AI-native

A program can use artificial intelligence in two very different ways. In the first case, the program already existed for a long time, and at some point a button with a little sparkle icon was added. Everything else keeps working as before. In the second case, the learning software stood at the center from the very beginning: without it, the product wouldn’t exist at all. Such products are called AI-native. So the term says nothing about how good something is, but rather what it was built from.

The difference between retrofit and new build

The term is above all an economic argument. Young companies use it to set themselves apart from large, older providers. Their message is: we didn’t have to retrofit anything, we built it right from zero. Large providers counter that they already own millions of customers and their data.

Behind this lies a genuine technical question. Anyone extending a twenty-year-old program with AI has to work within its old structure. Databases, menus, and workflows were built for humans, not for models. A new build can ignore these constraints and design the interface in a completely different way.

At the same time, “AI-native” has become an advertising word. Many companies use it even though, in their case, only a bought-in chatbot runs along on the side. A simple litmus test helps: imagine the AI fails. If a usable product still remains, it wasn’t AI-native.

How an AI-native product is built

In classic software, a team defines every step in advance. When the user clicks a button, exactly what the programmers wrote into that spot happens. Such rules are reliable but rigid. Every new situation requires a new rule.

AI-native applications replace part of these rules with a model that has learned from examples. The user describes their goal in plain language, and the software works out the steps itself. This makes it more flexible, but also less predictable: the same input can produce something slightly different twice.

That’s why the construction looks different from before. It requires checking loops that verify results before the user sees them. It requires ways for the model to access the company’s own data. And it requires a cost calculation per request, because every answer consumes computing time in a data center. With classic software, an extra click costs practically nothing.

Who calls themselves that—and where caution is warranted

The principle is most clearly visible in coding tools. Editors like Cursor are built around a language model that suggests code and rewrites entire files. Image generators or note-taking apps that produce summaries from voice recordings work similarly. In all these cases: without the model, only an empty shell remains.

In stock market and business news, the word mostly appears in the context of funding rounds. Investors pay higher valuations for “AI-native” companies because they see a structural advantage in them. Analysts therefore like to ask whether a provider is truly AI-native or just sounds like it. The term then determines very real sums of money.

It should not be confused with “cloud-native.” About ten years ago, that term referred to software built for remote data centers on the internet rather than for servers in one’s own basement. The pattern is the same: a new technology appears, and a distinction is drawn between retrofitted products and those built from the ground up. As back then, the term will likely become unnecessary in a few years, because it will have become self-evident.