AI favors incumbents more than startups. While everyone builds on similar models, true network effects come from proprietary data and consumer distribution, both of which incumbents own. Startups are left with narrow problems, but high-quality incumbents are moving fast enough to capture these opportunities.
Unlike cloud or mobile, which incumbents initially ignored, AI adoption is consensus. Startups can't rely on incumbents being slow. The new 'white space' for disruption exists in niche markets large companies still deem too small to enter.
Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.
The advantage from data network effects only materializes at immense scale. The difference between a startup with 3 customers and one with 4 is negligible. This means early-stage companies cannot rely on a data moat to win; the moat only becomes visible after a market leader is established.
AI capabilities offer strong differentiation against human alternatives. However, this is not a sustainable moat against competitors who can use the same AI models. Lasting defensibility still comes from traditional moats like workflow integration and network effects.
The long-held belief that a complex codebase provides a durable competitive advantage is becoming obsolete due to AI. As software becomes easier to replicate, defensibility shifts away from the technology itself and back toward classic business moats like network effects, brand reputation, and deep industry integration.
Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.
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.
AI drastically accelerates the ability of incumbents and competitors to clone new products, making early traction and features less defensible. For seed investors, this means the traditional "first-mover advantage" is fragile, shifting the investment thesis heavily towards the quality and adaptability of the founding team.
Google can dedicate nearly all its resources to AI product development because its core business handles infrastructure and funding. In contrast, OpenAI must constantly focus on fundraising and infrastructure build-out. This mirrors the dynamic where a focused Facebook outmaneuvered a distracted MySpace, highlighting a critical incumbent advantage.
New technology like AI doesn't automatically displace incumbents. Established players like DoorDash and Google successfully defend their turf by leveraging deep-rooted network effects (e.g., restaurant relationships, user habits). They can adopt or build competing tech, while challengers struggle to replicate the established ecosystem.