Posts tagged with entrepreneurship

McKinsey is nothing more than thousands of people who are either the most knowledgeable at what they do, or learning to become the most knowledgeable. Except many people go into consulting for the variety. Pretty early you have to focus and decide your specialty. Could be industry or function area, but the goal is to become the world’s expert so you can travel to different clients and help them with your expertise And a lot of times, that focus is based on the random projects you’ve had so far (and which you didn’t give much thought to). I know someone who ended up “focusing” on airline maintenance because his first project was 9 months with airlines.
Ellen Vrana

(Source: quora.com)




The entrepreneurial class, during its rule of scarce one hundred years, has created more massive and more colossal productive forces than have all preceding generations together. Subjection of Nature’s forces to man, machinery, application of chemistry to industry and agriculture, … — what earlier century had even a presentiment [of] such productive forces…?

The need of a constantly expanding market for its products chases businesspeople over the entire surface of the globe. They must nestle everywhere, settle everywhere, establish connexions everywhere.

Business has subjected the country to the rule of the towns. It has created enormous cities, has greatly increased the urban population as compared with the rural, and has thus rescued a considerable part of the population from the idiocy of rural life.

Entrepreneurs, wherever they have got the upper hand, have put an end to all feudal, patriarchal, idyllic relations. They have pitilessly torn asunder the motley feudal ties that bound man to his “natural superiors”….

Entrepreneurs cannot exist without constantly revolutionising the instruments of production, and thereby … the whole relations of society.

Charles Marx, 1848

(I just changed all reference to “the bourgeoisie”, which has an archaic or leftist ring to it, to “entrepreneurs”, which sounds more contemporary. Eat your heart out, Thomas Friedman.)

Read More

(Source: marxists.org)




in 1999, [I] made craigslist into a real company for it to survive effectively.

decided I didn’t personally need to make lots of money. NOT altruistic, just knowing when enough is enough.

Craig Newmark

(Source: quora.com)




(Swype is the $100 million exit that makes it easier for Android smartphone havers to type)

  • hippie
  • wanted to be able to talk with dolphins
  • lives in Nevada City, CA. (So do Joanna Newsom, Mariee Sioux, and Terry Riley … what’s up with that?!)
  • meditates
  • once he had invented one thing it was easier for him to get financing for the next one
  • working alone for … 7 years?! … before he came to Swype

(Source: video.mitef.org)




Silicon Valley “maker” culture insists that if you don’t like something, it’s incumbent on you to do better. Bollocks. If we applied that logic to everything we’d say:

  • Marcovaldo’s paintings bore me. Maybe you should paint a better one!
    image
  • The seats on this bus are uncomfortable. Maybe you should engineer some better bus seats!
  • I’m so frustrated that this plane won’t take off for another two hours. Maybe you should re-figure the logistics for the Civil Aviation Authority so planes can get off the ground faster!
  • Sylvia Plath is obnoxious. Maybe you should write some better poetry yourself. I don’t see you writing any poetry!
  • I hate Palazzo Pants. They’re coming back and I can’t stand it. So don’t buy any!
  • Economic theory is wrong. Maybe you should come up with a better theory!
  • Star Trek is racist and paternalistic. Well, I don’t see you writing a hit TV show that’s not racist!
  • I don’t like that restaurant. So don’t go there.

How could it seem reasonable to obligate someone to years of reparations for a one-minute whinge?

 

Kvetching may be a waste of time, but it’s also a natural part of life. We are a verbal species.
image
Just as innate as it is to

  • angrily debate politics or
  • to make an ugly face when you ask someone what they do and they say “Mathematics”,

it’s very simple and natural to express delight or disgust at good or bad design, craft, or taste—even outside one’s expertise. How is it incumbent on a whiner to spend ten years learning how to write software, because they said they disliked what you made?

