Full-Stack AI

For a program like ChatGPT to respond, many layers have to work together. At the very bottom are special computing chips, above them large data centers, then the software that turns all of this into a learning system. At the very top sits the app that a user ultimately operates. Normally, different companies divide these layers among themselves. Full-Stack AI means: a single company covers as many of these layers as possible on its own. The term comes from the software world, where “stack” refers to the pile of all the technical layers involved.

What control over the whole stack is worth

Whoever occupies only one layer is dependent on all the others. A start-up that only builds an app rents computing power from a cloud provider and uses other companies' AI models. If prices there rise, its own profit immediately shrinks. If the model provider changes its rules, the entire product can become unusable overnight.

Whoever, on the other hand, owns several layers earns money from each of them. A customer pays for the app, and the money stays within the company instead of moving on to suppliers. In addition, the layers can be tuned to one another. If the same company designs both the chip and the software, it can build both so that they fit together especially well. This is exactly the argument Apple has been making for years to justify its own processors.

But there is also a counterargument. Every additional layer costs enormous amounts of capital and specialized personnel. Building your own chip manufacturing is a multi-billion-dollar project spanning many years. Smaller companies therefore often do better if they do one thing really well and buy in the rest.

The layers of the stack from bottom to top

The lowest layer is the chips, usually graphics processors, or GPUs for short. These are computing units that can perform very many simple calculation steps simultaneously. That is exactly what AI needs. Above that lies the infrastructure: data centers with power, cooling, and fast networks between the machines.

The next layer is the model itself. A model is a program that has learned patterns from enormous amounts of data and generates answers from them. It is created during training, a one-time learning process that can take weeks. At the top layer sits the application, meaning the chatbot, the search function, or the assistance system in a car.

A full-stack provider ideally occupies all four layers. In practice, hardly anyone manages this completely. Most companies cover two or three layers and buy in the rest. The term therefore describes more of a direction than an achieved state.

Which corporations come closest to the ideal

Google is considered the best example. The company develops its own AI chips, operates its own data centers, builds the models of the Gemini series, and delivers them to end customers in Search, Android, and Gmail. Nvidia works from the other direction: known as a chip manufacturer, the company now also sells complete server racks and accompanying software.

In stock market news, the term comes up when dependencies are at issue. Analysts then ask how much profit a company has to hand over to its suppliers. A common misconception is to regard Full-Stack AI as a seal of quality. It says nothing about how good a product is, only about who owns the technology behind it.

For you as a user, the difference is rarely visible. It shows up indirectly: through prices, through the question of where your data is processed, and through how quickly new features appear. Whoever controls the entire stack can push through changes without having to coordinate with partners.