r/Physics • u/RoundElephant5876 • 6d ago

employed physicist

Those of you who have completed research physics and are currently working, how is it, what exactly do you do, are you satisfied, do you work inside your country (and if yes, which one) or abroad, online, how difficult was it for you to get your current job?

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u/slopoSpeshalK 5d ago

Not sure if an UNemployed physicist has the right to chime in here but the job market has been quite tough of late. I graduated with a PhD in computational Quantum Physics, and I am trying to enter the job market in Quantum startups as a scientific software dev. I'm hoping to be the guy that has cleaner code than the physicists but knows more physics than the softwware devs, kind of working at that interface, but it has been quite tough to get past the CV sending stage. I know these roles exist but jobs are typically curated for one or the other. It seems to be a lot about who you know these days, getting a referal fro different people you met throughout your PhD.

In the mean-time I got hired for a part time job as an AI expert promt engineer. Basically you go through your PhD or research paper hisotry and try to generate the meanest most contrived cutting edge problems from your very specific neiche and hope that whatver AI company is funding your project has an LLM that never seen anything like it before and gets stumped. It can be a quick buck but I have found the industry to be very fast paced, and sometimes quite exploitative. Not to mention that you are selling your knowledge to the highest bidder, but I can't choose too much in this economy.

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u/Ok-Parsley7296 5d ago

Im curious (and it will impact me in the future i guess), what kind of specific problems do you find that AI consistently fails at? Do you see this as a structural issue of current LLMs (like lack of extrapolation), or do you think it's just a matter of time and scale before they become perfect?

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u/slopoSpeshalK 4d ago

It is just a question of data. It just needs to see more solutions to problems it never saw before. It doesn't fail at much anymore to be honest. It does not do stupid mistakes, it usually has access to coding languages so given enough tokens in can interate and solve things fast. It scraped the whole of stack exchange and other forums, and has had an army of PhDs feeding it Uni grade problems for money over the past two years. Its learned basically everything we know and is getting real good at applying it. (the newest versions the public has no access to at least)

That being said, it has no way of anticipating the cutting edge of science. Things are new for us, our hypotheses fail all the time, and scientific consensus can be proved wrong for decades and decades. Things that we think we know but aren't true are a blind spot for AI becuase it learns of what we know. Unless the finding comes a huge set of different fields with huge banks of data and no corssover experts and the AI could identify a pattern (it is just one big probabilistic matchmaker after all).

The next little goldrush in the Physics tasking industry will be media. The AI must extract PhD grade data from plots, gifs videos and use reasoning methods to solve some task. But it will get good at that soon enough too.

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u/Ok-Parsley7296 4d ago

yeah, i'm doing my master in physics and honestly it can solve every homework problem, or exam problem, even if its phd-level. the thing i see is that the fast models are dumb, like its obvious that what makes these things so smart is exploring a lot of responses and choosing the best one, and thinkng for a long time. and i think that is bc what they do is they explore a lot of option branches and choose the best one. but think about the erdos problem chatgpt 5.5 has just solved, its a counterexample type of problem, so easy to try a lot of things and see wich one is the best.... and its the same in every textbook exercice, also there is data leackage

What im trying to say is that reasoning is expensive, and it gets more expensive as the complexity of the problem increases, and also when it does not have enough data to extrapolate so the "universe" of plausible options becomes big. thats where the limit is i think, there is not a single model where the fast response doesnt make silly mistakes all the time, and you dont want to spend 100 dollars in order to clean a dataset for example, a human would be cheaper at leat in the near future, but its scary. anyways, there is a reason why there arent super relevant papers done entirely by ai. i think they have tested chatgpt 5.5 in a lot of math problems, a lot, and it could only solve this one, a counterexample in number theory, but you seem to think ai is more capable than that right?

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u/slopoSpeshalK 4d ago

Yes it is more capable, but more so as a tool delegated to to find (sometimes unexpected) paths to solutions of interest. It has a much wider web of immediately accessible data than any human could ever imagine, but struggles to make sense of it when not given tangible direction. The relationship will have to be symbiotic, and I expect that to be the case until we move on from LLMs.

Even in the task engineering job I mentioned, the LLMs are solving quite complex and contrived problems with relative ease, but the solution has to always be of integer or fractional form and there are strict guidelines that the LLMs knows it can expect. It cannot do the human thing of extracting all the knowledge from my tasks and then becoming an active member in my field of many-body quantum systems, contributing to research literature and inventing novel ways of testing Quantum matter on its own.