If I say I didn’t like that restaurant and you say So don’t go there again, what just happened is that I expressed how something made me feel, and you instructed me. Maybe it is rational to not go there again … ok, fine … maybe it’s also rational to have feelings and to want to express them, even if I’m not going to take any further action beyond expressing myself.

It’s of course possible to override the natural instinct to complain. I could if I really wanted to. But Rails programmers in San Francisco already get enough remuneration. I’m not also going to grant them the power to dictate culture as well.

  • Linux is still hard for most people.
  • Mathematics is still boring for most people.
  • If someone complains to you, a totally fine response is: “I see”.




Albert Wenger, one of the owners of tumblr

At minute 31:

  • Google did not invent keyword advertising
  • GoTo, later renamed Overture, out of IdeaLab, invented it
  • and were acquired by Yahoo
  • Google improved upon the keyword search idea, turning keyword search into a viable business model
  • They realised there needs to be such a thing as a quality score—i.e., you don’t myopically give the ad space to the highest bidder. Long-term revenue maximisation required asking what the users want, and not p***ing them off.




It’s strange to me when internet advice-givers tell you to "Just build something—anything. Get moving. Get going." It’s like they’re the same people who tell you that taking risks is costless—that it’s always worth it. There are a lot of failed businesses cluttering up the past. In the case of internet start-ups you can actually look them up on Crunchbase.

image

To say “analysis-paralysis” is bad is not to say that doing random stuff is good. Doing an inch-deep reflection of hype should be even worse.

http://store.metmuseum.org/content/ebiz/themetstore/invt/80010981/80010981_01_l.jpg

I’m not saying sitting on your duff is the same as thinking things through. Real thinking, real research, takes a lot of energy and time. But I think that can be a reasonable investment if it keeps you from wasting your life on a business that’s dead before it starts, or that will end up making your life something you don’t want it to be (e.g. if biz is a net evil, or your role in it is not how you want to spend your life).

I don’t know what’s a reasonable timeline to spend researching a business idea but if I were doing another business I wouldn’t go forward until certain bars had been cleared: basically similar bars to what an investor would want to see before putting their money in.




@bos31337 Running a startup (MailRank) on Haskell (por jasonofthel33t)

Even though this is an advanced talk, there’s still something here for business people who know very little about software but are interested in web startups.

Namely, at Minute 20 BOS ticks off the things that a web app needs to do, like:

  • load balancing requests,
  • proxying data off…
  • for his Haskell code to bang on…
  • in the cloud,
  • receive requests from a Windows desktop software written in C#
  • coördinate those with what he already had,
  • store the data (thus evaluate a database appropriate for their problem),
  • worry about server throughput,
  • connect (bind) his (main) Haskell code to the database, to the server, to the webapp,
  • evaluate server software,

This surveys the moving parts in an internet-based business.




I’m bored of #ff meaning follow Fridays. Let’s do Failure Friday instead and talk about things we’e failed at.

  • I failed an arithmetic test.
  • I failed judo class.
  • I failed to attract interest with my CV.
  • I failed to be married or have a stable job by my 30th birthday.
  • I failed an entrance exam.
  • I failed most of my writing assignments.
  • I lost an important contest.
  • I lost a race. Badly.
  • I lost a client I thought I had secured.
  • I failed a client I thought I could help.
  • I failed to get paid what I thought I was worth.
  • I failed to be honest in a romantic relationship.
  • I failed to do anything cool for a few years.
  • I couldn’t walk on a mountain because I was so out of shape.
  • I failed to wear sunscreen.
  • I failed to read the prospectus.
  • I failed to get into my preferred university.
  • I failed to get someone to fall for me.
  • I didn’t know what I wanted or how to get it.
  • I failed to keep in touch with old friends.
  • I failed to impress people.
  • I failed to advocate for myself.
  • I failed to do things on time.
  • I lost Other People’s Money.
  • I failed to come up with good ideas.
  • I failed to give it my all.
  • I failed to lose weight.
  • I failed to meet expectations.
  • I failed to look “put together”.
  • I failed to stay organised.
  • I failed to Get Things Done.
  • I failed to cook a good dinner.
  • I failed to recognise the obvious signs.
  • I failed to learn what I was trying to learn.
  • Things did not go according to plan.

