a16z 对谈 Eve：请不到的「12 美分律师」、96% 被拒的案子，与小律所的十亿美元之路

信源说明：本文基于 a16z 频道访谈视频整理（视频 ID iRMmPjunm3Q，发布于 2026-10-30，全长约 52.2 分钟，英文转写 27 段覆盖 0–3130 秒（末段仅 10 秒、无内容），已完整读取，文中数字与引语均依英文转写）。对谈双方为 a16z 合伙人 Alex Rampell（主持人）与法律 AI 公司 Eve 联合创始人兼 CEO Jay（转写中仅以名相称）；Eve 为 a16z 投资对象，Rampell 片尾自称 partner，本文按利益相关方对谈处理。文中全部数字、案例与判断均为双方口述、节目未附可核验出处，本文未做独立核实，归属按「Rampell 称／Jay 认为／节目引用」分层标注；ASR 疑似错词以「转写疑作」标注，文末附校正表与完整转写原文。

先说结论

这期访谈真正的话题不是「AI 能不能写诉状」，而是一个被硅谷看漏了三十年的市场结构：美国法律界分两半，公司法律所按小时收费（Rampell 的例子：白鞋所两千美元一小时），而更大、更分散的原告代理（plaintiff law）按风险代理收费——只有打赢才拿钱，头部 Morgan & Morgan「利润很可能超过 Wilson Sonsini」（Rampell 称），但它的软件栈长期只有手机加邮箱（Jay 称部分独立律师连 Microsoft Word 都不买）。原因不在保守，而在这个行业卡死的从来不是软件，是劳动力：Jay 给出的门槛是「律师预期收费不到 5000 美元的案子不接」，劳动就业类案子 96% 被拒。Eve 想做的是把「接电话→立案→取证→和解」整条链路代理化，让一家 LA 律所把每律师服务客户数从 100 做到 500。对场外读者，本场最有价值的三个机制判断是：其一，劳动力约束行业里，软件市场其实是被低估的「影子劳动市场」；其二，应用层的护城河不在模型，而在不可抓取、高度本地化的办案数据；其三，「结果定价」正在从天然按结果收费的原告法外溢——席位制 SaaS 与律师计时收费可能同一条路走向衰落。

被忽视的另一半法律界：广告牌上的原告律师

Rampell 的开场观察：硅谷听到「法律」想到的是 Wilson Sonsini 这类公司法律所——哈佛法学院、两千美元一小时；但 101 公路和 I-95 沿线广告牌的另一半世界是原告律师，服务的是「没有资源、请不起所内律师（in-house）的普通人」。Jay 补充了商业模式的根本差异：公司法律所按小时计费，把律师时间导向越来越贵；原告律师收风险代理费（contingency）——只有替客户赢到钱才拿钱，通常从和解金中抽成（Jay 后文给的算例是约三分之一）。这正是普通人能与机构对抗的唯一「平局化」机制，也是 Jay 反复用的比喻：Eve 要做的是「给大卫对抗歌利亚的投石索」。

Rampell 用经济学第一课的供需曲线把这个结构讲成市场失败：一个只有 1000 美元损失的当事人，既付不起一千多美元一小时，风险代理律师也不愿接——抽成不够覆盖办案的劳动力成本。于是供给曲线和需求曲线之间留下一大片「双方都想成交却成交不了」的死区。Jay 确认这就是行业的真实约束：「需求不受限，受限的是劳动力供给」。

一道算术题：5000 美元门槛与 96% 被拒的案子

Jay 给出的行业算术（均为其口述）：律师接案有隐形门槛——预期收费不足 5000 美元不接，即便 5000 美元也常常亏钱，律所实际靠少数大额和解「押注」回本；低于免赔额的车祸小事故「美国没有律师会碰」，哪怕三四千美元对当事人就是「一个月工资有没有」的区别。他展开了一个职场骚扰案例的完整成本：律师接一个来电要花 30–45 分钟；美国雇佣关系基本是任意解雇（at-will），必须把事实挂到某个受保护类别或「诉因」（cause of action）上，这需要约 10 小时的检索与追问——做完可能发现案子不值 5000 美元。结果是他引用的行业数字：劳动就业类案子 96% 被拒，当事人除非自掏腰包否则无人代理。

Rampell 把这句话翻译成了本次访谈最有传播力的一句：这种市场里，律所真正想要的是「12 美分一小时的律师助理」——这样的劳动力市场不存在，所以需求死在那里；而 AI 的 token 成本恰好落在那个价位上。Jay 的对应说法是：案子从立案到结案要走六个月到一年、上千个动作，随着 Eve 准确率提高，长程任务可以被摊销成常数成本，「复杂工作的经济学被改写」。

Eve 的打法：从接电话到和解的全链路代理

按 Jay 的描述，Eve 不是文书工具，而是全链路代理：来电由 Eve 接听并按该律所的历史判断决定是否值得接、随即产出demand letter（索赔函）等前置工作；律所从「一人身兼数案在各个角色间搬运信息」变成只在需要人类判断时被拉入。他给的标杆案例：LA 一家律所把每律师管理的客户数从 100 做到 500，从接案、资格审查、消除律师与案件经理之间的「中转损耗」，一直到调解前的简报——告诉律师该聚焦什么、同类案子历史上和解到什么价位、用过哪些战术。产品线里还有一个叫 Auditor 的组件：夜里逐页分析每个案子，找「完美律师每次都会抓到」的疏漏——Jay 称几乎每个案子都有漏掉的信息。

两个值得记录的工程细节：其一，Jay 估计单个案子约有上万次来回沟通（10,000 back-and-forths），仅调取病历一项（要外呼保险公司和医疗机构）今天就耗时约两周，Eve 的外呼语音代理已在做这件事，并且「agent 已经在学习怎么绕过对方的 agent」；其二，被问「有没有完全无人工介入、从头到尾跑完的案子」时，Jay 答「还没有，但接近了」，称端到端能力已在部分客户处上线早期版本。他还主动划清了与基准测试的关系：Anthropic、OpenAI（他称双方合作密切）公开的法律基准关心的是「单次任务做得还行吗」，而 Eve 从第一天起只关心「代理式结果」。

至于增长瓶颈，Jay 的回答直白：需求不缺、事情做不完，「我们主要被工程师数量卡住」——仅把「移动车辆事故」这一类案子端到端跑通，他声称释放的价值就是「数千亿美元级」，而这只是一个子类。

护城河不在模型，在「不可抓取」的数据

Rampell 替所有应用层创业者问了那个问题：模型厂商一路向上打，垂直应用层会不会是路边被碾过的？Jay 的回答构成本场最有方法论价值的一段。

他的第一论点是「爱因斯坦也不落地」：就算给你一千个爱因斯坦做诉讼，你也不会要——把爱因斯坦空降到律所，他很长时间内毫无产能，因为缺的不是智力，是工作流、机构知识与本地知识。第二论点是数据的物理形态：原告法领域绝大部分数据不可抓取——Jay 称不到 1% 的案子会形成书面判决意见，绝大多数在诉前甚至 demand letter 之前就以谈判和解，「Geico 真实赔了 2.9 万美元」这类信息极有价值却从不公开。保险侧倒是集中化的：对手方用 Colossus 这类软件给「断一根拇指值多少钱」定价（Jay 的转述：它同时计算「我们最多能糊弄掉多少」），而原告侧的信息碎在当地律所手里。第三，本地异质性：辖区、法院、对方律师的行为模式都不同，「此刻做什么最能改善案子结果」这个问题在一个案子里要重复上千次，人脑装不下。

