Virginia is one of the most progressive states on AI regulation for lawyers, and it broke new ground on a question most states haven't touched: billing. Legal Ethics Opinion (LEO) 1901, adopted by the Supreme Court of Virginia in November 2025, explicitly supports value-based billing for AI-assisted work. The Virginia Bar Association also released a Model AI Policy for Law Firms.
AI Regulation in Virginia: The Current Landscape
Virginia's AI framework is notable for addressing the economics of AI in legal practice, not just the ethics. LEO 1901 was adopted by the Supreme Court of Virginia on November 24, 2025, making it one of the most authoritative AI-related ethics opinions in the country — it carries the weight of Supreme Court adoption, not just a bar committee recommendation.
The opinion addresses how lawyers may assess and charge reasonable fees when using AI. This is territory most states haven't entered. While other jurisdictions focus on competence and confidentiality, Virginia tackled the billing question that every firm adopting AI is asking: if AI reduces the time spent on a task, does the fee have to drop proportionally?
Separately, the Virginia Bar Association established a Task Force on AI in April 2024, which released a Model AI Policy for Law Firms. The model policy covers training, confidentiality, validation, oversight, and AI in decision-making. Combined with LEO 1901, Virginia offers both a billing framework and an operational blueprint for AI adoption.
What the Virginia Bar Says About AI
LEO 1901 (adopted November 24, 2025) addresses fees and AI directly. The key holdings: time-based billing must reflect actual time spent, but Rule 1.5 permits non-hourly value-based fees that account for the lawyer's skill, judgment, and results. Reduced time spent due to AI doesn't necessarily mean proportionally reduced fees. The opinion explicitly supports value-based billing for AI-assisted work.
This is a significant position. It tells Virginia's approximately 26,400 attorneys that they can capture the value of AI efficiency rather than passing all time savings directly to clients. A lawyer who uses AI to complete in two hours what previously took ten can bill based on the value delivered, not just the hours logged.
The VBA Task Force on AI (established April 2024) produced a Model AI Policy for Law Firms. The model policy addresses: training requirements for attorneys using AI, confidentiality protections for client data, validation and verification of AI outputs, oversight responsibilities, and guidelines for AI in decision-making. The policy also states that lawyers must not delegate judgment to machines and must verify all AI outputs.
Court Rules and Judicial Guidance
LEO 1901 was adopted by the Supreme Court of Virginia on November 24, 2025, giving it the highest level of authority short of a statute. This isn't just bar committee guidance — it's an opinion endorsed by the state's highest court.
The Supreme Court's adoption of LEO 1901 signals judicial-level engagement with AI in legal practice. Virginia courts are aware that AI is changing how legal work gets done and how it gets billed, and the Supreme Court has proactively addressed the billing question rather than waiting for disputes to force the issue.
Practical Implications for Virginia Attorneys
Virginia's billing framework changes the economic calculation of AI adoption. In states without fee guidance, firms worry that AI efficiency will compress revenue — if AI cuts research time in half, hourly billing cuts revenue in half. LEO 1901 gives Virginia attorneys a different path: value-based billing that accounts for skill, judgment, and results, not just hours.
This has massive implications for firm economics. A Virginia firm that builds a sophisticated AI workflow — reducing a 10-hour research project to 2 hours while maintaining quality — can bill based on the value of the work product rather than the time spent producing it. The client gets faster, potentially better work. The firm captures the value of its investment in AI infrastructure. That's the economics of supervised leverage in practice.
The Model AI Policy for Law Firms is equally practical. Instead of leaving firms to develop their own AI policies from scratch, Virginia provides a template covering training, confidentiality, validation, oversight, and decision-making. For Richmond, Virginia Beach, Norfolk, Arlington, Alexandria, and Fairfax — major legal markets with significant government contracting and corporate work — this template removes a major barrier to responsible AI adoption.
What Attorneys in Virginia Should Do
Study LEO 1901 carefully and consider restructuring your fee arrangements. If you're still exclusively billing hourly for AI-assisted work, you're leaving value on the table — and Virginia's Supreme Court has explicitly said you don't have to. Explore value-based, flat-fee, or hybrid billing models that account for the quality and efficiency of AI-assisted work.
Adopt the VBA's Model AI Policy for your firm. It covers training, confidentiality, validation, oversight, and decision-making — the full spectrum of responsible AI use. Customizing an existing model policy is faster and more thorough than building one from scratch. The VBA did the foundational work; adapt it to your firm's specific practices and tools.
Document how AI contributes to work product quality, not just efficiency. Under LEO 1901, the justification for value-based billing includes skill, judgment, and results. If AI helps you identify a case-changing precedent in two hours instead of ten, the value isn't just time savings — it's the strategic advantage delivered to the client. Documenting that value supports your billing under Virginia's framework.
The Bottom Line
Virginia broke new ground with LEO 1901's explicit support for value-based billing of AI-assisted work, adopted by the Supreme Court in November 2025. Combined with the VBA's Model AI Policy for Law Firms, Virginia gives its 26,400 attorneys both an economic framework and an operational blueprint for AI adoption.
AI-Assisted Research. This piece was researched and written with AI assistance, reviewed and edited by Manu Ayala. For deeper takes and the perspective behind the research, follow me on LinkedIn or email me directly.