When I published the book which is the “raison d’être” of this blog, I had shortly analyzed the correlations between venture capital level in the USA, the Nasdaq index and their relationship to “crises”. Each peak and bottom level could be easily explained. I updated it to today levels with idea of revisiting when we zill be out of the Covid19 crisis. Comments welcome!
I used today again Steve Blank’s marvellous definition of a startup that I had used last time in 2013 here.
You can listen to him giving it in Helsinki in 2011 at Aalto University
or through his Mooc here:
It’s obvious once you heard it and took so many years to be designed!
In these uncertain times of Coronavirus, I can only advise you to relax. One way for me was tonight through Recomposed by Max Richter – Vivaldi’s Four Seasons.
Speculation, bubbles, yes, they have always been around. I entered the VC world in the late 90s. Now we are in the unicorns era. Or were we?
I did my 537th startup cap. table a few days ago (see below). I had hesitated a little as I was not sure a company selling mattresses, even online, could be classified in my list of tech companies. But with VCs like NEA, IVP, Norwest on board and leading banks such as Goldman Sachs and Morgan Stanley as underwriters, it had all the needed pedigree. Or at least it looked like it.
What happened? Well the initial IPO price on the table below should have been $18, then it was fixed at $12 for the first day of trading and this morning CSPR is at $10.26. The unicorn is now a $400M company. And you may want to have a look at the price of the B, C and D preferred rounds on the table below. Yes disasters happen from time to time.
As a quick remined my latest list to be updated when I will have reached 550 tables.
I had to wait many years to discover there was a book written about science and innovation which convincingly shows there is not such thing as a linear model of innovation described usually as Basic research → Applied research → Development → (Production and) Diffusion
Thanks to Laurent for mentioning to me Pasteurs Quadrant: Basic Science and Technological Innovation by Donald Stokes. There is more on Wikipedia.
Pasteur himself apparently said: “There is not pure science and applied science but only science and the applications of science”. More precisely he seems to have said according to Wikipedia again:
« Souvenez-vous qu’il n’existe pas de sciences appliquées, mais seulement des applications de la science ».
(Remember that there are no applied sciences, but only applications of science)
« Non, mille fois non, il n’existe pas une catégorie de sciences auxquelles on puisse donner le nom de sciences appliquées. Il y a la science et les applications de la science, liées entre elles comme le fruit à l’arbre qui l’a porté »
(No, a thousand times no, there is not a category of sciences to which we can give the name of applied sciences. There is science and the applications of science, linked together like the fruit to the tree that carried it.)
I have agreed with this for so many years and for the same reasons I never really understood the concept of R&D, I mean why the concepts of research and development would be associated in the same unit, but that is a slightly different topic!
Here is a long extract from Stokes (taken from a pdf found here) worth reading I think:
The examples from the history of science that contradict the static form of the postwar paradigm call into question the dynamic form as well. If applied goals can directly influence fundamental research, basic science can no longer be seen only as a remote, curiosity-powered generator of scientific discoveries that are then converted into new products and processes by applied research and development in the subsequent stages of technology transfer. This observation, however, only sets the stage for a more realistic account of the relationship between basic science and technological innovation.
Three questions of increasing importance arise about the dynamic form of the postwar paradigm. The least important is whether the neatly linear model gives too simple an account of the flows from science to technology. An irony of Bush’s legacy is that this one-dimensional graphic image is one he himself almost certainly never entertained. An engineer with unparalleled experience in the applications of science, he was keenly aware of the complex and multiple pathways that lead from scientific discoveries to technological advances-and of the widely varied lags associated with these paths. The technological breakthroughs he helped foster during the war typically depended on knowledge from several, disparate branches of science. Nothing in Bush’s report suggests that he endorsed the linear model as his own.
The spokesmen of the scientific community who lent themselves to this oversimplification in the early postwar years may have felt that this was a small price to pay for being able to communicate these ideas to a policy community and broader public for whom science was always a remote and recondite world of affairs. This calculation may well have guided the draftsmen of the second annual report of the National Science Foundation as they stated the linear model in the simplistic language quoted earlier in this chapter. In any case, these spokesmen did their work well enough that the idea of an arrow running from basic to applied research and on to development and production or operations is still often thought to summarize the relationship of basic science to new technology. But it so evidently oversimplifies and distorts the underlying realities that it began to draw fire almost as soon as it was widely accepted.
