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.

As AI infrastructure giants become government-backed utilities, their investment appeal diminishes like banks after 2008. The next wave of value creation will come from stagnant, existing businesses that adopt AI to unlock new margins, leveraging their established brands and distribution channels rather than building new rails from scratch.

A key competitive advantage for AI companies lies in capturing proprietary outcomes data by owning a customer's end-to-end workflow. This data, such as which legal cases are won or lost, is not publicly available. It creates a powerful feedback loop where the AI gets smarter at predicting valuable outcomes, a moat that general models cannot replicate.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

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.

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.

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.

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.

As AI's bottleneck shifts from compute to data, the key advantage becomes low-cost data collection. Industrial incumbents have a built-in moat by sourcing messy, multimodal data from existing operations—a feat startups cannot replicate without paying a steep marginal cost for each data point.