Capturing Value from The Next 10 Billion Devices

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What can we learn from the last major diffusions of technology into our society (mobile & PC) and how will that apply to the Internet of Things? What strategies & business models should we consider to build sustainably profitable solutions.

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Capturing Value from The Next 10 Billion Devices

  1. 1. Capturing Value From The Next 10 Billion Devices Paul R Brody Vice President & Global Industry Leader, Electronics
  2. 2. Page 3 Our Discussion Today Entering  A  New  Era  In  Mobile  &  Social  Computing The  Next  Battleground:  Distributed,  Autonomous  Internet  of  Things The  Shape  of  Business  Models  To  Come Writing  The  Rules  of  The  Next  Marketplace
  3. 3. Page 4 You  can  see  the  computer  age   everywhere  but  in  the  productivity   statistics. Robert  Solow,  1987
  4. 4. Page 5 Computers  spread  through  enterprises  throughout  the  1970s  and  1980s  even  as   productivity  growth  stalled 0 5,625 11,250 16,875 22,500 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 IBM PC Apple II Macintosh Amiga Atari 400/800 Atari ST C 64 TRS-80 NeXT PET Other PC  Platform  Volumes,  1980-­‐1990   jeremyreimer.com 0% 1% 1% 2% 3% 1970s 1980s 1990s GPD  Per  Capita    Growth,  G7   OECD
  5. 5. Page 6 The  1980s  saw  intense  battles  to  define  the  shape  of  the  computing  world  as  multiple   Personal  Computer  ecosystems  battled  for  market  supremacy 0% 25% 50% 75% 100% 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 Mac Amiga PC C64 Apple II Atari ST Other PC  Platform  Market  Share,  1980-­‐1990   jeremyreimer.com
  6. 6. Page 7 Though  personal  computers  seemed  to  be  everywhere,  the  reality  is  that  we  had  only  just   started  to  really  consume  computing  power PC  Platform   Volumes,   1975-­‐2010   jeremyreimer.com PC  “Wins”
  7. 7. Page 8 The  reality  is  that  only  after  standards  had  been  established  and  scale  achieved  did   volumes  really  start  to  expand  enormously 0 100,000 200,000 300,000 400,000 1975 1979 1983 1987 1991 1995 1999 2003 2007 IBM PC Apple II Macintosh All OthersPC  Platform   Volumes,   1975-­‐2010   jeremyreimer.com PC  “Wins”
  8. 8. Page 9 It  was  only  then  that  economists  could  start  to  see  a  significant  increase  in  productivity   growth  from  the  rapid  expansion  of  the  personal  computer US  Productivity  Growth,   1960-­‐2007   Total  Factor  Productivity,  Average   Annual  Percentage   ! Information  Technology  &  US  Productivity   Growth,  Jorgenson,  Ho,  &  Samuels -­‐0.1% 0% 0.1% 0.2% 0.3% 0.4% 1960-­‐2007 2000-­‐2007 IT  Producing IT  Intensive Non-­‐IT  Intensive
  9. 9. Page 10 In  the  PC  industry,  the  market  development  era  had  to  be  completed  before  we  could  see   the  value  of  scale  and  productivity Perfect  The  Product Build  The  Ecosystem Establish  Control  Points Market  Development  Era IBM  PC  5150 Cut  Costs  &  Grow  Scale Focus  on  Value  Creation Refine  User  Experience Scale  &  Productivity  Era Dell  scaled  up  PC   business  with   Build  To  Order
  10. 10. Page 11 The  mobile  industry  today  is  where  the  PC  industry  was  in  1990:  just  out  of  the  first   battles  for  market-­‐share  and  into  the  period  of  scaling  up 0% 25% 50% 75% 100% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Symbian Windows Palm Blackberry Android iPhone Linux Others Smartphone  Platform   Market  Share  &  Shipments,   2000-­‐2012   jeremyreimer.com
  11. 11. Page 12 The  mobile  industry  today  is  where  the  PC  industry  was  in  1990:  just  out  of  the  first   battles  for  market-­‐share  and  into  the  period  of  scaling  up 0 150 300 450 600 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Symbian WinMobile PalmOS Blackberry Android iPhone Linux Others Smartphone  Platform   Market  Share  &  Shipments,   2000-­‐2012   jeremyreimer.com
