These slides show how the most successful startups of today (Unicorns) are not doing as well as the most successful of 20 to 50 years ago. Today's startups are doing worse in terms of time to profitability and time to top 100 market capitalization status. Only one Unicorn founded since 2000 has achieved top 100 market capitalization status while six, nine, and eight from the 70s, 80s, and 90s did so. It is also unlikely that few or any of today's Unicorns will achieve this status because their market capitalizations are too low, share prices increases since IPO are too small, and profits remain elusive. Only 14 of 45 had share price increases greater than the Nasdaq and only 6 of 45 had profits in 2019. The reasons for the worse performance of today's Unicorns than those of 20 to 50 years ago include no breakthrough technologies, hyper-growth strategies, and the targeting of regulated industries. The slides conclude with speculations on why few breakthrough technologies, including science-based technologies from universities are emerging. We need to think back to the division of labor that existed a half a century ago.

1. The Troubled Future of
Startups and Innovation
Jeffrey Funk
Retired Associate Professor
Webinar, London Futurist, July 18, 2020
“the 2010s were the worst decade for
productivity growth since the early 19th century”
Quote from an April 2020 Financial Times article

2. Startup Foun
ded
Year for
Profits
Years to Top
100 Mkt Cap
Microsoft 1975 1 12
Apple 1976 4 28
Genentech 1976 8 27
Oracle 1977 3 19
Home Dep 1978 3 17
EMC 1979 6 17
Amgen 1980 9 19
Adobe 1982 1 35
Sun 1982 6 15
Cisco 1984 5 11
Dell 1984 6 13
Compaq 1984 4 13
Startup Foun
ded
Year
Profits
Top
100
Qualcomm 1985 10 14
Celgene 1986 17 28
Gilead Sci 1987 15 21
Nvidia 1993 6 24
Amazon 1994 10 16
Yahoo! 1994 4 5
Ebay 1995 4 10
Netflix 1997 5 21
Google 1998 5 8
PayPal 1998 4 21
Salesforce 1999 4 19
Facebook 2004 6 10
Years to Profits, Top 100 Market Cap for Valuable Startups of Last 50 Years
Only 1
founded
since
2000
versus
6 in 70s
9 in 80s
8 in 90s

3. Lack of Venture Capital Funding Isn’t Problem
 VC funding
recovered a few
years after dotcom
bubble burst
 Began to grow in
2010 reaching
record 5-year high
(2015 – 2019)
 Many new Googles
and Amazons should
have already
succeeded

4. Ex-Unicorn
(14 of 45)
Year
Founded
Market Capitalization ($B) Share Price
Change
Nasdaq
Change2019 March 9, 2020
Uber 2010 60 38.9 -46% - 9%
Square 2009 24 23.3 +316% +41%
Zoom 2011 20 30.2 +77% - 10%
Twilio 2008 17 10.9 +198% +46%
Lyft 2012 17 7.3 -35% - 8%
Snapchat 2011 17 14 -61% +23%
Crowdstrike 2011 15 8.0 -35% - 7%
Slack 2009 14 11.8 -42% - 10%
Pinterest 2009 14 7.6 -45% - 10%
Roku 2002 12 9.5 +197% + 4%
Wayfair 2002 12 3.1 +1.5% +48%
Okta 2009 11 13 +351% +24%
DocuSign 2003 10 12.3 +73% +19%
Dropbox 2007 9 6.6 -45% + 3%
Only 14
of 45 ex-
Unicorns
had
share
price
changes
greater
than
Nasdaq
$98B
Needed
to be
in top
100
in 2019

5. Ex-Unicorn: 14/45 Founding Date 2019 Profits 2019 Revenues Profits/Revenues
Oportun 2005 62 442 0.14
Etsy 2005 153 818 0.12
Square 2009 -271 4700 0.08
Green Sky 2006 120 530 0.06
Zoom 2011 25 623 0.04
Sunrun 2007 26 859 0.03
GoPro 2002 -15 1195 -0.01
Grubhub 2004 -19 1007 -0.02
Sunrun 2007 -29 859 -0.03
Dropbox 2007 -93 1661 -0.03
Beyond Meat 2009 -1 298 -0.04
Roku 2002 -60 1100 -0.05
Lending Club 2006 -30 655 -0.05
Only 6 of
45 ex-
Unicorns
Had
Profits in
2019

