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Jeffrey FunkAuthor of Technology Change and the Rise of New Industries
Stanford University Press, 2013
For information on related issues and specific technologies, see http://www.slideshare.net/Funk98/presentations and http://www.slideshare.net/Funk97/presentations
Global Startups from Wall Street Journal◦ valuations over $1 Billion, still private (no IPO yet)
◦ have raised money in past four years
◦ at least one venture capital firm as investor
122 firms as of 25 September 2015◦ With 21 other startups that recently exited (IPOs,
acquisitions or decreasing value), total of 143 firms
High valuations mean investors believe these startups offer something valuable, unique, hard to copy◦ How should we search for the types of opportunities
that were exploited by the startups?
◦ What is the process by which they emerge?
◦ What process should we monitor?
How should firms and entrepreneurs search for valuable opportunities?
How can governments identify them in order to consider appropriate policies?
How should students search for them? These questions reflect fundamental questions◦ what is long-term evolutionary process (Nelson and
Winter, 1982*; Ziman, 2000; Murmann, 2004) by which new opportunities become candidates for commercialization?
◦ how can managers and policy makers use this information to “look forward and reason back,” in order to find opportunities and develop good strategies for their commercialization (Yoffie and Cusumano, 2015)
*Nelson R and Winter S 1982. An Evolutionary Theory of Economic Change, Harvard University Press
Did they emerge through process of
◦ Invention, commercialization, diffusion?
◦ So-called linear model of innovation
Most widely described process in economics,
emphasized by Joseph Schumpeter, Nathan Rosenberg,
Giovanni Dosi, Brian Arthur, and others
Science (new explanations of natural or artificial
phenomena) plays critical role in this process
◦ Enables creation and demonstration of new concepts
◦ Facilitates improvements in performance and cost
◦ Examples include organic transistors, LEDs, solar cells; quantum
dot displays and solar cells; quantum computers;
superconducting Josephson Junctions, carbon nano-tubes*
*Funk and Magee, 2015. Rapid Improvements with No Commercial Production: How do the improvements occur? Research Policy 44(3): 777-788, 2015.
Or did they emerge through a different process?
Rapid improvements in components, particularly those that
are defined as General Purpose Technologies (Paul David,
Timothy Bresnahan, Manuel Trajtenberg, Elhanan Helpman)
◦ enable new products, systems, and services to emerge (also
emphasized by Silicon Valley practitioners)
For example, improvements in integrated circuits (Moore’s
Law) enabled new forms of computers, mobile phones, and
other electronic products
More broadly speaking, improvements in ICs, lasers, glass
fiber, magnetic storage, computers enabled improvements
in Internet, which enabled new forms of content, services,
and access devices (e.g., smart phones) to emerge
See for example, Funk, J 2013. What Drives Exponential Improvements, California Management Review, Spring. Funk, J 2013. Technology Change and the Rise of New Industries, Stanford University Press. http://www.slideshare.net/Funk98/when-do-new-technologies-become-economically-feasible-the-case-of-electronic-products
What should managers, policy makers, and students be monitoring?
Should they monitor advances in science?
Or should they monitor something else?◦ improvements in components that can be defined
as general purpose technologies?
◦ new types of products, services, and systems that emerge from these improvements in components?
How should universities help students find these opportunities◦ By emphasizing advances in science?
◦ Or by emphasizing improvements in GPTs that enable new products, services, and systems?
From which of these processes do most radical innovations emerge?
Need an unbiased set of “opportunities” to investigate
Not enough recently founded firms in list of highest market capitalization◦ Only top 100, about 5 members founded since 1975
Not enough recent startups in Fortune 500◦ Takes many decades for composition of Fortune
500 to change
Chose billion dollar startup club for investigation
How prevalent was science-based process of invention, commercialization, and diffusion?
