Industries with historically low software adoption (like trial law or dentistry) are now viable markets. Instead of selling a tool, AI startups are selling an outcome—the automation of a specific labor role. This shifts the value proposition from a software expense to a direct labor cost replacement.

The most lucrative initial market for AI services like automated call handling is not tech startups, but local service businesses like plumbers and HVAC companies. These entrepreneurs lose money every minute they aren't serving a customer, making them highly motivated to pay for AI that automates non-core tasks.

AI will not primarily disrupt SaaS incumbents like Salesforce. Instead, its main economic impact will be automating repetitive labor, a market 40 times larger than enterprise software spend. AI-native companies are targeting labor-intensive roles like customer service, not trying to replace existing software subscriptions.

AI companies can accelerate enterprise adoption by focusing on workflows already outsourced to BPOs. This provides pre-codified standard operating procedures (SOPs), existing QA processes, and simpler change management, as replacing a vendor is easier than displacing an internal team.

When disrupting a market, selling enabling tools to incumbents (e.g., research agencies) is less effective than competing directly. Incumbents have misaligned incentives and are often low-intent "tire kickers," whereas their end-clients will readily switch for a better, faster, cheaper solution.

While AI can improve existing software categories, the most significant opportunity lies in creating new applications that automate tasks previously performed by humans. This 'software eating labor' market is substantially larger than the traditional SaaS market, representing a massive greenfield opportunity for startups.

Joe Lonsdale advises established SaaS companies to go on offense with AI. Instead of merely defending their core product, they should build AI agents on top of their platforms to automate customer workflows. This creates new, high-margin revenue streams by helping customers reduce headcount and increase efficiency.

The business model is shifting from selling software to selling outcomes. Instead of creating a tool and inviting users, create pre-trained agents that perform valuable work. Then, invite companies to a workspace where this 'team' of AI employees is ready to start delivering value immediately.

Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.