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Startup Reading List - Startup Strategy

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Steve Pell
Steve Pell

A pretty comprehensive reading list on startup strategy.

Business
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STARTUP  READING  LIST  –  JUNE  2012   Startup  strategy   What  is  a  startup,  the  basic  strategy,  what  not  to   do,  generating  and  testing  an  idea,  establishing   customer  intent,  starting  build  and  iterating   product,  making  money,  reaching  product   market  fit,  scaling,  business  development,   changing  direction,  competition  incumbents  and   first  mover  advantage,  failure,  business  metrics   for  startups                  
1     Contents   What  is  a  startup?  ..................................................................................................................................................  8   What  is  a  startup?  –  Eric  Ries  .............................................................................................................................  8   The  first  principles  of  startup  –  Steve  Blank  .....................................................................................................  10   Why  to  do  a  startup  (is  the  idea  worth  it?)  –  Peter  Thiel  (Blake  Masters)  .......................................................  13   Entrepreneurship  as  management  science  –  Eric  Ries  .....................................................................................  18   The  basic  strategy  (what  to  do)  ...........................................................................................................................  21   Startups  in  13  sentences  –  Paul  Graham  ..........................................................................................................  21   How  to  start  a  startup  –  Paul  Graham  .............................................................................................................  24   Understand  the  market  –  Rob  Fitzpatrick  ........................................................................................................  42   Milestones  to  startup  success  –  Sean  Ellis  ........................................................................................................  43   What  makes  a  great  startup  –  Sean  Ellis  ..........................................................................................................  46   The  startup  pyramid  (getting  to  product  market  fit)  –  Sean  Ellis  .....................................................................  48   Deconstructing  startup  growth  –  Sean  Ellis  .....................................................................................................  50   Speed  as  the  primary  business  strategy  –  Mike  Cassidy  ..................................................................................  52   Startups  rarely  do  anything  well  –  Eric  Paley  ...................................................................................................  54   No  plan  survives  contact  with  customers  –  Steve  Blank  ...................................................................................  55   Building  a  web  startup  –  Steve  Blank  ...............................................................................................................  57   One  way  to  start  a  startup  –  Naval  ..................................................................................................................  62   Moving  fast  –  Nivi  ............................................................................................................................................  62   The  benefits  of  thinking  small    –  Elad  Gil  .........................................................................................................  64   Elements  of  sustainable  companies  –  Sequoia  .................................................................................................  66   Is  stealth  mode  sensible  –  Elad  Gil  ...................................................................................................................  67   No  stealth    –  Chris  Dixon  ..................................................................................................................................  69   What  not  to  do  .....................................................................................................................................................  71   Mistakes  that  kill  startups  –  Paul  Graham  .......................................................................................................  71   The  most  common  mistakes  –  Mark  Suster  .....................................................................................................  82   The  9  deadly  startup  sins  –  Steve  Blank  ...........................................................................................................  86   Why  startups  fail    –  David  Skok  ........................................................................................................................  89   What  constitutes  a  startup  mistake?  –  Simeon  Simeonov  ...............................................................................  92   3  Startup  Lessons  I  Learned  the  Hard  Way  –  David  Cancel  ..............................................................................  94   Failing  by  not  knowing  what  you  don’t  know  –  Steve  Blank  ............................................................................  96   Startup  DOA    –  Ben  Yoskovitz  ...........................................................................................................................  98   The  top  20  reasons  startups  fail  –  Chubby  Brain  ..............................................................................................  99   Premature  scaling  leads  to  death  –  Steve  Blank  ............................................................................................  105  
2     Premature  scaling  –  Rip  Empson  ....................................................................................................................  107   Don’t  be  creative  about  the  wrong  things  –  Chris  Dixon  ................................................................................  113   Rewriting  code  is  suicide  –  Steve  Blank  ..........................................................................................................  113   Refactoring  yourself  out  of  business  –  Eric  Ries  .............................................................................................  115   Generating  and  testing  an  idea  ..........................................................................................................................  119   Ideas  for  startups  –  Paul  Graham  ..................................................................................................................  119   Writing  a  business  plan  –  Sequoia  ..................................................................................................................  127   Good  startup  ideas  are  secrets    –  Peter  Thiel  (Blake  Masters)  .......................................................................  128   How  I  come  up  with  new  startup  ideas  (in  4  steps)–  Rob  Fitzpatrick  .............................................................  139   Timing  your  startup  (Everything  has  been  tried  before)  –  Chris  Dixon  ...........................................................  142   Paradigm  breaking  ideas  –  Steven  Carpenter  ................................................................................................  144   You  Don't  Need  A  Good  Idea  To  Start  A  Great  Company  –  Elad  Gil  ...............................................................  146   The  myth  of  the  Eureka  moment  –  Chris  Dixon  ..............................................................................................  147   Developing  new  startup  ideas  –  Chris  Dixon  ..................................................................................................  148   How  to  Build  a  Billion  Dollar  Startup  (Just  fulfil  a  need)  –  Steve  Blank  ..........................................................  149   How  Can  You  Tell  If  Your  Market  Is  A  Good  One?–  Elad  Gil  ...........................................................................  150   Start  from  the  heart  (Do  what  you  believe)  –  Roy  Rodenstein  .......................................................................  152   Before  product-­‐market  fit,  find  passion-­‐market  fit  –  Naval  ...........................................................................  155   Why  Do  Market  Segments  Matter?  –  Brant  Cooper  ......................................................................................  156   Use  the  tools  you’re  displacing    –  Rob  Fitzpatrick  ..........................................................................................  158   Timing  is  everything  –  Chris  Dixon  .................................................................................................................  159   Why  smart  people  have  bad  ideas  –  Paul  Graham  ........................................................................................  160   Big  technologies  come  from  toys  –  Chris  Dixon  ..............................................................................................  167   Beware  of  the  word  interesting  –  Evan  Shore  ................................................................................................  168   The  fast  follow  –  Steven  Carpenter  ................................................................................................................  171   The  challenge  of  creating  a  new  category  –  Chris  Dixon  ................................................................................  173   Establishing  customer  intent  ..............................................................................................................................  174   The  customer  development  manifesto  –  Steve  Blank  .....................................................................................  174   Product  development  as  the  leading  cause  of  startup  death  –  Steve  Blank  ..................................................  175   The  Customer  Development  Manifesto:  Reasons  for  the  Revolution  (part  1)  –  Steve  Blank  ..........................  177   The  Customer  Development  Manifesto:  Reasons  for  the  Revolution  (part  2)  –  Steve  Blank  ..........................  180   The  Customer  Development  Manifesto:  The  Startup  Death  Spiral  (part  3)  –  Steve  Blank  .............................  183   Customer  Development  Manifesto:  Market  Type  (part  4)  –  Steve  Blank  .......................................................  185   Customer  Development  Manifesto:  The  Path  of  Warriors  and  Winners  (part  5)  –  Steve  Blank  .....................  187   What  happens  when  you  fail  to  validate  customers  –  Steve  Blank  ................................................................  189  
3     Customer  development  is  not  a  focus  group  –  Steve  Blank  ............................................................................  192   What  is  customer  development?  –  Eric  Ries  ...................................................................................................  194   The  Risk  Validation  Pyramid–  Rob  Fitzpatrick  ................................................................................................  197   Customer  development  for  web  startups  –  Steve  Blank  .................................................................................  199   Customer  development  for  consumer  startups  –  Ash  Maurya  .......................................................................  202   How  to  find  early  adopters  –  Brant  Cooper  ....................................................................................................  205   Customer  development  biases  –  Brant  Cooper  ..............................................................................................  207   How  to  find  prospective  customers  –  Cindy  Alvarez  .......................................................................................  209   What  to  learn  from  customer  development  –  Cindy  Alvarez  .........................................................................  211   Segmenting  a  market  –  Cindy  Alvarez  ............................................................................................................  214   Big  Picture  Customer  Development  Revisited  –  Sean  Ellis  ..............................................................................  216   What  customer  data  to  collect  –  Steve  Blank  ................................................................................................  217   Checklist  for  chaos  –  Steve  Blank  ...................................................................................................................  220   The  customer  development  conversation  –  Brant  Cooper  .............................................................................  222   Let’s  Fire  Our  Customers  –  Steve  Blank  ..........................................................................................................  225   Starting  user  research  –  Laura  Klein  ...............................................................................................................  227   You  Need  Personas  –  Cindy  Alvarez  ...............................................................................................................  230   A  day  in  the  life  of  your  customer  –  Ben  Yoskovitz  .........................................................................................  232   Survey  for  customer  development  –  Sean  Ellis  ...............................................................................................  234   You  Need  to  Make  “Wanting”  No  Longer  Free–  Cindy  Alvarez  ......................................................................  235   Strategy  is  not  a  to  do  list  –  Steve  Blank  ........................................................................................................  236   Getting  customer  intent  –  Eric  Ries  ................................................................................................................  238   Using  a  LOI  to  define  MVP  –  Eric  Ries  .............................................................................................................  241   Visionaries  lament  –  Eric  Ries  .........................................................................................................................  245   Validated  learning  about  customers  –  Eric  Ries  .............................................................................................  248   Vertical  markets  1  –  Steve  Blank  ....................................................................................................................  251   Vertical  markets  2  –  Steve  Blank  ....................................................................................................................  254   Vertical  markets  3  –  Steve  Blank  ....................................................................................................................  255   Vertical  markets  4  –  Steve  Blank  ....................................................................................................................  256   Starting  build  and  iterating  product  ...................................................................................................................  260   What  is  Lean  about  the  Lean  Startup?–  Eric  Ries  ...........................................................................................  260   Myths  about  lean  startup  –  Eric  Ries  ..............................................................................................................  261   Lean  startup  myths  –  The  Lean  Startup  Wiki  .................................................................................................  262   Lean  Startups  aren’t  Cheap  Startups–  Steve  Blank  ........................................................................................  264   Learning  is  better  than  optimisation  –  Eric  Ries  .............................................................................................  266  
4     Cash  is  not  king  –  Eric  Ries  .............................................................................................................................  270   Work  in  small  batches  –  Eric  Ries  ...................................................................................................................  272   The  five  whys  for  startups  –  Eric  Ries  .............................................................................................................  274   Conducting  a  five  whys  –  Eric  Ries  ..................................................................................................................  276   MVP  examples    –  Nivi  .....................................................................................................................................  280   MVP  doesn’t  mean  shit  –  Ben  Yoskovitz  .........................................................................................................  281   MVP  vs  MDP  -­‐  Andrew  Chen  ..........................................................................................................................  282   How  much  effort  to  spend  on  the  MVP  –  Vinicius  Vacanti  .............................................................................  285   Prioritising  features  –  Ben  Yoskovitz  ..............................................................................................................  285   Adding  new  features  –  Ben  Yoskovitz  .............................................................................................................  287   Embrace  technical  debt  –  Eric  Ries  .................................................................................................................  288   The  pace  of  startup    –  Ben  Yoskovitz  ..............................................................................................................  292   The  startup  rules  of  speed  –  Eric  Ries  .............................................................................................................  294   Rapid  iteration  with  hardware  –  Eric  Ries  ......................................................................................................  295   Running  (anything)  lean  –  Ash  Maurya  ..........................................................................................................  299   How  much  process  is  too  much  –  Eric  Ries  .....................................................................................................  301   Your  Best  Customers  Probably  Aren’t  –  Cindy  Alvarez  ...................................................................................  302   Holding  entrepreneurs  accountable  –  Eric  Ries  ..............................................................................................  305   Business  ecology  and  the  four  customer  currencies  –  Eric  Ries  ......................................................................  307   Good  enough  never  is  (or  is  it?)  –  Eric  Ries  .....................................................................................................  311   Don’t  launch  –  Eric  Ries  ..................................................................................................................................  315   No  corner  cases  –  Steve  Blank  ........................................................................................................................  318   Mobile  first,  web  second  –  Fred  Wilson  ..........................................................................................................  320   Making  money  (Business  models  and  monetisation)  .........................................................................................  321   The  business  model  /  customer  development  stack  –  Steve  Blank  .................................................................  321   Search  vs  execute  (the  relationship  between  customer  development  and  business  model  design)–  Steve  Blank  .......................................................................................................................................................................  324   The  Right  Business  Model  for  Your  Startup  –  Sean  Ellis  .................................................................................  329   8  questions  to  understand  the  business  model  –  Alex  Osterwalder  ...............................................................  331   13  Consumer  Internet  Business  Models  (Part  I)–  Steven  Carpenter  ...............................................................  335   13  Consumer  Internet  Business  Models  (Part  II)–  Steven  Carpenter  ..............................................................  342   Business  model  discovery  –  Ash  Maurya  ........................................................................................................  351   How  to  create  a  profitable  Freemium  startup  –  Andrew  Chen  ......................................................................  354   Dividing  free  and  paid  features  in  “freemium”  products  –  Chris  Dixon  ..........................................................  360   Freemium  as  a  model  –  Eric  Ries  ....................................................................................................................  362   The  revenue  plan  needs  to  match  the  market  –  Steve  Blank  .........................................................................  363  