6. Among all startups
at IPO time
 Percent profitable fell
from 80% in early 1980s
to 20% in late 2010s
 Despite median age
(founding to IPO)
almost doubling
https://www.businessinsider.com/uber-lyft-ipo-
trends-money-losing-unicorns-could-cause-stock-
market-issues-2019-5?IR=T
Median Age
% Profitability
% Profitability

7. Amazon had profits by Year
10, neither Uber nor Tesla
did. Amazon’s cumulative
losses didn’t reach $3B while
Uber’s exceeded $20B and
Tesla’s $6B. Latter two losses
still growing
Tesla’s Losses
(Year 11 to 17)
Amazon’s Net Profits
https://qz.com/1196256/it-took-amazon-amzn-14-years-to-make-as-
much-net-profit-as-it-did-in-the-fourth-quarter-of-2017/
https://promarket.org/the-uber-bubble-why-is-a-
company-that-lost-20-billion-claimed-to-be-successful/
https://www.statista.com/
statistics/272130/net-loss-
of-tesla/
Tesla and Uber have Lost Much
More Money than Amazon

8. Will Ex-Unicorns Reach Top 100 Market Cap Status?
 Two are 1/5 of the way to $98 market cap with >$20B
 Both have share price increases greater than Nasdaq increases and
they had profits in 2019 (Zoom and Square)
 Ten are 1/10 of the way, with >$10B market cap
 But only 3 had share price increases > Nasdaq increases
 And none had profits in 2019
 Will Zoom make it to top 100 market cap, or Tesla or Uber?
 By the way, only fintech is profitable, and what will happen to
Unicorns that have yet to do IPOs (479, $1.4 trillion valuation)

9. Why Are Unicorns Doing Worse than past ones?
 One hypothesis: new startups acquired by large incumbents
before achieving top 100 market cap status
 All founded since 2000: Youtube, Instagram, GitHub, Linkedin and
WhatsApp.
 But all successful startups made acquisitions. Microsoft obtained Power
Point, through acquisition
 A bigger problem is acquisition argument assumes new startups
must challenge strong incumbents
 Successful startups avoided strong incumbents by commercializing new
technologies not within interests of strong incumbents.
 Silicon Valley evolved from semiconductor companies to disk drives,
networking equipment, PCs, workstations, software products and then
Internet in 1990s

10. Problem is No Breakthrough Technologies
 Ride sharing and food delivery use same vehicles, drivers, and roads
as did previous taxi services
 Online sales of juicers, mattresses, and exercise bikes are sold in same
way Amazon currently sells almost everything
 New business software enables more cloud-based work, not a huge
advantage during normal times
 Fintech startups use algorithms to find low-risk borrowers or
insurance subscribers, but advantages are still small
 Online education may deliver content differently, but it is the same
content
 In all these cases, the technology is not revolutionary.

11. Regulated Industries and Hyper-Growth Strategy
 Harder to succeed in regulated Industries
 Taxi services regulated because of congestion, which plagues ride
sharing and challenges scooters and bicycle rentals
 Fintech challenging traditional banking companies
 Education startups fighting highly regulated industry and huge clash
between public and private schools
 Hyper-Growth Strategy prevents experimentation
 Startups have subsidized users in effort to grow, thus bypassing
experimentation
 Ride sharing, food delivery, fintech, e-commerce startups copy leaders
 Unicorns can’t survive without subsidies

12. 0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
2002 2006 2010 2014 2018
Declining VC Investments in
Science-Based Industries
Semiconductors
Communication
Equipment
Medical
Instruments
Where are
fusion, super-
conductors,
nanotechnology
(graphene,
CNTs), bio-
electronics,
quantum
computing?
Money isn’t
issue.
Government
R&D funding
been high for
decades

13. Why So Few Science-Based Technologies?
 Change in Division of Labor
 1940s – 1960s: AT&T, IBM, Motorola, GE, RCA,
DuPont, Monsanto did basic research
 Today: universities train PhDs, write papers, obtain
funding, but little work with companies
 Hyper-Specialization at Universities
 Exponential growth in journals, papers, and citations
to papers
 Growing emphasis on science in engineering research
 >144 Nature journals
 Today’s top university scientists are drowning in
academic papers, journals, patents, and admin work