数据合规的边界他也讲了：客户案子数据受律师—客户特权保护，Eve 永不在此类数据上训练；但律师行业本来就通过 CLE（继续法律教育）线下互通战术——State Farm 的抗辩套路全国重复，知道应对方法本身不是秘密；Eve 自雇律师、按平台案子的胜率做统计，「用 Eve 应该赢更多，这是我们追踪的指标」。

对手也在进化：感恩节前的证据突袭与「同日回击」

对抗系统的另一侧怎么反应？Jay 的判断是节奏不对称：被告侧（保险与机构律所）仍主要按小时计费，还没跟上原告侧的采用速度。他讲了一个具体战术：某律所的对手惯常把证据开示（discovery）请求压到感恩节或重大假期前一天送达，赌的就是律师休假中草率应付；这家律所在 Eve 上建了一个代理，当天回击——下一次对方再压时间点，回应立刻送到，「对面的反应是：这怎么回事？」（此句转写疑作「tactic’s toast」，即「战术泡汤了」）Jay 称类似变体正在大量出现，被告方开始意识到「对面也有资源时会发生什么」。

往长了看，Jay 认为未来的案子「更有牙齿」：例行来回由 agent 对 agent 完成，人类的差异价值在于对当事人、事实与本地对手的理解；Eve 上的客户反而把更多时间投回庭审工作——以前「开庭会拖垮整个律所」（庭审准备与陪审团访谈耗尽产能），行业被迫偏和解，现在才谈得上真正的辩护。制度层面他预期「并非一切都进法院」的分流会加深——退伍军人事务、EEOC（就业机会平等委员会）前置程序、企业调解仲裁都是现成框架——并出现 ADR（automated dispute resolution，自动化争议解决）：AI 辅助的中立调解方加速出事实，但判断仍留给人类，人来剔除偏见。Rampell 则贡献了一个亲测案例：他与 Wells Fargo 纠缠四小时未果，转而让 Codex/ChatGPT 起诉——「四小时后它真的起诉了 Wells Fargo」，连送达程序员（process server）都找好了，开庭日期排到二月。

从计时收费到结果定价：软件公司化的律所

Jay 给出的律所单位经济（口述算例）：一个 2.5 万美元的和解，律师抽约三分之一即 8000 美元；其中约一半花在人力，30% 花在营销（就是那些广告牌），剩下才是利润——这让它几乎无力投资「把下一个案子办得更好」。AI 改写的正是人力那一半：省下的钱最好花在更多 token、让 Eve 干更多活上，形成收入扩张的正循环。Rampell 补了他著名的 SaaS 类比：按席位按月收费是「中杯大杯超大杯」（tall/grande/venti），而被私有化的 Zendesk（他称其最后公开时 ARR 20 亿美元、由 Permira 与 Hellman & Friedman 收购）现在要面对的问题是「你需要多少席位？也许零个」。他的判断：原告法天然按结果收费，是所有软件与专业服务正在被迫走向的定价方式的「先行者」——计时收费不会死透，但会像席位制一样边缘化；移民法等其他领域本就该按结果定价。Jay 确认 Eve 自己就是这么演进的：第一天先按档收费，「一年半以前」全面转向结果定价。

行业结构上，Jay 预期两个方向同时发生：一是专科化再下沉——三十年前的社区律师什么都做，后来分知识产权、雇佣，现在分到「护理与出生缺陷」级，下一步会是「X/Y 事实模式下的软组织损伤」级，专科广告比 Morgan & Morgan 式全国广告牌便宜得多，「先在本地做精，再全国扩张」；二是头部整合——Morgan & Morgan 量级的律所（Eve 在服务其中一些）开始收购其他律所，因为过去最痛的并购后重整合（人员、流程、系统）被 Eve 压缩成「客户要的只是法律结果」，并购回本周期被缩短。Rampell 由此抛出标题里的问题：小律所能不能长成十亿美元收入生意？Jay 的回答是律所将「看起来更像软件公司」，补上错过的三十年软件革命；Rampell 则提醒现实边界——律所目前不能上市，「也许亚利桑那州例外」。片尾 Jay 给出规模注脚：Eve 至今「帮助了几十万客户」，而要触达的是「数亿人」。

机制、影响与启发

把本场内容压缩成四条可迁移的链条：

1. 劳动力约束行业的软件市场是被遮蔽的「影子劳动市场」。表面看软件支出小（连 Word 都不买）≠ 市场小，而是可交付结果被劳动力卡死（变化/信号）；AI 把「12 美分/小时的律师助理」变为存在（直接原因）；律所收入扩张而非成本收缩、被价格排除的需求（96% 被拒案、5000 美元以下死区）重新可服务（谁获得）；观察指标是垂直 AI 在法律、财税、医疗等劳动密集行业按结果计费的合同占比（看什么）；启发是评估 AI 应用层市场时，先问「这个行业缺的是软件还是人力」，缺人力的行业里软件预算会随 AI 一起膨胀（对读者）。
2. 护城河=不可抓取+本地化+使用中生成的数据。不到 1% 的案子见判决、辖区与对手行为千差万别（信号）；通用模型再强也缺机构与本地知识——「爱因斯坦不落地」（机制）；Eve 的 Auditor 夜审与胜率追踪在使用中生成独有数据集（影响）；启发：判断任何垂直 AI 的 defensiveness，看它的数据是「公开语料可替代」还是「只有干活才能攒出来」，后者才是 10/26 例会式的「评测即业务」型资产。
3. 对抗系统的不对称采用制造战术红利窗口。原告侧 AI 化、被告侧仍按小时（信号）；感恩节突袭对同日回击失效（机制）；红利最终会随对手适应（agent 对 agent）收敛，差异回到「对本地对手行为模式的独家数据」（二阶影响）；启发：在博弈型行业卖 AI，早采用者的超额收益有半衰期，产品要帮客户把窗口期红利沉淀为数据资产。
4. 定价单位向客户真实想要的单位迁移。原告法天然按结果收费、Eve 一年半前转向结果定价、Zendesk 面对「零席位」（证据链）；计时收费与席位制同样面临「客户不为投入买单、只为产出买单」的挤压（影响）；启发：AI 产品定价的第一个问题不是「按 token 还是按席位」，而是「客户的产出单位是什么、我能不能计量它」——计量不了产出，就只能继续卖投入。

未决问题本场没有答案：被告侧全面 AI 化后和解金额会不会系统性抬升（Colossus 们会被迫重训）；律师执业责任与特权边界如何在 agent 代理下界定；以及「帮助几十万客户」到「数亿人」之间，法院系统的吞吐能力本身就是新瓶颈——Rampell 的专利观察（USPTO 成为新瓶颈）是同一个问题的另一个切面。

