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 startups build on commoditized AI platforms like GPT, product differentiation becomes less of a moat. Success now hinges on cracking growth faster than rivals. The new competitive advantages are proprietary data for training models and the deep domain expertise required to find unique growth levers.

Instead of simply adding AI features, treat your AI as the product's most important user. Your unique data, content, and existing functionalities are "superpowers" that differentiate your AI from generic models, creating a durable competitive advantage. This leverages proprietary assets.

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.

The winning strategy in the AI data market has evolved beyond simply finding smart people. Leading companies differentiate with research teams that anticipate the future data requirements of models, innovating on data types for reasoning and STEM before being asked.

Point-solution SaaS products are at a massive disadvantage in the age of AI because they lack the broad, integrated dataset needed to power effective features. Bundled platforms that 'own the mine' of data are best positioned to win, as AI can perform magic when it has access to a rich, semantic data layer.

As algorithms become more widespread, the key differentiator for leading AI labs is their exclusive access to vast, private data sets. XAI has Twitter, Google has YouTube, and OpenAI has user conversations, creating unique training advantages that are nearly impossible for others to replicate.