  12. 12. Page 13 Social  networks  are  also  consolidating  into  a  small  group  of  very  big  players June  2009 Image  cc  From  Vincenzo  Cosenza,  vincos.it
  13. 13. Page 14 Social  networks  are  also  consolidating  into  a  small  group  of  very  big  players December  2013 Image  cc  From  Vincenzo  Cosenza,  vincos.it
  14. 14. Page 15 Though  the  volumes  may  seem  large,  only  about  20%  of  the  world  population  have   mobile  phones  or  are  connected  through  social  networks.    We’re  just  getting  started. Perfect  The  Product Build  The  Ecosystem Establish  Control  Points Market  Development  Era The  T-­‐Mobile  G1:   First  Android   Phone Cut  Costs  &  Grow  Scale Focus  on  Value  Creation Refine  User  Experience Scale  &  Productivity  Era The  Smartisan  T1   Android  Phone
  15. 15. Page 16 For  industry  participants,  the  implications  are  clear  as  well:  time  to  shift  your  approach   from  designing  business  models  and  ecosystems  to  enabling  productivity Development  Era Scaling  Era • Attempted  social  network  -­‐  Ping   • Added  new  services  like  books,   music,  video  and  apps • Product  line  extensions   • Shift  towards  fashion  and  marketing • Attempted  extensions  with  Smart  TV   apps,  music  store  &  movie  store   • Flood  market  with  offerings • Close  non-­‐performing  areas   • Simplify  product  line   • Use  scale  to  drive  out  cost • Consulting  offerings   • Customized  solutions   • Research-­‐led  engagements • High  volume  product  offerings   • $7  billion  in  scaling  investment   • Product-­‐led  engagements  with  clients
  16. 16. Page 17 Our Discussion Today Entering  A  New  Era  In  Mobile  &  Social  Computing The  Next  Battleground:  Distributed,  Autonomous  Internet  of  Things The  Shape  of  Business  Models  To  Come Writing  The  Rules  of  The  Next  Marketplace
  17. 17. Page 18 Those  who  cannot  remember  the   past  are  condemned  to  repeat  it. George  Santayana,  1906
  18. 18. Page 19 Even  as  social  &  mobile  enter  the  era  of  scale,  we  are  still  trying  to  define  the  universe  of   options  and  capabilities  in  the  Internet  of  Things  era Smart  Cities Smart  Infrastructure Connected  Home Medical  Wearables Smart  Watches
  19. 19. Page 20 However  the  market  evolves,  it  will  likely  be  shaped  by  a  set  of  technologies  now   emerging  and  converging  with  each  other Software  Defined  Supply  Chain Analytics  &  Cognitive  Computing Distributed  Computing How  to  manufacture  billions  of  smart  devices   easily  and  effectively  in  small  quantities  and  in  a   highly  customized  way. How  to  turn  data  into  useful  insight  and,  from   there,  into  recommendations  for  action. Computing  power  will  be  everywhere.    We  must   find  a  way  to  harness  it  to  keep  the  cost  and   complexity  of  managing  the  IOT  feasible.
  20. 20. Page 21 Software  Defined  Supply  Chain Analytics  &  Cognitive  Computing Distributed  Computing How  to  manufacture  billions  of  smart  devices   easily  and  effectively  in  small  quantities  and  in  a   highly  customized  way. How  to  turn  data  into  useful  insight  and,  from   there,  into  recommendations  for  action. Computing  power  will  be  everywhere.    We  must   find  a  way  to  harness  it  to  keep  the  cost  and   complexity  of  managing  the  IOT  feasible.
  21. 21. Page 22 The  combination  of  3D  printing  with  related  digital  manufacturing  technologies  is   reshaping  the  global  supply  chain 3 D P R I N T I N G O P E N S O U R C EINTELLIGENT ROBOTICS
  22. 22. Page 23 3D  printing  (aka  Additive  Manufacturing)  is  the  most  critical  of  these  new  technologies $0.00 $0.08 $0.15 $0.23 $0.30 2013 2018 2023 COST PER UNIT VOLUME PRINTED! $/CUBIC CM - BLENDED AVERAGE -79% -92% Over the next 10 years, 3D printing will become 92% cheaper than today. This technology will shift from being a tool for prototyping to one of mass manufacturing.
