Nature has written a few times about FROs, see https://www.nature.com/articles/d41586-022-00018-5.
It is worth noting that more conventional for-profit startups which are heavy on R&D can also benefit from government grants and tax rebates.
I am also trying to spin off from Academia. If you are interested in connecting, see email in my profile.
-- Have a founder / CEO / lead type who is well-connected and can play the part of "mad scientist." For this company, it was a guy with a phd in something data related who was very good at storytelling.
-- Find customers who can benefit from your research. In this company's case, it was government (which can take a lot of forms - cities, school districts, etc), non-profits, university researchers... from what I could tell, they were good about letting someone else find the grant dollars and they would provide the research. Over time they were able to take their reputation from those projects and then start selling the same process to corporate America.
-- This company had very little technology or even secret sauce. Sure, they tried to make it look like they had some magic, but once you started peeling away layers, it was clear that they were mostly just doing a lot of data crunching that no one else quite had the desire or skills to do. They delivered a result rather than a lot of worry about how they got there. That same message likely applies today - lots of young companies focusing on some magic new algorithm, but paying customers generally care a lot more about a result that has value to them over the tech you used to get there.
* Initial Graphistry funding was from showing compelling 10X technology demos started in my PhD through my extended network, which got us angel & seed and first early adopter customers, which let us hire a tiny team to build the real tech (which pivoted at our first 'big' customer) and start on our product discovery journey . Before then, ran out of my savings, and during a funding dry spell, same. All new R&D for last few years has been customer funded.
* Louie.AI: We have been reinvesting revenue here and signing on paying design partners to co-build with . (If you are on Splunk, OpenSearch, Databricks, AWS Neptune, or a graph DB, and want to use genAI for investigation & analyses, lmk!.) Skipping VC $ has felt much better as we are much more aligned with our design partners.
If I was going to do it from scratch again, I would either do a PhD and spin out when ready, or do a consultancy and build out from that. Using VC dollars and hoping you march into product market fit is horrible, a lot has to work out just right, yet you are trying to cook with a flamethrower and short fuse.
I'm not sure that's an elevator pitch to wow investors. I mean "I want to do research" sounds more like a career objective than a business. There's nothing wrong with that.
Startup seed money is a bet on potential.
If you really want to start a research company, start a research company and figure out the money later. If you want to escape academia, you might just get a job in the industry.
Good luck.
But if you don’t have a track record of excellent research productivity, then it’s tough to get funding to do research because it’s highly competitive and it’s winner take all payouts.
To a first approximation, you don't.
What is it that you want to achieve? Post-doc like research nirvana[1] with a longer time horizon? Solving a particular problem?
[1] YMMV, but in the best case, that's some version of optimal
If anyone have great non sensational examples of the opposite i'm all ears.
I remember becoming aware of this during the crypto wave where there was always a bunch of "researchers" with PhD's around paper-thin whitepapers - and these researches were often from very prestigious universities, and of the hundreds of these companies no one produced anything valuable - and i've seen the same thing in many sectors like nutrition, "healthcare", financial products etc.
I may be dating myself but back when I was involved a program normally consisted of a ~6 month/$100k feasibility study and a 1-2 year/$1-2M work plan.
Best part, you can use this clout to pick up a few contracts along the way and do some work for clients with actual interest in the field. There’s your money.
While studying my post grad some friends and I started a very prestigious computer vision research company. It was all presented as a very serious co with computer scientists who knew what they were doing. We had $0 in revenue, and our demos were mostly just us playing around with various off the shelf libraries behind the scenes and presenting it to the masses. This gave us glowing resumes and credibility in the industry. We later got real jobs with big tech companies.
Chicken or the egg problem, unfortunately
Maybe there's a niche market that they're targeting and they're just not being public about it until they're ready to enter that market, so as to not tip off the competition.
Or maybe they know that AI is important to many companies with deep pockets, and they convince investors that they have a better strategy than that being pursued by Google, Facebook, OpenAI, etc. In which case the plan is not to get customers, but to pull an OpenAI and leapfrog the state of the art, thereby becoming a prime acquisition target.
Yeah if you think investors are just throwing hundreds of millions of dollars at companies with zero growth or exit strategy, then you are naîve.
I am sure most of these companies will not receive any funding beyond their seed and series A because they weren't able to identify a product that they can sell.
I've thought a lot about these issues - I am actively working on creating new research and then commercializing it. However I think the incentives of investors (VCs and likely angels) are not aligned well with research development. As a result, I've landed on the bootstrap/self fund side of the argument, much like Midjourney. Find the low hanging fruit on the research side that can be monetized, and build off that.
Otherwise: venture capitalists, but I wouldn't consider those companies "research" in the traditional, academic sense. Most times, at least in AI and the present climate, VCs are betting on that company to develop some novel tech that will be acquired a big markup by a larger company who deems the technology valuable. Not sure that is so much "research" as "niche product development with a very targeted exit strategy".