Greg Lambert and Marlene Gebauer take The Geek in Review on the road to Atlanta for a conversation following the GDS AI Summit. Meeting with leaders from across industries offers a fresh perspective on familiar legal technology challenges: messy data, uncertain ownership, and the pressure to turn artificial intelligence experiments into measurable business improvements.
Law firms and legal departments face pressure to adopt AI, yet buying another tool does not answer the hardest question: Which problem needs solving? Dr. Maryam Salehijam, founder and CEO of Andarzi, joins TGIR hosts, Greg Lambert and Marlene Gebauer, to discuss how legal teams choose between existing systems, new vendors, and tools built for their own workflows. Her starting point is to observe the work, identify a useful outcome, and then decide what technology belongs in the process.
Salehijam describes legal teams paying for AI platforms while struggling to get consistent use from them. She urges organizations to examine tools they already own and ask vendors for more training before adding another subscription. A legal department’s use of ServiceNow for intake provides one example. The conversation also tackles shifting model capabilities, consumption pricing, and the cost of assigning an expensive model to a routine task.
What counts as a return on legal AI investment? Time and money matter, but Salehijam argues for measuring employee satisfaction as well. Repetitive contract review and document work consume attention lawyers would prefer to spend on judgment, business problems, and client relationships. She describes using anonymous feedback before and after a workflow change to learn whether people’s work has improved, while Greg raises the tension between hours saved and the billable hour.
The discussion turns to local AI models, data security, and the growing interest in AI-native law firms. Salehijam argues for decisions based on the task and the client’s requirements, including a direct conversation with clients about how their information should be handled. Greg presses her on whether smaller firms and large firms face different constraints. Marlene asks whether the emerging “legal engineer” role will endure, prompting a pointed exchange about technical skill, credentials, and what lawyers need to learn.
For teams putting AI into practice, Salehijam favors hands-on learning with peers, repeated use, and work product over a quick certificate. She also warns leaders to account for the time and effort a new workflow demands before promising immediate gains. In the closing questions, she reflects on a legal AI market with fewer new entrants than she expected and predicts more law firms and legal departments will ask whether owning a workflow serves them better than renewing another set of software seats.
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Music: Jerry David DeCicca
What happened when I pointed Jev, SALI, and CLOC’s Core 12 at every post we’ve ever written
I read Nate Jones’s post on Jev, TypeSafe’s new “System One” model, and he described it as an LLM that can only talk in multiple choice. It reads what you give it, you hand it the possible
Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries.
Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg.
The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice.
Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation.
Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
What happens when a 40-year-old legal data company decides its employees should start building their own software? This week on we talk with Best Lawyers CEO Phillip Greer and Senior Vice President of Research and Product Strategy Elizabeth Petit about an internal AI transformation that reaches far beyond adding ChatGPT to the corporate toolkit. Best
In this episode of The Geek in Review, we talk with Patrick Forquer, Chief Revenue Officer at Legora, about legal AI’s move from experimentation into daily legal work. Forquer explains why Legora has invested heavily in legal engineers, lawyers with practice experience who work alongside clients on adoption, workflow design, prompt and context engineering, and change management. The conversation also explores an emerging career path for lawyers who pair substantive legal knowledge with AI fluency, especially as firms search for people able to translate practice needs into working systems.
Legora’s acquisition strategy provides another lens on the company’s ambitions. Forquer describes a strategy aimed at building breadth across legal work while adding depth in litigation, commercial real estate, regulatory monitoring, and legal research. Recent acquisitions such as Wexler, Cadastral, and Graceview bring specialized capabilities into a broader agentic platform. Legora’s own 13-day acquisition process also serves as an example of how M&A diligence, document review, drafting, and analysis are beginning to move through shared AI environments.
A major portion of the discussion focuses on the difference between traditional workflow automation and agentic AI for legal work. Forquer draws a line between prebuilt automation and agentic systems: workflows follow predetermined steps, while agents receive a goal, gather context, form a plan, call tools, and work across longer tasks with human review. Context engineering therefore becomes increasingly important. Matter data, firm knowledge, permissions, legal skills, and connections to systems through tools such as MCP all shape the quality of agentic work. M&A due diligence already represents one area where longer-horizon agentic processes are gaining traction. Legora describes the same architecture through its agentic operating system, or aOS.
The discussion then turns to economics, pricing, and proof of adoption. Greg points to Crowell & Moring’s reported 91 percent attorney adoption and nearly 70 percent weekly usage, while Forquer argues login counts and activated licenses tell only part of the story. Legora tracks daily activity and depth of feature use, including tools such as Tabular Review, skills, playbooks, and extraction templates. Agent Pro’s shift to consumption-based pricing introduces another measurement challenge, with credits tied to usage alongside dashboards, spending controls, and project-level attribution. For law firms, a broader question follows: when AI compresses hours while increasing speed, scope, and output quality, traditional measures of efficiency and value start pulling in different directions.