转写校正表

转写原文（修正版）

[0s - 120s]

Historically, plaintiff law really didn’t need more than a cell phone, email address. That was kind of it. No software really solved the core problem, which is labor constraint. Most people think of AI as like, oh, you’re going to fire everybody and cut costs, and it’s like, no, actually, like if I use Eve, I make a lot more money for my firm. People can’t pay five hundred, six hundred dollars an hour on a blank check for however long the case might take. What Eve is aiming to do is really handle that whole thing end to end. One firm in LA went from managing about a hundred clients per attorney to five hundred. Wow, that is a type of thing that is possible. Why is Eve better than just kind of like raw intelligence? If you drop Albert Einstein into a law firm today, he will not be productive for quite some time. That is a general problem that Eve is solving. And why won’t that person be productive? Well, it’s due to a number of things. One is, hi everyone. I’m Alex Rampell, partner at Andreessen Horowitz, and I’m very happy to be here with Jay from Eve, co-founder and CEO. And everybody knows a lawyer, right? Everybody knows law, but most people, I think, especially in Silicon Valley, when they hear law and legal, they kind of think about corporate law, like really big, you know, supposedly white white shoe. I guess is the term law firms where you go to Harvard Law School and you bill two thousand dollars an hour. But there’s like another side of law which actually is bigger, more distributed, and I guess we would call that plaintiff law. So like every time you drive on 101, you see all these billboards, and maybe kind of talk about like what is, maybe start off like what is Eve? What form of law and legal AI do you focus on, and maybe a little bit about the industry in general? Because I feel like most Americans are familiar with it, but they don’t necessarily realize, and especially for people I think here in Silicon Valley, they’ve heard of Wilson Sonsini, but they haven’t heard of Morgan & Morgan, right? And Morgan & Morgan is probably a lot more profitable than Wilson Sonsini. But like, what what is plaintiff law, and also

[120s - 240s]

Kind of maybe explain Eve in your own words. Absolutely. One, thank you for having me here, Alex. Always fun to talk about Eve. What Eve is is is legal AI for real people, right? Like people like you, me, not institutions, not in-house legal, not the people that already have all the resources. But how do we actually provide legal services to people that otherwise can’t afford it? And this is how the plaintiff law industry really came about. Because what ended up happening was you talked about how Wilson Sonsini might charge two thousand dollars an hour, and that’s that’s really the business model. Lawyers’ times are expensive, and you’re trying to point lawyers towards more and more expensive time. The same is true for most lawyers. They they have a lot of bills to pay, and they need today legal work. The more work you put behind it, you can represent your facts better. You can get a better outcome. That is the state of world we live in today. To account for this, lawyers understand people can’t pay five hundred, six hundred dollars an hour on a blank check for however long the case might take. So the plaintiff industry came about on working on contingency law. So what this means is they only get paid when they win the case for the client, and this is what’s allowed them to even have any sort of leveling playing field with people versus institutions. And what Eve is aiming to do is really handle that whole thing end to end, of how do you represent a client all the way from the very first call to getting them a settlement? How do you do that end to end thing purely with AI and give that to the lawyers to then amplify the outcomes that can actually provide? And that’s what Eve is doing, and we’re really arming, you know, think about giving David a slingshot to take down Goliath. That’s what we’re doing. So what I find fascinating about this is you really I kind of think about econ one hundred and one, where you have a supply curve that goes up into the right and.

[240s - 360s]

What does that mean? Because remember, there’s price and quantity. So here’s here’s quantity, and here here’s price. So as the price goes up, right, then the quantity supplied goes up, right. So if I can make a hundred thousand dollars by doing something, wow, I’m totally in. But the demand curve is the opposite direction. That’s right. So you know, price and quantity go this direction. So the demand, the number of the units that I I want, actually, I should say, this this is quantity, this is price here. So you know, price goes down, quantity demanded goes up considerably. And if I think about applying this to law, like, what if I have a thousand dollars of damages, right? I can call a lawyer, even for contingency, right? They might not want to take my case because if they take a third of the outcome, it’s like they want a bigger case, and it’s going to require a certain amount of work. And AI really it it kind of liberates these market failures to a certain extent because you mentioned kind of giving David a bigger slingshot. Like a lot of times, like David just well, number one, if I have a thousand dollars of damages, I certainly can’t pay twelve hundred dollars an hour for a lawyer. So like I’m out for that. We’ll enter contingency law. The problem is that like the settlement night might not be big enough. So like the lawyer might want to serve me, but like he or she has paralegals that they have to pay and so on and so forth. But if the software can do the work, now the market opportunity is so much bigger, right? You can actually deliver justice for a lot of people that otherwise they weren’t priced out of it because how you how are you priced out of contingency? But like it’s not really in the interest of a lawyer that they want to serve you know a thousand customers that they get, but they can’t. They have to focus on the biggest ones to the detriment of the smaller ones because again, it’s kind of to the right of that equilibrium point. I don’t know if you agree with that or how you think about that. That is exactly right. I think that is the actual market problem we have today, which is you know if.

[360s - 480s]

If you go back to first principles, if if you’re a person, you deserve a great lawyer to represent your case, and usually only need a lawyer when your back is against the wall, right? The best of us, the worst of us, comes at this time, and we don’t have the skill sets to represent ourselves. That’s why this whole profession exists. That’s why the legal system is about third of our our government. It’s for this reason. Yeah. Um, and today, what’s happening is because lawyers still need to get paid. It’s still bottlenecked on legal work. Today, if a lawyer doesn’t make at least five thousand dollars of fees on a case, they do not take it on. Yeah. That is the market dynamics today, and even at five thousand, they’re losing money on a lot of cases. They’re really banking on few cases paying out very large. They’re not even guaranteed to win. Period. That’s right. So they already take that. That’s the contingency aspect. That’s exactly. But it’s kind of like even if I win, I lose because my costs of carrying the case are going to be higher than the actual potential outcome, even if I win. That’s exactly right. I mean, think about like most people here have been in a car accident, fender bender of some sort. If it’s below the deductible, no lawyer in America will touch that case, right? It’s just not economically worth it. Even if the deductible’s three thousand, four thousand dollars, to an individual, that is a difference between a paycheck being gone versus not, right? And there’s no representation they can get for that. Right. And the the same thing, and and the reason is actually from their side, we have to understand from their side too, right? So let let’s say you’ve been getting harassed at work, and your employer could stop it, like in hospitality industry. Let’s say you could, you know, tell your customers, “Hey, don’t harass my employees,” and change processes to make that happen. Today, that doesn’t happen because when that person gets calls, the lawyer on the other side has to pick up a call, feel that call. That’s about thirty to forty-five minutes of work, and then after that, go do legal research because in employment in America, pretty much everything’s at will, and you have to tie.

