Desiree Sainthrope is a formidable force in the legal world, bridging the gap between traditional trade compliance and the high-speed evolution of intellectual property in the age of artificial intelligence. As firms navigate the pressure to automate, her deep understanding of how institutional structures either foster or fail innovation provides a crucial lens for the industry. In this discussion, we explore the existential shift currently facing Big Law as it confronts the rise of AI-native firms that build their workflows from scratch. We examine the financial and structural barriers—from annual profit distributions to the rigid billable hour—that make large-scale adaptation difficult, the specialized agility of newer firms targeting the tech sector, and the potential for internal operations to serve as the front line for digital transformation.
Traditional partnership models often prioritize annual profit distribution over long-term technological investment. How does this fiscal structure specifically impede a firm’s ability to pivot toward an AI-native model?
The core of the issue lies in the immediate financial expectations of the partners who drive these massive institutions. Because partners typically take their profits off the table every single year, asking them to sacrifice a significant portion of that income for one or even a few years to fund a technological overhaul is an incredibly difficult proposition. This focus on short-term gains creates a natural friction with the long-term vision required to lower prices today in hopes of capturing a larger market share over the next decade. Transitioning away from the billable hour, which has been the gold standard for success for generations, feels like a direct threat to the individual partner’s bottom line. Until the financial incentive shifts toward reinvestment rather than immediate annual extraction, the aggressive adoption of new workflows will remain a secondary priority compared to maintaining the status quo.
AI-native firms seem to thrive by focusing on specific, high-volume tasks like contracting for startups. Why is it so much harder for a large-scale firm to implement these same efficiencies across its entire practice?
The sheer scope of service that Big Law provides acts as a significant drag on universal AI adoption. While an AI-native firm might concentrate exclusively on a single practice area like contracting—which is perfectly suited for automation—a large firm has to manage a vast array of complex matter types, including high-stakes litigation and financial regulatory problems. These areas involve nuanced human judgment and unpredictable variables that are much harder to automate than a standard nondisclosure agreement. Furthermore, client comfort levels vary wildly; while a tech founder might be eager to experiment with a novel business model, a massive enterprise often has rigid outside counsel guidelines that may actually limit or prohibit the use of certain AI tools. This creates a fragmented landscape where a firm cannot simply impose an across-the-board approach to how technology is used on every task.
Many new legal founders suggest that their real competition isn’t other law firms, but the language models themselves. How does this change the value proposition for a client who is choosing between a human attorney and an AI agent?
The value proposition is shifting toward providing a “qualified attorney’s advice” that is supercharged by the model, rather than just the model itself. In the past, a small growth-stage company might have gone without any legal representation at all because the costs of a major firm were simply out of reach. Now, AI-native firms are pricing themselves to be a viable alternative to someone simply relying on a tool like Claude for their legal needs. They are offering a hybrid solution: the speed and low cost of an AI-driven workflow combined with the professional oversight of a person who understands the law. This allows them to capture a segment of the market—new founders and tech-adjacent industries—that was previously underserved or ignored by traditional firms.
If the billable hour is such a massive roadblock, are there specific sectors within Big Law that are better positioned to adopt AI-native workflows because they already use alternative pricing?
We are seeing a much stronger incentive for change in practices that have already embraced value-based pricing, such as those serving private equity or private funds. In these specific areas, the firm isn’t just selling hours; they are selling a result or a successful transaction, which means any tool that speeds up their work directly increases their profit margin. For these teams, adopting AI-native tools to rebuild their workflows is a logical case-by-case decision rather than a firm-wide existential crisis. They can look at their internal processes and identify where an AI agent can achieve major efficiency gains without the psychological hurdle of losing billable hours. It’s less about becoming an AI-native firm overnight and more about introducing those workflows where the financial incentives already align with speed.
We often focus on how lawyers use AI, but how can the back-office and legal operations teams within a large firm lead the way in becoming AI-native?
The transformation of internal workflows is perhaps the most exciting and under-discussed frontier for large firms. We’ve seen examples where security teams have built dedicated AI agents specifically to assess access logs for vulnerabilities—a task that might have been impossible to perform manually at that scale previously. Legal operations teams are also in a unique position to rebuild workflows around AI from the ground up, using new tools to take on types of work that weren’t even on their radar a year ago. By focusing on these front-office and back-office service delivery roles, a firm can move toward being “AI-native” in its operations even if its primary billing model remains traditional. This allows the firm to keep up with client demands and achieve efficiency gains in the “dark matter” of the firm’s daily business.
What is your forecast for the balance of power between traditional Big Law and these emerging AI-native competitors?
I believe we will see a two-tiered evolution where AI-native firms dominate the high-volume, lower-complexity market for startups and tech companies, while Big Law retains its grip on the most complicated, high-stakes financial and litigation matters. However, the real winner will be the large firms that successfully integrate AI-native workflows into their existing structures without trying to reshape their entire identity. We are likely to see firms selectively deploy AI agents for specific tasks—like security audits or contract reviews—while maintaining the billable hour for complex regulatory work that requires heavy human intervention. The pressure from clients to speed up adoption will only increase, and while the billable hour won’t disappear tomorrow, the firms that refuse to automate their internal workflows will eventually find themselves unable to compete on efficiency or price. In the end, the “AI-native” label will matter less than the ability to deliver expert advice at a pace that matches the modern market.
