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

This week we welcome American Association of Law Libraries leaders Jenny Foster, AALL President for 2025-2026, and Jessica Whytock, AALL Vice President and President-Elect. The conversation offers a preview of the 2026 AALL Annual Meeting & Conference in Cleveland, Ohio, along with a thoughtful look at how the association is supporting legal information professionals during a period of institutional, technological, and professional change.

Foster reflects on a leadership year focused on transparency, communication, and meaningful opportunities for member participation. From strengthening channels between members and AALL leadership to intentional volunteer appointments across committees and juries, she describes an association built through relationships. The goal is to ensure newer, mid-career, and seasoned law librarians all have a visible place in shaping the profession’s future.

Advocacy also plays a central role in the discussion. Foster explains how AALL continues its work on access to legal information, public policy, and coalition-building, even amid staffing transitions. The association’s Government Relations Committee has continued meeting with members, offering advocacy training, rebuilding connections with peer organizations, and aligning its work with AALL’s strategic priorities. For law librarians, advocacy is both a long-term commitment and a practical responsibility tied to preserving authoritative legal information.

The 2026 conference theme, “Leading with Aloha,” gives the Cleveland meeting its distinct point of view. Foster shares how aloha, rooted in kindness, unity, humility, patience, and meaningful connection, became a framework for leadership during uncertain times. More than 65 programs will explore topics ranging from generative AI and legal scholarship to physical collection strategy, access challenges, and the changing role of legal information professionals. Local programming connected to Cleveland’s history will bring an added sense of place to the gathering.

Whytock looks ahead to her upcoming presidency with a focus on clear pathways for engagement, leadership, grants, scholarships, committee service, and professional growth. Both leaders see artificial intelligence as a catalyst for a deeper conversation about the identity and value of legal information professionals. Their message is straightforward: the future of law librarianship rests in human judgment, critical thinking, ethical discernment, context, access, and a community willing to bring more voices into the room. The 2026 AALL Annual Meeting in Cleveland offers a place for those conversations to move from aspiration into action.

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 AALL 2026 Annual Meeting Preview with Foster and Whytock: Leading with Aloha, Legal AI, and the Future of Law Libraries

In this episode of The Geek in Review, we welcome Greg Dickason, Chief Technology Officer at LexisNexis, for a wide-ranging conversation on agentic legal AI, Lexis+ AI Protégé, and the movement from AI chat toward AI work. Dickason frames the shift through a simple contrast: earlier legal AI answered questions, while agentic workflows take on multi-step assignments, conduct research, create drafts, verify citations, and move legal professionals closer to finished work product. For law firms and legal departments trying to understand where AI goes next, this episode places agentic AI squarely inside legal workflow, legal research, drafting, and risk management.

A major theme of the conversation is trust. Dickason explains how Shepard’s Verify extends the familiar Shepard’s signal beyond traditional research screens and into uploaded work product. Rather than asking lawyers to rely on AI-generated text without a verification layer, LexisNexis is building citation checking into the workflow, giving lawyers a path to confirm whether cited authority exists, whether authority is still good law, and how later courts treated the cited case. For lawyers worried about hallucinated citations, AI-generated briefs, and unreliable authority, this verification layer becomes part of the product architecture, rather than an afterthought.

The discussion also explores the relationship between LexisNexis and Anthropic, along with the rise of legal AI skills. Dickason describes a market where model choice, orchestration, and legal skills increasingly matter as separate layers. Anthropic, OpenAI, Google, and other model providers offer impressive foundations, yet legal work needs more than general-purpose intelligence. Large law workflows require legal content, expert reasoning, matter-specific playbooks, and firm-defined processes. Dickason notes the ability to upload firm playbooks as skills, giving firms a path to bring their own way of working into Protégé.

Security receives equal billing with accuracy. As firms place client documents into AI vaults and connect work product to legal AI platforms, Dickason explains bring your own key, or BYOK, through a practical office-and-locked-cabinet analogy. The point is control: client content sits encrypted, access depends on the user’s key, and access stops when the key is withdrawn. He also discusses legal chunking, indexing, vector stores, retrieval-augmented generation, and knowledge graphs as part of building AI systems suited for legal documents, rather than generic file handling.

