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.
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)
and
« 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” [1], 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.
[1] 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!
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.
By Yann Legendre for Le Monde
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:
https://www.poetryfoundation.org/poems/147907/let-america-be-america-again
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]
I had a few days ago a conversation about why I thought titles such as CEO or CTO were not such a good idea in start-ups. I thought this was a close debate and apparently not. So let me try to elaborate.
A good quote – I just found trying to structure my thinking – is “CEO means in a startup Chief Everything Officer”! CTO means someone who does not want to interact with customers while Business Development means the opposite. But you would not use VP of Sales in a small team…
In the book Startup Nation, there is something similar: “The multitasking mentality produces an environment in which job titles — and the compartmentalization that goes along with them — don’t mean much.”
There are two articles worth reading: first, my favorite start-up guru, Steve Blank, Job Titles That Can Sink Your Startup. Second, Start-ups should eliminate job titles by Jeff Bussgang.
Steve Blank explains titles are for established companies with knwon business models and known processes: Companies Have Titles to Execute a Known Business Model. […] Therefore the job title “Sales” in an existing company is all about execution around a series of “knowns.” [For example] Did he have a repeatable and scalable business model? Did he have a well understood group of customers? […] Startups Need Different Titles to Search For an Unknown Business Model. You didn’t need a VP of Sales, you needed something very different. Searching around a series of unknowns. You needed a VP of Customer Development
I am not sure I am allowed to do the following but here is a long extract from Bussgang: “Job titles make sense for mature companies, not for start-ups. […] At business school, I learned all about titles and hierarchies and the importance of organizational structure. When I joined my first start-up after graduation, e-commerce leader Open Market, I found the operating philosophy of the founder jarring: He declared no one would have titles in the first few years. If you needed a title for external reasons, our founder told us, we should feel free to make one up. But we would avoid using labels internally. In other words, there would be no “vice-president” or “director” or other such hierarchical denominations.
Why? Because a start-up is so fluid, roles changes, responsibilities evolve and reporting structures move around fluidly. Titles represent friction, pure and simple, and the one thing you want to reduce in a start-up is friction. By avoiding titles, you avoid early employees getting fixated on their role, who they report to, and what their scope of responsibility is – all things that rapidly change in a company’s first year or two.
So when I co-founded Upromise, I instituted a similar policy. We had an open office structure and functional teams, but a fluid organizational environment and rapid growth. One of our young team members changed jobs four times in her first year. Only after the first year, as we settled into a more stable organizational structure and I recruited senior executives who were more obviously going to serve as my direct reports on the executive team did I begin to give out titles (CTO, CMO, CFO, etc.). But you can establish role and process clarity without having to depend on titles.
Here is Steve Blank visual summary of all this:
In his four steps to the epiphany, he adds a quick check list about this:
Goal of phase O-b: Set up the Customer Development Team. Agree on Customer Development team methodology and goals.
Author: Whoever is acting as CEO
Approval: Entire Founding Team/Board
Presenter: CEO
Time/Effort: 1/2-l day meeting of entire founding team
A- Review the organizational differences between Product and Customer Development – Traditional titles versus functional ones.
1. No VP of Sales
2. No VP of Marketing
3. No VP of Business Development
B-Identify the four key functional roles for the first four phases of a startup
1. Who is the Business Visionary
2. Who is the Business Execution
3. Who is the Technical Visionary
4. Who is the Technical Execution
C-Review the goals of each of the roles for each of the four Customer Development phases
D-Enumerate 3 to 5 Core Values of the Founding Team
1. Not a mission statement
2. Not about profit or products
3. Core ideology is about what the company believes in
Phase O-b Exit Criteria: Buy-in of the team and board for functional job descriptions, right people in those jobs, core values
PS: you may find more interesting advice from Steve Blank in
How To Find the Right Co-Founders? – https://steveblank.com/2014/09/16/who-do-you-need-on-your-startup-team/
Why Founders Should Know How to Code – https://steveblank.com/2014/09/03/should-founders-know-how-to-code/
Building Great Founding Teams – https://steveblank.com/2013/07/29/building-great-founding-teams/
and you may want to listen to Randy Komisar about entrepreneurship skills
PS2: I revisited my blog and saw the tag “team” was also relevant, direct link is here www.startup-book.com/tag/team/
I just read two very nice guides about deeptech entrepreneurship. They’ve been published by BPI, the French Public Investment Bank. Either you read French or you will only read a couple of quotes I translated. I have however put the slideshare links at the end of the post.
