When Michael Aiello told Weil, Gotshal & Manges earlier this month that he was leaving for Cravath, taking five M&A partners with him, the firm’s statement came with a sting. Aiello and his team, Weil said, were “leaving the firm for a smaller platform”. Cravath. A smaller platform. A week later Bloomberg reported that Weil was weighing its options, a merger with a competitor among them. According to people familiar with the matter, the firm’s leaders had grown more open to a combination, some had held informal talks to test the water, and the firm was canvassing its own partners. Weil’s answer was short: it “is not engaged in merger discussions with any firm”. Reuters, Above the Law and eventually the Financial Times picked up the story. Almost all of them were repeating Bloomberg. Read the denial slowly and it denies very little. But the rumour itself rests on one set of anonymous sources. Partners leave firms. That is not news. Wachtell lost its litigation co-chair and five colleagues to Gibson Dunn this summer and has seen around a dozen partners go this year. Cravath has lost about a dozen too. Nobody suggests either should go looking for a merger partner. What sets Weil apart is not the count but who is leaving. The co-head of private equity went to Paul Weiss in August, and a group of his colleagues followed. The co-head of private funds went to Simpson Thacher. The London co-managing partner went to Sullivan & Cromwell. Then Aiello, chair of the corporate department and of the committee set up to steer the firm through its leadership succession. The Financial Times counts 23 partners gone since April. These are not partners drifting off the edges. They come from the centre. The problem for Weil is not a handful of big names walking out. It is that partners are losing faith in the platform. In my 2015 book Death of a Law Firm I described how the departure of one or more major rainmakers can set off a run on the bank. It undermines morale, erodes confidence and pushes other partners to start looking elsewhere, long before the damage shows up in the revenue figures. A bank run does not need an insolvent bank. It only needs depositors who begin to wonder whether everyone else is about to leave. Every departure makes the next one a little more rational, and the partners who can move most easily, those with portable clients, move first. In Chapter 9 of my recent book Law Firm Partner Compensation, I look at why partners move. Money is rarely the main reason. Most laterals move away from their firm rather than towards a particular destination, and what sets them in motion is a loss of faith in the platform: doubts about the strategy, about the calibre of the colleagues, about whether the firm will ever achieve greatness. When partners feel their pay lags the competition, the strategy is usually to blame. Which is why paying a wavering partner more rarely keeps them. It treats the symptom, not the cause. The firms most exposed are the personality-driven ones, where the departure of one or two key individuals triggers a talent cascade that the compensation system has no mechanism to arrest. So would a merger stop the run? I do not see how. Weil is too large and too profitable to be rescued by a combination. It earned roughly $2 billion last year and $5.6 million per equity partner, twentieth in the Am Law 100. Compare that with the two firms that did go down this road. In the last year it reported as an independent firm, Shearman & Sterling took in $907 million and $2.5 million per equity partner, both down sharply on the year before. Cadwalader, in its final year of independence, took in $616 million and $3.5 million per equity partner, also lower than a year earlier. Weil is more than twice the size of either and far more profitable. Those firms needed a merger. Weil would have to go looking for one, and almost any plausible partner would dilute the income of exactly the people it can least afford to lose, the ones Paul Weiss and Simpson Thacher are already calling. As I wrote in my book, US partners in a high-margin practice do not leave a transatlantic merger for more money. They leave because they refuse to subsidise a global infrastructure that does nothing for their practice. The recent precedents are not encouraging. Shearman & Sterling went into its merger with Allen & Overy with falling revenue, falling profits and a partnership that had shrunk by 40% in ten years. After the deal the combined firm cut a tenth of its partners and closed offices to lift profitability. By October last year more than 170 legacy partners of the two firms had left or retired. Two years on, profit per equity partner is back where Allen & Overy stood before the merger. Not ahead of it. Back. Cadwalader lost about 30 partners before it agreed to join Hogan Lovells, and at least 17 more in the two months after the deal was announced, many of them litigators conflicted out by Hogan Lovells clients. Conflicts are never optional in a merger. Somebody always has to go. Nor is there much evidence that the combined firm does better later. Bloomberg Law looked at the 18 largest US law firm mergers of the past fifteen years. Two thirds of the merged firms grew profit per partner and revenue per lawyer more slowly than their competitors afterwards. A merger buys size. Weil already has size. What Weil lacks is a reason for its best partners to stay. It could be leadership. The firm is in the middle of a handover, with Ramona Nee taking over from Barry Wolf in January, and the committee built to manage that handover has just lost its chair. But I think the deeper cause is