The Capability Distribution Indicator
The fourth of Coyle's proposed dashboard indicators is distributional — not merely of income, which existing metrics track, but of capability. Who has access to AI tools? Who is being augmented, and who is being displaced? Is the capability expansion reaching populations that most need it, or is it concentrating among those already advantaged? Coyle's concern about AI monopolization — her call for a CERN for generative AI, her advocacy for interoperability principles borrowed from telecoms regulation — reflects the conviction that the distributional dimension of the transition is as important as aggregate productivity, and that aggregate metrics conceal distributional reality.
In the AI Story
The framework draws directly on Amartya Sen's capability approach: economic development should be evaluated by what people are able to do and be, not merely by what they consume. The capability distribution indicator translates this philosophical framework into a measurable dashboard element that could stand alongside GDP in quarterly reporting.
The indicator matters because the AI transition produces distributional effects that income metrics miss. When AI tools are available at low cost to anyone with an internet connection, the surplus they generate is potentially progressive — a developer in Lagos receives the same capability amplification as an engineer in San Francisco, at the same price, which means the surplus relative to what the tool replaces is far larger for the developer in Lagos. This is the democratization of capability that Segal celebrates, and the celebration is warranted. But the distributional benefit is invisible to every metric policymakers currently use to evaluate inequality, because the metric measures income and wealth — market outcomes — not capability and surplus.
Equally, the indicator would capture the downside distribution. AI-driven displacement is unevenly distributed across occupations, regions, and demographic groups. Workers whose tasks are most AI-substitutable face displacement pressure disproportionate to the aggregate employment statistics. The capability indicator would make this distribution visible in a way that unemployment rates alone cannot.
Coyle's advocacy for AI infrastructure interventions — a public option for computing, data commons, interoperability mandates — depends on the capability distribution being visible. A political system that cannot see capability gaps cannot be mobilized to address them. Building the indicator is prerequisite to building the institutions that would distribute capability more equitably.
Origin
The framework draws on Sen's Development as Freedom (1999), Martha Nussbaum's central capabilities list, and Coyle's institutional advocacy through the Bennett Institute and her work on AI governance. The specific proposal for a capability distribution indicator synthesizes these sources with the operational requirements of a dashboard metric.
Key Ideas
Beyond income. Distribution must be measured in terms of what people can do, not merely what they earn.
Progressive surplus. Low-cost AI tools may generate disproportionately large surplus for previously excluded populations — a benefit invisible to income metrics.
Asymmetric displacement. AI substitution pressure is unevenly distributed across occupations and regions; capability metrics make this visible.
Governance prerequisite. Distribution interventions require distribution visibility; building the metric is prerequisite to building the policy response.
Appears in the Orange Pill Cycle
Further reading
- Amartya Sen, Development as Freedom (Anchor Books, 1999)
- Martha Nussbaum, Creating Capabilities: The Human Development Approach (Harvard University Press, 2011)
- Diane Coyle, 'We Need a CERN for Generative AI', Project Syndicate, 2023