News you can use: What we announced in AI this month

In March, we made big strides in our models and accessibility – from announcing Gemini 2.5, our most intelligent AI model, to Gemma 3, our most capable and advanced version of the Gemma open-model family. We also introduced Gemini Code Assist for individuals, a free version of our AI coding assistant. 

In the industry, we shone light on telecom and gaming. We went to Mobile World Congress, where we displayed key agent use cases where AI is becoming an imperative in telecom. At the same time, we talked to leaders in the games industry, who told us how they’re using Google Cloud AI to drive unprecedented advancements in game development, including smarter, faster, and more immersive gaming experiences. 

Top announcements 

The Gemini family is growing – we introduced Gemini 2.5, a thinking model designed to tackle increasingly complex problems. Gemini 2.5 Pro Experimental leads common benchmarks by meaningful margins and showcases strong reasoning and code capabilities. Learn all about it here.

We also introduced Gemma 3, the most capable model you can run on a TPU or GPU. To help you get started, we shared guides on how to deploy your AI workloads on Cloud Run and Vertex AI.

In the world of open-source, we announced Claude 3.7 Sonnet, Anthropic’s most intelligent model to date and the first hybrid reasoning model on the market, is available in preview on Vertex AI Model Garden. Claude 3.7 Sonnet can produce quick responses or extended, step-by-step thinking that is made visible to the user. Explore our sample notebook and documentation to start building.

Finally, we took a step forward in security. We introduced AI Protection, a set of capabilities designed to safeguard AI workloads and data across clouds and models — irrespective of the platforms you choose to use. Our Office of the CISO suggests these five do-it-today, actionable tips on how leaders can help their organizations adopt AI securely.

News you can use

Wondering how to make the most of your AI? When it comes to Gemini 2.0, we created a helpful guide that teaches you how to optimize one of enterprises’ most time-intensive tasks: document extraction. 

From an infrastructure perspective, we broke down the four top use cases for AI Hypercomputer and ways to get started. In this guide, you’ll learn everything from affordable inference to reducing delivery bottlenecks. Take Moloco, for example. Using the AI Hypercomputer architecture they achieved 10x faster model training times and reduced costs by 2-4x.

And, speaking of cost – do you know the true cost of enterprise AI? Enterprises need ways to optimize large AI workloads because these resources can still be quite expensive. Learn how to calculate your AI cost and dig into these five tips to optimize your workflow on Google Cloud Platform.

Hear from our leaders 

We closed off this month with our monthly column, The Prompt. In this installment, we hear from Suraj Poozhiyil, AI product leader, on how AI agents depend on “enterprise truth” – your enterprise’s unique context – to be successful. Here’s a snippet:

We’ve heard it before – AI is only as good as the data you put into it. When it comes to agents, AI agents are only as good as the context you give them. Enterprise truth is the answer to questions like, “What is our company’s policy for creating a purchase order?” and “What is the specific workflow for approvals and compliance?”

Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud, read our weekly updates, The Overwhelmed Person’s Guide to Google Cloud.