Everyone is familiar with the concept of hallucinations, and in the legal world, these aren't just figments of the imagination but rather misleading or fictional citations sometimes found in legal briefs. Lawyers and even judges have been called out for these inaccuracies, highlighting a growing concern around the reliability of AI-generated content in legal documents. The real amusement, however, does not come from these errors themselves but from the professional mishaps they can cause.

While these 'hallucinations' can be caught and corrected by diligent humans, the AI's seductive confidence often leads to less scrutiny, potentially dumbing down the legal professionals who rely on it. But there's a subtler, potentially more hazardous issue at play — AI's completeness, or lack thereof.

In the realm of intellectual property, where searching through millions of documents is standard practice, missing a single prior-art document could lead to a multimillion-dollar mistake in patent litigation. AI tools, tasked with finding every relevant piece of prior art, face what is known as the problem of 'unknown unknowns' — critical information that the AI fails to retrieve simply because it doesn't know it needs to look for it.

Melange, a patent analytics firm, discovered this risk firsthand when expanding their document search capabilities. Initially dealing with 40 million documents, they aimed to scale to the entire global corpus of approximately 450 million patent documents. Their experience underscores a crucial lesson: the quality of an AI model isn't the only factor that matters — the infrastructure supporting it is just as critical.

Partnering with Pinecone, a vector database provider, Melange improved their system's ability to handle vast amounts of data without sacrificing reliability or completeness. This partnership underscores a crucial shift in focus from the AI's 'brain' to its 'nervous system,' emphasizing the need for robust infrastructure to support the vast data AI must analyze.

This scenario isn't unique to patent searches. Legal discovery, involving even larger volumes of data, faces similar challenges. Despite the progress in AI technology, the infrastructure capable of supporting these immense loads without degradation is vital. An AI might be able to generate the right answers, but if it's working with incomplete data, its outputs are still not 'right.'

In the end, as the legal profession continues to integrate AI into more of its practices, the focus must also shift to ensuring these systems can handle the full scope of data needed to make accurate and complete legal judgments. The debate over AI's role in law is no longer just about whether AI can be trusted, but whether the entire system supporting AI is up to the task.