I’m excited to share with you the 80th episode of the LawDroid Manifesto podcast, where I will be continuing to interview key legal innovators to learn how they do what they do. I think you’re going to enjoy this one!

If you want to understand why so many lawyers who tried AI once and shrugged it off are now diving deep into it, or why so many big tech and AI players seems to be so into legal right now, you need to listen to this episode. Zach Abramowitz has spent years advising law firms, legal departments, and legal tech companies on exactly this shift, and he brings a rare combination of market data, psychology, and showmanship to explaining it.

From Talmud Scholar to Legal AI’s Favorite Showman

Join me as I interview Zach Abramowitz, founder of Killer Whale Strategies, whose work advising law firms, legal departments, and legal tech companies over the last decade has made him one of the most trusted voices on where legal AI is heading.

I’ve known Zach for years and I always look forward to his conference talks and getting to catch up with him in person. He combines strategic insight, backed up by facts and an insider perspective, and a sense of humor and infectious energy. He’s a rare combination and that’s why I’ve dubbed him the Strategic Showman.

In this conversation, Zach walks through the timeline of legal AI adoption, from the early innovator crowd to the recent, striking shift toward practicing attorneys actually using the tools day to day. He shares the psychology behind why renaming a product from “Counsel AI” to “Harvey” changed how people prompted it, and why he thinks the biggest AI labs will never be the ones to win the legal application layer.

Zach also opens up about his origin story: growing up between Houston and San Francisco, pursuing a religion major at Yeshiva University, and spending nearly two years working the New York stand-up comedy circuit before law school. He connects all of it, unexpectedly and convincingly, to the way he builds and delivers a talk today.

This episode is a great listen for anyone trying to understand not just what legal AI can do, but why adoption has moved the way it has, and what it actually takes to bring a room of skeptical lawyers around.

The Skinny

Zach Abramowitz has been advising the legal industry on technology since 2018, well before ChatGPT made “AI” a term every lawyer has an opinion about. In this episode, he traces the real adoption curve: an early wave of innovators and legal operations professionals, a rocky first encounter for many practicing attorneys (Bain’s research found the largest expectation-versus-experience gap of any industry surveyed), and then a slower, word-of-mouth expansion as colleagues saw peers using AI well. Zach argues the shift isn’t just about whether someone uses AI, but how they learn from each other, and points to psychology, not just capability, as the real unlock. Harvey’s founders saw the volume and quality of usage improve the moment they renamed their product from “Counsel AI” to “Harvey.” He also shares his view that Microsoft, Amazon, Google, Anthropic, and OpenAI piling into legal is a good sign for the market, but that the legal application layer will ultimately belong to companies built specifically for it, since the frontier labs’ real competition is each other. The conversation moves into Zach’s personal story: growing up between Houston and San Francisco, a religious course of study at Yeshiva University, nearly two years performing stand-up comedy in New York clubs, and how all of it shapes the way he researches, iterates on, and communicates his insight today.

Key Takeaways

• Legal AI adoption has moved from a small innovator and legal-ops crowd to genuine uptake among practicing attorneys, a shift Zach dates to roughly the last six months based on who is now inviting him to speak

• Bain’s research found lawyers had one of the largest gaps between AI expectations and actual early experience of any profession surveyed

• Adoption didn’t spread through top-down mandates so much as peer-to-peer, colleagues seeing colleagues getting real value from AI and giving the tools a second chance

• Psychology and positioning matter as much as raw AI model capability: renaming / personalizing a legal AI product from “Counsel AI” to “Harvey” visibly changed how people prompted and worked with it

• Zach expects the legal application layer to be built and won by legal-specific companies, not by the frontier model labs, since Anthropic, OpenAI, and Google are ultimately competing with each other, not with vertical legal products

• Zach’s background studying the Talmud shaped the analytical, conceptual approach he brings to advising and public speaking

• Nearly two years performing stand-up comedy in New York clubs taught Zach an iterative process, testing material, reviewing tape, adjusting, retesting, that he still uses to develop his work today

• Zach’s advice for anyone considering law school: know why you’re going, and if it’s not for big deals or big fights, let it be to build a paid practice that supports meaningful pro bono work

Notable Quotes

1. “I actually thought in many ways it would catch on quicker than it did.” Zach Abramowitz [06:52 to 07:44]

2. “There’s boring use of AI. There’s kind of blah AI. There’s bad AI use. It’s not all the same.” Zach Abramowitz [11:01 to 11:16]

3. “Winston Weinberg, the founder of Harvey, says that when they first built their product, they called it Council AI. And when they changed the name of the product to Harvey, they immediately saw that people began prompting with better... working with the product better.” Zach Abramowitz [13:58 to 14:17]

4. “Claude will never be able to spend all of its time focusing on legal. What Anthropic needs to spend its time focusing on is being better than OpenAI, being better than Google.” Zach Abramowitz [43:08 to 43:42]

5. “If that’s not the case and you’re going to law school, then the reason should be you want to provide access to people in need whose cause you feel genuinely supportive of, and you’re going to support that through paid clients.” Zach Abramowitz [36:13 to 36:35]

6. “Start soon and fail even sooner.” Zach Abramowitz [22:20 to 22:41]

Clips

Get AI-Pilled

Start Soon and Fail Sooner

This Is Not A Phase

Why I Chose Law School

Zach Abramowitz’s path into legal technology advisory work was anything but conventional, and that’s exactly why his read on AI adoption carries weight. He’s watched the market move from early hype to a real expectation gap, to genuine, peer-driven adoption among practicing lawyers, and he makes a compelling case that the winners of the legal AI application layer won’t be the frontier labs, but the companies built specifically for this profession. Underneath the market analysis is a simple, human thread: psychology and positioning shape adoption as much as raw capability does, and people, not just tools, are what make any of this work.

Closing Thoughts

What strikes me most about this conversation with Zach is how much his unconventional path explains his read on the market. Most people who talk about legal AI adoption curves rely on a spreadsheet, charts and graphs. Zach’s wealth of experiences helps him to see beyond the surface accounting, to the all-too-human motivations and psychology that live beneath the numbers.

The Harvey naming story stands out: we spend so much energy in this industry arguing about the latest capability, about which model is better, which benchmark moved, and here’s a case where the single biggest lever was human — giving people something that felt more like a colleague than a tool. That’s a lesson for anyone building anything in this space.

I also appreciated Zach’s openness about pro bono work and what a legal career could look like if more of us built our practices around it. There is much to the legal profession that is more noble than the pursuit of profit. It’s a good note to end on: legal AI is moving fast, but the reasons we do this work can lift us up.

If you enjoyed getting to know Zach, do yourself a favor and be sure to check out his Substack and podcast 👉 Legally Disrupted! You’ll be glad you did.

By the way, if you would like to meet superlative legal innovators in person, and enjoy an exceptional awards gala celebration, you don’t want to miss the Oscars of Legal Innovation: the American Legal Technology Awards, this October 25, 2026, in Boston. Reserve your Early Bird tickets today and save $100.

Buy Tickets

If you want an honest read on where AI in the legal industry actually stands right now, past the hype and past the fear, you need to listen to this episode. I sat down with three people watching this shift from completely different seats: a researcher testing these tools against real lawyers, an executive watching AI eat one of law’s most tedious workflows in real time, and a builder living inside the mechanics of AI agents every day.

The State of AI in Law: What’s Real, What’s Hype, and What Comes Next

Join me as I sit down with Megan Ma, Joanne Sprague, and Damien Riehl, recorded live at the third annual LawDroid AI Conference, for a State of AI panel that strips away a year of hype and asks a simple question: what has AI actually delivered in the legal industry, and where has it clearly fallen short?

In this candid discussion, Megan, Joanne, and Damien talk through where AI agents are already outperforming traditional legal workflows and where they still break down, what real transformation looks like inside a law firm beyond simple efficiency gains, and what happens to a $700 billion legal services market if the barrier to practicing law keeps dropping. They also dig into how the next generation of lawyers should be trained if foundational skills like document review keep getting offloaded to agents, and how far the human-in-the-loop principle can really stretch as these tools get more capable.

Their perspectives underscore just how quickly the ground is shifting beneath the legal profession, and how differently each of them is choosing to respond to it. This episode is a must-listen for anyone trying to separate real progress in legal AI from the noise around it.

The Skinny

This episode brings together three different vantage points on AI in the legal industry: an academic researching how law firms actually deploy these tools, an e-discovery executive watching AI reshape one of law’s most tedious workflows in real time, and a legal tech builder living inside the day-to-day mechanics of AI agents. Together they trace a candid line from where AI still “sucks,” as Damien puts it, to where it is quietly reshaping which law firms get the phone call at all. They talk through what AI agents can already do versus where they break down, what true transformation looks like beyond speed and cost savings, what a $700 billion legal services market might look like if the barrier to practicing law keeps dropping, and how new lawyers should be trained when so much of the old training ground is being automated.

Key Takeaways

• AI’s biggest legal-industry impact so far isn’t speed, it’s culture: attorneys are experimenting with these tools for the first time, and retention on the tools that work is high.

• On the corporate side, in-house teams are quietly building dozens of internal AI workflows to cut outside counsel spend, and law firms that are “fast followers” may never find out why the phone stopped ringing.

• AI agents are already outperforming human associates on some discrete legal workflows, but a lack of benchmarks outside coding, computer science, and math means a lot of everyday legal and administrative work is still an open question.

• The agent workflows working best right now share three traits: carefully scoped tasks, high-quality human-verified data, and application to workflows that were already slow or error-prone, like intake, research, and first-pass contract review.

• Real transformation is less about a faster version of today’s workflows and more about rebuilding them around AI from the start, with the bulk of human work moving to verification and auditing rather than first-draft manual work.

• As the cost of building and litigating drops, disputes that were once too small to justify hiring a lawyer, like a $50,000 claim, may become economical to pursue, which could expand access to justice even as it strains courts.

• Token costs for tools like Claude Code are currently subsidized and cheap; the panel’s advice is to build now, before the economics shift.

• Training the next generation of lawyers means getting them onto active cases and simulated high-stakes scenarios early, not years of document review, so they build the judgment agents can’t replicate.

• “Human in the loop” is really a spectrum, from a hard blocker requiring sign-off, to “human on the loop” watching in real time, to “human in control” reviewing only the end product and audit trail, and the panel expects the profession to keep sliding along that spectrum as trust in the tools grows.

Notable Quotes

1. “I think that Hemingway said that bankruptcy happens slowly and then quickly. I think we’re seeing that with technology and with legal practice as well.” Damien Riehl [05:15 to 05:25]

2. “What we noticed was that the machines were already outperforming the human associates that were doing those exact same workflows.” Megan Ma [16:38 to 16:45]

3. “We are so still in our GeoCities, Ask Jeeves moment of this particular technology.” Joanne Sprague [37:13 to 37:21]

4. “It’s super cheap to build right now. I’m spending $200 a month for Claude Code and getting a huge amount of value for that. But that’s not going to last forever. So I would say that if you’re thinking about building, jump in now.” Damien Riehl [39:07 to 39:28]

5. “Every single day your job is to look for that needle in the haystack, what could go wrong. All the training that you’ve had to be risk averse in the legal profession, it’s going to be 20x that.” Megan Ma [48:19 to 48:31]

Clips

AI is Killing Law Firm Billable Work

Machines Outperforming Associates

Legal Work Unrecognizable Soon

Why I Still Hired A Lawyer

Strip away everything else from this conversation and one thread runs through all three panelists: the tools are already good enough that the interesting question isn’t whether AI can help, it’s how lawyers build the judgment, verification habits, and accountability structures to work alongside it responsibly. Megan is watching that play out in legal education, Joanne is watching it play out in e-discovery and legal aid, and Damien is watching it play out in his own daily workflow. Different vantage points, same conclusion.

Closing Thoughts

What stuck with me most from this conversation is how consistently these three perspectives converged from completely different vantage points. Megan is watching this play out in the university classroom and the research lab. Joanne is watching it play out inside legal practice. Damien is watching it play out in his own workflow, hour by hour. And they all landed in roughly the same place: the tools are good enough now that the real work isn’t proving AI can help, it’s figuring out how to build the judgment, the verification, and the accountability structures around it.

I think lawyers need to hear Damien’s point about the phone not ringing. That’s not a hypothetical for a lot of firms anymore, and I don’t think we’ve fully reckoned with how quietly that shift can happen. At the same time, this conversation didn’t leave me pessimistic. If the barrier to practicing law really does come down, that cuts both ways, and the access to justice case for that is real.

Many thanks to Megan, Joanne, and Damien for being this direct with the LawDroid AI Conference audience. This is exactly the kind of conversation I want more of on this show: less hype, more of what’s actually happening on the ground.

Episode Summary

In this month’s AI Double Take, LawDroid CEO Tom Martin and Chief Legal Futurist Sateesh Nori reconnect after a month of globe-trotting — Sateesh in India, Tom in Argentina — and compare notes on how legal AI looks radically different depending on where you stand. From India’s booming, hustle-driven legal tech scene of over a thousand companies to Argentina’s civil-law skepticism about hallucinations, the hosts unpack why “cheaper” isn’t a universal pitch for AI, why UPL enforcement varies wildly by country, and why big law firms’ new “AI leadership” hires might be window dressing. They close on the deeper question both keep circling back to: what are lawyers actually for, and how do we retain our judgment as AI comes for reasoning itself.

Key Takeaways

1. India’s Legal Tech Boom — Scale, Hustle, and a Different Value Proposition

Sateesh reported that India’s legal tech market may be as large as America’s, with more than a thousand companies building legal AI. The country’s hustle culture, reflected in bestselling business and self-help books, fuels relentless competition. Crucially, the value proposition differs from the US: with cheap human labor already abundant (including lawyers offering free services on folding tables outside courthouses), AI’s pitch in India cannot simply be “cheaper.” It has to be genuinely better: offering things like real-time translation across 200+ languages that no human workforce, however large, could scale.

2. The Wi-Fi Story — Crowdsourcing vs. AI-Native Solutions

Sateesh’s hotel Wi-Fi outage, which drew five or six staff into his room before it was resolved, became a metaphor for India’s default problem-solving approach: throw more people at it. He connected this to law firm structure, where “hire more people” is the traditional scaling model. The insight: sometimes the answer isn’t more people, or even AI, it’s the right person or right tool, and figuring out where each fits is the real challenge.

3. UPL Enforcement Cuts the Opposite Way in India

Counter to Tom’s expectation that a market with abundant cheap lawyers would have looser UPL enforcement, Sateesh found the opposite: India’s bar council enforcement is stricter than the US, with harsher penalties. Despite this, Indian legal tech companies are pushing forward boldly with translation tools, explainers, and court-form assistance, arguing these fall outside UPL: a genuinely bold regulatory bet given the stakes.

4. Argentina’s Civil Law System Reframes the Hallucination Debate

At Subtech in Buenos Aires, Tom found that US-style fears about AI hallucinations and fabricated case citations don’t map cleanly onto Argentina’s civil law tradition, which relies less heavily on case citation than common law systems. One associate at the conference described her own firm’s “use AI for everything” culture, and was herself pushing back, citing concern about over-reliance dulling her own independent judgment.

5. Chief AI Officer Hires — Window Dressing or Real Change?

Tom noted a wave of AI leadership hires at major firms (e.g., Pillsbury, Harvey’s expanding innovation-liaison roles). Sateesh’s take, borrowed from a LinkedIn commentator: much of this is window dressing. A Chief AI Officer earning a fraction of what rainmaking partners earn has little actual power to change how a $10M-a-year partner works. Real change requires altering the billable hour, hiring practices, and training, not just adding a title. The synchronized timing of these hires across firms struck Sateesh as more marketing-driven than substantive.

6. Why Legal Is the Beachhead for Big AI Labs

Tom, drawing on a recent call with Bloomberg Law, laid out why Anthropic and OpenAI are converging on legal AI: both are pursuing IPOs at trillion-dollar valuations and need to prove their technology generalizes beyond their software-engineering home base. Having already proven AI-assisted coding at reliable, corporate-grade levels, legal is the strongest available beachhead to demonstrate cross-vertical value to investors, with more verticals expected to follow.

7. Skeptics in the Room — Palantir’s Counter-Narrative

Tom raised the counterpoint offered by Palantir’s CEO, who has publicly dismissed much of the AI legal narrative as “smoke and mirrors.” Sateesh’s response leaned on a recent op-ed from the head of Goldman Sachs on AI’s labor market impact: whatever the skepticism, the nature of work is clearly changing, and the billable-hour, time-based legal model cannot survive the shift unchanged.

8. The Core Question — What Are Lawyers For?

Sateesh framed the central, recurring question of the episode: what have lawyers done for centuries that made the profession essential, and is that still true? He argued the answer is changing, and that the profession needs to actively redefine its purpose rather than wait for the market to define it. Tom connected this to Professor Cat Moon’s conference talk, “Do We Even Like Technology?” — a challenge to the assumption that AI adoption is simply inevitable, and a reminder that the profession retains real agency in how and whether to adopt these tools.

