The new economics of AI

A CEO guide to managing the economic forces shaping the future of agentic AI.

That tectonic rumble you hear is AI shifting the forces that drive business. Companies looking to benefit from this transformation will need to understand how AI is reshaping the economics of business—and how to navigate those economics to create value.

The dynamics shaping agentic economics

AI usage and shifting business dynamics mean that even while unit token costs are declining, spend continues to climb. The number one challenge for the CEO and board is how to navigate those dynamics to both manage costs and create new value.
Is that AI agent worth it? Agentic economics and the modern operating modelFrom AI table stakes to AI advantage: Building competitive moatsThe decision dividend: How AI creates economic value

Demand forces: How to manage AI consumption

To better control agentic AI demand, CIOs shouldn’t simply ratchet back consumption. Instead, leaders are building financial operations, or FinOps, AI practices to predict demand and manage usage.
The cost of intelligence: How CIOs can manage AI demand at scale

Supply forces: How to manage AI infrastructure costs

How can AI inference be made economically sustainable at scale? AI infrastructure competitiveness may depend on how efficiently systems convert power, memory bandwidth, networking, and silicon into inference, making cost per token and energy per token the defining measures of AI economics.
Frontiers of compute: The technologies to reduce AI inference costs

Most businesses are still asking the wrong questions about agentic AI

The decision to scale an agent is a complex and fast-changing economics decision, not a technical one. Leaders need a much better sense of what their employees and agents are doing, and what business outcomes they want to achieve.
Cost versus value: Managing agentic AI system performance

The practical economic decisions when implementing agentic AI

Leaders charged with implementing agentic programs need to get three things right: Identify where the value is, determine what drives the unit economics of AI-based workflows, and understand how to adapt to the evolving dynamics of agentic economics.
Where AI agents pay off: A practical guide to the economics of agentic workflows
Decrease in inference costs
Amount companies can save on their AI costs
Reduction in cost per token through model optimization
Single agent workflow cost compared to cost to run a multi-agent team

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