The episode closes with a broader view of legal AI’s impact on junior associates, legal training, and access to law. Dickason does not predict the end of junior lawyers. Instead, he sees AI helping junior lawyers become senior faster through mock trials, mock depositions, and richer training environments. He also warns of risks from agent volume, security vulnerabilities, and legal systems struggling to keep pace with AI-enabled industries. The message is pragmatic and optimistic: agentic legal AI will change legal work, yet the winners will be those who combine trusted content, secure systems, verification, workflow design, and human judgment.

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

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

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading LexisNexis CTO Greg Dickason on Agentic Legal AI, Protégé, Shepard’s Verify, and the Future of Legal Work

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

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

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

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

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

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

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

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

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

A few weeks ago I ran the numbers on the token cost panic. I took the scariest figure in legal AI, the finding that agentic workflows burn a thousand times more tokens than a chat query, and followed it all the way down to a dollar amount on a real deal. The panic did not survive the arithmetic. The piece is here if you want the full walk-through.

This is not that piece. The panic has moved on since I wrote it, and the new versions are smarter than the old one. The thousand-times number has quietly retired, because a thousand times almost nothing is still almost nothing. In its place are three fresher anxieties, and they deserve a real answer. The first says the model makers have a monopoly now, the price of a token is climbing, and it will climb forever, so you had better lock in a flat rate or build your own models before it does. The second says forget the price of a token, watch the meter: every time the AI reads your contract it ticks, and a long agentic session reads your contract over and over and over. The third does not bother with an argument at all. It just points at a number. One company spent five hundred million dollars on AI in a single month, and the number is so large it does the panicking for you.

All three are wrong. They are wrong in more interesting ways than the original, which is the only reason I am writing this down instead of linking to the first piece again. But underneath the new costumes it is the same body. Every version of this panic makes the same mistake and reaches the same conclusion. So let us stop swatting the individual numbers and name the thing that keeps generating them.

The Mistake Underneath All of It

Here is the error, stated once, because everything below is a variation on it.

A token is the unit a model uses to bill you. It is not the unit your work is measured in, it is not the unit your client pays for, and it is not the unit anything you care about is denominated in. It is a meter reading. The entire genre of token panic consists of staring at the meter reading as though it were the fare, the destination, and the quality of the ride all at once.

It is not any of those things. It is the meter. And a meter, by itself, tells you nothing about whether you are getting a good deal. A taxi meter reading of forty dollars is a bargain to the airport and a robbery around the block. The number on the meter is the least informative number in the entire transaction, because it means nothing until you put it next to what the ride was worth. Every piece in this genre forgets that, and forgets it in a slightly different way. Let me take them in turn.

“Prices Only Go Up”

Start with the monopoly story, because it has a real fact inside it. Yes, the newest frontier model costs more per token than last year’s newest model. That part is true. What the story does with it is the problem.

It draws a line through two dots and calls it a trend. Frontier prices up, therefore prices up forever, therefore lock in a flat rate before the meter eats you. But you are watching the wrong number. The price of a frontier token is not your cost. Your cost is what it takes to finish a task, and the cost of finishing a given task has been in freefall for two straight years. The same capability that ran on the most expensive model available in 2022 runs today on something on the order of two hundred and eighty times cheaper. Last year’s frontier is this year’s mid-tier is next year’s free default. The token at the very tip of the frontier gets a little pricier each release; everything behind the tip collapses in price behind it. Gartner expects another ninety percent drop in inference cost by 2030.

Watching the frontier price and concluding that AI is getting more expensive is reading the thermometer and announcing a fever, while ignoring that you are holding the thermometer over a candle. The evidence that the baseline is getting cheaper often sits right there in the same articles raising the alarm, quoted from the experts and then left unaddressed. You do not build a cost strategy on the one number in the system that is engineered to always be the highest.Continue Reading Bride of the Token Cost Panic

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

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

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

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

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

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

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

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

Transcript:

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