Blame It On The AI: Company tried to pin labor law violation on ChatGPT.

The Tech Isn’t The Key: Legal AI needs to build a winning user experience if it’s going to capture Biglaw.

Everyone In MAGA Land Has Notes For Jeanine Pirro This Week: ‘Never mention her friggin’ name to me,’ fumed the man demanding she fire a prosecutor.

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In the mid-1940s, which Supreme Court justice refused to recuse himself from a case argued by his former law partner. A fellow justice was so incensed that he fired off a public cable to Congress naming the justice and accusing him of unethical conduct — the only time in the Court’s history one justice has publicly charged another this way.

Hint: The Supreme Court, the famous line goes, is “nine scorpions in a bottle,” and these two were the ones doing the stinging. The accuser sent his broadside from Germany, where he had a rather more infamous set of defendants.

See the answer on the next page.

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Breaking news: business and society, including the legal industry, have generally decided AI may have some benefit that is worth leveraging. Many of us have grown markedly more comfortable with using AI in our day-to-day lives and activities. Need help polishing that email you want to send?  Check. Want a quick outline to facilitate a strategic discussion? Check. Want to quickly summarize or compare a set of documents? Check. Want to see what you’d look like as an action figure?! Check.  

Just make sure you remove any use of the em dash from AI’s output to avoid associated stigma. (Frustrating, on a personal note, as a writer who used to make frequent use of the em dash before AI was doing so!)

The evidence has continued to mount over the last few years. According to 2025 Pew Research Center findings, Americans are interested in AI and want it involved in their daily activities. Stanford’s 2025 AI Index also found that a majority of businesses are using, investing in, and experimenting with AI. Based on Thomson Reuters 2025 Generative AI in Professional Services Report & ILTA’s 2025 Technology Survey, the legal industry is here for it; legal continues to embrace and expand its usage of AI. 

However, there’s a catch: comfort and adoption with AI are rising faster than judgment maturity. I’m a big advocate and user of AI, but I also recognize time-tested pearls of wisdom apply here: “Just because you can do something, doesn’t mean you should do that thing.” Or perhaps you prefer “with great power comes great responsibility.”

The Stakes: What If the Machine Is Wrong?

The legal profession and industry are built on core principles of logic and rules. The intentional use of language and reasoning are central to the practice of law, and practitioners carry an important responsibility in serving clients. All attorneys know these tenets well, as they are woven into the fundamentals of legal training. IRAC, which stands for Issue, Rule, Application, Conclusion, is the foundational legal analysis and writing outline taught in law schools, and it demands attention to detail and clear congruence between facts and applicable rules. As service professionals, legal practitioners are also duty-bound to follow the rules of professional conduct and shape their practice in accordance with ethical standards.

Adherence to these duties and principles is imperative for legal. They reflect the exercise of professional responsibility and the very essence of client service. They demand the autonomous exercise of professional judgment. They cannot and should not be delegated to AI.

There is ample evidence that the legal profession and industry are aware of the dangers of not exercising proper professional judgment over the use of AI:

• The oft-cited Mata v. Avianca, Inc., where attorneys were sanctioned for submitting hallucinated cases, emphasized that the duty of verification cannot be delegated. The issue was that professional judgment was not exercised; the core issue wasn’t that AI was used, but rather that AI was not properly supervised.

• Professional liability insurance is evolving, and related guidance cautions against lawyers’ reliance on unverified AI outputs, as outlined in “From innovation to exposure: artificial intelligence risks for legal professionals.” Failure to supervise AI could expose firms to malpractice claims.

• Beyond Mata, courts are increasingly scrutinizing AI-assisted filings and requiring variable certifications. This is highlighted in “Which Federal Courts Have AI Judicial Standing Orders?” The message is not anti-technology; it is pro-judgment and verification.

• The American Bar Association’s (ABA) Standing Committee on Ethics and Professional Responsibility issued Formal Opinion 512 in July 2024, emphasizing, among other things, that lawyers are required to develop a reasonable understanding of the capabilities and limitations of AI use as part of the duty of competence.  

