Future of general LLM work (interp/inference/alignment) vs agentic/physical AI (VLA, multimodal) for career [D]

I'm at a crossroads with two grad school options that would take me in somewhat different research directions, and I wanted some general advice on these fields, their growth, and industry alignment. I'm leaving out the specifics of the programs since I'm tryna compare the research/career trajectories.

The first direction is general LLM work, like alignment, safety, optimization, interp, etc. (different subfields but centred around LMs). From what I've seen, jobs around LLMs seem much more common right now, including ML systems/infra roles, and the skills are fairly transferable across different areas of ML.

The other direction is agentic/physical AI, like agents, multimodal, VLAs, robotics, etc. There are fewer roles right now and fewer companies doing this kind of work, but there's a lot of investment/hype in it, and there seems to be a lot of growth. At the same time, it's also more specialized and harder to enter since there are more prereqs (vision, robotics, etc.). Also yes, agentic AI and physical AI are very different, but those are the research fields for one of the grad schools.

I'm wondering how people view the trajectories of these fields over the next 4-6 years. Which is safer to bet on, upsides, etc. I know a lot changes in this time and how the field is always growing and changing, and both of these fields will probably still be alive by then, still asking tho.

More importantly, I'm wondering about the transferability between these fields. How difficult would it be to go from LLM/interp/inference work/research into multimodal/VLA/physical AI later, versus going from physical AI/VLA work/research back into more general foundation model work? Which skills from either direction will remain valuable? Obv it depends on the type of research done, someone doing optimization in VLAs will be able to transfer more easily than someone doing video depth research, and I know there are a lot of other factors involved like degree type, advisor, location, etc., but for this question I'm mostly interested in the fields themselves.

my current thoughts are that general LLM work is safer and more transferable while physical/agentic AI is more specialized with more growth potential, but idk, things can change. The reason I'm asking is that the grad school with agentic/physical AI is significantly stronger for research/work/funding/advisor, and would set up a better career, BUT the research field is more specialized, hence the transferability question. I know a PhD develops transferable research/technical skills and are viewed well, but if the research/work done is specialized and hard to transfer, it'll be harder to find roles regardless. The other grad school is a master's and is stronger for its internship outcomes.

interested in hearing from people currently working/researching in either area.