Indeed, the linear model has been such an easy target that it has tended to draw fire away from two other, less simplistic misconceptions imbedded in the dynamic form of the postwar model. One of these was the assumption that most or all technological innovation is ultimately rooted in science. If Bush did not subscribe to a linear image of the relationship between science and technology, he did assert that scientific discoveries are the source of technological progress, however multiple and unevenly paced the pathways between the two may be. In his words,
new products and new processes do not appear full-grown. They are founded on new principles and new conceptions, which in turn are painstakingly developed by research in the purest realms of science.
Even if we allow for considerable time lags in the influence of “imbedded science” on technology, this view greatly overstates the role that science has played in technological change in any age. In every preceding century the idea that technology is science based would have been false. For most of human history, the practical arts have been perfected by “‘improvers’ of technology,” in Robert P. Multhauf’s phrase, who knew no science and would not have been much helped by it if they had.
But the deepest flaw in the dynamic form of the postwar paradigm is the premise that such flows as there may be between science and technology are uniformly one way, from scientific discovery to technological innovation; that is, that science is exogenous to technology, however multiple and indirect the connecting pathways may be. The annals of science suggest that this premise has always been false to the history of science and technology. There was indeed a notable reverse flow, from technology to science, from the time of Bacon to the second industrial revolution, with scientists modeling successful technology but doing little to improve it. Multhauf notes that the eighteenth-century physicists were “more often found endeavoring to explain the workings of some existing machine than suggesting improvements in it.”
Tinkerings is not a word, I think, that is easily or commonly associated with innovation. And yet! It appears several times on this blog as indicated by the #tinkering tag. And here is the reference to a nice article by Paul Millier entitled The engineer, the tinkerer and the innovator (L’ingénieur, le bricoleur et l’innovateur). I’m not sure if you can have free access so here is a brief excerpt:
The other way of innovating is that of the “Tinkerer” , who, on the contrary, collects pieces to get an idea of the whole. He collects disparate elements from the left and the right which he preserves “in case it could be useful”. He brings them together, he assembles them, he organizes and reorganizes until suddenly – almost surprisingly – it makes sense and the innovator can say “eureka”. Damn, but it’s obvious! How had we not thought about it before? The suitcase and the wheel existed separately from each other until the day when someone brought them together to make the suitcase on wheels. How could we imagine traveling without a suitcase on wheels today? This is a radical innovation.
 The term “Tinkering” used here has no derogatory character, quite the contrary. It corresponds to a profound distinction proposed by Claude Lévi Strauss in “La pense sauvage” (1962) between two approaches (both valid) that of the “scientist”, of the “engineer” who defines himself by a project, and that of the “Tinkerer” who deals with the “means at hand” (and therefore with the client in the case of innovation). This term is quite positive for this author as for all those who, in strategic analysis, semiotics or sociology, have appropriated this notion.
The word “Tinkering” in innovation, I discovered it with a magnificent text by Tom Wolfe, The Tinkerings of Robert Noyce, which I strongly encourage you to (re)read!
PS: Not mentioning the death of Clayton Christensen on 23 January 2020 would have been a mistake and just mentioning it as a postcript here is probably another one, but I could not say anything better than
1- The Economist – https://www.economist.com/business/2020/01/30/clayton-christensens-insights-will-outlive-him
2- or Nicolas Colin – https://europeanstraits.substack.com/p/what-europe-could-learn-from-clay
If you did not know Clayton Christensen, you are lucky, you can now discover his work!
Following my recent post, The largest technology companies in Europe and the USA in the last 10 years, I needed to add a quick follow-on which comes from the fact that many people asked me two additional questions:
– but what about China?
– but what about biotech?
I am not a specialist of either dimension but I tried to do a similar exercice in the past and yesterday. Here are the results:
I have a small doubt about the year of that last table (best effort only…) and all the data in a single pdf here: Top China and Biotech
Also a short synthesis to be compared with the previous post:
It’s just after reading on Twitter that Google had just become a trillion dollar company (In honor of Google becoming a $1T company today), and also after reading Nicolas Colin’s concerns about European technology companies (Will Fragmentation Doom Europe to Another Lost Decade?) that I remembered I used to compare US and European tech former startups.
