Why meta agents must become the economic intelligence layer of the agentic enterprise

In “Micro and macro agents: The emerging architecture of the agentic enterprise,” I proposed a three-layer architecture for enterprise AI. Micro agents execute specialized tasks. Macro agents orchestrate end-to-end business processes. Meta agents provide governance through monitoring, compliance, security, and human oversight. As enterprises begin deploying thousands — and eventually tens of thousands — of…

Why AI infrastructure needs a new operating model

The next AI infrastructure crisis may come from unmanaged inference capacity. For the past several years, the AI infrastructure conversation centered on one question: how do we get more compute? That made sense. Enterprises needed GPUs, cloud capacity, foundation models and room to experiment. Compute became shorthand for AI readiness. Production AI changes the operating…

Don’t let your company be fooled by AI efficiency

The scenario isn’t hypothetical: Some of the companies that went furthest in replacing people with AI have had to backtrack. For example, in 2024 Klarna became a European benchmark for what AI could do for a company. Its AI assistant handled two-thirds of customer service chats in its first month, performing the equivalent of 700 full-time agents. As…

The AI assurance gap: CIOs need proof that agentic AI controls actually work

Enterprises have spent decades learning how to audit people and software. Agentic AI creates a third category: systems that interpret instructions, call tools and act across workflows without a mature assurance model built around them. In my work as a leader and investor across technology-enabled businesses, I have spent years around automation, cybersecurity, compliance, workflow…