[aheilbut]

The problem with this fantasy is that easily automated and distributed tasks are not the rate-limiting steps in most biomedical research. The hard parts (in addition to designing the right experiments and analyzing data..) are in constructing and validating relevant model systems and doing the specific experiments to address questions of interest.

These are extremely dependent on the question being studied and often are not amenable to automation, and may require very rare, expensive, and difficult-to-handle samples. For example, my collaborators work with transgenic mice that are a model for a particular disease, and these mice have to be bred then aged to 12 weeks until they exhibit the phenotype before we can even start doing an experiment. In another model, they have to do brain surgery on each mouse and then wait several weeks for the phenotype.

The 'easy' parts, such as DNA synthesis and sequencing, are already highly standardized and automated, and there is fierce competition to improve the technology and bring costs down.

[cing]

This is certainly the bottleneck in my research. I ran thousands of core years of computer simulation in the first year of my PhD. I have all the data I need to write a PhD thesis but I'm still years from graduating due to the aforementioned bottlenecks. An arsenal of software I've written in numpy/scipy/pandas saves time, but only goes so far when you're trying to carve out stories from your data to write papers.