  23. 23. Page 24 Using  these  new  manufacturing  technologies,  the  required  scale  to  produce  a  product   efficiently  is  up  to  90%  lower  than  current  manufacturing  methodologies 90% LESS VOLUME REQUIRED 0 25 50 75 100 2012 Traditional 2017 Digital 2022 Digital 17 25 100 3 29 100 17 24 100 2 24 100 AGGREGATE NORMALIZED! MINIMUM ECONOMIC SCALE
  24. 24. Page 25 The  net  result  is  a  much  more  flexible,  responsive  supply  chain HARDWARE CONSTRAINED BUILD A MOLD OR CAST HARDWIRE PRODUCTION LINE DEVELOP EMBEDDED CHIP SOFTWARE DEFINED PRINT PARTS DIRECTLY BY SOFTWARE RECONFIGURE ASSEMBLY THROUGH SOFTWARE DIGITAL CONTROLS USING SOFTWARE
  25. 25. Page 26 When  you  use  a  supply  chain  that  is  built  on  3D  printing,  the  results  are  dramatic Software Defined Supply Chain - 2012Case Example:! ! To manufacture efficiently, you need the scale that comes from covering a whole market in the traditional model
  26. 26. Page 27 When  you  use  a  supply  chain  that  is  built  on  3D  printing,  the  results  are  dramatic Software Defined Supply Chain - 2017Case Example:! ! By 2017, 3D printing and robotic assembly make it simple and easy enough to start manufacturing regionally.
  27. 27. Page 28 When  you  use  a  supply  chain  that  is  built  on  3D  printing,  the  results  are  dramatic Software Defined Supply Chain - 2022Case Example:! ! By 2022, we forecast that most mew manufacturing capacity will be shifting back towards a localized model
  28. 28. Page 29 Software  Defined  Supply  Chain Analytics  &  Cognitive  Computing Distributed  Computing How  to  manufacture  billions  of  smart  devices   easily  and  effectively  in  small  quantities  and  in  a   highly  customized  way. How  to  turn  data  into  useful  insight  and,  from   there,  into  recommendations  for  action. Computing  power  will  be  everywhere.    We  must   find  a  way  to  harness  it  to  keep  the  cost  and   complexity  of  managing  the  IOT  feasible.
  29. 29. Page 30 Cognitive  computing  will  allow  us  to  blend  unstructured  information  with  structured  data Unstructured  data  like  medical   papers  give  guidelines: Structured  data  from  systems   shows  an  individual  patient: What  is  the   right  course  of   treatment?
  30. 30. Page 31 Without  cognitive  computing  -­‐  a  kind  of  electronic  common  sense  -­‐  we  will  be   overwhelmed  with  the  complexity  and  data  required  to  manage  smart  devices Very  stylish Not  nearly  smart  enough
  31. 31. Page 32 Software  Defined  Supply  Chain Analytics  &  Cognitive  Computing Distributed  Computing How  to  manufacture  billions  of  smart  devices   easily  and  effectively  in  small  quantities  and  in  a   highly  customized  way. How  to  turn  data  into  useful  insight  and,  from   there,  into  recommendations  for  action. Computing  power  will  be  everywhere.    We  must   find  a  way  to  harness  it  to  keep  the  cost  and   complexity  of  managing  the  IOT  feasible.
