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Notes -
I am in experimental nuclear/particle physics, more on the making-stuff-run than paper-writing side of things. For me personally LLMs are a huge boon.
I mean, on the one hand, they have made huge parts of my skillset obsolete. Before I was one of the few people in my department who knew how to run gdb to hunt down a segfault in some terrible ROOT program. This is not an employable skill any more -- Claude can do this far better and more reliably than I ever could at a tiny fraction of my cost.
But as someone lucky enough to have a job, Claude is like having a PhD student who happens to have read every book on every programming language ever working for you. Tasks which would have taken me half a year (because I lacked the specific skills) can now be done in weeks. I have even taken a liking to systemd of all things, because I don't need to learn the syntax myself. Going down some Linux rabbit hole (how do I figure out why an interrupt handler runs at 100% cpu?), which would have taken me days before is now a matter of an hour.
Still, it used to be that in my field, a lot of students were employed in data analysis. Generally, this involved writing terrible ROOT code to get some physics channel out of a dataset, then turning this into a publication and a PhD thesis. In the short term, they also benefit from LLMs -- at least for the code writing part, which used to be a majority of the work.
But solving for equilibrium, it seems unlikely that anyone will employ PhD students for data analysis in the future. They are expensive (compared to LLMs), they suck at programming, they start out with a lot less domain knowledge than the bots, they come with interpersonal conflicts which require management. In the past, there was no way around them (short of employing programmers, which would take larger salaries and require physicists to work interdisciplinarily). These days, a senior physicist can probably use an LLM agent to do some analysis without spending more time mentoring than she would for a PhD student.
Data analysis aside, experimental physics still contain plenty of manual tasks which are not easily automated. We have used PhD students to glue photomultipliers to scintillators before, and these jobs will still require doing.
The other question is the long term career path of physics PhDs. Before, most of them (in my field) ended up in the software industry, which is what kept our system running. Prospects there are probably not so great at the moment.
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