I like to think of startups as research projects. You start with a hypothesis, which is usually "there is sufficient market for <idea>". Then you do the experiments to prove the hypothesis, which is the actual startup. How you go about proving the hypothesis is just as important as the hypothesis you start with. In the world of science, if your experiments have flaws they won't be accepted as proof of your hypothesis. In the startup world, flaws don't necessarily discredit the hypothesis, but then fail to prove it when you go bust.
This is the foundation for the Lean Startup methodology espoused by Steve Blank, Eric Reis, Ash Maurya and others, but until recently it has been articulated in the jargon of the business community such that it is difficult to see in terms of the scientific method.
I think Ash Maurya's upcoming book, "The Customer Factory", is perhaps the first attempt to codify the Lean Startup entirely in terms of how a scientist builds and tests models. As a technical person, not a business person, I am eagerly awaiting this book in hopes that it frames the startup process in the same ways that I tend to see the world (in terms of axioms, hypotheses and experiments).
Well, I don't expect The Customer Factory to be a philosophy book. Instead, I expect it to be full of practical ideas and tools. Most business books, even those about Lean, start from what I see as an alien perspective. Sure, I can glean some valuable insight and maybe put it into practice, but it doesn't speak my language. A practical book on business models that speaks my language would be a valuable thing indeed.
Ah, I understand now, that's a good point! When it comes to "ideas as tools", finding a source that's on the same wavelength as you does make a huge difference.
And hey, if we end up reaching your goals, then who cares what book helped us get there? We win and just like with a firefight, who is left standing is all that matters at the end of the day.
> I like to think of startups as research projects. You start with a hypothesis, which is usually "there is sufficient market for <idea>". Then you do the experiments to prove the hypothesis, which is the actual startup.
"There is sufficient market for <idea>" (or a variation of it, such as "there is sufficient demand for <solving this problem (efficiently)>" is usually the question you try to answer before shoving money into a research project...
I see it in a different non so-scientific way. My company is not a startup but my issues were more about how to move forward with thousands of, probably, good choices. And analyzing it in a scientific way was not feasible if you need to move fast.
