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.

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.

Companies like Sierra can't justify a 100x ARR valuation by targeting the existing software market (e.g., $8B Service Cloud). The bet is that they will capture a significant portion of the much larger human labor market ($200B+ for support agents). This represents a fundamental transition of spend from human capital to software.

The true market opportunity for AI is not merely replacing existing software but automating human labor. This reframes the total addressable market (TAM) from the ~$400 billion global software industry to the $13 trillion US-only labor market, representing a thirty-fold increase in potential value.

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.

Traditional software automated standardized processes but struggled with complex human interactions like call center support. Generative AI's ability to understand natural language allows software to automate these nuanced tasks, dramatically expanding the total addressable market by tackling problems that were previously impossible to solve with code.

Unlike Vertical SaaS which sells software licenses to IT departments, Vertical AI sells outcomes by replacing human labor. This allows it to tap directly into a company's much larger labor P&L, creating a significantly bigger total addressable market and enabling outcome-based pricing models.

Countering the idea of a zero-sum SaaS market, Box CEO Aaron Levie argues that AI agents create net-new value. By performing complex knowledge work on existing data (like analyzing contracts), agents allow software platforms to capture budget previously allocated to human labor, thus expanding the total addressable market.

Elad Gil argues that the total addressable market for AI companies is not limited to traditional seat-based software pricing. Instead, it encompasses the multi-trillion dollar human labor market that AI can augment or automate.