One method is to examine each startup and the scientific basis for the radical innovation/ opportunity
Easier method is to examine U.S. patents held by members of startup club and scientific papers cited by patents◦ Scientific papers are defined as papers published in
journals that are in science citation index◦ Contrast papers in physical and life science journals with
those in engineering (e.g., IEEE) and computer science (e.g., Association of Computing Machinery) journals
◦ Compare importance of scientific papers across categories of startups
What is impact of rapidly improving components on emergence of new products, services, and systems?◦ Microprocessors, memory, displays, other ICs, lasers
◦ Internet and its access devices are treated as “components” that enable new content, services, and systems
Analysis of startups◦ Read descriptions provided by Wall Street Journal, web pages
of startups, articles about startups
◦ What specific improvements enabled opportunities to emerge?
◦ After defining categories (mostly used WSJ categories) and sub-categories, determined specific improvements for each sub-category and startup
◦ Looked for specific terms in startup descriptions
◦ In the end, it was mostly about Internet vs. non-Internet startups and the access devices for Internet-related startups
Company Latest Valuation Total Equity Funding Last Valuation
Uber $51.0 billion $7.4 billion August 2015
Xiaomi $46.0 billion $1.4 billion December 2014
Airbnb $25.5 billion $2.3 billion June 2015
Palantir $20.0 billion $1.6 billion October 2015
Snapchat $16.0 billion $1.2 billion May 2015
Didi Kuaidi $16.0 billion $4.0 billion September 2015
Flipkart $15.0 billion $3.0 billion April 2015
SpaceX $12.0 billion $1.1 billion January 2015
Pinterest $11.0 billion $1.3 billion February 2015
Dropbox $10.0 billion $607 million January 2014
WeWork $10.0 billion $969 million June 2015
Lufax $9.6 billion $488 million March 2015
Theranos $9.0 billion $400 million June 2014
Spotify $8.5 billion $1.0 billion April 2015
DJI $8.0 billion $105 million May 2015
Zhong An Online $8.0 billion $934 million June 2015
Meituan $7.0 billion $1.1 billion January 2015
Square $6.0 billion $495 million August 2014
Stripe $5.0 billion $290 million July 2015
ANI Technologies (Ola Cabs) $5.0 billion $903 million September 2015
Snapdeal $5.0 billion $911 million August 2015
Stemcentrx $5.0 billion $250 million September 2015
Zenefits $4.5 billion $596 million May 2015
Cloudera $4.1 billion $670 million March 2014
Dianping $4.0 billion $1.4 billion March 2015
The Top 25 Firms as of 25 September, 2015
Category U.S. Europe China India Other Total
Software 38 1 2 41
E-Commerce 10 3 9 2 2 26
ConsumerInternet
19 5 7 2 4 37
Financial 7 4 3 1 15
Hardware 7 2 1 10
BioTech, Bio-Electronics
7 1 8
Energy 2 2
Space 1 1
Other 1 1 1 3
Total 92 14 22 6 9 143
Number of Startups, by Category and Country
Most are Internet Related (119)
Note: some of the startups were redefined and the smaller categories were combined, based on the descriptions by the Wall Street Journal and other sources
0
5
10
15
20
25
30
before
2001
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Number of Startups Founded by Year and Category
software
consumer internet
e-commerce
financial
hardware
bio-electronic/bio-tech
other
Category Total Number
Percentage of Startups with Patents
≥ 1 patent ≥ 10 patents ≥ 50 patents
Software 41 63% 29% 7.3%
E-Commerce 26 3.9% 3.9% 0%
ConsumerInternet
37 23% 17% 0%
Financial 15 6.6% 6.6% 6.6%
Hardware 10 90% 70% 20%
Healthcare 8 88% 50% 0%
Energy 2 100% 100% 50%
Space 1 100% 0% 0%
Other 3 0% 0% 0%
Total 143 39% 23% 4.9%
Percentage of All Startups, by Numbers of Patents
Category Total Number
Percentage of Startups with Patents
≥ 1 patent ≥ 10 patents ≥ 50 patents
Software 38 66% 32% 7.9%
E-Commerce 10 10% 10% 0%
ConsumerInternet
19 39% 28% 0%
Financial 7 14% 14% 14%
Hardware 7 86% 72% 28%
BioTech, Bio-Electronics
7 86% 56% 0%
Energy 2 100% 100% 50%
Space 1 100% 0% 0%
Other 1 0% 0% 0%
Total 92 54% 34% 7.6%
Percentage of U.S. Startups, by Numbers of Patents
Category TotalNumber ofstartups