5     Business  Model  Analysis,  Part  1:  Key  Questions  –  Tom  Eisenmann  ...............................................................  366   Business  Model  Analysis,  Part  2:  Platforms  and  Network  Effects  –  Tom  Eisenmann  .....................................  370   Business  Model  Analysis,  Part  3:  Switching  Costs  –  Tom  Eisenmann  .............................................................  374   Business  Model  Analysis,  Part  4:  Racing  for  Scale  –  Tom  Eisenmann  ............................................................  376   Business  Model  Analysis,  Part  5:  Virality–  Tom  Eisenmann  ...........................................................................  379   Business  Model  Analysis,  Part  6:  LTV  and  CAC  –  Tom  Eisenmann  ..................................................................  381   Business  Model  Analysis,  Part  7:  Bundling  –  Tom  Eisenmann  ........................................................................  384   Business  Model  Analysis,  Part  8:  Crossing  the  Chasm  –  Tom  Eisenmann  ......................................................  385   Business  Model  Analysis,  Part  9:  Outsourcing  –  Tom  Eisenmann  ..................................................................  387   Stop  asking  “But  how  will  they  make  money?”  –  Andrew  Chen  .....................................................................  388   Reaching  product  market  fit  ..............................................................................................................................  392   Product  market  fit  is  the  only  thing  that  matters  –  Marc  Andreessen  ...........................................................  392   Zero  to  product/market  fit  –  Andrew  Chen  ....................................................................................................  396   When  has  a  consumer  startup  hit  product/market  fit?  –  Andrew  Chen  ........................................................  404   What  is  a  market?  –  Eric  Ries  .........................................................................................................................  408   Scaling  (growing  the  business  post  product  market  fit)  .....................................................................................  411   Keys  to  unlocking  startup  growth  –  Sean  Ellis  ................................................................................................  411   Taking  the  mystery  out  of  scaling  a  company  –  Ben  Horowitz  .......................................................................  412   Make  No  Little  Plans:  Defining  the  Scalable  Startup  –  Steve  Blank  ................................................................  416   Add  another  zero  –  Brad  Feld  .........................................................................................................................  419   4  types  of  scale  –  Nivi  .....................................................................................................................................  420   Key  Elements  of  a  Massively  Scalable  Startup  –  Sean  Ellis  .............................................................................  420   Business  development  ........................................................................................................................................  423   Business  Development  for  Early  Stage  Startups  –  Charlie  O’Donnell  .............................................................  423   Pitching  strategic  partners  –  Chris  Dixon  .......................................................................................................  424   Pitching  partners  –  Chris  Dixon  ......................................................................................................................  426   Outsourcing  –  Fred  Wilson  .............................................................................................................................  427   Outsourcing  vs  offshoring  –  Fred  Wilson  ........................................................................................................  428   Outsourcing  product  development  -­‐  VIVEK  WADHWA  ...................................................................................  430   Changing  direction  (The  pivot)  ...........................................................................................................................  435   Pivot,  don’t  jump  –  Eric  Ries  ...........................................................................................................................  435   Pivoting  –  Chris  Dixon  .....................................................................................................................................  437   Anatomy  of  a  pivot  –  Eric  Ries  ........................................................................................................................  438   Pivoting  the  business  model  –  Steve  Blank  .....................................................................................................  441   Panic  at  the  Pivot  –  Aligning  Incentives  By  Burning  the  Boats  –  Steve  Blank  .................................................  444  
6     Competition,  incumbents  and  first  mover  advantage  ........................................................................................  447   We’re  just  like  those  others  guys,  but  better  –  Steve  Blank  ...........................................................................  447   First  mover  advantage  doesn’t  work  for  startups–  Steve  Blank  .....................................................................  451   Thoughts  on  incumbents  from  a  startup's  perspective–  Chris  Dixon  .............................................................  453   Indifference  is  Your  Real  Competitor  –  Sean  Ellis  ...........................................................................................  454   Never  Enough  Competition  –  Charlie  O’Donnell  .............................................................................................  455   Competition  -­‐  The  Pros  and  Cons  –  Fred  Wilson  .............................................................................................  456   Incumbents  –  Chris  Dixon  ...............................................................................................................................  457   Competitive  analysis  kills  –  Steve  Blank  .........................................................................................................  458   Be  the  first  mover  –  Brad  Feld  ........................................................................................................................  460   Competition  –  Brad  Feld  .................................................................................................................................  462   Competition  is  overrated  –  Chris  Dixon  ..........................................................................................................  463   On  competition  and  markets  –  Peter  Thiel  (Blake  Masters)  ...........................................................................  464   Starting  strategy  –  Steve  Blank  ......................................................................................................................  474   Why  competition  is  not  failure  –  Mark  Suster  ................................................................................................  475   Winning  by  being  better  (Bill  Gross)  –  Mark  Suster  .......................................................................................  479   Failure  ................................................................................................................................................................  484   When  you  want  to  quit  –  Jason  Cohen  ...........................................................................................................  484   When  Do  You  Throw  in  the  Towel  On  Your  Struggling  Project?  –  Vinicius  Vacanti  ........................................  488   Patience  &  Persistence  –  Bijan  Sabet  .............................................................................................................  492   There’s  Always  a  Plan  B  –  Steve  Blank  ...........................................................................................................  493   Achieving  a  failure  –  Eric  Ries  .........................................................................................................................  495   Built  to  fail  -­‐  Andrew  Chen  .............................................................................................................................  497   Why  The  ‘Fail  Fast’  Mantra  Needs  to  Fail,  Fast  –  Mark  Suster  .......................................................................  500   Business  metrics  for  startups  .............................................................................................................................  505   Learning  is  better  than  optimisation  –  Eric  Ries  .............................................................................................  505   Metrics  for  startups  –  Steve  Blank  .................................................................................................................  509   Daily  data  –  Brad  Feld  ....................................................................................................................................  513   The  three  numbers  that  matter  –  Brad  Feld  ...................................................................................................  514   More  metrics  for  startups  -­‐  ALISTAIR  CROLL  ..................................................................................................  516   Metrics  to  drive  good  behaviour    –  David  Skok  ..............................................................................................  519   Using  metrics  in  startup  –  Mark  Suster  ..........................................................................................................  526   Why  vanity  metrics  are  dangerous  –  Eric  Ries  ................................................................................................  531   It’s  a  startup  not  a  spreadsheet  –  Eric  Ries  ....................................................................................................  533   Assorted  ideas  and  inspiration  ...........................................................................................................................  536  
7     The  future  of  the  internet    –  Chris  Dixon  ........................................................................................................  536   Ambitious  ideas  –  Paul  Graham  .....................................................................................................................  537   Maximizing  capacity  utilization  as  a  startup  premise  –  Chris  Dixon  ..............................................................  546   Understanding  the  history  of  the  tech  startup  market  –  Peter  Thiel  (Blake  Masters)  ....................................  546   What  happens  after  web  2.0  (ideas)  –  Peter  Thiel  (Blake  Masters)  ...............................................................  553   Luck  vs  Skill  (statistics  vs  calculus)  –  Peter  Thiel  (Blake  Masters)  ...................................................................  566   Green  tech  (ideas)  –  Peter  Thiel  (Blake  Masters)  ...........................................................................................  584   Visions  of  the  future  (ideas)  –  Peter  Thiel  (Blake  Masters)  ............................................................................  606   On  Biotech  (ideas)–  Peter  Thiel  (Blake  Masters)  ............................................................................................  619   Artificial  intelligence  (ideas)  –  Peter  Thiel  (Blake  Masters)  ............................................................................  634        
8     What  is  a  startup?   What  is  a  startup?   What  is  a  startup?  –  Eric  Ries   http://www.startuplessonslearned.com/2010/06/what-­‐is-­‐startup.html   Monday, June 21, 2010 What is a startup? I think most people have a fairly specific image that gets conjured up when they hear the word startup. Maybe it’s the “two guys in a garage” made famous by HP, or the idea of Jobs and Wozniack walking barefoot and shaggy through the Homebrew Computer Club. Maybe it’s the more recent wunderkinds like Zuckerberg or Brin and Page. What all of these pictures have in common is a narrative that goes something like this: scrappy outsiders, possessed of a unique genius, took outrageous risks and worked incomprehensible hours to beat the odds. But this cinematic view of entrepreneurs is flawed in many ways. Let’s start with the most basic. It leads people to mistakenly believe that any time they see two guys in a garage attempting the impossible, that’s a startup. Wrong. It also causes them to miss the numerous other kinds of startups that appear in less-glamorous settings: inside enterprises, non-profits, and even governments. And because both small businesses and startups have a high mortality rate, sometimes these images lead us to believe that any small business is a startup. Wrong again. So let’s begin with a definition of a startup that captures its essential nature, and tries to leave behind the specific associations of the most famous startups. A startup is a human institution designed to deliver a new product or service under conditions of extreme uncertainty. Let’s take each of these pieces in turn. First, I want to emphasize the human institution aspect, because this is completely lost in the “two guys in a garage” story. The word institution connotes bureaucracy, process, even lethargy. How can that be part of a startup? Yet, the real stories of successful startups are full of activities that can rightly be called institution-building: hiring creative employees, coordinating their activities, and creating a company culture that delivers results. Although some startups may approach these activities in radical ways, they are nonetheless key ingredients in their success. Isn’t the word human redundant in this definition? What other kinds of institutions are there, anyway? And yet, we so often loose sight of the fact that startups are not their products, their technological breakthroughs, or even their data. Even for companies that essentially have only one product, the value the company creates is located not in the product itself but with the people and their organization who built it. To see proof of this, simply observe the results of the large majorities of corporate acquisitions of startups. In most cases, essential aspects of the startup are lost, even when the product, its brand, and even its employment contracts are preserved. A startup is greater than
9     the sum of its parts; it is an acutely human enterprise. And yet the newness of a startup’s product or service is also a key part of the definition. This is a tricky part of the definition, too. I prefer to take the most expansive possible definition of product, one that encompasses any source of value for a set of people who voluntarily choose to become customers. This is equally true of a packaged good in a grocery store, an ecommerce website, a non- profit social service or a variety of government programs. In every case, the organization is dedicated to uncovering a new source of value for customers, and cares about the actual impact of its work on those customers (by contrast, a monopoly or true bureaucracy generally doesn’t care and only seeks to perpetuate itself). It’s also important that we’re talking innovation, but this should also be understood broadly. Even the most radical new inventions always build upon previous technology. Many startups don’t innovate at all in the product dimension, but use other kinds of innovation: repurposing an existing technology for a new use, devising a new business model that unlocks value that was previously hidden, or even simply bringing a product or service to a new location or set of customers previously underserved. In all of these cases, innovation is at the heart of the company’s success. Because innovation is inherently risky, there may be outsized economic returns for startups that are able to harness the risk in a new way – but this is not an essential part of the startup character. The real question is: “what is the degree of innovation that this business proposes to accomplish?” There is one last important part of this definition: the context in which the innovation happens. Most businesses – large and small alike – are typically excluded by this context. Startups are designed to confront situations of extreme uncertainty. To open up a new business that is an exact clone of an existing business, all the way down to the business model, pricing, target customer, and specific product may, under many circumstances, be an attractive economic investment. But it is not a startup, because its success depends only on decent execution – so much so that this success can be modeled with high accuracy. This is why so many small businesses can be financed with simple bank loans; the level of risk and uncertainty is well enough understood that a reasonably intelligent loan officer can assess its prospects. Thus, the land of startups is a unique place, where the risks themselves are unknown. Contrast this with other high-risk situations, like buying a high-risk stock. Although the specific payoff of a specific risky stock is not known, investing in many such stocks can be modeled accurately. Thus a decent financial advisor can give you a reasonably accurate long-term expected return for a set of risky stocks. When the “risk premium” is known, we are not in startup land. In fact, when viewed in retrospect, most startups appear like no-brainers. Probably the most famous example today is Google: how did we ever live without it? Building that particular product was not nearly has risky as it seemed at the time; in fact, I think it is a reasonable inference to say that it was almost guaranteed to succeed. It just wasn’t possible for anyone to know that ahead of time. Startups are designed for the situations that cannot be modeled, are not clear-cut, and where the risk is not necessarily large – it’s just not yet known. I emphasize this point because it is necessary to motivate large amounts of the theory of the lean startup. Fundamentally, the lean startup is a methodology for coping with uncertainty and unknowns with agility, poise, and ruthless efficiency. It is a completely different experience from the equally hard job of executing in a traditional kind of business, and my goal is not to disparage those other practitioners – after all, most startups aspire to
10     become non-startups someday. Still, these differences matter, because the “best practices” that are learned in other contexts do not transplant well into the startup soil. In fact the most spectacular startup failures result when people were in a startup situation but failed to recognize it, or failed to recognize what it meant for their behavior. This definition is also important for what it excludes. Notice that it says nothing about the size of the company involved. Big companies often fail because they find themselves in a startup situation but are unable to reorient in time to cope with this situation; this specific pathology is explored in The Innovator’s Dilemma. This kind of crisis can be precipitated by many external factors: macroeconomic changes, trade policy, technological change, or even cultural shifts. But most often, the entrant of a startup into a previously calm market precipitates this kind of crisis. This has significant implications for general managers in enterprise, about which you can read more at HBR: Is Entrepreneurship a Management Science?   The  first  principles  of  startup  –  Steve  Blank   What’s A Startup? First Principles. Posted on January 25, 2010 by steveblank Success consists of going from failure to failure without loss of enthusiasm . Winston Churchill Everyone knows what a startup is for – don’t they? In this post we’re going to offer a new definition of why startups exist : a startup is an organization formed to search for a repeatable and scalable business model . A Business Model Ok, but what is a business model? A business model describes how your company creates, delivers and captures value. Or in English: A business model describes how your company makes money. (Or depending on your metrics for success, get users, grow traffic, etc.) Think of a business model as a drawing that shows all the flows between the different parts of your company. A business model diagram also shows how the product gets distributed to your customers and how money flows back into your company. And it shows your company’s cost structures, how each department interacts with the others and where your company fits with other companies or partners to implement your business. While this is a mouthful, it’s a lot easier to draw. Drawing A Business Model Lots of people have been working on how to diagram and draw a business. I had my students drawing theirs for years, but Alexander Osterwalder’s work on business models is the clearest description I’ve read in the last decade. The diagram below is his Business Model template . In
11     your startup’s business model, the boxes will have specific details of your company’s strategy. Alexander Osterwalder's Business Model Template (At Stanford, Ann Miura-Ko and I have been working on a simplified Silicon Valley version of this model. Ann will be guest posting more on business models soon.) But What Does a Business Model Have to Do With My Startup? Your startup is essentially an organization built to search for a repeatable and scalable business model. As a founder you start out with: 1) a vision of a product with a set of features, 2) a series of hypotheses about all the pieces of the business model: Who are the customers/users? What’s the distribution channel. How do we price and position the product? How do we create end user demand? Who are our partners? Where/how do we build the product? How do we finance the company, etc. Your job as a founder is to quickly validate whether the model is correct by seeing if customers behave as your model predicts. Most of the time the darn customers don’t behave as you predicted. How Does Customer Development, Agile Development and Lean Startups Fit? The Customer Development process is the way startups quickly iterate and test each element of their business model. Agile Development is the way startups quickly iterate their product as
12     they learn. A Lean Startup is Eric Ries’s description of the intersection of Customer Development, Agile Development and if available, open platforms and open source. (This methodology does for startups what the Toyota Lean Production System did for cars.) Business Plan Versus Business Model Wait a minute, isn’t the Business Model the same thing as my Business Plan? Sort of…but better. A business plan is useful place for you to collect your hypotheses about your business, sales, marketing, customers, market size, etc. (Your investors make you write one, but they never read it.) A Business Model is how all the pieces in your business plan interconnect. The Pivot How do you know your business model is the right one? When revenue, users, traffic, etc., start increasing in a repeatable way you predicted and make your investors happy. The irony is the first time this happens, you may not have found your company’s optimal model. Most startups change their business model at least once if not several times. How do you know when reached the one to scale? Stay tuned. More in future posts. Lessons Learned A startup is an organization formed to search for a repeatable and scalable business model. The goal of your early business model can be revenue, or profits, or users, or click-throughs – whatever you and your investors have agreed upon. Customer and Agile Development is the way for startups to quickly iterate and test their hypotheses about their business model Most startups change their business model multiple times.      
13     Why  to  do  a  startup  (is  the  idea  worth  it?)  –  Peter  Thiel  (Blake  Masters)   http://blakemasters.tumblr.com/post/20400301508/cs183class1   Peter Thiel’s CS183: Startup - Class 1 Notes Essay Here is an essay version of my class notes from Class 1 of CS183: Startup. Errors and omissions are my own. Credit for good stuff is Peter’s entirely. CS183: Startup—Notes Essay—The Challenge of the Future Purpose and Preamble We might describe our world as having retail sanity, but wholesale madness. Details are well understood; the big picture remains unclear. A fundamental challenge—in business as in life—is to integrate the micro and macro such that all things make sense. Humanities majors may well learn a great deal about the world. But they don’t really learn career skills through their studies. Engineering majors, conversely, learn in great technical detail. But they might not learn why, how, or where they should apply their skills in the workforce. The best students, workers, and thinkers will integrate these questions into a cohesive narrative. This course aims to facilitate that process. I. The History of Technology For most of recent human history—from the invention of the steam engine in the late 17 th century through about the late 1960’s or so— technological process has been tremendous, perhaps even relentless. In most prior human societies, people made money by taking it from others. The industrial revolution wrought a paradigm shift in which people make money through trade, not plunder. The importance of this shift is hard to overstate. Perhaps 100 billion people have ever lived on earth. Most of them lived in essentially stagnant societies; success involved claiming value, not creating it. So the massive technological acceleration of the past few hundred years is truly incredible. The zenith of optimism about the future of technology might have been the 1960’s. People believed in the future. They thought about the future. Many were supremely confident that the next 50 years would be a half-century of unprecedented technological progress. But with the exception of the computer industry, it wasn’t. Per capita incomes are still rising, but that rate is starkly decelerating. Median wages have been stagnant since 1973. People find themselves in an alarming Alice-in-Wonderland-style scenario in which they must run harder and harder—that is, work longer hours—just to stay in the same place. This deceleration is complex, and wage data alone don’t explain it. But they do support the general sense that the rapid progress of the last 200 years is slowing all too quickly. II. The Case For Computer Science Computers have been the happy exception to recent tech deceleration. Moore’s/Kryder’s/Wirth’s laws have largely held up, and forecast continued growth. Computer tech, with ever-improving hardware and agile development, is something of a model for other industries. It’s obviously central to the Silicon Valley ecosystem and a key driver of modern technological change. So CS is the logical starting place to recapture the reins of progress.
