Planet of Women

 

Appendix 1. AI and Feminist Economics

There is an economic precedent worth naming explicitly: Care Economics, within the broader Feminist Economics tradition. It helps us think seriously about goods and services that have no line item in GDP. Even granting that GDP, as currently constructed, is the right metric for evaluating AI’s economic impact, if AI generates enormous consumer surplus while displacing a great deal of formal output for a relatively small quantity of tokens, the score will still be misread. The Stiglitz Commission argued exactly this in 2009, and they were right that GDP is a poor proxy for well-being.

The Feminist Economics literature documented these issues at scale. Marilyn Waring showed in 1988 that the committees who drafted the original System of National Accounts were 91.7% male. One sentence in the founding document dismissed much of women’s economic contribution, raising children, maintaining households, caring for the elderly and sick, as “of little or no importance” to the national accounts. Duncan Ironmonger calculated that Australia’s household economy was 78% the size of its entire market economy. The UK’s Office for National Statistics put household production at 63.1% of measured GDP. The International Labour Organization estimated 16.4 billion hours of unpaid care work performed daily, worth $11 trillion a year, three times the global technology industry. By the conventions of national accounting, all of it has zero value.

This is not ancient history. It is not a resolved methodological debate. It is the same production boundary, updated but structurally unchanged, that is about to encounter AI-generated output at industrial scale. AI could push a large fraction of work out of the priced-and-produced region into just-produced, with the cost delinked from the production.

The economist Margaret Reid proposed a test in 1934 that remains the sharpest diagnostic: if work could be delegated to a paid third party, it is productive. When a family hires a housekeeper, the housekeeping enters GDP. When a family member does the same work, it does not. The act is identical. The accounting treatment depends entirely on whether money changes hands.

AI makes virtually every information task delegable. A large language model can draft a legal brief, analyze a financial statement, write a marketing plan, triage a patient complaint, generate code, or compose a research summary. In each case, the work was previously performed by a paid human and counted in GDP.

If an AI is asked to take notes on a medical consultation today, the only place that transaction can show up in the national accounts is buried in the bill from the AI company. Nowhere is the use itself reported in a way that would let disinflation or output be calculated correctly. We are using the same old GDP ruler we always have, while the production function pushes more of the economy into the no-man’s-land of Dark Output.

A candid note on what our own framework inherits from this history. Displacement Dark Output measures only paid market labor: BLS wages, BLS employment counts, O*NET work activities. It does not measure AI’s impact on unpaid care work, household production, or the informal economy. We invoke Waring and Ironmonger to establish that the production boundary is constructed and politically contested, then build a measurement system that operates entirely within that boundary. This is a deliberate choice, not an oversight. The data infrastructure for measuring market labor displacement (BLS, O*NET, employer surveys) exists and is auditable. The infrastructure for measuring household AI adoption does not, and inventing it would stack measurement uncertainty on measurement uncertainty. But the limitation is real. Dark Output reproduces a known exclusion. The 16.4 billion daily hours of unpaid care work that the International Labour Organization documented are no more visible in our framework than in the one we critique. We do not claim otherwise. Our key observation is that the problem documented by these alternative frameworks is about to get worse. Much of the displacement we measure also risks falling disproportionately on occupations with high female employment shares (administrative work is 72% female, BLS). We do not yet disaggregate by gender, but it is a logical extension.

All AI uses in the non-transactional production sphere are another form of Dark Output. When someone uses AI to do a domestic task faster, more easily, or better than before, the activity does not move from produced/priced into produced/unpriced; it just enlarges the produced/unpriced economy.

Appendix 2. AI and Feminist Economics

Since the 1990 Griliches conference, service accounting has improved, but in targeted ways. BLS expanded service producer price indexes, with PPI service coverage reaching more than 70% of the services sector by 2009 and the headline PPI system moving to Final Demand-Intermediate Demand in 2014 to include services, construction, government purchases, and exports. BEA moved to chain-type Fisher quantity and price indexes, integrated GDP-by-industry with input-output accounts, capitalized software and R&D, and improved treatment of difficult sectors like finance, insurance, and R&D.

But the core problem remains. BEA still says most detailed NIPA components are measured in dollars, not units, so real quantity is usually estimated by deflating current-dollar spending with a price index. That works tolerably when the transaction, product, and price index all still describe the same thing. It breaks down when AI moves service work into subscriptions, tokens, or internal production. The accounts can see receipts, wages, and sampled prices, but not necessarily the legal memo, literature review, HR task, or code review that still got done. It also has no unit of quality, if an AI augmented literature review is 10x more exhaustive there is no current method to capture that fact.