structural. Weil is stuck in the middle. It is too big to behave like Wachtell or Cravath, which have chosen to stay small and charge for scarcity. And it is not profitable enough to pay what Kirkland ($11.1 million per equity partner), Paul Weiss ($8.6 million) or Simpson Thacher ($8.6 million) pay, which are precisely the firms bidding for its people. Plenty of large firms earn less per partner than Weil. Few of them compete for the same clients and the same rainmakers against rivals paying half as much again. As I wrote in Requiem for a Lockstep, a compensation system only holds as long as the trust and shared ambition beneath it are renewed. Once partners start doing the arithmetic on the offers their neighbours are getting, no formula survives for long. Can it be fixed? Yes, though if it were easy everyone would do it. Paul Weiss is a classic example that shows what it takes. In 2009 it was a firm best known for litigation, with revenue under $700 million. Chairman Brad Karp decided it needed private equity, public M&A and restructuring work. It built on its relationship with Apollo, brought in seven private equity partners from O’Melveny & Myers in 2011 and, in 2016, lured Scott Barshay from Cravath with pay his old firm would not match. In 2024 it broke its modified lockstep and added a non-equity tier. Last year it took $3.26 billion in revenue and $8.6 million per equity partner. It took the best part of fifteen years. I am not holding Paul Weiss up as a model of culture. I am holding it up as proof that a deliberate strategy can change a firm’s economics. There is an irony in it too. The firm now hiring Weil’s private equity partners might be the one showing Weil the way out. Weil was right about one thing. Cravath is a smaller platform. The trouble is that partners do not choose platforms by size. This article is part of a series drawing on the themes of Law Firm Partner Compensation by Jaap Bosman and Jaime Fernández Madero. If you would like to know more about this topic, read the book. Our book Law Firm Partner Compensation is available worldwide on Amazon, national online book sellers, and can be ordered at your favorite bookstore

Management Summary Latham & Watkins has spent several years and put a 900-person technology division to work on its own AI infrastructure: Nvidia GPU servers in data-centre space only Latham staff can enter, running an open-weight model the firm fine-tunes itself. It will be reported as an infrastructure story. It is really a custody story. The trigger is concrete, not theoretical. Days before this article, OpenAI admitted it “cannot rule out” that a customer’s own data had shaped one of its models, a sentence OpenAI didn't volunteer lightly. Latham’s CIO has said plainly that confidentiality, and the cost of paying a vendor by the token, are what owning the model buys back. Open-weight means Latham owns the model rather than renting access to one. Which size it runs is not public. Reinforcement learning lets the firm keep training it against its own checkable outcomes, narrowing the reliability gap with Kirkland’s approach without closing it: Kirkland's rule engine cannot hallucinate, a trained model still can. Kirkland bet on data and judgement. Latham has bet on the model and the infrastructure beneath it. Both are the same wager: the technology between a client’s problem and the firm’s answer is now too important to rent. Harvey, the leading vendor, is already building the same architecture for itself. This is a market direction. For everyone below this tier, particularly national champions who cannot match the spend, judgement alone, once sufficient, no longer is. Firms left renting will either lose their best mandates or become subcontractors to the firms that build. Latham & Watkins takes AI in house Latham & Watkins has spent several years and put a technology division of more than 900 specialists to work, on something almost no other law firm has attempted: its own AI infrastructure. Multiple Nvidia GPU servers now sit in leased data-centre space that only Latham staff can enter, running Nvidia’s open-weight Nemotron 3 models, which the firm’s own engineers fine-tune under its own roof. This is not a subscription. Latham did not rent intelligence from a vendor. It bought the compute and brought the model in-house, joining a very small group of firms now choosing to own the technology stack rather than lease it. Why a firm with $8.3 billion in revenue would choose to build rather than rent comes down to two things its CIO, Rene Mendoza, has been plain about: confidentiality and cost. “Sometimes we may have information that is so sensitive, client information that we really want to protect, we don’t want to put it to any cloud vendor,” Mendoza has said, alongside concerns about the “consumption costs” of paying a vendor by the token as usage climbs. The first concern got a vivid illustration on 8 September, when OpenAI announced it had solved one of mathematics’ six remaining Millennium Prize Problems. Two academics had reached a closely related result using several AI models, Claude and OpenAI’s own Codex among them, and OpenAI’s own model arrived at its answer within days of first hearing rumours about their progress. Asked whether that work had shaped its system, OpenAI wrote a sentence it presumably wishes it hadn’t had to: “While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.” (OpenAI) Read as a lawyer rather than a mathematician, that sentence is the whole argument for owning your own infrastructure in one line. A vendor with every incentive to say no, could not quite bring itself