Google has published a new explainer on what “full stack” means in AI, describing the layers that now sit between chips and finished products. The piece covers infrastructure, models, developer tools and applications as parts of one system.
The framing matters because AI performance is increasingly shaped by more than a model checkpoint. Hardware, serving systems, data pipelines and product design all affect cost, latency and usefulness.
For builders, the post is a reminder that competitive AI products depend on integration across the stack, not only access to a strong foundation model.