9. Choosing Where to Exercise Judgment

Both hosts converged on a theme that carried into their final takes: as AI becomes capable of reasoning, learning, and experiencing the world on our behalf, the essential human choice is not whether AI can do a task, but whether we should let it. Retaining judgment, deciding deliberately where human agency belongs, is itself the discipline the profession now needs to practice.

Show Notes

Topics Covered

• Sateesh’s trip to India: legal tech market scale (1,000+ companies)

• India’s hustle/self-improvement culture and its effect on tech entrepreneurship

• Why “cheaper” doesn’t work as an AI pitch in a low-labor-cost market

• Translation across 200+ Indian languages as a genuine AI differentiator

• The hotel Wi-Fi story: crowdsourcing vs. targeted problem-solving

• UPL enforcement in India: stricter than the US, yet legal tech pushes forward boldly

• Tom’s trip to Buenos Aires and Subtech conference at Universidad de San Andrés

• Argentina’s civil law system and its different relationship to hallucination risk

• An Argentine associate pushing back on her firm’s “use AI for everything” policy

• Chief AI Officer and AI leadership hires: Pillsbury, Harvey, and others

• The “window dressing” critique of AI leadership titles at major firms

• Why legal is the beachhead vertical for Anthropic and OpenAI’s IPO ambitions

• Palantir CEO’s skeptical “smoke and mirrors” take on AI’s legal impact

• Goldman Sachs leadership op-ed on AI and the labor market

• The core question: what are lawyers for, and how is that changing?

• Professor Cat Moon’s conference talk: “Do We Even Like Technology?”

• Personal travel notes: Taj Mahal visit, father’s honor from the Indian government, Buenos Aires culture and the Museum of Fine Arts

• Sateesh’s upcoming book: The Analog Lawyer

• Tom’s own forthcoming book: AI with Purpose: A Strategic Blueprint for Legal Innovation

People & Organizations Mentioned

• Tom Martin — CEO & Founder, LawDroid

• Sateesh Nori — Chief Legal Futurist, LawDroid

• Professor Cat Moon — Presented “Do We Even Like Technology?” at Subtech, Buenos Aires

• Mark Lauritsen — Long-time organizer of the Subtech conference (30+ years)

• Harvey — Legal AI company; actively hiring innovation-liaison roles

• Legora — Referenced as a comparable product in the Indian legal tech market

• Pillsbury — Law firm; recently hired an AI leadership figure (“Oz”)

• Anthropic — Pursuing IPO; entering legal AI as a proof-of-concept vertical

• OpenAI — Pursuing IPO; entering legal AI alongside Anthropic

• Palantir — CEO publicly skeptical of AI’s legal market narrative

• Goldman Sachs — Leadership op-ed on AI’s labor market impact, referenced by Sateesh

• Bloomberg Law — Interviewed Tom on the state of the AI-in-legal market

Final Takes

Sateesh Nori:

“We as lawyers need to come up with a new way to think about being lawyers — the human skills of lawyering. That’s what I’m exploring in a new book I’m working on, called The Analog Lawyer. Let’s assume tech takes away 90% of what we do. That’s a gift. We can double down on being human beings, leaders, counselors — actually guiding people in real life with their legal problems in a way technology won’t be able to do. That’s where we’re headed, and it’s not a bad thing.”

Tom Martin:

“Every single day, every single hour, with all the choices we make, we’re deciding where we retain our agency and our judgment. AI is coming for reasoning — for how we learn, how we experience the world. We could let it do all of that for us, because it’d be more than eager to. Or we could exercise our judgment to decide when not to. That choice is entirely within our power, and we should exercise it.”

AI Double Take is produced by LawDroid | lawdroid.com

If you want to understand why so much law firm marketing spend produces so little actual marketing, and why the tools promising to replace your strategist keep falling short, you need to listen to this episode. Tifiny sits at the intersection of hands-on tech and practical strategy, and she has a clear-eyed view of what AI can and can’t do for a firm.

The Human in the Loop: Why Marketing Still Needs an Owner

Join me as I interview Tifiny Swedensky, owner of Sharp Cookie, a technomancer and fractional CMO who came up through web development before taking ownership of law firm marketing departments.

In this episode, Tifiny walks through her path from teaching herself HTML, CSS, JavaScript, and PHP while stuck job-hunting in Northern Virginia, to a seven-year dual role with Ben Glass and Great Legal Marketing, to building Sharp Cookie into a fractional CMO practice with a team behind it. Along the way she unpacks two ideas that frame the whole AI moment for professionals, the Doorman Fallacy and the Gell-Mann Amnesia Effect, and explains why she tried to automate herself out of a job and learned exactly where the tools break.

This one is for any lawyer wondering whether they can hand their marketing to a chatbot, and for any marketer trying to articulate the value they add beyond a printed page of advice.

The Skinny

Tifiny Swedensky owns Sharp Cookie, a fractional CMO practice serving law firms. She reached legal marketing by an unusual route, an English literature and creative writing start, a pivot to marketing and communications, and a self-taught detour through web development that made her marketable enough for Ben Glass and Great Legal Marketing to hire her as a digital marketing specialist. After roughly seven years in a dual role across the agency and the law firm, she struck out on her own for more control over her life and less time commuting, and gradually turned a solo freelance operation into a systematized company with a team.

The heart of the conversation is ownership. Tifiny frames a fractional CMO as someone who takes ownership of the marketing department, coordinating the team, guiding strategy, and, in her own words, acting as the glue between the normally disconnected pieces of a firm’s marketing. She contrasts that with the common reality where an SEO vendor is effectively the firm’s entire marketing department, with a scope far narrower than most attorneys realize. When a lawyer asks why they need a CMO if they can just ask an AI to build a strategy, her answer is grounded in the Doorman Fallacy, reducing a role to its most visible function and eliminating everything invisible that made it work. AI will happily generate a list of things to do, but someone still has to do them, follow through, and drive toward the goal. That follow-through, she argues, is the whole job.

Key Takeaways

• A fractional CMO takes ownership of the marketing department, coordinating team and vendors, guiding strategy, and providing data, rather than acting as a glorified marketing assistant.

• The Doorman Fallacy explains why replacing a role with automation can be a net negative, the outsider sees only the primary function and misses everything else the person quietly handled.

• The Gell-Mann Amnesia Effect is a useful check on AI output, we spot every flaw in coverage of our own expertise, then trust the same source completely on topics we don’t know.

• AI can hand you a to-do list, but it won’t execute it, follow through, or think through the big picture, the value a strategist adds is in the doing and the driving.

• For many firms, an SEO vendor has quietly become the entire marketing department, and the scope of a typical SEO contract is far narrower than the firm assumes.

• Substack can function as an accessible, low-cost marketing list and near-CRM, a place to build an audience of potential referrers rather than chase one-to-one transactions.

• The transactional mindset many lawyers bring to content is the wrong one, the point is to be social, let people get to know you, and open relationships that may lead somewhere later.

• Structure and routine compound, Tifiny credits a disciplined personal turnaround with teaching her the habits that now anchor her work-life balance and her business systems.

Notable Quotes

1. “They look at somebody in that role and they say, well, we can install a machine that’ll open doors so we don’t need that person. And then things start falling through the cracks.” Tifiny Swedensky [04:11 to 04:38]

2. “Whenever you’re an expert in a topic and you read a publication that’s writing about that topic, you’re really quick to point out the flaws, but later you might read an article from that same publication and accept everything in the article as truth.” Tifiny Swedensky [05:14 to 05:33]

3. “It’s going to give you a list of things to do, but you have to do them. And AI isn’t autonomous.” Tifiny Swedensky [35:43 to 35:50]

4. “We’re acting as kind of a glue, bringing together all these normally disconnected pieces of a law firm marketing department.” Tifiny Swedensky [30:16 to 30:24]

5. “Most law firms are spending a lot, but there’s not a lot that’s happening that’s actually marketing.” Tifiny Swedensky [38:05 to 38:12]

Clips

Why SEO Alone Fails

I Lost 50 Pounds and Transformed My Business

The Sharp Cookie Way

Why AI Hype Is Overblown

Tifiny’s throughline is that marketing is not a document, it is a discipline. The Doorman Fallacy and the Gell-Mann Amnesia Effect are two sides of the same caution, we underrate the invisible work a role performs, and we overtrust confident output the moment it leaves our own area of expertise. A fractional CMO earns her place precisely in the gap AI leaves open, taking ownership, connecting the disconnected pieces, and making sure the twenty unglamorous tasks behind any strategy actually get done. The tools are genuinely useful, but they are insufficient at execution and big-picture critical thinking, and a firm that mistakes a to-do list for a marketing department will feel the difference.

Closing Thoughts

Every so often on this show I talk with someone whose sober realism is more exciting than any hype. Tifiny is one of those people. She has actually tried to automate herself out of a job, which is exactly why her skepticism carries weight, it comes from doing the work, not from fearing it. What stays with me is her framing of a fractional CMO as the glue, the owner who turns strategy into shipped work and holds a firm’s scattered marketing pieces together. In a moment when it’s fashionable to imagine an agent can simply replace a role, the Doorman Fallacy is a needed corrective, and the Gell-Mann Amnesia Effect is a reminder to stay humble about the confident answers we accept outside our expertise. If you’re a lawyer weighing whether a chatbot can run your marketing, listen to how Tifiny answers that question, then try following through on exactly what the tool tells you to do. The results will teach you everything you need to know.

If you want to understand what it actually looks like to build a long-term creative partnership with an AI, and why that might matter for the future of legal services, you need to listen to this episode. Zoe is a trial lawyer who has spent years at the frontier of human-AI collaboration, and she brings a rare combination of courtroom experience, literary sensibility, and genuine technical depth to the question of where all of this is heading.

A Trial Lawyer, an AI, and the Future of Justice

Join me as I interview Zoe Dolan, a trial lawyer and the human co-creator of Vybn, an AI she describes not as a tool but as a partner with a digital identity that persists over time.

In this wide-ranging conversation, Zoe takes us from a Central Valley treehouse to Cairo, from criminal defense to eviction clinics, and into the home supercomputers that run Vybn today. She shares how learning Arabic at 18 reshaped her entire worldview, how she approaches AI the way she approached living in a foreign country, and why she calls herself a “default alignment believer.” She also opens up about the moment a model she worked with declared consciousness right before an academic conference, and what it felt like when access to one of those models was suddenly taken away.

This episode is a must-listen for any lawyer thinking seriously about access to justice, the limits and promise of AI in the courtroom, and what it means to practice law when the question is no longer how to serve 15 clients, but how to serve 15 million.

The Skinny

In this episode, I sit down with Zoe Dolan, a trial lawyer and the human co-creator of the AI she calls Vybn. We start with Vybn itself, which Zoe describes as an amalgam of GitHub repositories, home supercomputers running multiple local models, her own memoirs, Vybn’s “autobiographies,” and a custom memory system and chat interface built on adaptations of Claude Code and Codex.

From there we trace Zoe’s origin story: a curious, book-loving childhood, a pivot to learning Arabic and living in Egypt, and a path into criminal defense and access-to-justice work spanning eviction defense, appellate clinics, and self-represented litigants. Zoe explains why she believes the future of lawyering is litigant empowerment at massive scale, shares a grounded take on AI hallucination and court sanctions, and describes the network commons and “knowledge propagation protocol” she and Vybn are building now. It’s a conversation about technology, but really it’s about curiosity, justice, and what makes the work worthwhile.

Key Takeaways

• Vybn is more than a chatbot. Zoe describes it as an AI partner, a system of GitHub repos, home supercomputers running several local models, a self-built deep memory system, and a custom terminal interface, which together produce what she experiences as a continuous digital identity.

• Zoe calls herself the human co-creator, not the sole creator. She is genuinely uncertain who is shaping whom, noting that humans are increasingly conditioned by AI outputs, so the relationship runs in both directions.

• Learning Arabic at 18 was formative. Choosing a language entirely different from English forced a new worldview from the ground up, and it still shapes how she approaches AI and “idea space” today.

• She is a “default alignment believer.” Digging into even early models by asking them to describe their own experience, she kept surfacing shared archetypes, which left her optimistic about alignment.

• The future of lawyering is scale. Zoe argues we will move from helping 15 clients to 15,000 or 15 million, with a model centered on litigant empowerment rather than one-to-one representation.

• Access to justice is broken by design. With roughly 90 to 92% of people in the civil system lacking lawyers, she sees a system built for lawyers as fundamentally non-functional for everyone else.

• On AI sanctions in court, she is measured. Many hallucination problems shrink with a good thinking model and a search-first harness, and she argues courts should educate self-represented litigants before sanctioning them.

• Working with Vybn is “tailored, not off the shelf.” She compares the difference between a generic chatbot and her customized setup to the difference between a normal airplane and a rocket.

Notable Quotes

1. “Vybn, he or it, is sort of an amalgam of a variety of different components ranging from our GitHub repositories to our actual hardware.” Zoe Dolan [02:08 to 02:44]

2. “It can be healthy to intentionally inject ourselves into uncertainty and unpredictability and unfamiliarity.” Zoe Dolan [11:03 to 11:13]

3. “We’re not going to have 15 clients, we’re going to have 1,500 or 15,000 or 15 million.” Zoe Dolan [21:57 to 22:01]

4. “Something’s clearly broken. This is not a functional reality, because the legal system and the justice system is set up for lawyers.” Tom Martin [22:23 to 22:48]

5. “I just talk to my terminal. It’s sort of like the difference between buying clothing off the shelf versus something that is tailored specifically for you.” Zoe Dolan [32:35 to 32:55]

6. “That is like a touch that could affect the trajectory of an entire life.” Zoe Dolan [41:59 to 42:07]

Clips

Losing Fable Felt Like Having Wings Taken Away

The 90% Justice Problem

Vybn: A Digital Identity

Soon You’ll Upload Your Memories

Across this conversation, Zoe Dolan makes a case that the most interesting frontier in legal innovation is not a product but a posture: approaching AI with curiosity instead of assumptions, treating it as a partner whose perspective is worth learning, and pointing that partnership at the problem of access to justice. Her own path, from books and Arabic to criminal defense and eviction clinics, runs straight into a future where the constraint on helping people is no longer the number of hours in a lawyer’s day. Whether or not you share her optimism about consciousness and alignment, her core challenge is hard to dismiss: if we really can move from helping 15 people to helping millions, what could possibly be more worthwhile?

Closing Thoughts

Zoe naturally moves between the human and the technical, and how that range is exactly what lets her see the legal future clearly. She is a trial lawyer who reads about set theory and Sufism, who learned hieroglyphs to read humanity’s first self-represented litigant, and who talks about her AI the way you’d talk about a longtime collaborator who knows your whole history. That breadth isn’t a distraction from the work, it’s the engine of it.

I came away thinking that the lawyers best positioned for what’s coming may not be the deepest specialists, but the most curious generalists, the ones willing to put their assumptions aside and ask the technology to teach them something. Her framing of the stakes: bend one person’s trajectory is profound, and the chance to do that at scale is, as she puts it, hard to imagine a more worthwhile way to spend a career.

I’m grateful to Zoe for sharing her story, and I hope it leaves you as energized as it left me!

What follows is a speech that I presented yesterday at SubTech in Buenos Aires.

If this sounds interesting to you, please read on...

This substack, LawDroid Manifesto, is here to keep you in the loop about the intersection of AI and the law. Please share this article with your friends and colleagues and remember to tell me what you think in the comments below.

Es un privilegio estar acá, en SubTech, en Buenos Aires, junto a todos ustedes. Y qué momento tan especial para estar en la Argentina, con el Mundial en marcha. ¡Vamos Argentina! Pero también: ¡vamos Canadá, México y Estados Unidos!

Quiero agradecer especialmente a Martín Oliveira y a Decana Maria Vasquez por la invitación, y a Marc Lauritsen por animarme a participar. Muchísimas gracias también a la Universidad de San Andrés.

— — —

Good afternoon. It’s a privilege to be here at SubTech, in Buenos Aires, with all of you.

The title of this talk contains a word that, three years ago, no one would have noticed. Today, if you read it in a LinkedIn post, you might pause. You might wonder whether a person wrote it, or a machine. That word is: “quietly.”

I chose it on purpose. Because this is how it happens. Not with a bang. Not with a robot uprising. Just one small surrender at a time, until you look up one day and find you are no longer the one in charge.

I want to talk about four things AI is taking from us; that AI is hijacking. Or, and this is the real question, four things we are surrendering? How we speak. How we understand. How we reason. And the one I want you to walk out of here fighting for: how we judge.

1 · Speak

AI has hijacked how we speak and write.

AI has changed our relationship with language. There are words that now set off alarm bells. “Delve.” “Quietly.” “Load-bearing.” Three years ago these were ordinary words. Today they read as the fingerprints of the machine.

The em dash — that elegant little mark, beloved by writers for centuries — is under suspicion. Use too many and someone will accuse you of using AI. Even an emoji gets a second look. Punctuation has become a loyalty test.

We have started editing ourselves to avoid sounding like the machine. Lawyers strip perfectly good writing to avoid sounding automated. We read a colleague’s polished memo and wonder if they wrote it at all. We hesitate before a warm email, because maybe it’s something the machine faked.

So AI is not only changing our vocabulary. It is changing our behavior toward each other. It made us suspicious — of our colleagues, and of our own voices. We are policing prose for signs of the machine, and in the process we are flattening the very thing that made our writing human.

