Google explained what a full-stack approach to AI is and why the company needed it

Google has published an explanation of its approach to AI development — the so-called full-stack, or end-to-end method, which has been the foundation of the company's AI work for over a decade.

What "full stack" means

In technology, it is customary to divide a product into layers: hardware, operating systems, frameworks, user applications. "Full stack" means that a company controls all levels at once, not assembling a solution from ready-made third-party components. Google built exactly such a vertically integrated pyramid for its AI systems — from silicon to the interface of the end user:

• Chips — proprietary TPU (Tensor Processing Units), created for machine learning workloads. The first generation appeared in 2015, today the sixth is in development.
• Infrastructure — data centers and high-speed networks optimized for distributed model training across tens of thousands of accelerators simultaneously.
• Frameworks — TensorFlow, JAX, and Keras, developed for internal needs and then opened to the global developer community.
• Models — from BERT and PaLM to the current Gemini 2.5 with Flash and Ultra variants.
• Applications — Google Search, Translate, Photos, Maps, and dozens of other products with a combined audience of billions of users.

Why this is a competitive advantage

When a company owns each layer of the stack, it optimizes it for specific tasks without dependence on third-party vendors. TPU is designed to work perfectly with JAX. JAX — to maximize the architectural features of TPU.

This closed optimization loop is unavailable to companies that buy chips from one vendor, frameworks from another, and rent cloud from a third. Full control accelerates the research cycle: engineers can simultaneously change chip architecture and adapt the software stack. In a fragmented ecosystem, this would require lengthy negotiations between multiple organizations or waiting for updates from an external provider.

There is also direct economic logic. Proprietary chips allow training and serving models at internal cost, not commercial provider prices. For a company whose AI services process billions of requests per day, even minimal differences in the cost of one computing cycle translates into enormous savings at scale.

How it works in practice

Pre-training a model of Gemini Ultra scale requires tens of thousands of chips working simultaneously for several weeks. Without proprietary TPUs and specialized infrastructure, such scale is either technically impossible or financially ruinous. Google invested in vertical integration long before the era of generative AI. Today, this foundation is turning into a sustainable advantage in the race for frontier models — a barrier to entry that many companies without similar infrastructure cannot overcome. Opening tools — TensorFlow in 2015, JAX later — attracted thousands of external researchers and made fragments of Google's stack an industry standard. This way, internal technology turned into a tool for forming an ecosystem and a magnet for leading researchers around the world.

What this means

The full-stack approach is not just an architectural solution, but a long-term strategy that determines the speed of iterations, the cost of computation, and the quality of AI products. Companies wishing to compete at the level of frontier models face a rigid choice: build their own stack — or accept dependence on those who have already built it.