Most AI companies compete for one piece of the business. NVIDIA builds chips and OpenAI builds models, while many startups build apps on systems someone else owns. Google is trying to control the whole chain: the chips, the cloud, the tools, the models and the products where AI reaches users. That gives it a direct path from a breakthrough to a product, and from a product to profit.
Everything needed to build, run and deliver AI is known as the AI stack. A better model can attract attention, but it is still only one part of that stack. Google has more of the stack under one roof than most competitors, and each piece strengthens the next.
The stack starts with data centres, cloud systems, chips and storage. Google runs its own data centres around the world and designs its own AI chips, called TPUs. Its chip work focuses on the moment a model produces an answer, where cost, energy use and performance are part of the same problem. The cost of computing decides how far a product can grow before its margins come under pressure.
Next come the tools. Google’s TensorFlow gives teams a way to build AI systems and run them on different kinds of hardware. Work built with it can run with little or no change on anything from phones to large computing setups. Less friction between building, testing and launching AI means faster execution.
Above the tools sit the models. Gemini, the company’s main family of AI models, works across text, images, audio and video. It includes versions designed for phones and smaller devices. That fits a company whose products already span cloud, mobile, search and media.
At the top of the stack, the products are even harder to copy. Search, YouTube and Android are daily habits for billions of users. When AI improves, the company can move the improvement into products people already use, so a new feature does not start with zero users.
For other companies, the risk is dependency. A business can build useful AI products by renting computing power, using outside models and relying on outside platforms. Each of those dependencies reduces its control over cost, speed, timing and flexibility.
