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SellYourAIData Research

Research on the market for AI training data

Source-cited reviews of how business data is bought, priced and licensed for AI. Every claim links to a public source.

6 results

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The State of Business Data Licensing for AI, 2026

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-001

Credible evidence shows licensing of platform content, publisher archives, stock media, clinical datasets and creator video, and newer reporting describes sales of internal records by closing companies. Intermediaries and pay-per-use...

Reported AI Data Licensing Deals, 2023 to 2026: A Review

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-002

We tabulated 33 reported arrangements across platforms, news publishers, books and academic publishing, music and entertainment, stock media, bankruptcy and wind-down sales, and expert-data programs. Financial terms were undisclosed for...

Which Enterprise Data Types AI Developers Value, and Why

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-003

Buyers name a consistent set of enterprise data types: team messaging and email, documents and procedures, ticketing and project records, CRM and business-platform records, and source code with its review history. Their stated value...

Pricing Models in AI Training Data Licensing

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-004

Reported structures include fixed annual fees, one-time payments for historical archives plus recurring fees, per-unit rates for images, video, text and books, tiered ranges on buyer program pages, per-task and per-environment prices for...

Legal Frameworks for Selling Business Data to AI Developers

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-005

Across regimes the central questions are whether the data is identifiable, whether the seller's notices and contracts permitted the transfer, whether de-identification meets the applicable standard and can be evidenced, and whether the...

De-identification Methods for Enterprise Text Data

SellYourAIData Research. 2026 Oct 20. SYAD-R-2026-006

Rule-based, statistical, transformer and LLM-based detectors perform strongly on benchmark categories such as names and emails in clinical text, but recall is lower and more variable for ambiguous categories such as usernames and keys, and...

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