I got to work on interesting problems like language modelling, neural program induction, question answering / information retrieval, and conversation AI in the context of enterprise problems. Found the problem space incredibly fascinating and the fundamental research often lacking. I was able to get in a funded AI phd program this year. Many of the NLP and deeplearning challenges we faced in both startups have become the inspiration for my phd work (exploring augmenting deeplearning methods with external knowledge graphs for causal reasoning over text).
I went straight from undergrad to grad school, because 1) I had the privilege/opportunity of doing so and 2) I felt if I started collecting money in tech, I wouldn't be motivated to return to academia as I'd probably just make do with exploring and learning in my free time.
I originally built a product for data collection in order to answer my research questions, and realized that other people probably had the same problem.
If they could, something would be terribly wrong: Something that could be done by two cofounders in the proverbial garage, and paid for by commercial industry, was instead consuming valuable research funding. A PhD, like any form of research, prioritises in discovering the unknown and expanding what we know, rather than seeking a commercial goal. (The exception is projects that "win the lottery": they went digging somewhere that's useful research but commercially unpromising, and stumbled on something directly monetisable. This is the case for most biotech, but it is - by definition - rare.)
But the people who do PhDs...that's a different matter. By the time you're eligible to do research, you've hauled yourself to the frontier of your chosen field, and fully grokked everything humanity knows so far about your topic. This kind of deep expertise can often pay off in solving an adjacent commercial goal.
Take my history: I did my PhD in usable programming systems. The actual day-to-day work (controlled experiments, inventing and analysing imaginary hardware extension, writing papers) wasn't even a little bit commercially relevant. But the experience, the surrounding reading, the techniques, and the knowledge I picked up from colleagues really set me up for when I started my startup.
These days, I head up https://anvil.works, making a programming environment for the web that doesn't suck (along with a cofounder who did his PhD a few offices down, in human-computer interaction). Would Anvil make a good research topic? Hell, no - it's recycling good ideas from the last 20+ years. Do our research experiences make us really well positioned to solve this problem? Absolutely.
When I met my co-founder, he was actually already working on a solution for this. Obviously, we vibed immediately We're now working on a reading tool that allows you to build a knowledge base directly from your reading. The tool already supports importing and reading of pdf, epub, and capturing webpages. The tool has an integrated reader that allows you to do a whole bunch of stuff, including incremental reading and creating flashcards directly from annotations Various other features are in there and we have a long list of features coming up. We also have a native Android app coming out in the near future
This is what we're building: https://getpolarized.io Would love to hear your feedback. We're actually very close to a major release. if you're curious, you can play around with the beta of that release here: https://beta.getpolarized.io (you'll need an account to do so)
Not quite a startup in the chem space, but 100% a problem that I encountered every day and I'm sure many other PhDs, software engineers, and other knowledge workers encounter :)