Percentage of Startups with Patents Citing Scientific Papers (SPs)
Citing ≥ 1SP
Citing ≥ 10different SPs
Citing ≥ 50different SPs
Software 41 27% 2.5% 0%
E-Commerce 26 0% 0% 0%
ConsumerInternet
37 2.7% 0% 0%
Financial 15 0% 0% 0%
Hardware 10 50% 0% 0%
BioTech, Bio-Electronics
8 88% 75% 75%
Energy 2 50% 50% 0%
Space 1 0% 0% 0%
Other 3 0% 0% 0%
Total 143 18% 5.6% 4.2%
Percentages of All Startups,
by Numbers of Scientific Papers Cited in Patents
Category TotalNumber
Percentage of Startups with Patents Citing Scientific Papers (SPs)
Citing ≥ 1 SP
Citing ≥ 10 different SPs
Citing ≥ 50 different SPs
Software 38 28% 2.6% 0%
E-Commerce 10 0% 0% 0%
Consumer Internet
19 0% 0% 0%
Financial 7 0% 0% 0%
Hardware 7 57% 0% 0%
BioTech, Bio-Electronics
7 86% 71% 71%
Energy 2 50% 50% 0%
Space 1 0% 0% 0%Other 1 0% 0% 0%Total 92 23% 7.6% 5.4%
Percentages of U.S. Startups, by Numbers of Scientific
Papers Cited in Patents
Only 4.2% of all startups have patents that cite 50 or more different papers
Only 5.6% of all startups have patents that cite 10 or more different papers
Even for U.S. startups, the percentages are still low◦ Only 5.4% of U.S. startups have patents that cite 50
or more different papers
◦ Only 7.6% of U.S. startups have patents that cite 10 or more different papers
75% of BioTech/Bio-Electronic startups had patents that cited more than 50 scientific papers
One of two (50%) of energy startups had patents that cited more than 10 scientific papers
In comparison◦ Only 2.5% of the software startups
◦ Only one of the 78 e-commerce, consumer Internet, or hardware startups had patents citing 1 or more scientific papers
The BioTech/Bio-Electronics and energy startups had patents that cited papers in pure scientific journals◦ Physics
◦ Chemistry
◦ Biology
Only one startup outside of these categories cited papers in pure scientific journals◦ Palantir, a software startup
◦ It cited more than 10 scientific papers including Nucleic Acids Research and Bioinformatics
Advances in science directly played an important role in the emergence of about 10 of 143 radical innovations exploited by startups
Few scientific papers cited in patents◦ Of the few papers cited in patents, they were mostly
for startups in Bio-Tech/Bio-Electronic category
Few science-based products◦ No carbon nanotubes, graphene, nano-particle,
quantum dot, superconductor, display, LEDs, OLEDs, new forms of integrated circuits, membranes, or quantum computers
Thus, traditional process (invention, commercialization and diffusion) only explains how opportunities emerged for a small number of startups◦ Monitoring advances in science only help for a
small number of startups
What about improvements in components?◦ Can they explain the emergence of opportunities
that were exploited by startups?
◦ Let’s look at the startups for each category, beginning with e-commerce
Sub-Category
Number Names of Firms
Clothing and Accessories
9 Fanatics, Vancl, Gilt Groupe, Mogujie, JustFab, LaShou, Zalando, Global Fashion Group, Lazada
Broad Variety of Products
7 Flipkart, Contextlogic, Snapdeal, Coupang, Koudai Shopping, Quikr, JD.com
Furniture,InteriorDesign
5 Home24, Honest Co, FarFetch, Wayfair, Fab
OtherSpecialtyFashion Sites
3 Warby Parker, Blue Apron, Beibei
Discount coupons
2 Coupons.doc, Meituan
Total 26
Number of E-Commerce Startups by Sub-Category
17 of 28 startups focus on fashion related products ◦ clothing, accessories, furniture, interior design, other
Sales of fashion products only started growing recently*◦ Early e-commerce was dominate by music, videos,
books, electronic products ◦ U.S. sales of fashion products are now much higher than
music, videos, books, electronic products (next slide)
Improvements in Internet speed and bandwidth◦ Enabled more complex and aesthetically pleasing web
pages and thus fashion related opportunities (next slide)
◦ Early books on Internet emphasized problems for fashion products**
*The Ongoing Evolution of US Retail, Journal of Economic Perspectives, Ali Hortacsu and Chad Syverson, Vol 29, No 4, pp. 89-112** Evans P and Wurster T 2000. Blown to Bits, HBS Press. Liebowitz S 2002 Rethinking the Network Economy, AMACOM.
