Investments in Legal AI: Defense Lagging Behind

The Rise of Investments in Legal AI

The legal technology sector is experiencing rapid growth, but this momentum is unevenly distributed. According to a report from Crunchbase News, investment in legal tech startups has significantly increased, with a marked interest from investors in AI. Goldman Sachs estimates that 44% of legal tasks could be automated in the future, which has fueled enthusiasm for legal software.

Legal AI companies focused on plaintiffs have particularly benefited from this trend. For example, EvenUp raised $370 million, Eve received $164 million, Supio $85 million, and Darrow $63 million. In total, this amounts to approximately $682 million, or 71% of the capital invested in legal AI. Investors seem to favor this segment due to the standardization of workflows, which facilitates the adoption and funding of software.

This investor interest is not hard to understand. Law firms representing plaintiffs tend to share more standardized workflows around client intake, case evaluation, medical review, and demand generation—all areas where AI can automate repetitive work and improve efficiency. As these firms adopt software, the category becomes easier to understand, distribute, and finance.

Untapped Potential on the Defense Side

In contrast, the legal defense sector remains largely underdeveloped. Corporate legal departments and law firms managing high volumes of defense still rely on fragmented systems, spreadsheets, and email coordination processes. For these entities, litigation is often treated as a service function rather than supported by software.

This situation creates a significant yet complex opportunity. Companies such as retailers, insurers, and healthcare systems often manage vast litigation portfolios but lack an overview of risks, settlement patterns, and the performance of external counsel. The challenge lies in structuring a software category around these needs.

Part of the reason legal AI on the defense side has lagged is structural. Workflows vary significantly by industry, case type, and regulatory context, making the market less standardized than practices on the plaintiff side. Purchasing decisions often go through legal advisors, legal operations teams, and relationships with external counsel, which can lengthen sales cycles and make the category less immediately viable for investors.

Towards Transformation Through AI

AI could play a crucial role in transforming defense workflows. By automating and systematizing processes, it would allow comparable cases to emerge, signal risks earlier, and compare outcomes across portfolios. This would make the defense segment less of a niche and more of an underdeveloped area with significant growth potential.

Last fall, Crunchbase News reported that funding for legal technologies reached historic highs in 2025, reinforcing the speed with which investor attention has turned to AI-enabled legal workflows. As plaintiff-side firms become quicker to source, evaluate, and pursue claims with software, operational pressure on defense teams increases. At the same time, AI makes it more feasible to transform chaotic litigation workflows into systems capable of surfacing comparable cases, signaling risks earlier, and comparing outcomes across portfolios.

An Opportunity for Investors

Currently, there is no clear leader backed by investments in defense litigation AI. For investors, this presents an open question: could the next sustainable legal AI company emerge from a business category whose software stack is still in formation?

For venture capitalists, this is the type of asymmetry to watch: a large enterprise market with measurable pain points, improving technical feasibility, and no entrenched category leader for now. What investors should monitor is whether startups in the category can pair proprietary outcome data with repeatable enterprise adoption—the combination most likely to produce a sustainable category leader.

An emerging approach on the defense side is benchmarking settlements and exposures: using historical resolution data to estimate settlement ranges, legal expenses, and risks of cases across similar matters. In practice, this could mean comparing claims by jurisdiction, plaintiff firm, type of claim, or other operational variables to help internal teams make faster and more consistent decisions.

If the category develops, a potential competitive advantage could arise from proprietary outcome data. The details of defense-side settlements, the economics of cases, and resolution patterns are often difficult to reconstruct from public records alone.

A platform that aggregates and normalizes these signals across clients could build a data asset that becomes more useful as it scales—a familiar dynamic in vertical software, and a potential early signal for investors of a sustainable advantage in legal AI on the defense side.