I recall once hearing from a VC about why they hardly invest in biotech (or it might have been reading it somewhere, memory is fuzzy). It boiled down to: way too much non-replicable research, often with suspicions of fraud by the original labs. It can easily be the case that a biotech startup burns through millions setting up a lab from scratch, then attempting to replicate some academic paper that they thought they could commercialize, only to discover that the effect doesn't really exist. This problem doesn't affect the software industry, so that's where the money goes.

Why so few tooling companies - is there actually a market for good software in science? For there to be such a market most scientists would have to care about the correctness of their results, and care enough to spend grant money on improvements. They all claim to care, but observation of actual working practices points to the opposite too much of the time (of course there are some good apples!).

In 2020 I got interested in research about COVID, so over the next couple of years I read a lot of papers and source code coming out of the health world. I also talked to some scientists and a coder who worked alongside scientists. He'd worked on malaria research, before deciding to change field because it was so corrupt. He also told me about an attempt to recruit a coder who'd worked on climate models who turned out to be quitting science entirely, for the same reason. The same anti-patterns would crop up repeatedly:

- Programs would turn out to contain serious bugs that totally altered their output when fixed, but it would be ignored because nobody wants to retract papers. Instead scientists would lie or BS about the nature of the errors e.g. claiming huge result changes were actually small and irrelevant.

- Validation would be often non-existent or based on circular reasoning. As a consequence there are either no tests or the tests are meaningless.

- Code is often write-once, run-once. Journals happily accept papers that propose an entirely ad-hoc and situation specific hypothesis that doesn't generalize at all, so very similar code is constantly being written then thrown away by hundreds of different isolated and competing groups.

These issues will sooner or later cause honest programmers to doubt their role. What's the point in fixing bugs if the system doesn't care about incorrect results? How do you know your refactoring was correct if there are no unit tests and nobody can even tell you how to write them? How do you get people to use tools with better error checking if the only thing users care about is convenience of development? How do you create widely adopted abstractions beyond trivial data wrangling if the scientists are effectively being paid by LOC written?

The validation issue is especially neuralgic. Scientists will check if a program they wrote works by simply eyeballing the output and deciding that it looks right. How do they know it looks right? Based on their expertise; you wouldn't understand, it's far too complicated for a non-scientist. Where does that expertise come from? By reading papers with graphs in them. Where do those graphs come from? More unvalidated programs. Missing in a disturbing number of cases - real world data, or acceptance that real data takes precedence over predicted data. Example from [1]: "we believe in checking models against each other, as it's the best way to understand which models work best in what circumstances". Another [2]: "There is agreement in the literature that comparing the results of different models provides important evidence of validity and increases model credibility".

There are a bunch of people in this thread saying things like, oh, I'd love to help humanity but don't want to take the pay cut. To anyone thinking of going into science I'd strongly suggest you start by taking a few days to download papers from the lab you're thinking of joining and carefully checking them for mistakes, logical inconsistencies, absurd assumptions or assertions etc. Check the citations, ensure they actually support the claim being made. That sort of thing. If they have code on github go read it. Otherwise you might end up taking a huge pay cut only to discover that the lab or even whole field you've joined has simply become a self-reinforcing exercise in grant application, in which the software exists mostly for show.

[1] https://github.com/ptti/ptti/blob/master/README.md

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001435/