The Ongoing Evolution of US Retail, Journal of Economic Perspectives, Ali Hortacsu and Chad Syverson, Vol 29, No 4, 2015 pp. 89-112
Furniture, Sporting Goods, Clothing
Fashion, clothing, furniture
E-Commerce Changed from Digital Products to Fashion, Clothing, Furniture
Increases in Speed Enable Increases in Web Page SizeAnd Number of Objects (pictures, videos, flash files)
Large number of 28 startups in two largest emerging economies and are mobile related◦ 9 from China and 2 from India
◦ 16 of them primarily depend on access from smart phones
Both these trends consistent with improvements in Internet and Internet-related devices◦ As cost and performance of Internet improved, Internet
diffusion spread to countries such as China and India and new access devices such as mobile phones
◦ Smart phones required improvements in microprocessors, flash memory, displays, WiFi
◦ Smart phone-targeted services attract young people and other fashion-conscious shoppers to Internet
◦ This impacts on popularity of fashion-related products
Emergence of Smart Phones Partly Driven by Improvements in Wireless Systems
Apple ◦ released iPhone in 2007◦ introduce app system in 2008
Android phones first introduced in 2008 Why at this time?◦ In addition to improvements in wireless systems◦ Improvements in flash memory enabled storage of
apps◦ Improvements in microprocessors enabled higher
bandwidth services (3G), bigger apps, WiFi◦ Improvements in displays enable higher resolution,
colors, frame rates
Source for next few slides: Technology Change, Economic Feasibility and Creative Destruction:The Case of New Electronic Products and Services, Under Review, Industrial and Corporate Change
Type of Product
Final Assembly Standard Components1
Number ofData Points
Average(%)
Number ofData Points
Lower Estimatefor Average2 (%)
Smart Phones 28 4.2% 26, 28 76%, 79%
TabletComputers
33 3.1% 33, 33 81%, 84%
eBook Readers 6 4.9% 6, 9 88%, 88%
Game Consoles 2 2.4% 2, 2 64%, 70%
MP3 Players 2 3.4% 2, 9 74%, 75%
Large ScreenTelevisions
2 2.4% 2, 2 82%, 84%
Internet TVs 2 5.7% 2, 2 57%, 61%
Google Glass 1 2.7% 1, 1 62%, 64%
Standard Components Determine Cost and Performanceof Many Electronic Products
1 Values as a percent of total and material costs; 2 Excludes mechanical components, printed circuit boards, and passive components
Type ofProduct
# ofDataPoint
Memory
Micro-Proc-essor
Display
Camera
Connectivity,Sensors
Bat-tery
PowerMgmt
Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8%
Tablets 33 17% 6.6% 38% 2.9% 6.3% 7.3% 2.5%
eBookReaders
9 10% 8.1% 42% .30% 8.3% 8.3% Not available
GameConsole
2 38% 39% none none Not available
none 5.8%
MP3Players
9 53% 9% 6% none Not available
4% 3.5%
TVs 2 7% 4.0% 76% none Not avail. none 3.0%
InternetTVs
2 16% 31% none none 10.5% none 3.5%
GoogleGlass
1 17% 18% 3.8% 7.2% 14% 1.5% 4.5%
Contribution of Specific “Standard Components” to Costs of Selected Electronic Products
Measure iPhone iPhone 3G iPhone 4 iPhone 5 iPhone 6
OperatingSystem
1.0 2.0 4.0 6.0 8.0
Flash Memory 4, 8, 16GB 8 or 16GB 8, 16, 64GB 16, 32, 64GB 16, 64, or 128GB
DRAM 128MB 128MB 512MB 1GB 1GB
ApplicationProcessor
620MHz Samsung 32-bitRISC
1 GHz dual-core Apple A5
1.3 GHz dual-core Apple A6
1.4 GHz dual-coreApple A8
GraphicsProcessor
PowerVR MBX Lite 38 (103MHz)
PowerVRSGX535 (200MHz)
PowerVRSGX543MP3 (tri-core, 266 MHz)
PowerVR GX6450(quad-core)
CellularProcessor
GSM/GPRS/EDGE
Previous plusUMTS/HSDPA3.6Mbps
Previous plusHSUPA5.76Mbps
Previous plus LTE,HSPA+, DC-HSDPA, 4.4Mbps
Previous plus LTE-Advanced, 14.4Mbps
Displayresolution
163 ppi (pixels per inch) 326 ppi 401 ppi
CameraresolutionVideo speed
2 MP (mega-pixels) 5 MP30 fps,480p
8 MP30 fps at 1080p
8 MP60 fps at 1080p
WiFi 802.11 b/g 802.11b/g/n
802.11 a/b/g/n 802.11 a/b/g/n/ac
Other Bluetooth 2.0 GPS,compass,Bluetooth2.1,gyroscope