  32. 32. Page 33 Thanks  to  Moore’s  law,  it  will  soon  be  cheaper  and  easier  to  put  a  fully  powered  system   on  chip  platform  into  even  the  simplest  systems  than  to  customize  an  embedded  chip Full ARM SoC as powerful as many cell phones with 2GB of RAM. Boots when connected. Runs Mac OS Core (XNU) Receives MPEG stream and converts it to HDMI output. The  Apple  Lightning  to  HDMI  Connector Source:  ExtremeTech.com  report  on  Apple  lightning  HDMI  connector  cable,  retrieved  March  2013
  33. 33. Page 34 Significant  recent  advances  in  the  software  of  distributed  computing  mean  that  we  may   soon  be  able  to  harness  and  use  that  computing  power  that  will  be  everywhere Billions  of  Devices Millions  of  Locations Terabytes  of  storage  &  bandwidth The  cloud  is  moving  out  of  your  data   center  and  into  your  doorknob. Image  Flickr  Creative  Commons  License
  34. 34. Page 35 The  solution  to  harnessing  all  this  distributed  computing  power  is  now  visible:  BitCoin Traditional banks are built on private, centralized systems: There is one central ledger for accounts, identities, and transactions. Account owners Bank balances Transaction records New Transactions In Bitcoin, the central functions are distributed to all the participants in the system: Thanks to cheap computing power and clever process design, BitCoin enables truly distributed transaction processing. Every user has access to their own copy of the entire transaction ledger in a long file called the BLOCK CHAIN:
  35. 35. Page 36 BitCoin  is  built  on  the  concept  of  distributed  consensus  -­‐  all  participants  can  see  all  the   transactions  and  many  participants  verify  the  work  of  each  transaction Transactions are confirmed by CONSENSUS Multiple ecosystem participants check on each transaction to provide REDUNDANT VERIFICATION No single point of failure No need to trust all the participants
  36. 36. Page 37 Take  away  the  financial  component  of  BitCoin  and  you  have  a  powerful  decentralized   computing  system  that  can  be  used  for  all  kinds  of  systems Take Bitcoin and remove the financial component You a have powerful distributed transaction processing system Account owners Bank balances Transaction records Any transaction- intensive processing activity Transaction processing engines are the foundation of many key technology systems: Travel Resrvations Billing Systems Health Records Social Media Device Data Documents Both old… And new…
  37. 37. Page 38 Case  Example:  GitChain  project  marries  distributed  computing  and  software  development   in  a  single  scalable  platform GitHub: A Centralized S/W Development System GitChain: A Decentralized S/W Development System •Same  basic  features  as   GitHub   •Better  local   performance  with  slow   networks   •Better  security  &   redundancy •Check  In  /  Check  out   software  to  develop   •Share  and  copy  code   with  other  developers   •Build  a  social  network   through  professional   work
  38. 38. Page 39 Though  relatively  young  and  immature,  BitCoin  is  growing  a  rate  reminiscent  of  past   platforms  like  Facebook  and  Twitter 0 1,000,000,000 2,000,000,000 3,000,000,000 4,000,000,000 BitCoin NYSE Twitter Facebook Transactions Per Day! Various Online Services Standard Scale! As of April 2014 1 100 10,000 1,000,000 100,000,000 10,000,000,000 BitCoin NYSE Twitter Facebook Transactions Per Day! Various Online Services Log Scale! As of April 2014 0 22,500 45,000 67,500 90,000 2009 2010 2011 2012 2013 2014 BitCoin Transactions Per Day! Overall Growth Trend Standard Scale! As of April 2014
  39. 39. Page 40 The  combination  of  these  technologies  will  allow  us  to  build,  scale  up,  and  manage   networks  of  billions  of  devices Software  Defined  Supply  Chain Analytics  &  Cognitive  Computing Distributed  Computing
  40. 40. Page 41 Our Discussion Today Entering  A  New  Era  In  Mobile  &  Social  Computing The  Next  Battleground:  Distributed,  Autonomous  Internet  of  Things The  Shape  of  Business  Models  To  Come Writing  The  Rules  of  The  Next  Marketplace
  41. 41. Page 42 The  future  is  already  here.    It’s  just   not  very  evenly  distributed. William  Gibson
  42. 42. Page 43 Our  research  suggests  too  many  companies  are  trying  to  build  a  smart-­‐phone  ecosystem   based  on  apps  and  subscriptions  and  that  may  not  be  realistic No  Apps No  Subscription No  Problem
  43. 43. Page 44 The  web  made  digital  services  easy  to  search,  use,  and  purchase DISCOVER USE PAY Online  Payment  icon  (cc)  by  Slawek  Jurczyk  from  the  Noun  Project