[480s - 600s]

A problem to, you know, like a protected protected class of some kind, and it’s called causes of action. So you have to tie to a cause of action, and that work requires them to go spend ten hours, yeah, to go research your case law, research the facts, go back and forth, and ask them more follow up questions. See if they qualify. And while they spent all that time, they might still realize, hey, this doesn’t even make me five thousand dollars, right? So that is why ninety six percent of cases on the labor and employment side, for example, gets rejected, right? So you can never find anybody to represent you unless you come out of pocket. In many cases, people don’t have money to come out of pocket. That’s that’s exactly right. That’s exactly right. So you just kind of take it unless something really egregious happens. And that is the current state. And it’s all bottlenecked on legal labor. And this is what Eve is meant to do: is to how do you now take that ten hours is now immediate with Eve. A call comes in. Eve actually answers the call and automatically uses his insights on the law firm to know whether or not this is a case the lawyer has to take on or not, and then hit the ground running with a demand or the initial preliminary work done, right? And that completely changes the economics of that type of case. And we’re still at the very beginning stages. You know, like what’s what’s happening is Eve is getting more and more capable of handling outcomes end to end over a very long horizon. These cases take six months, a year to take to launch, and through them, probably a thousand actions back and forths that are happening. And as accuracy of Eve is getting higher and higher, this long horizon becomes possible. So the more this becomes possible, even more complicated work gets amortized down to a constant case. So now all these people without representation get representation. Yeah, and if you really ask yourself, like, how many times in your life, if asking for legal help wasn’t a friction point, would you have asked it? That number.

[600s - 720s]

Is way higher than we think. Yeah. So it’s a funny story. This is true story. I was in Blue Bottle, a little coffee shop in Los Altos, and I was explaining Eve to somebody probably nine months ago. And I was explaining it because most people think of AI as like, oh, you’re going to fire everybody and cut costs, and it’s all about cost savings. And it’s like, no, actually, like if I use Eve, I make a lot more money for my firm. And it’s not because I’m cutting costs. It’s like I can take on more cases. It’s actually revenue expansion. So I’m explaining this as as an outsider investor can, so not as well as you probably would. And at the end of this, I mean, we were talking about many topics, but Eve came up, and this guy comes up to me, looks a little angry. He has a big dog, and I’m kind of afraid of big dogs. And he’s like, “What was that company you were talking about?” I was like, “Oh, is he going to get mad?” He’s like, “I’m a trial attorney. I want to try it.” Oh, great. So I got you a new customer there. Thank you. But the whole point is that you expand the revenue opportunity, but maybe kind of walk through like what do the dynamics because everybody is driven on 101 or i95. I95 is the corridor that goes from Florida all the way up to Maine or something, you know, up and down the Eastern Seaboard. And it’s like I’ve driven almost all of that, and it’s like I feel like half of the ads are for trial attorneys, plaintiff law. So they’re obviously advertising there because you want they want to get called. It’s like, hey, I I have this accident or I have this issue. But what do the economics look like? So like I spend all this is why again I think Eve is so fascinating because I have to turn down 90% of the leads that I’m getting. I assume right or like what what are the numbers look like? I spend money on a billboard. I get lots of inbound. How many of those turn into cases? How many of those cases turn into revenue? Maybe kind of like a before and after because you are you’re not a cost savings play for lawyers. You’re a revenue expansion play for them. That’s that’s from the legal side, but what what what are the economics look like for a law firm? That that’s exactly right. When most people drive by, they see the face and they forget about the people behind them and the amount of work every single call actually takes. That’s that’s what happens today. That’s what.

[720s - 840s]

Is there to help with, and the math on the law firm side is very simple. The more people you represent, typically speaking, the more money you make. But because you can take on more people, and you honestly are denying and not taking on many cases because they’re too expensive to take on, the demand from their perspective is not really constrained. It’s really the supply of labor. Like, do I have enough people inside to actually service that problem? And to make things more complicated, different firms here are in different realms of problems where the bottleneck keeps shifting, right? So, like a small firm might just have one attorney that has a case manager or some sort of office manager that fields the initial call and just notes it down, and then the lawyer then responds back in their free time. Right. They’re kind of running chaotically, running around, and then there’s a next level, which is they find a way to systematize it. You know, they have a an intake team of call cent like call center somewhere that intakes it, and now that becomes a new bottleneck. So now, when you increase your marketing spend, let’s say you looked particularly attractive in that billboard, and a lot of people are calling in, suddenly now that team is overwhelmed, and you’re limited again by how many people you can represent. Right. And this is the daily challenge these small businesses are dealing with. It’s they’re small businesses; they’re not massive enterprises. They want to represent individuals, and everyone here went to law school, not business school. They want to be there for the people. So we’re basically consolidating all of that into the E platform, such that they are getting pulled in only for what they need to, right? Like their lawyer insights, their particular insights on how do they take this case. Eve keeps raising the floor. How do I take it from this floor to the next level? And that is where I believe really all lawyers spend time talking to their people and helping get client outcomes. Well, and this is kind of the the market failure point to.

[840s - 960s]

To a certain extent, which is, I could serve all 5,000 leads that came in if only I could hire a paralegal for 12 cents per hour, right, or whatever the token cost of it. But it’s like I can’t, so I would just. There’s no labor market that could actually function, but with AI now, I can. I mean, do do you have anecdotes of just like kind of before and after? It’s just like you know, like the hair club for men. It’s like you know, here I am before, here after, I have a nice head of hair. But like, what what’s that for? Kind of your best customers that you work with. I imagine the revenue transformation is pretty significant. Yeah, one one firm in LA went from managing about 100 clients per attorney to 500. Wow, right, and that is that is a type of thing that is possible. And they went to to their credit. They worked really hard to achieve this. They utilized Eve up to the max of all the way from intake, take fielding the call, doing qualification, automatically removing all the relay problem that they have of passing things from one attorney to another to a case manager. All that is now handled within agents in Eve, and all the way to settlement, where it’s actually briefs before they go to a mediator discussion that actually tells you here’s what you need to focus on, here’s what the settlement outcome should look like for cases like this that we worked on, and here are the tactics used before that got you here. Yeah, and that is what the future looks like. It’s truly moving the case forward continuously, and you’re helping making it do a better job than it could do by itself. That is, I think, the future of law, right? Where you as a lawyer are really going to look more like a almost a data scientist and engineer, engineering something to be better and better, and that is a shift the top law firms on Eve mentally kind of go through. Yeah, well, I kind of think of if you if you look at your.

[960s - 1080s]

Average law firm where they don’t really know AI and and actually like this is kind of an interesting kind of take on on the world of of AI enabled applications in general like what was the software stack for law before well if you’re a big firm like Wilson Sonsini probably the most important thing is like your your time tracker right because you’re billing by the hour if you’re in contingency law it’s like well your time matters but you’re not billing your customers on that you probably need to have like a you know each case here’s how much money I’m spending on the case but like I imagine your primary tool was Microsoft Word and there wasn’t and this is how like VCs would have like gotten this industry wrong before which is like oh well the software market for plaintiff law is small they all buy Microsoft Office yeah Microsoft Office is kind of expensive so therefore like the software spend is like in the tens of billions of dollars a year or something seemingly comically small but wait a minute the outcomes that they deliver is in the hundreds of billions of dollars a year they can’t deliver more outcomes because they’re really constrained on labor and they can’t hire labor for twelve cents an hour because it doesn’t exist so it turns out the software market is kind of like this shadow labor market and it’s like massively bigger than anybody would have expected and and like it’s always just a weird thing because it’s like you can’t say the software market is small because you’re a you’re a legal software company but people know Harvey or they know like corporate loss or like you know e discovery tools that have been around but I imagine like what was the main software stack before Eve entered what was it just Microsoft Word or like what what what was a in order for me to go into business I graduate from law school I want to be in this particular you know sub sector of the field what was my software stack you know this is the unfortunate thing about plaintiff law and institutions is historically plaintiff law really didn’t need more.