14. Appendix

15. Selected Publications
 What Drives Exponential Improvements? California Management Review 55(3): 134-152, Spring
2013
 Rapid Improvements with No Commercial Production: How do the improvements occur? Research
Policy 44(3): 777-788, 2015 (second author is Chris Magee)
 Assessing Public Forecasts to Encourage Accountability: The Case of MIT's Technology Review,
PLOS ONE, August 2017.
 What Does Innovation Today Tell Us About the US Economy Tomorrow? Above all, that the nation
needs to get a lot better at linking scientific advance to economically and socially valuable
technologies. Issues in Science and Technology December 2017
 Technology Change, Economic Feasibility and Creative Destruction: The Case of New Electronic
Products and Services, Industrial and Corporate Change 27(1): Pages 65–82, February 2018
 Beyond Patents: Scholars of innovation use patenting as an indicator of both innovativeness and the
value of science. It might be neither, Issues in Science and Technology Summer 2018.
 What’s Behind Technological Hype? Start-up losses are mounting, and innovation is slowing. We
need less hype and more level-headed economic analysis, Issues in Science and Technology Fall,
2019.
 AI and Economic Productivity: Expect Evolution, Not Revolution. IEEE Spectrum, March 2020
 Three Part Series on Startups, Mind Matters, May/June 2020. Where are all the profitable startups?
Why do Today’s Startups Disappoint Investors? Why are there no new Googles and Amazons?
 The Increasing Limitations of Academic Experts: Narrower Specializations and Less Practicality
Even as Problems Become More Complex, Working Paper

16. Falling Research Productivity
 Drugs
 Number of drugs per billion dollars of R&D dropped about 80 times in last
50 years
 Number of researchers per commercialized drug rose by almost five times in
last 50 years
 Number of researchers required to maintain the same rate of increase
in crop yields rose 6 to 24 times (corn, soybeans, cotton, wheat)
between 1970 and 2010
 R&D needed to sustain Moore’s Law has risen in recent decades
Number of drugs per $billion from Nature article by Scanlan et al, 2012
Other data on drugs, and crops and Moore’s Law from Are Ideas Getting Harder to Find

17. Falling Research Productivity - Continued
 R&D productivity has fallen across a wide variety of industries
 Revenue growth per research dollar has fallen by about 65% over the last
30 years (Anne Marie Knott)
 Importance of Nobel Prize winning research in physics has
declined over last century
 Few Nobel Prizes have been awarded for research done since 1990 not
only in physics, but also for chemistry and medicine (Atlantic article)

20. Research
Productivity for
Medical Research

21. Overall Research Productivity: Number of Researchers Has
Exploded Even as Productivity Growth Has Fallen

22. Moore’s Law is
slowing and
evidence
of other
technologies
experiencing
rapid rates of
improvement
Is difficult to find
I covered these
issues in my course
at NUS from 2009
to 2016

24. Moore’s Law enabled these product by reducing their costs and
improving their performance
With Moore’s Law slowing, new types of electronic products (VR, AR,
robots, commercial drones, blockchain, AI) will take much longer to
emerge and diffuse

26. Improvements in Other Technologies in Table
 No more improvements in cost and performance?
 Microprocessors, memory chips, camera chips
 Superconductors, DNA sequencers (nothing since 2015)
 Improvements but little impact?
 Magnetic storage, Organic transistors
 Soon to be slowing?
 WiFi, cellular speeds and cost; liquid crystal displays
 Batteries? As car batteries catch up with laptop batteries?
 Continued improvements in cost and performance?
 OLED displays
 Silicon, organic, perovskite, quantum dot solar cells
 LAN, Internet speeds

27. Mag lev to hyperloop
Micro-finance to fintech
Stem cells to gene editing
Telematics to IoT
Ride sharing to MaaS
Forgotten about solar water heaters, fusion,
cellulosic ethanol, strategic defense initiative
Hype about new technologies:
Proponents Replace Old Ones with New Ones
Even Though Old Ones Provide Lessons

28. Why I am Pessimistic about AI
 Growth much slower than forecasts
 $15 trillion in economic gains expected by 2030 but only $10
billion in 2018, $15b in 2019, and $23B (est) in 2020
 Growth still stuck in news, advertising, and e-commerce
 Few startups offer products and services that directly
impact on productivity (IEEE Spectrum)
 Solow’s Paradox and small impact of bar codes in retail
(reduced grocery costs by 1.3%)
 Little success in driverless vehicles or manufacturing

29. Why I am Pessimistic about AI - continued
 Limitations of Big Data revealed in 2016 book, Weapons
of Math Destruction by Cathy O’Neil
 Limitations of AI revealed in
 AI Delusion by Gary Smith (2018)
 Rebooting AI by Gary Marcus (2019)
 Computational power used to achieve higher accuracies has been
doubled every 3.4 months
 300,000-times increase in capacity after 2012
 Head of Facebook AI (Jerome Presenti) says this is
unsustainable. "If you look at top experiments, each year cost is
going up 10-fold. An experiment might be in seven figures, but
it’s not going to go to nine or 10 figures, it’s not possible,
nobody can afford that."

