MikeOSS and the New Bargaining Power in Legal AI

Will Chen, a developer, saw enterprise legal AI demos and realized their premium pricing wasn’t justified by basic features like chat interfaces or prompt templates. By building and releasing MikeOSS, he showed that much of what is marketed as sophisticated legal AI can be reproduced by skilled individuals.

When Will appeared on a fireside chat with Jamie Tso and Raymond Sun of Legal Quants shortly after, the conversation moved quickly to what MikeOSS actually revealed. Three bargaining power shifts are happening at once. Each follows from the same underlying move: someone who was assumed not to understand what they were buying figured it out. That happened first between firms and vendors. It is now happening between innovative lawyers and the firms that employ them, as well as between clients and outside counsel. The client shift is the one most firms are not watching closely enough. When an in-house team runs the same calculation Will ran, watching a demo and asking what it would cost to build, the case for sending routine work outside gets harder to make.

What You Are Actually Paying For

Vendors charge what they charge because buyers have not been able to evaluate what they are buying. MikeOSS changes that. The chat interfaces, the playbooks, the document review tables: those are replicable, as Will demonstrated. What companies like Harvey and Legora legitimately earn their fees for is the enterprise wrapper: security, deployment, configuration, and support. That is a service business, not a moat. Before your next renewal, the question worth asking is which of those two things you are actually paying for, and whether the price reflects it.

The next round of legal AI value will not come from general platforms. Will was clear about this in the fireside chat: generalist tools will be copied, and the firms that build on top of them for a specific workflow or jurisdiction will be the ones creating defensible value. That applies to vendors building on MikeOSS, and it applies to the lawyers inside your firm who understand a workflow well enough to improve it. General capability is becoming a baseline. Depth is where the advantage will be.

The Harder Problem Is Inside the Firm

The harder challenge is inside the firm, and this is where you need to be honest with your partners. Most compensation systems reward production. They do not reward the creation of tools that make other lawyers more productive. If you want that to change, build incentives around what you actually want to reward. A lawyer who makes twenty other lawyers more productive has created real value for the firm. Integrate that recognition into formal evaluations alongside billable production.

Consider this: If a junior lawyer created a tool that saved forty hours on a fixed-fee matter, how would your firm reward them? Typically, origination credit goes elsewhere, and fewer billable hours may even penalize the innovator. Law firms lack an equity-sharing system like those used by software companies.

Will described how most firms reward output rather than the creation of tools that boost others’ productivity. Jamie pressed him on this misalignment. The implication of that conversation is direct: if your firm wants real innovation, the compensation system has to recognize it.

What Happens to Your Billing Model

Jamie raised pricing directly: If AI increases lawyer productivity, what happens to billing? Fewer hours mean the rate-times-hours formula works against you. Move to value-based or fixed pricing before clients require it. Most firms haven’t. They add AI subscriptions but keep billing unchanged. Clients will soon get better tools, making this model hard to justify.

Raymond Sun pressed Will on this directly. Will’s answer was that client relationships still matter, but clients will increasingly want measurable results from AI. Right now, saying the firm uses Harvey is no longer a differentiator. Clients will want to see concrete outcomes. A firm that cannot show what its AI investment produces is competing on a claim that the whole market is already making.

Raymond Sun’s questions pushed toward the strategic position of law firms and in-house teams. If AI-native firms charge premium prices, where does the money come from? If client legal budgets remain constrained, will in-house teams use open-source tools to do more themselves?

Will agreed that this is a real possibility. In-house teams may not replace outside counsel for complex transactions or high-risk litigation. But they may use open-source tools and enterprise AI subscriptions to handle more repeatable work internally.

The Real Lesson

This is why MikeOSS matters. It is not only a product. It is a signal that the cost of building is falling and that law firms should no longer treat legal AI as a black box.

Commercial legal AI platforms will still matter. Many firms will prefer supported, secure, enterprise-ready tools. But the existence of open-source alternatives should make the market more honest. Vendors will need to show where their value really sits. Firms will need to understand which workflows are worth buying, which are worth building, and which should be redesigned altogether.