So here are some testimonies:
Do not be afraid to start your startup, even if it may seem complex and endless. Whether the result is positive or a little less, it is an adventure that you will not be taken away from, just like a PhD. Entrepreneurship brings so much into your life, into your curriculum. Entrepreneurship is a continuous training that can only be rewarding.
The transition from my doctorate to the status of entrepreneur came naturally. The technology of […] was my doctoral topic, we had already developed several prototypes that we had evaluated and which were promising. We could not stop there without giving end users, who really need it, the benefit of this innovation. We decided to create the startup and to launch it until the commercialization of the device.
The world of entrepreneurship opened me up to new horizons and brought me experiences that I never imagined when we started a few years ago.
The creation of a startup is a very beautiful experience, a human one first of all. By creating […] I met people I would never have otherwise. It’s also a work experience, because doing research and ending up with a finished product is not at all the same thing. Finally, as a laboratory director I consider that valorization is part of my missions, and it also brings us a lot of visibility at the regional level, because we create value and jobs.
What drives you to do that is a human experience: be willing to go to the very end of a topic that you are passionate about. Do not do it because it’s fashionable but because it fascinates you.
When you go from researcher to entrepreneur everything changes: the way former colleagues and friends look at you, the prospects of professional evolution. The question must be asked: “Am I aligned with my personal values?”
To go from scientist to entrepreneur is often to put what you like aside. You have to get into finance, IP, contracts … It’s a real change of mindset. In parallel, meetings and the emergence of new opportunities require a real agility in the way of thinking and constantly questioning the vision of your work.
Contributing to the creation of […] allowed me to discover an unknown world, that of the industrial world and marketing, and brought me a lot of things: the additional respect of my colleagues, the recognition and the […] gratitude […] for the positive impact (to come) on the economic activity of the region. This brought me a real satisfaction because my academic research finds consumers and therefore a real usefulness. And more people are working on my ideas since I started the business.
This is one of the best books about innovation I have read in years. The importance of crazy ideas, not the recipe on how to make them successful, but the attitude to make them less crazy. And more importanly, crazy ideas have much more impact on our lives than we may think. A must read. Here are some extracts to convicne you…
Loonshot : a neglected project, widely dismissed, its champion written-off as unhinged.
The Loonshot thesis :
1. The most important breakthroughs come from loonshots, widely dismissed ideas whose champions are often written off as crazy.
2. Large groups of people are needed to translate those breakthroughs into technologies that win wars, products that save lives, or strategies that change industries.
3. Applying the science of phase transitions to the behavior of teams, companies, or any group with a mission provides practical rules for nurturing loonshots faster and better. [Page 2]
“Bush changed national research the same way Vail changed corporate research. Both recognized that the big ideas – the breakthroughs that change the course of science, business, and history – fail many times before they succeed. Sometimes they survive through sheer chance. In other words, the breakthroughs that change our world are born from the marriage of genius and serendipity.” [Page 37]
“But the ones who truly succeed – the engineers of serendipity – play a more humble role. Rather than champion any individual loonshot, they create an outstanding structure for nurturing many loonshots. Rather than visionary innovators, they are careful gardeners. They ensure that both loonshots and franchises are tended well, that neither dominates the other, and that each side nurtures and supports the other.” [page 38]
“As we will see over the next chapters, managing the touch and the balance is an art. Overmanaging the transfer causes one kind of trap. Undemanaging that transfer causes another.” [Page 42]
A project champion: On the creative side, inventors (artists) often believe that their work should speak for itself. Most find any kind of promotion distasteful. On the business side, line managers (soldiers) don’t see the need for someone who doesn’t make or sell stuff – for someone whose job is simply to promote an idea internally. But great project champions are much more than promoters. They are bilingual specialists, fluent in both artist-speak and soldier-speak, who can bring the two sides together. [Page 63]
Contrarian answers, with confidence, create very attractive investments. [Page 63]
LSC: Listen to the Suck with Curiosity. LSC, for me, is a signal. When someone challenges the project you’ve invested years in, do you defend with anger or investigate with genuine curiosity? [Page 64]
Some famous creators of Loonshots:
https://en.wikipedia.org/wiki/Akira_Endo_(biochemist)
https://en.wikipedia.org/wiki/Juan_Trippe
https://en.wikipedia.org/wiki/Edwin_H._Land
Years later, Land became known for a saying: “Do not undertake a program unless the goal is manifestly important and its achievement nearly impossible.” [Page 96]
“Then the author has an amazing thesis about team size. “I will show that team size plays the same role in organizations that temperature does for liquids and solids. As team size crosses a “magic number”, the balance of incentives shifts from encouraging a focus on loonshots to a focus on careers.” [Page 164]
This magic number is
“Where G is the salary growth rate with promotion (for example 12%); S is management span – if it is narrow, each manager has a small number of direct reports and there are many hierarchical layers, whereas if it is wide, there will be more direct reports and less hierarchy – E is the equity fraction which ties your pay to the quality of your work. The final parameter F for fitness is return on politics vs. project-skill fit.
