I'm not really defending the "Lean Startup" approach; I don't think it's very socially useful or interesting, but I do think it's probably profitable on average (hence why VCs like it).
The approach of doing a university spinoff using your own tech is a model I do like, and I agree it's a way to develop new tech and also bring it out of the lab. But I think the academic part there is key: you get some R&D runway up front before you start the startup and need to make money, rather than doing a startup up-front. You can then "afford" to start the startup once at least some of the high-risk work has been done and you're reasonably close to a product. Since you mention Wolfram, that's probably an even better example of that than Google, given Wolfram's rather lengthy prologue to his startup: he spent 8 years at Caltech (1979-1987, first as PhD student, then as professor) developing the Symbolic Manipulation Program, a precursor to Mathematica. Then he spun off a company in 1987 and launched Mathematica in 1988. I don't think he would've been able to do the same kind of initial R&D in a startup as he was able to do at Caltech.
fair enough -- i misunderstood your post. i consider "PhD turns their research into a startup" to be a great approach and completely antithetical to the lean startup model which says you do customer development first, not putz around in your lab for 5 years developing nascent tech.
But when would you decide to transition from research to startup? Probably when you decide that you can apply that research to something profitable, and do it very quickly, and when the research seems to be at a point where development is scalable (i.e. if you throw more people at the problem then the problem will be solved faster.)
I'd think you'd want most or all of the experimenting to be out of the way.
The approach of doing a university spinoff using your own tech is a model I do like, and I agree it's a way to develop new tech and also bring it out of the lab. But I think the academic part there is key: you get some R&D runway up front before you start the startup and need to make money, rather than doing a startup up-front. You can then "afford" to start the startup once at least some of the high-risk work has been done and you're reasonably close to a product. Since you mention Wolfram, that's probably an even better example of that than Google, given Wolfram's rather lengthy prologue to his startup: he spent 8 years at Caltech (1979-1987, first as PhD student, then as professor) developing the Symbolic Manipulation Program, a precursor to Mathematica. Then he spun off a company in 1987 and launched Mathematica in 1988. I don't think he would've been able to do the same kind of initial R&D in a startup as he was able to do at Caltech.