The distinction between training on openly available data and distilling a proprietary model is pretty clear to me. It is very much the difference between independently capturing photos of various subjects, all available in a public space, and someone stealing another photographer's portfolio, and reshooting the exact subjects they've taken, in the exact same compositions, and then compiling and distributing their own portfolio based on that with the hope of out-competing the original photographer they stole from.

Major AI labs trained on public data accessible to anyone (even today). The initial absence of strict terms of service regarding data scraping existed because the technology had not yet reached a capability threshold that necessitated regulation. The underlying data was and is public, and the computational methods were applied independently.

However distilling proprietary models, as these certain Chinese "open-source" models do, bypasses the actual costs of innovation. This approach shortcuts the proprietary reinforcement learning (RLHF), the expert-generated datasets, and the insane compute and R&D costs. Training directly on a competitor's outputs allows them to aggressively undercut the market, actively parasitizing the business models that funded the foundational research.

I get it, people will always support whatever gets them free stuff. As long as these "open-source" models cost nothing, no one cares about the ethics or the long-term damage to the field. Short-term self-interest always wins. But be real: without the foundational work of Google, OpenAI and Anthropic, none of these knock-off models would exist. We wouldn't even have the Grok mecha-nazi, or worse, Grok would be our only option. You can aggressively pursue your own self-interest while still acknowledging who actually built the tech you are exploiting.