Judgment maturity is becoming a market expectation. In-house departments are not passive observers. They see the value of integrating AI capabilities and are increasingly seeking ways to bring legal work in-house, as borne out in the Association of Corporate Counsel’s survey results, “Generative AI’s Growing Strategic Value for Corporate Law Departments.” As a result, corporate legal teams are critically evaluating how law firms deploy generative AI and report a perceived lack of awareness regarding outside counsel’s use of the technology on their legal matters. Some even expect to push for changes to billable hour arrangements with outside counsel because of generative AI’s impact.

The challenge is converting system awareness and industry pressure into personal professional judgment.

Shifting From Tool Training to Judgment Development

Clearly, a majority of law firms recognize that AI is the biggest technology pushing significant change in the industry over the next 3-5 years, but there is a great deal of variation in the approaches firms are taking in response to this realization. Early AI enablement efforts at law firms focused on core elements of access, security policies centered around proper use, and basic prompting techniques. Prudence dictated building a solid training foundation.  

But there is emerging recognition that the legal AI journey requires evolving beyond the basics toward something less technical, and yet just as fundamentally important and substantive: developing human judgment and instinct that guides our use (and at times, intentional non-use) of AI. Recent initiatives by law firms and legal AI providers have included:

• Evolving approaches to junior associate training to encourage and facilitate development of the lawyer’s “judgment” muscle through multi-modal, scenario-based training models, as highlighted in “Reinventing Associate Training for the Age of AI”

• Treating AI training as a continuing education responsibility of firms, core to associate and attorney development. According to “The Grace to Dabble: Two Biglaw Firms Look to an AI-First Future,” some firms are even putting skin in the game by providing billable hour credit to associates.

• The growth of AI-specific leadership roles within firms to inject context and oversight at the practice level within practice groups, rather than centralizing AI governance solely as an IT function; and

• Practice-specific training simulations teaching and guiding attorneys to know and practically apply the limitations of AI-generated work product, including identifying hallucinated citations, comparing AI-generated drafts to human drafts, and conducting structured verification reviews. 

Early Building Blocks for an AI Judgment Framework

While there is variation in how organizations are supporting the development and maturation of judgment skills, below are some common threads that have been emerging.

1. Teaching AI Literacy and Limitations through Scenarios. Beyond helping legal professionals to understand the basics of AI and the pitfalls to be on the lookout for, such as hallucinations, bias, and outdated training data, programming presents users with realistic scenarios where AI outputs are plausible but may be wrong. This requires learners to critically assess and verify, rather than passively accept outputs. Harvard Law School even offers an “AI and the Law” executive education program where scenario-based roleplays are used to navigate AI-fueled dilemmas.

2. Ethical Reasoning Baked In. As previously mentioned, attorneys have ethical obligations tied to their use of AI, particularly in relation to competence, confidentiality, communication, and supervision. Programs have seen success embedding these topics into scenario-based training materials. The key is to not treat ethics as a separate compliance module, but instead treat it as an organizing framework for judgment calls attorneys must make in their AI use. 

3. Risk-Tiered AI Use Cases. Not all uses of AI carry the same risk. Differentiating these use cases can help to heighten risk awareness, while avoiding arbitrarily dampening the prospective value of AI for lower-risk tasks. Borrowing from ediscovery and technology-assisted review defensibility frameworks, organizations can generally categorize AI use cases and scale human oversight according to risk. For example:

4. AI Reliance Checklists/ Practice Resources. Workflows that incorporate the use or reliance on AI should also integrate protocols to facilitate baseline Human-in-the-Loop verification on proper usage. This step is analogous to citation checking, due diligence checklists, or document review quality control measures. AI output review is a skill to be developed, not a formality. Modeling from guidance emerging from judicial orders and bar opinions, these checklists might include queries such as:

• Have all citations been independently verified?

• Are assumptions factually supported?

• Does this output affect client rights?

• Would disclosure be required or prudent?

5. Practice- and Role-Tailored Training. Resist the temptation to deploy one-size-fits-all training programs. I am a big believer that, like politics, all change and innovation is local. Learning to exercise judgment on the use of AI cannot be abstract.   

A litigator assessing AI-assisted research output applies different filters and faces different judgment demands than a transactional attorney reviewing an AI-drafted contract clause. Likewise, a first-year associate drafting a contract with the assistance of AI has different judgment demands than a general counsel evaluating an enterprise AI platform. Effective training programs segment by practice group, seniority level, and/or function and build practice-specific modules that situate reasoning in recognizable professional contexts that resonate with learners.

