Hype rather than realistic attempts to solve things is actually a valid explanation for that. They want to give the impression it’s a solve-everything machine even if it’s not well suited to areas outside of CS and math (or really anything where the logic of what is being done is not 1:1 with syntax, which is where LLMs are clearly excelling)
Was anyone under the impression that AI would, in its very first attempt at biological research, suddenly be able to get answers without having to need physical follow-through on its leads? Even if AI had a hypothetical cure for cancer, it would need a human with cancer and a biolab and governmental approval to actually do anything with it.
The nature of biology makes the feedback loops much larger and more expensive, which means the AI has to follow the "normal" collaborative scientific process instead of being able to hunker down in its proverbial study and work all night.
Why can’t it build a simulator for this stuff? It has textual and formulaic explanations of everything it’s working with. Seems like it should be less than a days work if it can solve all of math in a few hours?
Any computational bottleneck on running such a simulator should also be solvable right, Anthropic have already told us this is the singularity already
If you are saying you have made a really cool piece of enterprise security automation software, then okay I am impressed this is a very powerful fuzzer.