OBEY  THE  POWER  LAW  

The  importance  of  hit-­‐driven  thinking  in  venture  capital  and  technology  

Babe  Ruth:  Home  runs  define  success.  

In  VC,  a  fund’s  best  investment  returns  more  $  than  all  other  investments  combined.  

As  expected,  great  venture  capital  funds  have  more  “home  run”  investments.  

And  great  funds’  home  runs  are  of  greater  magnitude.    

But  great  funds  also  lose  money  more  oOen  than  good  ones  do.  

To  be  hit-­‐driven,  need  to  trade-­‐off  probability  of  success  with  magnitude  of  impact.  

100%  

0%  

High  Low  

Probability  of  success  

Magnitude  of  impact  

Consensus  

Non-­‐    consensus  

Right  Wrong  

Nature  of  belief  

Outcome  

In  VC,  this  translates  to  being  “non-­‐consensus  right.”  

“Vulture”  Money  already  in,  race  to  remaining  profit  opportunity  

“Thunderlizard”  Big  breakthroughs  not  obvious  at  :me  

of  investment  

“Lemming”  Lowest-­‐risk  failure  

via  safety  in  numbers  

“Babe  Ruth”  Big  swing  and  a  

miss  

How  valuable  is  “consensus”?  Expert  predicUons  oOen  have  liVle  predicUve  value.  

Most  of  us  think  of  the  world  as  Normally  Distributed.  

But  power  laws  govern  most  of  what  happens  in  technology.  

Here  are  some  reasons  why.  

Zero  marginal  cost  of  so=ware  reproduc@on  makes  hit  products  dispropor@onately  profitable    Consumer  demand  for  new  technologies  is  usually  very  difficult  to  predict    Mutually  reinforcing,  mul@-­‐step,  posi@ve  feedback  loop  between  plaIorms  and  applica@ons    

 

“Companies  spend  more  than  $2  trillion  on  acquisi@ons  every  year.      

Study  a=er  study  puts  the  failure  rate  of  mergers  and  acquisi@ons  somewhere  between  70%  and  90%.”  

Example  #1:  Most  tech  M&A  transacUons  are  complete  failures.    

But  the  few  that  succeed  can  be  completely  transformaUve.  

Example  #2:  Winner-­‐take-­‐most  markets  (applicaUons)  

Example  #2:  Winner-­‐take-­‐most  markets  (infrastructure)  

Example  #2:  Winner-­‐take-­‐most  markets  (search)  

Example  #3:  “10x  engineers”  

Task   Best  engineer   Worst  engineer  

Coding  @me   1   20  

Debugging  @me   1   25  

Program  size   1   5  

Program  execu@on  speed  

10   1  

Example  #4:  InsUtuUonalised  Hit-­‐Making.  

Example  #5:  Technology  Udal  waves  and  incumbent  failure.  

Embrace  power  curve  thinking  in  your  organisaUon.  

Incen@vise  outcome  value,  not  frequency  of  success      Make  dispropor@onately  big  investments  in  a  few  products  designed  to  appeal  to  large  audiences    Make  failure  allowable  (not  desirable):  Quan@ty  +  Learning  =  Quality    Find  secrets  (“non-­‐consensus  rights”)    Don’t  overweight  expert  predic@ons