to. Open-Weight “Open-weight” is what makes Latham’s approach different from simply choosing a cheaper vendor. GPT and Claude are closed: a firm which has a subscription gets an API and a promise, and every query still leaves the building. Nvidia published Nemotron 3’s actual weights, the trained parameters themselves, for anyone to download, and the Nemotron family shares a mixture-of-experts (MoE) design that routes each fragment of text to a handful of specialist sub-networks rather than the whole model, and a hybrid architecture that threads the usual attention mechanism through faster Mamba layers to handle very long documents. Latham has not said which size it runs, and the difference matters: Nvidia’s largest Nemotron 3 variants carry well over a hundred billion parameters with only a fraction active on any query, while the smallest is a fraction of that again and would suit a firm optimising for query volume over raw capability. What is public either way is that the weights are open, which means Nvidia’s own training method, extensive reinforcement learning against verifiable, checkable rewards rather than simple imitation of examples, is available to Latham’s engineers too. That lets the firm keep training its own copy of the model against outcomes it can check itself, whether a citation is real, whether an extracted clause matches the source document, a genuine step toward reliability, not just confidentiality, though still a different step from the one Kirkland & Ellis took. Compared with Kirkland & Ellis Kirkland committed $500 million to Palantir to turn decades of messy fund paperwork into structured, linked data, and then handed the judgement calls to a deterministic engine rather than a language model. “The probabilistic engine reads. The deterministic engine judges. That division of labour is the whole game,” as I wrote here in an earlier article. A rule either passes or it does not; there is no probability distribution to be wrong within. [read more here] If Latham is also grounding its model in its own archive of matters and precedent, a technique called retrieval-augmented generation rather than training in the strict sense, that closes part of the gap with Kirkland: the model answers with institutional knowledge no rented vendor model has access to, a real echo of Kirkland’s proprietary-data advantage. But retrieval only changes what the model reads before it answers. The answer itself is still generated, still probabilistic, and can still misstate or blend what it retrieved in a way no deterministic check would allow. Training a model to be right more often, however it is trained, is not the same as building a system that cannot, by construction, be wrong. Kirkland remains superior on raw accuracy for exactly that reason. That difference is relevant, but it is also points at the same underlying bet. Neither firm is buying software off the shelf, and the rest of the market is starting to notice, even Harvey, the leading vendor, is now training its own model, called Tenet, on an open-weight foundation with a native one-million-token context window, run on Harvey’s own infrastructure rather than through third-party APIs, while Legora continues to route tasks across whichever outside model does the job best. Harvey’s move is similar architecture as Latham has built, at the vendor level rather than the firm layer. The difference that survives is custody. Latham owns the infrastructure its model runs on. A firm using Harvey is trusting Harvey’s custody of it instead of its own, however open the weights underneath. This signals a bifurcation Chapter 14 of my book on partner compensation asks why an exceptional partner, once AI lets them work with a small team, still needs the firm at all. Among the honest answers it gives is “the AI platform itself, which at enterprise scale requires investment that individuals cannot make alone.” Latham and Kirkland are that answer made concrete, at a price no partner, no boutique, and increasingly no vendor relationship alone can meet. That is what makes this a bifurcation rather than an upgrade cycle. A small number of firms are converting capital into an institutional asset no lateral can replicate by switching firms. Everyone else, including the national champions who are the obvious first call in their own market, is left competing on judgement alone, which was always necessary but is no longer sufficient. They will never be able to match this spend and cannot ignore it without watching their best clients notice the gap. Two outcomes follow, and neither is comfortable: lose the mandate outright, to a firm that can now offer more reliable answers, tighter confidentiality, or a lower cost per query, or keep it by accepting a smaller share, doing the execution work underneath a firm like Kirkland or Latham rather than owning the client relationship. Being the best lawyers in the room stops being enough as a smal number of elite firms will employ superior technology. This article is part of a series drawing on the themes of Law Firm Partner Compensation by Jaap Bosman and Jaime Fernández Madero. If you would like to know more about this topic, read the book. Our book Law Firm Partner Compensation is available worldwide on Amazon, national online book sellers, and can be ordered at your favorite at your favorite bookstore

