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: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

This week on The Geek in Review, we talk with Ryan McClead of Sente Advisors about his new book on AI agents, written in collaboration with Claude. McClead explains how a short best practices guide grew into a full book after his work with Claude Cowork revealed something larger than tool tips or prompt advice. The result is part field guide, part warning label, and part first-person report from the edge of agentic AI adoption in legal work.

McClead’s process flips the traditional writing model. Instead of staring at a blank page, he asked Claude to generate an outline and draft, then spent weeks shaping, cutting, challenging, and refining the work. The book became a study in collaboration, with McClead serving as author, editor, supervisor, and occasional bouncer when the AI wandered too far from the point. His description of training Claude toward his voice, “more Anthony Bourdain and less Bobby Flay,” gives the episode one of its best lines and one of its most useful lessons.

A central idea from the conversation is “executable knowledge.” McClead argues knowledge management teams need to think beyond content meant for humans to find and read. The next stage is knowledge structured, so AI agents understand when to use it, how to apply it, and how to turn it into repeatable workflows. For law firms, this raises practical questions around scale, security, permissions, data quality, and governance. It also creates a new role for KM and innovation teams as builders of reusable legal intelligence.

The discussion also moves past prompt engineering as the main AI skill. McClead describes a shift from prompting to delegation, where users set goals, provide context, invite clarifying questions, and supervise the work product. The human role does not shrink in this model. It becomes more focused on judgment, direction, taste, and knowing when to take the work away from the AI before endless iteration turns progress into mush.

By the end of the episode, McClead frames AI agents less as replacements and more as strange new colleagues whose usefulness depends on the expertise of the person directing them. Good lawyers, KM professionals, and innovation leaders get faster and more effective. Poor processes get accelerated too, which is where the danger sits. For legal organizations, the message is clear: start small, learn the tool, build guardrails, and prepare for a future where clients ask not only for legal answers, but for legal workflows they can run.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Download it as a PDF for free here.
Or purchase a printed copy here.

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.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

This week on The Geek in Review, we talk with Keith Maziarek, founder of Lucratic Method and Bodhi Solutions, about the shifting economics of legal work, AI’s impact on pricing, and why law firms and clients need better commercial conversations. Keith brings more than two decades of experience in pricing, profitability, legal project management, and business-of-law strategy from firms including DLA Piper, Perkins Coie, and Katten. His new consulting work focuses on aligning client value with law firm operations, a topic gaining urgency as AI changes how legal work gets produced, measured, and priced.

Keith argues the legal industry has spent too much time asking what technology firms use, while ignoring how economic models, client expectations, and service delivery structures support the work. For him, the problem is less about whether BigLaw is broken and more about both firms and clients being “tone deaf” to each other’s business realities. Firms talk about realization rates. Clients talk about cutting spend. The better conversation starts with mutual value, risk, predictability, staffing, and clarity around which work deserves premium treatment and which work should be systematized.

The discussion turns directly to generative AI and the mistaken assumption that faster work must always mean cheaper work. Keith makes an important distinction between routine, high-volume work and complex, high-stakes legal matters. AI will reduce variance and improve budget predictability in many workflows, especially where tasks are repeatable and pattern-based. But in complex work, AI’s greater value might come from better preparation, broader analysis, and stronger outcomes, rather than dramatic cost reduction. The Neil Katyal Supreme Court preparation example gives this point a useful frame. AI might not reduce time, but it might improve judgment.

Keith also explores how AI will reshape law firm staffing and leverage. Fewer junior associates might be needed for some traditional tasks, but firms will need more data professionals, technologists, process experts, and other allied professionals to make AI-driven work reliable. This raises hard questions about associate development, talent pipelines, compensation, and the future shape of the partnership model. The old pyramid might narrow into something closer to a specialized team, with carefully selected lawyers and business professionals working together around data, process, and client value.

The episode closes with Keith’s view of the next phase of legal transformation. Firms are still experimenting, but the experimental period will give way to sharper questions about revenue models, profitability, AI-enabled service delivery, and whether certain work belongs inside the firm, with an ALSP, or in a hybrid model. His crystal ball points toward a market where firms with mature commercial thinking gain ground, while firms slow to rethink pricing, staffing, and process risk falling behind. As Keith suggests throughout the conversation, the future of legal work is not only about smarter tools. It is about whether firms learn to run better businesses.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

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: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

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: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

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: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

This week on The Geek in Review, we talk with Kristina Satkunas of CounselLink about what the numbers are saying in a legal market that still talks about change while clinging hard to old billing habits. Kris discusses the hard data behind outside counsel spend, drawing on CounselLink invoice data and Harbor survey results to compare what legal departments say they expect with what the bills are already showing. She makes the case that the objective data is stubbornly clear. Rates are rising, demand is not falling, and the biggest firms continue to capture a larger share of work.

