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That is a very interesting example but it serves very much to illustrate my point: this is latent knowledge, we effectively already have it but we just haven't put 2 and 2 together. Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.

So while you start off with 'disagreement' in fact it appears we agree far more than we disagree. Yes, it is important that this work is done. But you clearly illustrate that the envelope did not in fact expand. But all the time scientists around the world do in fact expand the envelope of our knowledge and one of my fears is that by undercutting a lot of such knowledge work by putting this digital combine to work on it that we will lose the infrastructure to do the rest. We'll see a lot of progress in a very short time and then we'll find out that because we took away that class of problems that it has become almost impossible to keep the infrastructure, incentive structure and educational paths in place to be able to do the next round.

You see a similar thing happening in software development. All of a sudden it has become a lot harder to start as a career. And lots of people are not even going to try this direction if there is no first rung on the ladder.

Of course it is progress. But there are orders here: filling in the blanks is super useful and for sure you can - or should I say could - make a living with that. But there is another level above that which is where the total area is increased. That's not meant as a sleight against people that were ensuring all the ground was covered, it's just establishing the fact that there are multiple levels to knowledge.

If you want to make it seem as if I'm pissing on people that were doing useful science that's your privilege, but there is a reason we use terms like 'groundbreaking' about discoveries that are different.

As for lost careers: I've been schlepping bits my whole life, and probably an RTX 6000 could re-run that in under a year, and no doubt what I did was of less significance than that done by the lowliest of scientists. Still, I don't derive my sense of self value from what future technology can do any more than I think that a day laborer should compare themselves with a tractor or a combine.

I'm not sure of your background, but FWIW everyone in my PhD program in my elite CS program had varying levels of anguish and angst when realizing that their efforts were mostly suited for "coloring in the blanks." The PhD Grind [1] felt like a poignant capture of those feelings.

[1]: https://dokumen.pub/the-phd-grind-a-phd-student-memoir-3.htm...

You are just echoing the words of the previous commenter in this thread. For my answer, please see there to avoid repetition.

Typical quantum simulation papers do not target 100% accuracy, they target "chemical accuracy [1]". That said, chemistry simulations do tend to be less tolerant of approximations because you are sampling an unknown distribution and so can't easily tell if a shot had a failure. But the linked article is about a subroutine for Shor's algorithm, where it is very obvious if you succeeded or not because you either recovered the private key or you didn't.

[1]: https://chemistry.stackexchange.com/questions/62001/why-is-c...

i just skimmed the article, but it does sound like it would be detectable if the case you had is one that the algorithm doesn't handle. In any case, it sounds like it is very rare that you have one of those cases vs existing heuristics, which is a simplification every time.