A recent practitioner commentary offers a confident assessment of the current state of large language models (LLMs) in legal practice, arguing that the primary barriers to adoption are no longer questions of intelligence or reliability but rather issues of infrastructure and workflow integration. Writing from the perspective of a lawyer who uses advanced models daily, the author contends that modern systems have already reached a level of practical competence sufficient for much of routine legal work, and that the profession’s hesitation reflects outdated assumptions about hallucinations and model limitations.
Central to the argument is the claim that hallucinations, once the dominant concern surrounding generative AI, have largely receded as a meaningful obstacle. According to the author’s experience, newer models rarely produce fabricated information, and overall error rates compare favorably with those of competent junior associates. This view reflects a broader shift in perception: rather than treating LLMs as experimental tools requiring constant skepticism, the author frames them as increasingly dependable collaborators capable of supporting substantive legal tasks.
The post also challenges prevailing narratives about the intellectual difficulty of legal work. While acknowledging that certain cases demand deep expertise, the author suggests that the majority of legal tasks rely on skills such as careful reasoning, synthesis of precedent, structured writing, and research , areas where modern LLMs already excel. By reframing legal practice as process-driven rather than exclusively intellectually rarefied, the commentary positions AI as well aligned with the day-to-day realities of the profession.