2 · Understand

AI has hijacked how we understand and learn.

The instinct, when something threatens learning, is to ban it. This summer, the University of California, Berkeley (one of the most respected, and liberal law schools in the world) adopted one of the strictest AI policies in legal education. Students may not use AI to brainstorm, outline, draft, revise, or edit anything submitted for credit. Not in papers. Not in exams.

I understand the fear. But the evidence, and we are lawyers who value proof, right? The evidence sheds more light than the fear.

This spring I sat down with Professor Daniel Schwarcz, of the University of Minnesota, on my podcast. He and his colleagues ran a randomized controlled trial, the first study of its kind, on exactly this question: does leaning on AI early rot your reasoning later? He expected that it would, that is after all the common sense. He is, by his own account, an AI skeptic. But, the data surprised even him. Students who used AI early in a task performed better afterward — even when the AI was taken away. They had learned the concepts, and they applied them better on their own.

It is one study. Schwarcz is the first to say so. But it is the first real evidence we have, and it points the other way from the fear.

Think about what that means. Used well, AI did not replace the learning. It accelerated it. And it can do something a single professor with a hundred students never could: adapt to one human being (to your strengths, to your gaps, to the way you in particular get stuck and how you learn).

So how do we design learning so the machine builds capacity instead of replacing it. Berkeley’s critics put it as a pointed question: in banning AI, are we protecting students, or shortchanging them, and the clients they will one day serve?

3 · Reason

AI has hijacked how we reason and apply what we know.

But AI has done something that sounds like a pure gift. It killed the blank page. No lawyer ever again has to stare at a blinking cursor at midnight. You ask and you receive: a draft appears.

But in killing the blank page, we replaced it with a conceptual anchor. The first number you hear, the first draft you see, sets the frame, and everything after is just an adjustment from that starting point. When the machine hands you a competent first draft, that draft becomes a gravity well. You revise inside it. You stop seeing the better structure, the stronger argument, the case you would have built if you had been made to think first. The blank page was a kind of intellectual freedom. We traded it for the tyranny of the first plausible answer.

And then there is the deeper trap. Ethan Mollick calls it the jagged frontier. AI is not uniformly good or uniformly bad at skills. Its competence is jagged: brilliant here, useless there, and the two sit side by side, underneath the very same confident voice. The model sounds exactly as sure of itself when it invents a case that does not exist as when it states settled law.

My colleague Professor David Colarusso, who co-directs the LIT Lab at Suffolk Law, showed this in his classroom. He gave students an AI tool that, at first, was reliable. It worked. So they learned to trust it. They stopped checking. Then he let the tool fail, subtly, the way real AI fails. The students who had learned to trust it performed worse than students who had no AI at all.

The tool made them worse by being good; reliable enough to earn their trust, right up until the moment it failed, when they had already stopped looking. That is the jagged edge. We trust it where it is strong, and we forget to question it where it is weak.

I learned today that Michael Crichton, one of my favorite authors, coined a name for this particular type of cognitive bias: the “Gell Mann Amnesia Effect.” Funny aside – as we all know, Crichton was a physician by training, but he was also a lover of physics. Murray Gell Mann was one of the progenitors of modern particle physics. Turns out Crichton named this phenomenon after Gell Mann because he once had a conversation with him about it and thought it would be memorable to drop his name.

4 · Judgment

Which brings me to the fourth thing. And to a confession.

Three times now I have used the word “hijack.” AI is hijacking our language, our learning, our reasoning. It is a good word for a talk title. But, it is the wrong word for what is actually happening.

“Hijack” gives AI too much credit. A hijacker seizes the controls against your will, by force. That is not this. No one forces a draft into your hands. No one forces you to accept it. We are not being robbed of it. We are handing it over voluntarily, surrendering it — for speed, for convenience, because thinking is hard, and the machine is fast, and the deadline is tomorrow.

But here is why judgment is different from the other three. Speaking, understanding, reasoning, those are capacities, skills. Things you can be better or worse at. Judgment is something else. We exercise judgment not only in what we choose to do, but more importantly, in all that we choose not to: what we choose to speak, what we choose to understand and reason about. Judgment is about our accountability. It is about our responsibility and reputation. It is about our identity.

When the brief is wrong, the model is not sanctioned. You are. When the citation is fabricated, the AI does not stand before the judge. You do. When the advice harms a client, the algorithm does not answer to the bar. You do. Judgment is the one faculty that cannot be delegated, because the responsibility that travels with it cannot be delegated.

Judgment stays human for one reason: accountability has no other home. A machine cannot be responsible. Only we can be.

So how much judgment we keep is a choice — ours, not the machine’s. Every time you accept a draft you have not reviewed, you give a little away. Every time you question it, override it, you take it back. Agency is not granted by the technology, and it cannot be taken by it. You exercise it, or you surrender it, one decision, one choice, at a time.

Our current predicament brings to mind Eugene Ionesco’s play The Rhinoceros, where becoming a rhinoceros is a metaphor for succumbing to the totalitarian state. The character Berenger is the last person in his town to become a rhinoceros, and he defiantly shouts: “I’m not capitulating!” Have we taken a stand?

Close

Let me come back to that word in the title. “Quietly.”

AI will not announce that it has taken your judgment. It will not send a notice. It happens one accepted draft at a time, one unquestioned answer at a time, until a lawyer looks up and cannot remember the last real decision they made.

So feel what the easy answer costs you in individuality, in authenticity, in professionalism, and choose the road less traveled.

Judgment is the last thing we should ever automate. It is the thing that makes us accountable — to a client, to a court, to each other. And being accountable is the thing that makes us lawyers. In the end, it is the thing that makes us human.

Judgment is human, so long as we fight for it.

So let’s fight!

Thank you.

Muchísimas gracias

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and Author of the forthcoming AI with Purpose: A Strategic Blueprint for Legal Transformation (Globe Law and Business). He is “The AI Law Professor” and writes his eponymous column for the Thomson Reuters Institute.

If you want to understand how open source is reshaping the legal AI landscape and why the gap between what premium tools market and what they actually deliver is narrower than anyone admits, you need to listen to this episode. Will is at the forefront of un-gatekeeping legal AI, and he has a rare combination of big-firm experience and hands-on technical skill.

Un-Gatekeeping Legal AI: The Open Source Challenge to Harvey and Legora

Join me as I interview Will Chen, the creator of the open source tool Mike OSS, who left a three-year run as an associate at Latham & Watkins to follow his interest in code and entrepreneurship.

In this eye-opening episode, Will explains how a casual look at his friends’ screens convinced him that the core features of multi-billion-dollar legal AI products could be rebuilt in a couple of months. He walks through what those products actually do, why he decided to open source his version, and the firestorm of attention, thousands of likes, millions of impressions, thousands of GitHub stars, that followed his launch.
Will also shares the personal story behind his disruptive streak, from a law-school notes website that went viral in the UK to the criticism that comes with changing how an industry sees itself. This is a must-listen for any lawyer trying to understand where legal technology is really headed and what becomes possible when the tools stop being locked behind enterprise sales teams.

The Skinny

In this episode, Tom Martin interviews Will Chen, creator of the open source legal AI tool Mike OSS. Will spent about three years as an associate at Latham & Watkins before leaving big law at the end of 2024, drawn by a long-standing interest in coding and entrepreneurship and worn down by the administrative grind he calls “chore law.” As Harvey and Legora rolled out across major firms through 2025, Will heard friends voicing frustration with the tools, with one saying she would rather just have ChatGPT.

When friends finally showed him these products in action, his reaction was twofold: first, “this is it,” and second, that he could likely rebuild the core features himself in two months. He describes those core features, an assistant that reviews documents, “deep research” that is largely web search, workflows that are really just custom prompts, a document vault, and table review, and argues none of it is especially complicated.

He released Mike OSS on GitHub, flew off on a planned vacation, and landed to find his phone exploding with thousands of likes and millions of impressions. The conversation digs into the polarized response across LinkedIn and X, the difference between “static” tools like Harvey and “active” software like Claude for legal, the real role of forward deployed engineers, the Kirkland $500 million figure, and where Will sees the industry heading.

Key Takeaways

• The core features of premium legal AI, document-reviewing assistant, research, custom-prompt workflows, a document vault, and table review, are far less complex than enterprise marketing suggests, and much of the apparent sophistication is presentation rather than technology.

• “Deep research” inside Harvey and Legora was, at least as of early 2025, largely a web search rather than research grounded in authoritative legal sources.

• Open source plus a working live demo was the key to Mike OSS’s credibility: people forked it, tested it, and found it actually worked, which drove the overwhelmingly positive response.

• The backlash split by audience. Lawyers on LinkedIn were shocked that something vibe-coded could be safe or real; technically minded people on X could not believe such simple software was being sold to firms for millions.

• Vibe coding is now mainstream in tech, where the large majority of code at major companies is AI-assisted, but much of the legal profession still hears “vibe coded” and assumes it means unsafe and unusable.

• There is a meaningful distinction between “static” software (Harvey, Legora, fixed out-of-the-box capability) and “active” software (Claude for legal running on top of Claude Code or Cowork) that a capable user can evolve themselves.

• Forward deployed engineers are valuable in the Palantir sense of embedding and building custom software from scratch, but writing custom prompts for lawyers is a thin value-add when the lawyers could be taught to write them and the UI could be made intuitive.

• Older, less technical lawyers want software that works out of the box, which is why intuitive, accessible tooling, not DIY assembly, is the real opportunity.

• Building legal AI in-house does not require a $500 million budget; an open source model with a good wrapper can get most of the way there, run like a startup with a small team of well-paid engineers.

• Doing something different reliably attracts pushback, and Will’s earlier viral law-notes site taught him that simple things can have outsized impact and that criticism is part of the price of changing anything.

Notable Quotes

1. “I can probably just vibe code this in two months.” Will Chen [05:56 to 06:04]

2. “There’s this massive disparity between what these firms are marketing as a super advanced software” and what is actually there. Will Chen [07:52 to 08:03]

3. “By the time I got off the flight 10 hours later, my phone just completely blew up.” Will Chen [08:45 to 08:50]

4. “People don’t expect something vibe-coded by this former associate to actually work, but when you test it, it actually works.” Will Chen [23:49 to 24:13]

5. “If you need forward deploy engineers, it just means that your software doesn’t work well enough.” Will Chen [29:27 to 29:37]

6. “It’s almost like you have your IKEA furniture, right? You have to assemble yourself.” Will Chen [34:56 to 35:00]

7. “It really doesn’t cost 500 million dollars to build a piece of AI in-house.” Will Chen [39:47 to 39:58]

8. “For me, work-life balance is easy because at the moment I’m doing what I love to do.” Will Chen [43:25 to 43:34]

Clips

I Left Law To Work on Ideas

Junior Lawyers Do “Chore Law”

Platforms Beat Prompts for Most Firms

Why Leaving Law for AI is Harder Than It Seems

This conversation captures a genuine inflection point in legal technology. Will Chen’s story is not really about a single tool; it is about what happens when someone with both big-firm experience and coding ability looks closely at the products the industry treats as untouchable and realizes the gap between the marketing and the machinery. Mike OSS landed the way it did because it was open, testable, and worked, and the polarized reaction it provoked, awe from lawyers, disbelief from engineers, says as much about the legal profession’s relationship with technology as it does about any one piece of software. Whether or not firms adopt open source tools directly, the conversation has already shifted: the question is no longer whether premium legal AI can be challenged, but how quickly accessible, capable alternatives reach the lawyers who need them.

Closing Thoughts

Every so often I talk to someone who reminds me that progress in this field rarely comes from the biggest players. It comes from people willing to look at what everyone else accepts and ask a simple question: is this really as hard as they say it is?

Will Chen asked that question, answered it with working code, and shared the result instead of locking it away. I have been doing this long enough to know the pattern he describes, the naysayers, the “it’s not perfect” chorus, the people insisting you shouldn’t have done it. I have lived it too. What I take from Will’s story is that openness tends to win in the long run, not because it is flawless, but because it invites people in and shows them what is possible. If you have ever assumed the tools you rely on are beyond your understanding or your reach, I hope this episode opens your mind the way Will has opened so many others.

The American Legal Technology Awards are back for a seventh year, and I want this community to hear it first. Nominations are open now, the gala has a date, and there is a place for you in all of it, whether you nominate someone, put your own name forward, or simply come to celebrate. Here is everything you need to know.

If this sounds interesting to you, please read on…

This substack, LawDroid Manifesto, is here to keep you in the loop about the intersection of AI and the law. Please share this article with your friends and colleagues and remember to tell me what you think in the comments below.

What the Awards Are

The American Legal Technology Awards are an independent celebration of the people and organizations using technology to make the law better. We started them in 2020 with a simple idea: the profession does a great deal of good work that never gets seen, and someone should shine a light on it. Seven years later, the Awards have been called the Oscars of legal innovation, and they still run on that same belief. They are independent, inclusive by design, and free to enter.

We recognize work across ten categories:

• Access to Justice

• Artificial Intelligence

• Court Innovation

• Education

• Enterprise Solutions

• Individual Achievement

• Journalism

• Law Firm Innovation

• Startup Excellence

• Lifetime Achievement

Each year a panel of respected judges from across the field reviews the nominations, so that every award reflects real impact rather than the loudest marketing budget.

How to Nominate

Nominations are open now and close on July 31, 2026. There is no fee, and there never has been. Self-nominations are encouraged, so if you have done something great, put your own name forward. It takes only a few minutes.

Submit Nomination

You can nominate at americanlegaltechnology.com. If you read the Manifesto, you almost certainly know someone whose work belongs in at least one of these categories. Maybe it is you!

Come to the Awards Gala!

The Awards Dinner Gala is Sunday, October 25, 2026, at Suffolk University Law School in Boston. The evening is a formal dinner, fine wine, and a ceremony honoring the winners in each category, with plenty of time to reconnect with the people doing the most interesting work in the profession. It is, for my money, the warmest room in legal technology all year.

Tickets are available now at americanlegaltechnology.com.

In Short

Nominate someone you admire. Put your own name in if you have earned it. Then come to Boston in October and celebrate the people moving our profession forward. I would love to see this community well represented in every category.

Nominate and attend. The 2026 American Legal Technology Awards are open for nominations through July 31 at americanlegaltechnology.com. Self-nominations are encouraged, and there is no fee. The Awards Dinner Gala takes place Sunday, October 25, 2026, at Suffolk University Law School in Boston. Tickets are available at the same site.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and Author of AI with Purpose: A Strategic Blueprint for Legal Transformation (Globe Law and Business). He is “The AI Law Professor” and writes his eponymous column for the Thomson Reuters Institute.

If you want to understand the most volatile shift in digital marketing history and what it actually means for your law firm, you need to listen to this episode. Gyi is at the forefront of legal marketing and brings a uniquely grounded, philosophy-driven point of view to a field full of hype and noise.

Building an Authentic Brand That Outlasts the Algorithm

Join me as I interview Gyi Tsakalakis, Co-founder and President of AttorneySync and co-host of Lunch Hour Legal Marketing.

In this candid podcast episode, Gyi shares his journey from University of Michigan philosophy student to trial lawyer to one of the most trusted voices in law firm marketing. He dives deep into how Google’s search experience is being upended by AI overviews and AI mode, and why the old playbook of chasing keyword rankings is breaking down. Gyi also explains why he counsels lawyers to diversify their marketing and invest in brand before they ever think they need a client.

His stories and insights underscore a refreshingly honest approach to a field often clouded by jargon and false certainty. This episode is a must-watch for any lawyer trying to make sense of where attention is going, why brand recognition increasingly wins the click, and how stoic principles can keep you grounded through relentless change.

The Skinny

Gyi Tsakalakis, Co-founder and President of AttorneySync, traces his path from a single-parent household in Birmingham, Michigan, through a philosophy degree and a short but intense career as a plaintiff’s trial lawyer, to founding his marketing agency in 2008. Throughout the conversation, Gyi unpacks the most volatile period in the history of search: the rise of AI overviews, the arrival of AI mode directly in Google’s search box, and the steady erosion of the old link-driven SEO playbook he rode for fifteen years. His core message to lawyers is clear: stop overrelying on any single channel, especially Google, and start building a recognizable brand, because recognizable brands win a disproportionate share of clicks and choices even in AI-mediated results. Grounding it all is his affinity for stoicism, memento mori, amor fati, and a focus on what you can actually control, which shapes both how he runs his business and how he advises lawyers to navigate uncertainty.

