Those two buckets only contain the work in physics that have a sustained presence in popular media. But take gravitational wave astronomy as a counterexample. It doesn't make it into the news much, but I'm pretty sure the entire field is less than ten years old.

Yes, good example. They already got a Nobel prize, one of the quickest in history.

This is the kind of physics we might need more of.

So then you could argue that that's what the folks who are smashing particles and building new models are trying to get to.

Smashing particles helps test existing theories and hypotheses. We do it with particle accelerators because that's one of the ways of getting to the uncharted territory, which is where you need to be if you want to push the boundaries. And maybe remember that the sexy stuff that makes it into the news isn't the whole of the thing. The LHC is also, for example, doing practical climate science: https://en.wikipedia.org/wiki/CLOUD_experiment

And building new mathematical models is part of figuring out how to make sense of observations that don't quite fit the current models. That is a messy process, and I think that our retrospective perspective on what that process is like might be colored by survivor bias. We remember Einstein and his theory of special relativity. We mostly don't remember the preceding few decades' worth of other attempts by other physicists to resolve conflicts between existing non-unified theories (in this case Newton's and Maxwell's models) or making sense of things like the Michelson-Morley experiment. I don't really know that history myself, but I would not at all be surprised if many of those efforts were also having trouble figuring out how to produce testable hypotheses.

And also, big picture, I think that it's important for any lover of science to remember to celebrate the entire enterprise, not just its headline successes. Expecting consistent results is tacitly expecting scientists to have some way of knowing ahead of time which avenues of inquiry will be most fruitful. If we had access to an oracle that could tell us that, we wouldn't actually need science anymore.

> They already got a Nobel prize, one of the quickest in history.

From some aspects, it was late. Gravitation waves were predicted decades ago. It's almost unfair to predict something but then have engineering take decades to catch up to be able to prove/disprove the theory. This is just commentary on the notion of being right decades before the world is ready for it. Of course, it can go the other way where one is assumed to be right but then isn't (e.g., many components of string theory).

Just because there happens to be economical viability for a field currently doesn't mean that field needs less introspection. Exactly what research contributions the people who are throwing hundreds of millions of dollars worth of GPUs at the next random "research" problem at the top of the queue at Microsoft or Google are making to deserve a Nobel?

Too often there is near zero intuition for why research in AI yields such incredible results. They're mainly black boxes that happen to work extremely well with no explanation and someone at a prestigious institution just happened to be there to stamp their name on top of the publication.

Big difference between research in AI and any non-computational/statistical/luck-based science.

> most physics

That's an interesting definition of "most physics". I mean, I find high-energy physics as fascinating as the next guy but there are other fields, too, you know, like astrophysics & cosmology, condensed-matter physics, (quantum) optics, environmental physics, biophysics, medical physics, …

The corners of physics I have some contact with (climate/weather modelling and astrophysics) seem pretty dynamic to me. Each generation of CMIP models seems to be significantly better than the previous.

What's the core technology making these models significantly better?

Not neural nets. CMIP models are largely dynamical models.

Awesome, where do I learn more about this?

I wonder if your list approaches AI at some point!

The Nobel committee seems to think so