Understand AI economics
The argument, the evidence and the models
Start here if you want to know what is happening and why: why AI cost is visible while AI value is not, which models explain the gap, what the evidence shows, and where the site's own positions might be wrong. Every claim is sourced or labelled as interpretation, and the site's proposals are marked as proposals. If you want the practical version, the Implement section turns the same ideas into templates and steps.
Start with the argument
Four pieces that carry the case, in the order to read them.
Framework
The AI Economics Map: From Compute to Business Value to Capital Allocation
One map of how AI resources become business outcomes and funding decisions, showing what external standards cover and where leadership judgement begins.
Hub proposal, v0.9
The AI Value Gap
Why AI cost is visible and AI value is not, and how attribution coverage measures the difference.
Framework
The AI Abundance Paradox
When generating work is cheap, the constraint moves to verifying and absorbing it. AI value is then capped by review, decision and change capacity.
Essay
The Token Is the Meter, Not the Value
Tokens make AI consumption measurable and priceable. They do not tell an organisation whether anything valuable happened.
The models
Frameworks for cost, value and maturity, each labelled as the site's proposal, an adapted idea or an explainer.
Index
All frameworks
Every model on the site, grouped by the job it does and labelled by origin.
Reference explainer
AI TCO Framework
Seven cost layers, most of which never appear on the model invoice.
Adapted concept
AI Economics Maturity Model
Five levels from fragmented spend to governed, provable value.
Reference explainer
Token economics explained
The anatomy of a token bill: input and output, caching, context, agent loops, and where the meter ends.
The evidence
Verified numbers with their caveats, dated reviews, and a record of how the site's positions are holding up.
Reference
Key numbers
Verified statistics with source, sample, date and caveat.
Evidence
Evidence Review, Q3 2026
What new evidence on AI cost and value arrived in Q3 2026, what it means for this publication's positions, and what we changed on the site as a result.
Evidence
What the Engineering Evidence Says About AI Coding Value
Studies of AI coding assistants point different ways. Task speed, delivery performance and perceived productivity differ, so check which one a claim rests on.
Reference
Position ledger
Dated house positions and how they are holding up.
Challenge it
The strongest case against the site's own positions, and where the field may go next.
Open questions
The debates and the frontier
Does enterprise AI pay off? Should AI govern itself? Six questions argued from both sides, and essays on what may come next.
Essay
Automation Share Is Not Value
Measuring AI value by the share of work done without people rewards removal, not value. What the Tokenomics Foundation's first measure needs alongside it.
Essay
What Agentic AI Changes in AI Economics
Agents break the assumption that a person starts each AI task. What that changes in cost, attribution and governance, and which models still hold.
Ready to put it to work?
The same ideas as templates, steps and worked cases.