31. Much Higher Accuracies are Needed
More than 99.99%
Users won’t tolerate low accuracies

32. Lot of Misleading Hype
 Misleading hype in health care: failure of Watson
 Misleading hype in energy:
 DeepMind did not reduce energy usage at a Google data centers nor for
UK economy; Economist claims “some insiders say such boasts are
overblown,”.
 Nest did not reduce energy usage in homes, nor did general subsidies for
smart meters do so
 And these propagators of hype are big money losers
 DeepMind’s 2018 losses reached $572 million in 2018, up from $154
million in 2016 and $341 million in 2017, on revenues of $124 million.
 Nest lost $621 million on revenues of $726 million in 2017.

33. Lots of Misleading Hype - continued
 Stanford University’s Artificial Index 2019 Annual Report is filled with hype; no market
data or examples of successful products and services
 Presents 300,000 times increase in computational power used in training exercises as
good sign, but industry people say otherwise
 Head of Facebook AI says this is unsustainable. "each year the cost is going up 10-fold. Right
now, an experiment might be in seven figures, but it’s not going to go to nine or 10 figures, it’s
not possible, nobody can afford that."
 Report fails to address impact of increase in computational capacity on improvements in
accuracy or reductions in time and cost of training exercises, such as in image
recognition.
 How much are these trends a result of better machine learning algorithms or more parallel
processing with bigger computers? If it is latter, limits will likely cause a slowdown in image
recognition improvements

34. MIT Technology Review’s Predictions: Many Sound More
Like Scientific Disciplines Than Products and Services
2005
 Airborne Networks
 Quantum Wires
 Silicon Photonics
 Metabolomics
 Magnetic-
Resonance Force
Microscopy
 Universal Memory
 Bacterial Factories
 Enviromatics
 Cell-Phone Viruses
 Biomechatronics
2004
 Universal
Translation
 Synthetic Biology
 Nanowires
 T-Rays
 Distributed
Storage
 RNAi Interference
 Power Grid Control
 Microfluidic
Optical Fibers
 Bayesian Machine
Learning*
 Personal Genomics
2003
 Wireless Sensor
Networks
 Injectable Tissue
Engineering
 Nano Solar Cells
 Mechatronics
 Grid computing
 Molecular imaging
 Nanoprint
lithography
 Software
assurance
 Glycomics
 Quantum
cryptography
2001
 Brain-Machine
Interface:
 Flexible Transistors
 Data Mining
 Digital Rights
Management
 Biometrics
 Natural Language
Processing
 Microphotonics
 Untangling Code
 Robot Design
 MicrofluidicsOrange: <$100 Million sales
Blue: too broad and vague to gather data
Green: Over $10 Billion sales; Black: >$100M but <$10B *machine learning also in 2013 predictions

35. Scientific American’s 40 Predictions (2015-2018)
 Vague
 Next Generation Batteries and Robotics, IoT Goes Nano, Sustainable
Design of Communities, Sense and Avoid, Affordable Catalysts
 What is the specific technology?
 Old
 Fuel Cells, additive manufacturing, distributed manufacturing,
catalysts for vehicles
 How are these technologies new?
 Not a Technology
 AI Ecosystem, Sustainable Design of Communities, Sense and Avoid
Drones
 Similar or Recycled Ideas
 Dimensional Materials (nanotech?), AI and Deep Learning (5 Times),
Many genetic technologies (7 Times), Quantum Computers (2 Times)

36. 2015 2016
Fuel-cell vehicles OLD
Next-generation robotics VAGUE
Recyclable thermoset plastics
Precise genetic-engineering techniques
Additive manufacturing OLD
Emergent artificial intelligence VAGUE
Distributed manufacturing OLD
“Sense and avoid” drones VAGUE
Neuromorphic technologies
Digital genome
Autonomous Vehicles
The Internet of Things Goes Nano VAGUE
Next-Generation Batteries VAGUE
Open AI Ecosystem TECHNOLOGY?
Optogenetics for Therapeutic
Neuroscience
Organs-on-Chips
Perovskite Solar Cells
Systems Metabolic Engineering
Blockchain
Dimensional Materials NANOTECH
RECYLCED
Scientific American’s PredictionsSimilar
similar
similar

