About AI Economics Hub

Editorial mission

AI Economics Hub is built around a simple premise: enterprise AI now requires its own serious management language. Many organisations can describe AI activity. Far fewer can explain AI cost, ownership, value, and portfolio consequence in a way that supports disciplined investment and operating decisions.

The mission of the site is to reduce that gap. It aims to provide structured, vendor-neutral analysis for leaders who need to understand AI as an economic and operational system rather than as a stream of tools, pilots, and announcements.

Why AI economics needs a dedicated resource

AI economics is not adequately covered when it is treated only as a technical topic, only as a finance topic, or only as a transformation theme. The most important enterprise questions cut across all three. What does AI really cost? Which part of the cost is shared platform versus local demand? What counts as credible proof of value? Which disciplines need to adapt for inference-era economics? How should leaders interpret a market evolving around several adjacent control problems rather than one clean category?

These questions justify a dedicated editorial resource because the language, frameworks, and operating assumptions are still immature in many organisations.

Who this publication serves

The publication is designed for:

• CIOs and CTOs
• CFOs and finance transformation leaders
• CAIOs, CDOs, and VPs of AI
• Heads of Engineering and CPOs
• FinOps leads and cloud economics teams
• TBM practitioners and technology value teams
• ITFM leaders and IT finance directors
• SPM leads, Heads of Portfolio Management, and PMO leaders
• FinOps consultants and management consulting partners
• Technology analysts, advisory teams, and research communities

What the site covers

The site focuses on a small set of themes that recur across serious enterprise AI conversations: value proof, cost structure, ROI, operating disciplines such as FinOps, TBM, ITFM and SPM, market structure, and the reference language leaders need to use those topics consistently.

Sources and methodology

The publication draws on a blend of primary and institutional research sources, including:

• FinOps Foundation, especially the annual State of FinOps reports and FOCUS specification work
• TBM Council, including Taxonomy, Framework, and State of TBM materials
• McKinsey, especially the annual State of AI survey
• Deloitte research on AI ROI and Humans x Machines operating models
• IDC research on AI cost governance, infrastructure risk, and shadow spend
• Gartner market and spending predictions, including AI governance platform coverage
• Forrester and PwC outlooks where they add useful enterprise context
• IBM Institute for Business Value and Think Circle research
• MIT CISR research on enterprise AI maturity and scaled ways of working
• LSE and adjacent academic work on AI leadership and transformation

The editorial method is deliberately restrained. Some claims are definitional. Some are analytical. Some are practical interpretations based on comparing multiple sources and observed enterprise patterns. The site aims to be clear about which is which.

What the site does not aim to cover

The site is not intended to be a general AI news publication. It does not try to track every model release, funding round, or product announcement. It is not primarily a technical implementation guide except where technical design choices have direct economic consequences. It is also not a procurement site or a theatrical ranking engine. Market analysis is included where it improves management decisions.

Feedback and thoughtful collaboration

Thoughtful feedback is welcome, especially where it improves a framework, clarifies a source base, strengthens a market interpretation, or exposes an important gap in current coverage. The aim is not to expand for its own sake. It is to improve the reliability and usefulness of the resource for people trying to make better AI decisions under real enterprise constraints.