For the literature review piece the key problem is that LLMs are exquisitely bad at working with even the simplest kind of scientific evidence: citations [1, 2]. They will get better, but it is not clear that LLMs can deal effectively with the very sparse kind of evidence that appears in the literature. Also, generating hypotheses isn't exactly the rate limiting step, the bigger issue tends to be when you get people with pet projects/hypotheses in positions of power that dictate funding priorities (e.g. the decades long Alzheimer's Aβ disaster).
For automation and instrumentation of labs the vision is on point and there is interest, and active work, if not large amounts of funding, to bring that vision to reality [3, 4, 5]. However, we simply don't have the tooling needed to be able to express the full complexity of experimental protocols in a way that can be verified. Sure you can write a python script to control a robot, but it is exceptionally difficult to extract the scientific meaning from that.
My PhD work was to develop a formal language for scientific protocols, and I'll be continuing to develop it, but there is still a long way to go.
1. https://doi.org/10.7759/cureus.39238 2. https://doi.org/10.1016/j.mcpdig.2023.05.004 3. https://www.youtube.com/watch?v=_gXiVOmaVSo&t=865s 4. https://doi.org/10.1109/JIOT.2020.2995323 5. https://ccc.ucsf.edu/sites/ccc.ucsf.edu/files/Marshall_W_CCC...