The episode closes with a look at what law firm innovation leaders should prepare for next. Forquer identifies the data layer as one of the central issues behind successful agentic AI. Secure access to documents, matter-level permissions, governance, firm knowledge, and well-structured context determines how far agents progress into complex legal work. Talent matters alongside infrastructure, which brings the conversation back to legal engineers and new hybrid roles spanning law, AI, knowledge management, and data governance. The episode leaves innovation and KM leaders with a practical agenda: improve data governance, build legal engineering skills, align stakeholders around risk and outcomes, and measure value through work product, adoption depth, and client impact.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Fresh from AALL in Cleveland, Greg reflects on a conference filled with legal information professionals who understand how technology performs under real working conditions. These librarians purchase products, train users, support law schools and courts, and often serve as internal advocates for legal technology. Their expertise makes vendor engagement especially valuable, yet major product announcements were scarce. Marlene balances Greg’s conference report with stories from her hiking trip through Zion and Bryce Canyon, plus a brief comparison of Ohio and Utah karaoke culture.
The conversation turns to the rapid growth of innovation attorney positions across law firms and legal organizations. Greg and Marlene describe these professionals as translators who connect legal practice, technology, workflow design, and organizational change. Firms are searching beyond traditional legal career paths for people who combine technical fluency with strong interpersonal skills. For law students and junior lawyers facing uncertainty around AI, these emerging roles offer broader career options beyond the familiar associate track.
Marlene explores the growing use of AI personas and simulations for professional development. Deposition witnesses, opposing counsel, negotiation partners, and drafting reviewers now appear as interactive characters with distinct goals and behaviors. Lawyers receive a place to practice, make decisions, and receive feedback before working with clients or appearing in court. Greg connects simulation-based learning with legal fiction, including his Beyond the Model series, which uses a fictional law firm to explain AI systems, business pressures, and changes in legal work.
The discussion takes a serious turn with a reported AI benchmarking incident involving an agentic model, a breached sandbox, and unauthorized access to Hugging Face resources in search of an answer key. Greg and Marlene examine the episode as a warning about containment, accountability, and excessive faith in technical guardrails. From there, they consider the renewed importance of knowledge management and security as AI systems gain access to documents, financial information, client data, and institutional expertise. Greg predicts growing attention around AI harnesses, structured software layers designed to guide model behavior and produce predictable outputs.
Marlene closes with examples of AI moving into client intake, business qualification, and workflow decisions, including an AI legal receptionist designed for smaller firms. The larger shift involves moving beyond simple tool adoption toward redesigned workflows, staffing models, pricing structures, and client service. Token costs are creating immediate budget pressure, while clients are questioning which AI expenses belong on their bills. Greg and Marlene argue firms must connect AI spending with legal judgment, measurable value, and responsible delivery, rather than treating consumption as a proxy for progress.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
LINKS:
Here is a chronological list of the substantive topics and sources referenced in the episode:
Continue Reading From AI Personas to Rogue Agents: Rethinking Legal Training, Security, and Value
In this episode of The Geek in Review, Greg Lambert hosts a solo conversation with Triona Buckley, Chief Product Officer at Actionstep, about generative AI’s growing influence on mid-market law firms. Buckley challenges a common assumption about legal AI: faster task completion does not always remove friction. An associate might produce a draft within seconds, only to transfer the burden upstream to a senior lawyer responsible for reviewing sources, reconstructing reasoning, and correcting mistakes.
Buckley argues law firms should shift their attention from speed to systems. Standalone drafting and research tools address individual tasks, while system-level AI connects work across an entire legal matter. Embedded within everyday workflows, AI helps lawyers locate information, reduce administrative work, and preserve more time for client advice and professional judgment. The goal is a smoother operating model, rather than a collection of isolated tools producing faster documents.
The conversation also examines institutional knowledge, especially within firms lacking large knowledge management or innovation teams. Buckley describes an approach where AI captures decisions, context, and reasoning as lawyers work. This creates a continuously expanding record of how the firm handles matters, advises clients, and applies professional judgment. Governance still plays a central role, including clear audit trails showing whether a person or an AI agent performed each action.
Greg and Triona then explore AI as an individual tutor for junior lawyers. Remote and hybrid work have weakened the traditional apprenticeship model built around observation and informal office conversations. Drawing upon decades of firm experience, an AI tutor might question an associate’s assumptions, prompt additional research, and reinforce the firm’s preferred methods. Such systems offer structured practice while preserving the essential mentoring relationship between senior and junior lawyers.
Another major theme is the hidden cost of delayed time entry. Actionstep’s Trace passive time capture technology monitors work across practice management, email, and document applications, then presents lawyers with matter-linked, billing-ready entries. More accurate records help firms recover otherwise forgotten time while producing better data for pricing, staffing, client estimates, and profitability analysis. Those insights grow more important as clients push firms toward fixed fees and output-based pricing.
Buckley believes mid-market law firms hold several advantages during the AI transition. They often operate with fewer systems, maintain closer client relationships, and move through organizational change faster than larger enterprises. Success will still require disciplined implementation, trusted internal champions, connected data, and sustained attention to client service. Her message is optimistic but direct: firms with strong relationships, clean data, and a clear economic strategy will be better prepared for agentic AI and the changing business of law.