In many cases the magic number M equals 150… [pages 195-200]
Safi Bahcall has many other rich descriptions including the importance of power laws in innovations [Page 178] or this one [Page 240]
For a loonshot nursery to flourish – inside either a company or an industry – three conditions must be met:
1. Phase separation : separate lonnshot and franchise groups
2. Dynamic elequilibrium: seamless exchange between the two groups
3. Critical mass: a lonnshot group large enough to ignite.
Applied to companies, the first two are the first Bush-Vail rules discussed in part one. The third, critical mass, has to do with commitment. If there is no money to pay for hiring good people or funding early-stage ideas and projects, a loonshot group will wither, no matter how well designed. To thrive, a loonshot group needs a chain reaction. A research lab that produces a successful drug, a hit product, or award-winning designs will attract top talent. Inventors and creatives will want to bring new ideas and ride the wave of a winning team. The success will justify more funding. More projects and more funding increase the odds of more hits – the positive feedback lopp of a chain reaction.
How many projects are needed to achieve critical mass? Suppose odds are 1 in 10 that any one loonshot will succeed. Critical mass to ignite the reaction with high confidence requires investing in at least two dozen such loonshots (a diversified portfolio of ten of those loonhsots has a 65 percent likelihood of producing at least one win; two dozen, a 92 percent likelihood).” [Pages 240-1]
Disruptive innovation again [Page 263]
Use “disruptive Innovation” to analyze history; nurture loonshots to test beliefs.
In an article addressing recent controversy about the notion of disruptive innovation, Christensen explains why Uber is not disruptive, by his definition, and why the iPhone also began as a sustainable innovation. In Chapter 3, we saw that American Airlines – a large incumbent, not a new entrant – led the airline industry after deregulation with many brilliant “sustaining” innovations targeted to high-end customers. Hundreds of low-cost, specialty airline startups, “disruptive innovators” failed.
If the transistor, google, the iPhone, Uber, Walmart, IKEA, and American Airlines’ Big Data and other industry-transforming ideas were all initially sustaining innovations, and hundreds of “disruptive innovators” fail, perhaps the distinction between sustaining vs. disruptive, while interesting academically or in hindsight, is less critical for steering businesses in real time than other notions.
That, at least, is why I don’t use the distinction in this book. I use the distinction between S-type and P-type because teams and companies or any large organization develop deeply held beliefs, sometimes consciously, often not, about both strategies and products – and loonshots are contrarian bets that challenge those beliefs. Perhaps everything that you are sure is true about your products or your business model is right, and the people telling you about some crazy idea that challenges your beliefs are wrong. But what if they aren’t? Wouldn’t you rather discover that in your own lab or pilot study, rather than read about it in a press release from one of your competitors? How much risk are you willing to take by dismissing their idea?
We want to design our teams, companies, and nations to nurture loonshots – in a way that maintains the delicate balance with our franchises – so that we avoid ending up like the Qianlong emperor. The one how dismissed those “strange or ingenious objects”, the same strange and ingenious objects that returned in the hands of his adversaries, years later, and doomed his empire.
A short second post following my recent one, here. Short notes.