NB: I don’t intend Failure Friday as a pity party. It just bugs me when people try to act flawless and successful. Infinitely wise with inerrant self-command. Even apparent failures are successes in disguise. Sorry stories modulate into major key as the lessons learned were invaluable rungs on the ladder of upward progress so in the end it all worked out for the best.

What is that? You’ll probably just make people who are already down feel worse by doing that. And not make anyone feel better.




Lucas’ “rational expectations” revolution in macroeconomics has been tied to the ending of stagflation in the world’s largest economy, and to the reintroduction of “psychology” into finance and economics. However, I never felt like the models of “expectation” I’ve seen in economics seem like my own personal experience of living in ignorance. I’d like to share the sketch of an idea that feels more lifelike to me.

http://www.olivierlanglois.net/images/voro2.jpg

First, let me disambiguate: the unfortunate term-overlap with “statistical expectation” (= mean = average = total over count = ∑ᵢᴺ•/N = a map from N dimensions to 1 dimension) indicates nothing psychological whatever. It doesn’t even correspond to “What you should expect”.

If I find out someone is a white non-Hispanic Estadounidense (somehow not getting any hints of which state, which race, which accent, which social class, which career track…so it’s an artificial scenario), I shouldn’t “expect” the family to be worth $630,000. I “expect” (if indeed my expectation is not a distribution but rather just one number) them to be worth $155,000. (scroll down to green)

Nor, if I go to a casino with 99% chance of losing €10,000 and 1% chance of winning €1,000,000 (remember the break-even point is €990,000). “On average” this is a great bet. But that ignores convergence to the average, which would be slow. I’d need to play this game a lot to get the statistics working in my favour, and I mightn’t stay solvent (I’d need to get tens of millions of AUM—with lockdown conditions—to even consider this game). No, the “statistical expectation” refers to a long-run or wide-space convergence number. Not “what’s typical”.

Not only is the statistical expectation quite reductive, it doesn’t resemble what I’ve introspected about uncertainty, information, disinformation, beliefs, and expectations in my life.

File:Coloured Voronoi 3D slice.svg

A better idea, I think, comes from the definition of Riemann integration over 2+ dimensions. Imagine covering a surface with a coarse mesh. The mesh partitions the surface. A scalar is assigned to each of the interior regions inscribed by the mesh. The mesh is then refined (no lines taken away, only some more added—so some regions get smaller/more precise and no regions get larger/less precise), new scalars are computed with more precise information about the scalar field on the surface.
a scalar field

NB: The usual Expectation operator 𝔼 is little more than an integral over “possibilities” (whatever that means!).

(In the definitions of Riemann integral I’ve seen the mesh is square, but Voronoi pictures look awesomer & more suggestive of topological generality. Plus I’m not going to be talking about infinitary convergence—no one ever becomes fully knowledgeable of everything—so why do I need the convenience of squares?)

I want to make two changes to the Riemannian-integral mesh.

image
image

 

First I’d like to replace the scalars with some more general kind of fibre. Let’s say a bundle of words and associations.

(You can tell a lot about someone’s perspective fro the words they use. I’ll have to link up “Obverse Words”, which has been in my drafts folder for over a year, once I finish it—but you can imagine examples of people using words with opposite connotation to denote the same thing, indicating their attitude toward the thing.)

http://i780.photobucket.com/albums/yy90/AlexMLeo/felixsbrain.jpg

Second, I’d like to use the topology or covering maps to encode the ignorance somehow. In my example below: at a certain point I knew “Rails goes with Ruby” and “Django goes with Python” and “Git goes with Github” but didn’t really understand the lay of the land. I didn’t know about git’s competitors, that you can host your own github, that Github has competitors, the more complex relationship between ruby and python (it’s not just two disjoint sets), and so on.

