Updated July 28, 2026
Five Questions Every CDO Should Ask About AI and SemanticsThe metrics powering your AI agents right now, like margin per square foot or total economic cost, probably don't belong to you. They belong to whichever platform computes them today. Snowflake, Databricks, the frontier LLM labs, Microsoft, Salesforce, Google, and…
Updated July 23, 2026
Don’t Let Microsoft Be Your WardenMicrosoft just made the case for AtScale by playing Warden Norton, the prison boss from "The Shawshank Redemption." On Tuesday, Sara Vredevoogd from Microsoft's Power BI team announced that Microsoft 365 Copilot can now query Power BI semantic models directly.…
Updated July 22, 2026
My Case for the Semantic Layer, Made Live on Atlan’s PodcastI recently joined Austin Kronz on Atlan's podcast, WTF is the Context Layer, to discuss one of the biggest sources of confusion in enterprise AI: the distinction between semantic layers, context layers, and ontologies. We covered a lot of ground,…
Updated July 20, 2026
Snowflake Semantic Views vs. Universal Semantic Layer: Which One Does Your Enterprise Actually Need?Snowflake Semantic Views let you define metrics, dimensions, and relationships directly inside Snowflake, with no extra tooling required. A universal semantic layer sits above your warehouse and works across Snowflake, other data platforms, and every BI tool you run on…
Updated July 13, 2026
One Governed Layer, Now Live Across Excel, Power BI, and Horizon ContextPower BI and Excel users can soon query governed Snowflake Semantic Views directly. No mirrored data. No business logic was rebuilt outside the warehouse. That's the news customers have been asking for and AtScale formally announced at Snowflake Summit 2026.…
Updated July 13, 2026
The Real AI Sovereignty Fight Is About MeaningIn the last few weeks, the sovereignty conversation has moved from academic to urgent. Palantir's Alex Karp went on CNBC to explain the logic behind his company's partnership with NVIDIA and spent 20 minutes arguing that enterprises are handing frontier…
Updated July 13, 2026
The Metadata Trap: Why Three “Context” Platforms Can Give You Three Different AnswersA regional VP asks her company's new AI agent for last quarter's revenue. It says $42M. Twenty minutes later, a finance analyst asks a different copilot the same question. It says $44M. Both agents are grounded in the company's brand-new…
Updated July 9, 2026
Three Companies Solved Their AI Problem Before AI Was the ProblemAn agent is only as reliable as the definitions it's built on. And every company racing to deploy agentic AI right now is about to learn it the hard way. Here's what's actually happening at TELUS, Vodafone, and Slickdeals, three…
Updated July 7, 2026
Why I Joined AtScale: The Semantic Layer Inflection PointOver four and a half years at Snowflake, I watched enterprises run into a problem that's about to get much worse: every major platform has built its own semantic layer, and none of them talk to each other. Many platforms…
Updated July 1, 2026
Enterprise AI: Separate the Reasoning from the CalculationAt this year’s Semantic Layer Summit, practitioners from Anthropic, Chevron, WPP, Accenture, NVIDIA, and OpenHands, compared notes on why production AI keeps breaking down. The consensus? The LLM is not the bottleneck. It’s context. The organizations succeeding in production don’t…