  44. 44. Page 45 With  physical  beacons  and  connected  devices,  search  and  discovery,  usage,  and  payment   will  become  just  as  simple  in  real  life  as  online DISCOVER USE PAY
  45. 45. Page 46 Technology  companies  are  creating  the  devices  necessary  to  instrument,  use  and  pay  for   services  and  asset  usage DISCOVER USE PAY
  46. 46. Page 47 The  power  of  Internet  of  Things  will  be  to  increase  the  leverage  from  physical  assets  and   to  create  new,  digital  markets  for  physical  goods  and  services Unlocking Capacity Creating New Markets Reducing Risk Improving Efficiency Creating New Value
  47. 47. Page 48 Services  like  UBER  capture  unused  capacity  and  make  it  available  through  an  online Drivers  and  customers  can  both   see  the  marketplace: Analytics  tells  drivers   where    to  find  customers: UBER  (and  similar  services)  are  using   data  to  bring  LIQUIDITY  to  markets:
  48. 48. Page 49 The  results  are  striking  in  terms  of  economic  value  created: Sources:  Uber,  New  York  Taxi  &  Limousine  Commission,  Boston  Taxi  Commission,  UBER  fares  based  on  UberX Today,  average  Taxi  utilization   is  relatively  low: 55% UBER  fares  are  lower  than   regular  taxi  prices -­‐18% …but  Uber  drives  report   higher  incomes: +22%
  49. 49. Page 50 The  speed  and  scale  with  which  Uber  has  grown  as  spawned  a  wave  of  investment: The  number  of  new  digital  online  services  that  do  this  is   growing  enormously: UBER  (and  similar  services)  are  using   data  to  bring  LIQUIDITY  to  markets: Just  550  Employees   Estimated  $1bn  in  revenue   $10bn  Valuation
  50. 50. Page 51 Our Discussion Today Entering  A  New  Era  In  Mobile  &  Social  Computing The  Next  Battleground:  Distributed,  Autonomous  Internet  of  Things The  Shape  of  Business  Models  To  Come Writing  The  Rules  of  The  Next  Marketplace
  51. 51. Page 52 He  who  has  the  gold,  makes  the   rules. Unknown
  52. 52. Page 53 If  we  want  to  see  some  real  battles,  we  should  take  a  look  at  the  fights  going  on  between   existing  industry  leaders  and  disruptive  attackers  using  the  Internet  of  Things Car  Sharing Apartment  Sharing Recent  Regulatory   Battles  Over  Market   Disruption
  53. 53. Page 54 Despite  dominating  existing  industries,  incumbents  (so  far)  seem  to  be  losing  the  battle   against  market  disruptions Products come and go. Systems last longer. Relationships endure.
  54. 54. Page 55 It’s  important  for  our  economic  growth  that  innovators  win  these  regulatory  battles US  Productivity  Growth,  1960-­‐2007   Total  Factor  Productivity,  Average  Annual  Percentage   Information  Technology  &  US  Productivity  Growth,  Jorgenson,  Ho,  &  Samuels -­‐0.075% 0% 0.075% 0.15% 0.225% 0.3% 1960-­‐2007 IT  Producing IT  Intensive Non-­‐IT  Intensive IT Intensive Industries IT Share of CapEx Securities contracts & investments 85% Air transportation 68% Professional Services 63% Broadcasting and telecom 57% Educational services 55% Newspaper & book publishers 55% Management of companies 54% Administrative and support services 50% Water transportation 48% Machinery 34% Federal General government 30% Retail Trade 16%
  55. 55. Page 56 The  list  of  industries  that  have  yet  to  really  be  transformed  by  IT  and  to  leverage  IT  is   enormous,  and  it  is  the  biggest  area  of  opportunity  for  the  Internet  of  Things Non-IT Intensive Industries IT Share of CapEx Farms 1% Real estate 1% Oil and gas extraction 3% Accommodation 7% Utilities 7% Amusements and recreation 8% Electrical equipment appliances 11% Federal Government enterprises 11% Ambulatory health care services 12% Fabricated metal products 14% Motion picture and sound recording 14% Warehousing and storage 14% Smart  Planting  Technology RFID  wrist  bands  at  DisneyLand 3D  printed  solid  objects Smart  containers  &  warehouses Smart  hotel  rooms  &  door  locks Electronic  Medical  Records
  56. 56. Page 57 When  it  comes  to  transforming  our  economy,  we’ve  only  just  gotten  started 48% 50% 2% IT  Producing IT  Intensive Non-­‐IT  Intensive 44% 53% 3% Economic  Share  IT   Producing,  Intensive  &   Non-­‐Intensive  Industries   ! Share  of  Total  Economic  Output,   Information  Technology  &  US   Productivity  Growth,  Jorgenson,  Ho,  &   Samuels 1960-­‐1995  Average 2000-­‐2007  Average
  57. 57. Paul R Brody LinkedIn.com/In/PBrody @pbrody Twitter & Weibo: @pbrody

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