Data Loops

Some AI builders like myself boast about our technology, but in reality, most AI apps face a challenging landscape. Within a 2-4 month window, we must establish a competitive moat to differentiate themselves from competitors who can rapidly copy our features. And, a single tweet from Sam Altman about the next release can eradicate the market.

These ‘wrappers’ around existing platforms often carry significant platform risk, because the core value stems from the underlying APIs rather than the startups’ unique offerings. As major platforms flex their muscles—leveraging vast infrastructure, data, and capital—they can easily expand features, potentially rendering these wrapper startups obsolete.

Well, there’s still room for optimism. While large models have made significant inroads into the application layer—such as Adobe-Odyssey, ChatGPT-Canvas-Cursor, and ChatGPT-Grammarly—startups can still carve out a competitive edge by creating unique data loops at the application level.

By developing deep expertise in niche industries or specific use cases, we can leverage domain knowledge to establish a sustainable advantage through data loops.

We should think about these:

• Does this AI application have unique and direct access to untapped data?
• Can your app convince users to legally opt in to provide their data, and is that data being refined and improved?
• For B2B, do they have exclusive contracts that grant them market superiority? A unique defensive data moat that no one else can replicate.