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.

LINKS

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

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

Email: geekinreviewpodcast@gmail.com

MusicJerry David DeCicca

Transcript

Continue Reading Patrick Forquer on Legora’s Agentic AI, Legal Engineering, and Consumption-Based Pricing

It turns out that tech companies don’t sit outside of government as just ordinary vendors. This week on The Geek in Review podcast, we welcome back Texas A&M University School of Law professor Hannah Bloch-Wehba to talk about accountability of Big Tech, AI regulations, government surveillance, and the intertwining of public authority and private tech infrastructure. Bloch-Wehba traced the dependency between the two powers all the way back to the 1930s in her article “How Tech Took Over,” in how the tech sector became a foundation for national security and economic growth.

Today’s hybrid form of governance, where Bloch-Wehba explains how a handful of private companies supply data and cloud systems, along with decision-making infrastructures across multiple governmental agencies. It is a struggle for traditional constitutional doctrines to adjust to the modern technology and the operations provided by contractors that are providing their core foundational operations.

The issues also enter into the criminal law enforcement areas and Bloch-Wehba’s “Rights, Knowledge, and Capture in the Datafied State,” discusses how trade-secret claims are throwing a barrier between proprietary data systems and criminal defendant’s ability to examine the systems that are being used to convict them in the courts. There is a strangeness in the judicial systems where corporate choices are shaping the legal process being followed, rather than corporate governance following established legal norms.

Bloch-Wehba’s “Information Law Pluralism” covers how privacy rules, audits, impact assessments, disclosure duties, researcher access, and independent review as parts of a broader system governing exactly how knowledge is shared, validated, and even produced. There seems to be no single device that transparently provides accountability. In addition, she lists how a political campaign program against states attempting to regulate AI companies and products is weakening state transparency even further.

Finally, we cover Bloch-Wehba’s “Rethinking Federal Support for Journalism” where she argues that platform payments give rise to the risk of replacing a governmental dependency gets switched for journalist and new organizations being financially tied to companies they must scrutinize. Ideas floated like an AI tax provide some alternative funding possibilities for supporting local and public-interest journalists.

LINKS

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 Who Governs Big Tech? Hannah Bloch-Wehba on AI Regulation, Police Surveillance, and Public Accountability

We welcome back Brad Blickstein, CEO at Blickstein Group, to discuss how private equity principles may provide law firms with an alternative approach to profitability, governance, and even long-term growth. Blickstein’s new book, WWPED: What Would Private Equity Do? was written to walk firms through how treating topics like pricing, technology, talent, and client relationships as part of the enterprise value instead of overhead expenses after year-end partnership distributions.

Pulling from Jae Um’s topics of Cream, Core, and Commodity framework, Blickstein talks about the legal work as the primary competitive battleground. Much like businesses that provide baked goods, firms have to separate the customized legal judgment from the repeatable legal processes, technology, and what alternative legal services providers offer. Law firm leaders should understand what scalable work is, begin building consistent systems to deliver that work, and truly professionalize pricing over relying upon what a partner’s gut tells them.

We also cover the Blickstein Group’s 2026 Law Firm COO Survey where technology adoption and investment ranks as the leading strategic initiative with 38.1% identified practice silos as the largest structural issue and 27% of COOs listed lack of operational authority as another prime issue. COOs are struggling with being tasked with modernizing law firms, but not given the authority to actually overcome the base issues of decentralized partnerships, competing incentives, and overall firm political structures.

Add AI into the mix, and the pricing question becomes even more important. Some two-thirds of the COOs surveyed confessed that they were not formally measuring any return on investment (ROI) in which they could later measure any law productivity or direct revenue increases. Blickstein points out that faster work in a billable hour model is not the type of math that law firms want to calculate, and that firms have to address this directly and redesign their overall pricing model on value received by the client, not hours worked by the lawyers. We all discuss the issues of alternative fee arrangements (AFAs) have face in the more than 30 years since Blickstein originally published an article titled “Alternative Billing Making a Comeback.” AFAs bring with it issues of shadow billing, client trust factors, and the need to express value not tied to the amount to time spent on the work.

We also break down the corporate buyer side and address the Blickstein Group’s 18th Annual Law Department Operations Survey which identifies AI pilot projects in corporate legal departments, but very few operational deployments. These may be tied to the long running issue of poor data hygiene along with business objectives that are not clearly tied to overall corporate strategy.