14     III. The Future For Progress A. Globalization and Tech: Horizontal vs. Vertical Progress Progress comes in two flavors: horizontal/extensive and vertical/intensive. Horizontal or extensive progress basically means copying things that work. In one word, it means simply “globalization.” Consider what China will be like in 50 years. The safe bet is it will be a lot like the United States is now. Cities will be copied, cars will be copied, and rail systems will be copied. Maybe some steps will be skipped. But it’s copying all the same. Vertical or intensive progress, by contrast, means doing new things. The single word for this is “technology.” Intensive progress involves going from 0 to 1 (not simply the 1 to n of globalization). We see much of our vertical progress come from places like California, and specifically Silicon Valley. But there is every reason to question whether we have enough of it. Indeed, most people seem to focus almost entirely on globalization instead of technology; speaking of “developed” versus “developing nations” is implicitly bearish about technology because it implies some convergence to the “developed” status quo. As a society, we seem to believe in a sort of technological end of history, almost by default. It’s worth noting that globalization and technology do have some interplay; we shouldn’t falsely dichotomize them. Consider resource constraints as a 1 to n subproblem. Maybe not everyone can have a car because that would be environmentally catastrophic. If 1 to n is so blocked, only 0 to 1 solutions can help. Technological development is thus crucially important, even if all we really care about is globalization. B. The Problems of 0 to 1 Maybe we focus so much on going from 1 to n because that’s easier to do. There’s little doubt that going from 0 to 1 is qualitatively different, and almost always harder, than copying something n times. And even trying to achieve vertical, 0 to 1 progress presents the challenge of exceptionalism; any founder or inventor doing something new must wonder: am I sane? Or am I crazy? Consider an analogy to politics. The United States is often thought of as an “exceptional” country. At least many Americans believe that it is. So is the U.S. sane? Or is it crazy? Everyone owns guns. No one believes in climate change. And most people weigh 600 pounds. Of course, exceptionalism may cut the other way. America is the land of opportunity. It is the frontier country. It offers new starts, meritocratic promises of riches. Regardless of which version you buy, people must grapple with the problem of exceptionalism. Some 20,000 people, believing themselves uniquely gifted, move to Los Angeles every year to become famous actors. Very few of them, of course, actually become famous actors. The startup world is probably less plagued by the challenge of exceptionalism than Hollywood is. But it probably isn’t immune to it. C. The Educational and Narrative Challenge Teaching vertical progress or innovation is almost a contradiction in terms. Education is fundamentally about going from 1 to n. We observe, imitate, and repeat. Infants do not invent new languages; they learn existing ones. From early on, we learn by copying what has worked before. That is insufficient for startups. Crossing T’s and dotting I’s will get you maybe 30% of the way there. (It’s certainly necessary to get incorporation right, for instance. And one can learn how to pitch
15     VCs.) But at some point you have to go from 0 to 1—you have to do something important and do it right—and that can’t be taught. Channeling Tolstoy’s intro to Anna Karenina, all successful companies are different; they figured out the 0 to 1 problem in different ways. But all failed companies are the same; they botched the 0 to 1 problem. So case studies about successful businesses are of limited utility. PayPal and Facebook worked. But it’s hard to know what was necessarily path-dependent. The next great company may not be an e-payments or social network company. We mustn’t make too much of any single narrative. Thus the business school case method is more mythical than helpful. D. Determinism vs. Indeterminism Among the toughest questions about progress is the question of how we should assess a venture’s probability of success. In the 1 to n paradigm, it’s a statistical question. You can analyze and predict. But in the 0 to 1 paradigm, it’s not a statistical question; the standard deviation with a sample size of 1 is infinite. There can be no statistical analysis; statistically, we’re in the dark. We tend to think very statistically about the future. And statistics tells us that it’s random. We can’t predict the future; we can only think probabilistically. If the market follows a random walk, there’s no sense trying to out-calculate it. But there’s an alternative math metaphor we might use: calculus. The calculus metaphor asks whether and how we can figure out exactly what’s going to happen. Take NASA and the Apollo missions, for instance. You have to figure out where the moon is going to be, exactly. You have to plan whether a rocket has enough fuel to reach it. And so on. The point is that no one would want to ride in a statistically, probabilistically-informed spaceship. Startups are like the space program in this sense. Going from 0 to 1 always has to favor determinism over indeterminism. But there is a practical problem with this. We have a word for people who claim to know the future: prophets. And in our society, all prophets are false prophets. Steve Jobs finessed his way about the line between determinism and indeterminism; people sensed he was a visionary, but he didn’t go too far. He probably cut it as close as possible (and succeeded accordingly). The luck versus skill question is also important. Distinguishing these factors is difficult or impossible. Trying to do so invites ample opportunity for fallacious reasoning. Perhaps the best we can do for now is to flag the question, and suggest that it’s one that entrepreneurs or would-be entrepreneurs should have some handle on. E. The Future of Intensive Growth There are four theories about the future of intensive progress. First is convergence; starting with the industrial revolution, we saw a quick rise in progress, but technology will decelerate and growth will become asymptotic. Second, there is the cyclical theory. Technological progress moves in cycles; advances are made, retrenchments ensue. Repeat. This has probably been true for most of human history in the past. But it’s hard to imagine it remaining true; to think that we could somehow lose all the information and know-how we’ve amassed and be doomed to have to re-discover it strains credulity. Third is collapse/destruction. Some technological advance will do us in.
16     Fourth is the singularity where technological development yields some AI or intellectual event horizon. People tend to overestimate the likelihood or explanatory power of the convergence and cyclical theories. Accordingly, they probably underestimate the destruction and singularity theories. IV. Why Companies? If we want technological development, why look to companies to do it? It’s possible, after all, to imagine a society in which everyone works for the government. Or, conversely, one in which everyone is an independent contractor. Why have some intermediate version consisting of at least two people but less than everyone on the planet? The answer is straightforward application of the Coase Theorem. Companies exist because they optimally address internal and external coordination costs. In general, as an entity grows, so do its internal coordination costs. But its external coordination costs fall. Totalitarian government is entity writ large; external coordination is easy, since those costs are zero. But internal coordination, as Hayek and the Austrians showed, is hard and costly; central planning doesn’t work. The flipside is that internal coordination costs for independent contractors are zero, but external coordination costs (uniquely contracting with absolutely everybody one deals with) are very high, possibly paralyzingly so. Optimality—firm size—is a matter of finding the right combination. V. Why Startups? A. Costs Matter Size and internal vs. external coordination costs matter a lot. North of 100 people in a company, employees don’t all know each other. Politics become important. Incentives change. Signaling that work is being done may become more important than actually doing work. These costs are almost always underestimated. Yet they are so prevalent that professional investors should and do seriously reconsider before investing in companies that have more than one office. Severe coordination problems may stem from something as seemingly trivial or innocuous as a company having a multi-floor office. Hiring consultants and trying to outsource key development projects are, for similar reasons, serious red flags. While there’s surely been some lessening of these coordination costs in the last 40 years—and that explains the shift to somewhat smaller companies—the tendency is still to underestimate them. Since they remain fairly high, they’re worth thinking hard about. Path’s limiting its users to 150 “friends” is illustrative of this point. And ancient tribes apparently had a natural size limit that didn’t much exceed that number. Startups are important because they are small; if the size and complexity of a business is something like the square of the number of people in it, then startups are in a unique position to lower interpersonal or internal costs and thus to get stuff done. The familiar Austrian critique dovetails here as well. Even if a computer could model all the narrowly economic problems a company faces (and, to be clear, none can), it wouldn’t be enough. To model all costs, it would have to model human irrationalities, emotions, feelings, and interactions. Computers help, but we still don’t have all the info. And if we did, we wouldn’t know what to do with it. So, in practice, we end up having companies of a certain size. B. Why Do a Startup?
17     The easiest answer to “why startups?” is negative: because you can’t develop new technology in existing entities. There’s something wrong with big companies, governments, and non-profits. Perhaps they can’t recognize financial needs; the federal government, hamstrung by its own bureaucracy, obviously overcompensates some while grossly undercompensating others in its employ. Or maybe these entities can’t handle personal needs; you can’t always get recognition, respect, or fame from a huge bureaucracy. Anyone on a mission tends to want to go from 0 to 1. You can only do that if you’re surrounded by others to want to go from 0 to 1. That happens in startups, not huge companies or government. Doing startups for the money is not a great idea. Research shows that people get happier as they make more and more money, but only up to about $70,000 per year. After that, marginal improvements brought by higher income are more or less offset by other factors (stress, more hours, etc. Plus there is obviously diminishing marginal utility of money even absent offsetting factors). Perhaps doing startups to be remembered or become famous is a better motive. Perhaps not. Whether being famous or infamous should be as important as most people seem to think it is highly questionable. A better motive still would be a desire to change the world. The U.S. in 1776-79 was a startup of sorts. What were the Founders motivations? There is a large cultural component to the motivation question, too. In Japan, entrepreneurs are seen as reckless risk-takers. The respectable thing to do is become a lifelong employee somewhere. The literary version of this sentiment is “behind every fortune lies a great crime.” Were the Founding Fathers criminals? Are all founders criminals of one sort or another? C. The Costs of Failure Startups pay less than bigger companies. So founding or joining one involves some financial loss. These losses are generally thought to be high. In reality, they aren’t that high. The nonfinancial costs are actually higher. If you do a failed startup, you may not have learned anything useful. You may actually have learned how to fail again. You may become more risk-averse. You aren’t a lottery ticket, so you shouldn’t think of failure as just 1 of n times that you’re going to start a company. The stakes are a bit bigger than that. A 0 to 1 startup involves low financial costs but low non-financial costs too. You’ll at least learn a lot and probably will be better for the effort. A 1 to n startup, though, has especially low financial costs, but higher non-financial costs. If you try to do Groupon for Madagascar and it fails, it’s not clear where exactly you are. But it’s not good. VI. Where to Start? The path from 0 to 1 might start with asking and answering three questions. First, what is valuable? Second, what can I do? And third, what is nobody else doing? The questions themselves are straightforward. Question one illustrates the difference between business and academia; in academia, the number one sin is plagiarism, not triviality. So much of the innovation is esoteric and not at all useful. No one cares about a firm’s eccentric, non-valuable output. The second question ensures that you can actually execute on a problem; if not, talk is just that. Finally, and often overlooked, is the importance of being novel. Forget that and we’re just copying. The intellectual rephrasing of these questions is: What important truth do very few people agree with you on?
18     The business version is: What valuable company is nobody building? These are tough questions. But you can test your answers; if, as so many people do, one says something like “our educational system is broken and urgently requires repair,” you know that that answer is wrong (it may be a truth, but lots of people agree with it). This may explain why we see so many education non-profits and startups. But query whether most of those are operating in technology mode or globalization mode. You know you’re on the right track when your answer takes the following form: “Most people believe in X. But the truth is !X.” Make no mistake; it’s a hard question. Knowing what 0 to 1 endeavor is worth pursuing is incredibly rare, unique, and tricky. But the process, if not the result, can also be richly rewarding.   Entrepreneurship  as  management  science  –  Eric  Ries   http://blogs.hbr.org/cs/2010/01/is_entrepreneurship_a_manageme.html   Is Entrepreneurship a Management Science? After ten years as an entrepreneur, I started writing a blog called Startup Lessons Learned. My original goal was to provide support and encouragement to other entrepreneurs who, like me, had some unorthodox ideas about how to run companies. Along the way, I encountered two big surprises. They led me to a question I had never previously considered: is it possible that entrepreneurship is actually a management science? For most of us, the phrase "management science" conjures up a decidedly non-entrepreneurial image, and for good reason. The preeminent management science, for much of the twentieth century was general management, pioneered by twentieth century giants like Peter Drucker and Alfred P. Sloan. As an entrepreneur, I often saw the best practices of general management fail startups. Applied out of context, they were dangerous. This conflict has played out in many companies that I've worked with. My most recent startup created a marketplace for customers to buy and sell virtual goods for their 3D avatar. So you can imagine how I expected some skepticism when pitching ideas about technology innovation to, say, the U.S. Army. This was my first surprise: they understood that innovation needs to be understood at the level of principles, not just tactics. Some tactics, like Continuous Deployment, are controversial, even among people with my background. But the really vexing questions came from entrepreneurs who inundated me with questions from other backgrounds. Could these techniques be used by enterprise software companies? Hardware manufacturers? Biotech startups? Answering those questions requires understanding the principles of entrepreneurship in a rigorous way. I began to call this new methodology "the lean startup." It is a rigorous application of lean principles to the problem of new product innovation. Inspired by the lean manufacturing revolution (and excellent books like Lean Thinking), I started with a first fundamental question: in a startup, what activities are value-creating and which are waste?
19     Usually, new projects are measured and held accountable to milestones and deadlines. When a project is on track, on time, and on budget, our intuition is that it is being well managed. This intuition is dead wrong. Most startups fail because they are building something that nobody wants. Enamored with a new technology or a radical new product, many entrepreneurs never find a set of customers who will buy it. Each new feature added to such a product is actually wasted effort, even if it's done on-time and on-budget. In product that nobody wants, all the features get thrown away. This problem vexes venture-backed founders and general managers alike. Anyone who is tasked with creating disruptive innovation will find it familiar. In short, for an innovation on the wrong course, executing well doesn't increase the odds of success. What's needed, I realized, is a new definition of progress in a startup, one that recognizes that a startup's primary mission is to penetrate the fog of the unknown and find out what their customers ultimately will want and accept. I call this unit of progress "validated learning about customers." Because learning is the goal, the progress that startups make requires a very different kind of management. This led me to a second surprise. As I was traveling the country talking to people about the lean startup, I kept meeting people in the audience who seemed out of place. They weren't what we usually think of as entrepreneurs. They were general managers, most working in very large companies, who were tasked with creating new product innovations. They were smart, well-read, and understood Clay Christenson's Innovator's Solution cold. They were adept at organizational politics: they knew how to form autonomous divisions with separate P&Ls, and could shield controversial or disruptive teams from meddling. And — my biggest surprise yet — they were visionaries. Like the startup founders I have worked with for years, they could see the future of their industries, and were prepared to take bold risks to seek out new and innovative solutions to the problems their companies would face. They found the lean startup helpful. In fact, they considered it obvious that these principles — rapid iteration, a focus on validated learning, and reducing cycle time from idea to learning — applied to their situation. Here's why. In my quest to put the practice of entrepreneurship on a rigorous footing, I had begun with this definition of a startup: A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty. Originally, I used this definition to help entrepreneurs understand why the "best practices" of general management so often failed them. They can't thrive when transplanted into the soil of extreme uncertainty that is the province of startups. But this definition is surprising: it doesn't say anything about the size of the company, sector of the economy, or industry. Looking past my old preconceptions, I realized that entrepreneurs are everywhere: in government, in the occasional garage, and yes, in the traditional organization. All entrepreneurs face the same fundamental challenges: How do we know if we're making progress? How do we know if customers will want the product we are building? And, if they do, how do we know what kind of value we can create with it?
