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ARTICLE · 13 MAY 2026

AI Training Data And Transfer Pricing

Mayer Brown
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Transfer pricing complexities emerge as artificial intelligence systems increasingly rely on vast training datasets, raising critical questions about how multinational corporations should value and allocate these intangible assets across jurisdictions. The intersection of AI development and international tax law presents novel challenges for determining arm's length pricing when data crosses borders within corporate structures.

United StatesTechnology
Anthony D. Pastore
Anthony D. Pastore
Jason Osborn
Jason Osborn
Lucas Giardelli
Lucas Giardelli
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“Data is the new oil.” When a mathematician made this observation two decades ago, he was addressing an audience of marketers who were sitting on mountains of data but struggling to “refine” it into something valuable. In the artificial intelligence (“AI”) era, hyperscalers, AI labs, and other companies often face the opposite challenge: they know how data can train their models, yet they struggle to find enough of it. One option might be to acquire data from third parties. But a better option could be to obtain it from a foreign affiliate, since that data might be more directly relevant to the company’s business.

This article examines the transfer pricing questions that can arise in this space. Suppose a U.S. technology company wants to create a new AI model. It writes the model code and owns the relevant IP in the United States, but it needs spe cific user or customer data to train the model. This data includes not merely con tact information like names and addresses, but also the

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This Mayer Brown article provides information and comments on legal issues and developments of interest. The foregoing is not a comprehensive treatment of the subject matter covered and is not intended to provide legal advice. Readers should seek specific legal advice before taking any action with respect to the matters discussed herein.

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