[1080s - 1200s]

Than a cell phone email address. Honestly, even Microsoft Word, some people on the solos would not buy it for cost reasons or other reasons. No, it’s it’s because if you think about it, you actually don’t do that much redlining. It’s a trust boundary situation, right? So your your one side is kind of opposition to the other side. You’re not really collaborating. Yes, in the same way that you are when you’re doing contract review or or something along those lines. So that was kind of it, and no software really solved a core problem, which is exactly what you mentioned. They’re labor constrained. That is the core of how they do anything at all, and because they have labor and they’re good at managing their labor, they found a way to solve in every problem to them look like solve with labor, and that’s how they’ve they’ve grown up. And as a result of that, what ended up happening was a lot of resources on software went towards defense institutions because that’s where the budgets were. For Harvey, you know, really any company, honestly, legal software company today, vast majority of them are on the defense side because that’s where the budgets are. Right. Right. And that’s what’s easy to build for, easy to get money for, and on the on the plaintiff side, these are small business that are watching their pocketbooks every month. Right. If you’re not driving outcomes for them from day one, you’re out. You’re not getting renewed. They’re they’re gonna curse you out. You know, like anything anything they can do, they’re going to come after you, right? And that’s what’s made things so difficult. But what we realized is, well, their core problem is we know what their incentives are. They want to represent more people. They want to win, and we can do that now. You know, there’s once the beauty of software today is once you do a task manually, even tasks that include crunching new piece of data and transforming it, that’s kind of what AI is really good at. You can now encode it as a process. That is a fundamentally new shift, that has never happened before. That’s what they were trying to do with.

[1200s - 1320s]

People operations, but never worked because every single case is still different enough that you can’t do it. It’s still bottlenecked on hiring smart enough people to be able to handle it. And now, one smart person can actually handle, like we talked about, five hundred different cases of very different facts, very different situations, and how to even keep that in your brain. Like it’s hard, right? But now you don’t have to. And I guess one of the things that a lot of founders are thinking about right now at the application layer is like, uh-oh, maybe I’m roadkill from the big guys, right? Like the labs that produce the intelligence, maybe they’re going to go tackle MySpace next, and I’m I’m toast. And there are many ways of defending against that, but defending kind of sells it short because if you’re able, like if you have a network effect, or if you have data that actually nobody else has, you can deliver better outcomes. So you know who cares if everybody uses ChatGPT or or Claude behind the scenes or on their own? But maybe talk about like I’m sure you’ve thought about this a lot. You know why is Eve better than just kind of like raw intelligence? And how does it get better? Because it does. The more practitioners that use the product, yeah we’re we’re betting on we’re betting on these models getting better and better. And our value is exactly that. You know if you have thousand Albert Einsteins to work with to do litigation or or you know the best attorney ever to to do every single task, we would take it. The problem is if you drop Albert Einstein into a law firm today, he will not be productive for quite some time. And that is a general problem that Eve is solving, right? And why won’t that person be productive? Well, it’s due to a number of things. I think one is in plaintiff law, the vast majority of data is actually non-scrapable. About less than a percent of cases actually settle and get turned into case opinions. Right. Vast majority settle pre-demand or pre-litigation, and some even before that.

[1320s - 1440s]

Right, where it’s actually just negotiation of, hey, this is how I was wronged. Please fix it. And to make that concrete, it’s like I’m in a car accident. I’m not at fault. You know, the adversary, if you will, like the other side’s insurance company, is like, oh, you weren’t that hurt. We’ll give you a dollar, right? They always try to like deny, deny, deny. Like rule number one of an insurance carrier is deny. Rule number two is deny. You know, just goes on. Those are all the rules of insurance carriers. So now I hire a lawyer, and to actually get what I’m owed because I actually am injured and my car is actually totaled and they just don’t want to pay me. You get a settlement. Doesn’t go to case. It doesn’t go to actual trial. But you know, that’s right. Right, that Geico actually did pay twenty nine thousand dollars, and that’s very, very useful information. I assume that’s right because insurance is the one that’s dealing with hundred thousand different. So they have the shared community. So they have. They know all the cases, but because it’s sharded across all the different local firms, they don’t know exactly Geico’s every single move. That’s exactly right, and you know they even have ominous sounding software like Colossus, which tracks. You know they they’re they try to basically say a broken thumb. This is how much we think it’s worth, which is a hard thing to do. In their defense, like it’s a hard thing to do, but they are doing that, but also saying this is how much we can get away with giving them away for, and irrespective of that individual’s actual need of of their particular situations, and that’s what the legal system is meant to fix. That’s that’s what it’s there for. And what now is happening is, and this is this is the interesting bit and the challenge is, if you go and Google how do you write an amazing demand letter for again State Farm for soft tissue injury cases like that today exists nowhere, even for a new paralegal that is joining a plaintiff firm, this is something they have to learn. And on top of that, it gets more complicated because when you’re in local jurisdiction, the local environment, like the court systems, your opposing counsel, they all behave a little different.

[1440s - 1560s]

And that context matters too. So what Eve is doing is it’s turning this historically incredibly difficult to utilize multi-attribute, multi-dimensional information that is basically what is the best thing I can do at this particular moment to improve the outcome of the case. Yeah. And that same question is repeated thousands of times during the case, and a human brain is just not capable of kind of keeping all of that in mind. Um, and but AI is, right? And and that is the type of data we help build for law firms, and they’re seeing the immediate value from it. And the first place where they see value is, of course, intake. Who do I even take on? Who am I good at representing? And maybe start there and keep getting better. Right. And I imagine because these aren’t giant firms, right? Like if I’m a lawyer in New Jersey with a four-person, four-attorney practice, and over there there’s another lawyer in Arizona with a seven-attorney practice, but their counterparties tend to be State Farm and Geico and and and whatnot at the limit. It’s actually fine to share information. I guess how do you think about data? Because I imagine this is always a this is always you know hard for companies when you’re getting off the ground. It’s like no, no, this is my data. But there’s also there’s a give to get. I assume it’s kind of like you know I work with a dental AI company, and it’s like well the dentist in Iowa can’t serve patients in Florida, but they want to learn from the collective actions of each other. But I guess how do you think about you know just data sequestration and and whatnot? Does that ever become an issue, or how do you think about using it? We we’ve largely followed the rules of engagement that already exist in this industry, but we’ve amplified it with software. That’s kind of our stance, right? So the rules rules of engagement is for their own cases they want to be very private. Like that is important by law, it’s attorney-client privilege.