GPS, compass,Blue-tooth 4.0,gyroscope, voicerecognition
Previous plus finger-print scanner, near-field communication
Evolution of iPhone in Terms of Measures of Performance
Fps: frames per second480p: progressive scan of 480 vertical lines
760 songs, 4000 pictures (4 megapixel JPEG), four hours of video, or 100 apps/games, or some combination of them
Equal usage◦ 190 songs , 1000 pictures
◦ one hour of video , 25 apps/games
Was 4GB of flash memory necessary, or would less have been sufficient?
The average iPhone user downloaded 58 apps (58% of flash memory) in 2008 and 2009
Emergence of iPhone depended on improvements in memory and other components (microprocessors, displays)
Returning to billion dollar startup club
Sub-Category Number Names of Firms
Ride Sharing 7 Uber, Didi Dache, Kuaidi Dache, Ola Cabs, Lyft, Grabtaxi, BlaBla Car
Fresh & PreparedFood Delivery
5 Delivery Hero, Zomato, Instacart, Hello Fresh, Ele.me
Audio and Video 5 Spotify, Shazam, Snapchat, KIK Interactive, Tango
Games and Movies
5 Kabam, Garena Online, Fan Duel, Draft Kings, Legendary Entertainment
Social Networking 6 Houzz, NextDoor, Eventbrite, Pinterest, Lamabang, Vox Media
Hotels 2 Airbnb, Tujia
Health Care 2 Oscar Healthcare, ZocDocOther 5 Yello Mobile, APUS, BuzzFeed, Dianping,
Plural InsightTotal 37
Number of Consumer Internet Startups by Sub-Category
All 37 provide services that were not available during early years of Internet (late 1990s and early 2000s) ◦ because Internet did not have sufficient bandwidth
and/or mobile phones did not have sufficient capability
This is particularly true of startups that were founded in China or India or that depend on mobile phones◦ 9 from China or India
◦ 25 of 37 services mostly depend on smart phones and often on smart phone apps
◦ this includes all ride sharing, food, and hotel-related services, and most audio/video, social networking, and “other” services
Ride sharing apps (e.g., Uber) have become most famous of these apps
Also food delivery, audio/video, two social networking (NextDoor, Eventbrite), hotels, and three “other” (Yello Mobile, APUS, Dianping)
These opportunities emerged after the iPhone and app store were introduced by Apple◦ First iPhone 2007
◦ Apple App store 2008
◦ Android 2008
Multi-player online games (bandwidth)◦ Kabam, Garena Online
◦ Fan Duel, Draft Kings (also new form of sports management games)
Digital movies (computing power)◦ Legendary Entertainment
Social Networking (bandwidth for calculating connections)◦ Houzz, NextDoor, Eventbrite, Pinterest, Lamabang,
Vox Media
◦ Also BuzzFeed in blogs and Oscar in health care
Education: Plural Insight (bandwidth)
Sub-Category Number Names of Firms
Sales, Human Resource, Inventory Enterprise software(Software-as-a-Service)
14 Shopify, Apttus, Coupa, Qualtrics, Zenefits, Automattic, CloudFlare, InsideSales.com, Sprinklr, Deem, AppDynamics, Slack, Medalia,
Domo Security software 5 Tanium, Good Technology, Lookout, Okta,
Zscaler
Database, data storage software
6 Nutanix, Simplivity, MarkLogic, PureStorage, MongoDB, Actifio
Big Data Software and Services
4 Palantir, Cloudera, Hortonworks, MuSigma
Online Ad Software 3 InMobi, AppNexus, IronSource
Cloud storage 2 Dropbox, Box
Software Dev. Tools 2 Twilio, Github
Other Tools 3 DocuSign, Evernote, New Relic
Integration Platforms 1 MuleSoft
Internet of Things Platform
1 Jasper Technologies
Total 41
Number of Software Startups by Sub-Category
All these opportunities involve cloud computing
Cloud computing (and Saas) emerged as improve-
ments in Internet speed and bandwidth occurred
Various types of enterprise software, software
development and other tools, and platforms are
accessed or downloaded via cloud by organizations
and to lesser extent individuals
◦ Organizations use software for internal use or on website
◦ This includes enterprise software for human resources, sales,