When I didn’t know about Economics or Business or Accounting or Finance, I classed them all together. But now they’re so clearly very very different. I don’t even see Historical Economists or Bayesian Econometricians or Instrumental Econometricians or Dynamical Macroeconomists or Monetary Economists or Development Economists as being very alike. (Which must imply that my perspective has narrowed relative to everyone else! Like tattoo artists and yogi masters and poppy farmers must all be quite different to the entire class of Economists—and look even from my words how much coarse generalisation I use to describe the non-econ’s versus refinement among the econ’s.
image
These meshes can have a negative curvature (with, perhaps a memory) if you like. You know when you think that property actuaries are nothing at all like health actuaries that your frame-of-reference has become very refined among actuary-distinguishment. Which might mean a coarse partitioning of all the other people! Like Bobby Fischer’s use of the term “weakies” for any non-chess player—they must all be the same! Or at least they’re the same to me.)

image

Besides the natural embedding of negatively-curved judgment grids, here are some more pluses to the “refinement regions” view of ignorance:

  1. You could derive a natural “conservation law” using some combination of e.g. ability, difficulty, how good your teachers are, and time input to learning, how many “refinements” you get to make. No one can know everything.

    (Yet somehow we all are supposed to function in a global economy together—how do we figure out how to fit ourselves together efficiently?

    And what if people use your lack of perspective to suggest you should pay them to teach you something which “evaluates to valuable” from your coarse refinement, but upon closer inspection, doesn’t integrate to valuable?)
  2. Maybe this can relate to the story of Tony—how we’re always in a state of ignorance even as we choose what to become less ignorant about. It would be nice to be able to model the fact that one can’t escape one’s biases or context or history.
  3. And we could get a fairly nice representation of “incompatible perspectives”. If the topology of your covering maps is “very hard” to match up to mine because you speak dialectics and power structures but I speak equilibria and optima, that sounds like an accurate depiction. Or when you talk to someone who’s just so noobish in something you’re so expert in, it can feel like a very blanket statement over so many refinements that you don’t want to generalise over (and from “looking up to” an expert it can also feel like they “see” much more detail of the interesting landscape.)
  4. Ignorance of one’s own ignorance is already baked into the pie! As is the beginner’s luck. If I “integrate over the regions” to get my expected value of a certain coarse region, my uninformed answer may have a lot of correctness to it. At the same time, the topological restrictions mean that my information and my perspective on it aren’t “over there” in some L2-distance sense, rather they’re far away in a more appropriately incompatible-with-others sense.

In conclusion, I’m sure everyone on Earth can agree that this is a Really Nifty and Cool Idea.

File:ApproximateVoronoiDiagram.png

 

I’ll try to give a colourful example using computers and internet stuff since that’s an area I’ve learned a lot more about over the past couple years.

A tiny portion of Doug Hofstadter’s “semantic network”.  via jewcrew728, structure of entropy

First, what does ignorance sound like?

  • (someone who has never seen or interacted with a computer—let’s say from a non-technological society or a non-computery elderly rich person. I’ve never personally seen this)
  • "Sure, programming, I know a little about that. A little HMTL, sure!”
  • "Well, of course any programming you’re going to be doing, whether it’s for mobile or desktop, is going to use HTML. The question is how.

OK, but I wasn’t that bad. In workplaces I’ve been the person to ask about computers. I even briefly worked in I.T. But the distance from “normal people” (no computer knowledge) to me seems very small now compared to the distance between me and people who really know what’s up.

A few years ago, when I started seriously thinking about trying to make some kind of internet company (sorry, I refuse to use the word “startup” because it’s perverted), I considered myself a “power user” of computers. I used keyboard shortcuts, I downloaded and played with lots of programs, I had taken a C++ course in the 90’s, I knew about C:\progra~1 and how to get to the hidden files in the App packages on a Mac.

