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May 20, 2026

The Agentic AI Advantage: How Token Economics Separates the Programmes That Scale From the Ones That Stall

Google now processes 3.2 quadrillion tokens a month, up from 480 trillion a year ago. While vendors compete on per-token prices, the organisations pulling ahead on AI are the ones tracking a different metric entirely: tokens per business outcome. This article explains why per-token price is the wrong number to optimise, introduces tokens per resolved outcome as the unit that makes agentic AI economics legible, and sets out the three architectural patterns – task decomposition, context discipline, and evaluation-driven model selection – that create durable cost and performance advantages in agentic AI programmes.
Apr 29, 2026

The Stack You Built for Your Analysts Won’t Work for Your Agents

While nearly two-thirds of enterprises have experimented with AI agents, fewer than 10% have successfully scaled them, with 80% citing data limitations as the core obstacle. This article explores why the modern pipeline-centric data stack, designed for human analysts, struggles with autonomous AI systems, and examines the critical shift toward semantic metadata, upstream quality enforcement, and machine-readable context.
Apr 24, 2026

Your Data Team Is Shipping Faster Than It Can Be Trusted. That’s a Problem.

dbt Labs’ 2026 State of Analytics Engineering Report found trust in data has surged to 83% as the top organisational priority — the steepest single-year rise ever recorded — driven by 71% of data professionals concerned about incorrect AI outputs reaching stakeholders. With only 7% of enterprises saying their data is completely AI-ready and Gartner finding organisations that see returns invest four times more in data foundations, this article examines the acceleration-to-governance gap and what agent-ready data infrastructure actually requires.