You disagree with the statement "there were always obvious fixes that could've prevented it" with the response "no no, these were very obvious fixes that could've prevented it?"
You're missing the point about complex failures.
It's that if this particular path were unavailable, there are countless other similar paths. At sufficient scale and complexity, hitting one of those other countless paths is virtually guaranteed.
Let's say I drive Los Angeles to New York City. You look at the route I took and say, gee wiz, aren't you lucky that a tree didn't fall right there on Route 66? If a tree had fallen there (if we had "patched the particular route you took"), you would've been screwed!
But that's obviously not true. There were an infinite number of routes I could've taken. Any one of them would be equally "obviously preventable" by the same hypothetical tree falling across the whatever road I happened to end up taking. But you can't put trees across every single path between Los Angeles and New York City. The smarter I am and the more complex the map between us, the more impossible it becomes to put trees across all possible paths.
I disagree. To use an analogy, air travel in the US is relatively extremely safe - not 100%, but we've built up a culture around air safety that is very robust. Conversely, when I order packages online, sometimes they never show up, or the box is banged up, or the box is missing things, etc.
They're both complex systems, but clearly there is a much higher level of care given to human air travel than package delivery. A lot of the article basically saying that OpenAI gave "package delivery" level of care when they should have given "air travel" level of care.
At the very least I think the systems that run these tests should be fully, 100% air gapped. I'm not pretending that's easy given how much compute and data these systems use, but it is doable, and I think all AI development should be paused until that can be assured.