Actionstep’s U.S. Midsize Law Firm Priorities Report
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Predictions about artificial intelligence often focus on job losses and shrinking demand for lawyers. Filevine CEO and co-founder Ryan Anderson and product manager John Rizner offer a sharply different forecast. Drawing on the Jevons paradox, they argue greater efficiency will make legal services accessible to more people, encourage deeper legal research, and create work once excluded by cost. AI might reduce the effort required for individual tasks while expanding the overall volume and ambition of legal representation.
The shift holds major implications for the access-to-justice gap. Faster drafting, research, and document review would allow lawyers to serve more clients without sacrificing professional judgment. Anderson expects family law, immigration, bankruptcy, criminal defense, and employment litigation to experience some of the earliest growth. Motions, witnesses, and legal theories once abandoned over expense become economically viable, although courts face their own capacity crisis as more disputes and arguments enter the system.
Rizner explains how Filevine’s legal AI platform, Lois, applies machine learning to one of legal research’s oldest problems: traditional citators often return different results. Lois combines citation graphs with semantic analysis to locate opinions discussing related legal doctrines even when no direct citation connects the cases. A panel of models then evaluates potential conflicts and produces a structured memo. The goal is richer legal analysis focused on the precise holding or proposition a lawyer needs, rather than a simple flag attached to an entire opinion.
Accuracy still demands disciplined human review. Filevine organizes citation verification into three levels: confirming the cited case exists, determining whether the case supports the claimed proposition, and checking whether the authority is still good law. The conversation also examines Rizner’s research into how different large language models approach efficient breach of contract. OpenAI, Google, and Anthropic models produced dramatically different recommendations, revealing embedded legal and economic preferences beneath seemingly neutral answers.
The guests also explore how AI changes legal drafting, law firm economics, and the billable hour. Filevine’s acquisition of Pincites, now Lois for Word, reflects Microsoft Word’s continuing role as the shared language of legal documents, redlines, formatting, and negotiations. Efficiency does not automatically eliminate hourly billing. Lawyers might instead use saved time to produce more thoroughly researched arguments, stronger contracts, and work product approaching senior-level depth. Firms still need incentives rewarding efficiency rather than treating faster work as lost revenue.
Looking ahead, Anderson and Rizner predict a proliferation of frontier and open-source models tailored to firms, individual lawyers, and specific client relationships. Legal teams will increasingly pair proprietary knowledge with selected models to produce highly specialized analysis. Yet model choice introduces jurisprudential bias, accuracy risks, and serious training concerns for junior lawyers. AI expands the range of available options, while experienced legal judgment decides which arguments deserve trust, which sources require verification, and which advice should reach the client.
John Rizner Slides Filevine Primary Presentation – 2026
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
What does legal AI value look like once speed stops serving as the headline metric? In this episode of The Geek in Review, Greg Lambert and Marlene Gebauer speak with Nikki Shaver, co-founder and CEO of Legal Technology Hub and a member of the inaugural Financial Times Law 50. Shaver argues that law firms need to move beyond time saved toward efficacy: stronger output, stronger client outcomes, and more effective legal advice.
The conversation examines why the billable hour is far from finished yet no longer serves as the sole measure of legal value. Shaver compares hourly timekeeping to a taxi meter: useful for internal visibility, yet insufficient as the price signal for work transformed by AI. Workflow mapping, client discussions, and pricing discipline become central where an AI-enabled process compresses weeks of effort into hours.
Corporate legal departments are adopting AI at a faster pace, bringing new pressure to outside counsel. Some in-house teams see AI as a route to keep more work inside, while others see room for firms to take on work that previously sat outside budget limits. Shaver frames the strategic question around delivering more for clients, especially in practice areas where a firm holds differentiated expertise.
AI has not produced the promised empty calendar. Instead, lawyers report fuller schedules, longer documents, and a growing verification tax. Shaver flags the rise of 40-page forms, bloated redlines, and outputs that look polished yet lack sound reasoning. The episode makes a practical case for concise drafting, human review, and critical reasoning before any AI-generated material reaches a client or counterparty.
Agentic AI raises the stakes. Legal Technology Hub’s AI Agents in Law Map tracks hundreds of solutions, yet governance has not kept pace with new autonomy, connectors, and downstream system access. Shaver urges firms to establish traceability, unique identifiers, risk-based human oversight, enforceable policies, and a clear view of where data travels.
For firms aiming past baseline adoption, Shaver draws a line between routine personal use and strategic transformation. Daily use builds fluency, but competitive advantage grows from proprietary workflows, data foundations, client-facing collaboration spaces, and focused investment in the practices where a firm already excels. Her crystal-ball view is blunt: trusted judgment will become a scarce premium asset, AI-native firms will rise, and traditional firms will launch AI-native subsidiaries of their own.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Continue Reading Nikki Shaver on Legal AI Strategy, Agentic Governance, and Trusted Judgment