Brad gets to be one of the first to answer our new question of “what’s true today that wasn’t true a year ago?” A nice lead in to our Crystal Ball question. We cover AI token pricing and having to compete with the new “AI native firms” that are spinning up from former BigLaw partners.

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

LINKS

Transcript:

Continue Reading Brad Blickstein on Private Equity Thinking, AI Pricing, and the Law Firm Business Model

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.

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

LINKS:

Here is a chronological list of the substantive topics and sources referenced in the episode:

Transcript:

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

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 AI Is Shifting the Bottleneck: Actionstep’s Triona Buckley on Building Smarter Mid-Market Law Firms

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

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 Why AI Will Create More Legal Work, Not Less: Filevine’s Rizner and Anderson on Research, Access, and Human Judgment

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.

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 Nikki Shaver on Legal AI Strategy, Agentic Governance, and Trusted Judgment

[Ed. Note: This is part of an ongoing series at The Geek in Review Substack page. – GL]

Leo Huang did not usually come to the twelfth floor, and he never came to it at seven-forty in the morning. So when Cooper looked up from his coffee and found him in the doorway, still in yesterday’s shirt with a laptop held against his chest like a clipboard, he could tell something was going on, and from the look on Leo’s face, probably something bad.

“I think we almost sent a client something we shouldn’t have,” Leo said. “I think the intake agent cleared it, and I think it’s been clearing things it shouldn’t for a while, and I can’t find anyone whose job it is to care.”

Cooper put the coffee down. “Sit. Start at the beginning, slowly. What got cleared.”

Leo set the laptop on the desk and turned it. On the screen was the intake queue, the one the business-intake team and the junior associates worked off of every morning, the inbound non-disclosure agreements and engagement requests that the firm’s intake agent read overnight, classified by risk, and stacked into three colors. Green for standard, clear to send. Yellow for a human look. Red for a partner. The agent drafted the first-pass response too, so that by the time a person opened the item, there was already a reply sitting under it, polite and competent and ready to go out.

“This one came in Monday,” Leo said, and opened it. “Counterparty NDA, mutual, looks like every other one. The agent tagged it green. Standard, clear to send. The response was already drafted. Someone on intake would have sent it this morning, today, because that’s what you do with green, you send it.” He scrolled. “But I was staffed on the underlying matter, so I actually read the thing. And buried in the confidentiality section there’s a non-solicit. Twelve months, their employees and ours. That’s not a confidentiality term. That’s a restrictive covenant riding inside an NDA, and we have a house rule that any embedded non-solicit goes to a partner, because Maya made it a rule after that mess last year.”

“And the agent missed it.”

“The agent didn’t miss it. That’s the part that kept me up. It saw the non-solicit. It just decided it was boilerplate. Because the playbook it’s reading still says standalone non-solicits in NDAs are standard market and don’t need escalation. Which was true. Last spring. Before the rule changed.”

Cooper felt the morning go quiet around him.

“So I went looking for the playbook it’s reading,” Leo said. “It’s pointed at a document in the old KM folder. The one we superseded in March. The current house position, the one with the non-solicit rule, lives in a different place now, and nobody ever told the agent. It’s been reading a dead document with total confidence since the spring. Every green tag it’s handed out since March, it handed out from a rulebook we threw away.”

“How many is that.”

“I don’t know. That’s the other thing.” Leo looked at him. “I went to find out who owns this agent so I could tell them. There’s no one. It was a pilot. I built the clause-extraction piece, the part that pulls terms out, eighteen months ago, but I don’t own the thing it became. Somebody in KM stood up the production version, and somebody used to check it on Fridays, and I went and asked, and the person who checked it moved to the Houston office in the spring. The Friday review went with her. Except it didn’t go anywhere. It just stopped.”

Cooper was already standing, already reaching for the marker he kept by the door.

“Don’t send anything green until I get back to you,” he said. “I’m getting the room.”

