Richard Seroter, who leads developer experience at Google Cloud, explains that “full-stack” AI means delivering a complete system where every layer—from hardware to user interface—is connected and owned by a single provider.
Who is Richard Seroter?
Seroter joined Google as a product manager roughly three years ago. He now heads the developer relations and technical writing teams. His group also covers product engineering for languages and frameworks, alongside the Open Source Programs Office. The team’s job is to help software developers build using Google Cloud products. They create the programming languages and frameworks, meet with the community to share best practices, and write the documentation. The goal is to give developers the confidence to get things done.
What does full-stack mean in tech?
The term originally appeared in software development about a decade ago. It referred to applications. Historically, building an app required multiple specialised teams. One group built user interfaces, another handled server-side logic, and a third managed databases. The concept of a “full-stack engineer” emerged to describe a developer who could work across all these functions independently. Instead of handing off components between people, a full-stack engineer could take an idea from a rough concept to a fully running piece of software.
How does this apply to AI?
Google has taken that end-to-end principle and applied it to artificial intelligence. If you want to deliver value with AI, you can buy disparate parts from different vendors and try to stitch them together yourself. Alternatively, you can look for an integrated system where everything needed is already connected.
An intentional AI stack requires a cohesive combination of layers to get a job done: compute infrastructure, an AI model, an orchestration platform and the user interfaces. At Google, the company has invested in every single layer. It provides hardware like Tensor Processing Units (TPUs). It uses frontier models developed by Google DeepMind, such as the Gemini family of models. It offers the Gemini Enterprise Agent Platform. It includes interfaces people use daily, like Maps and Gmail. The company has done the work to find all the necessary components and put them inside the box.
Has Google always planned this?
It was a deliberate, decades-long strategy. The bet on custom TPUs is over 10 years old. The company recognised early on that there is massive value in owning the supply chain and raw infrastructure when serving the world’s most important internet services. Owning that thread throughout the entire stack lets the company deliver a level of service, performance and reliability that is very hard to achieve if you are at the mercy of multiple parties.
Does this limit builders?
Locking people in does not align with Google’s ethos. The company gives away foundational technology and source code that the entire industry depends on. The AI platform is described as “opinionated but extensible” and “batteries included”. This means everything needed to build and run an application is ready to go out of the box.
However, if you want to use another company’s AI model instead of Gemini, or hook up different software instead of Google Workspace, you can plug those right in. The company wants you to use its products every day based on the completeness of the platform, not because you were forced into a closed choice.
What are the other benefits?
Because Google manages the entire stack, from running the underlying infrastructure to delivering Gmail, there is massive system reliability. If a technical failure happens at one layer, the company’s ownership of the platform allows it to catch and handle it at another layer easily. There is also an economic advantage. Since the company is not paying third-party vendors for anything, customers do not have to absorb those fees. This means the company can offer remarkably competitive pricing.
Where do you start?
The company wants to make technology accessible to billions of people who do not have an engineering degree. It provides clear front doors depending on what you are trying to achieve. There are three starting points recommended.
- If you want to take a creative idea and quickly build a prototype web application, Google AI Studio is a place to start. You can build a prototype in just a few minutes and deploy it directly to Cloud Run with the click of a single button.
- If you are looking for a low-code option to automate your day-to-day work, try Gemini Enterprise Platform. You can build workflows to clean up your inbox or parse complex spreadsheets without ever having to write or look at a single line of code.
- For those looking to orchestrate more complex application or agent builds, the Antigravity platform is powerful. Its rich surfaces allow you to build sophisticated systems without requiring advanced programming knowledge.
Whatever you are trying to make and whatever level of developer skill you have, there is a Google full-stack tool ready to help you.
What it means
Builders no longer need to hunt for compatible hardware, models, and interfaces from different vendors. A single provider manages the reliability and cost, while still allowing external tools to be plugged in if needed.
