---
id: "quote-data-valuation-objection"
type: "quote"
source_timestamps: ["¶2"]
tags: ["industry-defense", "transaction-costs"]
related: ["claim-data-valuation-feasible", "contrarian-data-valuation-possible"]
speakers: ["E. Glen Weyl", "Raul Castro Fernandez"]
speaker: "E. Glen Weyl and Raul Castro Fernandez"
sources: ["tail1"]
sourceVaultSlug: "hbr-seg-tail1"
originDay: 1
articleStem: "hbr-tail-109-ai-pay-fair-rates-content"
sourceUrl: "https://hbr.org/2026/06/how-ai-companies-can-pay-fair-rates-for-the-content-they-need"
sourceTitle: "How AI Companies Can Pay Fair Rates for the Content They Need"
---
# The technical objection to data valuation

> "AI companies counter that training on available data constitutes fair use and that even if a market in data were desirable, compensating millions of creators is technically impossible: the cost of figuring out what any given piece of data is worth, researchers have argued, would swallow most of the value that data creates in the first place."

— [[entity-e-glen-weyl|E. Glen Weyl]] and [[entity-raul-castro-fernandez|Raul Castro Fernandez]]

## Context

The authors summarize the primary industry defense they spend the rest of the article dismantling. This is the objection that [[claim-data-valuation-feasible]] and [[contrarian-data-valuation-possible]] directly refute — by showing the valuation metrics ([[concept-data-mixture-weights]], [[concept-scaling-laws-valuation]]) are already produced for free during training.