[1560s - 1680s]

And we don’t train on that data ever. Yeah. Right. However, what attorneys also do is to share this knowledge. They actually travel. They attend CLE, and they share tactics each other on how they went up against State Farm’s X and Y defense, because State Farm repeats that defense across the board. Right. And not picking on State Farm, anyone is fair game here. But that is that is what they’re already doing. So we hire our own attorneys and track success rates of cases for our platform, and we want to help. If you’re using Eve, you should have more success. That’s what we track. And we have this product called Auditor, which actually analyzes every single case through every single page overnight and looks for situations that a perfect attorney would catch every time. And across the board, unfortunately, vast majority of cases have almost every single case has missed information. Right. And now that’s no longer a thing. Yeah. And maybe talk about the future of where where things go, because on the one hand, I can imagine a world where it’s like one of these days Geico is going to get a little bit smarter, and they’re probably going to like you see their deny deny deny plan. That’s how they got smart before, right? Because they realized it is an adversarial system. Like you know, I lost my house in a fire, and I’m going to claim I had a Picasso in there, and then they’re going to think that I’m claiming that I have a Picasso. They’re just going to deny the claim, deny deny. Like so, each party is kind of fighting each other, but they’re going to get superpowers. I mean, you see this playing out a little bit with Muse and all the consumer agents, where I want a reservation at this restaurant that’s impossible to get, and they have a human answering the phone. I can DDoS them now that I have to pay one penny per hour or something, and then eventually they’re going to realize that they’re getting DDoS and they’re going to prevent that. So you know, I guess on the one hand, like how do you think about you know your your adversaries here, not not your adversaries, but like your clients’ adversaries?

[1680s - 1800s]

How are they adapting to a world where it’s like, you know, it was great for Geico where they could say, “Hey, you have two thousand dollars of damages.” The consumer’s like, “Yeah, I did have maybe twenty five hundred dollars of damages,” but it’s like they never really had to worry about that before because of the quantum. And now, unfortunately for them, but fortunately for the Davids of the world, like people that are actually aggrieved and and have been victims, now they can fight back. And you’re powering that. That’s kind of one thing. But also, you mentioned long horizon agents because you know what does the practice of law look like when a lot of the stuff can actually be done by AI. You had, I think, you framed it as like the the attorneys are almost like the data scientists of the future. But you know, maybe talk about like what does the adversary or the adversarial process look like? You know, what what what are the the State Farms and Geico’s using potentially as a result of you entering the scene? But also, like how big big question. But how does law evolve and emerge? Just given that you know you’re kind of manipulating words and finding facts and doing all this kind of stuff, where like the humans are not as important. They obviously have an important role of shepherding the case to completion. But like, what does the AI do versus not do? Yeah, I mean, ultimately, my my framework for thinking about this is ultimately when you have an individual, they’re going to need representation. Whether that representation, like who does the work on the representation, you can assume it’s ninety nine percent AI, maybe ninety nine point nine percent AI. There’s always going to be that little bit that is the human touch, right? And we do believe that people are going to make a positive difference to the outcomes of the of the case. And that is exactly what you have, right? So, like, what you have on the on the defense side or really institution side is what’s always happened, which is they have more resources, they have more money, they have more time, which is actually another weapon they have, which is quite powerful. And individuals are in.

[1800s - 1920s]

In debt to pay their fees, and that is a very powerful tool they have, and that is that is the strength they’re working with, and they’re trying to rely on centralization and standardization. On the defense side, you have a attorney that is fighting your case and pushing them to get to litigation, and I think that is going to be what happens. I think a lot more cases are going to have teeth, and all the routine back and fowards I think is going to happen through agents talking to agents. I think that’s you know it’s crazy future world, but I think that’s where we’re headed to, and the difference maker is what does the attorney understand about that client, that set of facts, and their local environment of who they’re going up against locally in that law in in that defense firm, and they’re using that insight to make a difference for their client, and I don’t think that ever goes away. It’s it’s in fact something we’re seeing already where many customers on Eve are actually choosing to spend more time on their trial work, yeah, because it actually enables that. Previously, going to trial took down your entire firm. Suddenly, you’re just spending all your time doing trial trial prep, interviewing juries. You want things to settle. Exactly, it’s more of a settlement motion. That’s exactly right. And instead, I think things are going more into real advocacy. How do I actually make our point known? How do I, you know, my client is against back of the wall, they’re doing bad things, they’re doing good things. How do I represent them in what actually matters for them? Well, I imagine this is kind of the data science side of what you’re doing as well with this giant dataset that’s probably never been accumulated before, because as you mentioned, all these things it’s so bespoke. There’s no like communal repository for like all of the settlement amounts, but like I’ve seen this with litigation finance a little bit where if I’m a lawyer, if I’m a trial attorney, and I’m not getting paid by the hour by you, my client, well, where do I pay myself and my staff and my office?

[1920s - 2040s]

Rent and everything else. Well, there are companies that will show up and say, “I will fund this case.” That’s right, right. And I’m funding this case because I’m counting on you winning it. And like, they’ll do all sorts of clever things, like here’s the judge, and here’s this. Like, they’re using data. So outside of, I think those guys have been very data science forward. But historically, like this data just like never really lived anywhere. And now that it does, you can make smarter decisions as an attorney. I’m like, do I take it to trial or do I accept the settlement or do I push back? Oh, Geico offered me twelve thousand dollars. Should I push back and fight for fifteen? Should I, you know, take it to trial and try to win a hundred? Like, how do I make those decisions? That’s exactly right. That’s which is why we can help them make the best decision every single case and know when an optimal outcome has been achieved. Yeah, and it’s still, I would say, it’s still the attorney’s responsibility to push it even further, right? And I think that is what I meant by data science like work because they’re using the power of software to do work you never even thought to do to improve the outcome. And I mean, where do you? This is a very broad question, of course, but like, where does the legal system go? I was having some some trouble with Wells Fargo, and I’m like, you know what? I’ve tried like for four hours to do this. I’m just going to sue them. So I went to Codex, you know, ChatGPT. I was like, sue Wells Fargo. Four hours later, it had sued Wells Fargo. It actually found a process server. Um, I got the notice of like we’re going to court, but you know the date is in February. Like another kind of similar vein is happening with patent law, where before it’s so much work to go file a patent, now it’s less work to file a patent, but you still have the USPTO, which is the bottleneck. So I guess I mean, if you were to prognosticate, like where does the legal industry go now that AI is becoming table stakes, and you’re delivering, you’re you’re part of that table, obviously for plaintiff law, but if you think about the entire practice of law in general, like how does it change if you were to fast forward five years? What does it look like? What’s similar? What’s different? Most likely, I think it’s inevitable.