6. Measuring Judgment Maturity. As the management principle states, we should measure what matters. KPIs for AI training should move beyond indicators on whether users are accessing AI tools to tracking metrics that demonstrate proficiency and maturation in the exercise of professional judgment, such as:

• Citation defect rates

• Review protocol compliance

• Audit documentation of AI-assisted workflows

• Practice-specific AI standards adoption

7. Cultural Shift to AI-Enabled Talent. Finally, the ultimate, and perhaps longer-term, success of building AI judgment frameworks will be evidenced by a mindset shift. As AI-assisted workflows become the norm, traditional views on professional identity and legal team roles are evolving. “Human-in-the-Loop” will take on a new and very tangible meaning. This shift may look something like:

In the emerging model of AI-enabled talent, the speed of content generation is likely not the top indicator of premium skill. The focus will be more on context and risk calibration, error detection, ethical reasoning, and supervision of AI outputs, informed by legal subject-area expertise.  

The Skill Machines Can’t Replace

Early on, the AI race was about getting access and standing up use cases. The next phase, which is critical to sustainability in legal, is about judgment and defensibility. Courts have sanctioned AI misuse, and bars have clarified overriding duties. Clients are asking questions about AI governance. The organizations that will lead are those that move beyond teaching legal professionals to prompt better, to teaching them how to decide better.

Brendan W. Miller, J.D. is a legal innovator: a curious, seasoned litigator and corporate attorney, technologist, strategist, and change agent. To Brendan, legal innovation is about continually being relevant for clients, by making the business and practice of law easier, better, and more valuable.

The post Teaching Judgment In The Age Of AI: Building Legal Professionals Who Rely On AI Responsibly appeared first on Above the Law.

As a thought experiment, imagine only one person had a superintelligent lawyer. They would have an unfair advantage in court — even if they were wrong on the merits. That would lead to a worse society. But now imagine everyone has a superintelligent lawyer. In this case, justice would be carried out much more fairly and efficiently than it is today when there is often an imbalance in skills and resources in litigation.

— Mark Zuckerberg, Meta Platforms CEO and founder, offering a glimpse of how he envisions a future where superintelligent artificial intelligence is available to help everyone succeed, in “The Future Is for Everyone: The Path to a Positive AI Future,” a 14-page letter that he wrote and posted earlier this week on the Meta website.

Staci Zaretsky is the managing editor of Above the Law, where she’s worked since 2011. She’d love to hear from you, so please feel free to email her with any tips, questions, comments, or critiques. You can follow her on Bluesky, X/Twitter, and Threads, or connect with her on LinkedIn.

The post Mark Zuckerberg Thinks Everyone Should Have A Superintelligent AI Lawyer appeared first on Above the Law.

Matt Bruenig flagged a weird sign-of-the-times NLRB matter out of Texas. Daniela Melendez worked for an aftermarket auto parts company called Autofit as an administrative assistant at $22 an hour. Two or three days in, another employee asked what Melendez was making. Melendez told her, and the other employee printed out Melendez’s job posting, marching into a boss’s office and asked to be paid the same. Management told other employee that she didn’t qualify.

Sixteen days later, Melendez was fired.

When Melendez applied for unemployment, the company — via Chief Logistics Officer Elham Emadi’s administrative assistant Esperanza Guevara — had to explain itself to the Texas Workforce Commission in writing. This is where things get screwy:

The employee was terminated due to a number of performance issues, which included serious breaches of confidentiality and difficulties in teamwork. As part of her role, she was explicitly instructed to keep sensitive information confidential, yet she shared pay details with colleagues.

According to the decision, after Melendez learned about this rationale, she asked whether talking about pay was against the law. The answer is, of course, no. But firing someone for talking about pay very much is against the law.

Autofit had to explain why it admitted to breaking the law. Its first attempt came in a sworn statement taken during the Board’s investigation, where the company argued that when it wrote “she shared pay details with colleagues,” it didn’t mean talking about pay, but “taking information or sales commissions of others and sharing it or using it for herself.”

Since that’s not how words work, this didn’t fly.

Autofit’s own lawyer teed up attempt two at trial: you see, the company hadn’t admitted anything because ChatGPT wrote it! The author of the statement testified that she used the free version because she wanted a more professional sounding response, and then copied and pasted the output directly into the form. She couldn’t recall whether she read it thoroughly, and figured ADP would fix any errors. She had not mentioned any of this to anyone until witness preparation.

