Everything tech giants will hate about the EU’s new AI rules

The European Union is moving to force AI companies to be more transparent than ever, publishing a code of practice Thursday that will help tech giants prepare to comply with the EU's landmark AI Act.

These rules—which have not yet been finalized and focus on copyright protections, transparency, and public safety—will initially be voluntary when they take effect for the biggest makers of "general purpose AI" on August 2.

But the EU will begin enforcing the AI Act in August 2026, and the Commission has noted that any companies agreeing to the rules could benefit from a "reduced administrative burden and increased legal certainty," The New York Times reported. Rejecting the voluntary rules could force companies to prove their compliance in ways that could be more costly or time-consuming, the Commission suggested.

The AI industry participated in drafting the AI Act, but some companies have recently urged the EU to delay enforcement of the law, warning that the EU may risk hampering AI innovation by placing heavy restrictions on companies.

Among the most controversial commitments that the EU is asking companies like Google, Meta, and OpenAI to voluntarily make is a promise to never pirate materials for AI training.

Many AI companies have controversially used pirated book datasets to train AI, including Meta, which suggested that individual books are individually worthless to train AI after being called out for torrenting unauthorized book copies. But the EU doesn't agree, recommending that tech companies designate staffers and create internal mechanisms to field complaints "within a reasonable timeframe" from rightsholders, who must be allowed to opt their creative works out of AI training data sets.

The EU rules pressure AI makers to take other steps the industry has mostly resisted. Most notably, AI companies will need to share detailed information about their training data, including providing a rationale for key model design choices and disclosing precisely where their training data came from. That could make it clearer how much of each company's models depend on publicly available data versus user data, third-party data, synthetic data, or some emerging new source of data.