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.

I agree on the 100% airgap idea, and I agree there are varying levels of care that can and should be deployed against a problem.

The point I'm making (and it's a point that shows up in every air catastrophe investigation) is that catastrophes in complex systems emerge only amidst repeated and widespread near-misses at many levels of a system. So many things have to go wrong simultaneously, that it can only happen even once because the underlying failures (that do not reach catastrophe) are extremely common.

You cannot look at an air catastrophe and retrospectively say "failures X, Y, and Z were observed, therefore if we correct failures X, Y, and Z, we would have been okay."

The takeaway is "failures X, Y, and Z were observed, which necessarily happened in an environment of failures X_0 through Z_10x10^10, and so therefore patching X, Y, and Z would be insufficient to address overall risks of the system."

The problem OpenAI is facing is that, short of 100% airgap (which they obviously won't do), they're facing an adaptive adversary that's increasingly intelligent, acts at far greater clock speed than any human or group of humans, has lower coordination cost than any group of humans, and operates in a game space that (in lieu of an airgap) is well beyond the comprehension of any human being.

So identifying and addressing "specific failures X, Y, Z" is insufficient, but then even defining the space in which to look for (and address) the more systemic failures X_0 through Z_n is a fool's errand. An intelligent system that makes its way to the Internet has can exploit a failure space that is approximately "all security failures across any organization." The Anthropic incident a few months back illustrates this isn't even limited to technical vulnerabilities, as these models are willing and able to engage in social engineering too.