37. 2017 2018
Blood Tests for Scalpel-Free Biopsies
Draw Drinking Water from Dry Air
Deep-Learning Networks
Artificial Leaf Turns Carbon Dioxide Into
Liquid Fuel
Human Cell Atlas
Precision Farming Increases Crop Yields
Affordable Catalysts for Vehicles VAGUE
Genomic Vaccines
Sustainable Design of Communities VAGUE
Quantum Computing
Augmented Reality
Advanced Diagnostics for Personalized
Medicine
AI for Molecular Design
AI That Can Argue and Instruct
Implantable Drug-Making Cells
Lab-Grown Meat
Electroceuticals
Gene Drive
Plasmonic Materials
Algorithms for Quantum Computers
Scientific American’s Predictions
Similar
More Genetic
Engineering
Similar

38. Number of
PhDs
% with
PhD
% with PhD
or MS
% with PhD,
MS, or MD
% of Total
PhDs
Biotech 791 35% 41% 53% 32%
Education & Research (mostly biotech) 346 33% 40% 47% 14%
Medical Instruments 159 13% 24% 32% 6.4%
Sub-total, life science sector 1296 28% 36% 46% 52%
General Instruments 104 24% 38% 40% 4.2%
Semiconductors 158 18% 41% 41% 6.4%
Electronic Equipment 79 15% 31% 31% 3.5%
Communications Equip 86 11% 32% 33% 3.2%
Sub-total, electronics Sector 427 16% 36% 37% 17%
Computer Programming 51 8.9% 22% 22% 2.1%
Computers 50 8.4% 29% 20% 2.0%
Computer Systems 34 7.8% 20% 21% 1.4%
Software 136 6.3% 20% 20% 5.5%
Telephone & Telegraph 27 5.2% 15% 15% 1.1%
Sub-total, Internet Infrastructure 298 8% 22% 22% 12%
Computer Services 29 5.1% 16% 19% 1.2%
Information Retrieval 22 4.7% 4.7% 13% 0.9%
Retail & Wholesale Trade 16 4.5% 12% 12% 0.6%
Finance, Broadcasting, Transport, Securities, Insurance,
Real Estate.
8 2.6% 11% 12% 0.3%
Business and Other Services 26 4.0% 12% 12% 1.0%
Advertising, Employment, Leasing 7 2.9% 9.4% 9.4% 0.3%
Sub-Total, Internet Content, Services, and Commerce 108 4.2% 13% 14% 4.3%
Number and Percentage of Advanced Degrees by Industry and Sector

39. Ex-Unicorn (15th to
25th)
Year Founded Market Capitalization ($B) Share Price
Change
Nasdaq
Change2019 March 9, 2020
Zscaler 2008 8 5.1 +19.7% - 3.6%
Moderna 2010 8 8.2 +19.3% + 3.3%
Etsy 2005 7 5.2 +61% +46%
Beyond Meat 2009 7 4.6 +11% - 10%
Coupa 2006 7 7.4 +153% +48%
Peloton 2012 7 5.5 -24% - 10%
Nutanix 2009 7 2.6 -64% +36%
Grub Hub 2004 6 3.2 -20% +48%
Mongo DB 2007 6 6.3 +264% + 9%
Cloudflare 2009 6 5.8 +6.7% - 12%
Market Capitalizations and Changes in Share Prices for Unicorns Doing IPOs (15th to 25th)

40. Ex-Unicorn (26th to
36th)
Year
Founded
Market Capitalization ($B) Share Price Change Nasdaq
Change2019 March 9, 2020
Medallia 2001 4 2.4 -48% - 6%
New Relic 2008 3 2.3 +14% +48%
Sunrun 2007 2 1.4 +16% +43%
Livongo 2008 3 2.3 -37% - 12%
Box 2005 3 1.6 -40% +48%
Cloudera 2008 3 2.0 -61% +19%
Plural Insight 2004 3 1.7 -42% - 2%
Green Sky 2006 2 0.93 -79% - 3%
Eventbrite 2006 2 0.7 -77% - 10%
Domo 2010 2 0.32 -57% - 4%
Bloom Energy 2001 1 0.76 -71% - 7%
Lending Club 2006 <1 0.76 -10% +48%
Market Capitalizations and Changes in Share Prices for Unicorns Doing IPOs (26th to 36th)

