, and stronger interdisciplinary collaboration between lawyers and technologists so legal education aligns with Lawyer 3.0 realities.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript

Continue Reading Lawyer 3.0 and the Milkshake Test: Ray Brescia on Legal AI, Client Value, and the Next Wave of Lawyering