Data Loops

Some AI builders, myself included, love to boast about our technology. But let’s be honest: most AI apps are navigating a brutal landscape. We have a 2-4 month window to build a competitive moat—something that sets us apart before competitors copy every feature we launch. And let’s not forget: a single tweet from Sam Altman about the next big release can erase an entire market overnight.

These “wrapper” startups, built on top of existing platforms, carry massive platform risk. Their value often comes from the underlying APIs, not from anything uniquely their own. When the big players—with their endless infrastructure, data, and capital—decide to expand their features, these startups can be wiped out in an instant.

But it’s not all doom and gloom. Even as large models push deeper into the application layer (think Adobe-Odyssey, ChatGPT-Canvas-Cursor, and ChatGPT-Grammarly), there’s still a path for startups to win: by creating unique data loops at the application level.

The key is to go deep—really deep—into niche industries or specific use cases. By leveraging domain expertise, startups can build sustainable advantages through data loops. Here’s what we should be asking:

• Does this AI app have unique, direct access to untapped data?

• Can it convince users to legally opt in and share their data—and is that data being refined and improved over time?

• For B2B, do they have exclusive contracts that give them an edge? A defensible data moat that no one else can replicate.

In a world where features can be copied overnight, the real differentiator is the ability to create and sustain proprietary data loops that others can’t easily touch.