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.
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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.
AI tools are taking over foundational research and drafting, tasks traditionally done by junior associates. This automation disrupts the legal profession's apprenticeship model, raising questions about how future senior lawyers will gain essential hands-on experience and skills.
Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.
A new technology's adoption depends on its fit with a profession's core tasks. Spreadsheets were an immediate revolution for accountants but a minor tool for lawyers. Similarly, generative AI is transformative for coders and marketers but struggles to find a daily use case in many other professions.
Current AI tools are empowering laypeople to generate a flood of low-quality legal filings. This 'sludge' overwhelms the courts and creates more work for skilled attorneys who must respond to the influx of meritless litigation, ironically boosting demand for the very profession AI is meant to disrupt.
At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.
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.
Historically, developer tools adapted to a company's codebase. The productivity gains from AI agents are so significant that the dynamic has flipped: for the first time, companies are proactively changing their code, logging, and tooling to be more 'agent-friendly,' rather than the other way around.
Contrary to the belief that PMs are the earliest tech adopters, go-to-market functions (sales, marketing, support) are leading agent adoption. Their work involves frequently recurring, pattern-based tasks that are a perfect fit for automation, putting them ahead of the curve.
Harvey is building agentic AI for law by modeling it on the human workflow where a senior partner delegates a high-level task to a junior associate. The associate (or AI agent) then breaks it down, researches, drafts, and seeks feedback, with the entire client matter serving as the reinforcement learning environment.
