Google explains its full-stack approach to AI and why the company needed it

Google has published an explanation of its approach to AI development, the so-called full-stack method, which has underpinned the company's AI work for more than ten years.

What "full stack" means

In technology, a product is usually divided into layers: hardware, operating systems, frameworks and user-facing applications. "Full stack" means a company controls every level at once rather than assembling a solution from off-the-shelf components made by others. Google built exactly this kind of vertically integrated pyramid for its AI systems, from silicon to the end-user interface:

• Chips: its own TPUs (Tensor Processing Units), built for machine learning workloads. The first generation appeared in 2015, and the sixth is current today.
• Infrastructure: data centers and high-speed networks optimized for distributed model training on tens of thousands of accelerators at once.
• Frameworks: TensorFlow, JAX and Keras, developed for internal needs and later 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 several billion users.

Why it is a competitive advantage

When a company owns every layer of the stack, it can optimize for specific tasks without depending on third-party suppliers. The TPU is designed to work ideally with JAX, and JAX is designed to make the most of the TPU's architectural features. Companies that buy chips from one vendor, a framework from another and rent cloud capacity from a third have no access to such a closed optimization loop.

Full control speeds up the research cycle: engineers can change the chip architecture and adapt the software stack at the same time. In a fragmented ecosystem, that would require lengthy negotiations between several organizations or waiting for updates from an outside supplier.

There is also a direct economic logic. Its own chips let the company train and serve models at internal cost rather than at commercial providers' prices. For a company whose AI services handle billions of requests a day, even a minimal difference in the cost of a single compute cycle adds up to enormous savings at scale.

How it works in practice

Pretraining a model on the scale of Gemini Ultra requires tens of thousands of chips working simultaneously for several weeks. Without its own TPUs and specialized infrastructure, such a scale is either technically out of reach or financially ruinous.

Google invested in vertical integration long before the generative AI era. Today that head start is turning into a lasting advantage in the race for frontier models: an entry barrier that many companies without comparable infrastructure may not be able to clear.

Opening up its tools, TensorFlow in 2015 and JAX later, attracted thousands of outside researchers and made parts of the Google stack an industry standard. An internal technology thus became a tool for shaping an ecosystem and a magnet for the world's leading researchers.

What this means

The full-stack approach is not just an architectural decision but a long-term strategy that determines iteration speed, compute cost and the quality of AI products. Companies that want to compete at the frontier-model level face a stark choice: build their own stack, or accept dependence on those who have already built one.