According to Flexport's CEO, large incumbents hold significant AI advantages over startups. They possess vast proprietary data for model training, the domain expertise to target high-value problems (features, not companies), and instant distribution, allowing them to deploy AI solutions to thousands of customers overnight.

To avoid being made obsolete by a frontier AI model, startups need a strong moat. The three most defensible moats are: 1) building hardware, which AI cannot physically replicate, 2) establishing strong network effects where value increases with more users, and 3) operating in a complex, regulated industry requiring human interaction.

Major tech and fintech players, including Apple, Google, and Stripe, have opted to integrate with Visa's network rather than build a competing one from scratch. This dynamic turns potential disruptors into partners, reinforcing Visa's deep moat and demonstrating the prohibitively high cost of replicating its global infrastructure.

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.

While AI can write code, Affirm CEO Max Levchin states it can't replicate the true moats of a fintech company. These include deep capital markets relationships, a full suite of money transmitter licenses (which take ~18 months to acquire), and years of building consumer trust.

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.

Fears of AI disintermediating platforms like Booking.com may be overblown. AI agents would need to replicate decades of user ratings, global payment infrastructure, and deep supplier relationships from scratch—a monumental task that makes it more likely incumbents will simply integrate AI themselves.

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.

Oren Zeev argues against the narrative that AI will kill all incumbents. He believes businesses with operational complexity, deep data moats, and strong distribution are not easily disrupted. These companies are more likely to leverage AI to their advantage, while simpler software companies are at greater risk.