[2040s - 2160s]

That you’re going to have to create systems where not everything goes to the court system, and this already is a framework that is used in law, right? Like, I mean, for some types of veterans’ affairs cases, that goes through a different committee. For even labor employment, that goes through EEOC in a lot of cases before it goes through. And you know, employers have figured out different ways to mediate and have arbitration that doesn’t necessarily go through the court system. Right. And this is this is the framework that actually naturally evolves, and I expect that to happen more and more. And I think the tweak that’s going to happen is that for some pockets of it, we’re probably going to go through some version of like ADR, automated dispute resolution, where AI-enabled mediators or central parties that have a neutral view of both sides are going to use AI to really speed up how they come up with judgment. And I don’t think the judgment will really be replaced, but what they will get is a set of facts that shows historically here’s how this happened, here’s how this happened, and it’s up to them to remove the biases that might exist. And I think we’re just going to spend more and more time on that side of things. And do you think the small law firms get bigger? Because that would be the other thing. It’s like if I could only handle 100 cases per attorney, now I can handle 500. But in the future, maybe 5,000. You know, why do I limit my office to like a very, very small New Jersey location? You know, maybe I should go more broad. And you’re going to have kind of larger, almost like corporate-like law entities, and like I, I think the biggest trial attorney firm is Morgan & Morgan. Is that right? And they have billions of dollars in revenue, but your average firm is is quite small. But you can imagine, powered by Eve, and with all the changes that we’re talking about, it’s like the small firm could actually become a billion-dollar revenue business. It’s kind of like venture out.

[2160s - 2280s]

Versus kind of like, but what you would call lifestyle business. You were a VC once upon a time. You know that term, some somewhat pejorative term for like you make profits. But how do you think? Like, is is that a reasonable take that like some of these small guys can actually? I mean, obviously you can’t go public as a as a law firm for now. Maybe in Arizona or something you can. But how do you think there’s a chance for some of these local mom and pop stores, if you will, to become very very large institutions? I think the transformation that’s going to happen is a lot of these law firms are going to look more like software companies. I think they’re going to finally participate in the software revolution that’s been happening for the last thirty years. They’ve been left out of, right? And if you if you kind of deep dive, you’ll understand why. Let’s say you have a twenty five thousand dollars settlement. The attorney gets maybe a third of that. Let’s call it eight thousand for simplicity. And you know, like out of that eight thousand, about half of it goes towards labor spend. Thirty percent goes to marketing, and the rest maybe is profit, right? Like that. That’s kind of the business as it exists today. Because of that business, it is so difficult to reinvest back into servicing your next client better, right? Like the only way you can really do that is by hiring a better attorney today, right? What changes is now they can actually accrue a database and data set that is unique to them that gets them results that is better than their competitors for a subset of law, right? And this is kind of specialization. And historically, this industry already went through that. You know, like thirty years ago, there was a you know your neighborhood lawyer that handled everything. You know, from from employment cases to IP to what have you. Yes, exactly, exactly. And and now what we have are specializations of practice areas. You have someone that’s really good at IP, someone that’s good in employment, and now it’s getting even more specific, right? Like someone that’s good at let’s say nursing birth defects or or something along those lines.

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And I can anticipate that to go even another level, where now someone could get really good at soft tissue injury cases that involve X and Y fact patterns, and it advertising for those cases might be a lot more cost effective than some other larger firm like Morgan Morgan, right? Just showing their billboard everywhere. So get good at something locally and then expand. That’s right. Just across the entire country because you have such high competence around that one field. That’s exactly right. And so I think we’re going to see that type of specialization, and I think we’re also going to see the opposite, where some of the large, we’re also working with some on the scale of Morgan Morgan, like some of them actually are coming together. So those ones that got us big are actually going around buying other law firms because they realized with Eve they can very quickly consolidate what used to be a very painful reintegration process. Now it can be done on exactly what the client wants, which is hey, the client wants a legal outcome, and Eve is made just for that. Yeah. So by doing that, they get to largely work away from a lot of the change management, work workforce changes, operation changes, because Eve handles a lot of that for them. They can speed up their time to making that acquisition worth it. Well, the funny thing is that there’s this movement underway to outcome oriented pricing. So like software SaaS used to be per seat per month, and I used to joke it was like the tall grande venti model of software selling. It’s like you go to like any SaaS company’s website. This is why you know Claude can clone them all so readily. They all look the same. It’s like seventy nine dollars a month, ninety nine dollars a month, one twenty nine a month. You know, tall grande venti, and it was per seat. And like look at a company like Zendesk, which had two billion dollars of ARR when they were last a public company, they were taken private by Permira and Hellman and Friedman, and now they’re like, uh oh, how many seats do you need? Like maybe zero. We we need to deliver outcomes. So everybody in the software world is kind of like in a lot of AI companies, like we deliver outcomes, we charge for outcomes. The interesting.

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thing is that the form of law that you focused on was always outcome oriented from the very beginning. But you have other types of law that are not outcome oriented, where I guarantee all the clients would much, much, much prefer that it was outcome based, right? I mean, sometimes it’s tricky. Like I have a trust and estate attorney at McDermott Will & Emery. She’s very, very good. She’s like so expensive. Like I guess my outcome, the outcome is my death, so I don’t know if I want that. But you know, you would prefer to just say like there is a fixed cost, but there’s often this data, you know, in in their defense, the reason to charge by the hour is not so much to rip off the client, but there’s data asymmetry between what I want in my head and how I articulate it. Maybe the the the scope of the project is changing over time, but you know, other forms of law, I would argue, they probably should look more like plaintiffs’ law. They should have outcome oriented pricing as well, right? Or like immigration law, that should be outcome oriented. Like everything should be outcome oriented pricing, but it’s hard because there’s a data deficiency, if you will, in terms of like how will I win this case on behalf of the defendant? But I guess like how do you think other forms of like you could probably expand well beyond plaintiffs’ law? I don’t know if you agree with me, but like how do you think about just like law in general changing? Because I kind of feel like plaintiffs’ law was like ahead of everything that we’re seeing right now. It’s like everybody wants outcomes. Nobody wants to pay for the ingredients or the inputs. This was the only kind of field, probably, of business where it’s like it was always outcome oriented. But kind of shouldn’t other parts of law work that way? That is our exact belief as well, right? So like the the hard part has been it is hard, you know, when when you’re on the defense side or even the plaintiff side, the question around a new case is it ten hours of work, hundred hours of work? In many cases, you really don’t know until you take it on. And for plaintiffs, that is why we had to go so deep into each practice area into achieving outcomes. And you know, we were doing what is now called.

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Long horizon agents like last year. That’s just what was needed to drive results, and now it’s getting better and better to actually drive legal outcomes. And ultimately, I think what is needed for democratization of law is, from a set of facts, you need to know how much compute resources you need to arrive at a outcome, and that happens through both aggregation of and better usage of data, and better usage of actually running the compute to achieve that outcome. And so we’re doing both, right? So like that is why we’re laser focused on outcomes, and we want to drive outcomes. And eventually, this David versus Goliath thing is happening everywhere. You know, it’s not just in in you know big law or in house legal; those are just where the budgets are, so they actually can write basically a blank check for an interim period of time. And the the forcing function around how much is this actually worth in outcomes to me is not known, right? Like, I mean, you know this. Like when you go fundraise, you might get hit with like a an hundred k check. What is the exact outcome of that? You know, like it’s very hard to know what the value of this, but that is the only way they can price today, right? And that’s because they don’t know what the expected value of their work actually is. Well, that’s the asymmetry as well. It’s like I don’t know. It’s like okay, you need to hire a lawyer for your Series C. Man, what does that mean? Well, it could be like very very simple. It could be very complicated. So it’s very hard to have a flat rate. That’s right. Because they could get burned in. They can’t get burned in the low direction, right? It’s like I charge you twenty five thousand dollars. I spend an hour on this, but they could get burned if it’s like I have to now spend four thousand hours of attorney time. I could lose money on this, which of course every single firm. Oh, well, actually, like as you know in corporate law, these are loss leader products, and then you try to make it up on the M and A or IPO or something. But obviously a little bit different than plaintiff law. How do you guys charge? How did you? Because I imagine you had a. It’s not like on day one when you started the business, you knew exactly what you were going to charge for like.

