I think the opposite is actually true: you get better data when you don't feel the samples. This is a highly unpopular view right now but I have to wonder what's going on when things like Amgen getting 11% reproducibility of foundational papers (http://www.nature.com/nature/journal/v483/n7391/full/483531a...) are happening. There are things that are hard to automate because of their mechanical nature, but I think that for the reproducibility of science somewhat distancing the human from the process is a good thing. Of course, this adds short-term costs (which may sometimes be unacceptable) and requires a lot of behavior change for how people are used to working. I will say that we put a lot of thought into giving you the fidelity of interaction such that you can still make "breakthroughs from your errors" on Transcriptic, and improving those reporting capabilities are an ever-ongoing process.
I'll also say that this challenge is bigger than just one company. Some things may make more sense to do via Science Exchange, for example if the method requires some very customized hardware or there are only a few experts in the world who are sufficiently familiar with an unusual method's sensitivities. I'm also excited to see what Riffyn comes out with to help labs understand where reproducibility comes from. We're just getting started, but I can't see a path forward that puts more humans at benches rather than less. The humans should be free to do real science.
I have exactly the opposite opinion. Even scientists are captiviated by scientism - the idea that there is something poisonous about human subjectivity and imprecision and that removing subjectivity (and gathering more data) is necessarily a good. Sydney Brenner, for example has a 'money quote' about the path that biologists take: "low input, high throughput, no output science". Note that this quote doesn't make sense in an environment that doesn't faddishly flock to high througput 'big data' solutions.
Your example is greatly flawed. The biggest consumers of highly parallelizeable workflows is the pharmaceutical industry. Highly parallel medchem was a big fad and the number of drugs that it produced for its efforts is disappointing. The fact that 11% of Amgen's results are irreproducible is if anything a condemnation of parallel scaleup, at least in the context of an operator with a strong motive for selective interpretation.
Another big problem is that when you bring your numbers up, you 'get what you are looking for'. Precision optimization can optimize for an artefact. I joke I like to make is that sloppy science is good, because if you keep seeing the same result under a noisy platform, what you're seeing is probably real and, more encouragingly, robust.
You are absolutely correct that the lack of standardization in assay preparation harms productivity and reproducibility in the sciences.
Molecular biology has embraced standardization and automation to a great degree but it is an outlier.
I've encountered enormous resistance from practitioners trying to apply automation into cell biology.
Biology is enormously held back from the tools for improving productivity have to come from computer science but there is huge amounts of interdisciplinary friction.
I'll also say that this challenge is bigger than just one company. Some things may make more sense to do via Science Exchange, for example if the method requires some very customized hardware or there are only a few experts in the world who are sufficiently familiar with an unusual method's sensitivities. I'm also excited to see what Riffyn comes out with to help labs understand where reproducibility comes from. We're just getting started, but I can't see a path forward that puts more humans at benches rather than less. The humans should be free to do real science.