Eric Schmidt and its coauthors emphasize the importance of teams, of people and of products. For example:
“In our previous book, How Google Works, we argue that there is a new breed of employee, the smart creative, who is critical to achieving this speed and innovation. The smart creative is someone who combines technical depth with business savvy and creative flair. […] As we were researching this book and talking to the dozens of people Bill had coached in his career, we realized that this thesis misses an important piece of the business success puzzle. There is another , equally critical, factor for success in companies: teams that act as communities. integrating interests and putting aside differences to be individually and collectively obsessed with what’s good for the company. […] But adhering to these principles is hard, and it gets even harder when you add factors such as fast-moving industries, complex business models, technology-driven shifts, smart competitors, sky-high customer expectations, global expansion, demanding teammates… […] To balance the tension and mold a team into a community, you need a coach, someone who works not only with individuals but also with the team.” [Pages 22-4]
“Bill started his business career as an advertising and marketing guy, then added sales to his portfolio after joining Apple. But through his experiences in the tech world, in his stints at Apple, Intuit, Google, and others, Bill came to appreciate the preeminence of technology and product in the business pecking order. “The purpose of a company is to take the vision you have of the product and bring it to life,” he said once at a conference. “Then you put all the other components around it – finance, sales, marketing – to get the product out the door and make sure it’s successful.” This was not the way things were done in Silicon Valley, or most other places, when Bill came to town in the 1980s. The model then was that while a company might be started by a technologist, pretty soon the powers that be would bring in a business guy with experience in sales, marketing, finance, or operations, to run the place. These executives wouldn’t be thinking about the needs of the engineer and weren’t focused on product first. Bill was a business guy, but he believed that nothing was more important than an empowered engineer. His constant point: product teams are the heart of the company. They are the ones who create new features and new products.” [Pages 67-8]
About teams again, and trust : “Not surprisingly when Google conducted a study to determine the factors behind high-performing teams, psychological safety came out at the top of the list [1]. The common notions that the best teams are made up of people with complementary skill sets or similar personalities were disproven; the best teams are the ones with the most psychological safety, And that starts with trust.” [Page 84]
About talent: Bill looked for four characteristics in people. The person has to be smart, not necessarily academically but more from the standpoint of being able to get up to speed quickly in different areas and then make connections. Bill called this the ability to make “far analogies”. The person has to work hard, and has to have high integrity. Finally, the person should have the hard-to-define characteristic: grit. The ability to get knocked down and have the passion and perseverance to get up and go at it again.” [Page 116]
And finally, may be most importantly, about founders: “He held a very special place in his heart for the people who have the guts and skills to start companies. They are sane enough to know that every day is a fight for survival against daunting odds and crazy enough to think they can succeed anyway. And retaining them in a meaningful way is essential to success in any company. Too often we think about running a company as an operating job, and as we have already examined, Bill considered operational excellence to be very important. But when we reduce company leadership to its operational essence, we negate another very important component: vision. Many times operating people come in, and though they may run the company better, they lose the heart and soul of the company.” [Page 178]
In conclusion, People, People, People.
[1] More details about the study can be found in James Graham, “What Google Learned from Its Quest to Build the Perfect Team” New York Times, February 25, 2016.
Excellent issue of the famous French review Esprit on The Ideology of Silicon Valley. You’ll find contibutions by Emmanuel Alloa, Jean-Baptiste Soufron, Fred Turner, Shoshana Zuboff, Antonio Casilli and Jean-Pierre Dupuy.
I was especially struck by the surprising interview with Fred Turner: Do not be Evil. Utopias, borders and brogrammers. This is in fact a translation from LogicMag, which you can find here: Don’t Be Evil. It is really worth reading! It is his explanation of the roots of Silicon Valley that surprised me the most and how they still influence today this region which in the end is not very ideological, even if the other authors have different points of view.
For example: “It owes its origins to 1960s communalism. A brief primer on the counterculture: there were actually two countercultures. One, the New Left, did politics to change politics. It was very much focused on institutions, and not really afraid of hierarchy. The other—and this is where the tech world gets its mojo—is what I’ve called the New Communalists. Between 1966 and 1973, we had the largest wave of commune building in American history. These people were involved in turning away from politics, away from bureaucracy, and toward a world in which they could change their consciousness. They believed small-scale technologies would help them do that. They wanted to change the world by creating new tools for consciousness transformation.” Then on the influence today: “It varies depending on the company. Apple is, in some ways, very cynical. It markets utopian ideas all the time. It markets its products as tools of utopian transformation in a countercultural vein. It has co-opted a series of the emblems of the counterculture, starting as soon as the company was founded. At other companies, I think it’s very sincere. I’ve spent a lot of time at Facebook lately, and I think they sincerely want to build what Mark Zuckerberg calls a more connected world. Whether their practice matches their beliefs, I don’t know. About ten years back, I spent a lot of time inside Google. What I saw there was an interesting loop. It started with, “Don’t be evil.” So then the question became, “Okay, what’s good?” Well, information is good. Information empowers people. So providing information is good. Okay, great. Who provides information? Oh, right: Google provides information. So you end up in this loop where what’s good for people is what’s good for Google, and vice versa. And that is a challenging space to live in.”
Jean-Pierre Dupuy in “The New Data Science” brilliantly explains there is no data science. Science is about causes, data are more about correlations which can not really help in predictions (I hope I understood his message!). Let me quote one sentence: “The ideology that accompanies big data, meanwhile, announces the advent of new scientific practices that, putting the theoretical requirement in the background, endanger the advance of knowledge and, more importantly, undermine the foundations of a rational ethic.” Really brilliant!