The concern that artificial intelligence will shrink the workforce is real and widespread. Economics offers a nuanced framework that challenges the simple "AI takes jobs" narrative. Vincent Venturella recently hosted a webinar exploring the true impact of AI on legal industry jobs.
What economic concepts apply to the rise of AI in legal?
Two main economic concepts apply to the rise of AI in the legal industry: the lump of labor fallacy and the Jevons paradox.
The lump of labor fallacy is the mistaken belief that there is a fixed amount of work to be done in the economy. Under this view, if AI eliminates a category of tasks, the people who did those tasks are simply left without work. History tells a different story. Many of the ten most common jobs from the early 20th century no longer exist in any recognizable form. Yet employment has expanded dramatically. Many of the jobs that exist now, including roles like AI data scientist or prompt engineer, didn't exist then because the conditions that created them hadn't yet arrived. Every major technological shift ultimately results in a larger total number of jobs, even when it disrupts specific roles.
Meanwhile, the Jevons paradox states that when something becomes cheaper and more efficient to use, total consumption of it tends to increase, not decrease. The original example involved coal. As steam engines became more efficient, requiring less coal to operate, there was a widespread expectation that coal consumption would decrease. Instead, it multiplied. Cheaper engines led to more engines being used in more places.
For legal operations professionals, the practical implication is clear. Efficiency gains driven by AI are likely to increase expectations about the work you can produce rather than reduce demand.
Is AI a task machine or a job machine?
AI is a task machine, not a job machine. It excels at completing individual workflows but lacks the judgment required to perform an end-to-end job as a person would.
This distinction matters most for legal operations professionals. Think of your role as a pyramid. At the top sits your job title, below that are your workflows, and at the base are individual tasks like running reports, reviewing documents, and checking outputs.
AI is currently very good at tasks. It isn't good at complete workflows, and it's nowhere near capable of doing a total job, particularly one that depends on human judgment and interaction. Vincent cited research findings that AI produced professional-quality output on individual tasks roughly 50% to 60% of the time across various roles.
However, when asked to execute a complete project, the success rate dropped to around 2%. The gap between completing a task well and delivering a finished project is significant. A human remains essential to verify accuracy, apply context, and course-correct when needed.
What does this mean for legal professionals?
AI handles research-heavy tasks, which frees attorneys to spend more time on strategy, counseling, and judgment-driven work. Historically, attorneys spent roughly 80% of their time on research and 20% on high-value advisory work. AI is beginning to invert that ratio.
Importantly, this shift isn't resulting in smaller legal teams. Most surveys of corporate legal departments report equal or larger incoming staff counts. Legal teams are increasingly looking for junior professionals who arrive AI-trained and ready to work alongside these tools. They need people who can validate AI output, manage workflows, and apply their expertise to the outputs rather than the inputs.
Vincent pointed out that when the low-value, rote tasks are handled by AI tools, “the creative, the novel, and the bespoke become the province of legal operations.”
What to look for in your legal ops team and technology
For legal operations leaders thinking about AI readiness, a few principles are worth keeping in mind:
- Hire and develop for AI fluency: The most valuable legal ops professionals going forward will be those who can work effectively alongside AI tools.
- Insist on human-in-the-loop design: Look for technology that keeps humans at critical decision points rather than removing them.
- Start with high-volume, repetitive tasks: Focus initial AI investment on defined tasks like invoice review, report generation, and data aggregation.
- Think in terms of capacity, not cuts: The goal isn't to do the same work with fewer people. It's to do more valuable work with the same people.
If you want to stay ahead of a fast-moving landscape, the other webinars in this series are worth your time. Register for the remaining webinars in the series here.