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Your way of thinking is interesting, but in all of the examples you give, the execution was vastly more difficult than the idea. Furthermore, most of them weren

Your way of thinking is interesting, but in all of the examples you give, the execution was vastly more difficult than the idea. Furthermore, most of them weren't commercial products and couldn't have been done by a startup.

graycat 12y ago
> the execution was vastly more difficult than the idea But the execution was fully routine, that is, low risk. > couldn't have been done by a startup Right. But maybe in the future, and because of my main point: It's fully possible for the planning to be rock solid and lead to a successful product with low risk. That's the main point -- it really is possible, indeed, in applied science and engineering, nearly standard. That the examples were not commercial products need not detract much from the main point -- again, we can plan just on paper and have a low risk, high payoff project. But now we have enhanced opportunities: The math I assumed can be cheap, for someone with an appropriate math background, dirt cheap, fast, fun, easy, and low risk. Then for the product, I assumed that that is just software. So the guy who does the math also does the software -- we're still in the low budget area. Next, the product needs computer hardware, but now enough computer hardware to execute 100,000 lines of code can be less than $2000. If the product is just a Web site supported by ads, then for just a focused start, where I agree with Altman, might do quite well: E.g., maybe the start up sends Web pages of 400,000 bits per page. Each page has on average 5 ads. From the Mary Meeker data at KPCB, maybe get paid $2 per 1000 ads displayed (CPM = $2). Maybe have an Internet connection with 25 million bits per second upload bandwidth. Maybe half fill that 24 x 7. Then the monthly revenue would be 2 * 5 * 25 * 106 * 3600 * 24 * 30 / ( 2 * 400,000 * 1000 ) = 810,000 dollars. That's enough to fuel organic growth. So, on average, how many Web pages sent per second? 25 * 106 / ( 2 * 400,000 ) = 31.25 Depending on the project, that might take several computers at $2000 each, but if early on send just 1 page a second, and for some projects early on one computer could do that, and get monthly revenue of 1 * 2 * 5 * 3600 * 24 * 30 / ( 1000 ) = 25,920 dollars so that in one month have free cash flow enough to buy computers enough to send 31.25 Web pages a second. The 31.25 Web pages a second is a lot of Web pages per month, is 31.25 * 3600 * 24 * 30 = 81,000,000 So how to justify this? Well, as we assumed, the first good or a much better solution for the problem is a "must have". The problem has been selected so that we can attack it essentially just with math. We do the math, as theorems and proofs, and check the proofs carefully -- fairly routine and low chances of errors for a well trained pure/applied mathematician. We convert the math to software and check the software -- again, fairly routine with low risk of errors. Now we have the product, and it is a "must have". Then we go to Altman's lecture and use what he said about viral growth. And we use the assumption that the problem is such that many people want the solution (I hope I made that assumption clear also). What's left to do? Get the checks from the ad networks. Yes, very much do need to meet the assumptions, find the solution, write the software, etc., and if can't then return to (1) and try again. Once I had a project that looked pretty good on the alternative approach, but the software was going to be too much work because I'd need to write a lot of code for a lot of data collection and with too little information about what the APIs and interfaces were or would be. So, I dropped the project. That is, back to step (1). You are correct that nearly all the successful, famous projects I listed were, yes, routine, but expensive to execute. And for a start up, we need projects that are cheap to execute. Do such projects exist? I believe so.
graycat 12y ago
It appears that we are not considering the same issues, i.e., we are not communicating, E.g., right up front in my post I said, as a summary, and overview, and a statement of my purpose in my post: > I want to propose that it can be possible to have an idea, test it, essentially just on paper, and, if it passes the test, be quite sure the resulting product will be good and fairly sure the resulting company will be successful. To me, this statement of mine about the idea is very different from what Altman said and very different for the plan and the resulting execution and business. Here I will avoid taking enough words to quote enough from Altman's lecture to show these differences; I assume we agree that, on the idea Altman and I are saying very different things. For your > in all of the examples you give, the execution was vastly more difficult than the idea. Sure, but if you are concerned about this then you are not really responding to what I wrote. All the examples (A)-(O) do illustrate what I said about the idea. For execution I said that with a good idea, execution should be routine and low risk. Projects (A)-(O) also illustrate this point -- routine and low risk -- about execution. So, with a sufficiently good idea, execution can be routine and low risk. Here, too, what I am saying is different from what Altman said. You are also concerned about the amount of effort in the execution. Right. And, right, mostly the projects (A)-(O) do not illustrate low effort. Okay. But we can't use those projects to conclude that there are no projects, start ups, that follow what I said about ideas and that have have low execution effort. So, for effort in execution, can we also have projects that have low execution effort and, thus, are suitable for start ups? Well, except maybe for the project (L) RSA I listed, the projects in (A)-(O) do not illustrate low effort. But I do believe that, as we select projects, we can also have some that follow what I described and also have low execution effort. How? Broadly, just do what is the focus of Hacker News -- Information technology that exploits Moore's law and current infrastructure software. E.g., in The Happy Demise of the 10X Engineer By Sam Gerstenzang at http://a16z.com/2014/07/30/the-happy-demise-of-the-10x-engineer/ http://a16z.com/2014/07/30/the-happy-demise-of-the-10x-engin... is a discussion of the question: "How long before we have a billion-dollar acquisition offer for a one-engineer startup?" So, this is from A16Z: They are guessing that the issue you raised about "effort", we should be able to have a "billion-dollar ... one-engineer startup". So, A16Z and I agree that now, in information technology start ups, low effort in execution is reasonable to expect. The A16Z arguments add to mine and do not conflict. I would suggest that my arguments help show the way to the A16Z information technology "billion-dollar ... one-engineer startup" with low risk and low effort. Why don't we have a lot of examples of projects that followed my arguments, and were "billion-dollar start ups" with low effort? Sure: What I am saying is. so far in practice, mostly new for information technology start ups -- instead of what I described people have followed what Altman described in his lecture. I'm saying that there is a better way, heavily in the planning. That what I am saying seems new for information technology start up can seem good news unless find the thinking wrong. Nutshell View: I'm claiming that we can do good planning and with a good plan execution can be routine and the intended success low risk. A key to a reliable path from plan to success is some math for the core of the product. Another key is a problem where the first good or a much better solution is a "must have". My project examples (A)-(O) illustrated that such planning, the role of math, routine execution with low risk, and "must have" solutions are possible. Yes, nearly all the project examples (A)-(O) had high effort. For effort, there's evidence, e.g., from A16Z, that now, due to Moore's law, etc., that for some information technology start ups we might get all of these project attributes along with low effort. Here what I'm saying is very different from what Altman said and maybe good news.

