mlsanityData to 14 Oct 2026

A Sustainable AI Economy Needs Data Deals That Work for Generators

arXiv 2601.09966 ↗First posted 15 Jan 2026cs.LG cs.AI cs.HCcited by 3

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Abstract (arXiv)

We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults - missing provenance, asymmetric bargaining power, and non-dynamic pricing - as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 40 of its 132 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

40 of 132 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026How Hyper-Datafication Impacts the Sustainability Costs in Frontier AIcited by 1
  2. 2026Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AIcited by 0
  3. 2026An Investigation of the NeurIPS and ICML 2025 Position Trackscited by 0