Comment by ungovernableCat

I don't think that's at all an accurate characterization of prior skepticism, and indeed it's quite obvious how the systems we've built to improve AI code generation work, and they were also an obvious thing to try (in parallel with the models themselves getting more capable).

Despite the improvements, though, we still see plenty of code that is "slop", because it's directed by someone with little idea of what to do, but also because it's harder to test for (or even measure) code quality than correctness. The "solved problem" rhetoric depends very strongly on your standards.

In other fields the problem is similar, and typically worse. Plenty of AI music sounds acceptable, not clearly flawed, and even mildly "interesting" to me; but I think this is much more a commentary on music than it is on AI. Prose continues to be abhorrent in nearly every application; while I've seen examples of people getting really impressive output, it takes a level of effort that defeats the purpose of almost everyone who wants AI to assist their writing (i.e., just generate a comprehensible report on some findings and get on with their day). Art is a whole different world; the quality of results clearly strongly depends on the quality of prompting, it's much better/worse in some genres than others, humans vary widely in their ability to notice "mistakes", etc.

Eventually maybe, but the problem is hard from both directions. Answering basic questions such as "what does aspirin actually do at a biochemical level?" or "design a drug to reduce beta amalyoid plaques" are both very non trivial. Nevermind something complex like 'cure cancer'.

It's often not necessarily about visibility, but real iteration time.

For instance, let's say we are doing biology on plants, which is still much easier than doing human trials. No matter how motivated claude might get, you aren't getting a corn plant to grow in 40 seconds. Real world research pipelines involve stages where even greenhouses don't do, and sacks of grain are shipped to Hawaii, or Brazil, or wherever, to try to get practical data for a given test.

If your computer simulation is so accurate you can skip a step now done with biology, great, but it gets harder the larger your problem is. I might see if a molecule kills bacteria kind of quickly, but what does a molecule do to a real human being? Not so easy.

I don't follow how "we can see tiny stuff" = "it is possible to build closed system"?

Seeing tiny stuff doesn't magically give you enough info for Claude to reason with the system predictably (the way it can with source code).

No its not, this is complete dunning kruger, please provide an example.