Hallucinations are wild… but they aren’t that wild:

Guevara would have us believe that ChatGPT made up the language that sensitive information includes pay. For example, why didn’t ChatGPT say another type of sensitive information, like trade secrets. I therefore do not credit Guevara’s explanation that ChatGPT added pay without Guevara’s help.

That’s Judge Sharon Steckler not buying the idea that ChatGPT just decided to make up a reason to fire Melendez without any prompting. An illegal reason to fire her at that.

Even if ChatGPT spit out language about pay, Guevara could have reviewed it, found it inaccurate, and removed the part about pay.

With the company in the bind between admitting that it filed a termination explanation with the state including fake justifications or conceding that the explanation included the genuine admission that the company fired her illegally, the judge credited the latter explanation.

The company also lodged 16 affirmative defenses, including a Seventh Amendment jury trial demand and the now-standard Article II claim that the Board itself is unconstitutional because presidents are god emperors with unchecked power over the executive branch as long as they’re a Republican. Neither carried the day.

The remedy is reinstatement, backpay, pecuniary harms, file expungement, and a notice on the wall for 60 days.

There is one more detail worth sitting with. Autofit’s witnesses testified that the company demands employees be trustworthy above all, and the administrative assistant job was described repeatedly at hearing as a sensitive position. Outsourcing a state agency filing to a free chatbot was apparently not a breach of sensitivity, but talking about your own paycheck was.

Garbage in, garbage out.

(Check out the opinion here…)

Joe Patrice is a senior editor at Above the Law and co-host of Thinking Like A Lawyer. Feel free to email any tips, questions, or comments. Follow him on Twitter or Bluesky if you’re interested in law, politics, and a healthy dose of college sports news.

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What happens when AI starts making decisions on its own?

The next generation of AI isn’t limited to drafting emails or summarizing documents. Autonomous AI agents can perform multi-step tasks, interact with third-party systems, execute transactions, and make decisions with minimal human oversight. These capabilities promise significant efficiency gains, but they also introduce risks that many organizations and their insurers are only beginning to understand.

According to recent reports, cyber insurers are increasingly scrutinizing how organizations deploy autonomous AI and whether existing cyber insurance policies adequately address the new exposures these systems create. The concern isn’t simply that AI creates new cyber threats. It’s that organizations may unknowingly introduce risks that fall outside the assumptions underlying their insurance policies.

AI Changes More Than Technology

Traditional cybersecurity focuses on protecting systems against unauthorized access, malware, ransomware, and data breaches. Autonomous AI poses a different challenge.

Instead of merely assisting employees, AI agents may be authorized to access client files, send communications, interact with financial systems, retrieve confidential information, or make operational decisions on the firm’s behalf. The authorizations are not significantly different than permissions granted to apps on your smartphone to the data stored on your phone (personal and client). As organizations grant these systems greater authority, questions of accountability become significantly more complex.

If an autonomous AI agent exposes confidential client information, authorizes an inappropriate transaction, or makes a decision that causes financial harm, was it a cyber incident, a professional liability issue, or an operational failure?

The answer may not be as straightforward as existing insurance policies assume.

Governance Matters More Than Ever

For law firms, this is less about buying new insurance and more about implementing thoughtful governance.

Firm leadership should know where autonomous AI is used, what information it can access, what decisions it is authorized to make, and what safeguards are in place to prevent unintended actions. Just as importantly, firms should maintain meaningful human oversight for high-risk activities involving client data, financial transactions, or legal work product.

These questions increasingly mirror the cybersecurity conversations firms have already had about privileged access, vendor management, and cloud security. AI governance is becoming another component of enterprise risk management rather than a standalone technology initiative.

Don’t Wait Until Renewal

Cyber insurance applications have become significantly more detailed over the past decade. Questions about multifactor authentication, endpoint detection, backups, and incident response planning have become commonplace as insurers have learned that these controls materially affect risk. AI governance may be next.

Organizations that can demonstrate clear policies, documented oversight, access controls, and responsible deployment of autonomous AI will likely be better positioned as underwriting evolves. Firms that cannot explain how AI operates in their environment may face additional scrutiny, coverage limitations, or difficult conversations after an incident.

Conversation Is Bigger Than Insurance

Whether cyber insurance policies ultimately change is almost beside the point.