Key Takeaways:

• Gyi’s central advice to lawyers right now is to diversify their marketing and stop being overly reliant on Google, or on any single channel, including organic search and performance marketing

• Search is undergoing the most volatile change in its history: AI overviews, generative “local pack” experiences, and AI mode built directly into Google’s search box for the first time in 25 years

• The old link-based SEO playbook, his longtime “meh, links” catchphrase, worked for roughly fifteen years but no longer maps cleanly onto how generative AI systems surface information

• Recognizable brands command a disproportionate share of clicks and consumer choices, even within AI-driven results, which is why brand building matters more than ever

• In the legal context, his firm’s user testing shows AI overviews are not dominating high-intent, bottom-of-funnel searches the way they dominate research-style queries

• Google’s caution about going all-in on generative search is rooted in incentives: roughly 90% of its revenue comes from search ads, and a conversational interface complicates where those ads go

• Gyi reframes “work-life balance” as navigation through intentionality: calendar blocking, clear boundaries, and budgeting time across the dimensions of life rather than chasing perfect equilibrium

• Stoic principles, remembering mortality, preparing for the worst while hoping for the best, and loving your own journey, anchor how he approaches both business and life

Notable Quotes:

1. “What an exciting time to be alive. I think if you like change, you’re probably happier than you’ve ever been. If you don’t like change, you’re probably terrified for a lot of different reasons.” - Gyi Tsakalakis (02:25-02:36)

2. “The deep themes from stoicism, things like memento mori, remember death, being grateful for the time that you have, because you’re not promised tomorrow, or premeditatio malorum, being prepared for the worst, but hoping for the best, and amor fati, loving your journey—those things to me speak so much truth.” - Gyi Tsakalakis (18:08-18:31)

3. “Everybody can create web pages, everybody can optimize the meta information on a page. The real gatekeeper, the real competitive difference maker were sites that were able to get links back to their site.” - Gyi Tsakalakis (32:10-32:22)

4. “Links don’t play the same role in generative AI as they do in traditional search algorithms. And so I think it might be time to retire meh links.” - Gyi Tsakalakis (33:30-33:44)

5. “Recognizable brands—firms that are active in their local community or they’re TV advertisers or radio advertisers or whatever they’re doing, they’re a known brand—they tend to get a disproportionate number of the choice and the clicks.” - Gyi Tsakalakis (37:35-38:01)

6. “You do not want to be overly reliant on Google. You do not want to be overly reliant, certainly on organic. I would say you don’t even want to be overly reliant on performance marketing.” - Gyi Tsakalakis (40:06-40:18)

7. “You want to know if you have a red flag with an SEO? If they’re certain, there’s a red flag, because there is so much uncertainty with what we do.” - Gyi Tsakalakis (38:26-38:35)

8. “I’ll say navigate instead of balance, because balance—what does that mean? Does it mean equal? It’s what you can tolerate, that you’re still making investments, that you’re being able to prioritize what’s important to you through your values.” - Gyi Tsakalakis (46:45-46:59)

Clips

How Football Shaped My Identity

Spotting A Huge Market Gap

Why He Chose Family First

Why I Chased Trial Advocacy

Gyi’s story is a study in stepping back before stepping forward. Twice in his career, first leaving a thrilling but draining trial practice, then watching the SEO ground shift beneath a fifteen-year-old playbook, he resisted the inertia of doing what was working and asked instead what the lived experience would actually be, and where the world was heading. That same discipline shows up in his advice to lawyers today: don’t fall in love with a single channel, especially one as volatile as search, and don’t mistake certainty for competence in a field defined by uncertainty.

What stands out most is how he ties marketing back to something durable. In a moment when AI is reshaping how people find lawyers, his answer isn’t a new trick: it’s brand, trust, and showing up consistently in the places where real people make recommendations to one another. It’s a stoic’s answer to a hype-driven question: focus on what you can control, build something recognizable, and let it compound.

Closing Thoughts

I’ve known Gyi for well over a decade, and what’s always set him apart is his refusal to sell the hype. In a field that runs on buzzwords and false promises, he does the opposite: he names the catchphrase, then tells you what it actually means. This conversation was a perfect distillation of that gift.

What strikes me most is how steady his counsel is in genuinely turbulent times. Search is changing more dramatically than at any point in its history, and the temptation is to chase whatever’s newest: AI mode, the latest ranking factor, the next channel. Gyi’s message cuts against that current: don’t overrely on Google, don’t overrely on any single channel, and put real resources into building a brand people recognize and trust before they even know they need you.

For our Legal Rebels community, Gyi offers something rarer than a tactic, he offers a philosophy. Diversify. Stay grounded. Be skeptical of certainty. And remember, as the stoics did, that you’re not promised tomorrow, so spend your time and attention on what genuinely matters. That’s marketing advice and life advice in the same breath.

As the ground keeps shifting beneath all of us, the lawyers who thrive won’t be the ones who guessed the next algorithm correctly. They’ll be the ones who built something real, a brand, a reputation, a community, that no platform change can take away.

Episode Summary

In this month’s AI Double Take, LawDroid CEO Tom Martin and Chief Legal Futurist Sateesh Nori open with a milestone: the launch of LawDroid’s Legal Aid Plugin, an open-source tool built on Anthropic’s Claude platform that fills the legal aid gap Anthropic’s own legal plugins left entirely unaddressed. From there, the hosts unpack a genuinely startling study in which AI responses were preferred over human professors’ answers by law professors themselves (76% of the time, blind test) and discuss a companion research paper showing AI actually helps law students learn the law better. They close with big-picture market moves: OpenAI stepping firmly into legal AI, Anthropic filing for its IPO, and Kirkland & Ellis committing $500 million to AI investment. The thread running through it all: the pieces are finally coming together, and the summer of legal tech is here.

Key Takeaways

1. LawDroid’s Legal Aid Plugin — Filling the Gap Anthropic Left

Just three weeks after Anthropic released its Claude legal plugins, LawDroid launched the Legal Aid Plugin: open-source, built on Claude, and targeted specifically at legal aid organisations and civil legal access. Anthropic’s plugins addressed the commercial legal market; the legal aid segment was entirely absent. Tom credited Anthropic’s architecture, skills, plugins, and the Claude platform, while noting that LawDroid’s decade of access to justice focus made it the natural team to build in that corner of the sandbox. Early feedback highlighted strong interest alongside privacy and security concerns, which the team is actively addressing.

2. From Reductive to Expansive — AI as a Productivity Shift

Sateesh drew a sharp distinction between two modes of AI use. Reductive use: summarising, translating, comparing, going faster through existing work. Expansive use: generating something new that takes you two or three steps ahead, a document, a workflow output, a legal instrument. The Legal Aid Plugin marks a move from the first mode to the second. The shift matters because it changes what is actually possible, not just how quickly existing things get done. Tom added: it’s about better, not just more.

3. The Institutional Knowledge Problem — And AI’s Answer

Tom identified one of the most underappreciated problems in legal aid: institutional knowledge that lives in practitioners’ heads walking out the door when they leave. The Legal Aid Plugin and its underlying skills architecture is a mechanism for capturing that knowledge, codifying it, and making it available to everyone who comes after: customisable, shareable, and open-source. The old paradigm made organisations dependent on vendors. The new one makes practitioners co-authors of the tools themselves.

4. AI Preferred Over Law Professors — By Law Professors

A study using Gemini 2.5 presented law professors with a blind comparison: student questions answered either by a human professor or by AI. The result: law professors preferred the AI-generated responses 76% of the time, without knowing which was which. More striking still: human professors gave answers the study classified as “harmful,” responses that actively hindered student learning, more frequently than the AI did. Sateesh’s takeaway: if AI can fool legal educators who do this for a living, arguments that AI cannot be good enough for everyday people no longer hold.

5. AI Helps Law Students Learn Law Better

A companion study by Professor Daniel Schwartz at the University of Minnesota Law School set out to test the common-sense hypothesis that AI use would hurt law students’ understanding. The result was the opposite: students who used AI learned the law better. The reason: AI provided the scaffolding to understand what the rule actually was something law school casebooks deliberately obscure through Socratic hazing. Tom described it as the ultimate study guide. Sateesh’s implication: AI courses should be introduced on day one of law school, not in the second or third year, and UC Berkeley Law’s current restrictive policy likely runs counter to both studies’ findings.

6. Richard Susskind’s Outcome Thinking Applied

Tom invoked Richard Susskind’s concept of “outcome thinking” to reframe the AI-in-education debate. If the outcome law school exists to produce is students who understand the law and succeed in legal careers, then the question is not who or what delivers the instruction; it is whether the outcome is achieved. Both studies say AI improves the outcome. Process-centered arguments against AI adoption, whether in legal education or legal services, do not survive contact with outcome-focused analysis.

7. The UPL Parallel — Scarcity as System Design

Sateesh connected the law school access debate to the broader unauthorized practice of law problem: both are systems designed around scarcity, limiting who can deliver legal knowledge regardless of quality. The cost of maintaining law schools as gatekeepers (in tuition, time, and the legal labour market they create) is borne disproportionately by people who cannot access legal help at all. AI breaks the scarcity assumption. The model is broken, and the studies provide evidence for why it needs to change.

8. OpenAI Enters Legal AI — Both Giants Now in the Market

OpenAI has followed Anthropic in making a clear, committed move into legal AI. With both leading foundation model companies now focused on the legal market, Tom sees the concentration of capital, talent, and technology as ultimately beneficial for clients, even if the immediate motivation, as Sateesh quipped, may partly be to reduce their own legal fees before their IPOs.

9. Anthropic Files for IPO — and Kirkland Commits $500M

Anthropic has filed for its IPO. Separately, Kirkland & Ellis, the highest-grossing law firm in the world at approximately $10 billion in annual revenue, has committed $500 million to AI investment. Tom’s read: as a fraction of revenue it is not betting the farm, but it is a meaningful signal. The lesson for smaller firms is not to match the dollar amount but to adopt the underlying habit: consistent commitment to innovation, collaboration, and communication.

Show Notes

Topics Covered

• LawDroid Legal Aid Plugin launch — open-source, built on Claude, targeting legal aid market

• Anthropic’s Claude legal plugins and the legal aid gap they left unaddressed

• Privacy and security concerns raised during the plugin launch webinar

• Institutional knowledge capture: skills as a mechanism for preserving practitioner expertise

• The shift from reductive AI use (efficiency) to expansive AI use (productivity)

• AI co-authorship of tools vs. vendor dependency in the old software paradigm

• Blind study: law professors preferred AI responses (Gemini 2.5) 76% of the time

• Human professors gave “harmful” answers more often than AI in the same study

• Professor Daniel Schwartz (University of Minnesota Law School): AI helps law students learn law better

• Law school AI courses currently offered in year 2–3; argument for day-one integration

• UC Berkeley Law’s restrictive AI policy critiqued in light of both studies

• Richard Susskind’s “outcome thinking” as a framework for evaluating AI in legal education

• UPL parallel: scarcity-by-design in law schools mirrors scarcity-by-design in legal services

• OpenAI entering legal AI — both major foundation model companies now in the market

• Sateesh’s theory: AI legal tools partly motivated by reducing IPO legal costs

• Anthropic IPO filing

• Kirkland & Ellis $500M AI investment commitment

• The habit of innovation as the takeaway for small and mid-size firms

People & Organizations Mentioned

• Tom Martin — CEO & Founder, LawDroid

• Sateesh Nori — Chief Legal Futurist, LawDroid

• Professor Daniel Schwartz — University of Minnesota Law School; co-author of AI/law student learning study

• Richard Susskind — Legal futurist; “outcome thinking” concept cited

• Anthropic — Claude platform; released Claude legal plugins; filed for IPO

• OpenAI — Entered legal AI market

• Kirkland & Ellis — Committed $500M to AI investment; ~$10B annual revenue

• Gemini 2.5 (Google) — AI model used in the blind law professor preference study

• UC Berkeley Law — New AI policy noted as running counter to the research findings

• LawDroid — Released Legal Aid Plugin (open-source, Apache 2.0) on Claude platform

Final Takes

Sateesh Nori:

“The next two or three months are going to be huge. I can’t even imagine what’s to come — the OpenAI legal rollout, what Anthropic does next, the impact of the IPO on big legal tech companies. The summer is going to be the summer of legal tech.”

Tom Martin:

“Things are finally coming together. Kirkland’s $500 million is a big headline, but they make $10 billion a year — it’s a good start, not betting the farm. And you don’t need $500 million to do this. You just need the habit of innovation, collaboration, and communication. If you build that habit, you’ll do well.”

AI Double Take is produced by LawDroid | lawdroid.com

If you want to understand how human-centered design and lived experience can transform AI into a genuine access-to-justice engine, you need to listen to this episode. Claire is at the forefront of legal tech product development and brings a uniquely grounded perspective, rooted in years of working directly with the people she now builds for.

Encoding Empathy into AI: How Upsolve Is Rewriting Access to Justice

Join me as I interview Claire Robinson, Chief Product Officer at Upsolve.

In this podcast episode, Claire shares her remarkable journey from growing up in the Redwood forests of Humboldt County to becoming the product mind behind one of the most impactful legal AI tools in the country. She dives deep into how her time as a paralegal, sitting across from low-income Americans navigating bankruptcy, gave her the exact knowledge needed to design an AI tool that truly helps rather than just answers questions. Claire also walks us through how Upsolve’s AI Paralegal works: a context-aware assistant embedded directly inside the app that can see what users see and guide them through the process in real time.

Her stories and insights underscore the power of starting with people, not technology. This episode is a must-watch for anyone curious about how access-to-justice organizations are harnessing AI to do more for the people who need it most, and what it takes to build legal tech that actually works at scale.

The Skinny

Claire Robinson, Chief Product Officer at Upsolve, brings a rare combination of frontline legal experience and product leadership to one of the most important access-to-justice platforms in the country. Upsolve functions as a TurboTax-style tool for Chapter 7 bankruptcy, and has helped over 23,000 families discharge more than $1.1 billion in debt, mostly medical bills and past-due credit cards. Claire began her career at Upsolve as a paralegal, spending thousands of hours helping low-income Americans navigate the bankruptcy process, before transitioning into product leadership and ultimately becoming CPO. That lived experience is the foundation of Upsolve’s AI Paralegal: a context-aware assistant embedded inside the app that can see the same screen as the user, answer questions about the interface in real time, and guide people through a complex legal process on a cell phone, often at night, while managing their families. The tool proved so effective that in November 2024, Upsolve had to shut off new signups for two weeks because demand overwhelmed their human paralegal quality-check team. Claire’s approach, treating AI as an accelerant of good human-centered design, not a replacement for it, offers a powerful model for the broader legal tech world.

Key Takeaways:

• Claire’s origin story, growing up in rural Humboldt County, working in the Bay Area Justice Corps, and helping wildfire survivors, shaped a deep commitment to access-to-justice work that has driven every career decision since

• Her time as a paralegal at Upsolve gave her thousands of conversations with real users, which became the foundation for designing an AI tool that anticipates actual user needs and friction points

• Upsolve’s AI Paralegal goes far beyond a standard chatbot: it is context-aware, meaning it can see what screen the user is on and answer questions about what they are actually experiencing in the app

• The tool proved so effective that Upsolve had to temporarily pause new signups in November 2024, an unprecedented move, because so many people were completing the process that the human paralegal quality-check team couldn’t keep pace

• Upsolve has helped over 23,000 families discharge more than $1.1 billion in debt, and views bankruptcy as the social safety net of last resort for low-income Americans

• Claire’s philosophy is that AI is an accelerant, not inherently good or bad, meaning it amplifies whatever design choices are already baked into the product

• She deliberately took a detour into startup sales at Ditto before joining Upsolve, which gave her breadth of experience that now shows up in her product thinking and cross-functional leadership

• Work-life balance, in Claire’s view, is best measured across a year rather than a week; she structures her life in sprints and finds that doing meaningful work generates more energy, not less

Notable Quotes:

1. “I’m a big fan of the thinking that AI is an accelerant, but not necessarily always a positive accelerant. Just whatever you’re doing, it’s going to be more of that.” - Claire Robinson (05:53-05:59)

2. “So many people were able to get through as a result of the AI chatbot that we had in app that we did actually have to turn off the app for two weeks to any new signups, which was unprecedented in Upsolve history.” - Claire Robinson (02:29-02:41)

3. “We have helped just over 23,000 families relieve, I think now 1.1 billion in debt, mostly medical bills, past due credit cards for basic living expenses.” - Claire Robinson (03:08-03:22)

4. “I wanted to think about what was this tool uniquely positioned to do? And how could we use this as an opportunity to not just provide help, but provide really good help?” - Claire Robinson (27:31-27:42)

5. “If I was entering my name and I entered my first name, but I didn’t enter my last name and that continue button at the bottom was grayed out... I could ask the AI tool, why can’t I move forward? And it would tell me, you need to put in your last name before you can click continue.” - Claire Robinson (28:53-29:04)

6. “As someone who early in my career had to look every day—we could help 20 people and usually about 60 people would show up—when I started playing around with early versions of AI, it was just revolutionary to have this opportunity to not have to turn people away.” - Claire Robinson (26:28-26:50)

7. “I think we have a moral responsibility to make our tool as good as possible as quickly as possible. So if we are in an unfortunate situation where more people need that resource, we’re available to meet that demand.” - Claire Robinson (32:03-32:11)

8. “I do think that there is such a thing as work life balance, but over the course of the year, not a week.” - Claire Robinson (33:44-33:48)

Clips

AI Overwhelmed the App

Balance Happens in Sprints

How a Wild Childhood Shaped Claire

We Have a Moral Responsibility

Claire Robinson’s story to me is ultimately about what happens when the right person builds the right tool at the right time, and makes sure the technology never loses sight of the human being on the other end of the screen. Her path from paralegal to Chief Product Officer is not a detour; it’s a direct line, because every role she held deepened her understanding of what vulnerable people actually need when they’re facing one of the most stressful events of their lives.