Law firm partner returning from holiday. 3 things on your to-do-list right now. Book Law Firm Partner Compensation Once September arrives, the leisurely summer feeling is replaced by the regular drum of the office. Summer is more than a time to unwind and relax with family and friends, away from the office. It is also a time to recharge and get new energy and inspiration. Judging from the comments, a number of you used the break to read my book on Law Firm Partner Compensation (www.lawfirmpartnercompensation.com). The feedback has been overwhelmingly positive, thank you for that. I remember as a kid the excitement when the new school year started. I went to buy new books and a fresh agenda. Everything new, holding a promise of a new year, in which I could have a fresh start and a clean, well-organized way of studying. Unfortunately, after a week or so at school, all good resolutions had faded. The challenge is to convert inspiration into action. All too often within days back at the office, or even hours, routine sets in, inspiration and resolutions are forgotten, and everything pivots back to how it was before. This happens every time and it is a missed opportunity. To help you avoid that this year, here are three things that should be on your priority list right now. 1. Invest in talent The time-based pyramid model is running out of road. For decades a law firm's structure was simple: a broad base of associates billing hours, a thin layer of partners capturing the margin. AI is now eroding the fundament that sits at the base of this business model: every routine task can be done faster. Associates will need more than knowledge of the law to remain valuable. (Chapter 14 in the book) The leading firms are already recruiting differently. Some US firms have gone as far as recruiting students on campus who will not start their first year until this September, locking in talent early because they know the competition for people who can operate at this new level is only going to get tighter. Big-four accountancy firm Ernst & Young has reserved 100 million in bonusses for ‘human’ skills. What should you be looking for in that talent? Focus on critical independent thinking and the 7-Core Dimensions©: understanding the client's business, creativity, practice development, practice management, people skills, presence and confidence, integrity (Chapter 4 in the book) This matters more, not less, as AI takes over production. As the pyramid transitions into a diamond, the associates who remain need to be more capable. Focused on judgement and client communication rather than document production. The gap between an average lawyer and an exceptional one is no longer capped by the hours in the day. A lawyer with real judgement, directing AI, can produce what used to take an entire practice group. A lawyer without that judgement just gets faster at producing average work. Recruit and develop on the 7-Core Dimensions©, and build them into how you train and pay people, and you end up with more of the first kind. 2. Develop alternative pricing models The hourly rate is not a legal tradition. It is a mid-twentieth-century invention that became standard practice from the 1950s onward, and it is about to sunset. For most of legal history lawyers charged based on the value of the work at the end of a matter. The billable hour shifted the risk of inefficiency onto the client: the longer a task took, the more the firm earned. That arrangement worked well and law firm profitability has grown consistently substantially over the past decades. AI breaks that business model, because it attacks and erodes the time a task takes, and the legal market is not growing fast enough to absorb the difference. Here is the arithmetic. Take a firm of 200 lawyers generating $500,000 revenue per lawyer, 50 equity partners, a 50% profit margin. The firm spends $1 million on AI that makes its lawyers 5% more efficient. If the volume of work stays the same, the firm bills fewer hours for the same output. Revenue drops roughly 5%, to $95 million. Costs go up by the $1 million spent on the technology, to $51 million. Profit falls from $50 million to $44 million, a 12% drop, and profit per partner falls from $1 million to $880,000. That is a conservative estimate. Firms that keep billing by the hour while adopting AI are effectively cutting their own profit. This is where the Value Matrix© comes in. What a client is willing to pay depends on two things: the return on investment the work generates for them, and how many other lawyers could do the work equally well. It has never depended on how many hours it took. Price on that basis before your clients do it for you. In-house teams are investing in technology, over half have already adopted generative AI themselves, and most expect to rely less on outside counsel because of it. Getting good at fixed fees and value-based pricing now, while you still have room to experiment, beats being forced into it later with no margin left to give. (Chapters 2 and 14 in the book) 3. Get out of your silo and break down the walls Most blue-chip clients do not hire an individual lawyer. They hire the firm, expecting access to the full range of talent, contacts and experience the brand represents. That only works if partners actually operate as a team rather than as a collection of individual practices sharing a building and a billing system. When partners hoard clients and refuse to bring in other departments for fear of losing credit, the firm's collective value stays locked up. The sum of everyone's revenue ends up lower than it would be with real cooperation, even if a few individuals do better for themselves in the short run. We call the alternative swarm intelligence: combining the collective knowledge, experience and creativity of the whole partnership instead of leaving it trapped in individual practices. I know a premier AmLaw 100 firm where partners routinely consult the wider partnership for ideas on a matter. That habit is part of why they keep beating firms with more individually talented partners but less willingness to talk to each other.