Established SaaS firms avoid AI-native products because they operate at lower gross margins (e.g., 40%) compared to traditional software (80%+). This parallels brick-and-mortar retail's fatal hesitation with e-commerce, creating an opportunity for AI-native startups to capture the market by embracing different unit economics.

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.

Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.

The economic incentive for VCs funding AI is replacing human labor, a $13 trillion market in the US alone. This dwarfs the $300 billion SaaS market, revealing the ultimate goal is automating knowledge work, not just building software.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.

Instead of pursuing complex, open-ended consulting projects, partners can scale more effectively by creating productized, "turnkey AI" offerings for specific business units like legal or marketing. This approach lowers the adoption barrier for customers by delivering predictable results for a defined use case, making it easier to sell into departments or smaller businesses.

The transition from AI as a productivity tool (co-pilot) to an autonomous agent integrated into team workflows represents a quantum leap in value creation. This shift from efficiency enhancement to completing material tasks independently is where massive revenue opportunities lie.

The paradigm shift with AI agents is from "tools to click buttons in" (like CRMs) to autonomous systems that work for you in the background. This is a new form of productivity, akin to delegating tasks to a team member rather than just using a better tool yourself.