Deep Tech Unicorn Startups Are Unprofitable, Why?

Many have argued that deep tech startup Unicorns will do better than the low-tech money-losing startups that I have covered in previous articles in this series on startups and technologies. Beginning with Uber, Lyft, and WeWork, these articles have shown that today’s startups are doing worse than those founded 20 to 50 years ago. Only one startup founded since 2000 (Facebook in 2004) has achieved top 100 market capitalization versus six in the 1970s, nine in the 1980s, and eight in the 1990s[i] and none of America’s ex-Unicorns are even close to being ranked in the top 100, as shown in the second article in this series[ii]. Ex-Unicorns are also much less profitable than the most successful startups founded 20 to 50 years ago. The third and fourth articles[iii] in this series showed that only six of 45 ex-Unicorns had profits in 2019 despite most of them being founded before 2010, or more than 10 years ago, and the subsequent articles provided explanations for these low profits[iv] in industries from fintech to business software, consumer Internet, and ride sharing.

This is the 13th article in this series, addressing deep tech startups that will purportedly do better than other startups. Like previous articles, it focuses on Unicorns, startups valued at $1Billion or more, and ex-Unicorns, ones that have done IPOs and thus report net income and other financial information every quarter.

Table 1 lists 32 Unicorns and ex-Unicorns (mostly American) and their founding year, status, and technology. Eight of them have done IPOs and thus are public companies, six have been acquired, three have been liquidated, and 14 are still privately held. They are targeting biotech/health (12), AI/Big Data (8), sensors/AVs (4), wearables (3), satellites/space (2), and other (3). Other includes 3D printing, storage, and energy (fuel cells).

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Table 2 summarizes net income for 12 ex-Unicorns, 10 of which are public and two of which were acquired by Alphabet. The data shows that half of them have losses greater than 50% of revenues and 10 of the 12 have losses greater than 30% of revenues. And this is despite them being founded at least nine years ago with more than half founded at least 12 years ago.

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These losses are much higher than those of startups in general and of the most successful startups 20 to 50 years ago. About 20% of startups were profitable at IPO time in the last few years, down from 80% in the early 1980s, compared to the zero percent in Table 2. The 20% figure is also comparable to the percent profitable during the dotcom bubble, yet the medium time to founding is more than double it was during the dotcom bubble. Longer time periods from founding to IPO should lead to higher profitability at IPO time.

If we consider the most successful startups of the last 50 years, those attaining top 100 market capitalization status, we can conclude that these deep tech startups should have been profitable by now. The first article showed that 13 of 24 had profits by year five and another 9 had profits by year 10. Only two took longer than 10 years and these were biotechnology startups, an industry that takes longer for startups to become profitable than others because they must wait many years for drug approvals.

Four of the 12 startups shown in Table 2 are biotechnology/health startups as are 12 of the 31 in Table 1. It is reasonable to expect them to take longer to become profitable than other deep tech startups, and thus to some extent, the lack of profits for startups in Table 1 is not surprising. Yet, even if we exclude the biotechnology/health startups from Table 2, we are left with eight publicly-held and 20 privately-held startups that are far from profitable.

A final issue concerns Unicorns that have not yet done IPOs (see Table 3). All of them should have done IPOs by now, but they have not, and this should raise flags of concern. My sixth article in this series considered current Unicorns for all industries[v], as did my ninth[vi], tenth,[vii] and eleventh articles on fintech, business software and consumer Internet respectively. These articles found that current Unicorns that have announced income figures are much less profitable than are ex-Unicorns[viii], a dangerous trend for Unicorns because so few ex-Unicorns are profitable. They also found that startups announcing IPOs in 2020 are less profitable than are ex-Unicorns.

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Hype will enable many of these startups to survive. Covid19 has given hope to biotech startups, Tesla has given hope to EV and hydrogen startups, and Elon Musk has given hope to SpaceX. But just to name one, SpaceX cannot generate sufficient revenues from launching satellites unless it or another company provides a new application for satellites such as rural Internet access, a tough application to sell. It’s vaunted reusable rockets are also taking longer to reduce costs than was expected.

The big question is why are these deep tech startups so unprofitable? After all, deep tech startups have been successful in the past, the best example being the semiconductor startups of the past that gave Silicon Valley its name. Other deep tech startups were successful in networking equipment (e.g., Cisco, 3Com), medical equipment, and of course biotech (Celgene, Gilead Sciences, Amgen).

Are they less successful now than in the past because there is less to commercialize, there are more regulations, incumbents are stronger, or they are doing something different than in the past? A tough question. My conclusion is that fewer breakthrough technologies are coming out than decades before and the ones that are coming out are taking longer to successfully commercialize. AI/Big Data, sensors/AVs, wearables, satellites, 3D printing, and fuel cells have all been over-hyped, their costs and performance are still disappointing, and their diffusion will continue to be slow. For instance, improvements in the accuracy of image classification on the ImageNet dataset over time, which can be seen as a proxy for broader progress in supervised learning for image recognition, has mostly been driven by improvements in computational capacity[ix].

Overall, few examples of successful breakthrough technologies can be found since the iPhone was introduced in 2007. Other than OLEDs and solar cells, there are a long string of mostly disappointments. Yes AI, electric vehicles, drones, and Internet of Things are diffusing and thus an analysis in 10 years might come to different conclusions, but for the 2010s, there was little to commercialize.

In summary, deep tech Unicorns and ex-Unicorns are highly unprofitable. None of the 12 deep tech startups reporting net income are profitable and it is unlikely that any of the other 19 startups in Table 1 are profitable. Overall, these results are consistent with the small number of profitable business software[x] (1 of 27), fintech[xi] (4 of 18), and consumer Internet (1 of 27) startups that are described in previous articles. It is hard to be optimistic about today’s startups.

[i] https://medium.com/@jeffreyleefunk/the-most-valuable-startups-founded-since-1975-none-have-been-founded-since-2004-8bc142b67051

[ii] https://medium.com/@jeffreyleefunk/unicorn-ipos-continue-to-disappoint-investors-37c2007e7b73

[iii] https://medium.com/@jeffreyleefunk/are-there-any-industries-in-which-ex-unicorns-are-profitable-747eca652170

https://medium.com/@jeffreyleefunk/how-successful-are-todays-startup-unicorns-893043f32d24

[iv] https://medium.com/@jeffreyleefunk/why-are-todays-startup-unicorns-doing-worse-than-those-of-the-past-1c8ece718ab0 https://medium.com/@jeffreyleefunk/should-we-fail-fast-hard-and-often-or-think-carefully-about-investments-de7bccc21a69

[v] https://medium.com/@jeffreyleefunk/can-business-software-unicorns-become-profitable-c038d4db5b98

[vi] https://medium.com/@jeffreyleefunk/can-fintech-and-insuretech-unicorns-become-profitable-2b586f50f81

[vii] https://medium.com/@jeffreyleefunk/can-business-software-unicorns-become-profitable-c038d4db5b98

[viii] https://medium.com/@jeffreyleefunk/what-will-happen-to-todays-privately-held-unicorns-valued-at-1-4-trillion-13f507797487

[ix] https://medium.com/@jeffreyleefunk/how-fast-is-ai-improving-pattern-recognition-accuracy-and-computational-power-e1366689a120

[x] https://medium.com/@jeffreyleefunk/can-business-software-unicorns-become-profitable-c038d4db5b98

[xi] https://medium.com/@jeffreyleefunk/can-fintech-and-insuretech-unicorns-become-profitable-2b586f50f81

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