The Geek In Review

Greg Lambert & Marlene Gebauer
  • 4.7 (26)
  • BUSINESS
  • UPDATED WEEKLY

Welcome to The Geek in Review, where podcast hosts, Marlene Gebauer and Greg Lambert discuss innovation and creativity in legal profession.

  1. Flatiron Law Group's Lennie Nuara on Talent-First AI, M&A Workflows, and the Future of Legal Practice

    1D AGO

    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 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.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

    1h 1m
  2. Orbital CTO Andrew Thompson on Practice Area AI, Real Estate Law, and the Future of Legal Work

    APR 27

    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 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.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

    47 min
  3. Greg Mazares Sr. on AI, E-Discovery, and the Future of Human-Led Legal Services

    APR 20

    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 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.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

    40 min
  4. CounselLink’s Kris Satkunas on Rising Legal Spend, Law Firm Rates, and the Future of Value-Based Pricing

    APR 13

    CounselLink’s Kris Satkunas on Rising Legal Spend, Law Firm Rates, and the Future of Value-Based Pricing

    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: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

    35 min
  5. From Document Review to Fact Intelligence, Gregory Mostyn on How Wexler.ai Is Reshaping Litigation

    APR 6

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

    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: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

    32 min
  6. Texas Trailblazers and the Hard Truth About AI in Legal Work

    MAR 30

    Texas Trailblazers and the Hard Truth About AI in Legal Work

    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: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠Transcript:

    47 min
  7. From Translation to Transformation: Paula Reichenberg on AI, Legal Quality, and the Future of Good Enough

    MAR 23

    From Translation to Transformation: Paula Reichenberg on AI, Legal Quality, and the Future of Good Enough

    This week we welcome Paula Reichenberg, founder of Neuron, for a sharp and thoughtful conversation about legal translation, artificial intelligence, and what happens when professional expertise collides with tools that look polished but still miss the mark. Paula shares her path from M&A and capital markets law into business school, legal services, machine learning, and finally legal tech entrepreneurship. What started as frustration with inefficiencies inside law firms grew into a translation business, then evolved again as machine translation improved and forced a harder question about survival, adaptation, and quality. Paula explains how her early company, Hieronymus, found success by handling sensitive, high-stakes legal translations in Switzerland, especially where precision and confidentiality mattered most. But as machine translation improved, the market for average work started to disappear. Clients began doing more on their own, leaving only the hardest, highest-value assignments for specialists. Rather than ignore the shift, Paula leaned into it. That decision led her back to university, into data science and machine learning, and toward building Neuron, a company focused less on replacing expertise and more on improving the process around imperfect AI output. A central theme of the discussion is the uncomfortable truth that many users do not care as much about excellence as professionals do. Paula makes the point with refreshing honesty. AI often produces work that is mediocre, but for a large share of users, mediocre is enough. That creates both a market shift and a professional dilemma. In legal translation, as in legal drafting more broadly, the issue is rarely whether AI produces something flawless. The issue is whether the user notices what is wrong, has the time to fix it, and has the systems in place to improve the result efficiently. Paula argues that the real value is not in claiming perfection. It is in helping experts find the mistakes faster, correct them with less pain, and avoid wasting hours doing work that feels like cleanup on aisle five. The conversation also digs into trust, user behavior, and the strange authority people give to AI-generated answers. Paula recounts how, in one negotiation, a party trusted ChatGPT’s answer more than a human tax lawyer’s detailed explanation, even when the AI response was wrong. That anecdote opens up a broader discussion about confidence, presentation, and why polished outputs often feel more persuasive than expert judgment. Greg and Marlene connect that idea to legal systems, translation quality, and access to justice, especially where technology might offer better service than overworked and underfunded human systems. The result is not a simple pro-AI or anti-AI position. It is a grounded look at where human excellence still matters, where automation fills gaps, and where the future may split between mass-market convenience and premium, highly tailored expertise. Looking ahead, Paula sees consolidation coming to legal tech, along with a growing push toward seamless interfaces that bring best-in-class features into one place. For Neuron, that means becoming an embedded layer inside other legal tools rather than forcing lawyers to juggle yet another standalone platform. Her crystal ball view is both stylish and sobering. The legal industry is not simply moving toward automation. It is sorting itself into tiers of service, quality, and expectation. And if Paula is right, the future belongs to those who understand where “good enough” ends and where true expertise still earns its premium. Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack [Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]     ⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript

    41 min
  8. Anthropic’s Matt Samuels and Den Delimarsky - Claude & MCP: Building the USB-C for the Legal Tech Stack

    MAR 16

    Anthropic’s Matt Samuels and Den Delimarsky - Claude & MCP: Building the USB-C for the Legal Tech Stack

    This week, we sit down with two guests from Anthropic, Matt Samuels, Senior Product Counsel, and Den Delimarsky, a core maintainer of the Model Context Protocol, or MCP. Together, they unpack why MCP is drawing so much attention across the legal industry and why some are calling it the USB-C for AI. For law firms long burdened by disconnected systems, scattered data, and the infamous integration tax, MCP offers a shared framework for connecting models to the places where real work and real knowledge live, from iManage and Slack to email, data lakes, and internal tools. Den explains that the promise of MCP is not tied to one model or one vendor. Instead, it creates a standardized way for AI tools to securely interact with many different systems without forcing organizations to build one-off integrations every time they want to connect a new source. The conversation gets especially relevant for legal listeners when Greg and Marlene press on issues like permissions, ethical walls, least-privilege access, and auditability. The answer from Anthropic is reassuring. MCP is built to work with familiar enterprise security concepts such as OAuth and role-based access, meaning firms do not have to throw out their security model in order to explore new AI workflows. Matt brings the legal and operational lens, translating MCP into practical use cases for lawyers, legal ops teams, and security leaders. He describes how AI becomes far more useful once it has access to the systems lawyers already rely on every day, while still operating within carefully defined administrative controls. The discussion highlights a key shift in how firms should think about AI. This is no longer about asking a chatbot a clever question and getting a polished paragraph back. With MCP, firms are moving toward systems where AI can retrieve, correlate, summarize, draft, and support actions across multiple platforms, all while staying inside the guardrails set by the organization. The episode also explores how MCP fits into the rise of agentic workflows, apps, plugins, and skills. Rather than treating AI as a static assistant, Anthropic describes a future where these tools become active participants in legal work, pulling together information from multiple sources, helping assemble case timelines, drafting notes into a shared document, and supporting lawyers in a far more integrated workspace. The conversation around skills is especially useful for firms thinking about standard operating procedures, preferred drafting styles, escalation rules, and repeatable work product. Skills and MCP do different jobs, but together they start to look like the operating system for structured legal workflows. By the end of the conversation, one message comes through clearly. The legal profession is still early in this shift, but the pace is picking up fast. Both Matt and Den encourage listeners to stop treating these tools like abstract future concepts and start experimenting with them now. At the same time, they offer an important note of caution. As much as these systems promise speed and efficiency, lawyers still need to protect the craft of lawyering, their judgment, and the human choices that matter most. For firms trying to make sense of where AI is headed next, this episode offers a grounded and practical look at the infrastructure layer that could shape what comes next. Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack [Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]   ⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

    56 min
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4.7
out of 5
26 Ratings

About

Welcome to The Geek in Review, where podcast hosts, Marlene Gebauer and Greg Lambert discuss innovation and creativity in legal profession.

Information

  • Creator
    Greg Lambert & Marlene Gebauer
  • Years Active
    2018 - 2026
  • Episodes
    351
  • Rating
    Clean
  • Copyright
    © Greg Lambert & Marlene Gebauer
  • Show Website
    The Geek In Review

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