Previously, startups had years before incumbents copied their innovations. With AI coding assistants, incumbents can now replicate features in weeks, not years. This intensifies the battle, making a startup's ability to rapidly acquire distribution its most vital competitive advantage for survival.

As AI and better tools commoditize software creation, traditional technology moats are shrinking. The new defensible advantages are forms of liquidity: aggregated data, marketplace activity, or social interactions. These network effects are harder for competitors to replicate than code or features.

The AI revolution may favor incumbents, not just startups. Large companies possess vast, proprietary datasets. If they quickly fine-tune custom LLMs with this data, they can build a formidable competitive moat that an AI startup, starting from scratch, cannot easily replicate.

Contrary to popular narrative, established companies hold a significant advantage over AI-native startups. Their vast proprietary data and deep, opinionated understanding of customer problems form a powerful moat. The key is successfully leveraging these assets to build unique, data-driven AI solutions, which can create a bigger advantage than a pure tech-first approach.

As AI application layers become easier to clone, the sustainable competitive advantage is moving down the tech stack. Companies with unique, last-mile user interaction data can build proprietary models that are cheaper and better, creating a data flywheel and a moat that is difficult for competitors to replicate.

Stripe’s payments model shows how AI creates powerful data flywheels. Their massive, proprietary transaction dataset trains superior models, which improves the product, attracts more customers, and widens their data advantage, making it nearly impossible for new competitors to catch up.

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.

As AI makes building software features trivial, the sustainable competitive advantage shifts to data. A true data moat uses proprietary customer interaction data to train AI models, creating a feedback loop that continuously improves the product faster than competitors.

The gap between the top few AI labs and the rest is growing, not shrinking. Demis Hassabis explains this is because these labs leverage their own superior tools for coding and math to accelerate development of the next generation of models, creating a powerful compounding advantage that makes it harder for others to catch up.