Where Venture Capital Agrees on LegalTech: AI, Billing Models & Data Moats | Part 2/3 - LegalTech Investment Thesis Series

This Part in the LegalTech Investment Thesis Series examines the major areas of convergence across leading venture capital perspectives on LegalTech and AI-driven legal services. While firms such as General Catalyst, Sequoia, a16z, Lightspeed, Battery Ventures, and The LegalTech Fund approach the market through different lenses; ranging from AI-native services and workflow infrastructure to legal operations and augmentation tools; a closer reading reveals several recurring structural assumptions about how AI will reshape legal work. Across these theses, common themes emerge around workflow automation, the limitations of the billable-hour model, proprietary legal datasets as competitive moats, and the role of AI in expanding access to commercial justice. These convergences are important because they reveal where investors broadly believe value will accrue within the future LegalTech market.

The Convergences

1. The Perception of Legal Services: Across all of the theses, one of the structural obstacles towards lawyers adopting AI-driven tools is the perception that lawyers are generally risk-averse. Risk-aversion is a cultural and professional barrier. Furthermore, this is associated with the law firm structure, as partners typically profit, leading to less capital investment in technology, which is an economic barrier. This is especially true in a high-stakes profession like law, where a single error, such as citing a wrong case or submitting AI-generated case law, could lead to a malpractice suit and client loss. The best example of this is Sullivan & Cromwell apologizing to a U.S. bankruptcy court after submitting inaccurate citations, despite having an internal firm policy governing the use of such tools. 

   
   

   In India, as well, the legal industry has been slow to adapt to technology. Indian courts adopted video conferencing only in 2019 due to the Covid-19 pandemic, highlighting that adoption can occur, but not because of an inherent need to improve legal services through technology. LegalTech startups must align their products with this perception or bypass it by selling to in-house counsel and Alternative Legal Service Providers (ALSPs). Lightspeed’s portfolio company EvenUp does sell to lawyers, but primarily to personal injury lawyers (a category more likely to adopt given the nature of their practice).

2. Billable Hour as the Central Obstacle: Sector-specific theses, such as a16z’s, Lightspeed, Battery Ventures, and The LegalTech Fund (TLTF), identify the billable model as the primary adoption barrier. This is because legal services were billed by the hour rather than by the outcomes produced. Different venture capital investors have predicted that different law firms, based on size, will adopt AI.

   
   

   For example, Battery Ventures' thesis identifies distinct buyer personas to understand their perspectives on billing and the cost of acquiring a tool. BigLaw will take a cautious approach to adopting AI, and this is usually handled by digital officers or innovation heads. a16z also predicts that pure time-saving products would be a difficult sell to lawyers. Lightspeed notes that there is no business model for fixed-fee services and cites Wilson Sonsini’s practice as an example of such offerings for startups in corporate services, leveraging BigLaw’s expertise and domain-specific tools.

   
   

   TLTF also identifies the “Law Firm 2.0,” in which non-lawyer ownership is allowed under Arizona (ABS) to create an AI-Native Law Firm that can function like a software company and raise outside venture capital. Early-movers such as Eve, EvenUp, and Soxton represent investments in areas of law, such as personal injury and startups, that follow an outcome-based model, such as a share of the settlement or subscription services.

   
   

   In India, law firms still bill by the hour, and this will take time because billable hours are ingrained in lawyers' cultural identity. However, law firms such as Shardul Amarchand Mangaldas & Co., Khaitan & Co, Cyril Amarchand Mangaldas, Trilegal, and AZB & Partners have also been adopting AI tools, including Harvey, Legora, Lucio and others. For the time being, law firms will reduce billables while offering a hybrid structure in which some tasks are fixed, such as due diligence and legal research. Tasks that require judgment, such as negotiation and correction of incorrect provisions, will be charged on a billable basis.

   
   

   The value of LegalTech accrues in areas of practice such as fixed-fee work and contingency-based models, like startups and personal injury, where BigLaw has been expensive due to its billable model. This is because of client demands for efficiency and the use of LLMs, which create better cost predictability for them. BigLaw is adapting to alternative billing models under competitive pressure.  

