Successfully reported this slideshow.
Your SlideShare is downloading. ×

The Troubled Future of Startups and Innovation: Webinar for London Futurists

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Loading in …3
×

Check these out next

1 of 49 Ad

The Troubled Future of Startups and Innovation: Webinar for London Futurists

Download to read offline

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.

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.

Advertisement
Advertisement

More Related Content

Slideshows for you (20)

Similar to The Troubled Future of Startups and Innovation: Webinar for London Futurists (20)

Advertisement

More from Jeffrey Funk (19)

Advertisement

The Troubled Future of Startups and Innovation: Webinar for London Futurists

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 14. Appendix
  15. 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. 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. 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)
  18. 18. Research Productivity for Medical Research
  19. 19. Overall Research Productivity: Number of Researchers Has Exploded Even as Productivity Growth Has Fallen
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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."
  26. 26. Much Higher Accuracies are Needed More than 99.99% Users won’t tolerate low accuracies
  27. 27. 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.
  28. 28. 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
  29. 29. 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
  30. 30. 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)
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. 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)
  35. 35. Ex-Unicorn (26th to 36th) Year Founded Market Capitalization ($B) Share Price Change Nasdaq Change2019 March 9, 2020 Medallia 2001 4 2.4 -48% - 6% New Relic 2008 3 2.3 +14% +48% Sunrun 2007 2 1.4 +16% +43% Livongo 2008 3 2.3 -37% - 12% Box 2005 3 1.6 -40% +48% Cloudera 2008 3 2.0 -61% +19% Plural Insight 2004 3 1.7 -42% - 2% Green Sky 2006 2 0.93 -79% - 3% Eventbrite 2006 2 0.7 -77% - 10% Domo 2010 2 0.32 -57% - 4% Bloom Energy 2001 1 0.76 -71% - 7% Lending Club 2006 <1 0.76 -10% +48% Market Capitalizations and Changes in Share Prices for Unicorns Doing IPOs (26th to 36th)
  36. 36. Ex-Unicorn (37th to 45th) Year Founded Market Capitalization ($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)
  37. 37. 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)
  38. 38. 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)
  39. 39. 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)
  40. 40. 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)
  41. 41. https://nationalmaglab.org/magnet-development/applied-superconductivity-center/plots http://magnet.fsu.edu/~lee/plot/plot.htm 2018 (left) vs. 2014 (right) for superconductors
  42. 42. From paul martin

×