In honor of Google becoming a $1T company today, presenting its pre-IPO financials…lmao😳
So here are my past tables and also a short synthesis in the end. The full data in pdf in the end too.
USA vs. Europe in 2020
USA vs. Europe in 2018
USA vs. Europe in 2016
USA vs. Europe in 2014
USA vs. Europe in 2012
USA vs. Europe in 2010
USA vs. Europe: the Synthesis over the decade
If you prefer to download it all and a little more: Top US Europe (in pdf)
This article is linked,even it may not look obvious, to the previous one, Talent vs Luck: the role of randomness in success and failure.
I recently discovered in newspaper Le Monde the economist Deirdre McCoskey. I am apparently not the only French person to have ignored her: “The last in a series of 18 books (while waiting for a 19th that she is about to publish, and a 20th that she is in the process of completing), many of which have marked economic science and have been translated into a dozen languages … but not into French. Markus Haller, eponymous boss of the Geneva publishing house, translated one, The Secret Sins of Economic Science (2017 – the original was published in 2002!): “French publishers are flocking to French economists who have become stars in the United States but superbly ignore the American economists who generate the most debate.” I will definitely have to make the effort to read her. The article Deirdre McCloskey, économiste libertarienne d’un autre genre is available for a fee on Le Monde, here are two extracts …
More than the neoclassical equation “capital x labor x technological innovation = wealth”, more than the advent of the rule of law, of democratic institutions or the modern state which the “institutionalists” defend, it is, she says, the production of intellectuals and artists who create a shift in the ethics of time to make freedom, creativity and individual innovation the new moral virtues in place of honor, rank and submission to the Church and to the prince.
Liberal values freed successively white men, slaves, Irish Catholics, Jews, victims of fascism, colonized people, women, victims of communism, gays and … transgender people. Neither slavery nor the exploitation of workers has made us wealthier, she says; on the contrary, it is their liberation that makes us all richer, more creative. Quoting the African American poet Langston Hughes – “Let America return to the land where every man is free” – and she adds, “And every woman”.
NB here a link to Let America Be America Again By Langston Hughes:
I must thank my friend and colleague Fuad for pointing to me a remarkable research article entitled Talent vs Luck: the role of randomness in success and failure. You can find the paper on Arxiv in pdf format.
I must say this resonates with research I did in the past on serial entrepreneurs, where I discovered there was no real correlation between experience and success in high-tech entrepreneurship. Here is a link to this work: Serial entrepreneurs: are they better?
If you are interested, just download and read the paper. Here are some short teasers from their paper:
It is very well known that intelligence (or, more in general, talent and personal qualities) exhibits a Gaussian distribution among the population, whereas the distribution of wealth – often considered a proxy of success – follows typically a power law (Pareto law), with a large majority of poor people and a very small number of billionaires. Such a discrepancy between a Normal distribution of inputs, with a typical scale (the average talent or intelligence), and the scale invariant distribution of outputs, suggests that some hidden ingredient is at work behind the scenes. In this paper, with the help of a very simple agent-based toy model, we suggest that such an ingredient is just randomness. [Page 1]
There is nowadays an ever greater evidence about the fundamental role of chance, luck or, more in general, random factors, in determining successes or failures in our personal and professional lives. In particular, it has been shown that scientists have the same chance along their career of publishing their biggest hit; that those with earlier surname initials are significantly more likely to receive tenure at top departments; that the distributions of bibliometric indicators collected by a scholar might be the result of chance and noise related to multiplicative phenomena connected to a publish or perish inflationary mechanism; that one’s position in an alphabetically sorted list may be important in determining access to over-subscribed public services; that middle name initials enhance evaluations of intellectual performance; that people with easy-to-pronounce names are judged more positively than those with difficult-to-pronounce names; that individuals with noble-sounding surnames are found to work more often as managers than as employees; that females with masculine monikers are more successful in legal careers; that roughly half of the variance in incomes across persons worldwide is explained only by their country of residence and by the income distribution within that country; that the probability of becoming a CEO is strongly influenced by your name or by your month of birth; that the innovative ideas are the results of a random walk in our brain network; and that even the probability of developing a cancer, maybe cutting a brilliant career, is mainly due to simple bad luck. Recent studies on lifetime reproductive success further corroborate these statements showing that, if trait variation may influence the fate of populations, luck often governs the lives of individuals. [Page 2]