20     But because every startup also strives to become an institution, answering these questions requires more than just disciplined thinking at the whiteboard. It requires the coordination of many different people, working in concert to answer them. In other words, it requires management. The management challenges presented by entrepreneurship are different. For example, how do we hold an entrepreneurial team accountable for making progress? This is a core challenge faced by venture capitalists and corporate CFOs alike. A team might be making progress against their plan, might be hitting milestones, might even be earning revenue. But none of those artifacts constitute progress. Learning is progress. In short, it's demonstrating the ability to use what we've learned to change customer behavior, with key metrics as the common source of evidence. To measure learning requires a shift of attention from the output of models to their inputs. I'll show you how to apply this to entrepreneurial situations in my next post. Eric Ries is the author of StartupLessonsLearned.com and is an adviser to many startups, companies, and venture capital firms.    
21     The  basic  strategy  (what  to  do)   The  basic  strategy  (what  to  do)   Startups  in  13  sentences  –  Paul  Graham   http://www.paulgraham.com/13sentences.html   Want to start a startup? Get funded by Y Combinator. Watch how this essay was written on Etherpad. February 2009 One of the things I always tell startups is a principle I learned from Paul Buchheit: it's better to make a few people really happy than to make a lot of people semi-happy. I was saying recently to a reporter that if I could only tell startups 10 things, this would be one of them. Then I thought: what would the other 9 be? When I made the list there turned out to be 13: 1. Pick good cofounders. Cofounders are for a startup what location is for real estate. You can change anything about a house except where it is. In a startup you can change your idea easily, but changing your cofounders is hard. [1] And the success of a startup is almost always a function of its founders. 2. Launch fast. The reason to launch fast is not so much that it's critical to get your product to market early, but that you haven't really started working on it till you've launched. Launching teaches you what you should have been building. Till you know that you're wasting your time. So the main value of whatever you launch with is as a pretext for engaging users. 3. Let your idea evolve. This is the second half of launching fast. Launch fast and iterate. It's a big mistake to treat a startup as if it were merely a matter of implementing some brilliant initial idea. As in an essay, most of the ideas appear in the implementing. 4. Understand your users.
22     You can envision the wealth created by a startup as a rectangle, where one side is the number of users and the other is how much you improve their lives. [2] The second dimension is the one you have most control over. And indeed, the growth in the first will be driven by how well you do in the second. As in science, the hard part is not answering questions but asking them: the hard part is seeing something new that users lack. The better you understand them the better the odds of doing that. That's why so many successful startups make something the founders needed. 5. Better to make a few users love you than a lot ambivalent. Ideally you want to make large numbers of users love you, but you can't expect to hit that right away. Initially you have to choose between satisfying all the needs of a subset of potential users, or satisfying a subset of the needs of all potential users. Take the first. It's easier to expand userwise than satisfactionwise. And perhaps more importantly, it's harder to lie to yourself. If you think you're 85% of the way to a great product, how do you know it's not 70%? Or 10%? Whereas it's easy to know how many users you have. 6. Offer surprisingly good customer service. Customers are used to being maltreated. Most of the companies they deal with are quasi-monopolies that get away with atrocious customer service. Your own ideas about what's possible have been unconsciously lowered by such experiences. Try making your customer service not merely good, but surprisingly good. Go out of your way to make people happy. They'll be overwhelmed; you'll see. In the earliest stages of a startup, it pays to offer customer service on a level that wouldn't scale, because it's a way of learning about your users. 7. You make what you measure. I learned this one from Joe Kraus. [3] Merely measuring something has an uncanny tendency to improve it. If you want to make your user numbers go up, put a big piece of paper on your wall and every day plot the number of users. You'll be delighted when it goes up and disappointed when it goes down. Pretty soon you'll start noticing what makes the number go up, and you'll start to do more of that. Corollary: be careful what you measure. 8. Spend little. I can't emphasize enough how important it is for a startup to be cheap. Most startups fail before they make something people want, and the most common form of failure is running out of money. So being cheap is (almost) interchangeable with iterating rapidly. [4]But it's more than that. A culture of cheapness keeps companies young in something like the way exercise keeps people young. 9. Get ramen profitable. "Ramen profitable" means a startup makes just enough to pay the founders' living
23     expenses. It's not rapid prototyping for business models (though it can be), but more a way of hacking the investment process. Once you cross over into ramen profitable, it completely changes your relationship with investors. It's also great for morale. 10. Avoid distractions. Nothing kills startups like distractions. The worst type are those that pay money: day jobs, consulting, profitable side-projects. The startup may have more long-term potential, but you'll always interrupt working on it to answer calls from people paying you now. Paradoxically, fundraising is this type of distraction, so try to minimize that too. 11. Don't get demoralized. Though the immediate cause of death in a startup tends to be running out of money, the underlying cause is usually lack of focus. Either the company is run by stupid people (which can't be fixed with advice) or the people are smart but got demoralized. Starting a startup is a huge moral weight. Understand this and make a conscious effort not to be ground down by it, just as you'd be careful to bend at the knees when picking up a heavy box. 12. Don't give up. Even if you get demoralized, don't give up. You can get surprisingly far by just not giving up. This isn't true in all fields. There are a lot of people who couldn't become good mathematicians no matter how long they persisted. But startups aren't like that. Sheer effort is usually enough, so long as you keep morphing your idea. 13. Deals fall through. One of the most useful skills we learned from Viaweb was not getting our hopes up. We probably had 20 deals of various types fall through. After the first 10 or so we learned to treat deals as background processes that we should ignore till they terminated. It's very dangerous to morale to start to depend on deals closing, not just because they so often don't, but because it makes them less likely to. Having gotten it down to 13 sentences, I asked myself which I'd choose if I could only keep one. Understand your users. That's the key. The essential task in a startup is to create wealth; the dimension of wealth you have most control over is how much you improve users' lives; and the hardest part of that is knowing what to make for them. Once you know what to make, it's mere effort to make it, and most decent hackers are capable of that. Understanding your users is part of half the principles in this list. That's the reason to launch early, to understand your users. Evolving your idea is the embodiment of understanding your users. Understanding your users well will tend to push you toward
24     making something that makes a few people deeply happy. The most important reason for having surprisingly good customer service is that it helps you understand your users. And understanding your users will even ensure your morale, because when everything else is collapsing around you, having just ten users who love you will keep you going. Notes [1] Strictly speaking it's impossible without a time machine. [2] In practice it's more like a ragged comb. [3] Joe thinks one of the founders of Hewlett Packard said it first, but he doesn't remember which. [4] They'd be interchangeable if markets stood still. Since they don't, working twice as fast is better than having twice as much time.   How  to  start  a  startup  –  Paul  Graham   http://www.paulgraham.com/start.html   Want to start a startup? Get funded by Y Combinator. March 2005 (This essay is derived from a talk at the Harvard Computer Society.) You need three things to create a successful startup: to start with good people, to make something customers actually want, and to spend as little money as possible. Most startups that fail do it because they fail at one of these. A startup that does all three will probably succeed. And that's kind of exciting, when you think about it, because all three are doable. Hard, but doable. And since a startup that succeeds ordinarily makes its founders rich, that implies getting rich is doable too. Hard, but doable. If there is one message I'd like to get across about startups, that's it. There is no magically difficult step that requires brilliance to solve. The Idea
25     In particular, you don't need a brilliant idea to start a startup around. The way a startup makes money is to offer people better technology than they have now. But what people have now is often so bad that it doesn't take brilliance to do better. Google's plan, for example, was simply to create a search site that didn't suck. They had three new ideas: index more of the Web, use links to rank search results, and have clean, simple web pages with unintrusive keyword-based ads. Above all, they were determined to make a site that was good to use. No doubt there are great technical tricks within Google, but the overall plan was straightforward. And while they probably have bigger ambitions now, this alone brings them a billion dollars a year. [1] There are plenty of other areas that are just as backward as search was before Google. I can think of several heuristics for generating ideas for startups, but most reduce to this: look at something people are trying to do, and figure out how to do it in a way that doesn't suck. For example, dating sites currently suck far worse than search did before Google. They all use the same simple-minded model. They seem to have approached the problem by thinking about how to do database matches instead of how dating works in the real world. An undergrad could build something better as a class project. And yet there's a lot of money at stake. Online dating is a valuable business now, and it might be worth a hundred times as much if it worked. An idea for a startup, however, is only a beginning. A lot of would-be startup founders think the key to the whole process is the initial idea, and from that point all you have to do is execute. Venture capitalists know better. If you go to VC firms with a brilliant idea that you'll tell them about if they sign a nondisclosure agreement, most will tell you to get lost. That shows how much a mere idea is worth. The market price is less than the inconvenience of signing an NDA. Another sign of how little the initial idea is worth is the number of startups that change their plan en route. Microsoft's original plan was to make money selling programming languages, of all things. Their current business model didn't occur to them until IBM dropped it in their lap five years later. Ideas for startups are worth something, certainly, but the trouble is, they're not transferrable. They're not something you could hand to someone else to execute. Their value is mainly as starting points: as questions for the people who had them to continue thinking about. What matters is not ideas, but the people who have them. Good people can fix bad ideas, but good ideas can't save bad people. People What do I mean by good people? One of the best tricks I learned during our startup was a rule for deciding who to hire. Could you describe the person as an animal? It might be hard to translate that into another language, but I think everyone in the US knows what
26     it means. It means someone who takes their work a little too seriously; someone who does what they do so well that they pass right through professional and cross over into obsessive. What it means specifically depends on the job: a salesperson who just won't take no for an answer; a hacker who will stay up till 4:00 AM rather than go to bed leaving code with a bug in it; a PR person who will cold-call New York Times reporters on their cell phones; a graphic designer who feels physical pain when something is two millimeters out of place. Almost everyone who worked for us was an animal at what they did. The woman in charge of sales was so tenacious that I used to feel sorry for potential customers on the phone with her. You could sense them squirming on the hook, but you knew there would be no rest for them till they'd signed up. If you think about people you know, you'll find the animal test is easy to apply. Call the person's image to mind and imagine the sentence "so-and-so is an animal." If you laugh, they're not. You don't need or perhaps even want this quality in big companies, but you need it in a startup. For programmers we had three additional tests. Was the person genuinely smart? If so, could they actually get things done? And finally, since a few good hackers have unbearable personalities, could we stand to have them around? That last test filters out surprisingly few people. We could bear any amount of nerdiness if someone was truly smart. What we couldn't stand were people with a lot of attitude. But most of those weren't truly smart, so our third test was largely a restatement of the first. When nerds are unbearable it's usually because they're trying too hard to seem smart. But the smarter they are, the less pressure they feel to act smart. So as a rule you can recognize genuinely smart people by their ability to say things like "I don't know," "Maybe you're right," and "I don't understand x well enough." This technique doesn't always work, because people can be influenced by their environment. In the MIT CS department, there seems to be a tradition of acting like a brusque know-it-all. I'm told it derives ultimately from Marvin Minsky, in the same way the classic airline pilot manner is said to derive from Chuck Yeager. Even genuinely smart people start to act this way there, so you have to make allowances. It helped us to have Robert Morris, who is one of the readiest to say "I don't know" of anyone I've met. (At least, he was before he became a professor at MIT.) No one dared put on attitude around Robert, because he was obviously smarter than they were and yet had zero attitude himself. Like most startups, ours began with a group of friends, and it was through personal contacts that we got most of the people we hired. This is a crucial difference between startups and big companies. Being friends with someone for even a couple days will tell
27     you more than companies could ever learn in interviews. [2] It's no coincidence that startups start around universities, because that's where smart people meet. It's not what people learn in classes at MIT and Stanford that has made technology companies spring up around them. They could sing campfire songs in the classes so long as admissions worked the same. If you start a startup, there's a good chance it will be with people you know from college or grad school. So in theory you ought to try to make friends with as many smart people as you can in school, right? Well, no. Don't make a conscious effort to schmooze; that doesn't work well with hackers. What you should do in college is work on your own projects. Hackers should do this even if they don't plan to start startups, because it's the only real way to learn how to program. In some cases you may collaborate with other students, and this is the best way to get to know good hackers. The project may even grow into a startup. But once again, I wouldn't aim too directly at either target. Don't force things; just work on stuff you like with people you like. Ideally you want between two and four founders. It would be hard to start with just one. One person would find the moral weight of starting a company hard to bear. Even Bill Gates, who seems to be able to bear a good deal of moral weight, had to have a co- founder. But you don't want so many founders that the company starts to look like a group photo. Partly because you don't need a lot of people at first, but mainly because the more founders you have, the worse disagreements you'll have. When there are just two or three founders, you know you have to resolve disputes immediately or perish. If there are seven or eight, disagreements can linger and harden into factions. You don't want mere voting; you need unanimity. In a technology startup, which most startups are, the founders should include technical people. During the Internet Bubble there were a number of startups founded by business people who then went looking for hackers to create their product for them. This doesn't work well. Business people are bad at deciding what to do with technology, because they don't know what the options are, or which kinds of problems are hard and which are easy. And when business people try to hire hackers, they can't tell which ones aregood. Even other hackers have a hard time doing that. For business people it's roulette. Do the founders of a startup have to include business people? That depends. We thought so when we started ours, and we asked several people who were said to know about this mysterious thing called "business" if they would be the president. But they all said no, so I had to do it myself. And what I discovered was that business was no great mystery. It's not something like physics or medicine that requires extensive study. You just try to get people to pay you for stuff. I think the reason I made such a mystery of business was that I was disgusted by the idea of doing it. I wanted to work in the pure, intellectual world of software, not deal with customers' mundane problems. People who don't want to get dragged into some kind of work often develop a protective incompetence at it. Paul Erdos was particularly
28     good at this. By seeming unable even to cut a grapefruit in half (let alone go to the store and buy one), he forced other people to do such things for him, leaving all his time free for math. Erdos was an extreme case, but most husbands use the same trick to some degree. Once I was forced to discard my protective incompetence, I found that business was neither so hard nor so boring as I feared. There are esoteric areas of business that are quite hard, like tax law or the pricing of derivatives, but you don't need to know about those in a startup. All you need to know about business to run a startup are commonsense things people knew before there were business schools, or even universities. If you work your way down the Forbes 400 making an x next to the name of each person with an MBA, you'll learn something important about business school. After Warren Buffett, you don't hit another MBA till number 22, Phil Knight, the CEO of Nike. There are only 5 MBAs in the top 50. What you notice in the Forbes 400 are a lot of people with technical backgrounds. Bill Gates, Steve Jobs, Larry Ellison, Michael Dell, Jeff Bezos, Gordon Moore. The rulers of the technology business tend to come from technology, not business. So if you want to invest two years in something that will help you succeed in business, the evidence suggests you'd do better to learn how to hack than get an MBA. [3] There is one reason you might want to include business people in a startup, though: because you have to have at least one person willing and able to focus on what customers want. Some believe only business people can do this-- that hackers can implement software, but not design it. That's nonsense. There's nothing about knowing how to program that prevents hackers from understanding users, or about not knowing how to program that magically enables business people to understand them. If you can't understand users, however, you should either learn how or find a co-founder who can. That is the single most important issue for technology startups, and the rock that sinks more of them than anything else. What Customers Want It's not just startups that have to worry about this. I think most businesses that fail do it because they don't give customers what they want. Look at restaurants. A large percentage fail, about a quarter in the first year. But can you think of one restaurant that had really good food and went out of business? Restaurants with great food seem to prosper no matter what. A restaurant with great food can be expensive, crowded, noisy, dingy, out of the way, and even have bad service, and people will keep coming. It's true that a restaurant with mediocre food can sometimes attract customers through gimmicks. But that approach is very risky. It's more straightforward just to make the food good. It's the same with technology. You hear all kinds of reasons why startups fail. But can you think of one that had a massively popular product and still failed?