The firms that benefit most from AI will not necessarily be the firms that buy the most impressive platform. They will be the firms that understand their own work deeply enough to know where AI can create economic value.

That is the real lesson of MikeOSS.

Legal AI strategy cannot simply be a software purchase. It touches pricing, compensation, governance, client service, training, and profitability. The firms that understand that will have more bargaining power than the firms that simply buy what they are sold.

When the Phone Stops Ringing

What Big Law Figured Out – Part One of Four

The most dangerous threat to your firm will not announce itself. Clients will not explain why they move on. The work just stops.

Jae Um, legal analyst and founder of Lumio, explained this on the AI and the Future of Law podcast, hosted by Jen Leonard of Creative Lawyers and Bridget McCormack of the American Arbitration Association. In some practices, the phone simply stops ringing. You are left guessing. A client found a cheaper way and the work got done elsewhere. No announcement, no discussion.

One in-house counsel completed a $10,000 matter with a $20/month tool. The former law firm never knew. You cannot measure what you never received.

That is the nature of this threat. Missed matters go untracked, making the competitive loss invisible.

Most firms focus on visible work at risk: commoditized, high-volume matters with price pressure. But a bigger risk is work leaving the firm unnoticed. When clients handle legal matters elsewhere, no one notices until the pattern has continued for months. The key question is not where price pressure appears, but where work disappears before you see it.

The most exposed position, in my experience, is serving clients you barely know. If you do not truly understand these clients’ businesses, you will not spot a problem before it becomes a decision. When a $20/month tool is viable, the client weighs it against your cost. If your value is not clear, they choose differently. They will not say why; they will simply stop calling.

If clients are solely focused on price, the risk of loss is high. Lawyers must be able to communicate their value beyond just the price, including judgment, experience, track record in court, $ won, $ saved, reputation, creativity, references, etc.

A general counsel in Um’s analysis said it plainly: “If a firm isn’t cannibalizing its own inefficient billable hours, we will find a firm that will.”

Take that as a forecast. Clients already have alternatives, and they are signalling what happens if you do not act first.

Um described how this pressure arrives to a room full of managing partners in London. It never comes as one event. New business gets harder to win, and existing matters shrink. Realization rates slip, and it will be hard to say why.

If you are asking the right questions, you are already ahead. A Cleary senior partner advised: envision the business that would put yours out of business. This explains the threat faster than any market analysis.

For each major practice area, ask: how hard is it for clients to solve this another way? The competitor may not be another firm, but a $20/month subscription. If the honest answer is “not very hard,” that area is more exposed than you may be treating it.

The most at-risk work is process-driven: matters where clients with the right tool and some internal capacity can reach an acceptable result without you. Think work that follows a predictable process and produces a predictable result. The work least at risk requires judgment that the client cannot buy off the shelf, especially where the stakes are material and getting it wrong costs far more than the tool costs to try. Most firms have both: the question is whether you know which is which.

If your firm is smaller, you have a real advantage here. Close client relationships are an early-warning system, but only if you use them that way. Ask clients directly what they are handling without you. Learn what tools or services they are already using. Knowing this before you need it gives you time to respond.

This is not a cause for paralysis. The same disruption pulling work away from firms that are not paying attention is creating real opportunity for those that are. If you understand what you deliver and can make that case against the alternatives, you will be in a stronger position at the end of this period than you are now.

Don’t wait for silence to signal risk. Contact clients now and ask specifically why their needs are changing. Taking initiative to reach out demonstrates attentiveness, not desperation. Approach these conversations as opportunities to help clients with their challenges and reinforce your commitment to their success. Be proactive: schedule conversations, request candid feedback, and use what you learn to adapt immediately. The managing partners who do this consistently and directly are the ones who stay ahead of shifting client expectations.

