Every year brings a new legal-technology miracle. In 2026, the most aggressively promoted one may be “AI for discovery.” If you have attended even a single conference lately, you have heard the pitch. AI will slash review costs. AI will eliminate drudgery. AI will—apparently any day now—fetch your coffee. That last claim remains unproven.
What tends to get lost in the enthusiasm surrounding AI for discovery is a basic but critical distinction: not all AI is the same. The market often groups two very different technologies under a single oversized umbrella labeled AI, and the difference between them matters enormously in discovery. Definitions are in order: Technology-assisted review (TAR) is the old, reliable workhorse. It is extractive. It finds what is already there based on mathematical patterns. As an article in the Richmond Journal of Law and Technology demonstrates, it has been in use for more than a decade, is well understood, and has enjoyed broad judicial acceptance.
TAR has earned respect from courts and practitioners who value measurable performance metrics, transparent workflows, and repeatable validation. The Sedona Conference TAR Primer remains the foundational explanation of why TAR works, how it can be audited, and how precision and recall can be evaluated.
Generative AI—large language models such as ChatGPT, Claude, and Gemini—is the new, charismatic intern. It is creative. It quickly generates new text based on probability. It is dazzling at first encounter, articulate, fast, and often helpful. It is also prone to making things up when under pressure.
Generative AI lacks TAR’s long judicial track record in discovery workflows. Chatbots are trained to produce plausible text, not to classify documents according to legal standards. They do not inherently understand responsiveness, confidentiality, privilege, or legal intent. Independent evaluations, including the Stanford HAI Index, consistently warn that while generative models are powerful, they remain unpredictable in risk-sensitive contexts.
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