While not in formal business frameworks, speed of execution is the most critical initial moat for an AI startup. Large incumbents are slowed by process and bureaucracy. Startups like Cursor leverage this by shipping features on daily cycles, a pace incumbents cannot match.
Early-stage founders should not prematurely optimize for defensibility. The primary focus must be on solving a real problem and building something people want. Moats are a defensive strategy that only becomes relevant once a startup has created value worth protecting.
Anyone can build a simple "hackathon version" of an AI agent. The real, defensible moat comes from the painstaking engineering work to make the agent reliable enough for mission-critical enterprise use cases. This "schlep" of nailing the edge cases is a barrier that many, including big labs, are unmotivated to cross.
In the AI era, network effects are less about connecting users (like Facebook) and more about data acquisition. The more users interact with a product, the more proprietary data (keystrokes, clicks, workflows) is collected. This data is then used to train and improve the model, creating a better product that attracts more users.
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.
Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.
In a rapidly evolving space like AI, being the first mover can be a disadvantage if you bet on the wrong technical approach (e.g., fine-tuning vs. application logic). Second movers can win by observing the market, identifying the first mover's flawed strategy, and building a superior product on the correct technical foundation.
Unlike SaaS which sells to limited software budgets (e.g., 1% of revenue), vertical AI agents automate core business functions. This allows them to tap into much larger operational and labor budgets. Companies can capture 4-10% of a customer's total spend by replacing expensive human-led tasks like customer support.