This article is the first in my “What Big Law Figured Out” series, inspired by the AI and the Future of Law podcast featuring Jae Um. In Part two, learn how to design AI investment around a distinct competitive strategy, not by following others. Part three will walk you through essential foundations to put in place before any investment discussion. Act on these insights today to outpace competitors tomorrow.

What Changes When Intelligence Becomes Cheap?

Mark Alcazar opened our recent Law Firm Profitability group session, “AI Agents for Law Firms,” with this question (See session recording here.) The evidence shows that AI is able to handle large parts of what law firms do. A significant portion of the cognitive scaffolding for legal work, including routine drafting and document formatting, is already handled at near-zero cost. Your judgment and your client relationships are not going anywhere, but the production layer around them is another matter.

Most of the law firms I speak with are using AI for narrow, low-stakes tasks, if at all. The session I hosted with Mark Alcazar and John Fitzpatrick from Apex Velocity Catalysts was designed to change that. Everything they showed was live. Tools they are building and using in their own work today. What those demonstrations showed is worth your attention.

Building Without a Developer

The first thing they demonstrated was how fast something useful can be built without a software developer. Mark built a working new-matter intake form in roughly five minutes using Claude Cowork. He wrote a detailed prompt describing what the form should do, and Claude returned working HTML: a form that populates a table and exports to a spreadsheet. Not production-ready, but a great starting point. The distance between “we need a tool for this” and “here is a working prototype” is now measured in minutes, not months. Cost and technical complexity used to keep custom tools out of reach, but both have dropped considerably.

Improving the Output

The second thing they showed was how to improve the AI’s output. Most of you are already using Claude or ChatGPT for some part of your work, and the output quality varies based almost entirely on how the prompt is written. Two techniques made an immediate difference. The first is context: a bare prompt like “draft an engagement letter” returns something generic. Adding jurisdiction, matter type, client background, and the purpose of the letter produces something that resembles actual work your firm would do. The second is more interesting. Instruct the model to create a scoring rubric, score its own output against it, and iterate until it meets a defined threshold. In the demonstration, Claude scored its first draft at 86 out of 100 and kept improving. The mechanism works, and the results are noticeably better.

Agents vs. General AI

The third area was the distinction between general AI and purpose-built agents. General AI is versatile but unfocused. An agent is built for a single defined workflow, such as your NDA process or conflict check. Because the scope is narrower and the instructions are specific, agents are measurably more reliable for the workflows they support. John demonstrated a full NDA workflow: intake, drafting, multi-agent review, negotiation with version tracking, client approval, and e-signature integration. Early indicators from firms adopting this kind of focused approach suggest the gains come precisely from that focus. One workflow done reliably outperforms a broad tool used inconsistently.

Before You Deploy

Before any of this goes into practice, certain things need to be settled deliberately.

What can the AI do without human approval? That is a decision your firm has to make deliberately, not by default. Mark’s own agent is instructed never to send an email on his behalf, even though it is technically capable of doing so. He set that limit, the system did not.

How does AI-generated work get verified before it reaches a client? Whether that is a second agent reviewing against a rubric, or a structured checklist process, the principle is the same: one step produces, another step evaluates.

Where is your client data going? Consumer versions of Claude and ChatGPT carry different protections than enterprise accounts. If your firm is using consumer tools for client-related work, that needs to be resolved before any other adoption decision.

Where to Begin

On the question of where to begin: every firm has what Mark called “drag.” It is the repetitive, error-prone work that consumes time without generating strategic value. It is spread across every role and usually invisible because everyone is too busy doing it to examine it. The exercise is simple. Identify the drag and put a number on it: hourly rate multiplied by time spent each week. Prioritize based on what automation would recover.

Then pick one workflow and make it work before touching anything else. Forward-thinking firms are not trying to transform everything at once. They are proving value in one place and expanding from there.

Colin Cameron is President of Profits for Partners and founder of the Law Firm Profitability group on LinkedIn. The session was presented by Mark Alcazar and John Fitzpatrick of Apex Velocity Catalysts.