Litigation is costly because it's an arms race to explore a vast combinatorial space of legal arguments. Sufficiently powerful and cheap AI could search this space so exhaustively that no useful new moves remain, effectively ending the arms race and placing a natural ceiling on legal costs.

AI models reason well on Supreme Court cases by interpolating the vast public analysis written about them. For more obscure cases lacking this corpus of secondary commentary, the models' reasoning ability falls off dramatically, even if the primary case data is available.

AI's capabilities are highly uneven. Models are already superhuman in specific domains like speaking 150 languages or possessing encyclopedic knowledge. However, they still fail at tasks typical humans find easy, such as continual learning or nuanced visual reasoning like understanding perspective in a photo.

OpenAI's new GDPVal framework evaluates AI on real-world knowledge work. It found frontier models produce work rated equal to or better than human experts nearly 50% of the time, while being 100 times faster and cheaper. This provides a direct measure of impending economic transformation.

Contrary to its reputation for slow tech adoption, the legal industry is rapidly embracing advanced AI agents. The sheer volume of work and potential for efficiency gains are driving swift innovation, with firms even hiring lawyers specifically to help with AI product development.

Unlike simple "Ctrl+F" searches, modern language models analyze and attribute semantic meaning to legal phrases. This allows platforms to track a single legal concept (like a "J.Crew blocker") even when it's phrased a thousand different ways across complex documents, enabling true market-wide quantification for the first time.

Within the last year, legal AI tools have evolved from unimpressive novelties to systems capable of performing tasks like due diligence—worth hundreds of thousands of dollars—in minutes. This dramatic capability leap signals that the legal industry's business model faces imminent disruption as clients demand the efficiency gains.

Venture capitalist Keith Rabois observes a new behavior: founders are using ChatGPT for initial legal research and then presenting those findings to challenge or verify the advice given by their expensive law firms, shifting the client-provider power dynamic.

The legal profession's core functions—researching case law, drafting contracts, and reviewing documents—are based on a large, structured corpus of text. This makes them ideal use cases for Large Language Models, fueling a massive wave of investment into legal AI companies.