The more important takeaway is that autonomous AI is compelling organizations to rethink risks. The same technology that promises greater efficiency also raises new questions about accountability, governance, and professional responsibility.

For law firms, AI should not be viewed as merely another productivity tool. It should be managed with the same discipline applied to any technology that can access confidential information or make decisions that affect clients.

Cyber insurance may eventually adapt to these new realities. The firms that succeed will be those that adapt first.

Michael C. Maschke is the President and Chief Executive Officer of Sensei Enterprises, Inc. Mr. Maschke is an EnCase Certified Examiner (EnCE), a Certified Computer Examiner (CCE #744), an AccessData Certified Examiner (ACE), a Certified Ethical Hacker (CEH), and a Certified Information Systems Security Professional (CISSP). He is a frequent speaker on IT, cybersecurity, and digital forensics, and he has co-authored 14 books published by the American Bar Association. He can be reached at mmaschke@senseient.com.

Sharon D. Nelson is the co-founder of and consultant to Sensei Enterprises, Inc. She is a past president of the Virginia State Bar, the Fairfax Bar Association, and the Fairfax Law Foundation. She is a co-author of 18 books published by the ABA. She can be reached at snelson@senseient.com.

John W. Simek is the co-founder of and consultant to Sensei Enterprises, Inc. He holds multiple technical certifications and is a nationally known digital forensics expert. He is a co-author of 18 books published by the American Bar Association. He can be reached at jsimek@senseient.com.

The post Your Cyber Insurance May Not Be Ready For Autonomous AI appeared first on Above the Law.

For years, legal marketing was predictable. Someone with a legal question searched on Google, and if a firm did its job with SEO, they earned a click to the website. Looking for recommendations on legal representation directed them to Chambers or other ranking sites. Researching a lawyer or firm was a time-consuming task, sifting through profiles, media stories, biographies, and other sites which bubbled up to the top of the search results. Despite ongoing algorithmic changes to Google, SEO was a proven marketing strategy no matter what the searcher asked.

We live in the zero-click world where machines provide the answers before site results, even in the coveted #1 spot, are seen. AI visibility (sometimes called GEO or AEO) is no longer new. Many firms are trying to figure it out, and most are defaulting to a single strategy of adding technical elements to their websites and pumping out more content. But while AI algorithms keep changing, what will stay true is that AI visibility is not a single-strategy problem. There are three separate paths firms need to explore and prioritize to be included in answers and recommendations, and to proactively shape their own story and reputation. These three paths are directly tied to how legal service buyers now use AI engines.

The Expertise Path

It starts with a legal question. Maybe someone wants to know about the right of publicity statutes in California. In the past, a solid page on the website with all the SEO bells and whistles rewarded the firm with a top result. Now, AI Overviews, ChatGPT, Perplexity, or any number of AI engines can give that answer before a search result is ever seen. Ahrefs found that the presence of an AI Overview correlated with a 58% lower average click-through rate for the top-ranking page.

Visibility on this path comes in two forms: a citation, where the firm is the source behind the answer, credited in a footnote or a link at the bottom, or a direct mention, where your firm or a lawyer is mentioned directly inside the answer itself. Both count. The second is worth more, since the reader picks up your name without having to click anything. There’s actually a third outcome firms tend to overlook, which is that your content shapes the answer and nobody hears about it. That happens constantly. The engine reads your alert, uses what it learned, and moves on. No traffic, no credit, and no way for you to know it happened.

Getting into those answers starts with deciding which questions you want to own. Not broad practice areas, but the questions clients ask before they know they need a lawyer. Run a focused set through the engines and see what comes back. Then rewrite with the answer up front. Original analysis beats another summary of the same decision, especially when it publishes while the issue is still live. Site structure and markup help, though less than some vendors claim. What you need from your lawyers is a point of view worth quoting. Marketing can handle much of the rest, which makes this the most manageable path.

The Recommendation Path

This path starts when the buyer asks who should handle the matter. They may want an intellectual property lawyer in Chicago or a firm with experience in cross-border investigations. The engine is not explaining the law anymore. It is building a shortlist, using signals such as directories, rankings, outside coverage, and what lawyers publish under their own names.

