Jerry Levine

This is a good result, in my opinion. I hope this rational finds its way into other civil litigation involving discovery of AI chatbot use. If you disagree, I'd be very interested to hear the reason. Let's debate!

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California enters 2026 by putting in place a number of new AI laws relating to transparency, consumer protection, healthcare, data brokers, and liability. While a recent Executive Order from President Trump signals movement towards preemption and a national framework, California shows no signs of ceasing to be a leader in regulating AI. Companies should ensure compliance with new laws that came into effect at the start of the year and prepare now for others that will become operative in the near future. Read our full analysis for practical checklists, timelines, and implementation guidance: https://lnkd.in/ebBe3iDW #AOShearman #AI #California

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This is definitely an interesting one…it brings to mind for me the simple principle that if a third-party is present during legal discussions that isn’t also representing you, it breaks privilege. The fact that the AI isn’t a lawyer and on top of that stores chats in some form of logging function on the backend that nth parties could access, should give pause to not just legal professionals but the average person as well. A bigger question is if a firm rolls their own AI trained for their particular practice area and that isn’t exposed outside of the firm, would _that_ be considered privileged…? 🤔

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Trade shows like #CES are incredible launchpads for new tech, but they also pose real IP risks.   Ilya Mirov and I compiled this guide to help innovators, startups, and technology companies stay protected. It covers best practices for aligning IP filings with product launches, navigating international laws, and building a global enforcement strategy. https://lnkd.in/ghvafuqi

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1 Comment

It's been about two years since law firms started adopting AI tools. Even though the results should be resoundingly positive, the feedback, quite frankly, is mixed based on all of the surveys I have read. Firms report concerns around inconsistent internal adoption, varied client permissiveness regarding AI usage, varying accuracy levels, lack of training, lack of clarity around workflow improvement, and little positive change to firm economics. The firms that are winning the AI-enabled law firm race share a common trait: they studied their data before implementing AI. Those that are struggling took the "ready-fire-aim" approach, jumping on the bandwagon after believing the promises of AI salespeople that GenAI is an out-of-the-box panacea. Make no mistake—I may be the BIGGEST fan of AI adoption in the legal industry, but AI must be adopted in a smart and strategic manner. Smart people don't start taking medicine without understanding their underlying condition; the same should be true of how firms adopt AI. Billing data continues to be the key component for guiding an AI strategy. The Critical Challenges: -Transitioning to Value-Based Pricing. Your billing data reveals which matters are actually profitable under the billable hour. This baseline is essential for setting competitive fixed fees that protect margins while meeting client demands for predictability. -Eliminating Training Inefficiencies. Historic matter data shows exactly where junior lawyer time added value versus where it inflated costs. As AI eliminates routine junior tasks, this analysis helps you redesign training programs and staffing models that work. -Shifting from Input to Output Pricing. Years of time entries tell you what it actually costs to deliver specific outcomes. Without this intelligence, you're guessing at project-based pricing—and likely leaving money on the table or overpricing yourself out of opportunities. -Investing in the Right AI Tools. Your billing patterns reveal which practice areas have the highest volume of repetitive, high-margin work—the sweet spot for AI automation. This data drives ROI-focused technology decisions, not vendor promises. Your billing data is a strategic goldmine. It shows you: • Where you're most efficient (and why) • Which clients and matters are actually profitable • What optimal staffing looks like by matter type • How to price competitively in a value-based world The firms winning in 2026 won't just blindly implement AI—they're leveraging years of operational intelligence to transform strategically, not reactively.

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1 Comment

Law firm AI labs are solving the wrong problem. Law firms sell billable time. Software companies sell scale. Build a tool that automates away billable hours, and you've built something that fights your own revenue model. Trace the incentives back far enough and the outcome is predictable. The Washington Journal of Law, Technology & Arts (University of Washington Law) found the same pattern: when a service provider automates the tasks it bills for, the automation works against the economics of the firm building it. The University of Chicago Business Law Review has a name for what happens instead: "captured innovation." Rather than build software designed for self-sufficiency, incumbents build tools that extend the existing service model. Same people, same work, new interface. The real cost is that budget and headcount go toward technology that reinforces the billable model instead of replacing it. General counsel offices face the same trap. Building AI in-house optimizes for one matter at a time; a platform scales across the whole docket. A litigation team doesn't need to become a software company and make more internal AI hires to get software-grade efficiency. Point the innovation team at the workflow instead, buy a purpose-built platform — that's where they add value a technology vendor doesn't. Buy the tool, build the process around it. Sources: Rapoport & Tiano, "Fighting the Hypothetical," 20 Wash. J.L. Tech. & Arts 41 (2025): https://lnkd.in/gtRzFQb8 Showalter & Edelson, "Captured Innovation," Univ. of Chicago Bus. L. Rev., Vol. 4 (2025): https://lnkd.in/ggFRiKEX #LegalTech #LawFirmManagement #LegalOperations #CorporateCounsel #AIinLaw

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1 Comment

Everything I know about legal AI got sharper outside the legal AI bubble. I say that from deep inside it. Same conferences, same threads, same arguments about the same handful of tools. Most of it is recognition. Current inside the bubble is the Harvey certification. Its great that Harvey is helping people. Its great marketing. But it doesnt tell me much about whether you've built something. Whether you can deal with the random issues that always pop up. Which is why I love getting outside the bubble. Outside I have to watch. When I help my wife in her sales role. When I help my friend in his small business. When I trade model tricks with my friend in AI ecommerce. I get to learn because I have no specific expertise. I'm simply there to help. Which means trying, and yes sometimes failing. But always learning. Go get outside your legal bubble. Go build something with someone in an entirely different department. You'll learn what you can take back to legal. You'll be better for it.

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WE BUILD BETTER! Why?? Because we are challenged to find solutions that are not what the industry believes but what the industry is really asking for. The notion to accept “we have always used this product” was not something we wanted our user to conform to. Legal research and citations should be treated as world class decision making. Hallucinations or possible hallucinations should be treated as an immediate review process. We built something that is going to improve the process.

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