Buyer programmes for company data: eligibility, prices and payment terms of 37 organisations, October 2026
Year: 2026
Type: article
Abstract: Since 2026 AI data vendors, marketplaces and intermediaries have published programmes through which an ordinary company can sell or license its internal records for training AI models. This paper compares the published terms of 37 organisations with a public way in, as compiled in an open dataset reviewed on 16 October 2026, together with a register of the contract clauses 16 of them publish and a dated log of 110 market events. Twenty-four take company records, 17 of them buying for their own account. Of those 24, 14 publish a floor (median $100,000) and 9 a ceiling (median $1 million; highest $5 million). Eligibility rules are sparse: six publish a headcount minimum (mostly 20 to 40 employees), two a minimum of three years, three a rule about country or language, and eight say they take companies that are closing or closed. Where a time to payment is stated it runs from 7 days after the data passes review to about 60 days, and several programmes pay only on the buyer's acceptance. Exclusivity is stated by few and deletion terms by six; one programme's terms say deletion does not require deleting trained models. Ten publish structured introduction fees, up to $100,000 per deal. No published ceiling is backed by an independently reported payment of that size: one programme has payments confirmed by sellers, at tens to hundreds of thousands of dollars, and seven report their own. We describe the data, the classification and the limits of comparing posted terms. Keywords: AI training data; data licensing; company records; buyer programmes; data markets; contract terms; posted prices; referral fees Use of AI: this paper was prepared with generative AI tools, which compiled and analysed the open datasets and drafted the text. The author takes responsibility for its content. Competing interests: the author is CEO of AIData.Broker, which acts for companies that license their data to AI developers and publishes the open datasets the paper draws on. Also at aidata.broker/research/paper-buyers.
Author: Heymans, Yuma
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7222864642
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