To ensure accuracy in its legal AI, LexisNexis unexpectedly hired a large number of lawyers, not just data scientists. These legal experts are crucial for reviewing AI output, identifying errors, and training the models, highlighting the essential role of human domain expertise in specialized AI.

Rather than relying on a single LLM, LexisNexis employs a "planning agent" that decomposes a complex legal query into sub-tasks. It then assigns each task (e.g., deep research, document drafting) to the specific LLM best suited for it, demonstrating a sophisticated, model-agnostic approach for enterprise AI.

Traditional recruiting tools rely on keyword searches (e.g., "fintech"). Juicebox uses LLMs to semantically understand a candidate's profile. It can identify an engineer at a payroll company as a "fintech" candidate even if the keyword is absent, surfacing a hidden talent pool that competitors can't see.

The judicial theory of "originalism" seeks to interpret laws based on their meaning at the time of enactment. This creates demand for AI tools that can perform large-scale historical linguistic analysis ("corpus linguistics"), effectively outsourcing a component of legal reasoning to AI.

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.

Successful vertical AI applications serve as a critical intermediary between powerful foundation models and specific industries like healthcare or legal. Their core value lies in being a "translation and transformation layer," adapting generic AI capabilities to solve nuanced, industry-specific problems for large enterprises.

AI tools can instantly parse, reformat, and summarize dense documents like congressional bills, which would otherwise require significant manual cleanup. This capability transforms workflows for analysts and researchers, reallocating time from tedious data preparation to high-value strategic analysis.

The most significant recent AI advance is models' ability to use chain-of-thought reasoning, not just retrieve data. However, most business users are unaware of this 'deep research' capability and continue using AI as a simple search tool, missing its transformative potential for complex problem-solving.

Don't underestimate the size of AI opportunities. Verticals like "AI for code" or "AI for legal" are not niche markets that will be dominated by a few players. They are entire new industries that will support dozens of large, successful companies, much like the broader software industry.