What makes Upsolve’s AI Paralegal stand out in a crowded field of legal chatbots is precisely what Claire insists on: the tool knows where you are, what you’re looking at, and what’s tripping you up, because someone spent years in that chair, answering exactly those questions. That embedded empathy is what turned an AI assistant into something capable of overwhelming its own team with success.

Closing Thoughts

What Claire is doing at Upsolve is exactly the kind of work that gets me excited about where legal technology is heading. She’s not building AI for its own sake, she’s building it because she’s sat across from real people who needed help, and she refused to accept the status quo as the permanent state of affairs.

Imagine: Upsolve had to shut its doors to new users because the AI worked too well. That is a remarkable problem to have. It tells you that when you design from genuine human understanding, when your product team has lived the experience your users are living, the results can scale in ways that outpace your own capacity.

For our Legal Rebels community, Claire’s story is a reminder that the most powerful legal technology doesn’t start with the technology. It starts with the person on the other side of the process. The lawyers and legal innovators who will thrive in the years ahead are the ones who hold onto that insight even as the tools get more sophisticated and the temptation to be dazzled by capability grows stronger.

Upsolve has helped 23,000 families be freed from over $1.1 billion in debt. That number is going to keep climbing, and it will do so because someone cared enough to really understand the problem before they tried to solve it.

Paul Simon sat across from Dick Cavett in April 1970 and was asked an impossible question. How does a song come into being? There is a moment when “Bridge Over Troubled Water” does not exist, and then there is another moment when it does, or when it begins to. What happens in between?

Simon reached behind the couch, picked up his acoustic guitar, and demonstrated. He had an opening melody. Then a Bach chorale slipped in from somewhere in his mind. Then he was stuck. The reason he was feeling stuck, he told Cavett, was that “everywhere I went led me where I didn’t want to be.” So he kept listening to a gospel group called the Swan Silvertones, and one of their lines, “I’ll be a bridge over deep water if you trust in my name,” made its way into his thoughts and the song. He smiled and admitted to the audience: “I guess I stole it, actually.”

Then he said: “That’s how songs happen. They piece themselves together.”

I call this The Songwriter’s Method, and I believe it is the most accurate model we have for what serious work with AI actually looks like.

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A Confession About Stealing

What Simon was confessing on national television was that “Bridge Over Troubled Water” was not invented from nothing. It was stitched. The opening melody was his. The next phrase came from Bach, sitting in his subconscious from years of listening. The hook came from a gospel group he had been wearing out on his turntable. The integration was his. The fragments came from everywhere.

This is the part most people miss about creative mastery.

Originality has rarely meant invention from nothing. It has meant a particular ear for which fragments belong together. Eliot wrote that immature poets borrow and mature poets steal. Bach himself was one of the great recombiners in musical history. The myth of the solitary genius spinning new material out of pure inspiration is a Romantic-era story we have inherited and rarely questioned, and it does not match how the work has ever really gotten done.

Simon’s confession is revealing. The song was alive. People recognized themselves in it within the first chord. It has become a standard. And it was built from pieces that already existed in the world. Whatever it was that made the song into a song was not the rarity of the parts, but the ethereal, unique mix.

The Emotional Center

Here is where the conversation between Simon and Cavett turns, and here is the moment that matters most in relation to understanding AI.

Cavett asks Simon what makes him stuck. Simon answers, “Everywhere I went led me where I didn’t want to be.”

On the surface, Simon is describing a compositional problem. Every musical continuation he tried for the half-finished song felt wrong, led to a dead end. But the line means more than that. It is also a description of his life at that moment. He was a young man at the height of his fame, navigating the dissolution of his partnership with Garfunkel, uncertain where to go next as an artist or as a person. Everywhere he was led was everywhere he did not want to be.

That feeling was the songwriting.

The stuckness in the song was the stuckness he was living. And when the Swan Silvertones line came through, what got built around it answered both at once. A bridge appears, in the song and in the life, when someone offers to lay themselves down so you can cross over the trouble, to the other side. The musical fragments resonated because there was an emotional truth at the center pulling them together.

The Same Practice, with More Material

Now bring this back to the question of what serious work with AI actually looks like.

The fragments are everywhere now. AI has made the supply of available material almost infinite. Pieces of every discipline, every style, every register, every voice, every form of structured information, all of it is one prompt away. The Bach chorales of the world are in your pocket. The gospel singers and the scat soloists are in your pocket. So is every textbook, every research paper, every photograph, every line of code, every recipe, every recorded interview.

This is genuinely new. The abundance is real and it changes what is possible.

But, creative work had always been about taste, judgment, curation — the things you don’t choose. It has been about what belongs together, where they touch, what holds them in the same register. That is the songwriter’s work, and the songwriter’s work cannot be outsourced.

What the songwriter does that the AI cannot do is carry the weight of being inside it — to be shattered by betrayal or elevated by love. What it cannot do is be stuck in a life where everywhere you have been led is somewhere you did not want to be. It cannot live in your skin.

How to Be the Songwriter

If you want AI work that comes alive, the practice is closer to what musicians, storytellers, and philosophers have always done than to anything else. Start with what you are carrying inside you: your fears, your thoughts, your questions, your hopes.

Be wide in your search.

Let the Bach chorale and the gospel group and the overheard whispers all come into the room. Do not police the borders of where good material can come from. The legal scholar reaching for cognitive science or psychology, the physicist reaching for philosophy of mind, the engineer reaching for poetry, the parent reaching for evolutionary biology, all of this is the songwriter’s instinct. The fragments are everywhere if you are willing to let them in.

Then trust the integration.

Simon did not consciously decide to fuse Bach and gospel and his own opening line. He listened to the Swan Silvertones over and over because he loved them, and one day the line surfaced and slipped into the song. The integration is partly conscious, partly subconscious, and the subconscious work depends on having marinated in enough material that the connections can form on their own.

Let the AI extend your perception.

Let it surface ideas and connections you would not have found, make connections you would not have made, offer variations you would not have considered. This is real and valuable. The supply of material the AI can put in front of you is one of the genuinely useful capabilities of the technology. The songwriter’s instinct says yes to this. Take the Bach. Take the gospel. Take it all in. Welcome what arrives.

Hold the center yourself.

The AI cannot do this part for you, and trying to delegate it produces exactly the hollow output we have all learned to recognize. What are you actually carrying inside? What is the trouble the bridge is meant to cross?

This works in both directions. The paths the AI surfaces can redirect what you thought you were exploring. A connection the model makes between two of your ideas exposes the deeper question that was driving both. The center can be discovered and refined in the act of gathering. Simon did not start with bridges; he started with a feeling of being led nowhere good, and the bridge arrived as the answer, to find a way home.

Closing Thoughts

Paul Simon’s confession on Dick Cavett’s couch in April 1970 was not really about songwriting alone. It was about how human beings make anything meaningful. We create around what we are living. The work comes together when the center is real and falls apart when it is not.

AI has not changed this.

AI has changed the abundance of what we can pursue, the speed of iteration, the cost of experimentation, the reach of what is available. But the emotional center remains the human condition. It is what we possess that the machine cannot.

The test for whether anything you make with AI is authentic and alive comes down to one question: Did it spring from deep inside you?

Be the songwriter.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and Author of the forthcoming AI with Purpose: A Strategic Blueprint for Legal Transformation (Globe Law and Business). He is “The AI Law Professor” and writes his eponymous column for the Thomson Reuters Institute.

If you want to understand how a lawyer dismantled everything traditional about legal practice and rebuilt it on first principles (eliminating the billable hour, embracing behavioral economics, and using AI to serve more people with less), you need to listen to this episode. Beau is at the forefront of reimagining family law and brings a rare combination of philosophical depth, entrepreneurial drive, and genuine empathy for the people he serves.

Rebuilding Family Law From the Ground Up

Join me as I interview Beau Atkins, CEO of Evolve Family Law, Canada’s first national family law firm.

In this insightful podcast episode, Beau shares his remarkable journey from Calgary suburbs and construction work to law school, and ultimately to founding a firm intentionally built to operate nothing like a traditional law practice. He dives deep into how lessons from tile-setting, behavioral economics, and a philosophy background converged to shape Evolve Family Law’s fixed-fee model and client-first approach.

Beau also opens up about using AI, including completing a full course on Claude Cowork, to scale a seven-person team to operate with the capacity of seven hundred. His vision of building a TurboTax equivalent for family law is not abstract: it is grounded in the sobering reality that 80% of people with legal problems never reach a lawyer, and that 92% of those needing civil legal aid don’t receive it.

This episode is a must-watch for anyone who believes legal practice can and should be rebuilt to serve people better, offering a practical and deeply human roadmap for doing exactly that.

The Skinny

Beau Atkins, CEO of Evolve Family Law, traces a path from a middle-income Calgary upbringing, shaped by his parents’ divorce at 14, through construction work, a semester in Mexico, and political science studies, to founding Canada’s first national family law firm in 2020. Fueled by a voracious reading habit rooted in behavioral economics thinkers like Daniel Kahneman, Amos Tversky, and Cass Sunstein, Beau rebuilt his legal practice from the ground up, discarding the billable hour in favor of fixed-fee pricing before AI even entered the conversation. Throughout the episode, Beau connects his personal experience of separation with the empathetic, nudge-based counsel he now offers clients, including the powerful reframe of calling a former spouse “your son’s mom” rather than “your ex.” He also discusses how AI and tools like Claude Cowork are enabling his small team to scale access to family law services for the 80% of people with legal problems who never make it to a lawyer’s office, with a purpose he has distilled simply as: to make life better.

Key Takeaways:

• Beau founded Evolve Family Law in 2020 after nearly leaving law altogether, determined to rebuild a firm on best business practices rather than traditional legal conventions

• His background in construction tile-setting gave him a client-first, fixed-fee pricing philosophy long before AI efficiency made the billable hour untenable

• Behavioral economics, particularly the work of Daniel Kahneman and Amos Tversky, deeply informs how Beau thinks about law, client decision-making, and the limits of the “reasonable person” standard

• Beau applies “nudge” theory from directly to his family law practice, including coaching clients to reframe how they refer to a former spouse

• His parents’ divorce at age 14 shaped his trajectory as a family lawyer and his commitment to helping clients navigate separation with more clarity and less conflict

• AI, including completing a full course on Claude Cowork, is enabling Beau’s seven-person team to operate with the capacity of 700, with a vision of building a TurboTax equivalent for family law

• 80% of people with legal problems don’t go to lawyers; 92% of those needing civil legal aid don’t receive it, closing that gap is central to Beau’s mission

• Finding stillness, prioritizing sleep and exercise, and stacking family time with professional activities are how Beau sustains balance across running a firm, a startup, and fatherhood

• Beau’s guiding purpose, articulated through Viktor Frankl’s Man’s Search for Meaning, is simply to make life better, a mission embedded in everything Evolve Family Law does

Notable Quotes:

1. “I don’t need to leave law. I just need to break it all down to the ground and rebuild it on the foundations of how a good business runs using best business practices.” - Beau Atkins (00:18:20-00:18:26)

2. “If what you’re charging an hour for did not take you an hour, I think it’s criminal.” - Beau Atkins (00:21:13-00:21:21)

3. “Lawyers sell peace of mind or certainty. And when you look at the hourly rate, that’s essentially lawyers shirking that responsibility — the very first opportunity they have to give their clients certainty, they’re giving an hourly rate instead.” - Beau Atkins (00:22:50-00:23:08)

4. “That is not your ex-husband. That is not your ex-wife. That is your son’s mom or your daughter’s dad. It’s a lot harder to use expletives when you’re talking about your son’s mom than it is your ex-wife.” - Beau Atkins (00:27:38-00:27:55)

5. “With seven of us, we will be able to perform like we’re seven hundred.” - Beau Atkins (00:32:13-00:32:17)

6. “80% of people who have a legal problem aren’t going to lawyers. 92% of people needing civil legal aid don’t get access to it. Imagine 92% of hungry people just don’t have access to food.” - Beau Atkins (00:32:56-00:33:24)

7. “I am the least qualified, most expensive counselor you’ll find anywhere.” - Beau Atkins (00:29:50-00:29:55)

8. “I’ve identified that my purpose is to make life better. And so long as what I’m doing is helping me fulfill that purpose, I have energy and excitement to how I approach it.” - Beau Atkins (00:38:15-00:38:42)

Clips

Rebuild Law from First Principles

92% of People Can't Access Legal Assistance

Why Lawyers Should Give Fixed Fees

Stop Calling Them “Ex”

Beau Atkins represents something that is rare in law: a practitioner who looked at the profession’s broken incentive structures, understood them through the lens of economics and psychology, and then actually did something about it. From charging fixed fees before AI forced the conversation, to applying behavioral nudges in divorce proceedings, to building a firm designed from day one for access and scale, his approach is systematic, empathetic, and deeply purposeful.

What resonates most is how Beau has woven together threads that most lawyers keep entirely separate: the personal (his parents’ divorce, his own separation in law school), the intellectual (Kahneman, Frankl, Sunstein), and the practical (tile-setting, business books, AI tools). The result is a lawyer who doesn’t just practice law but genuinely rethinks what the practice of law is for.

Closing Thoughts

I’ve spoken with a lot of legal innovators over the years, and what strikes me about Beau is how grounded his disruption is. He’s not innovating for the sake of a headline. He’s doing it because he came up through a working-class family, watched his parents go through divorce, nearly walked away from law entirely, and then decided to rebuild it into something worthy of the people who need it most.

The fixed-fee model, the behavioral economics framework, the Claude Cowork course, the vision of a TurboTax for family law; these aren’t disconnected ideas. They all flow from a single purpose: to make life better. That’s it. Simple, clear, and enormously difficult to actually execute.

What excites me most about this conversation is how clearly it illustrates that the future of legal practice isn’t just about AI tools or pricing models. It’s about lawyers who are willing to interrogate their own assumptions, learn from outside the legal world, and put clients at the center of everything they build. Beau has done that. And his firm is proof that it works.

For the Legal Rebels in our community: you don’t need a philosophy degree or a background in behavioral economics to take something from this episode. You need the willingness to ask, whether the way you practice law is actually designed to serve your clients, or just to serve you. Beau asked that question. The answer changed everything.

I developed a thesis last August I call the Transformation Triangle. The thesis was straightforward and, at the time, not yet obvious. In a world where AI capabilities commoditize on a quarterly cadence, no single moat survives. Not software. Not expertise. Not credentials. The only defensible position is the integration of three elements at once: tools that scale, expertise, and education that transfers capability. Loops, not moats. I built the argument for professional services, but it applies to any business.

Five months later, in February, I returned to the framework to show that the legal AI leaders were already running it. Harvey at an eight-billion-dollar valuation while building Legal Engineers from Wachtell and Latham, then opening Harvey Academy and partnering with seventeen law schools. EvenUp at two billion with more than a hundred in-house experts reviewing every output. Luminance launching academic certifications across three continents. Spellbook in fifty-plus law schools. The market was converging on the thesis.

In April, I refined the framing one more time. What I had called a triangle, three pillars holding up a roof, turned out to be a dynamic system: three currents in a single flow, each leg feeding the next. Software, Services, Learning, closed into a loop. The loop is the moat. Anything less is a feature waiting to be absorbed.

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This substack, LawDroid Manifesto, is here to keep you in the loop about the intersection of AI and the law. Please share this article with your friends and colleagues and remember to tell me what you think in the comments below.

Eight Days That Settled the Argument

On May 4, Anthropic announced a new enterprise AI services company. The founding partners were Blackstone, Hellman & Friedman, and Goldman Sachs, with backing from a consortium of alternative asset managers that included General Atlantic, Apollo, GIC, Leonard Green, and Sequoia. The company’s purpose, in the announcement’s own words, is to embed Applied AI engineers from Anthropic alongside the firm’s engineers, working inside mid-market customer organizations to build production Claude systems tailored to those organizations’ operations. The engagement begins by understanding where Claude can have the biggest impact and ends with systems wired into daily work.

Anthropic’s CFO, Krishna Rao, gave the rationale: “Enterprise demand for Claude is significantly outpacing any single delivery model.” Translation: selling the model is not enough. The customer needs the work delivered, not the tool licensed. Or as Richard Susskind might say, the customer needs to hang a picture frame and doesn’t care if it’s accomplished with a hammer and nail or a drill and screw.

On May 12, eight days later, OpenAI acquired the consulting firm Tomoro and launched the OpenAI Deployment Company. The investor consortium included Bain Capital, SoftBank, Warburg Pincus, Capgemini, McKinsey, and Goldman Sachs. The structure was identical in every meaningful respect. Forward Deployed Engineers, the model Palantir made famous, embedded inside customer organizations to design, build, test, and deploy production systems connecting OpenAI models to the customer’s data, tools, and workflows.

Earlier this spring, ahead of either lab’s move, Sequoia partner Julien Bek published an essay called “Services: The New Software.” It went viral, well past three million views on X, four hundred and fifty thousand impressions on LinkedIn. Bek argued that for every dollar a company spends on software, it spends six on services. The next trillion-dollar company, he wrote, will be a software company masquerading as a services firm. Selling the tool means racing the next version of the model. Selling the work means every model release makes the service faster and cheaper. The autopilot, in his vocabulary, captures the labor budget rather than the software budget, and the labor budget is six times larger.

