My guess is that in the end, problems will come when the established companies slow down in acquisitions and the VC companies and angel investors get tired of startups which can't show profit.
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.
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.
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.
It's a great way to alert a much bigger competitor of an emerging market so they can eat your lunch, though.
My view is that those are not very promising
"technical" directions, exploitations, or
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
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.
Right. They are overwhelmingly
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.
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
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.
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
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:
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 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.
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
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
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.
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
My project might become a big thing.
The user interface is just a
simple HTTP, HTML, CSS Web site,
also simple enough for
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
in a way with good promise
of good results is a
challenge, one that I
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,
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,
I think it's exactly this as well. The way I see it (and I'm a financial idiot so I'm probably totally off), the bubble pop won't be when "the stock market" decides the companies aren't valuable anymore, rather the game is up when the Big Corps (Google/FB/Microsoft/etc) stop buying.