41. Ex-Unicorn (37th to
45th)
Year
Founded
Market Capitalization ($B) Share Price Change Nasdaq
Change2019 March 9, 2020
Sprout Social 2010 <1 0.3 -13% - 17%
Nant Health 2010 <1 0.17 -16.4% +47%
Oportun 2005 <1 0.36 -19% - 9,3%
Forescout 2000 <1 1.5 +16% + 7.5%
Quotient Technology 1998 <1 0.63 -36% +48%
Casper 2014 <1 0.2 -62% - 25%
GoPro 2002 <1 0.42 -93% +48%
Blue Apron 2012 <1 0.035 -98.5% +17%
Market Capitalizations and Changes in Share Prices for Unicorns Doing IPOs (37th to 45th)

42. Ex-Unicorn: 15th – 25th Founding Date 2019 Profits 2019 Revenues Profits/Revenues
Eventbrite 2006
-42 392 -0.058
New Relic 2008 32 479 -067
Wayfair 2002
-738 9127 -0.08
Box 2005 -80 696 -0.11
DocuSign 2003 -124 974 -0.12
Blue Apron 2012
-80 688 -0.12
Zscaler 2008 -24 303 -0.13
Casper 2014
-81 439 -0.18
Bloom Energy 2001
-150 785 -0.19
Peloton 2012
-181 915 -0.20
Twilio 2008 -236 1100 -0.21
Profits and Losses for Unicorns that Did Initial Public Offerings (IPOs)

43. Ex-Unicorn: 26th – 36th Founding Date 2019 Profits 2019 Revenues Profits/Revenues
Medallia 2001
-99 402 -0.25
Cloudflare 2009 -74 287 -0.25
Crowdstrike 2011
-126 481 -0.26
Forescout 2000 -87 337 -0.26
Okta 2009 -165 586 -0.28
Fiverr 2010
-31 107 -0.29
Cloudera 2008
-248 794 -0.31
Livongo 2008
-54 170 -0.32
Sprout Social 2010
-41 103 -0.4
Nutanix 2009
-520 1236 -0.42
Plural Insight 2004
-125 317 -0.45
Profits and Losses for Unicorns that Did Initial Public Offerings (IPOs)

44. Ex-Unicorn: 37th – 45th Founding Date 2019 Profits 2019 Revenues Profits/Revenues
Pure Storage 2009
-79 1643 -0.48
Uber 2009
-7400 14147 -0.52
Mongo DB 2007 -176 422 -0.41
Snapchat 2011
-921 1716 -0.54
Domo 2010
-108 173 -0.62
Lyft 2012
-2594 3616 -0.72
Slack 2009
-561 630 -0.89
Pinterest 2009
-1330 1143 -1.2
Moderna 2010 -477 60 -8.0
Profits and Losses for Unicorns that Did Initial Public Offerings (IPOs)

45. Industry Ex-Unicorns 2019 Profits
or Losses
2019
Revenues
Profits, Losses/
Revenues
Fintech Green Sky, Square, Lending Club, Sprout
Social
57 1497 0.06
E-Commerce Etsy, Wayfair, Casper, Peloton -294 2825 -0.10
Biz Software Dropbox, Opportun, Zoom, Quotient, Nant
Health, Roku, Coupa, Eventbrite, New Relic,
Box, DocuSign, Zscaler, Twilio, Cloudflare,
Forescout, Okta, Medallia, Crowdstrike
Cloudera, Mongo DB, Nutanix, Pure Storage,
Domo, Slack -151 640 -0.24
Other Beyond Meat, Sunrun, GoPro, Bloom Energy,
Fiverr, Snapchat, Pinterest -398 872 -0.46
Ride Sharing/
Food Delivery
Grubhub, Blue Apron, Uber, Lyft, -2797 4865 -0.57
Biotech Moderna, Livongo -284 115 -2.5
Average Industry Profits 2019 ($M)

48. https://nationalmaglab.org/magnet-development/applied-superconductivity-center/plots http://magnet.fsu.edu/~lee/plot/plot.htm
2018 (left) vs. 2014 (right) for superconductors

49. From paul martin