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I agree a lot with #3 but disagree heavily with #4. There are canonical examples of PhD theses that became successful companies, not to mention the dozens of c

I agree a lot with #3 but disagree heavily with #4. There are canonical examples of PhD theses that became successful companies, not to mention the dozens of companies who exited (e.g. CV companies to Qualcomm).

kinkrtyavimoodh 9y ago
Yeah, Akamai, Google, to some extent Bose come to mind. Most PhD theses would NOT become companies, so this is good advice for a PhD student but not for a founder. If a founder finds a PhD thesis that is worth productionizing, then the fact that it was a PhD thesis is irrelevant.
- philipov 9y ago
  The advice is "Don't productionize your PhD" thesis," so I think it is in fact aimed at PhD students in the exact spirit you pointed out, not founders surveying the academic field.
dpwm 9y ago
For what it's worth, trends #4 and #5 are both qualified with "AI Specific" further down. There will be some PhD theses that do solve real problems, but many are not going to make a startup successful by themselves.
- dboreham 9y ago
  Also FedEx
  - gaadd33 9y ago
    FedEx wasn't based on a PhD thesis
indescions_2018 9y ago
Jawbone founder Hosain Rahman developed his microphone tech while still a Stanford EE I believe. There are also the Quantum Computing startups to spin out of Yale CS. Gene editing pioneered at MIT Broad Institute. And on and on. Regarding AI / ML as a service specifically. Many startups may find themselves facing the same issues plaguing IBM Watson. Deep learning considers trillion dimensional spaces. Most enterprise prediction problems are simply not of an astrophysical scale. And where domain specific expertise is required. Such as Google Maps use of CV in rendering highres satellite image data to SVG for the browser. Simply having grad student level familiarity with OpenCV might not be sufficient. A Year of Google & Apple Maps (warning: bandwidth & cpu intensive) https://www.justinobeirne.com/a-year-of-google-maps-and-apple-maps/ https://www.justinobeirne.com/a-year-of-google-maps-and-appl...
- selimthegrim 9y ago
  Schoelkopf and Devoret are in Applied Physics, not CS.
dahart 9y ago
The vast majority of PhD thesis based products fail, and they fail because the thesis solves a deep and narrow technical problem, and not a broad market problem. The examples of successes is both anecdotal and survivor bias, you have to look at all attempts and weigh all the successes against all the failures. There will be future successful businesses based on theses, among the many more failures. The advice here is really aimed at people who think that a great technical solution to something automatically makes it marketable, which includes many thesis authors. Someone who's done their real market due diligence, and knows which thesis to productize or which problem to solve, they have a good business idea. But for most PhDs, the advice is good advice -- don't sink all your time & money into a business based on your idea if you haven't done as much study of the market as you have on your thesis topic.

kinkrtyavimoodh 9y ago

Yeah, Akamai, Google, to some extent Bose come to mind. Most PhD theses would NOT become companies, so this is good advice for a PhD student but not for a founder. If a founder finds a PhD thesis that is worth productionizing, then the fact that it was a PhD thesis is irrelevant.

philipov 9y ago
The advice is "Don't productionize your PhD" thesis," so I think it is in fact aimed at PhD students in the exact spirit you pointed out, not founders surveying the academic field.

dpwm 9y ago

For what it's worth, trends #4 and #5 are both qualified with "AI Specific" further down. There will be some PhD theses that do solve real problems, but many are not going to make a startup successful by themselves.

dboreham 9y ago
Also FedEx
- gaadd33 9y ago
  FedEx wasn't based on a PhD thesis

indescions_2018 9y ago

Jawbone founder Hosain Rahman developed his microphone tech while still a Stanford EE I believe. There are also the Quantum Computing startups to spin out of Yale CS. Gene editing pioneered at MIT Broad Institute. And on and on. Regarding AI / ML as a service specifically. Many startups may find themselves facing the same issues plaguing IBM Watson. Deep learning considers trillion dimensional spaces. Most enterprise prediction problems are simply not of an astrophysical scale. And where domain specific expertise is required. Such as Google Maps use of CV in rendering highres satellite image data to SVG for the browser. Simply having grad student level familiarity with OpenCV might not be sufficient. A Year of Google & Apple Maps (warning: bandwidth & cpu intensive) https://www.justinobeirne.com/a-year-of-google-maps-and-apple-maps/ https://www.justinobeirne.com/a-year-of-google-maps-and-appl...

selimthegrim 9y ago
Schoelkopf and Devoret are in Applied Physics, not CS.

dahart 9y ago

The vast majority of PhD thesis based products fail, and they fail because the thesis solves a deep and narrow technical problem, and not a broad market problem. The examples of successes is both anecdotal and survivor bias, you have to look at all attempts and weigh all the successes against all the failures. There will be future successful businesses based on theses, among the many more failures. The advice here is really aimed at people who think that a great technical solution to something automatically makes it marketable, which includes many thesis authors. Someone who's done their real market due diligence, and knows which thesis to productize or which problem to solve, they have a good business idea. But for most PhDs, the advice is good advice -- don't sink all your time & money into a business based on your idea if you haven't done as much study of the market as you have on your thesis topic.