That matters because a firm can be excluded before anyone reaches its website. An InterCore study found that a legal directory was the first-cited source in 77.8% of 1,620 Perplexity Sonar answers to high-intent legal hiring queries. The study focused on consumer practices, so it should not be treated as a proxy for how general counsel choose firms for complex corporate work. Even so, it shows how much third-party sources can shape the list.

The work on this path happens largely outside the firm’s website. Rankings and legal directories need to support the position the firm wants to own, which means treating submissions and profiles as ongoing reputation assets rather than annual chores. Earned media matters for the same reason. AI engines often rely on outside validation when deciding which firms or lawyers belong in a recommendation, so visible commentary in respected publications can carry more weight than another self-published page. Marketing can coordinate that effort, but it takes sustained participation from the lawyers and practices the firm wants the market to recognize.

The Validation Path

This path starts after the buyer already has a name in mind. It may be a lawyer, a practice group, or the firm itself. The engine is no longer building the shortlist. It is checking whether the option in front of the buyer holds up.

This path matters because conflicting public information can weaken trust before the firm ever knows it was being considered. A firm may describe a practice one way on its website and another way in a directory. A lawyer’s LinkedIn profile may emphasize work that barely appears in the firm bio. Each source may be accurate on its own, but together they can produce an incomplete or confusing picture.

Start by comparing how your priority lawyers and practices are described across the channels buyers are most likely to encounter. The language does not need to match word for word, but the underlying facts and positioning should agree. Fix outdated listings, clarify where descriptions conflict, and assign someone to keep the information current. Marketing can coordinate the work, but validation reaches beyond marketing because no single department controls the full public record.

Your Approach Should Be Based on Priorities and Real Questions

Instead of trying to boost AI visibility everywhere at once, firms should focus first on the practices and lawyers most essential to their growth. From there, they can determine the questions buyers are likely to ask about these areas.

This distinction is important. Many law firms focus on what they want to communicate, rather than considering what buyers actually want to know. AI responds to specific prompts, not marketing schedules. If a firm hasn’t identified the right questions, it can’t guide the answers.

Marketing teams should collaborate closely with leadership and practice heads to develop targeted prompts for each key area. These should be rooted in the real prompts potential clients are using to answer their legal questions or evaluate and research firms and lawyers.

There won’t be a single solution. Some prompts might uncover gaps in content or show that the firm is missing from referral sources or is inconsistently represented online. By focusing on buyer questions, firms turn AI visibility from a vague goal into specific, actionable issues they can solve.

The Greatest Risk is Solving the Wrong Problem

The real risk is feeling confident about the wrong things. Repeatedly, law firms have fallen into the trap of creating and publishing what they think buyers want to know instead of giving buyers what they are asking for. That approach now has real consequences because of how legal buyers are using AI. Your firm can either keep going down that path with simplified, one-size-fits-all solutions, or take the time to do it right. Showing up more often is not the goal. Showing up when the buyer’s opinion and decision are being shaped is.

Josh Anisfeld is chief digital strategist at Baretz+Brunelle, a growth advisory firm to the world’s preeminent businesses in the legal industry.

The post Law Firms Don’t Have One AI Visibility Problem. They Have Three. appeared first on Above the Law.

Ice Miller just announced a significant associate salary increase for lawyers in its Manhattan office, bringing compensation “in line with the top of the New York market.” The firm didn’t spell out the numbers in its announcement, but that sure sounds like a match for Milbank’s new market scale, which starts at $235,000 for first-year associates.

UPDATE: Per Chase Stuart, “The Firm has matched the Milbank scale for first- and second-year attorneys, while making significant increases across the associate salary structure and taking a customized approach at other levels that reflects our client base and overall compensation model.”

There is, however, one important catch: the new salary scale won’t take effect until January 1, 2027.

Given the glacial pace of this year’s salary wars, that may actually be some pretty savvy timing.

Milbank kicked off the latest round of associate raises back in June, and while several firms quickly matched, most of Biglaw has spent the summer doing approximately nothing. Ice Miller, meanwhile, has several months before it actually has to start cutting bigger checks to its New York associates, and it seems pretty safe to assume that by January, the rest of Biglaw will have finally gotten on board with the new scale. (Right, Cravath?)

