Why AI Might Actually Create More Work for Lawyers
Gary Wiggins, chair of law firm Lowenstein Sandler, discusses how AI is transforming legal work by improving efficiency and quality rather than replacing lawyers. While AI reduces costs for routine tasks by up to 70%, it's creating more complex work, preserving employment, and shifting the profession toward higher-value strategic work, though fundamental questions about pricing models and true economic costs remain unresolved.
Summary
In this Odd Lots podcast episode, hosts Joe Weisenthal and Tracy Alloway interview Gary Wiggins, chair of Lowenstein Sandler (a 400-lawyer firm focused on private capital, venture, and structured finance), about AI's impact on the legal profession. Wiggins challenges the common assumption that AI will drastically reduce legal jobs, drawing parallels to previous technological disruptions like word processing and document management software that ultimately expanded rather than contracted the profession.
Wiggins identifies two ways AI is changing legal work: first, increasing efficiency in routine tasks (similar to past innovations), and second, serving as a "thought partner" and co-pilot that improves work quality from the outset—something previous technologies didn't do. He provides a concrete example of a due diligence project involving thousands of trust agreements that initially cost $10 million but was reduced to $3 million with AI assistance, demonstrating the Jevons Paradox in action: lower prices enable clients to pursue work that was previously uneconomical.
Regarding hiring and training, Wiggins notes that Lowenstein Sandler hasn't changed its hiring patterns for junior lawyers but is shifting the skill set required. Rather than having associates perform tedious grunt work like hand-blacklining documents, the firm now trains junior lawyers by having them work with AI tools, giving them immediate access to the firm's entire knowledge base of similar deals. This approach democratizes market knowledge while still preserving the human elements of negotiation, client interaction, and judgment.
On billable hours, Wiggins explains why this model persists despite universal complaints: clients and lawyers don't fully trust each other. While alternative fee arrangements have been proposed for 15+ years, they haven't taken hold. However, Wiggins predicts that AI will finally drive adoption of project-based and outcome-based pricing, while hourly rates at top firms continue to rise dramatically (up 10.1% in 2025, far exceeding inflation). This apparent contradiction occurs because AI makes billable hours more productive and valuable, so firms charge more per hour even as total hours and total fees decline.
On the technology stack, Lowenstein uses Harvey (which provides security layers, data privacy, and retrieval-augmented generation capabilities) and Microsoft Copilot, along with direct Claude access. These tools offer advantages over consumer models, particularly attorney-client privilege protection and ability to search proprietary case documents. Regarding building proprietary models, Wiggins dismisses this as unrealistic for a 400-lawyer firm—training a model from scratch costs $1.5 billion, which even Kirkland & Ellis's announced $500 million five-year commitment wouldn't fully cover. Instead, he envisions customization of existing models through playbooks and domain-specific applications.
A critical unresolved issue is pricing economics. All enterprise AI tools currently use "all-you-can-eat" pricing models that Wiggins suspects may be venture-capital subsidized. As the industry shifts to token-based pricing, the true cost of AI-enhanced legal work remains unknown. This uncertainty is significant because if token costs prove higher than expected, the 70% cost reductions could evaporate, fundamentally changing the value proposition.
Wiggins also addresses concerns about knowledge sharing and firm culture. Highly autonomous law partners may resist contributing their proprietary templates and deal knowledge to firm-wide AI systems, which could undermine the effectiveness of these tools. This creates tension between individual partner autonomy (a key to firm success) and collective knowledge pooling required for AI optimization. He suggests that strategic hiring of superstar lawyers may partly be motivated by getting their expertise into AI systems.
On the Jevons Paradox more broadly, both hosts and Wiggins recognize that while individual legal tasks become cheaper, total legal work may expand (more litigation, more patent applications, more due diligence), potentially creating a future where AI expands bureaucratic overhead indefinitely rather than reducing total work hours. Wiggins provides an example of a client that quadrupled patent filings due to AI improvements in their own processes, requiring proportionally more legal work despite lower per-unit costs.
About this episode
<p>It seems obvious that among the many industries that AI might disrupt, the legal profession might face some of the most adverse outcomes. When clerical, research-based tasks like searching through databases and reading contracts are automated, what is left for lawyers to do and how might they justify all those billable hours? In this episode we speak with Gary Wingens, chair and partner at the law firm Lowenstein Sandler. He talks about how his firm is using AI and why he thinks the technology could end up increasing legal work for lawyers as costs come down, creating a sort of “Jevon's paradox” for lawsuits, deals and litigation. We also talk about the billable hours model and training junior talent.<br /><br />Read more: <a href="https://www.bloomberg.com/news/articles/2026-07-07/ai-legal-startup-norm-valued-at-1-2-billion-in-funding-round?utm_medium=referral&utm_source=podcast&utm_campaign=odd_lots&utm_content=article">AI Legal Startup Norm Valued at $1.2 Billion Funding Round</a><br /><br />Only Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox — now delivered every weekday — plus unlimited access to the site and app. <a href="https://www.bloomberg.com/subscriptions/oddlots?in_source=oddlotspodcast">bloomberg.com/subscriptions/oddlots</a><br /><br /></p><p>See <a href="https://omnystudio.com/listener">omnystudio.com/listener</a> for privacy information.</p>
Key Insights
- Wiggins argues that AI is unlikely to reduce legal employment because it's creating new categories of work, similar to how word processing eliminated hand-blacklining but expanded overall legal productivity rather than contracting the profession.
- The firm reduced the cost of a due diligence project from $10 million to $3 million using AI, which caused the client to approve and execute the work—demonstrating that cost reduction can expand total legal business volume rather than reduce it (Jevons Paradox).
- AI is providing value as a 'thought partner' and co-pilot for lawyers in ways that previous technological innovations (templates, LexisNexis, document management) did not, potentially improving work quality and expanding strategic thinking rather than just automating routine tasks.
- Wiggins claims that billable hours persist despite universal complaints because of mutual distrust between clients and lawyers, but he predicts AI will finally enable the shift to project-based pricing by making total costs transparent and reducing uncertainty about scope.
- Top law firms are raising hourly rates by over 10% annually (far exceeding inflation), suggesting that AI makes each billable hour more productive and valuable even as total hours per engagement decline substantially.
- Knowledge and deal templates are difficult to pool firm-wide because successful law partners tend to be highly autonomous and resistant to sharing proprietary intellectual capital, creating a cultural tension that could limit AI effectiveness.
- Training a legal AI model from scratch costs approximately $1.5 billion, making it economically infeasible for firms under a certain size; even Kirkland's $500 million five-year commitment only covers partial model development, forcing most firms to customize existing models instead.
- The true cost of AI-enhanced legal work remains unknown because current enterprise models use venture-capital-subsidized all-you-can-eat pricing; the shift to token-based pricing could reveal that costs are significantly higher than the apparent 70% reductions currently observed.
Topics
Transcript
Now, a message from Meta. Meta is launching America's Workforce Academy. The program offers paid training, a job, and a path to America's future. Because the future is for everyone. Learn more at meta.com slash America's Workforce Academy. The thing about AI for business, it may not automatically fit the way your business works. At IBM, we've seen this firsthand. But by embedding AI across HR, IT, and procurement processes, we've reduced costs by millions, slashed repetitive tasks, and freed thousands of hours for strategic work. Now we're helping companies get smarter by putting AI where it actually pays off, deep in the work that moves the business. Let's create smarter business, IBM. When you're running a business, the best…
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