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
Startup Reading List - Startup Strategy
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Startup Reading List - Startup Strategy

  • 1. STARTUP  READING  LIST  –  JUNE  2012   Startup  strategy   What  is  a  startup,  the  basic  strategy,  what  not  to   do,  generating  and  testing  an  idea,  establishing   customer  intent,  starting  build  and  iterating   product,  making  money,  reaching  product   market  fit,  scaling,  business  development,   changing  direction,  competition  incumbents  and   first  mover  advantage,  failure,  business  metrics   for  startups                  
  • 2. 1     Contents   What  is  a  startup?  ..................................................................................................................................................  8   What  is  a  startup?  –  Eric  Ries  .............................................................................................................................  8   The  first  principles  of  startup  –  Steve  Blank  .....................................................................................................  10   Why  to  do  a  startup  (is  the  idea  worth  it?)  –  Peter  Thiel  (Blake  Masters)  .......................................................  13   Entrepreneurship  as  management  science  –  Eric  Ries  .....................................................................................  18   The  basic  strategy  (what  to  do)  ...........................................................................................................................  21   Startups  in  13  sentences  –  Paul  Graham  ..........................................................................................................  21   How  to  start  a  startup  –  Paul  Graham  .............................................................................................................  24   Understand  the  market  –  Rob  Fitzpatrick  ........................................................................................................  42   Milestones  to  startup  success  –  Sean  Ellis  ........................................................................................................  43   What  makes  a  great  startup  –  Sean  Ellis  ..........................................................................................................  46   The  startup  pyramid  (getting  to  product  market  fit)  –  Sean  Ellis  .....................................................................  48   Deconstructing  startup  growth  –  Sean  Ellis  .....................................................................................................  50   Speed  as  the  primary  business  strategy  –  Mike  Cassidy  ..................................................................................  52   Startups  rarely  do  anything  well  –  Eric  Paley  ...................................................................................................  54   No  plan  survives  contact  with  customers  –  Steve  Blank  ...................................................................................  55   Building  a  web  startup  –  Steve  Blank  ...............................................................................................................  57   One  way  to  start  a  startup  –  Naval  ..................................................................................................................  62   Moving  fast  –  Nivi  ............................................................................................................................................  62   The  benefits  of  thinking  small    –  Elad  Gil  .........................................................................................................  64   Elements  of  sustainable  companies  –  Sequoia  .................................................................................................  66   Is  stealth  mode  sensible  –  Elad  Gil  ...................................................................................................................  67   No  stealth    –  Chris  Dixon  ..................................................................................................................................  69   What  not  to  do  .....................................................................................................................................................  71   Mistakes  that  kill  startups  –  Paul  Graham  .......................................................................................................  71   The  most  common  mistakes  –  Mark  Suster  .....................................................................................................  82   The  9  deadly  startup  sins  –  Steve  Blank  ...........................................................................................................  86   Why  startups  fail    –  David  Skok  ........................................................................................................................  89   What  constitutes  a  startup  mistake?  –  Simeon  Simeonov  ...............................................................................  92   3  Startup  Lessons  I  Learned  the  Hard  Way  –  David  Cancel  ..............................................................................  94   Failing  by  not  knowing  what  you  don’t  know  –  Steve  Blank  ............................................................................  96   Startup  DOA    –  Ben  Yoskovitz  ...........................................................................................................................  98   The  top  20  reasons  startups  fail  –  Chubby  Brain  ..............................................................................................  99   Premature  scaling  leads  to  death  –  Steve  Blank  ............................................................................................  105  
  • 3. 2     Premature  scaling  –  Rip  Empson  ....................................................................................................................  107   Don’t  be  creative  about  the  wrong  things  –  Chris  Dixon  ................................................................................  113   Rewriting  code  is  suicide  –  Steve  Blank  ..........................................................................................................  113   Refactoring  yourself  out  of  business  –  Eric  Ries  .............................................................................................  115   Generating  and  testing  an  idea  ..........................................................................................................................  119   Ideas  for  startups  –  Paul  Graham  ..................................................................................................................  119   Writing  a  business  plan  –  Sequoia  ..................................................................................................................  127   Good  startup  ideas  are  secrets    –  Peter  Thiel  (Blake  Masters)  .......................................................................  128   How  I  come  up  with  new  startup  ideas  (in  4  steps)–  Rob  Fitzpatrick  .............................................................  139   Timing  your  startup  (Everything  has  been  tried  before)  –  Chris  Dixon  ...........................................................  142   Paradigm  breaking  ideas  –  Steven  Carpenter  ................................................................................................  144   You  Don't  Need  A  Good  Idea  To  Start  A  Great  Company  –  Elad  Gil  ...............................................................  146   The  myth  of  the  Eureka  moment  –  Chris  Dixon  ..............................................................................................  147   Developing  new  startup  ideas  –  Chris  Dixon  ..................................................................................................  148   How  to  Build  a  Billion  Dollar  Startup  (Just  fulfil  a  need)  –  Steve  Blank  ..........................................................  149   How  Can  You  Tell  If  Your  Market  Is  A  Good  One?–  Elad  Gil  ...........................................................................  150   Start  from  the  heart  (Do  what  you  believe)  –  Roy  Rodenstein  .......................................................................  152   Before  product-­‐market  fit,  find  passion-­‐market  fit  –  Naval  ...........................................................................  155   Why  Do  Market  Segments  Matter?  –  Brant  Cooper  ......................................................................................  156   Use  the  tools  you’re  displacing    –  Rob  Fitzpatrick  ..........................................................................................  158   Timing  is  everything  –  Chris  Dixon  .................................................................................................................  159   Why  smart  people  have  bad  ideas  –  Paul  Graham  ........................................................................................  160   Big  technologies  come  from  toys  –  Chris  Dixon  ..............................................................................................  167   Beware  of  the  word  interesting  –  Evan  Shore  ................................................................................................  168   The  fast  follow  –  Steven  Carpenter  ................................................................................................................  171   The  challenge  of  creating  a  new  category  –  Chris  Dixon  ................................................................................  173   Establishing  customer  intent  ..............................................................................................................................  174   The  customer  development  manifesto  –  Steve  Blank  .....................................................................................  174   Product  development  as  the  leading  cause  of  startup  death  –  Steve  Blank  ..................................................  175   The  Customer  Development  Manifesto:  Reasons  for  the  Revolution  (part  1)  –  Steve  Blank  ..........................  177   The  Customer  Development  Manifesto:  Reasons  for  the  Revolution  (part  2)  –  Steve  Blank  ..........................  180   The  Customer  Development  Manifesto:  The  Startup  Death  Spiral  (part  3)  –  Steve  Blank  .............................  183   Customer  Development  Manifesto:  Market  Type  (part  4)  –  Steve  Blank  .......................................................  185   Customer  Development  Manifesto:  The  Path  of  Warriors  and  Winners  (part  5)  –  Steve  Blank  .....................  187   What  happens  when  you  fail  to  validate  customers  –  Steve  Blank  ................................................................  189  
  • 4. 3     Customer  development  is  not  a  focus  group  –  Steve  Blank  ............................................................................  192   What  is  customer  development?  –  Eric  Ries  ...................................................................................................  194   The  Risk  Validation  Pyramid–  Rob  Fitzpatrick  ................................................................................................  197   Customer  development  for  web  startups  –  Steve  Blank  .................................................................................  199   Customer  development  for  consumer  startups  –  Ash  Maurya  .......................................................................  202   How  to  find  early  adopters  –  Brant  Cooper  ....................................................................................................  205   Customer  development  biases  –  Brant  Cooper  ..............................................................................................  207   How  to  find  prospective  customers  –  Cindy  Alvarez  .......................................................................................  209   What  to  learn  from  customer  development  –  Cindy  Alvarez  .........................................................................  211   Segmenting  a  market  –  Cindy  Alvarez  ............................................................................................................  214   Big  Picture  Customer  Development  Revisited  –  Sean  Ellis  ..............................................................................  216   What  customer  data  to  collect  –  Steve  Blank  ................................................................................................  217   Checklist  for  chaos  –  Steve  Blank  ...................................................................................................................  220   The  customer  development  conversation  –  Brant  Cooper  .............................................................................  222   Let’s  Fire  Our  Customers  –  Steve  Blank  ..........................................................................................................  225   Starting  user  research  –  Laura  Klein  ...............................................................................................................  227   You  Need  Personas  –  Cindy  Alvarez  ...............................................................................................................  230   A  day  in  the  life  of  your  customer  –  Ben  Yoskovitz  .........................................................................................  232   Survey  for  customer  development  –  Sean  Ellis  ...............................................................................................  234   You  Need  to  Make  “Wanting”  No  Longer  Free–  Cindy  Alvarez  ......................................................................  235   Strategy  is  not  a  to  do  list  –  Steve  Blank  ........................................................................................................  236   Getting  customer  intent  –  Eric  Ries  ................................................................................................................  238   Using  a  LOI  to  define  MVP  –  Eric  Ries  .............................................................................................................  241   Visionaries  lament  –  Eric  Ries  .........................................................................................................................  245   Validated  learning  about  customers  –  Eric  Ries  .............................................................................................  248   Vertical  markets  1  –  Steve  Blank  ....................................................................................................................  251   Vertical  markets  2  –  Steve  Blank  ....................................................................................................................  254   Vertical  markets  3  –  Steve  Blank  ....................................................................................................................  255   Vertical  markets  4  –  Steve  Blank  ....................................................................................................................  256   Starting  build  and  iterating  product  ...................................................................................................................  260   What  is  Lean  about  the  Lean  Startup?–  Eric  Ries  ...........................................................................................  260   Myths  about  lean  startup  –  Eric  Ries  ..............................................................................................................  261   Lean  startup  myths  –  The  Lean  Startup  Wiki  .................................................................................................  262   Lean  Startups  aren’t  Cheap  Startups–  Steve  Blank  ........................................................................................  264   Learning  is  better  than  optimisation  –  Eric  Ries  .............................................................................................  266  
  • 5. 4     Cash  is  not  king  –  Eric  Ries  .............................................................................................................................  270   Work  in  small  batches  –  Eric  Ries  ...................................................................................................................  272   The  five  whys  for  startups  –  Eric  Ries  .............................................................................................................  274   Conducting  a  five  whys  –  Eric  Ries  ..................................................................................................................  276   MVP  examples    –  Nivi  .....................................................................................................................................  280   MVP  doesn’t  mean  shit  –  Ben  Yoskovitz  .........................................................................................................  281   MVP  vs  MDP  -­‐  Andrew  Chen  ..........................................................................................................................  282   How  much  effort  to  spend  on  the  MVP  –  Vinicius  Vacanti  .............................................................................  285   Prioritising  features  –  Ben  Yoskovitz  ..............................................................................................................  285   Adding  new  features  –  Ben  Yoskovitz  .............................................................................................................  287   Embrace  technical  debt  –  Eric  Ries  .................................................................................................................  288   The  pace  of  startup    –  Ben  Yoskovitz  ..............................................................................................................  292   The  startup  rules  of  speed  –  Eric  Ries  .............................................................................................................  294   Rapid  iteration  with  hardware  –  Eric  Ries  ......................................................................................................  295   Running  (anything)  lean  –  Ash  Maurya  ..........................................................................................................  299   How  much  process  is  too  much  –  Eric  Ries  .....................................................................................................  301   Your  Best  Customers  Probably  Aren’t  –  Cindy  Alvarez  ...................................................................................  302   Holding  entrepreneurs  accountable  –  Eric  Ries  ..............................................................................................  305   Business  ecology  and  the  four  customer  currencies  –  Eric  Ries  ......................................................................  307   Good  enough  never  is  (or  is  it?)  –  Eric  Ries  .....................................................................................................  311   Don’t  launch  –  Eric  Ries  ..................................................................................................................................  315   No  corner  cases  –  Steve  Blank  ........................................................................................................................  318   Mobile  first,  web  second  –  Fred  Wilson  ..........................................................................................................  320   Making  money  (Business  models  and  monetisation)  .........................................................................................  321   The  business  model  /  customer  development  stack  –  Steve  Blank  .................................................................  321   Search  vs  execute  (the  relationship  between  customer  development  and  business  model  design)–  Steve  Blank  .......................................................................................................................................................................  324   The  Right  Business  Model  for  Your  Startup  –  Sean  Ellis  .................................................................................  329   8  questions  to  understand  the  business  model  –  Alex  Osterwalder  ...............................................................  331   13  Consumer  Internet  Business  Models  (Part  I)–  Steven  Carpenter  ...............................................................  335   13  Consumer  Internet  Business  Models  (Part  II)–  Steven  Carpenter  ..............................................................  342   Business  model  discovery  –  Ash  Maurya  ........................................................................................................  351   How  to  create  a  profitable  Freemium  startup  –  Andrew  Chen  ......................................................................  354   Dividing  free  and  paid  features  in  “freemium”  products  –  Chris  Dixon  ..........................................................  360   Freemium  as  a  model  –  Eric  Ries  ....................................................................................................................  362   The  revenue  plan  needs  to  match  the  market  –  Steve  Blank  .........................................................................  363  
  • 6. 5     Business  Model  Analysis,  Part  1:  Key  Questions  –  Tom  Eisenmann  ...............................................................  366   Business  Model  Analysis,  Part  2:  Platforms  and  Network  Effects  –  Tom  Eisenmann  .....................................  370   Business  Model  Analysis,  Part  3:  Switching  Costs  –  Tom  Eisenmann  .............................................................  374   Business  Model  Analysis,  Part  4:  Racing  for  Scale  –  Tom  Eisenmann  ............................................................  376   Business  Model  Analysis,  Part  5:  Virality–  Tom  Eisenmann  ...........................................................................  379   Business  Model  Analysis,  Part  6:  LTV  and  CAC  –  Tom  Eisenmann  ..................................................................  381   Business  Model  Analysis,  Part  7:  Bundling  –  Tom  Eisenmann  ........................................................................  384   Business  Model  Analysis,  Part  8:  Crossing  the  Chasm  –  Tom  Eisenmann  ......................................................  385   Business  Model  Analysis,  Part  9:  Outsourcing  –  Tom  Eisenmann  ..................................................................  387   Stop  asking  “But  how  will  they  make  money?”  –  Andrew  Chen  .....................................................................  388   Reaching  product  market  fit  ..............................................................................................................................  392   Product  market  fit  is  the  only  thing  that  matters  –  Marc  Andreessen  ...........................................................  392   Zero  to  product/market  fit  –  Andrew  Chen  ....................................................................................................  396   When  has  a  consumer  startup  hit  product/market  fit?  –  Andrew  Chen  ........................................................  404   What  is  a  market?  –  Eric  Ries  .........................................................................................................................  408   Scaling  (growing  the  business  post  product  market  fit)  .....................................................................................  411   Keys  to  unlocking  startup  growth  –  Sean  Ellis  ................................................................................................  411   Taking  the  mystery  out  of  scaling  a  company  –  Ben  Horowitz  .......................................................................  412   Make  No  Little  Plans:  Defining  the  Scalable  Startup  –  Steve  Blank  ................................................................  416   Add  another  zero  –  Brad  Feld  .........................................................................................................................  419   4  types  of  scale  –  Nivi  .....................................................................................................................................  420   Key  Elements  of  a  Massively  Scalable  Startup  –  Sean  Ellis  .............................................................................  420   Business  development  ........................................................................................................................................  423   Business  Development  for  Early  Stage  Startups  –  Charlie  O’Donnell  .............................................................  423   Pitching  strategic  partners  –  Chris  Dixon  .......................................................................................................  424   Pitching  partners  –  Chris  Dixon  ......................................................................................................................  426   Outsourcing  –  Fred  Wilson  .............................................................................................................................  427   Outsourcing  vs  offshoring  –  Fred  Wilson  ........................................................................................................  428   Outsourcing  product  development  -­‐  VIVEK  WADHWA  ...................................................................................  430   Changing  direction  (The  pivot)  ...........................................................................................................................  435   Pivot,  don’t  jump  –  Eric  Ries  ...........................................................................................................................  435   Pivoting  –  Chris  Dixon  .....................................................................................................................................  437   Anatomy  of  a  pivot  –  Eric  Ries  ........................................................................................................................  438   Pivoting  the  business  model  –  Steve  Blank  .....................................................................................................  441   Panic  at  the  Pivot  –  Aligning  Incentives  By  Burning  the  Boats  –  Steve  Blank  .................................................  444  