• • •

He got the room by ten. Not the firefighting version from the Friday in June when a government had reached across an ocean and switched off a model while everyone watched from their kitchens. The deliberate one, Governance Committee on twelve, door closed, three hours blocked. Nora came up from the administrative floor with her iPad and the look of someone who already suspected she would not enjoy the next three hours. Maya came in between a deposition and a closing, reading glasses pushed into her hair. Jesse joined from his lab in Chicago, a window on the wall screen with whiteboards behind him. And Arthur joined the way he joined everything now, a calm rectangle in the corner, the leather spines of his home study at his back.

Cooper wrote one sentence on the board, not a phrase this time but a full sentence, and stepped away so they could all read it.

Continue Reading When a Trusted AI Agent Goes Wrong and No One is Watching (Beyond the Model)

For law firms, artificial intelligence has often arrived as a choice between speed and control. Stephen Costigan, founder of Atlas AI, argues that choice deserves a rethink. In this episode of The Geek in Review, we speak with Costigan about private legal AI infrastructure, knowledge graphs, and why a firm’s internal work product may become its most valuable long-term asset.

Atlas AI focuses on turning documents, matter history, precedents, clauses, parties, and obligations into a curated legal knowledge graph inside a firm’s own environment. Costigan contrasts this approach with standard vector search and retrieval systems, which find text with similar language but often lack context around clients, matters, entities, and relationships. A knowledge graph offers structure, linking people, documents, clauses, and legal concepts in ways closer to how lawyers understand their work.

The conversation also explores data quality, a subject with enough baggage to fill a records room. Costigan argues firms no longer need year-long cleanup projects before seeing results. Agent-led curation, entity extraction, duplicate resolution, and ontology mapping reduce much of the manual sorting traditionally associated with knowledge management. Human judgment still matters, especially around practice-area vocabularies and lower-confidence results, but the machines get assigned more of the janitorial work.

Security and governance sit at the center of Costigan’s model. Rather than asking firms to trust a vendor’s assurances around privileged data, Atlas AI runs within a firm’s Azure environment, under firm-controlled keys and policies. Costigan frames this as a shift from confidentiality as a contractual promise to confidentiality as an architectural decision. For legal organizations handling sensitive client information, the location of data, embeddings, audit trails, and model interactions matters as much as the interface lawyers see on screen.

Looking ahead, Costigan predicts a divide between firms renting generic AI tools and firms building durable knowledge infrastructure from their own experience. As routine drafting, diligence, and review work compress, firms with structured and reusable internal intelligence may productize expertise, offer new fixed-fee services, and rely less heavily on traditional leverage models. The future question, Costigan suggests, will not center on which AI tool sits on a lawyer’s desktop. The bigger question will ask who owns the knowledge behind 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 Own the Graph: Stephen Costigan on Private AI, Knowledge Infrastructure, and Law Firm Advantage

Signals are the new black

I had the pleasure of attending my first LSSO – Raindance Conference a few weeks ago where at least a half dozen times (I honestly lost count) presenters talked about signals.

Last week, I hosted an episode of Harbor’s Legal Soundings Podcast and signals came up.

There were the headlines about Kirkland’s AI investment being a signal of something, and talk of Microsoft signals.

I’m starting to think if I had a nickel for every recent mention of “signals” I’d be as rich as the people collecting nickels related to agentic AI.

Signals are not new.  Military intelligence has had the Signals Corp since the invention of radios.  Competitive intelligence professionals have always been in the business of finding signals to avoid mistakes and predict opportunity.

For decades, intelligent analysts have sifted through vast amounts of information to separate meaningful developments from background noise. The job has never been simply to gather information; it has been to transform information into intelligence. For example, at one of the firms I worked at, a Practice Group Leader called me one day and asked me to “look for the signals to determine which National oil company would invest in the Canadian oil sands next.”  Those weren’t the exact words he used, but you get the point.

What’s different in the AI era is that the economics of information have fundamentally changed.

Information itself is no longer scarce.

Every law firm and their CI/ BD practitioners now have access to AI tools that can instantly summarize earnings calls, SEC filings, regulatory developments, news articles, LinkedIn activity, job postings, patents, podcasts, analyst reports, and social media conversations. The barriers to access have largely disappeared.

The competitive advantage is no longer who has the information. The competitive advantage is who can identify and act on meaningful signals before everyone else.

This may be the single most important shift occurring in business development and competitive intelligence today.

AI Hasn’t Eliminated Analysis. It Has Raised the Bar.

