A new ecosystem is emerging where law firms are not just end-users of Harvey's AI but also channel partners. They are leveraging their expertise to help their in-house legal clients adopt and implement the technology, creating a new, high-margin line of business for themselves as tech consultants and implementers.
Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.
Harvey's Forward Deployed Engineering team isn't just for building custom solutions. It's a strategic product discovery tool. By embedding engineers with large clients who have undefined GenAI needs, Harvey identifies and builds the next set of platform features, effectively using customer problems to pave its future roadmap.
For established software companies with sluggish growth, the path forward is clear: find a way to become relevant in the age of AI. While they may not become the next Harvey, attaching to AI spend can boost growth from 15% to 25%, the difference between a viable public company and a sale to a private equity firm.
Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.
Unlike coding with its verifiable unit tests, complex legal work lacks a binary success metric. Harvey addresses this reinforcement learning challenge by treating senior partner feedback and edits as the "reward function," mirroring how quality is judged in the real world. The ultimate verification is long-term success, like a merger avoiding future litigation.
Harvey's initial product was a tool for individual lawyers. The company found greater value by shifting focus to the productivity of entire legal teams and firms, tackling enterprise-level challenges like workflow orchestration, governance, and secure collaboration, which go far beyond simple model intelligence.
John Morgan built the legal tech platform Litify for his own firm's needs. He then leveraged his massive case referral network by requiring partner firms to adopt Litify. This created a captive market for his software and streamlined his core business operations, establishing a powerful, self-reinforcing flywheel.
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.
In a world where AI makes software cheap or free, the primary value shifts to specialized human expertise. Companies can monetize by using their software as a low-cost distribution channel to sell high-margin, high-ticket services that customers cannot easily replicate, like specialized security analysis.
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.