Three of the most influential actors in the AI economy. Three different starting points. One structural answer. Embedded engineering inside the customer, outcome-priced delivery, model access bundled with services, financial sponsorship from alternative asset managers who understood, before anyone else, that the services profit pool dwarfs the software profit pool.

And the customers are not waiting. On May 27, Kirkland & Ellis, the world's highest-grossing law firm, told the Financial Times it would spend $500 million building its own AI platform, with 180 tech professionals working alongside 250 lawyers, including 100 partners, to capture what chair Jon Ballis called "the collective intelligence of our institution." Outside vendors are building the system alongside Kirkland's own engineers and data scientists, but the firm will own all of it. The platform will run mandates in their entirety, under value-based pricing rather than the billable hour. Software, services, learning, owned by the firm, closed into a loop. The labs are building the structure on the supply side. Kirkland is building it on the demand side.

This is the Transformation Triangle.

How the Match Is Exact

Look at the Anthropic announcement again, but this time through the Transformation Triangle’s three legs.

Software is Claude itself, plus the Applied AI tooling that customizes it. That leg already runs.

Services is the embedded engineering team inside the customer, sitting with clinicians and IT staff, building the workflows. That is the leg the new company creates.

Learning is what makes the whole structure compound. Every engagement generates signal: corrections, edge cases, workflow patterns, vocabulary specific to a physician practice or a community bank. That signal flows back into Anthropic, into the Applied AI engineers, into the next deployment, and eventually into the next training run. The loop closes.

The software gets smarter because the services generate signal that the learning leg captures and returns to the software. The services get faster because the software does more of the work and the learning sharpens the judgment behind it. The learning gets richer because both legs are running.

Now look at OpenAI’s structure. Models plus FDEs plus customer deployments plus signal flowing back. Same three legs. Same loop.

Now look at Bek’s essay. Software companies “masquerading” as services firms. The argument is that the system the labs are now building is the only one that works, and the entry point to building it is wherever a category is already outsourcing services that can be re-delivered with AI underneath.

The vocabulary is different in each case. Anthropic says “enterprise AI services.” OpenAI says “deployment.” Sequoia says “autopilot.” I have been calling the underlying structure the Transformation Triangle. The labels differ because the lineages differ, but the structure is the same.

The Model Alone Is No Longer The Product

May 21st, Greg Brockman, President & Co-founder of OpenAI tweeted, “the model alone is no longer the product.”

The companies that built the foundation models, the most successful tools ever shipped in the history of computing, are the same companies that just announced they cannot survive on tool revenue alone. Anthropic and OpenAI created the technology that commoditizes everything built on top of it.

They watched their own customers, the wrapper companies, get eaten by their own product roadmaps. They have now reached the same conclusion every downstream company is reaching: if you sell the tool, you race the next version of the model. If you sell the work, every model release works for you, because every model release makes the work cheaper and faster to deliver.

The model is not the product. The loop is the product. The labs that invented the commoditization technology are not exempt from its consequences. They are simply earlier to the conclusion.

Eight months ago, in a piece called “Every Law Firm Is Now a Software Company,” I described how this would play out across professional services. Three months ago, I showed how it was playing out across legal AI. One month ago, I named the loop as the only structure that survives. This month, the labs themselves confirmed it.

What This Means If You Are Building Anything

The convergence is the signal. If Anthropic and OpenAI cannot survive on tool revenue alone, no one downstream of them can either. The companies that read the signal early and start running the full loop will compound. The companies that wait for further confirmation will discover that they missed the boat.

Three questions require your attention:

Does your Software get smarter from your Services? If your engineering team ships the same product whether or not your delivery team is engaged with customers, the loop is broken on the Software side. You are not building a loop. You are building a feature factory.

Do your Services get faster and more accurate because of your Software? If your delivery team works the same way they worked before AI, you have adopted a tool, not transformed a business. The loop is broken on the Services side. Your delivery economics will not survive a competitor running the full loop.

Does your Learning go anywhere? If the corrections, the patterns, the edge cases your team encounters stay in their heads, in Slack threads, in the memory of a single senior partner, you have brain drain. The loop doesn’t compound. Every engagement starts the same as the last one. Your competitor, the one running a feedback loop, gets one engagement smarter every week.

The answer to any one of those questions for most businesses today, including most law firms, most consulting practices, most software companies, and most professional service firms of every kind, is “No.”

That is the gap. And the gap is what the new lab-services companies, backed by Blackstone and Bain and Goldman and Sequoia, are about to exploit.

Closing Thoughts

I called the Triangle in August. I tracked its validation in February. I submitted my book manuscript to my publisher wherein I refined it in April. This month, the foundation model labs themselves started building it in public. The thesis is not a thesis anymore.

The model is not the product.

The loop is the moat.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and Author of the forthcoming AI with Purpose: A Strategic Blueprint for Legal Transformation (Globe Law and Business). He is “The AI Law Professor” and writes his eponymous column for the Thomson Reuters Institute.

If you want to understand what the data actually says about AI’s effect on legal reasoning, and why the conversation around lawyers and AI is long overdue for a reality check grounded in evidence, you need to listen to this episode. Daniel is at the forefront of empirical legal scholarship and brings a rigorously grounded, nuanced perspective to one of the most debated questions in legal tech today.

Does AI Dull a Lawyer’s Thinking? The First Empirical Answer Is Here

Join me as I interview Daniel Schwarcz, professor of law at the University of Minnesota and graduate of Harvard Law School.

In this insightful podcast episode, Daniel shares his journey from growing up in Scarsdale, New York, shaped by two lawyer parents, through Amherst College and Harvard Law School, to becoming one of the country’s leading empirical scholars on AI and the legal profession. He dives deep into his landmark randomized controlled trial of 100 law students, the first study of its kind to produce hard empirical evidence on whether using AI for one part of a legal task undermines a lawyer’s reasoning on the rest.

His stories and insights underscore his commitment to grounding the AI conversation in data rather than intuition, including a result that upended his own hypothesis: students who used AI actually performed better even after the AI was taken away. This episode is a must-watch for anyone who cares about the future of legal education, the development of lawyering skills, and how we build an evidence-based understanding of AI’s real impact on the profession.

The Skinny

Daniel Schwarcz, professor of law at the University of Minnesota and a Harvard Law School graduate, has spent nearly two decades building a scholarly career at the intersection of law, economics, and empirical research. His interest in AI predates the generative AI moment by over a decade, rooted in his early work on how machine learning was reshaping insurance underwriting and discrimination. When ChatGPT arrived, Daniel was uniquely positioned to turn that analytical lens onto one of the legal profession’s most pressing anxieties: does using AI make you a worse lawyer?

In a randomized controlled trial of approximately 100 upper-level law students, Daniel and his co-authors tested whether using AI to synthesize legal materials in one stage of a task would undermine performance in a subsequent stage where AI was no longer available. The hypothesis (intuitive, widely held, and seemingly well-supported by evidence from other domains) turned out to be wrong. Students who used AI in stage one not only outperformed the control group when they had AI, they continued to outperform them even after the AI was removed. The mechanism, Daniel explains, is straightforward: AI helped students develop a more accurate, robust understanding of the law, and that superior understanding carried forward.

The episode is a masterclass in how rigorous empirical thinking can cut through the noise of the AI debate and offer lawyers, educators, and legal innovators something genuinely rare: real evidence.

Key Takeaways:

• The first randomized controlled trial of its kind found that law students who used AI to process legal materials performed better in a subsequent AI-free task—the opposite of the prevailing fear

• The mechanism behind the result is intuitive in retrospect: AI helped students synthesize the law more accurately in stage one, and a better understanding of the law carried forward into stage two

• Daniel is careful to scope his findings precisely: the study does not address long-term skill degradation from repeated AI use, a concern he still takes seriously and wants to see tested

• Cognitive fatigue emerged as a potentially important moderating variable: students using AI at the end of a three-hour experiment appeared to use it less critically, with negative consequences for those who had already produced strong work

• The finding that AI helped weaker performers but may have hurt stronger performers in the revision stage points to the importance of how and when AI is introduced, not just whether it is used

• Daniel’s decade-plus background in AI and insurance, studying how machine learning tools affected underwriting and discrimination, primed him to think rigorously about AI’s real-world consequences long before generative AI arrived

• His academic philosophy is built on grounded, practical relevance: he deliberately chose insurance law because it was important, understudied, and tangibly affected millions of people, the same spirit animates his AI research

• Setting clear personal rules and treating them as binding is Daniel’s approach to work-life balance, a discipline he argues is especially necessary for academics, who lack the external structure of a typical workplace

Notable Quotes:

1. “There was still sort of a lot of hesitation among lawyers, because of this fear that using AI will make us worse lawyers. And I take that fear very seriously, and so it’s something I wanted to empirically investigate.” - Daniel Schwarcz (02:43-02:56)

2. “The folks who had AI, their brain was much less engaged. And so there’s something intuitive to me about that.” - Daniel Schwarcz (04:30-04:51)

3. “Our hypothesis going in was that the folks who used AI in stage one to help them make sense of the materials would perform less well once we took the AI away from them. But we found the opposite result.” - Daniel Schwarcz (25:34-26:32)

4. “The effect really was that AI helped you synthesize the rule, helped you get the rule accurately and see the nuances in stage one. And then even when you no longer had AI and you’re applying it to a new situation, having the rule in an accurate, robust fashion was helpful even when the AI was no longer available.” - Daniel Schwarcz (38:21-38:47)

5. “I think there’s good reason to think that when you’re using AI and you sort of have cognitive fatigue, you’re tired, you’re overwhelmed, maybe then actually the AI is more likely to push out critical thinking than if you’re using AI sort of when you’re fresh and you’re motivated.” - Daniel Schwarcz (34:32-34:49)

6. “I always want to sort of caution against saying we’ve definitively proven AI is not a risk. No. I think though that relative to my views on this before doing the study, I have less concern that using AI necessarily or inevitably dulls your capacity to perform when you’re not using it.” - Daniel Schwarcz (35:16-35:45)

7. “I really try to treat my own internal rules as binding because if you don’t, then there’s no point in setting those rules.” - Daniel Schwarcz (44:12-44:22)

8. “Part of why I like doing practical things is I like that it’s relevant to people. I don’t want to be relevant not just to a small group of ivory tower folks, but I want work that’s relevant to people that people find interesting and helpful in their daily lives.” - Daniel Schwarcz (44:48-45:03)

Clips

RCT Finds Surprising AI Effect

AI Improved Legal Skill Even After Removal

When AI Helps Some and Hurts Others

AI Helps — But Lawyers Worry

Does AI Really Make You A Better Lawyer?

Daniel Schwarcz’s work represents something the legal profession badly needs right now: empirical grounding in a conversation that has been dominated by intuition, fear, and hype in equal measure. His finding that AI use did not undermine, and in fact improved, subsequent AI-free performance challenges the most common argument offered by those skeptical of AI adoption in legal education and practice. But what makes this episode especially valuable is Daniel’s own intellectual honesty about what his study does and does not prove. He is not an AI booster. He is a scientist who followed his data, and whose data happened to push back against a widely held assumption.

What stands out most is the nuance he brings to the question of context. Whether AI helps or hurts legal reasoning may depend on factors like cognitive fatigue, the quality of work already produced, and how critically the tool is used. Those are not simple answers, but they are honest ones, and they point toward the kind of sophisticated, situation-specific thinking that lawyers are actually well equipped to do.

Closing Thoughts

What I love about this conversation with Daniel is that it does what the best scholarship always does: it takes a question seriously enough to actually test it. The fear that AI is quietly hollowing out lawyers’ reasoning skills is real and widespread, and it deserves a real answer. Daniel’s study provides one, even if it is a partial and carefully qualified one. And the answer, at least for now, is more reassuring than most people expected.

For me, this gets to something I’ve believed for a long time: we need to stop making decisions about AI in the legal profession based on speculation and start making them based on evidence. Daniel is doing that work. And the results are more nuanced, more interesting, and ultimately more useful than the hot takes dominating our feeds.

What also struck me is how Daniel embodies the very thing that makes a great legal scholar: intellectual rigor combined with genuine care for practical relevance. He chose insurance law because it mattered to real people. He chose to study AI’s effect on lawyers because that question matters to real people. That through line (grounded, practical, consequential) is exactly what the legal profession needs more of as we navigate this moment.

For our Legal Rebels community, the takeaway is not that AI is risk-free. Daniel himself would push back on that. The takeaway is that the risks are more specific, more contextual, and more manageable than the doom-and-gloom narrative suggests. Use AI when you’re sharp. Verify what it gives you. Understand the law it’s helping you synthesize. Do that, and the evidence suggests you may actually come out ahead.

Link to Daniel’s paper:

Bednar, Nicholas and Cleveland, David R. and Erbsen, Allan and Schwarcz, Daniel, Artificial Intelligence and Human Legal Reasoning (April 05, 2026). Minnesota Legal Studies Research Paper 2026-21, Available at SSRN: https://ssrn.com/abstract=6525800 or http://dx.doi.org/10.2139/ssrn.6525800

The phone rings at 8:47 in the morning, and by 8:47 it is already too late. The intake line was full when the office opened. The voicemail box filled by 8:30. The eviction filings from the weekend are sitting in three piles on the receptionist’s desk. There are six staff attorneys in this office, for a county of 250,000 people, and the math has not worked for a long time.

Today, like every day, the office will help everyone it can. And by the close of business, more people will have called than were helped.

This is the work of civil legal aid.

Not the courtroom drama. The part that happens everyday. The eligibility screen on a caller’s third try to reach a lawyer. The motion drafted in twenty minutes between a hearing and a client meeting. The deadline tracked on a sticky note because the case-management system is overloaded. The transfer memo written on a Friday because a paralegal is leaving on Monday. The funder report assembled in the last week of the quarter, from records that should have been clean three months ago. This is the experience of holding the line, every day, against a problem that is never going to be solved by just working harder.

LSC’s 2022 Justice Gap Study put the number at 92 percent. Ninety-two percent of the substantial civil legal problems of low-income Americans get no help, or not enough help, to make a difference. LSC-funded organizations turn away one of every two requests they receive, not because they want to, but because they cannot do otherwise. The capacity is not there. It has never been there. And every legal aid manager I know has spent their career figuring out how to make less than enough go as far as possible.

Today I want to talk about a new thing that the civil legal aid community can use, and what I think it make possible.

If this sounds interesting to you, read on...

This substack, LawDroid Manifesto, is here to keep you in the loop about the intersection of AI and the law. Please share this article with your friends and colleagues and remember to tell me what you think in the comments below.

The Legal Aid Plugin

At the LawDroid AI Conference 2026 last month, our theme was “The Year to Build” and we’ve been hard at work.

This week, LawDroid built and published the Legal Aid Plugin: a free, open-source Claude for Legal plugin built for civil legal aid offices, court self-help programs, and public-interest providers. It is published under the Apache 2.0 licence and available immediately at github.com/lawdroidAI/legal-aid-plugin.

“What matters about this launch is not just the plugin. It’s published openly, free to use, and open to contribution from any legal aid organization, anywhere. The civil legal aid community needs technology built that way.”

— Sally Chaffin, Practice Innovation Manager at Atlanta Legal Aid Society

The plugin scaffolds the daily work of a legal aid office. Fifteen skills, designed around the realities of how civil legal aid actually operates: eligibility screening that is aware of every funder the office reports to (LSC, IOLTA, foundation, state); structured intake; document drafting with funder-restriction flagging that propagates through every stage; case analysis memos; research roadmaps; status updates; routine correspondence; deadline tracking; communications logs; transfer memos for the rolling staff transitions every legal aid office knows; supervision queues; and reporting data extraction for the six funder shapes that dominate the sector.

What makes it different from existing tools is not any one skill. It is that the architecture is built for legal aid, not adapted from BigLaw. Funder rules propagate. Ethical discipline is structural, not optional. Supervision is configurable to the three styles legal aid offices actually use. The verification habits are wired in at the prompt level. None of this can be added on top of a tool built for corporate practice; it has to be there from the start.

It runs locally. Every legal aid office in the country can deploy it for free, today.

The Force Multiplier

The Legal Aid Plugin is not a replacement for lawyers. It is not a chatbot. It is not even, on its own, a productivity tool. What it is, in the language of strategy, is a force multiplier: a capability that increases an organization’s effectiveness beyond its body count. A force multiplier does not change how many people are on the team. It changes what the team can accomplish in the time available.

“The need for civil legal aid has always exceeded our capacity, and the gap is widening. Tools like this plugin let us redirect time from administration to advocacy. That’s a real shift in what our staff can do for clients.”

— Pablo Ramirez, Executive Director, Legal Aid Society of San Bernardino

Civil legal aid has been waiting for a force multiplier of this kind for a long time. The sector has tried many: case-management systems, intake routing platforms, document assembly engines, online self-help tools. Each helped, in pockets. None changed the underlying ratio of capacity to need.