(Chapter 6 in the book) Here is the uncomfortable part. Swarm intelligence and real collaboration are in the interest of both the client and the firm, and most partners will agree with that if you ask them directly. In practice, plenty still prefer to work alone, keeping their clients away from other partners rather than bringing the wider team in. Some of this is income: a shared mandate usually means shared credit, and a smaller number next to your name at year end. Some of it is trust, or the lack of it: a partner who doubts a colleague will deliver the same quality will not risk the client relationship to find out. A compensation system that puts individual performance under the magnifying glass, origination, hours billed, revenue booked under your own name, rewards exactly this instinct. It tells every partner that protecting their own book is the safer bet. There is also a tribal element underneath it. It is my firm against the competition, but my practice group against the firm, and my team against the practice group. Loyalty runs strongest at the smallest unit, not at the level of the partnership as a whole. A compensation system built solely on individual numbers reinforces exactly that. So look hard at what yours actually rewards. If it only measures individual production, it will keep producing individualism, no matter what the culture statement on your website says. (Chapter 5 and 12 in the book) Read the book! All three of these, talent, pricing and culture, are covered in more depth in Law Firm Partner Compensation, including the full framework behind the 7-Core Dimensions©, the Value Matrix© and what AI is actually doing to the economics of the profession. This article is part of a weekly series drawing on the themes of Law Firm Partner Compensation by Jaap Bosman and Jaime Fernández Madero. If you would like to know more about this topic, read the book. Our book Law Firm Partner Compensation is available worldwide on Amazon, national online book sellers, and can be ordered at your favorite at your favorite bookstore

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TGO Consulting - Award winning strategy consultants for the legal sector. Serving a global clientbase. Enhancing law firm profitability. what we do TGO Consulting are award winning business consultants focusing on the legal sector. We have a strong client base spanning most of Asia, Europe and the Americas. Our approach is fact based and result driven. We help our clients to maintain or improve their profitability. We work on the basis of a Financial Business Analysis© for which we have developed our own unique standardized model. This FBA© will highlight low hanging fruit and provide a benchmark against the market. Having decades of experience in the legal industry, we know the dynamics of partner groups inside out. During the process this will help overcome resistance and create buy-in. "everything must change for things to remain the same" - Guiseppe Tomasi di Lampedusa - Latham & Watkins vs Kirkland & Ellis? Back in the office Requiem for a Lockstep articles of interest about us If it comes to serving a global client base and experience in working in different jurisdictions across the world, TGO Consulting is second to none. While understanding your home market and culture, we bring a wealth of experience in best market practice around the world. We know the legal industry inside out, past, present and future. We know your competitors and we know your clients. we strongly focus on enhancing our clients’ profitability the power of truly offering global best practice our new book - LAW FIRM PARTNER COMPENSATION - is the most complete and authoritative treatment of the subject ever written. The book covers every major compensation system — lockstep, eat-what-you-kill, modified lockstep and black box — alongside the psychology, culture and strategic dimensions that formal analysis almost always misses. It addresses the impact of AI and private equity on compensation models, includes a dedicated chapter on China, and draws on insights from the US, Europe, Latin America and Asia. Co-authored with Jaime Fernández Madero. Available on Amazon and through bookstores worldwide. Order on AMAZON a new concept There is no linear relation between time and value. We created the Creation-Production-Divide Concept©, a revolutionary new way to explain where the value is. This concept will fundamentally change the business of law. we strongly believe being a lawyer is about human skills a human-centric approach Being lawyers ourselves and having gained almost two decades of experience in private practice and in-house, we understand the dynamics of the partner group like few others. Although we always focus on our clients’ financial performance, we are strongly aware that the business of law is a human business before anything else. Understanding peoples’ drivers and behaviours is key to achieving lasting results. power curve Succession remains a sensitive and complex topic. The TGO power curve© analysis immediately shows succession and leadership vulnerabilities in the firm. This is just one of our data-based models in use. in the press 1/1 Interview on legal technology in La Gazette du Palais 未来十年，律师事务所的五大趋势 Article on the future of the legal profession Feature article in ACC Docket on how to prioritize for inhouse lawyers

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