graycat 12y ago

> the execution was vastly more difficult than the idea But the execution was fully routine, that is, low risk. > couldn't have been done by a startup Right. But maybe in the future, and because of my main point: It's fully possible for the planning to be rock solid and lead to a successful product with low risk. That's the main point -- it really is possible, indeed, in applied science and engineering, nearly standard. That the examples were not commercial products need not detract much from the main point -- again, we can plan just on paper and have a low risk, high payoff project. But now we have enhanced opportunities: The math I assumed can be cheap, for someone with an appropriate math background, dirt cheap, fast, fun, easy, and low risk. Then for the product, I assumed that that is just software. So the guy who does the math also does the software -- we're still in the low budget area. Next, the product needs computer hardware, but now enough computer hardware to execute 100,000 lines of code can be less than $2000. If the product is just a Web site supported by ads, then for just a focused start, where I agree with Altman, might do quite well: E.g., maybe the start up sends Web pages of 400,000 bits per page. Each page has on average 5 ads. From the Mary Meeker data at KPCB, maybe get paid $2 per 1000 ads displayed (CPM = $2). Maybe have an Internet connection with 25 million bits per second upload bandwidth. Maybe half fill that 24 x 7. Then the monthly revenue would be 2 * 5 * 25 * 106 * 3600 * 24 * 30 / ( 2 * 400,000 * 1000 ) = 810,000 dollars. That's enough to fuel organic growth. So, on average, how many Web pages sent per second? 25 * 106 / ( 2 * 400,000 ) = 31.25 Depending on the project, that might take several computers at $2000 each, but if early on send just 1 page a second, and for some projects early on one computer could do that, and get monthly revenue of 1 * 2 * 5 * 3600 * 24 * 30 / ( 1000 ) = 25,920 dollars so that in one month have free cash flow enough to buy computers enough to send 31.25 Web pages a second. The 31.25 Web pages a second is a lot of Web pages per month, is 31.25 * 3600 * 24 * 30 = 81,000,000 So how to justify this? Well, as we assumed, the first good or a much better solution for the problem is a "must have". The problem has been selected so that we can attack it essentially just with math. We do the math, as theorems and proofs, and check the proofs carefully -- fairly routine and low chances of errors for a well trained pure/applied mathematician. We convert the math to software and check the software -- again, fairly routine with low risk of errors. Now we have the product, and it is a "must have". Then we go to Altman's lecture and use what he said about viral growth. And we use the assumption that the problem is such that many people want the solution (I hope I made that assumption clear also). What's left to do? Get the checks from the ad networks. Yes, very much do need to meet the assumptions, find the solution, write the software, etc., and if can't then return to (1) and try again. Once I had a project that looked pretty good on the alternative approach, but the software was going to be too much work because I'd need to write a lot of code for a lot of data collection and with too little information about what the APIs and interfaces were or would be. So, I dropped the project. That is, back to step (1). You are correct that nearly all the successful, famous projects I listed were, yes, routine, but expensive to execute. And for a start up, we need projects that are cheap to execute. Do such projects exist? I believe so.