My knowledge of internet business was a scatty array of:

  • Mark Zuckerberg
  • "venture capital"
  • programer kid internet millionaires
  • Kayak.com — very nice interface!
  • perl.
    Regular Expressions
    11th Grade
  • mIRC
  • TechCrunch
  • There seem to be way more programming going on to impress other programmers than to make the stuff I wanted!
  • I had used Windows, Mac, and Linux (!! Linux! Dang I must be good)
  • I knew that “Java and Javascript are alike the way car and carpet are alike”—but didn’t know a bit of either language.
  • I used Alpine to check my gmail. That’s a lot of confusing settings to configure! And plus I’m checking email in text mode, which is not only faster but also way more cooly nerdy sexy screeny.
  • Object-Oriented, that’s some kind of important thing. Some languages are Object-Oriented and some aren’t.
  • "Python is for science; Ruby is for web"
  • sudo apt-get install
    Sandwich
  • I had run at least a few programs from the command line.
  • I had done a PHP tutorial at W3CSchools … that counts as “knowing a little PHP”, right?

So I knew I didn’t know everything, but it was very hard to quantify how much I did know, how far I had to go.

image

A mediocre picture of some things I knew about at various levels. It’s supposed to get across a more refined knowledge of, for example, econometrics, than of programming. Programming is lumped in with Linux and rich programmer kids and “that kind of stuff” (a coarse mesh). But statistical things have a much richer set of vocabulary and, if I could draw the topology better, refined “personal categories” those words belong to.

Which is why it’s easier to “quantify” my lack of knowledge by simply listing words from the neighbourhood of my state of knowledge.

Unfortunately, knowing how long a project should take and its chances of success or potential pitfalls, is crucial to making an organised plan to complete it. “If you have no port of destination, there is no favourable wind”. (Then again, no adverse wind either. But in an entropic environment—with more ways to screw up than to succeed—turning the Rubik’s cube randomly won’t help you at all. Your “ship” might run out of supplies, or the backers murder you, etc.)

File:2Ddim-L2norm-10site.png

Here are some of the words I learned early on (and many more refinements since then):

  • Rails
  • Django
  • IronPython
  • Jython
  • JSLint
  • MVC
  • Agile
  • STL
  • pointers
  • data structures
  • frameworks
  • SDK’s
  • Apache
  • /etc/.httpd
  • Hadoop
  • regex
  • nginx
  • memcached
  • JVM
  • RVM
  • vi, emacs
  • sed, awk
  • gdb
  • screen
  • tcl/tk, cocoa, gtk, ncurses
  • GPG keys
  • ppa’s
  • lspci
  • decorators
  • virtual functions
  • ~/.bashrc, ~/.bash_profile, ~/.profile
  • echo $SHELL, echo $PATH
  • "scripting languages"
  • "automagically"
  • sprintf
  • xargs
  • strptime, strftime
  • dynamic allocation
  • parser, linker, lexer
  • /env, /usr, /dev,/sbin
  • GRUB, LILO
  • virtual consoles
  • Xorg
  • cron
  • ssh, X forwarding
  • UDP
  • CNAME, A record
  • LLVM
  • curl.haxx.se
  • the difference between jQuery and JSON (they’re not even the same kind of thing, despite the “J” actually referring to Javascript in both cases)
  • OAuth2
  • XSALT, XPath, XML

http://www.financialiceberg.com/uploads/iceberg340.jpg
http://www.emeraldinsight.com/content_images/fig/1100190504002.png


http://www.preventa.ca/images/im_risk_anatomy.jpg

This is only—as they say—“the tip of the iceberg”. I didn’t know a ton of server admin stuff. I didn’t understand that libraries and frameworks are super crucial to real-world programming. (Imagine if you “knew English” but had a vocabulary of 1,000 words. Except libraries and frameworks are even better than a large vocabulary because they actually do work for you. You don’t need to “learn all the vocabulary” to use it—just enough words to call the library’s much larger program that, say, writes to the screen, or scrapes from the web, or does machine learning, for you.)