  • 7. 6     Competition,  incumbents  and  first  mover  advantage  ........................................................................................  447   We’re  just  like  those  others  guys,  but  better  –  Steve  Blank  ...........................................................................  447   First  mover  advantage  doesn’t  work  for  startups–  Steve  Blank  .....................................................................  451   Thoughts  on  incumbents  from  a  startup's  perspective–  Chris  Dixon  .............................................................  453   Indifference  is  Your  Real  Competitor  –  Sean  Ellis  ...........................................................................................  454   Never  Enough  Competition  –  Charlie  O’Donnell  .............................................................................................  455   Competition  -­‐  The  Pros  and  Cons  –  Fred  Wilson  .............................................................................................  456   Incumbents  –  Chris  Dixon  ...............................................................................................................................  457   Competitive  analysis  kills  –  Steve  Blank  .........................................................................................................  458   Be  the  first  mover  –  Brad  Feld  ........................................................................................................................  460   Competition  –  Brad  Feld  .................................................................................................................................  462   Competition  is  overrated  –  Chris  Dixon  ..........................................................................................................  463   On  competition  and  markets  –  Peter  Thiel  (Blake  Masters)  ...........................................................................  464   Starting  strategy  –  Steve  Blank  ......................................................................................................................  474   Why  competition  is  not  failure  –  Mark  Suster  ................................................................................................  475   Winning  by  being  better  (Bill  Gross)  –  Mark  Suster  .......................................................................................  479   Failure  ................................................................................................................................................................  484   When  you  want  to  quit  –  Jason  Cohen  ...........................................................................................................  484   When  Do  You  Throw  in  the  Towel  On  Your  Struggling  Project?  –  Vinicius  Vacanti  ........................................  488   Patience  &  Persistence  –  Bijan  Sabet  .............................................................................................................  492   There’s  Always  a  Plan  B  –  Steve  Blank  ...........................................................................................................  493   Achieving  a  failure  –  Eric  Ries  .........................................................................................................................  495   Built  to  fail  -­‐  Andrew  Chen  .............................................................................................................................  497   Why  The  ‘Fail  Fast’  Mantra  Needs  to  Fail,  Fast  –  Mark  Suster  .......................................................................  500   Business  metrics  for  startups  .............................................................................................................................  505   Learning  is  better  than  optimisation  –  Eric  Ries  .............................................................................................  505   Metrics  for  startups  –  Steve  Blank  .................................................................................................................  509   Daily  data  –  Brad  Feld  ....................................................................................................................................  513   The  three  numbers  that  matter  –  Brad  Feld  ...................................................................................................  514   More  metrics  for  startups  -­‐  ALISTAIR  CROLL  ..................................................................................................  516   Metrics  to  drive  good  behaviour    –  David  Skok  ..............................................................................................  519   Using  metrics  in  startup  –  Mark  Suster  ..........................................................................................................  526   Why  vanity  metrics  are  dangerous  –  Eric  Ries  ................................................................................................  531   It’s  a  startup  not  a  spreadsheet  –  Eric  Ries  ....................................................................................................  533   Assorted  ideas  and  inspiration  ...........................................................................................................................  536  
  • 8. 7     The  future  of  the  internet    –  Chris  Dixon  ........................................................................................................  536   Ambitious  ideas  –  Paul  Graham  .....................................................................................................................  537   Maximizing  capacity  utilization  as  a  startup  premise  –  Chris  Dixon  ..............................................................  546   Understanding  the  history  of  the  tech  startup  market  –  Peter  Thiel  (Blake  Masters)  ....................................  546   What  happens  after  web  2.0  (ideas)  –  Peter  Thiel  (Blake  Masters)  ...............................................................  553   Luck  vs  Skill  (statistics  vs  calculus)  –  Peter  Thiel  (Blake  Masters)  ...................................................................  566   Green  tech  (ideas)  –  Peter  Thiel  (Blake  Masters)  ...........................................................................................  584   Visions  of  the  future  (ideas)  –  Peter  Thiel  (Blake  Masters)  ............................................................................  606   On  Biotech  (ideas)–  Peter  Thiel  (Blake  Masters)  ............................................................................................  619   Artificial  intelligence  (ideas)  –  Peter  Thiel  (Blake  Masters)  ............................................................................  634        
  • 9. 8     What  is  a  startup?   What  is  a  startup?   What  is  a  startup?  –  Eric  Ries   http://www.startuplessonslearned.com/2010/06/what-­‐is-­‐startup.html   Monday, June 21, 2010 What is a startup? I think most people have a fairly specific image that gets conjured up when they hear the word startup. Maybe it’s the “two guys in a garage” made famous by HP, or the idea of Jobs and Wozniack walking barefoot and shaggy through the Homebrew Computer Club. Maybe it’s the more recent wunderkinds like Zuckerberg or Brin and Page. What all of these pictures have in common is a narrative that goes something like this: scrappy outsiders, possessed of a unique genius, took outrageous risks and worked incomprehensible hours to beat the odds. But this cinematic view of entrepreneurs is flawed in many ways. Let’s start with the most basic. It leads people to mistakenly believe that any time they see two guys in a garage attempting the impossible, that’s a startup. Wrong. It also causes them to miss the numerous other kinds of startups that appear in less-glamorous settings: inside enterprises, non-profits, and even governments. And because both small businesses and startups have a high mortality rate, sometimes these images lead us to believe that any small business is a startup. Wrong again. So let’s begin with a definition of a startup that captures its essential nature, and tries to leave behind the specific associations of the most famous startups. A startup is a human institution designed to deliver a new product or service under conditions of extreme uncertainty. Let’s take each of these pieces in turn. First, I want to emphasize the human institution aspect, because this is completely lost in the “two guys in a garage” story. The word institution connotes bureaucracy, process, even lethargy. How can that be part of a startup? Yet, the real stories of successful startups are full of activities that can rightly be called institution-building: hiring creative employees, coordinating their activities, and creating a company culture that delivers results. Although some startups may approach these activities in radical ways, they are nonetheless key ingredients in their success. Isn’t the word human redundant in this definition? What other kinds of institutions are there, anyway? And yet, we so often loose sight of the fact that startups are not their products, their technological breakthroughs, or even their data. Even for companies that essentially have only one product, the value the company creates is located not in the product itself but with the people and their organization who built it. To see proof of this, simply observe the results of the large majorities of corporate acquisitions of startups. In most cases, essential aspects of the startup are lost, even when the product, its brand, and even its employment contracts are preserved. A startup is greater than
  • 10. 9     the sum of its parts; it is an acutely human enterprise. And yet the newness of a startup’s product or service is also a key part of the definition. This is a tricky part of the definition, too. I prefer to take the most expansive possible definition of product, one that encompasses any source of value for a set of people who voluntarily choose to become customers. This is equally true of a packaged good in a grocery store, an ecommerce website, a non- profit social service or a variety of government programs. In every case, the organization is dedicated to uncovering a new source of value for customers, and cares about the actual impact of its work on those customers (by contrast, a monopoly or true bureaucracy generally doesn’t care and only seeks to perpetuate itself). It’s also important that we’re talking innovation, but this should also be understood broadly. Even the most radical new inventions always build upon previous technology. Many startups don’t innovate at all in the product dimension, but use other kinds of innovation: repurposing an existing technology for a new use, devising a new business model that unlocks value that was previously hidden, or even simply bringing a product or service to a new location or set of customers previously underserved. In all of these cases, innovation is at the heart of the company’s success. Because innovation is inherently risky, there may be outsized economic returns for startups that are able to harness the risk in a new way – but this is not an essential part of the startup character. The real question is: “what is the degree of innovation that this business proposes to accomplish?” There is one last important part of this definition: the context in which the innovation happens. Most businesses – large and small alike – are typically excluded by this context. Startups are designed to confront situations of extreme uncertainty. To open up a new business that is an exact clone of an existing business, all the way down to the business model, pricing, target customer, and specific product may, under many circumstances, be an attractive economic investment. But it is not a startup, because its success depends only on decent execution – so much so that this success can be modeled with high accuracy. This is why so many small businesses can be financed with simple bank loans; the level of risk and uncertainty is well enough understood that a reasonably intelligent loan officer can assess its prospects. Thus, the land of startups is a unique place, where the risks themselves are unknown. Contrast this with other high-risk situations, like buying a high-risk stock. Although the specific payoff of a specific risky stock is not known, investing in many such stocks can be modeled accurately. Thus a decent financial advisor can give you a reasonably accurate long-term expected return for a set of risky stocks. When the “risk premium” is known, we are not in startup land. In fact, when viewed in retrospect, most startups appear like no-brainers. Probably the most famous example today is Google: how did we ever live without it? Building that particular product was not nearly has risky as it seemed at the time; in fact, I think it is a reasonable inference to say that it was almost guaranteed to succeed. It just wasn’t possible for anyone to know that ahead of time. Startups are designed for the situations that cannot be modeled, are not clear-cut, and where the risk is not necessarily large – it’s just not yet known. I emphasize this point because it is necessary to motivate large amounts of the theory of the lean startup. Fundamentally, the lean startup is a methodology for coping with uncertainty and unknowns with agility, poise, and ruthless efficiency. It is a completely different experience from the equally hard job of executing in a traditional kind of business, and my goal is not to disparage those other practitioners – after all, most startups aspire to
  • 11. 10     become non-startups someday. Still, these differences matter, because the “best practices” that are learned in other contexts do not transplant well into the startup soil. In fact the most spectacular startup failures result when people were in a startup situation but failed to recognize it, or failed to recognize what it meant for their behavior. This definition is also important for what it excludes. Notice that it says nothing about the size of the company involved. Big companies often fail because they find themselves in a startup situation but are unable to reorient in time to cope with this situation; this specific pathology is explored in The Innovator’s Dilemma. This kind of crisis can be precipitated by many external factors: macroeconomic changes, trade policy, technological change, or even cultural shifts. But most often, the entrant of a startup into a previously calm market precipitates this kind of crisis. This has significant implications for general managers in enterprise, about which you can read more at HBR: Is Entrepreneurship a Management Science?   The  first  principles  of  startup  –  Steve  Blank   What’s A Startup? First Principles. Posted on January 25, 2010 by steveblank Success consists of going from failure to failure without loss of enthusiasm . Winston Churchill Everyone knows what a startup is for – don’t they? In this post we’re going to offer a new definition of why startups exist : a startup is an organization formed to search for a repeatable and scalable business model . A Business Model Ok, but what is a business model? A business model describes how your company creates, delivers and captures value. Or in English: A business model describes how your company makes money. (Or depending on your metrics for success, get users, grow traffic, etc.) Think of a business model as a drawing that shows all the flows between the different parts of your company. A business model diagram also shows how the product gets distributed to your customers and how money flows back into your company. And it shows your company’s cost structures, how each department interacts with the others and where your company fits with other companies or partners to implement your business. While this is a mouthful, it’s a lot easier to draw. Drawing A Business Model Lots of people have been working on how to diagram and draw a business. I had my students drawing theirs for years, but Alexander Osterwalder’s work on business models is the clearest description I’ve read in the last decade. The diagram below is his Business Model template . In
  • 12. 11     your startup’s business model, the boxes will have specific details of your company’s strategy. Alexander Osterwalder's Business Model Template (At Stanford, Ann Miura-Ko and I have been working on a simplified Silicon Valley version of this model. Ann will be guest posting more on business models soon.) But What Does a Business Model Have to Do With My Startup? Your startup is essentially an organization built to search for a repeatable and scalable business model. As a founder you start out with: 1) a vision of a product with a set of features, 2) a series of hypotheses about all the pieces of the business model: Who are the customers/users? What’s the distribution channel. How do we price and position the product? How do we create end user demand? Who are our partners? Where/how do we build the product? How do we finance the company, etc. Your job as a founder is to quickly validate whether the model is correct by seeing if customers behave as your model predicts. Most of the time the darn customers don’t behave as you predicted. How Does Customer Development, Agile Development and Lean Startups Fit? The Customer Development process is the way startups quickly iterate and test each element of their business model. Agile Development is the way startups quickly iterate their product as
  • 13. 12     they learn. A Lean Startup is Eric Ries’s description of the intersection of Customer Development, Agile Development and if available, open platforms and open source. (This methodology does for startups what the Toyota Lean Production System did for cars.) Business Plan Versus Business Model Wait a minute, isn’t the Business Model the same thing as my Business Plan? Sort of…but better. A business plan is useful place for you to collect your hypotheses about your business, sales, marketing, customers, market size, etc. (Your investors make you write one, but they never read it.) A Business Model is how all the pieces in your business plan interconnect. The Pivot How do you know your business model is the right one? When revenue, users, traffic, etc., start increasing in a repeatable way you predicted and make your investors happy. The irony is the first time this happens, you may not have found your company’s optimal model. Most startups change their business model at least once if not several times. How do you know when reached the one to scale? Stay tuned. More in future posts. Lessons Learned A startup is an organization formed to search for a repeatable and scalable business model. The goal of your early business model can be revenue, or profits, or users, or click-throughs – whatever you and your investors have agreed upon. Customer and Agile Development is the way for startups to quickly iterate and test their hypotheses about their business model Most startups change their business model multiple times.      
  • 14. 13     Why  to  do  a  startup  (is  the  idea  worth  it?)  –  Peter  Thiel  (Blake  Masters)   http://blakemasters.tumblr.com/post/20400301508/cs183class1   Peter Thiel’s CS183: Startup - Class 1 Notes Essay Here is an essay version of my class notes from Class 1 of CS183: Startup. Errors and omissions are my own. Credit for good stuff is Peter’s entirely. CS183: Startup—Notes Essay—The Challenge of the Future Purpose and Preamble We might describe our world as having retail sanity, but wholesale madness. Details are well understood; the big picture remains unclear. A fundamental challenge—in business as in life—is to integrate the micro and macro such that all things make sense. Humanities majors may well learn a great deal about the world. But they don’t really learn career skills through their studies. Engineering majors, conversely, learn in great technical detail. But they might not learn why, how, or where they should apply their skills in the workforce. The best students, workers, and thinkers will integrate these questions into a cohesive narrative. This course aims to facilitate that process. I. The History of Technology For most of recent human history—from the invention of the steam engine in the late 17 th century through about the late 1960’s or so— technological process has been tremendous, perhaps even relentless. In most prior human societies, people made money by taking it from others. The industrial revolution wrought a paradigm shift in which people make money through trade, not plunder. The importance of this shift is hard to overstate. Perhaps 100 billion people have ever lived on earth. Most of them lived in essentially stagnant societies; success involved claiming value, not creating it. So the massive technological acceleration of the past few hundred years is truly incredible. The zenith of optimism about the future of technology might have been the 1960’s. People believed in the future. They thought about the future. Many were supremely confident that the next 50 years would be a half-century of unprecedented technological progress. But with the exception of the computer industry, it wasn’t. Per capita incomes are still rising, but that rate is starkly decelerating. Median wages have been stagnant since 1973. People find themselves in an alarming Alice-in-Wonderland-style scenario in which they must run harder and harder—that is, work longer hours—just to stay in the same place. This deceleration is complex, and wage data alone don’t explain it. But they do support the general sense that the rapid progress of the last 200 years is slowing all too quickly. II. The Case For Computer Science Computers have been the happy exception to recent tech deceleration. Moore’s/Kryder’s/Wirth’s laws have largely held up, and forecast continued growth. Computer tech, with ever-improving hardware and agile development, is something of a model for other industries. It’s obviously central to the Silicon Valley ecosystem and a key driver of modern technological change. So CS is the logical starting place to recapture the reins of progress.