marketing, operations, human resources
◦ Economic feasibility of these opportunities depended on
improvements in Internet bandwidth and speed
Almost half of these opportunities involve Big Data
◦ Big data is broad term for data sets so large or complex
that traditional data processing techniques are inadequate
◦ It tests much more complex models with many more
independent variables than does traditional data analysis
◦ It emerged as improvements in Internet speed and
bandwidth occurred – mentioned in descriptions of 18 of
41 startups
Organizations purchase
◦ big data software and services
◦ software services for sales, human resource, inventory
enterprise software suppliers also includes big data
functions
Organizations purchase Big Data software/services
◦ Purchase big data results through services or do big data
internally with software or services from
Palantir, Cloudera, MuSigma, Hortonworks
◦ Use data base software from
Nutanix, Actifio, Simplivity, MarkLogic, PureStorage, MongoDB
Organizations also purchase software services for
sales, human resource, inventory enterprise
software suppliers that includes Big Data functions
this includes Shopify, Apttus, Coupa, Deem, AppDynamics,
Sprinklr, Qualtrics, InsideSales.com
Opportunities emerged as improvements in
Internet speed and bandwidth occurred
Improvements in Internet speed and bandwidth
along with emergence of cloud computing and big
data caused other opportunities to emerge
Better algorithms for Security
◦ Tanium, Good Technology, Lookout, Okta, Zscaler
Online ads have become more sophisticated, both
in presentation and delivery
◦ InMobi, AppNexus, IronSource
Software development and integration have
become more expensive and important
◦ Twilio, Github, MuleSoft
Five of the 41 software startups (InMobi,
Good Technology, Lookout, Evernote,
Twilio) also depended on emergence and
diffusion of smart phones such as iPhone
and Android phones
Emergence of these phones and networks
depended on improvements in
microprocessors, flash memory, and
displays
◦ As noted above
See for example, http://www.slideshare.net/Funk98/when-do-new-technologies-become-economically-feasible-the-case-of-electronic-products
Sub-Category
Number Names of Firms
Peer-to peerlending
5 Lufax, Prosper Marketplace, Social Finance, Funding Circle, Lending Club
Mobilepayment
4 Stripe, One97 Communications, Adven, Square
E-commercePayment
2 Powa and Klarna
Other 4 Zhong an Online (insurance), HanhuaFinancial (credit guarantor), Credit Karma (credit scores), Sunrun (solar leasing)
Total 15
Number of Financial Startups by Sub-Category
Peer-to peer lending and “other” financial startups have benefited from emergence of ◦ Cloud Computing
◦ Big Data
Peer-to peer loan web sites offer loans to customers, considered too risky by banks◦ set rates using special algorithms, often lower than loan sharks
◦ Improvements in Internet bandwidth and speed helped websites assemble credit histories and credit scores with Big Data
Similar things happened with Klarna and “other” financial startups◦ Klarna’s handling of e-commerce payments depends on Big
Data analysis of consumer risks
◦ Credit guarantees, credit scores, insurance, solar leasing
◦ decisions are based on Big Data
Startups for e-commerce payment services (Klarna and Powa) benefited from increasing market for e-commerce, which benefited from improvements in Internet
Startups for mobile payment services have benefited from improvements in mobile phones◦ growth in text messaging services in early 2000s◦ emergence and growth of smart phones in last seven
years Opportunities outside of U.S., particularly Asia,
have benefited from spread of Internet to other countries◦ 7 in U.S., 4 in Europe◦ 3 in China, 1 in India
All ten hardware startups provide electronic products
