American and Chinese AI models: different companies, surprisingly similar data pipelines
The public discussion around artificial intelligence often treats American and Chinese models as if they come from completely different technological worlds. Politically and legally, they do operate under different jurisdictions. Their companies have different ownership structures, regulatory obligations, cloud environments and terms of service. Those differences matter.
But when we look at how the models themselves are trained, the broad pattern is strikingly similar. OpenAI, Anthropic, Google, Meta, Alibaba and DeepSeek all rely on some mixture of large public datasets, licensed or partner material, human-created examples, synthetic data and extensive post-training. The details vary, but the core industrial process is recognisable across borders.
For Māori organisations, this means “American model” versus “Chinese model” is not a sufficient risk framework. The better questions are what data sources were used, how transparent the developer is, what product is being used now, where current prompts are processed, what jurisdiction applies, and whether the model can be run locally without sending new Māori information back to the developer.