  • 15. 14     III. The Future For Progress A. Globalization and Tech: Horizontal vs. Vertical Progress Progress comes in two flavors: horizontal/extensive and vertical/intensive. Horizontal or extensive progress basically means copying things that work. In one word, it means simply “globalization.” Consider what China will be like in 50 years. The safe bet is it will be a lot like the United States is now. Cities will be copied, cars will be copied, and rail systems will be copied. Maybe some steps will be skipped. But it’s copying all the same. Vertical or intensive progress, by contrast, means doing new things. The single word for this is “technology.” Intensive progress involves going from 0 to 1 (not simply the 1 to n of globalization). We see much of our vertical progress come from places like California, and specifically Silicon Valley. But there is every reason to question whether we have enough of it. Indeed, most people seem to focus almost entirely on globalization instead of technology; speaking of “developed” versus “developing nations” is implicitly bearish about technology because it implies some convergence to the “developed” status quo. As a society, we seem to believe in a sort of technological end of history, almost by default. It’s worth noting that globalization and technology do have some interplay; we shouldn’t falsely dichotomize them. Consider resource constraints as a 1 to n subproblem. Maybe not everyone can have a car because that would be environmentally catastrophic. If 1 to n is so blocked, only 0 to 1 solutions can help. Technological development is thus crucially important, even if all we really care about is globalization. B. The Problems of 0 to 1 Maybe we focus so much on going from 1 to n because that’s easier to do. There’s little doubt that going from 0 to 1 is qualitatively different, and almost always harder, than copying something n times. And even trying to achieve vertical, 0 to 1 progress presents the challenge of exceptionalism; any founder or inventor doing something new must wonder: am I sane? Or am I crazy? Consider an analogy to politics. The United States is often thought of as an “exceptional” country. At least many Americans believe that it is. So is the U.S. sane? Or is it crazy? Everyone owns guns. No one believes in climate change. And most people weigh 600 pounds. Of course, exceptionalism may cut the other way. America is the land of opportunity. It is the frontier country. It offers new starts, meritocratic promises of riches. Regardless of which version you buy, people must grapple with the problem of exceptionalism. Some 20,000 people, believing themselves uniquely gifted, move to Los Angeles every year to become famous actors. Very few of them, of course, actually become famous actors. The startup world is probably less plagued by the challenge of exceptionalism than Hollywood is. But it probably isn’t immune to it. C. The Educational and Narrative Challenge Teaching vertical progress or innovation is almost a contradiction in terms. Education is fundamentally about going from 1 to n. We observe, imitate, and repeat. Infants do not invent new languages; they learn existing ones. From early on, we learn by copying what has worked before. That is insufficient for startups. Crossing T’s and dotting I’s will get you maybe 30% of the way there. (It’s certainly necessary to get incorporation right, for instance. And one can learn how to pitch
  • 16. 15     VCs.) But at some point you have to go from 0 to 1—you have to do something important and do it right—and that can’t be taught. Channeling Tolstoy’s intro to Anna Karenina, all successful companies are different; they figured out the 0 to 1 problem in different ways. But all failed companies are the same; they botched the 0 to 1 problem. So case studies about successful businesses are of limited utility. PayPal and Facebook worked. But it’s hard to know what was necessarily path-dependent. The next great company may not be an e-payments or social network company. We mustn’t make too much of any single narrative. Thus the business school case method is more mythical than helpful. D. Determinism vs. Indeterminism Among the toughest questions about progress is the question of how we should assess a venture’s probability of success. In the 1 to n paradigm, it’s a statistical question. You can analyze and predict. But in the 0 to 1 paradigm, it’s not a statistical question; the standard deviation with a sample size of 1 is infinite. There can be no statistical analysis; statistically, we’re in the dark. We tend to think very statistically about the future. And statistics tells us that it’s random. We can’t predict the future; we can only think probabilistically. If the market follows a random walk, there’s no sense trying to out-calculate it. But there’s an alternative math metaphor we might use: calculus. The calculus metaphor asks whether and how we can figure out exactly what’s going to happen. Take NASA and the Apollo missions, for instance. You have to figure out where the moon is going to be, exactly. You have to plan whether a rocket has enough fuel to reach it. And so on. The point is that no one would want to ride in a statistically, probabilistically-informed spaceship. Startups are like the space program in this sense. Going from 0 to 1 always has to favor determinism over indeterminism. But there is a practical problem with this. We have a word for people who claim to know the future: prophets. And in our society, all prophets are false prophets. Steve Jobs finessed his way about the line between determinism and indeterminism; people sensed he was a visionary, but he didn’t go too far. He probably cut it as close as possible (and succeeded accordingly). The luck versus skill question is also important. Distinguishing these factors is difficult or impossible. Trying to do so invites ample opportunity for fallacious reasoning. Perhaps the best we can do for now is to flag the question, and suggest that it’s one that entrepreneurs or would-be entrepreneurs should have some handle on. E. The Future of Intensive Growth There are four theories about the future of intensive progress. First is convergence; starting with the industrial revolution, we saw a quick rise in progress, but technology will decelerate and growth will become asymptotic. Second, there is the cyclical theory. Technological progress moves in cycles; advances are made, retrenchments ensue. Repeat. This has probably been true for most of human history in the past. But it’s hard to imagine it remaining true; to think that we could somehow lose all the information and know-how we’ve amassed and be doomed to have to re-discover it strains credulity. Third is collapse/destruction. Some technological advance will do us in.
  • 17. 16     Fourth is the singularity where technological development yields some AI or intellectual event horizon. People tend to overestimate the likelihood or explanatory power of the convergence and cyclical theories. Accordingly, they probably underestimate the destruction and singularity theories. IV. Why Companies? If we want technological development, why look to companies to do it? It’s possible, after all, to imagine a society in which everyone works for the government. Or, conversely, one in which everyone is an independent contractor. Why have some intermediate version consisting of at least two people but less than everyone on the planet? The answer is straightforward application of the Coase Theorem. Companies exist because they optimally address internal and external coordination costs. In general, as an entity grows, so do its internal coordination costs. But its external coordination costs fall. Totalitarian government is entity writ large; external coordination is easy, since those costs are zero. But internal coordination, as Hayek and the Austrians showed, is hard and costly; central planning doesn’t work. The flipside is that internal coordination costs for independent contractors are zero, but external coordination costs (uniquely contracting with absolutely everybody one deals with) are very high, possibly paralyzingly so. Optimality—firm size—is a matter of finding the right combination. V. Why Startups? A. Costs Matter Size and internal vs. external coordination costs matter a lot. North of 100 people in a company, employees don’t all know each other. Politics become important. Incentives change. Signaling that work is being done may become more important than actually doing work. These costs are almost always underestimated. Yet they are so prevalent that professional investors should and do seriously reconsider before investing in companies that have more than one office. Severe coordination problems may stem from something as seemingly trivial or innocuous as a company having a multi-floor office. Hiring consultants and trying to outsource key development projects are, for similar reasons, serious red flags. While there’s surely been some lessening of these coordination costs in the last 40 years—and that explains the shift to somewhat smaller companies—the tendency is still to underestimate them. Since they remain fairly high, they’re worth thinking hard about. Path’s limiting its users to 150 “friends” is illustrative of this point. And ancient tribes apparently had a natural size limit that didn’t much exceed that number. Startups are important because they are small; if the size and complexity of a business is something like the square of the number of people in it, then startups are in a unique position to lower interpersonal or internal costs and thus to get stuff done. The familiar Austrian critique dovetails here as well. Even if a computer could model all the narrowly economic problems a company faces (and, to be clear, none can), it wouldn’t be enough. To model all costs, it would have to model human irrationalities, emotions, feelings, and interactions. Computers help, but we still don’t have all the info. And if we did, we wouldn’t know what to do with it. So, in practice, we end up having companies of a certain size. B. Why Do a Startup?
  • 18. 17     The easiest answer to “why startups?” is negative: because you can’t develop new technology in existing entities. There’s something wrong with big companies, governments, and non-profits. Perhaps they can’t recognize financial needs; the federal government, hamstrung by its own bureaucracy, obviously overcompensates some while grossly undercompensating others in its employ. Or maybe these entities can’t handle personal needs; you can’t always get recognition, respect, or fame from a huge bureaucracy. Anyone on a mission tends to want to go from 0 to 1. You can only do that if you’re surrounded by others to want to go from 0 to 1. That happens in startups, not huge companies or government. Doing startups for the money is not a great idea. Research shows that people get happier as they make more and more money, but only up to about $70,000 per year. After that, marginal improvements brought by higher income are more or less offset by other factors (stress, more hours, etc. Plus there is obviously diminishing marginal utility of money even absent offsetting factors). Perhaps doing startups to be remembered or become famous is a better motive. Perhaps not. Whether being famous or infamous should be as important as most people seem to think it is highly questionable. A better motive still would be a desire to change the world. The U.S. in 1776-79 was a startup of sorts. What were the Founders motivations? There is a large cultural component to the motivation question, too. In Japan, entrepreneurs are seen as reckless risk-takers. The respectable thing to do is become a lifelong employee somewhere. The literary version of this sentiment is “behind every fortune lies a great crime.” Were the Founding Fathers criminals? Are all founders criminals of one sort or another? C. The Costs of Failure Startups pay less than bigger companies. So founding or joining one involves some financial loss. These losses are generally thought to be high. In reality, they aren’t that high. The nonfinancial costs are actually higher. If you do a failed startup, you may not have learned anything useful. You may actually have learned how to fail again. You may become more risk-averse. You aren’t a lottery ticket, so you shouldn’t think of failure as just 1 of n times that you’re going to start a company. The stakes are a bit bigger than that. A 0 to 1 startup involves low financial costs but low non-financial costs too. You’ll at least learn a lot and probably will be better for the effort. A 1 to n startup, though, has especially low financial costs, but higher non-financial costs. If you try to do Groupon for Madagascar and it fails, it’s not clear where exactly you are. But it’s not good. VI. Where to Start? The path from 0 to 1 might start with asking and answering three questions. First, what is valuable? Second, what can I do? And third, what is nobody else doing? The questions themselves are straightforward. Question one illustrates the difference between business and academia; in academia, the number one sin is plagiarism, not triviality. So much of the innovation is esoteric and not at all useful. No one cares about a firm’s eccentric, non-valuable output. The second question ensures that you can actually execute on a problem; if not, talk is just that. Finally, and often overlooked, is the importance of being novel. Forget that and we’re just copying. The intellectual rephrasing of these questions is: What important truth do very few people agree with you on?
  • 19. 18     The business version is: What valuable company is nobody building? These are tough questions. But you can test your answers; if, as so many people do, one says something like “our educational system is broken and urgently requires repair,” you know that that answer is wrong (it may be a truth, but lots of people agree with it). This may explain why we see so many education non-profits and startups. But query whether most of those are operating in technology mode or globalization mode. You know you’re on the right track when your answer takes the following form: “Most people believe in X. But the truth is !X.” Make no mistake; it’s a hard question. Knowing what 0 to 1 endeavor is worth pursuing is incredibly rare, unique, and tricky. But the process, if not the result, can also be richly rewarding.   Entrepreneurship  as  management  science  –  Eric  Ries   http://blogs.hbr.org/cs/2010/01/is_entrepreneurship_a_manageme.html   Is Entrepreneurship a Management Science? After ten years as an entrepreneur, I started writing a blog called Startup Lessons Learned. My original goal was to provide support and encouragement to other entrepreneurs who, like me, had some unorthodox ideas about how to run companies. Along the way, I encountered two big surprises. They led me to a question I had never previously considered: is it possible that entrepreneurship is actually a management science? For most of us, the phrase "management science" conjures up a decidedly non-entrepreneurial image, and for good reason. The preeminent management science, for much of the twentieth century was general management, pioneered by twentieth century giants like Peter Drucker and Alfred P. Sloan. As an entrepreneur, I often saw the best practices of general management fail startups. Applied out of context, they were dangerous. This conflict has played out in many companies that I've worked with. My most recent startup created a marketplace for customers to buy and sell virtual goods for their 3D avatar. So you can imagine how I expected some skepticism when pitching ideas about technology innovation to, say, the U.S. Army. This was my first surprise: they understood that innovation needs to be understood at the level of principles, not just tactics. Some tactics, like Continuous Deployment, are controversial, even among people with my background. But the really vexing questions came from entrepreneurs who inundated me with questions from other backgrounds. Could these techniques be used by enterprise software companies? Hardware manufacturers? Biotech startups? Answering those questions requires understanding the principles of entrepreneurship in a rigorous way. I began to call this new methodology "the lean startup." It is a rigorous application of lean principles to the problem of new product innovation. Inspired by the lean manufacturing revolution (and excellent books like Lean Thinking), I started with a first fundamental question: in a startup, what activities are value-creating and which are waste?
  • 20. 19     Usually, new projects are measured and held accountable to milestones and deadlines. When a project is on track, on time, and on budget, our intuition is that it is being well managed. This intuition is dead wrong. Most startups fail because they are building something that nobody wants. Enamored with a new technology or a radical new product, many entrepreneurs never find a set of customers who will buy it. Each new feature added to such a product is actually wasted effort, even if it's done on-time and on-budget. In product that nobody wants, all the features get thrown away. This problem vexes venture-backed founders and general managers alike. Anyone who is tasked with creating disruptive innovation will find it familiar. In short, for an innovation on the wrong course, executing well doesn't increase the odds of success. What's needed, I realized, is a new definition of progress in a startup, one that recognizes that a startup's primary mission is to penetrate the fog of the unknown and find out what their customers ultimately will want and accept. I call this unit of progress "validated learning about customers." Because learning is the goal, the progress that startups make requires a very different kind of management. This led me to a second surprise. As I was traveling the country talking to people about the lean startup, I kept meeting people in the audience who seemed out of place. They weren't what we usually think of as entrepreneurs. They were general managers, most working in very large companies, who were tasked with creating new product innovations. They were smart, well-read, and understood Clay Christenson's Innovator's Solution cold. They were adept at organizational politics: they knew how to form autonomous divisions with separate P&Ls, and could shield controversial or disruptive teams from meddling. And — my biggest surprise yet — they were visionaries. Like the startup founders I have worked with for years, they could see the future of their industries, and were prepared to take bold risks to seek out new and innovative solutions to the problems their companies would face. They found the lean startup helpful. In fact, they considered it obvious that these principles — rapid iteration, a focus on validated learning, and reducing cycle time from idea to learning — applied to their situation. Here's why. In my quest to put the practice of entrepreneurship on a rigorous footing, I had begun with this definition of a startup: A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty. Originally, I used this definition to help entrepreneurs understand why the "best practices" of general management so often failed them. They can't thrive when transplanted into the soil of extreme uncertainty that is the province of startups. But this definition is surprising: it doesn't say anything about the size of the company, sector of the economy, or industry. Looking past my old preconceptions, I realized that entrepreneurs are everywhere: in government, in the occasional garage, and yes, in the traditional organization. All entrepreneurs face the same fundamental challenges: How do we know if we're making progress? How do we know if customers will want the product we are building? And, if they do, how do we know what kind of value we can create with it?
