The idea seemed to many nothing short of absurdity. Before then, models were already considered large-scale if trained on a few dozen chips. In top academic labs at MIT and Stanford, PhD students considered it a luxury to have ten chips. In universities outside the US, such as in India, students were lucky to share a single chip with multiple peers, making do with a fraction of a GPU for their research.
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Without Ukie’s services, Graceware can no longer issue wide-scale automated takedowns for Cookie’s Bustle.
Andrej Karpathy described the pattern: “I ‘Accept All’ always, I don’t read the diffs anymore.” When AI code is good enough most of the time, humans stop reviewing carefully. Nearly half of AI-generated code fails basic security tests, and newer, larger models do not generate significantly more secure code than their predecessors. The errors are there. The reviewers are not. Even Karpathy does not trust it: he later outlined a cautious workflow for “code [he] actually care[s] about,” and when he built his own serious project, he hand-coded it.