Google Cloud sells its AI platform on a set of claims about Google as a company. Each claim has a concrete product, a piece of hardware or a published commitment behind it, and an exam question names the claim and asks for the thing underneath it. The claims also describe what a business gets when it buys the platform, so a product person reads them as a statement of what the vendor takes responsibility for.
The sections below take the AI first claim, what Google means by enterprise ready, the hardware underneath the platform, the open approach and the claim that building with AI no longer needs a research team.
The AI first claim
Google states that it has been building AI into its products for a long time, that the transformer architecture behind current models came out of Google research and that the same models running its own products are the ones it sells. The business reading of all three claims is that gen AI is a continuation of work Google was already doing.
The ecosystem claim sits beside the AI first argument. Search, Workspace, Android, Chrome and Cloud all carry the same underlying models, so an organisation using Google products meets AI inside the tools its people open every day.
What Google means by enterprise ready
Enterprise ready is Google's phrase for what a business needs in place before it puts a model in front of customers, and each of the qualities it covers has something concrete behind it.
Responsible. Google publishes a set of AI principles, applies safety filters to its models and builds against its own Secure AI Framework.
Secure and private. Customer data is encrypted in transit and at rest, Google does not train its models on that data and the customer controls where processing happens.
Reliable and scalable. The platform runs on the same infrastructure as Google's consumer products, with published availability commitments.
Governed. Access control, audit logging and data residency are configurable per workload.
Enterprise ready names the distance between a demonstration and a system a regulated business can put in front of its own customers. Most of that distance is these qualities, and model quality accounts for very little of it.
The infrastructure underneath the platform
Data centres and ordinary cloud computing are the base of the platform, and two kinds of processor do the work of training and inference above them.
GPUs are general purpose parallel processors. They are widely used for training and for inference, and Google Cloud offers them alongside its own silicon.
TPUs, or Tensor Processing Units, are Google's own silicon, designed for the tensor operations that machine learning performs. A TPU is built for that one job and no other, so it does the job faster and with less energy than a general purpose processor.
AI Hypercomputer is Google's name for the whole arrangement, with the hardware, the networking and the software treated as one system. The claim it supports is that training and serving at scale is a systems problem, and a faster chip on its own does not answer it.
The open approach
Google describes its approach to models and tools as open. The word covers three things.
- Open weight models such as Gemma, which a business can download and run on infrastructure of its own choosing.
- Third party and open models offered beside Google's own inside Model Garden.
- Open standards for connecting agents to tools, so the connections a business builds work outside Google as well.
The business argument is that a customer avoids lock in, since a workload can move to another cloud and models from several providers can sit behind one platform.
Democratising development
Google's claim about democratising development is that building with AI no longer requires a research team. The support for the claim is low code and no code tooling, pre trained models that need no training data of their own and APIs for common tasks such as speech, translation and document understanding.
This is the claim with the most immediate consequence for a product person, because it decides whether a capability costs a quarter of engineering work or an afternoon of configuration.