3. Data Moats as a Durable Competitive Advantage: Data moat refers to the advantage that LegalTech startups enjoy due to proprietary datasets such as a firm’s contracts, drafting style, risk-appetite, and research workflow. This would involve customizing a tool to a firm's preferences. GC’s thesis on data moats is explicit. AI-native service providers (LegalTech) acquire existing law firms to obtain a proprietary dataset for training their tools. This follows the roll-up strategy, in which AI systems compound their knowledge with each new piece of information. Lightspeed also believes in vertical-specific tools that solve 2-3 workflows. This means that tools need to be trained on specific areas, such as Capital Markets, M&A, and Patent Litigation, depending on the specialization. Domain-specific knowledge is required to deliver accurate answers that horizontal tools such as Gemini and OpenAI do not enjoy.

   
   

   Battery Ventures' thesis does not mention anything about domain-specific tuning or knowledge. Instead, the thesis sticks mainly to analyzing buyer personas and workflow categories. TLTF’s law firm, as a channel, is an example of this, highlighting how law firms can leverage technology to deliver superior services to clients. For example, law firms can use tools specifically trained to identify patterns in practices such as personal injury and startups. Law firms can digitally understand and capture client matter data, rather than using paper-based workflows.  Beyond the roll-up data moats are acquired through self-reinforcement and network effects.

   
   

   Sequoia’s services thesis frames this under the category of “autopilots,” in which outsourced tasks such as NDA reviews can be handled. As the AI model progresses, the data moat improves. One such Sequoia portfolio company, Crosby, reviews NDA agreements. a16z’s approach is more nuanced, noting network effects and that LegalTech startups cannot train on other firms' documents but can improve their tools based on their own documents, given the self-reinforcing effect of such tools.

   
   

   In India, LegalTech startups are tying up with case law databases such as Manupatra, SCC, and Jus Mundi (International Arbitration) to build data moats. Furthermore, these tools draw from commentaries. For example, in Jhana, they have built their own database for training on case law, commentaries, books, and templates based on lawyer experience. Global platforms such as Harvey and Legora are also tying themselves to such databases to gain India-specific context and a better understanding of jurisdiction.

4. Access to Justice as Both Market Expansion and Mission: Access to justice is a central theme of convergence across most of these. Access to justice refers to AI tools that can reduce the cost of legal services and create new markets where the legal industry cannot provide services, such as for small businesses. Access to justice should not be confused with justice for the common man. Rather, the convergence that is noted between these theses is “access to commercial justice” for businesses and startups.

   
   

   GC’s thesis explicitly states this by highlighting that small businesses spend close to $13,300 on legal services. AI can enable legal analysis at an accessible cost by reducing tasks performed by lawyers, allowing small businesses to access legal services. Market expansion is being driven by startups building AI-native law firms for a new set of customers left out by traditional legal services. The TLTF does explicitly mention access to commercial justice as a theme. However, TLTF sees SMBs as a trillion-dollar opportunity, aiming to include ODR and to focus on dispute prevention rather than exclusively on resolution.

   
   

   Lightspeed does not explicitly mention “access to commercial justice” because they are not confident in their full-stack legal services and thus are not directly interested in this theme. However, they are still interested in funding LegalTech tools across the contingency-fee model, document review, and e-discovery for areas such as immigration law, where rapid regulatory changes prompt speedy attorney responses. Immigration law has an access-to-justice angle. Sequoia, a16z, and Battery Ventures do not address this angle in detail and are more interested in the economic and transactional side of legal services.

   
   

   Access to commercial justice is relevant in the Indian context as well, as India’s MSMEs face structural barriers to legal services. As of March 2026, India has more than 7 crore MSME enterprises, accounting for 31.1% of India’s GDP. MSMEs often face multiple issues, including high legal costs, prolonged payment disputes, and weak contractual enforcement. AI-native legal services, platforms, ODR, and contract review can help MSMEs operate more efficiently and reduce legal costs.

   
   

---

This article forms part of the LegalTech Investment Thesis Series, a three-part exploration of how venture capital is evaluating AI-driven legal services and LegalTech infrastructure.

Here are the links to Part 1 and Part 3.

Authored by Harshith Viswanath, LegalTech Fellow at the Indian LegalTech Network (ILTN).