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What type of customer? I imagine your thinking has evolved on this as well. Absolutely. So I mean, from actually weirdly enough, from day one, we started probably with the with the you know what was it tall grande tall grande venti venti model. That’s that’s what we started with because we didn’t know any better, right? And then as we dug into it, we realized like what they care about is outcomes. What they care about is how do I serve more clients? So that actually forced us to be outcome based from very early days. No, we’re talking like a year and a half ago, which is pretty funny. That’s early in today’s world. AI like dog years times a hundred. Exactly. But that that is that is kind of what happened to us, right? And there’s so much room to grow. Where now that you know we we talked about that that value diagram of a third goes to attorneys, half of that is labor. That is half they don’t have to spend anymore, right? So how do you actually turn that into something more useful? And that way, the best way to get more useful work is actually to spend it weirdly on more tokens and more more work that Eve can do to make more money for your firm, right? And that is kind of the cycle we’re in today. And a lot of the market dynamics that is helping us is exactly this. Like we actually believe the billable hour model. I don’t think it’s going to die completely, but I think it’s going to be on the way of the seat based pricing model, where for some subset of law, that is probably the best way you can charge for the work because it’s very difficult to know value until an outcome many many years later. But for a lot of it, that’s not true anymore. You know, for for a lot of it, you actually know what the outcomes are, and you should be able to take your work down to an amortized amount and deliver that back, right? And that is what we are betting on, right? And this is why we focused on delivering legal outcomes from day one, end to end, and doing that in such a way.

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That it is as simple as a lawyer saying, “Here’s this client I got in. Give it to Eve. Eve will pull you in when you’re needed, and you’re good to go.” We’re like, see, we’ve talked about how plaintiff attorneys, like they are, they are supply constrained, not demand constrained, or many of them are, because they have this infinite influx of of clients that they can’t serve because the math doesn’t really work in terms of you know cost versus you know CAC versus LTV. It’s the the old fashioned thing for any business applies to them as well. You know where where are your constraints? Like what what kind of holds you back from growing faster? I mean, you’re obviously growing incredibly fast. What are your rate limiters? What What do you need as inputs to grow faster as an output? You know, it’s it’s really fascinating because a lot of the legal benchmarks, like even benchmarks that you share publicly, that Anthropic, OpenAI, many of whom we’re working very closely with, are working on, care a lot about like almost single outcome, right? Like here’s the task. Did it kind of do mostly okay? And the world is now moving into agentic work. For us, the only thing that ever mattered was the agentic outcomes, right? So like now that is getting more and more. The end outcome here is today we’re still at the point with technology and and processes where there’s still a decent amount of human touches needed per case. But by doing more and more work, we’ve been drastically reducing that month over month. And in our industry, we need to do that per case type, right? So like for every single case, we’re learning that like a science, like how do you actually resolve this case, and how do you build a mini attorney brain that is capable of crunching all these data points to that resolution and driving it across the board and working with all the tools they have access to, including Claude and whatnot, to drive that outcome. And that is a really hard problem, right? So we’re largely limited by engineers at this point. Like we have a limitless number of things to do to drive this forward. And the other fun part is we have demand. The moment.

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We are able to deliver on, let’s say, moving vehicle accident cases end to end. The amount of value that gets unlocked for the world from that is in the hundreds of billions of dollars, and that’s just one subtype. Actually, that’s a great question. Have you had a fully autonomous end to end case? It’s like answer phone onboard client, you know, get out, get get check from Geico, you know, like has that happened end to end or like not not yet, but we’re close. That is what that is why we need the engineers, right? So, like to put things in perspective, a single case has probably ten thousand back-and-forths, ten thousands, something like that. Because wow, here’s how you think about it, right? A client comes in. Let’s say it’s a PI case. Now you have medical records, so now you have to actually outbound call insurance companies and healthcare companies to get hold of the records that the the clients already had, right? So, like that is actually that takes two weeks today, and with Eve outbound agents, where we actually have voice AI that can do the outbound call, and funnily enough, that’s an area where we’re already seeing agents talking to other agents and learning how to get past right each other, which is hilarious. And that is like one step that’s needed, right? And then from there, you have to then create a document and actually have a conversation with the other side. So all of this are task we are completing end to end. So today, there’s a little bit of touch required where someone makes an outcome better or does something a little better, but exactly what we’re building right now is how do you actually drive this whole thing end to end? Yeah, and we we are seeing early signs of that where it’s actually live in some customers. And have you seen going back to the defense side and their colossus of of information? Have you seen their reaction just in general change? So as an example of that, it’s like, uh oh, these guys contacted me with this. I always need to settle because they probably have like pretty firm rules about this. They don’t just like randomly make it up as you go. I would assume because they’re dealing with this.

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Tens of thousands of times, if not more, per year. So if I’m Geico, it’s like, oh, you demanded this much. Well, my first attempt is going to settle here. But like, if their costs go down, maybe that changes, right? So have have you seen any change in reaction of your, you know, not your counterparty per se, but of your clients’ counterparties? I would say largely still, the defense side very much still works on hourly business model. So they haven’t quite caught up to the same pace as our attorneys. So like, there’s a lot of, I guess, pricing asymmetry here. What defense used to do for this one one law firm that we’re working with is they would wait, they would push out discovery request dates until a day before Thanksgiving or a major holiday, right? And then send discovery there because they would know the person would go on vacation and be stuck with all this workload over that vacation, and they wouldn’t do a good job at it or would rush it. And now what they used they because of such a repeated tactic by them, they created an agent on Eve to respond the same day. So the next time this came through, like they responded immediately with the responses, and the defense were like, “What is going on here? The tactic stone and and like we’re seeing there’s so many variants of that that is happening now, which is causing defense to realize, wait, is this this is what happens when the other side has resources, right? Oh crap, right. And and that’s that’s what’s fun to see. Well, Jay, this is a fantastic conversation. I learned a lot. Didn’t realize there were 10,000 touches every time you went through a case, but really fascinating and delighted to be your partner and bring Eve to the next the next 10,000 firms. Thank you. I mean, we we still have so much more to go. We probably helped a couple hundred thousand clients today, and we have hundreds of millions to touch. And I would love to be making a difference in their life every day. So if that’s of interest, please come join Eve. Awesome. Thank you.

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