Here's how I'm thinking about it:
When a product exposes its core functionality as foundational primitives that an LLM with tools can use.
Let's break that down a bit.
We're used to static interfaces with controls, filters, sub-menus, screens, etc. The patterns are well established.
Those products are built for you - the user.
But despite all the optimization, sometimes these products are a huge pain! There might be too many records to digest... it might be confusing to set up a certain feature... it takes too many clicks to get somewhere.
Why not let the AI do that work instead?
An AI native product takes the core functionality and exposes it as reusable functions. Then an agent - an LLM with tools - can use the core functionality on your behalf.
You tell it what you want to do, usually via a chat interface, and it does it for you.
Here are some examples:
Writing code
Old way (Vim, Emacs):
AI native way (Cursor, Claude Code):
Searching company documents
Old way (Google Drive):
AI native way (Notion):
Making presentations
Old way (Powerpoint):
AI native way (Gamma):
The important thing with AI native products is that you are still in the driver's seat. And you can still use the static interface if you want. In fact often it's quicker than asking the agent.
But otherwise, you say what you want to get done, and the product does it for you.
That's what an AI native product is.