In early 2026, OpenAI stated that DeepSeek had inappropriately based its model on ChatGPT by using OpenAI's data

Overview Debates Related claims Topics Sources

Courts should order the destruction of AI models trained on unauthorized copyrighted data

AI companies should be allowed to train on copyrighted content

Licensing AI voice clones does more harm than good for professional voice actors

Suno's v6 AI model is legally tainted by its previous infringing versions

Record labels bear responsibility for undermining musicians' livelihoods by licensing music to AI

Training AI models on copyrighted news constitutes fair use

AI music platforms should be liable for imitating artists' styles

Suno's training of AI models on copyrighted music constitutes fair use

Suno's licensing deals with major labels protect the livelihoods of musicians

Universal Music Group should license its artists' voices for AI music platforms

Major-label AI licensing agreements unfairly exclude independent and session musicians

AI-generated music should carry mandatory disclosures even when fully licensed

Distillation is legitimate competition, not intellectual property theft

The Anthropic settlement's default 50-50 split between authors and publishers is unfair to writers

Training AI models on legally acquired copyrighted books should be considered fair use

AI model distillation from competitor outputs constitutes intellectual property theft

The joint advisory alleged that Chinese AI firms routed distillation requests through native APIs, remote cloud providers, and third-party aggregators that automatically obfuscate user metadata to avoid detection

The joint advisory stated that the distillation campaigns were conducted "likely with Chinese government awareness," though it did not claim that Chinese intelligence played a direct role

The joint advisory accused DeepSeek of running an organized campaign since late 2024 to feed synthetic training data into its R1 and V3 models, calling DeepSeek's widely cited $5.6 million training figure misleading because it excludes the cost of distilled data

The joint advisory recommended that U.S. model providers monitor for anomalous prompts, usage spikes, and immediate maximum usage from new accounts to detect distillation campaigns