A number of replies here are noting (correctly) how this doesn't have much to do with AI (despite some sentences in this article kind of implicating it; the title doesn't really, fwiw) and is more of an issue with cloud providers, confusing ways in which security tokens apply to data being shared publicly, and dealing with big data downloads (which isn't terribly new)... ...but one notable way in which it does implic…
For me it's also interesting as a potential pathway for data poisoning attacks - if you have control over the data used to train a production model, can you modify the dataset such that it inserts a backdoor to any model trained subsequently trained over it? E.g. what if gpt was biased to insert certain security vulnerabilities as part of its codegen capabilities?
Firstly, the malicious data needs to form a significant portion of the data. Given that training data is on the order of terabytes, this alone makes it unlikely you’ll be able to poison the dataset.
Unless the entire training dataset was also stored in this 38TB, you’ll only be able to fine tune the model, and fine tuning tends to destroy model quality (or else fine tuning would be the default case for foundation models — you’d train it, fine tune it to make it “even better” somehow, then release it. But we don’t, because it makes the model less general by definition).