There is a widening gap between hope and reality. Legal departments may believe they are on the verge of controlling outside counsel costs, moving more work in house, or shifting matters to smaller firms, but Satkunas notes that the billing data has not caught up to those ambitions. She sees some room for in-house expansion in more routine areas like employment work, especially with AI helping legal teams absorb more volume, yet the largest and most sensitive matters are still flowing to outside counsel. That tension gives the episode much of its energy. Everyone sees pressure building in the system, but the old habits of legal buying and legal staffing remain firmly in place.

The discussion also gets into the mechanics of better decision-making, and where there is practical value for legal operations leaders. Satkunas emphasizes that data only becomes useful when departments have enough discipline in their enterprise legal management systems to categorize work correctly, clean out outliers, and separate different matter types instead of lumping everything into broad buckets like litigation. She also explains why finance data alone will not do the job. The real insight sits inside invoice-level detail, where hours, rates, firms, and timekeepers reveal what is happening beneath the headline spend numbers. For listeners trying to build a stronger legal ops function, this part of the conversation feels like a polite but firm warning that dirty data still tells stories, but some of them are fiction.

There is an obvious strain on the billable hour model that AI is placing on it. Satkunas notes that while average partner rate growth has hovered around 5 percent, top-end lawyers are often raising rates even faster, especially as firms try to protect revenue from the work and people they still believe clients will pay for. At the same time, she argues that alternative fee arrangements have remained stuck for years, though AI may finally force movement toward value-based pricing. If technology reduces the hours required to complete the work, then the old logic behind both hourly billing and many flat fees starts to wobble. That leaves firms facing an uncomfortable question, which is how to price legal services based on value delivered rather than time consumed.

We'd say that Satkunas is neither cheerleader nor doomsayer. She is a patient observer of a market trying to pretend nothing is happening while the floorboards creak under everyone’s feet. Her prediction is that real value-based billing will begin to appear in pockets over the next couple of years, even as firms continue squeezing what they can from the billable hour in the meantime. For law firm leaders, legal ops teams, and general counsel, this episode is a sharp reminder that disruption does not arrive with a trumpet blast. Sometimes it arrives as a spreadsheet, a trend line, and a guest who quietly points out that the data has been trying to warn us for years.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

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.

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.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

The latest episode of The Geek in Review finds Greg Lambert and Marlene Gebauer back from Dallas with a sharp, grounded recap of the Texas Trailblazers conference, an event that stayed close to the daily realities of legal work instead of drifting into glossy predictions. Their conversation centers on a legal industry trying to sort out what AI means right now, in billing, workflow, training, pricing, governance, and client expectations. What stands out most is the hosts’ focus on the practical tension between what the tools are capable of and what law firms and legal departments are structurally ready to absorb.

A major thread in the discussion is the risk of what one speaker called “cognitive surrender,” the habit of trusting AI output too quickly and handing off too much human judgment in the process. Greg and Marlene treat this as less of a software issue and more of a workflow and education issue. The point is not whether AI produces polished work. The point is whether organizations are building systems where review, judgment, and accountability still sit with people. Their conversation ties this concern to legal practice, education, and even K-12 learning, showing how widespread the temptation has become to accept fluent output without enough friction or scrutiny.

The episode also takes a hard look at the pressure AI is putting on the billable hour. Marlene frames the issue well when she notes that AI does not kill the billable hour so much as expose its weaknesses. Across the conference, the hosts heard repeated concern about the mismatch between efficiency gains and the financial structures law firms still rely on. If AI reduces the time needed for many tasks, then firms, associates, pricing teams, and clients all have new incentives to sort through. Greg and Marlene highlight the awkward moment the industry is in, where firms want to talk about value while clients are also eyeing the chance to pay less for faster work. The result is a growing need for honest conversations about pricing, outcomes, and what legal value should mean when time is no longer the cleanest measure.

What gives the episode its energy is the number of concrete examples pulled from the conference. The hosts discuss lower-cost multi-state surveys, large-scale analysis of rights-of-way documents, and internal workflow improvements built with existing tools like SharePoint and Copilot on little or no budget. These stories show AI not as abstract promise, but as a way to get work done that used to be too expensive, too tedious, or too slow to tackle at all. At the same time, Greg and Marlene stay skeptical in the right places, especially when the conversation turns to legal research, citation accuracy, and the idea that technology vendors have somehow solved problems that law librarians and researchers know are stubbornly difficult.

By the end of the episode, the biggest takeaway is not that the legal industry has a clear answer, but that waiting for certainty is no longer a serious option. Greg and Marlene come away from Texas Trailblazers with a sense that real progress is happening through testing, discussion, and repeated adjustment, not through perfect plans. Their recap captures an industry in transition, one where law firms, legal ops teams, vendors, and clients are all feeling the strain between old business models and new technical possibilities. The message is simple and urgent: start the conversations now, use the tools now, and get honest about what must change before the gap between what is possible and what is workable gets even wider.

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

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

⁠⁠⁠⁠⁠Email: [email protected]
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠Transcript: