#clouds

We’re seeing an interesting infrastructure tug of war today where GPU clouds are being pulled in two directions. For the economics of AI to work, the enterprise market needs to carve expensive hardware into smaller, shareable units and hand it to customers on demand, similar to how CPUs are doled in public cloud infrastructure. But the more the providers push GPUs to behave like elastic cloud infrastructure, the more they run into the reality that this GPU hardware was never built for safe multitenant use, fast fault recovery, or clean isolation between workloads. That tension is becoming one of the defining operational problems of the AI infrastructure market. When a gamer launches Steam or the Epic Games Store on their laptop, they don’t have to worry about which GPU is being scheduled, how memory is going to be divided, or really any of the security boundaries or hardware assignment issues on their PC. For consumer PCs, these issues are not just hidden from view, they are irrelevant

Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands. Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand. The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use ca