  • 21. 20     But because every startup also strives to become an institution, answering these questions requires more than just disciplined thinking at the whiteboard. It requires the coordination of many different people, working in concert to answer them. In other words, it requires management. The management challenges presented by entrepreneurship are different. For example, how do we hold an entrepreneurial team accountable for making progress? This is a core challenge faced by venture capitalists and corporate CFOs alike. A team might be making progress against their plan, might be hitting milestones, might even be earning revenue. But none of those artifacts constitute progress. Learning is progress. In short, it's demonstrating the ability to use what we've learned to change customer behavior, with key metrics as the common source of evidence. To measure learning requires a shift of attention from the output of models to their inputs. I'll show you how to apply this to entrepreneurial situations in my next post. Eric Ries is the author of StartupLessonsLearned.com and is an adviser to many startups, companies, and venture capital firms.    
  • 22. 21     The  basic  strategy  (what  to  do)   The  basic  strategy  (what  to  do)   Startups  in  13  sentences  –  Paul  Graham   http://www.paulgraham.com/13sentences.html   Want to start a startup? Get funded by Y Combinator. Watch how this essay was written on Etherpad. February 2009 One of the things I always tell startups is a principle I learned from Paul Buchheit: it's better to make a few people really happy than to make a lot of people semi-happy. I was saying recently to a reporter that if I could only tell startups 10 things, this would be one of them. Then I thought: what would the other 9 be? When I made the list there turned out to be 13: 1. Pick good cofounders. Cofounders are for a startup what location is for real estate. You can change anything about a house except where it is. In a startup you can change your idea easily, but changing your cofounders is hard. [1] And the success of a startup is almost always a function of its founders. 2. Launch fast. The reason to launch fast is not so much that it's critical to get your product to market early, but that you haven't really started working on it till you've launched. Launching teaches you what you should have been building. Till you know that you're wasting your time. So the main value of whatever you launch with is as a pretext for engaging users. 3. Let your idea evolve. This is the second half of launching fast. Launch fast and iterate. It's a big mistake to treat a startup as if it were merely a matter of implementing some brilliant initial idea. As in an essay, most of the ideas appear in the implementing. 4. Understand your users.
  • 23. 22     You can envision the wealth created by a startup as a rectangle, where one side is the number of users and the other is how much you improve their lives. [2] The second dimension is the one you have most control over. And indeed, the growth in the first will be driven by how well you do in the second. As in science, the hard part is not answering questions but asking them: the hard part is seeing something new that users lack. The better you understand them the better the odds of doing that. That's why so many successful startups make something the founders needed. 5. Better to make a few users love you than a lot ambivalent. Ideally you want to make large numbers of users love you, but you can't expect to hit that right away. Initially you have to choose between satisfying all the needs of a subset of potential users, or satisfying a subset of the needs of all potential users. Take the first. It's easier to expand userwise than satisfactionwise. And perhaps more importantly, it's harder to lie to yourself. If you think you're 85% of the way to a great product, how do you know it's not 70%? Or 10%? Whereas it's easy to know how many users you have. 6. Offer surprisingly good customer service. Customers are used to being maltreated. Most of the companies they deal with are quasi-monopolies that get away with atrocious customer service. Your own ideas about what's possible have been unconsciously lowered by such experiences. Try making your customer service not merely good, but surprisingly good. Go out of your way to make people happy. They'll be overwhelmed; you'll see. In the earliest stages of a startup, it pays to offer customer service on a level that wouldn't scale, because it's a way of learning about your users. 7. You make what you measure. I learned this one from Joe Kraus. [3] Merely measuring something has an uncanny tendency to improve it. If you want to make your user numbers go up, put a big piece of paper on your wall and every day plot the number of users. You'll be delighted when it goes up and disappointed when it goes down. Pretty soon you'll start noticing what makes the number go up, and you'll start to do more of that. Corollary: be careful what you measure. 8. Spend little. I can't emphasize enough how important it is for a startup to be cheap. Most startups fail before they make something people want, and the most common form of failure is running out of money. So being cheap is (almost) interchangeable with iterating rapidly. [4]But it's more than that. A culture of cheapness keeps companies young in something like the way exercise keeps people young. 9. Get ramen profitable. "Ramen profitable" means a startup makes just enough to pay the founders' living
  • 24. 23     expenses. It's not rapid prototyping for business models (though it can be), but more a way of hacking the investment process. Once you cross over into ramen profitable, it completely changes your relationship with investors. It's also great for morale. 10. Avoid distractions. Nothing kills startups like distractions. The worst type are those that pay money: day jobs, consulting, profitable side-projects. The startup may have more long-term potential, but you'll always interrupt working on it to answer calls from people paying you now. Paradoxically, fundraising is this type of distraction, so try to minimize that too. 11. Don't get demoralized. Though the immediate cause of death in a startup tends to be running out of money, the underlying cause is usually lack of focus. Either the company is run by stupid people (which can't be fixed with advice) or the people are smart but got demoralized. Starting a startup is a huge moral weight. Understand this and make a conscious effort not to be ground down by it, just as you'd be careful to bend at the knees when picking up a heavy box. 12. Don't give up. Even if you get demoralized, don't give up. You can get surprisingly far by just not giving up. This isn't true in all fields. There are a lot of people who couldn't become good mathematicians no matter how long they persisted. But startups aren't like that. Sheer effort is usually enough, so long as you keep morphing your idea. 13. Deals fall through. One of the most useful skills we learned from Viaweb was not getting our hopes up. We probably had 20 deals of various types fall through. After the first 10 or so we learned to treat deals as background processes that we should ignore till they terminated. It's very dangerous to morale to start to depend on deals closing, not just because they so often don't, but because it makes them less likely to. Having gotten it down to 13 sentences, I asked myself which I'd choose if I could only keep one. Understand your users. That's the key. The essential task in a startup is to create wealth; the dimension of wealth you have most control over is how much you improve users' lives; and the hardest part of that is knowing what to make for them. Once you know what to make, it's mere effort to make it, and most decent hackers are capable of that. Understanding your users is part of half the principles in this list. That's the reason to launch early, to understand your users. Evolving your idea is the embodiment of understanding your users. Understanding your users well will tend to push you toward
  • 25. 24     making something that makes a few people deeply happy. The most important reason for having surprisingly good customer service is that it helps you understand your users. And understanding your users will even ensure your morale, because when everything else is collapsing around you, having just ten users who love you will keep you going. Notes [1] Strictly speaking it's impossible without a time machine. [2] In practice it's more like a ragged comb. [3] Joe thinks one of the founders of Hewlett Packard said it first, but he doesn't remember which. [4] They'd be interchangeable if markets stood still. Since they don't, working twice as fast is better than having twice as much time.   How  to  start  a  startup  –  Paul  Graham   http://www.paulgraham.com/start.html   Want to start a startup? Get funded by Y Combinator. March 2005 (This essay is derived from a talk at the Harvard Computer Society.) You need three things to create a successful startup: to start with good people, to make something customers actually want, and to spend as little money as possible. Most startups that fail do it because they fail at one of these. A startup that does all three will probably succeed. And that's kind of exciting, when you think about it, because all three are doable. Hard, but doable. And since a startup that succeeds ordinarily makes its founders rich, that implies getting rich is doable too. Hard, but doable. If there is one message I'd like to get across about startups, that's it. There is no magically difficult step that requires brilliance to solve. The Idea
  • 26. 25     In particular, you don't need a brilliant idea to start a startup around. The way a startup makes money is to offer people better technology than they have now. But what people have now is often so bad that it doesn't take brilliance to do better. Google's plan, for example, was simply to create a search site that didn't suck. They had three new ideas: index more of the Web, use links to rank search results, and have clean, simple web pages with unintrusive keyword-based ads. Above all, they were determined to make a site that was good to use. No doubt there are great technical tricks within Google, but the overall plan was straightforward. And while they probably have bigger ambitions now, this alone brings them a billion dollars a year. [1] There are plenty of other areas that are just as backward as search was before Google. I can think of several heuristics for generating ideas for startups, but most reduce to this: look at something people are trying to do, and figure out how to do it in a way that doesn't suck. For example, dating sites currently suck far worse than search did before Google. They all use the same simple-minded model. They seem to have approached the problem by thinking about how to do database matches instead of how dating works in the real world. An undergrad could build something better as a class project. And yet there's a lot of money at stake. Online dating is a valuable business now, and it might be worth a hundred times as much if it worked. An idea for a startup, however, is only a beginning. A lot of would-be startup founders think the key to the whole process is the initial idea, and from that point all you have to do is execute. Venture capitalists know better. If you go to VC firms with a brilliant idea that you'll tell them about if they sign a nondisclosure agreement, most will tell you to get lost. That shows how much a mere idea is worth. The market price is less than the inconvenience of signing an NDA. Another sign of how little the initial idea is worth is the number of startups that change their plan en route. Microsoft's original plan was to make money selling programming languages, of all things. Their current business model didn't occur to them until IBM dropped it in their lap five years later. Ideas for startups are worth something, certainly, but the trouble is, they're not transferrable. They're not something you could hand to someone else to execute. Their value is mainly as starting points: as questions for the people who had them to continue thinking about. What matters is not ideas, but the people who have them. Good people can fix bad ideas, but good ideas can't save bad people. People What do I mean by good people? One of the best tricks I learned during our startup was a rule for deciding who to hire. Could you describe the person as an animal? It might be hard to translate that into another language, but I think everyone in the US knows what
  • 27. 26     it means. It means someone who takes their work a little too seriously; someone who does what they do so well that they pass right through professional and cross over into obsessive. What it means specifically depends on the job: a salesperson who just won't take no for an answer; a hacker who will stay up till 4:00 AM rather than go to bed leaving code with a bug in it; a PR person who will cold-call New York Times reporters on their cell phones; a graphic designer who feels physical pain when something is two millimeters out of place. Almost everyone who worked for us was an animal at what they did. The woman in charge of sales was so tenacious that I used to feel sorry for potential customers on the phone with her. You could sense them squirming on the hook, but you knew there would be no rest for them till they'd signed up. If you think about people you know, you'll find the animal test is easy to apply. Call the person's image to mind and imagine the sentence "so-and-so is an animal." If you laugh, they're not. You don't need or perhaps even want this quality in big companies, but you need it in a startup. For programmers we had three additional tests. Was the person genuinely smart? If so, could they actually get things done? And finally, since a few good hackers have unbearable personalities, could we stand to have them around? That last test filters out surprisingly few people. We could bear any amount of nerdiness if someone was truly smart. What we couldn't stand were people with a lot of attitude. But most of those weren't truly smart, so our third test was largely a restatement of the first. When nerds are unbearable it's usually because they're trying too hard to seem smart. But the smarter they are, the less pressure they feel to act smart. So as a rule you can recognize genuinely smart people by their ability to say things like "I don't know," "Maybe you're right," and "I don't understand x well enough." This technique doesn't always work, because people can be influenced by their environment. In the MIT CS department, there seems to be a tradition of acting like a brusque know-it-all. I'm told it derives ultimately from Marvin Minsky, in the same way the classic airline pilot manner is said to derive from Chuck Yeager. Even genuinely smart people start to act this way there, so you have to make allowances. It helped us to have Robert Morris, who is one of the readiest to say "I don't know" of anyone I've met. (At least, he was before he became a professor at MIT.) No one dared put on attitude around Robert, because he was obviously smarter than they were and yet had zero attitude himself. Like most startups, ours began with a group of friends, and it was through personal contacts that we got most of the people we hired. This is a crucial difference between startups and big companies. Being friends with someone for even a couple days will tell
  • 28. 27     you more than companies could ever learn in interviews. [2] It's no coincidence that startups start around universities, because that's where smart people meet. It's not what people learn in classes at MIT and Stanford that has made technology companies spring up around them. They could sing campfire songs in the classes so long as admissions worked the same. If you start a startup, there's a good chance it will be with people you know from college or grad school. So in theory you ought to try to make friends with as many smart people as you can in school, right? Well, no. Don't make a conscious effort to schmooze; that doesn't work well with hackers. What you should do in college is work on your own projects. Hackers should do this even if they don't plan to start startups, because it's the only real way to learn how to program. In some cases you may collaborate with other students, and this is the best way to get to know good hackers. The project may even grow into a startup. But once again, I wouldn't aim too directly at either target. Don't force things; just work on stuff you like with people you like. Ideally you want between two and four founders. It would be hard to start with just one. One person would find the moral weight of starting a company hard to bear. Even Bill Gates, who seems to be able to bear a good deal of moral weight, had to have a co- founder. But you don't want so many founders that the company starts to look like a group photo. Partly because you don't need a lot of people at first, but mainly because the more founders you have, the worse disagreements you'll have. When there are just two or three founders, you know you have to resolve disputes immediately or perish. If there are seven or eight, disagreements can linger and harden into factions. You don't want mere voting; you need unanimity. In a technology startup, which most startups are, the founders should include technical people. During the Internet Bubble there were a number of startups founded by business people who then went looking for hackers to create their product for them. This doesn't work well. Business people are bad at deciding what to do with technology, because they don't know what the options are, or which kinds of problems are hard and which are easy. And when business people try to hire hackers, they can't tell which ones aregood. Even other hackers have a hard time doing that. For business people it's roulette. Do the founders of a startup have to include business people? That depends. We thought so when we started ours, and we asked several people who were said to know about this mysterious thing called "business" if they would be the president. But they all said no, so I had to do it myself. And what I discovered was that business was no great mystery. It's not something like physics or medicine that requires extensive study. You just try to get people to pay you for stuff. I think the reason I made such a mystery of business was that I was disgusted by the idea of doing it. I wanted to work in the pure, intellectual world of software, not deal with customers' mundane problems. People who don't want to get dragged into some kind of work often develop a protective incompetence at it. Paul Erdos was particularly
  • 29. 28     good at this. By seeming unable even to cut a grapefruit in half (let alone go to the store and buy one), he forced other people to do such things for him, leaving all his time free for math. Erdos was an extreme case, but most husbands use the same trick to some degree. Once I was forced to discard my protective incompetence, I found that business was neither so hard nor so boring as I feared. There are esoteric areas of business that are quite hard, like tax law or the pricing of derivatives, but you don't need to know about those in a startup. All you need to know about business to run a startup are commonsense things people knew before there were business schools, or even universities. If you work your way down the Forbes 400 making an x next to the name of each person with an MBA, you'll learn something important about business school. After Warren Buffett, you don't hit another MBA till number 22, Phil Knight, the CEO of Nike. There are only 5 MBAs in the top 50. What you notice in the Forbes 400 are a lot of people with technical backgrounds. Bill Gates, Steve Jobs, Larry Ellison, Michael Dell, Jeff Bezos, Gordon Moore. The rulers of the technology business tend to come from technology, not business. So if you want to invest two years in something that will help you succeed in business, the evidence suggests you'd do better to learn how to hack than get an MBA. [3] There is one reason you might want to include business people in a startup, though: because you have to have at least one person willing and able to focus on what customers want. Some believe only business people can do this-- that hackers can implement software, but not design it. That's nonsense. There's nothing about knowing how to program that prevents hackers from understanding users, or about not knowing how to program that magically enables business people to understand them. If you can't understand users, however, you should either learn how or find a co-founder who can. That is the single most important issue for technology startups, and the rock that sinks more of them than anything else. What Customers Want It's not just startups that have to worry about this. I think most businesses that fail do it because they don't give customers what they want. Look at restaurants. A large percentage fail, about a quarter in the first year. But can you think of one restaurant that had really good food and went out of business? Restaurants with great food seem to prosper no matter what. A restaurant with great food can be expensive, crowded, noisy, dingy, out of the way, and even have bad service, and people will keep coming. It's true that a restaurant with mediocre food can sometimes attract customers through gimmicks. But that approach is very risky. It's more straightforward just to make the food good. It's the same with technology. You hear all kinds of reasons why startups fail. But can you think of one that had a massively popular product and still failed?