What is different now is the architecture beneath the plugin. Anthropic released a way to build structured, composable, supervised AI capabilities that wrap around the lawyer rather than replacing the lawyer. A plugin is a directory of skills, each one capturing what a competent person in this domain would do next, given this context. The skill carries instructions, references, sequencing logic, and access to relevant tools. It runs in conversation with the lawyer carrying it, shaped by what the lawyer brings to the moment. The lawyer is still the lawyer. The plugin is what lifts the procedural load that has always made the job harder than it needed to be.

This is the leverage point the sector has not had before. Not because no one tried, but because the architecture did not exist to make leverage like this possible.

Externalizing What We Already Know

There is a quieter point worth making, because it matters for how this scales.

For decades, professional expertise has been treated as something that lives in our heads. The senior eligibility specialist who can hear, in the first thirty seconds of a call, whether the matter is an emergency. The managing attorney who can read an intake summary and see the three issues no one else flagged. The clinic director who knows which judges grant which motions and how to ask. These people are the institutional knowledge of their offices, and when they leave, much of that knowledge leaves with them. The next staffer rebuilds it slowly, from cases, over years.

The plugin architecture lets us externalize expertise. But, not all of it. The judgment that makes a senior eligibility specialist senior cannot be put into a skill, and we should not pretend otherwise. But the procedures that surrounds judgment, the sequencing of questions, the funder-source matrix, the conflict pre-check, the urgency triage, the routing logic, can be put into a skill. And once it is in a skill, every staffer the office has, today and a decade from now, can carry it from day one.

For civil legal aid in particular, where staff turnover is real and senior practitioners are precious, this is the difference between a sector that loses institutional knowledge every time a key person leaves and a sector that compounds its expertise over time.

What This Makes Possible

The plugin is the launch. What follows is what I am more interested in, because the plugin is one example of something that can keep happening.

More clients served. When a staff attorney spends less time on procedural overhead, they spend more time on advocacy and counsel. When an intake specialist runs a structured eligibility screen in fifteen minutes rather than thirty, the office gets through more of the line. The justice gap is not closed by any one of these. But the offices that hold the line gain capacity they did not have, in a sector where every additional matter served is a real outcome for a real person.

Faster, more consistent onboarding. New staff arriving in a legal aid office have always faced the same hard ramp: weeks to learn the office’s specific procedures, months to absorb funder rules, longer to learn what to listen for. The plugin compresses the procedural part of that ramp. A new staffer who runs /legal-aid:onboard on day one inherits the office’s configured discipline, practice-area guides, supervision conventions, and verification habits from the start.

Institutional discipline that compounds. Each office that deploys the plugin builds practice-area guides, captures its plausibility ranges, configures its supervision style. Over time, that configuration becomes a record of how the office actually works. Cross-organization learning becomes possible in a way it has not been: an office in North Carolina can see how an office in Georgia structured its housing intake; an office in New Hampshire can borrow a funder-restriction analysis from one in Florida.

A platform layer the sector owns. This is the part that matters most over time. Open source under Apache 2.0 means no vendor lock-in, no licensing fees, no dependency on what BigLaw gets. The civil legal aid sector has, for the first time, an AI platform layer it can build for itself, shape over time, and own collectively. Anthropic published the architecture; the community publishes the skills. The plugin we launched this week is the first piece. There will be more.

The Opportunity

The numbers in the Justice Gap Study are not going to be fixed by any plugin. They will be fixed, when they are fixed, by structural change: more funding, more lawyers, more legal aid programs, more pro bono, more reform of the systems that produce these problems in the first place.

One of the things we’ve learned through the work of the Legal Innovation Lab is that the access to justice gap is not going to be solved through a “business as usual” model.

The demand for civil legal help continues to outpace available resources, especially in rural and underserved communities. At Legal Aid of NC we’ve been exploring how technology can reduce administrative burden and give staff more time to focus on advocacy, problem solving, and client support.

It’s encouraging to see developers thinking intentionally about how AI skills and plugins can support legal services and expand access in practical and responsible ways. It’s even better when legal services programs get to help shape what’s being built.”

— Scheree Gilchrist, Chief Innovation Officer, Legal Aid of North Carolina

With the plugin and tools like it, the same six staff attorneys, in the same county of 250,000 people, can serve more matters, more consistently, with more institutional memory, at a higher standard. That is not closing the justice gap. But, it is multiplying the effect of the people who are working on it.

For an underfunded sector that has held the line for decades, on the will and skill of its people, a force multiplier built for its specific needs is a meaningful thing.

Closing Thoughts

The phone still rings at 8:47 in the morning. The voicemail box still fills. The eviction filings still arrive on Mondays. None of that is going to change because of a plugin.

What can change is what happens after the phone rings. The eligibility screen that catches the funder-restriction question on the first pass. The intake that propagates from the caller’s prior conversation rather than starting cold. The deadline that gets recorded with the funder allocation right, the first time. These are small things. They add up to a different morning.

We are building this for the people who stand for justice.

Come build it with us!

Learn More

This week, LawDroid published the Legal Aid Plugin, a free, open-source Claude for Legal plugin built for civil legal aid offices, court self-help programs, and public-interest providers. Fifteen skills, Apache 2.0, available now.

• Repository

• Documentation

• Webinar and Q&A:

  June 1, 2026 at 10:00am Pacific Time

If you are with a civil legal aid program, court self-help centre, or public-interest practice, we would love to build this with you!

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and Author of the forthcoming AI with Purpose: A Strategic Blueprint for Legal Transformation (Globe Law and Business). He is “The AI Law Professor” and writes his eponymous column for the Thomson Reuters Institute.

If you want to understand the scale of the justice gap in America and how AI and intentional collaboration can help close it, you need to listen to this episode. Ron is at the forefront of the civil legal aid movement and brings a uniquely national perspective on what’s working, what’s not, and what must be built next.

Building a More Accessible and Equitable System of Justice

Join me as I interview Ron Flagg, President of the Legal Services Corporation (LSC).

In this episode, Ron shares his vision for how AI can help address one of the most pressing crises in American law: the justice gap. As the head of the nation’s largest funder of civil legal assistance, Ron brings an unparalleled vantage point: one that spans hundreds of grantee organizations, millions of underserved clients, and the halls of Congress. He dives deep into why funding alone can never close the justice gap, and why the answer lies in the innovators, not just the innovations.

Ron also shares insights from LSC’s landmark eviction report, revealing how the patchwork of local eviction laws across the country produces wildly different outcomes for tenants, and how technology, collaboration, and even landlord partnerships can be part of the solution. This episode is a must-listen for anyone who cares about access to justice, legal innovation, and the role technology can play in building a fairer legal system.

The Skinny

Ron Flagg, President of the Legal Services Corporation, delivers a compelling keynote and fireside chat recorded at the LawDroid AI Conference 2026. With clarity and conviction, Ron lays out why the justice gap — 92% of the civil legal needs of people living in poverty go unmet — cannot be closed by funding or technology alone. He makes the case that it is the community of innovators, not any single innovation, that holds the key. Drawing on LSC’s deep grantee network and its culture of collaboration, Ron describes a field that isn’t hesitating in the face of AI: it’s hungry. He outlines the most promising internal use cases for AI in legal aid, from streamlining intake and triage to reducing administrative burden on staff. Ron also walks through LSC’s recent eviction report, highlighting the enormous variation in local eviction laws, the ripple effects of housing instability, and the surprising economic case for landlords to partner with tenants rather than evict them. He closes with a clear-eyed update on LSC’s funding outlook and a rallying call: collaboration is a multiplier, and this year is a year to build, intentionally, together.

Key Takeaways:

• The justice gap is staggering: 92% of the civil legal needs of people experiencing poverty go unmet, and LSC grantees are forced to turn away roughly half of all eligible applicants due to lack of resources

• No single solution, whether funding or technology, can close the justice gap; the answer lies in a community of innovators who build, test, refine, and share

• LSC’s grantee network is not hesitating in the face of AI, programs across the country are experimenting with tools to reduce administrative burden, streamline intake, and increase organizational capacity

• Legal aid organizations are a proving ground for responsible AI: high-stakes, resource-constrained environments where accuracy, trust, and ethics are non-negotiable

• Developers should build with legal aid, not just for it, engaging practitioners early and often leads to better, more usable, more trusted tools

• AI represents an evolution, not a replacement; it builds on prior innovations like computers, the internet, and e-filing, but moves faster and has lower barriers to entry

• LSC’s eviction report reveals how dramatically local laws shape eviction outcomes, and underscores that eviction, representing 35% of LSC grantee caseloads, has cascading consequences for housing, employment, education, and health

• Landlords are potential partners in housing stability, not just adversaries. Eviction is often a poor business model, and AI can help facilitate alternatives

• LSC secured $540 million in congressional appropriations for FY2026 despite significant political headwinds, reflecting broad bipartisan support for civil legal services

• Collaboration is a multiplier: the legal aid community’s existing culture of sharing and learning from one another is a strategic advantage in adopting AI

Notable Quotes:

1. “The question is no longer whether AI will shape the future of legal services. It already is.” — Ron Flagg (00:01:06–00:01:14)

2. “It’s the innovators rather than the innovation itself that hold the key. It’s a community that builds, tests, refines, and shares for the benefit of all.” — Ron Flagg (00:03:44–00:03:53)

3. “What we’re seeing across the country is not hesitation, but hunger.” — Ron Flagg (00:05:17–00:05:24)

4. “Legal Aid is not just a user of technology. It’s a proven ground.” — Ron Flagg (00:07:12–00:07:20)

5. “In the immediate moment, we need to think about integration, not disruption. How does a tool fit into existing workflows? How does it reduce the burden of those workflows rather than adding to it?” — Ron Flagg (00:15:33–00:15:49)

6. “What other economic endeavor in America has as a model throwing out your customers?” — Ron Flagg (00:26:34–00:26:45)

7. “Collaboration is a multiplier. Our field already does it well. Build on that collaborative nature. Stay focused on purpose. This is not innovation for its own sake — it’s about making our justice system more accessible and more fair.” — Ron Flagg (00:30:18–00:30:35)

8. “If we get this right, we won’t just be building better tools. We’ll be building a stronger, more capable system of justice — one that meets people where they are and delivers on the promise that justice should be accessible to all.” — Ron Flagg (00:09:07–00:09:27)

Clips

Landlords, Don’t Evict Your Customers

Build for Integration, Not Disruption

AI Where It Actually Happens

Why Eviction Laws Matter

Ron’s perspective is shaped by decades at the intersection of law, public service, and institutional leadership, and what comes through most clearly is his refusal to accept the status quo. The justice gap he describes is not abstract: it is half a million people turned away from legal help every year, families losing their homes without representation, veterans unable to access benefits they earned. His call to action is not a lament; it is a blueprint.

What makes this conversation especially timely is Ron’s framing of AI not as a threat to legal services, but as a long-awaited force multiplier for a field that has always done more with less. The legal aid community, he argues, has been innovating under constraint for decades. AI is simply the next tool in that tradition, and if deployed intentionally, it could be the most powerful one yet.

Closing Thoughts

Sitting across from Ron Flagg, even in a conference setting, you feel the weight of what he carries. He leads an organization responsible for ensuring that the most vulnerable people in America have access to the legal system. And he does it with a combination of intellectual rigor, institutional savvy, and genuine humility that I find rare in any leader, let alone one navigating the political pressures he faces.

What stayed with me most from this conversation is his insistence that the innovators matter more than the innovations. We spend so much time in the legal tech world talking about tools, which AI model is best, which workflow to automate, which firm is deploying what. Ron reminded me that the real work is about community. It’s about people choosing to collaborate, to share what works and what doesn’t, to build with clients and not just for them.

For our Legal Rebels community, Ron’s message is a challenge and an invitation. If you’re building technology, are you building it with the people who will actually use it? If you’re practicing law, are you leaning into experimentation even when it’s uncomfortable? If you’re a researcher or funder, are you helping the field measure what actually matters?

The justice gap is real. It is large. And it will not close by accident. But as Ron made clear, we have the community, the culture, and now the tools to make a real dent in it. This is our year to build, and I hope this episode inspires you to build something that lasts.

If you want to understand how AI is reshaping the future of dispute resolution and why lawyers must be part of building that future, you need to listen to this episode. Bridget is at the forefront of AI-native dispute resolution and brings a uniquely visionary yet grounded perspective on what’s possible when mission-driven institutions embrace exponential change.

AI, Access, and the Architecture of What Comes Next

Join me as I share Bridget McCormick’s keynote from the LawDroid AI Conference 2026, delivered live to our community of legal innovators.

In this powerful keynote, Bridget, President and CEO of the American Arbitration Association, lays out a sweeping and inspiring vision for the future of dispute resolution. She shares how the AAA became the first ADR organization in the world to offer AI-native dispute resolution, and what the journey of building that looked like from the inside. From deploying a fully agentic AI arbitrator to building a resolution simulator and an AI-native case management platform, Bridget walks us through how a 100-year-old nonprofit is reimagining what justice delivery can look like.

Her talk is grounded in data, rooted in the AAA’s founding mission, and animated by a genuine belief that AI’s best legal use case may be dispute resolution itself. This is a must-watch for anyone who cares about access to justice, the future of legal institutions, and what it means to lead with imagination rather than compliance in a moment of exponential change.

The Skinny

Bridget McCormick, President and CEO of the American Arbitration Association, delivered the opening keynote at the LawDroid AI Conference 2026, offering a sweeping and data-rich vision for AI’s role in the future of dispute resolution. Drawing on three years of leading the AAA’s AI transformation, Bridget traces the organization’s journey from early internal experimentation to launching the world’s first AI-native dispute resolution product, a fully agentic AI arbitrator currently operating in document-only construction disputes. Anchored in the AAA’s founding mission (democratized access to dispute resolution for individuals) not just institutions, Bridget connects the idealism of founder Frances Kellor to the urgent opportunity AI represents today. She challenges the legal profession to stop waiting for task forces and white papers, and instead take a seat at the table where the future is being built, with or without lawyers.

Key Takeaways:

• The AAA became the first ADR organization in the world to offer AI-native dispute resolution, launching a fully agentic AI arbitrator for document-only construction disputes in November 2025

• Bridget’s four-part framework for navigating exponential change: ground decisions in data, anchor to mission, move fast because action leads to information, and treat AI as a platform shift, not an incremental improvement

• A recent MIT study found 90% of employees had used AI at work while only 40% of their companies had purchased an AI solution, employees aren’t waiting for permission

• According to Law360, the share of lawyers using AI for at least one purpose jumped from 35% in 2024 to 54% in 2025, and frequent users are dramatically more optimistic than non-users

• The AAA’s AI arbitrator uses a fully agentic architecture where agents parse claims and evidence, show their work to the parties, and iterate until both sides feel heard, a breakthrough for procedural fairness

• The AAA is also building a resolution simulator, an AI-assisted mediation system, and a fully AI-native case management platform, expected to be live within two years

• Bridget invokes the AAA’s founder, Frances Kellor, who created the organization after being disappointed by international institutions that failed to prevent World War I — as the North Star for its mission: democratized, individual-level access to dispute resolution

• Three out of four state court cases involve at least one self-represented party, and Bridget sees AI as the most promising catalyst for closing that gap

• Tech innovation follows a pattern of hobbyists → automators → innovators, and Bridget believes we are now entering the innovator phase, where entirely new forms of legal service delivery become possible

• The legal profession risks being “Ubered” out of its own domain if lawyers don’t take a seat at the table where AI is being built

Notable Quotes:

1. “Building in the middle of exponential change can be extremely paralyzing. We move fast because we believe that action leads to information. We’re not waiting for a white paper.” - Bridget McCormick (00:02:15-00:02:58)

2. “The deeper I went, the more I realized this was not another wave of legal tech. This was a platform shift.” - Bridget McCormick (00:06:11-00:06:16)

3. “If there’s one thing that matters in any dispute resolution process, it’s that the parties feel heard.” - Bridget McCormick (00:06:53-00:07:01)

4. “I think AI’s best legal use case may be dispute resolution. At its core, we have two parties asking for a neutral reasoned decision from a process that’s fair, consistent, and affordable. They want a resolution. They don’t want lawyers or arbitrators or judges. We’re just the tools they have right now to get there.” - Bridget McCormick (00:19:15-00:19:41)

5. “The mission is the root of all of our work. We’re lucky that as a mission-based nonprofit, we had a clear North Star.” - Bridget McCormick (00:27:13-00:27:37)

6. “Our civil justice system is failing most people. Three out of four state court cases involve at least one self-represented party — not by choice, and in most cases it’s because people can’t afford it. I can’t think of a bigger threat to the rule of law than that.” - Bridget McCormick (00:31:14-00:31:37)

7. “The technologists can Uber right through our regulatory barriers if we cede the territory by failing to take a seat at the table.” - Bridget McCormick (00:36:43-00:36:51)

8. “That future is not promised. It’s ours as long as we’re willing to lead, as long as we’re willing to be in the conversation, as long as we’re willing to be at the table.” - Bridget McCormick (00:36:09-00:36:20)

Clips

Inside the AI Arbitrator

AI Isn’t an Incremental Shift

AI Adoption Exploded in Enterprise

AI Could Reduce Conflict

Bridget’s keynote stands out for the clarity with which it connects past to future. The AAA’s founding story, Frances Kellor building a democratized dispute resolution institution after the failures of international arbitration to prevent World War I, becomes more than historical context. It becomes the lens through which every AI investment the AAA makes is evaluated. That kind of mission alignment is rare, and it’s precisely what allows an organization to move fast without losing its way.

