Google-Blackstone TPU Cloud and Anthropic MCP Tunnels Lead AI Infrastructure Advances in May 2026

Key points

• •Google and Blackstone launched a $5 billion joint venture to expand TPU-based AI infrastructure starting in 2024, aiming to build large-scale, production-grade TPU cloud data centers that challenge Nvidia’s dominance. This investment targets scalable, high-performance compute optimized for AI training and inference workloads to accelerate enterprise AI deployment.[Convergence Now][Proactive financial news]
• •Anthropic introduced MCP tunnels within its Claude Managed Agents platform, enabling secure and private access for AI agents to enterprise internal systems. This addresses security, governance, and compliance challenges for safely deploying AI agents with restricted access to sensitive data and systems in production environments.[InfoQ AI/ML]
• •Together AI demonstrated breakthrough inference performance and cost efficiency for next-gen coding agents, achieving 31% higher TPS than TensorRT-LLM, 2× better time-to-first-token at saturation, and 76% lower operational costs than Claude Opus 4.6. These metrics highlight significant progress in scalable model serving for AI developer tooling.[Together AI Blog]
• •Broadcom’s integration of VMware into AI infrastructure stacks enables scalable, production-grade AI workloads with enhanced virtualization and orchestration capabilities. This approach improves resource utilization and operational efficiency critical for deploying AI systems at scale in enterprise data centers.[Insider Monkey]
• •Google’s AI Studio offers a no-code, web-based platform for building native Android apps powered by AI in minutes, streamlining AI feature integration and accelerating developer productivity. This tool boosts the AI developer experience by reducing friction in app development workflows.[TechCrunch AI]
• •New throughput-optimal scheduling algorithms for LLM inference and AI agents optimize system efficiency under high demand, reducing latency and improving throughput for real-time AI production workloads. This research underpins AI serving architectures supporting large-scale deployments with better resource management.[ArXiv Machine Learning]

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