"AI-native" has become a marketing term, which means it is now noise. To make it useful again, it helps to define it operationally: an AI-native product is one whose core workflow assumes a model in the loop, and would not exist in its current form if you removed the model. That is a high bar, and most products that claim it do not clear it.
The test
Imagine removing the model. What is left? If the answer is "the same product, minus a helpful assistant," it is not AI-native; it is AI-augmented. If the answer is "nothing, because the workflow only makes sense with the model doing the heavy lifting," it is AI-native.
What this changes about the product
AI-native products tend to have fewer screens, because the model collapses what used to be multi-step UIs into intent + result. They tend to have more conversation and less navigation. They tend to be priced on outcomes rather than seats, because the value created per user is highly variable. And they tend to require eval infrastructure as a first-class engineering concern.
Why the distinction matters now
Bolted-on AI competes on parity with whatever else has bolted on AI. AI-native products compete on workflows that look impossible to anyone still designing for the manual world. The window for being the AI-native version of something is small. After that, the category is taken.