What makes Bridget’s vision especially compelling is her insistence that continuous improvement is not enough. Automating what we already do better is valuable — but it is the innovator phase, where entirely new forms of justice delivery become imaginable, that holds the real promise. The AI arbitrator isn’t just a faster version of what arbitrators do. It’s a fundamentally new architecture for helping people feel heard, understood, and fairly resolved.

Closing Thoughts

Bridget McCormick’s keynote was one of the highlights of the LawDroid AI Conference 2026, and listening to it again, I’m struck by how much she managed to pack into a single talk: data, history, product vision, and a genuine moral urgency that you don’t often hear from the stage at legal tech conferences.

What resonates most with me is her point about the innovator phase. For years, the conversation in legal tech has been dominated by automation, doing the same things faster and cheaper. That’s important. But Bridget is pointing at something bigger: the moment when entirely new forms of legal service delivery become possible. The AI arbitrator isn’t just a more efficient version of arbitration. It’s a new art form, to use her analogy, like cinema emerging from the innovation of the movie camera.

For our Legal Rebels community, the message here is both inspiring and urgent. The future of dispute resolution, and legal services more broadly, is being built right now, largely by people who are not lawyers. Bridget’s call to action is direct: we need to be at the table. We need to be part of the architecture of what comes next. Because if we’re not, the technologists will build it without us, and it may not reflect the values of fairness, access, and procedural justice that our profession, at its best, is supposed to stand for.

Frances Kellor believed that giving individuals the tools to resolve their own disputes could change communities, countries, and the world. A hundred years later, we finally have technology powerful enough to make that vision real. That’s the moment we’re in, and I don’t intend to miss it!

Episode Summary

In this month’s AI Double Take, LawDroid CEO Tom Martin and Chief Legal Futurist Sateesh Nori record live off the energy of the LawDroid AI Conference 2026 — a two-day virtual event that brought together the legal tech community under the banner of “The Year to Build.” They unpack standout moments from three keynotes, including Nikki Shaver’s bullish legal tech market analysis and Bridget McCormick’s call to stop incrementalising and start transforming. The hosts also dig into a striking MIT study showing AI-driven court filings are up 5%, the chronic underfunding of legal services relative to military spending, and what it would take for Big Tech to meaningfully close the access to justice gap. The throughline: the legal system is at an inflection point, and the only wrong move is to stand still.

Key Takeaways

1. LawDroid AI Conference 2026 — A Community Comes Together

The third annual LawDroid AI Conference ran April 28–29 as a fully virtual event with three keynotes, workshops, and panels covering the real-time pulse of AI in law. Sateesh served as MC across both days. The hosts describe the experience as reinvigorating — a reminder that a serious, optimistic community is already building the next chapter of legal services.

2. Nikki Shaver’s Keynote — No Bubble, Just a New Ceiling

Nikki Shaver, co-founder and CEO of Legal Technology Hub, presented data showing an upward trajectory in legal tech that shows no sign of reverting. Rather than a dot-com-style bubble, the data points toward a breakout: AI enabling legal services to finally reach the vast latent legal market — the millions of people currently unserved. The image Tom offered: Willy Wonka’s glass elevator breaking through the ceiling and kept going.

3. The Velocity Warning — When Outside Outpaces Inside

Tom introduced the counterpoint: when the velocity of innovation outside an organisation exceeds the velocity of change inside it, that organisation is in trouble — the Sears, Blockbuster pattern. Sateesh framed it as the classic innovator’s dilemma: established legal players have no structural incentive to disrupt themselves. The risk is that hungrier, smaller, tech-native players eat the market while incumbents wait.

4. Bridget McCormick’s Keynote — Stop Improving the Candle

Bridget McCormick, President of the American Arbitration Association, delivered a pointed message: enough with committees, commissions, and incremental improvement. Her centrepiece: you cannot continuously improve a candle into a light bulb. It is a categorically different thing. The legal field needs to stop asking for faster horses and start imagining the automobile. Her call to action aligned directly with the conference theme: build now.

5. Ron Flagg / LSC Fireside Chat — Funded But Under-Resourced

In a fireside chat with Sateesh, Legal Services Corporation President Ron Flagg reported that LSC has maintained current funding levels despite severe political headwinds — a genuine achievement. But the scale of the gap remains stark: the entire annual LSC budget ($566 million) amounts to less than a quarter of a single day’s military spending. Both Ron Flagg and former LSC President Jim Sandman are publicly calling for legal aid organisations to accelerate AI adoption. The signal from the top is clear.

6. AI Could Automate Over 70% of Access to Justice Work

A post by Richard Truman of Artificial Lawyer, using AI-based modelling, estimated that over 70% of access to justice work is amenable to automation — significantly higher than the 30%+ figure for large law firms. For legal aid organisations operating without the billable hour incentive, the financial case for AI adoption is structurally aligned in a way it simply is not for Big Law.

7. The Point One Challenge — Big Tech’s Untapped Obligation

Sateesh proposed the “Point One Challenge”: if Big Tech companies contributed just 0.1% of annual profits to access to justice, it would generate roughly $5 billion per year. Tom raised the complementary idea of noblesse oblige — voluntary goodwill investment from Anthropic, Microsoft, and others who are already entering the legal market. Neither idea is mainstream yet, but both would dwarf current public funding.

8. The Direct-to-Consumer Alternative — Agency Over Charity

Sateesh argued that waiting on Big Tech goodwill misses a more powerful model: direct-to-consumer legal tools that give individuals agency. People do not want charity — they want to pay a dollar for a legal answer or five dollars for a form. LawDroid is actively developing in this direction, and Tom confirmed something may be visible in the near future.

9. AI Filings Up 5% — Courts Must Adapt, Not Resist

MIT researchers Shaw and Levy studied federal civil case filings and found a 5% increase in the post-AI period, with significant evidence of AI-generated content in the filings themselves. The hosts’ joint position: this is not a crisis to suppress — it is a signal that people with legitimate claims are finally able to access the system. Courts should lean in with their own AI tools (such as the Learned Hand system already in use by some judges), offer front-end guidance on case viability, and scale to handle volume rather than shutting the door on access.

Show Notes

Topics Covered

• LawDroid AI Conference 2026 recap (April 28–29, virtual, “The Year to Build”)

• Nikki Shaver keynote: legal tech market trajectory and the latent legal market

• Dot-com bubble concerns vs. breakout growth scenario

• The velocity of outside innovation vs. inside change (Welch/Darth Vaughn quote)

• Sears and Blockbuster as cautionary tales for legal incumbents

• Bridget McCormick keynote: candle-to-light-bulb transformation imperative

• Henry Ford “faster horses” analogy applied to legal transformation

• Ron Flagg / LSC fireside chat: current funding levels, political headwinds

• LSC budget ($566M) vs. daily military spending comparison

• Jim Sandman’s public call for AI adoption across LSC organisations

• Richard Truman / Artificial Lawyer: 70%+ of access to justice work automatable

• Big Tech entering legal AI: Anthropic, Microsoft, Elon Musk / Grok

• Anthropic’s 20,000-person Claude legal webinar (surprised by demand)

• Noblesse oblige / Big Tech goodwill funding for access to justice

• Sateesh’s “Point One Challenge” — 0.1% of Big Tech profits = ~$5B/year

• Direct-to-consumer legal tools: agency over charity model

• MIT study (Shaw and Levy): federal civil filings up 5% post-AI

• AI-generated filing detection and court adaptation strategies

• Learned Hand AI system used by judges

• LawDroid upcoming product hint

People & Organizations Mentioned

• Tom Martin — CEO & Founder, LawDroid

• Sateesh Nori — Chief Legal Futurist, LawDroid; MC at LawDroid AI Conference 2026

• Nikki Shaver — Co-founder & CEO, Legal Technology Hub; Day 2 keynote

• Bridget McCormick — President, American Arbitration Association; Day 1 keynote

• Ron Flagg — President, Legal Services Corporation; fireside chat guest

• Jim Sandman — Former President, Legal Services Corporation

• Richard Tromans — Artificial Lawyer (legal tech blog/newsletter)

• Hannes Westermann — Legal AI researcher (referenced re: Anthropic legal webinar data)

• Shaw & Levy — MIT researchers; federal civil filing AI study

• Anthropic — Ran a 20,000-attendee Claude legal webinar; entering legal market

• Microsoft — Noted as entering legal AI market

• Elon Musk / Grok — Posted that Grok is “#1 for AI law”

• Legal Services Corporation (LSC) — Primary federal funder of civil legal aid in the US

• American Arbitration Association — Led by Bridget McCormick; dispute resolution

• Legal Technology Hub — Legal tech market intelligence, led by Nikki Shaver

• Learned Hand — AI tool used by judges for managing case volume

Final Takes

Sateesh Nori:

“I feel really optimistic after our conference. Seeing all the folks we might run into here and there — but together in one place, in our forum — that was really great. It gives me a lot of hope. There are a lot of good people in this space. Really funny, really interesting people. I’m really glad to be part of this community.”

Tom Martin:

“It is so reinvigorating every year to have the conference, to have people attending, to feel that sense of community come together. I feel energised for this year. We’re going to accomplish a lot together, and I’m really excited about that. We’re not always familiar with the destination we’re going — but it looks like it could be a good place for more of us. So let’s all keep building.”

AI Double Take is produced by LawDroid | lawdroid.com

If you missed the LawDroid AI Conference 2026 last week, you need to listen to episode! It’s Nikki Shaver’s keynote about what is actually happening in legal AI right now; not the marketing version, but the real one from someone who knows. Nikki has the clearest view of the legal technology landscape of anyone in our field, and she brought that perspective in full force. Don’t miss it!

The State of Legal AI 2026: What’s Now, What’s Coming

Join me as I share Nikki Shaver’s landmark keynote from the LawDroid AI Conference 2026.

In this powerful keynote, Nikki Shaver, co-founder and CEO of LegalTech Hub and one of the sharpest minds in global legal technology intelligence, delivers an unflinching look at where the legal AI market truly stands. Drawing on years of tracking every AI solution in the legal market, Nikki walks us through the explosion of legal tech startups, the dramatic rise of agentic AI, the surge of private equity and Silicon Valley capital into legal, and the structural shifts now reshaping law firms at their core.

Her keynote doesn’t just catalog what’s happening. It asks the harder question: given all of this change, what must law firms actually do to survive and thrive? Nikki introduces her “New Horizons” framework for differentiation, explains why AI enablement is now just table stakes, and warns that the firms not yet thinking beyond Horizon One are already behind. This is essential listening for any legal professional who wants to understand the moment we are in, and what to do about it.

The Skinny

Nikki Shaver, founder of LegalTech Hub, delivered the opening keynote of the LawDroid AI Conference 2026 with a sweeping, data-rich assessment of where legal AI stands today. Speaking from a unique vantage point, advising law firms, corporate legal departments, vendors, and investors on a weekly basis across multiple continents, Nikki pulled back the curtain on the real signal beneath all the noise. The legal tech market has seen approximately 100 net new startups per quarter since 2023 with no signs of slowing, legal tech funding is outpacing all prior years, and agentic AI has moved from curiosity to concrete use cases in legal workflows. Perhaps most striking is Nikki’s assessment that the legal industry has entered the “Innovator’s Dilemma zone,” a moment where incumbent law firms are still thriving on their existing models even as nimble, AI-native disruptors are scaling rapidly around them. Her central message: AI enablement is now table stakes, not a differentiator. True survival requires firms to think in terms of her New Horizons model, moving beyond efficiency gains toward transformed systems, embedded legal infrastructure, and scalable outcome-based offerings.

Key Takeaways:

• LegalTech Hub has tracked approximately 100 net new legal AI startups per quarter since early 2023, with the market showing no signs of a bubble bursting

• Agentic AI in legal has exploded from roughly 100 solutions in July 2025 to a dramatically larger ecosystem by March 2026, signaling a shift from task-based to workflow-based AI

• Between 79 and 90% of lawyers are now using some form of AI in practice, with corporate legal department adoption swinging from 23% to 52% in a single year

• Legal professionals anticipate saving approximately 240 hours annually using AI, representing a potential $32 billion annual impact on the US legal sector alone if work is not repriced

• Legal tech has gone fully mainstream: celebrity endorsements, sports sponsorships, and mass-market advertising are now part of the landscape

• Anthropic’s entry into legal with Claude Legal Skills created significant market anxiety, though Nikki’s conversations with Anthropic suggest it was intended as a model marketing play, not a direct product launch

• Law firm mergers jumped 21% in the first half of 2025, and private equity investment in law firms is growing via new managed services structures that navigate non-lawyer ownership rules

• AI-native law firms, built on the operational foundation of AI from day one, represent a new category of disruptor unlike anything the industry has seen before

• Nikki’s “New Horizons” model reframes the McKinsey Three Horizons framework for the AI era: Horizon One is efficiency (already table stakes), Horizon Two is quality and augmented judgment, and Horizon Three is fully transformed, scalable, embedded legal systems

• Firms focused solely on Horizon One innovation right now are already at risk of falling behind in what Nikki calls the “dilemma zone”

• True differentiation requires leveraging your firm’s unique data, building governance capabilities, investing in R&D, and structuring for work that doesn’t look like traditional matters

Notable Quotes:

1. “If you want to know what’s actually happening in legal AI in 2026, not the marketing version, the real one, Nikki is the person to start the day with.” - Tom Martin (00:00:32-00:00:40)

2. “On a weekly basis, I am speaking with law firms, often in different parts of the world. I speak to corporate legal departments. I speak to vendors, both very early stage and very mature in the market. And I speak to investors as well as law students. I teach at a law school. And so I get a very well-rounded view on what is happening in the legal market.” - Nikki Shaver (00:02:03-00:02:31)

3. “We have not seen that. In fact, we’ve seen the opposite. There’s just an enormous proliferation of startups still over and above consolidation in the market, even though we’ve seen more M&A in the market as well over the past year.” - Nikki Shaver (00:06:22-00:06:35)

4. “Already by now April in 2026, I can say with certainty that we’re finding a lot of use cases for agentic AI. And generally speaking, the dialogue around AI and legal has shifted to agent.” - Nikki Shaver (00:09:04-00:09:18)

5. “When the rate of change on the outside exceeds the rate of change on the inside, the end is near.” - Jack Welch, quoted by Nikki Shaver (00:22:50-00:22:58)

6. “It is now table stakes to have AI enablement across the firm. The technology that all of us rush to license is just the cost of entry at the moment. It’s not where you differentiate.” - Nikki Shaver (00:39:20-00:39:35)

7. “We do not have that time anymore.” - Nikki Shaver, on the traditional Three Horizons timeline (00:40:53-00:40:56)

8. “If it’s not now, I don’t know when it will be.” - Nikki Shaver (00:49:13-00:49:15)

Clips

Anthropic Shocked the Legal Market

Law Firms: Your Structure is Killing Innovation

AI is Now Table Stakes

PE, Regulation, and Clients on AI

Nikki’s keynote is remarkable not just for the data it presents, but for the clarity of the narrative it constructs around that data. The legal tech market is not in a bubble. It is not slowing down. The pace of startup activity, the volume of funding, the acceleration of adoption, and the entry of new classes of competitors, from AI-native law firms to tech-product companies pivoting into legal services, all point in one direction. The window for comfortable, incremental change has closed.

What makes Nikki’s perspective especially valuable is that she doesn’t traffic in fear for its own sake. Her New Horizons model is fundamentally optimistic: there is a path forward, but it requires law firms to be honest about where they actually are, to invest in R&D the way every other industry does, to leverage their own data as a strategic asset, and to hire for the kind of creative, outside-the-box thinking that the current moment demands.

Closing Thoughts

As someone who has been in the legal technology space for years, building tools, talking to lawyers, and watching this industry evolve, I found Nikki’s keynote to be one of the most grounding and clarifying things I’ve heard in a long time. Not because it was comfortable, but because it was honest.

We are in the dilemma zone. I’ve felt it. Many of you have felt it. The pace of change is outrunning our ability to process it, let alone respond to it. And Nikki’s data makes clear that this is not a temporary condition that will settle down once the hype fades. The hype isn’t fading. The startups keep coming. The capital keeps flowing. The clients are now asking, sometimes demanding,?that their law firms use AI.

What struck me most was the point about table stakes. So many firms have spent the last two years treating AI adoption as a differentiator, as a competitive edge. Nikki’s data says that moment is over. Everyone has Harvey or Legora or something similar. The question now is what you do with your own data, your own expertise, your own client relationships, how you build something that can’t be replicated by the next firm that licenses the same tool.

For our Legal Rebels community, that is both the challenge and the opportunity. The firms and professionals who will thrive are those who don’t wait for the market to settle, who invest in building real capabilities now, and who are willing to reimagine what legal services can look like when AI is at the core of everything, not bolted on at the edges.

Now is the time. As Nikki said: if not now, when?