They are not placed into sub-categories◦ since each of them provides a different type of
electronic product◦ Smart phones, rugged cameras, drones◦ Wearable health, augmented reality glasses◦ Storage hardware, audio headphones, assisted
vision◦ Gaming mouse, smart thermostats
All of these opportunities depended on improvements in electronic components and/or Moore’s Law-type improvements
Calculators Laptops MP3 PlayersDigital Video Set-top boxes E-Book Readers
Watches Games Web Browsers Digital TV PCs Mobile Digital Cameras Smart Phones
Phones PDAs Tablet Computers
Moore’s Law has been Making New Hardware for 50 Years
Remaining startups depended less on improvements in electronic components than did above ones
Energy, space, and “other” startups do not benefit greatly from improvements in electronic components
Only four of eight 8 healthcare startups might benefit from rapid improvements in components, including electronic components ◦ Theranos, Intarcia, Proteus Digital Health, 23andMe◦ Four other startups (Moderna, Stemcentrix, Adaptive
BioTechonlogies, CureVaC) offer drugs ◦ The drug related products involve advances in science,
and this is consistent with data on patents and papers presented above
Most startups exploited opportunities that
benefited from improvements in Internet
Improvements in Internet speed and bandwidth
enabled new forms of
◦ Internet content
◦ Services
◦ Software
At least 119 of 143 are Internet related
Some startups exploited opportunities that
benefited from improvements in electronic
components, or Moore’s Law type improvements
◦ 10 are related to electronic hardware
Most opportunities emerged from process by which improvements in electronic components or in Internet enabled new forms of products, services, and systems to emerge◦ 119 of 143 are Internet related
◦ 10 are related to electronic hardware
Advances in science directly played an important role in the emergence of opportunities for about 10 of 143 startups◦ Thus, traditional process (invention, commercial-
ization and diffusion) is limited to a small number of startups
◦ Advances in Science indirectly played important role in much more, through impact on ICs and Internet
Many improvements continue to occur◦ in Internet speed and cost
◦ in electronic components such as ICs, MEMS, bio-electronics
What does this mean for opportunities?◦ more cloud computing, software-as a service, and Big Data
applications
◦ better web sites including better aesthetics for fashion sites
◦ larger, more complex, and better apps for smart phones
◦ new forms of access devices
These changes will impact on e-commerce, consumer Internet, Finance, software, and hardware categories
The more analysis we do, the more detailed answer we can provide
Big Data, Machine Learning, Biometrics◦ Impact on accounting, legal, finance, retail, logistics, many
others
Internet of Things (IoT)◦ Better and cheaper ICs, MEMS, transceivers (WiFi, Bluetooth),
and energy harvesters are enabling all mechanical products to be connected to Internet
◦ Also Big Data services, software, and machine learning
◦ Which products can benefit the most from being attached to Internet?
Augmented Reality◦ Being implemented for many applications including
maintenance, logistics, manufacturing
Virtual Reality◦ Not just for games, but for R&D, training, and education
For more info, see: http://www.slideshare.net/Funk98/presentations
Wearable Computing and Health Care
◦ Better and cheaper bio-sensors, ICs, MEMS and
displays are enabling more health care related
wearable computing
◦ Big Data services, machine learning, and software will
also emerge and will likely provide most of the
opportunities
IT and Transportation
◦ Driverless vehicles
◦ Multiple passenger ride sharing
For more info, see: http://www.slideshare.net/Funk98/presentations
Thank You!
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