For years, many organizations equated competitive intelligence with information gathering: collect the data, build the dossier, distribute the report, repeat.

AI now performs much of that work in seconds. Summarization is becoming commoditized. Research is becoming commoditized. Even synthesis is becoming increasingly accessible.

As AI lowers the cost of analysis, human judgment becomes more valuable, not less.

The question is no longer ‘What do we know?’ The questions become ‘What matters, and what is likely to happen next?’ ‘Who will this impact and how can we help?’

That is a signal-detection and analysis paradigm shift.

Business Development Is Becoming a Timing Function

Business development has always been about relationships, and it still is. But passive relationships, the kind where contact is only made when a suit is filed, a transaction is imminent or there is a sporting event happening, will no longer suffice.  Success today will   depend on engaging clients at precisely the right moment.

Companies continuously emit signals: new executive hires, geographic expansion, product launches, website changes, patent filings, strategic partnerships, job postings, and regulatory disclosures, to name a few.

Individually, these data points are unremarkable. Collectively, they tell a story.

Historically, legal business development has been largely relationship-driven and reactive: build relationships, stay visible, wait for a legal event, and receive the call.

The AI era invites a different question: What signals indicate a client is about to face a legal challenge before they realize they need outside counsel?  We used to set up early warning signals at my previous firm but we were still later than we could be in today’s world. We had to wait for a class action to be filed to find it. Today, AI tools can monitor consumer complaints, regulatory investigations, product recalls, data breaches, and court filings in near real time.

The firms that recognize that story first gain an advantage because timing matters.

Law Firms Have a Unique Opportunity

Lawyers are already trained to think in this scenario planning kind of way.

They instinctively ask: What changed? What are the second-order consequences? What risks are emerging? What is likely to happen next? What similar things have happened in the past?

These are signal-detection skills. The opportunity is to apply that thinking earlier in the client lifecycle.

What Legal Signals Might Look Like

Signal What it might indicate Potential legal need
Hiring a Chief AI Officer or AI governance lead Accelerating AI adoption AI governance, privacy, compliance, intellectual property
Expanding into a new country International growth Employment, tax, regulatory, and data privacy advice
Acquiring a smaller firm Integration risk M&A, employment, antitrust, and contracts
Multiple cybersecurity job postings Increased cyber maturity or recent concerns Cybersecurity, privacy, and incident response
Leadership turnover Strategic change Employment, compensation, and governance
Significant litigation against a competitor Industry-wide scrutiny Risk assessment and compliance review

 

Strong signals are easy to spot. Everyone sees the merger announcement, major funding round, or significant litigation filing.

Weak signals are more interesting: a handful of AI governance hires, a subtle website update, a revised privacy policy, or participation in a new industry consortium.

Weak signals may seem insignificant but they reveal a strategic shift months before it becomes obvious. The organizations that consistently connect these dots early will outperform those that wait for certainty, because by the time certainty arrives, everyone else can see it too.

This Is Also a Talent Question

Law firms have traditionally rewarded relationship builders, rainmakers, and network strength.

Those skills remain indispensable, but firms may need to elevate curiosity, pattern recognition, industry fluency, strategic questioning, and the ability to connect weak signals into actionable hypotheses.  These may not be skills that lawyers readily possess; some firms are already creating hybrid teams that combine business development professionals, competitive intelligence specialists, knowledge management professionals, and practicing lawyers to do exactly this. Others will find that to properly detect and action the signals they need to upskill their teams, hire or outsource to stay competitive.

Conclusion

Information is no longer a scarce resource.

In the AI era, every firm can gather more, summarize faster, and monitor more broadly. The advantage belongs to the firms that can identify which signals matter, understand what they mean, and act before the need becomes obvious.

For law firms, that changes the role of competitive intelligence and business development. The goal is not simply to report what happened. It is to help lawyers and clients see what may happen next.

AI can surface the signs. Human judgment turns them into signals.

And given how often signals seem to be appearing lately — in conferences, client conversations, headlines, podcasts, and product pitches — I wonder if the The Five Man Electrical Band was song writing in 2026 instead of 1971, they would have been singing about signals instead of signs… But there is an important distinction. Signs tell you where things are. Signals hint at where things are going.

“Sign, sign, everywhere a sign.”