The path should go something like: at first knowing programming languages ⊃ ruby. Then knowing programming languages ⊃ ruby ⊃ rubinius, groovy, JRuby. At some point uncovering topological connections (neighbourhood relationships) to other things (a comparison to node.js; a comparison to perl; a lack of comparability to machine learning; etc.)

I could make some analogies to maths as well. I think there are some identifiable points across some broad range of individuals’ progress in mathematics, such as:

  • when you learn about distributions and realise this is so much better than single numbers!

    a rug plot or carpet plot is like a barcode on the bottom of your plot to show the marginal (one-dimension only) distribution of data

    who is faster, men or women?
  • when you learn about Gaussians and see them everywhere
    Central Limit Theorem  A nice illustration of the Central Limit Theorem by convolution.in R:  Heaviside <- function(x) {      ifelse(x>0,1,0) }HH <- convolve( Heaviside(x), rev(Heaviside(x)),        type = "open"   )HHHH <- convolve(HH, rev(HH),   type = "open"   )HHHHHHHH <- convolve(HHHH, rev(HHHH),   type = "open"   )etc.  What I really like about this dimostrazione is that it’s not a proof, rather an experiment carried out on a computer.  This empiricism is especially cool since the Bell Curve, 80/20 Rule, etc, have become such a religion.NERD NOTE:  Which weapon is better, a 1d10 longsword, or a 2d4 oaken staff? Sometimes the damage is written as 1-10 longsword and 2-8 quarterstaff. However, these ranges disregard the greater likelihood of the quarterstaff scoring 4,5,6 damage than 1,2,7,8. The longsword’s distribution 1d10 ~Uniform[1,10], while 2d4 looks like a Λ.  (To see this another way, think of the combinatorics.)
  • when you learn that Gaussians are not actually everywhere
    kernel density plot of Oxford boys' heights.

    histogram of Oxford boys' heights, drawn with ggplot.A (bimodal) probability distribution with distinct mean, median, and mode.
  • in talking about probability and randomness, you get stuck on discussions of “what is true randomness?” “Does randomness come from quantum mechanics?” and such whilst ignorant of stochastic processes and probability distributions in general.
  • (not saying the more refined understanding is the better place to be!)
  • A brilliant fellow (who now works for Google) was describing his past ignorance to us one time. He remembered the moment he realised “Space could be discrete! Wait, what if spacetime is discrete?!?!?! I am a genius and the first person who has ever thought of this!!!!” Humility often comes with the refinement.
  • when you start understanding symbols like ∫ , ‖•‖, {x | p} — there might be a point at which chalkboards full of multiple integrals look like the pinnacle of mathematical smartness—
    http://www.niemanlab.org/images/math-formula-chalkboard.jpg
  • but then, notice how real mathematicians’ chalkboards in their offices never contain a restatement of Physics 103!
    Kirby topology 2012
    http://whatsonmyblackboard.files.wordpress.com/2011/06/21june2011.jpg
    A parsimonious statement like “a local ring is regular iff its  global dimension is finite” is so, so much higher on the maths ladder than a tortuous sequence of u-substitutions.
  • and so on … I’m sure I’ve tipped my hand well enough all over isomorphismes.tumblr.com that those who have a more refined knowledge can place me on the path. (eg it’s clear that I don’t understand sheaves or topoi but I expect they hold some awesome perspectives.) And it’s no judgment because everyone has to go through some “lower” levels to get to “higher” levels. It’s not a race and no one’s born with the infinite knowledge.
 

I think you’ll agree with me here: the more one learns, the more one finds out how little one knows. One can’t leave one’s context or have knowledge one doesn’t have. And all choices are embedded in this framework.