For Ice Miller, bringing its New York associates up to market is also part of a broader push to build out its presence in the city, particularly in private equity, M&A, and private credit. New York Office Managing Partner Chase Stuart had this to say:

“Our private equity and private credit practices have grown substantially over the last five years, both in the sophistication of the work and the strength of our client relationships. We offer associates the opportunity to work on complex transactions and work with experienced partners who are heavily invested in their success and growth. Increasing associate compensation reinforces our commitment to building a premier destination for talented transactional lawyers in New York.”

If you’re trying to compete for transactional talent in New York, paying New York market salaries certainly helps.

Congratulations to Ice Miller’s New York associates on the raises. They may have to wait until January to see the money, but considering how slowly the rest of Biglaw is moving, the firm may have just skipped ahead to the ending of this salary war.

Remember everyone, we depend on your tips to stay on top of compensation updates, so when your firm announces or matches, please text us (646-820-8477) or email us (subject line: “[Firm Name] Bonus/Matches”). Please include the memo if available. You can take a photo of the memo and send it via text or email if you don’t want to forward the original PDF or Word file.

And if you’d like to sign up for ATL’s Bonus Alerts (which is the alert list we also use for salary announcements), please scroll down and enter your email address in the box below this post. If you previously signed up for the bonus alerts, you don’t need to do anything. You’ll receive an email notification within minutes of each bonus announcement that we publish. Thanks for your help!

Staci Zaretsky is the managing editor of Above the Law, where she’s worked since 2011. She’d love to hear from you, so please feel free to email her with any tips, questions, comments, or critiques. You can follow her on Bluesky, X/Twitter, and Threads, or connect with her on LinkedIn.

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Because, for Biglaw, exclusivity isn’t necessarily a priority. Veterans of the online legal research wars of the early 2000s remember that the big firms will pay for all the products and the real fight is over capturing the hearts and minds of the junior associates who have to open one program or the other. If that’s once again the field of contest, then vendors who spend their time touting technical benchmarks are fighting the wrong war.

If vendors need to capture the attention of the end user, the technology isn’t as crucial as the experience.

Lawyers are not prompt engineers. Nor are they all hanging on the latest fad developments out of Silicon Valley. They want a productivity tool that gives them trusted, accurate answers and they’d prefer it in a way that is easy and intuitive. And part of that is building a system that’s not going to fight with the user — in particular, a product that isn’t going to try to take tasks out of the lawyer’s hands that only add more time-consuming, panicked double-checking on the back end.

That’s the ongoing divide between AI’s vibecheck and hard data. Lawyers have mostly come around to the idea that AI improves efficiency. And yet, nearly 60 percent of in-house counsel told ACC and Everlaw they hadn’t noticed savings from outside counsel using generative AI. 8am’s financial health report, drawn from several million bills, found hours per case going up in nearly every practice area tracked. Bloomberg Law Chief Revenue Officer Kara DelVecchio described a Bloomberg Law event bringing in-house lawyers together with outside counsel to talk AI, with the client side all asking some version of “my bills went up 10%, where’s the efficiency?”

A lot of that efficiency is eaten up by lawyers haphazardly throwing AI at problems. DelVecchio explained that Bloomberg’s approach to AI has focused on constructing workflows that make sense for the industry. Citing the sentiment of a Biglaw CIO, she explained the firms want pre-built workflows — “here are the three things litigation attorneys do most” — rather than a model that lawyers can poke around trying to prompt. Armed with thousands of data points on attorney behavior, content usage, and sequencing, Bloomberg Law can construct the process that takes prompt engineering off the lawyer’s hands.

The conversation underscored that the legal AI conversation keeps creeping from the tools, to the context, and then to process. Every vendor resides somewhere different on that continuum, but as AI talk matures, it seems to move from “look what our tech can do” to “the tech is secondary to the context that we can provide” to “we build the processes that work for you.”

You might say it’s more about being a consultant than a tech provider. Or you might say it’s the inevitable triumph of the user experience. It’s a bit of both.

Users come at the process point from different perspectives. We’ve seen small law clamor for bespoke solutions that can deliver AI power to the firm’s idiosyncratic approach. Big firms can’t countenance every lawyer building an individual solution. In fact, one CIO asked DelVecchio “how do I teach people to do the thinking we want them to do?” — a reminder that as AI takes over tasks, it’s also becoming the lens through which juniors learn the law.

But in both cases, the lawyers want processes more than tech. That’s not as exciting to the private equity investors throwing cash at AI development in a quest to build artificial general intelligence and finally replace the meat sacks cutting into their profits. Lawyers don’t have to care about that, they just need their teams to get answers without hallucinating all over the place.

