Incumbent software companies are moving up the AI ladder by adding agents to their existing products, but they're limited to automating work within their own records. Vertical AI startups can compete by owning the full job across systems and building learning loops that improve over time—something general-purpose agents and bounded incumbents can't easily replicate.

• Incumbents are shifting from retrieval assistants to process and policy agents, but their advantage stops at the boundaries of the record they own; the customer's actual job spans multiple systems, teams, and companies that no single incumbent controls.
• Vertical startups win through focused learning loops: they see the full decision-making process, corrections, and outcomes that general agents miss, letting them train faster on what "good work" looks like for a specific job.
• Harvey's example shows the playbook—manufacturing a curriculum of 1,750 realistic legal scenarios with expert rubrics before touching customer data, rather than waiting years to accumulate historical examples.