So here are some striking results:
But to understand the real meaning of [these] findings it is important to distinguish the macro from the micro point of view. In fact, from the micro point of view, following the dynamical rules of the model, a talented individual has a greater a priori probability to reach a high level of success than a moderately gifted one, since she has a greater ability to grasp any opportunity that will come. Of course, luck has to help her in yielding those opportunities. Therefore, from the point of view of a single individual, we should therefore conclude that, being impossible (by definition) to control the occurrence of lucky events, the best strategy to increase the probability of success (at any talent level) is to broaden the personal activity, the production of ideas, the communication with other people, seeking for diversity and mutual enrichment. In other words, to be an open- minded person, ready to be in contact with others, exposes to the highest probability of lucky events (to be exploited by means of the personal talent). On the other hand, from the macro point of view of the entire society, the probability to find moderately gifted individuals at the top levels of success is greater than that of finding there very talented ones, because moderately gifted people are much more numerous and, with the help of luck, have – globally – a statistical advantage to reach a great success, in spite of their lower individual a priori probability. [Page 14]
The authors draw some practical recommendations: for example, for strategies about funding research among a diversity of talented people looking at the table [below], it is evident that, if the goal is to reward the most talented persons (thus increasing their final level of success [C]), it is much more convenient to distribute periodically (even small) equal amounts of capital to all individuals rather than to give a greater capital only to a small percentage of them, selected through their level of success – already reached – at the moment of the distribution. The histogram shows that the “egalitarian” criterion, which assigns 1 unit of capital every 5 years to all the individuals is the most effcient way to distribute funds. [Pages 17-18]
Finally, the environment may have a role, such as improbing education, hence talent: Strengthening the training of the most gifted people or increasing the average level of education produce, as one could expect, some beneficial effects on the social system, since both these policies raise the probability, for talented individuals, to grasp the opportunities that luck presents to them. On the other hand, the enhancement in the average percentage of highly talented people who are able to reach a good level of success, seems to be not particularly remarkable in both the cases analyzed, therefore the result of the corresponding educational policies appears mainly restricted to the emergence of isolated extreme successful cases. […] Also, it results that increasing the variance without changing the average, enhances the chances for more talented people to get a very high success. This, on one hand, could be considered positive but, on the other hand, it is an isolated case and it has, as a counterpart, an increase in the gap between unsuccessful and successful people. Increasing the average without changing the variance induces that also in this case the chances for more talented people to get a very high success are enhanced, while the gap between unsuccessful and successful people is lower than before. [Pages 20-21]
As a stimulating conclusion, the authors write: Our results highlight the risks of the paradigm that we call “naive meritocracy”, which fails to give honors and rewards to the most competent people, because it underestimates the role of randomness among the determinants of success. In this respect, several dfferent scenarios have been investigated in order to discuss more effcient strategies, which are able to counterbalance the unpredictable role of luck and give more opportunities and resources to the most talented ones – a purpose that should be the main aim of a truly meritocratic approach. Such strategies have also been shown to be the most beneficial for the entire society, since they tend to increase the diversity of ideas and perspectives in research, thus fostering also innovation. [Page 23]
It’s by reading Nicolas Colin’s always interesting newsletter, European Straits #149, 10 Tech Giants That Are (Almost All) in Bad Shape that I decided to revisit quickly the growth of 3 tech giants that I have been following for many years now: Google, Facebook and Tesla. And here are their numbers in terms of thousands of employees, revenue and profit in $M.
If you really love numbers, here is a little more: their average growth of 5 years is about 20% for Google, 40% for Facebook and about the same for Tesla (except that they never made a profit). Google is older so it is not a fair comparison. here is a more precise analysis.
So are the three tech giants threatened? I am not sure given this steady growth.