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Cloud CISO Perspectives: At Next ‘26, why we’re multicloud and multi-AI

Welcome to the second Cloud CISO Perspectives for April 2026. Today, Francis deSouza, COO Google Cloud and President, Security Products, explains why Google is multicloud and multi-AI, straight from Next ‘26. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the …

Adaptive Thinking: Large Language Models Know When to Think in Latent Space

Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While increasing the thinking budget yields smooth performance improvements at inference time, the relationship between LLM capability, query complexity, and optimal budget allocation remains poorly understood for achieving compute-optimal inference. To address …

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Extracting contract insights with PwC’s AI-driven annotation on AWS

This post was co-written with Yash Munsadwala, Adam Hood, Justin Guse, and Hector Hernandez from PwC. Contract analysis often consumes significant time for legal, compliance, and procurement teams, especially when important insights are buried in lengthy, unstructured agreements. As contract volumes grow, finding specific clauses and assessing extracted terms can become increasingly difficult to scale. …

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The founder’s AI foundation: The top announcements for startups from Next ‘26

The momentum is undeniable: the world’s fastest-growing AI startups are building with Google Cloud. Instead of stitching together fragmented point solutions, founders are building their businesses here because we offer the entire AI stack in a single, open environment. And, as we saw at Next ‘26 last week, we continue to advance the models, infrastructure, …

Local Mechanisms of Compositional Generalization in Conditional Diffusion

Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear. To make this concrete, we study length generalization, the ability to generate images with more objects than seen during training. In a controlled CLEVR setting (Johnson et al.,2017), we find …

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Connecting Agents to Decisions

The Palantir Ontology Palantir’s software powers real-time, human-agent decision-making in many of the most critical commercial and government contexts around the world. From disaster response to nuclear energy production, our customers depend on Palantir AIP to safely, securely, and effectively leverage AI in their enterprises — and drive operational transformation. While many factors contribute to achieving and scaling …

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Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic

Migrating a text agent to a voice assistant is increasingly important because users expect faster, more natural interactions. Instead of typing, customers want to speak and understand in real time. Industries like finance, healthcare, education, social media, and retail are exploring solutions with Amazon Nova 2 Sonic to enable natural, real-time speech interactions at scale. …

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50+ fully managed MCP servers now available for Google Cloud services

At Google Cloud Next ‘26, we announced that more than 50 Google-managed Model Context Protocol (MCP) servers are generally available or in preview, with more on the way. Why it matters: To move beyond experimental prototypes, AI agents must be able to access real-world data and solve complex problems autonomously. Google-managed Managed Context Protocol (MCP) …

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Automate repetitive tasks with Amazon Quick Flows

Consider a typical Monday morning: you’re manually copying data from several different systems to create a weekly report, then formatting it for different stakeholders. This single task can consume several hours that could be spent on more strategic work. Multiply this across your team, and these repetitive tasks add up quickly. Amazon Quick Flows automates these …

Learning Long-Term Motion Embeddings for Efficient Kinematics Generation

Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures through full video synthesis remains prohibitively inefficient. We model scene dynamics orders of magnitude more efficiently by directly operating on a long-term motion embedding that is learned from large-scale trajectories …