Ask HN: How did you turn your PhD into a startup?
I'm curious to learn more about folks who were able to build companies around their research ideas. Did you have know going into your doctoral studies that there may be commercial applications or discover it along the way. Also curious if there any interesting and complex problems where research applications could translate into business opportunities.
10 comments
Most PhDs shouldn't, or can't, be a startup.
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.
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.
I originally did a PhD in chemistry - spent loooooots of time organizing papers, research, files, etc... hated using platforms like Mendeley and many other inefficient file / knowledge management systems. Also extracting the information into one space and working with the knowledge was super clunky...
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 :)
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 :)
I’m selling a product that I built as a spin-off from my work towards an MSc.
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.
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.
how did you go from hey this might a common problem to let's build product?
I'd also be really interested to hear if anyone turned a startup into a PhD.
So I actually did that. I transitioned in applied AI research about four years ago (came from an creative writing background and did a variety of other things spanning technical project management, business analysis and data analytics). Over the past three years worked at two different AI startup where I lead most of the deeplearning research.
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 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).
Current graduate student here who would be absolutely thrilled to hear more about your story. What sort of creative writing did you do? What was your rationale for leaving industry?
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 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 wish could done it your way. My journey was rather haphazard but one I'm proud of none the less. Sorry for the confusion, I studied creative writing in college. I initially gained most of my tech skill volunteering to build websites for non-profits and self study and eventually got a masters degree. Most of my job came from being able to sit at the intersection of business and technical teams since I was communicate relatively well about technical to non-technical audiences.
Working with applied AI problems also showed many of the limitations of the current research. I felt there was opportunities to move the needle on fundamental research through applying to more complex problems.
As for leaving industry, it a mixture of motivations. Most my of experience in AI is through applied work and self learning. I really wanted to build a strong theoretical foundation so I could have to ability to solve more complex problems that I saw in our startups. The second was intellectual curiosity and passion. I'm fascinated by words, NLP and AI. I love storytelling and find it fascinating that our experiences both socially and with reality are mediated through language. In many ways working on NLP and AI has made think deeply about how we use language to communicate and be humans. Finally, I'm I kind lucked in opportunities to do deep learning research without a phd and formal training. This really isn't replicable outside of the startup space as most ML/AI research requires a phd.
Working with applied AI problems also showed many of the limitations of the current research. I felt there was opportunities to move the needle on fundamental research through applying to more complex problems.
As for leaving industry, it a mixture of motivations. Most my of experience in AI is through applied work and self learning. I really wanted to build a strong theoretical foundation so I could have to ability to solve more complex problems that I saw in our startups. The second was intellectual curiosity and passion. I'm fascinated by words, NLP and AI. I love storytelling and find it fascinating that our experiences both socially and with reality are mediated through language. In many ways working on NLP and AI has made think deeply about how we use language to communicate and be humans. Finally, I'm I kind lucked in opportunities to do deep learning research without a phd and formal training. This really isn't replicable outside of the startup space as most ML/AI research requires a phd.
Spark would be a good example