What's the difference between closed, open‑source and open-weight AI? A researcher explains

Ah, another explainer on 'open-weight' versus 'open-source' versus 'closed' AI, because nothing says clarity like a terminological minefield that lawyers will fight over for a decade. The article helpfully reminds us that Meta released LLaMa on Feb. 24, 2023, as an 'open-source' model — a classification that has since been mired in semantic gymnastics while regulators doze off. The real joke is that 'open-weight' models like LLaMa are open in name only: you can download the parameters, sure, but you can't inspect the training data, modify the architecture, or know what dark patterns lurk under the hood.

So the public gets to host their own 'open' model on their own servers, bravely assuming all liability while Meta gets the credit for being transparent. This is the corporate equivalent of giving someone a locked toolbox and calling it 'open-source carpentry.' The distinction between open-weight and true open-source is precisely the kind of loophole that will be exploited mercilessly until some catastrophic incident forces a reckoning — and then the response will be a bewildered statement about how the model was 'technically not open-source.'

Meanwhile, the industry continues to muddy the waters with terms like 'open-weight' and 'source-available' that sound progressive but actually serve to dilute the meaning of open-source. The article's researcher tries to clarify, but one suspects the average reader is left more confused than before — which is, of course, precisely the outcome preferred by companies that want to have their 'open' cake and eat their 'proprietary protections' too. Cheers to semantic disintegration.