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I see it as the cost of forming a startup is much lower now so they can stay private longer.

I don't think that is true. Sales and marketing is still very expensive. SaaS needs a lot more cash investment than traditional software, since you are only making the money back gradually. Many of these unicorn software companies are raising a half dozen rounds.

Also, the easier it becomes to write software the for the internet, the more a startup has to do. Yahoo! could get to a breakout stage just by having an HTML page full of links. That's not going to cut it these days. So I'm not sure overall if starting a company is much cheaper, even at the early stage.




I don't agree. Lots of startups don't have sales and marketing in the early stages. The grow through word of mouth or iterate/pivot to find something that becomes a hit. Somebody like Yahoo would need to buy and maintain a lot of servers to scale up but now with cloud computing, you can grow quite a bit with Amazon AWS until you implement your own infrastructure.


Obviously it varies quite a bit. Some consumer companies can spend very little on sales and marketing. But many consumer companies and nearly all B2B companies spend an enormous amount. Look at a company like New Relic. They took four rounds of venture, plus two rounds of private equity. They were spending 70% of their operating budget on sales and marketing. It was expensive the whole way. It costs a lot of money to develop a product to the point where it is better than the status quo, and then a lot more money to market it.


would love to hear examples of successful startups that did not do any marketing. especially ones that are tech/Internet startups.

also - while AWS can make infrastructure convenient to scale up, rarely is it cheaper. It certainly can feel cheaper in the beginning as its pay-as-you-go, but averaged out over N years it's not. AWS also has reserved pricing to aid with this, but most startups are not in a position to commit to either real hardware or 3 year contracts up front.


zenefits, zenpayroll, slack were basically word of mouth


Zenefits and zenpayroll have easy to spot ads running on google right now.

Most startup's spend an enormous amount on marketing to get any traction. I'm sure there is more examples like Slack that did not use much marketing, but they are very rare.

If you build a startup and hope to iterate your way into being viral, this is bad planning in my opinion, no matter how awesome what your building is. Unfortunately, one that I had to learn the hard way.


If you build a startup and hope to iterate your way into being viral

It's a great way to alert a much bigger competitor of an emerging market so they can eat your lunch, though.


Zenefits has a massive sales team now, I don't know if it always did.


In fact as a founder I don't think there is anything cheap or easy about it. Especially if you are trying to do anything with significant technical challenges like with computer vision, deep learning, VR etc...


> Especially if you are trying to do anything with significant technical challenges like with computer vision, deep learning, VR etc...

My view is that those are not very promising "technical" directions, exploitations, or "challenges".

My view: Take in data, manipulate it, put out results of the manipulations. Want the results to be valuable in some important sense. For that value, want more powerful manipulations.

Well, any such manipulations are necessarily mathematically something, understood or not, powerful or not. For more powerful manipulations, proceed mathematically, i.e., exploiting powerful classic results and, maybe, doing some new derivations, right, complete with theorems and proofs.

This work needs a background in pure and applied math, but given that background the derivations require just ideas, paper, pencil, and, hopefully, access to a computer with D. Knuth's TeX for writing up the results. Not really expensive.

My view is that it is much better to exploit relatively classic pure and applied math than anything pursued in computer science.

Won't find a lot of traffic going that direction.


I can't really parse what you are stating.

You don't think CV, ML/DL, VR are worth pursuing? Or are you saying that those are not "mathematically" technical? If the latter then you are decidedly wrong as proven by any number of research teams at MSFT/FB/GOOG etc...

>Not really expensive.

So applied math researchers aren't expensive? Tell that to every PhD Mathematician at Google/FB.


> Or are you saying that those are not "mathematically" technical?

Right. They are overwhelmingly merely heuristic. The methodology is to guess, with heuristics, and then try it and find out (TIFO method) on real data, maybe adjust, and use it when it appears to work. There's next to nothing in theorems and proofs before hand that show that the manipulations will be powerful or yield valuable results.

There is a long history of good applied math where, once the theorems are proved, there isn't a lot of doubt about how the real world application will go. E.g., (1) GPS, (2) the earlier version for the US Navy, (3) error correcting coding for, say, satellite data communications, (4) phased array passive sonar, (5) optimal allocation of anti-ballistic missiles to incoming warheads, .... There's much more making good applications of math, e.g., Wiener filtering, the Neyman-Pearson result in advanced radar target detection, in cases of engineering where, once the engineering is done, there's not a lot of doubt about how good the practical results will be. No guessing. No TIFO. Low risk. High payoff. E.g.,

http://iliketowastemytime.com/sites/default/files/sr71_black...

As designed, unrefueled range 2000+ miles, altitude 80,000+ feet, speed Mach 3+, never shot down. Just as planned. Just as clear from the engineering, based on quite a lot of applied math.

Uh, for (5), really don't want to have to use the TIFO method! Instead, want to know with high confidence before someone pushes a big red button.