Earning those lawyer eyeballs relies in part on a smooth experience. That’s not just a matter of a substantively sound outcome — though that’s critical — or a single-pane model that avoids constant toggling between tools — though the universe of plug-ins and connections will deliver that — but it’s an aesthetic that foregrounds a pleasant user experience.

Software has ethos too, and a good experience tells the user that the vendor built a quality product that they can open up and it won’t fight them. Building that into the product is going to be the key to long-term viability. Because lawyers are going to vote with their clicks and getting in the door isn’t going to matter if attorneys aren’t choosing to use the product.

Joe Patrice is a senior editor at Above the Law and co-host of Thinking Like A Lawyer. Feel free to email any tips, questions, or comments. Follow him on Twitter or Bluesky if you’re interested in law, politics, and a healthy dose of college sports news.

The post The AI User Experience Is More Important Than Ever appeared first on Above the Law.

“Do me a favor, Steve, when I’m on camera… And you don’t want to talk about something like Jeanine Pirro, never mention her friggin’ name to me, because that woman still refuses to fire John Crabb, the prosecutor who was involved in putting you and me behind bars, okay?” Navarro said.

Bannon, sensing where this was headed, tried to move things along: “Stop, we love Jeanine.” Navarro does not love Jeanine, indeed, he was just getting started.

The John Crabb in question is a career federal prosecutor and longtime fixture of the D.C. U.S. Attorney’s Office, with a résumé that runs from the Benghazi prosecution to the Jan. 6 cases. His offense, in Navarro’s telling, was helping convict both Navarro and Bannon of contempt of Congress for defying the House committee’s subpoenas, cases that sent each of them to federal prison for four months.

“So don’t you dare to [mention] Judge Jeanine. I, you know, I’m just telling you, that guy, how is that guy still working here, pointing me in a courtroom and said I was above the law. Those… You get my drift. That’s a professional– that guy is on the inside, Steve, of our Justice Department. That guy was responsible for so much… He went after you, he should be gone. Jeanine, are you listening?”

For real, the “our Justice Department” is chilling — and telling. The whole grievance rests on the premise that the DOJ belongs to MAGA now, and that a prosecutor who did the actual job of prosecuting is the one who committed the offense. Crabb’s sin was pointing at Navarro in a courtroom and saying he acted as though he were above the law. Which, given that Navarro is on this podcast demanding a prosecutor be purged for prosecuting him, is holding up reasonably well as a legal theory.

And here’s the kicker! Crabb has already been on the receiving end of punishment for the high crime of doing his job. He was among the senior D.C. prosecutors demoted and reassigned to misdemeanor work back in the early days of the administration’s housecleaning of the office. Navarro isn’t calling for a head; he’s calling for a head that’s already rolled, and he’s furious that the man is still permitted to draw a government paycheck at all.

None of this lands on Pirro at a good moment. She has been the improbable U.S. Attorney since jump, installed off the evening cable lineup, then presiding over an office where grand juries kept no-billing her cases and one prosecution got handed to a dance photographer. And now she’s absorbing incoming from her own side. Navarro made a point of reminding her that “the boss said ‘she choked'” on the Reflecting Pool case, the same episode that had Trump publicly musing about replacing her days earlier.

Strip away the “friggin'”s and the on-camera laughing fit and what’s left is a sitting White House adviser using a livestream to instruct the top federal prosecutor in the capital on which of her line attorneys to fire. That is the weaponization of the Justice Department stated out loud with absolutely no subtext left to decode.

Earlier: Trump Taps Judge Jeanine For Key DOJ Role After Consulting Evening Cable Lineup
No Bills Are Only The Beginning Of Jeanine Pirro’s F-Ups
Jeanine Pirro Brought In Dance Photographer To Prosecute Lawmakers And It Went Exactly As You’d Expect
Donald Trump Likely To Fire Jeanine Pirro For Having One Foot In Reality

Kathryn Rubino is a Senior Editor at Above the Law, host of The Jabot podcast, and co-host of Thinking Like A Lawyer. AtL tipsters are the best, so please connect with her. Feel free to email her with any tips, questions, or comments and follow her on Twitter @Kathryn1 or Bluesky @Kathryn1