The primary threat from AI disruptors isn't immediate customer churn. Instead, incumbents get "maimed"—they keep their existing customer base but lose new deals and expansion revenue to AI-native tools, causing growth to stagnate over time.

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.

Large companies often focus R&D on high-ticket items, neglecting smaller accessory categories. This creates a market gap for focused startups to innovate and solve specific problems that bigger players overlook, allowing them to build a defensible niche.

Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.

Economist Bernd Hobart argues that large enterprises are too risk-averse for early AI adoption. The winning go-to-market strategy, similar to Stripe's, is for AI-native companies to sell to smaller, agile customers first. They can then grow with these customers, mature their product, and eventually sell the proven solution back to the legacy giants.

Incumbents are disincentivized from creating cheaper, superior products that would cannibalize existing high-margin revenue streams. Organizational silos also hinder the creation of blended solutions that cross traditional product lines, creating opportunities for startups to innovate in the gaps.

For incumbent software companies, an existing customer base is a double-edged sword. While it provides a distribution channel for new AI products, it also acts as "cement shoes." The technical debt and feature obligations to thousands of pre-AI customers can consume all engineering resources, preventing them from competing effectively with nimble, AI-native startups.

Promote IQ succeeded by targeting large retailers, a market other startups avoided due to its notoriously difficult and long sales cycle. They turned this pain point into a strategic advantage. By mastering the difficult sales process, they created a high barrier to entry that gave them time and space to dominate the category before competitors could catch up.

Metropolis couldn't sell its SaaS solution to incumbent parking operators because their business model relied on inefficient labor. These companies operate like staffing agencies on a cost-plus model, creating a fundamental disincentive to adopt tech that would reduce their core revenue stream.