It's undeniable there's a metric gigaton of hype surrounding the technology. It drives the enormous amounts of money and infrastructure being poured into it, which in turn demands more hype to justify the investment. The history of hyperbole is already evident, as new models continue to be trained to reach promises which now-retired models were already supposed to deliver.
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.
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