> So applied math researchers aren't expensive?

For evaluating the cost of a startup, commonly pay the founder $0.00 per year until there is revenue or at least funding. :-)! Sorry 'bout that.

E.g., I worked in artificial intelligence at IBM's Watson lab. Part of the work was to monitor the health and wellness of server farms and their networks. No theorems. No real guarantees of the power of the data manipulations or the value of the results. I did an upchuck, derived some new math, and published it. The math says that we know in advance the false alarm rate. The AI work didn't. The usual approaches to machine learning don't do such things because they don't approach the work as assumptions, theorems, and proofs.

For Ph.D. applied mathematicians (I am one) at Google, once Google ran a lot of recruiting ads, and I sent them a resume and got a phone interview.

They asked what my favorite programming language was, and I said PL/I. Apparently the only acceptable answer was C++. It was clear enough that my answer of PL/I essentially ended the interview.

Why PL/I? It has some total sweetheart scope of names rules. The exceptional condition handling is super nice (get an implicit pop of the stack of dynamic descendancy with just the right clean up). The data structures are nearly as powerful as classes and much faster in execution. Threading (tasking) in the language. Pl/I does really nice things with automatic storage -- C doesn't. And there's more.

C++? We know the history: Unix was a baby Multics, on an 8 KB DEC box. C was a dirt simple language, no runtime. All function calls for every little thing, e.g., string manipulations -- the first version of PL/I was like that, but the later versions compiled such things and were much faster. PL/I does just wonderful things with arrays, but C doesn't really have arrays.

Then C++? That was, along with Ratfor, an example of Bell Labs liking pre-processors. So, C++ was a pre-processor to C. Instead, PL/I was carefully designed.

My selection of PL/I over C++ was not wrong.

Google laughed at my naming PL/I. The laugh is on Google. Uh, Linux is a version of Unix which was a baby version of Multics which was written in, may I have the envelope, please (drum roll), right, PL/I.

It was clear that my Ph.D. in applied math and experience were of no interest at all. None. Zip, zilch, zero. C++? Sure. Ph.D. in applied math? Nope -- worthless.

Okay. It was Google's decision. But, now I get to make a decision: I'm not impressed by the power of the role of math at Google. At QUALCOMM, maybe. At Renaissance Technologies, sure. At Google, nope.

I still prefer PL/I to C++. Sorry 'bout that! But I wouldn't want to use either language in production now.

Now I program on Windows, not Linux, and on Windows I use the .NET Framework. To do that, for a language, I have just two leading choices, C# or the .NET version of Visual Basic (VB). The difference is mostly just the flavor of syntactic sugar, and I prefer the more verbose flavor of VB.

For FB, I never applied -- it seemed totally hopeless.

I'm doing my own startup, right, based on some applied math I derived as in my post here.

A few weeks ago I got all the code running I first planned to do. Now that the code is running, I see a few tweaks. Then I will load some initial data -- have been having fun collecting some. Then on to alpha test, beta test, going live, getting publicity, users, ads, and revenue.

Hopefully people will like the results (from the math, although users will not be ware of anything mathematical); if so, then I stand to have a nice startup.

Much of my confidence in the work is the theorems and what they say about the power of the data manipulations and the resulting value of the results.


I'll be interested to see how it goes for you.


I've noticed posts because of your use of italics. I can't tell if you are crazy or onto something. Would you mind sharing the name of your startup?


I'm not "crazy", not at all.

Math is supposed to be useful. There's a long track record that it can be. I studied math hoping it would be useful, and I believe that it is for my project.

Doing some applied math might seem unusual, but it's not "crazy". The unusual part indicates an opportunity.

A "name"? For my work so far, I've not needed a static IP address so have not paid extra for one from my ISP. So neither do I have a domain name yet.

I won't get a static IP address or a domain name until just before I go live, ASAP.

My startup is for Internet search, discovery, recommendation, curation, notification, and subscription for safe for work Internet content where keywords/phrases work at best poorly.

My project might become a big thing.

The user interface is just a simple HTTP, HTML, CSS Web site, also simple enough for smart phones.

So, my software takes in data, manipulates it, and sends the user the results. The crucial core of the manipulations is from some math I derived based on some advanced prerequisites I got mostly in grad school.

Right, the users will see the results but not be aware of any of the math. What the user does with the Web site and the results they get back will seem intuitively reasonable and maybe even natural, but actually doing the data manipulations in a way with good promise of good results is a challenge, one that I addressed mathematically.

The theorems give good evidence that with some good data the results for the users will be good. Given what I'm betting on this project, I want the good evidence, up front, long before TIFO results, traction, etc.

My main use of italics is a common one, mark a word as being used in a sense maybe not the same as in a literal dictionary definition and, thus, needing some caution, reinterpretation, and/or apology.




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