graycat 12y ago

It appears that we are not considering the same issues, i.e., we are not communicating, E.g., right up front in my post I said, as a summary, and overview, and a statement of my purpose in my post: > I want to propose that it can be possible to have an idea, test it, essentially just on paper, and, if it passes the test, be quite sure the resulting product will be good and fairly sure the resulting company will be successful. To me, this statement of mine about the idea is very different from what Altman said and very different for the plan and the resulting execution and business. Here I will avoid taking enough words to quote enough from Altman's lecture to show these differences; I assume we agree that, on the idea Altman and I are saying very different things. For your > in all of the examples you give, the execution was vastly more difficult than the idea. Sure, but if you are concerned about this then you are not really responding to what I wrote. All the examples (A)-(O) do illustrate what I said about the idea. For execution I said that with a good idea, execution should be routine and low risk. Projects (A)-(O) also illustrate this point -- routine and low risk -- about execution. So, with a sufficiently good idea, execution can be routine and low risk. Here, too, what I am saying is different from what Altman said. You are also concerned about the amount of effort in the execution. Right. And, right, mostly the projects (A)-(O) do not illustrate low effort. Okay. But we can't use those projects to conclude that there are no projects, start ups, that follow what I said about ideas and that have have low execution effort. So, for effort in execution, can we also have projects that have low execution effort and, thus, are suitable for start ups? Well, except maybe for the project (L) RSA I listed, the projects in (A)-(O) do not illustrate low effort. But I do believe that, as we select projects, we can also have some that follow what I described and also have low execution effort. How? Broadly, just do what is the focus of Hacker News -- Information technology that exploits Moore's law and current infrastructure software. E.g., in The Happy Demise of the 10X Engineer By Sam Gerstenzang at http://a16z.com/2014/07/30/the-happy-demise-of-the-10x-engineer/ http://a16z.com/2014/07/30/the-happy-demise-of-the-10x-engin... is a discussion of the question: "How long before we have a billion-dollar acquisition offer for a one-engineer startup?" So, this is from A16Z: They are guessing that the issue you raised about "effort", we should be able to have a "billion-dollar ... one-engineer startup". So, A16Z and I agree that now, in information technology start ups, low effort in execution is reasonable to expect. The A16Z arguments add to mine and do not conflict. I would suggest that my arguments help show the way to the A16Z information technology "billion-dollar ... one-engineer startup" with low risk and low effort. Why don't we have a lot of examples of projects that followed my arguments, and were "billion-dollar start ups" with low effort? Sure: What I am saying is. so far in practice, mostly new for information technology start ups -- instead of what I described people have followed what Altman described in his lecture. I'm saying that there is a better way, heavily in the planning. That what I am saying seems new for information technology start up can seem good news unless find the thinking wrong. Nutshell View: I'm claiming that we can do good planning and with a good plan execution can be routine and the intended success low risk. A key to a reliable path from plan to success is some math for the core of the product. Another key is a problem where the first good or a much better solution is a "must have". My project examples (A)-(O) illustrated that such planning, the role of math, routine execution with low risk, and "must have" solutions are possible. Yes, nearly all the project examples (A)-(O) had high effort. For effort, there's evidence, e.g., from A16Z, that now, due to Moore's law, etc., that for some information technology start ups we might get all of these project attributes along with low effort. Here what I'm saying is very different from what Altman said and maybe good news.