Tag Archives: Disruptive innovation

Obama and Silicon Valley, a common vision of the future?

Rarely have I read two articles giving a vision as close apparently of the challenges and issues of the future of the planet as I’ll mention in a moment. I say apparently, because behind some consistencies about a confident vision of the future, lie fairly fundamental differences about the challenges.

But I will allow myself a digression before commenting these tow articles. A third article was published on a very different subject in the paper edition the New Yorker dated Oct. 10, 2016 – again apparently as it deals about the past and the present! It is entitled He’s Back. This article reminded me that my two most important readings in 2016 (and perhaps in the 21st century) are those that I mentioned in the post Has the world gone crazy? Maybe…, namely the tremendous Capital in the 21st Century by Thomas Piketty and the no less remarkable In the disruption – How not to go crazy? by Bernard Stiegler. I need to give the title of the digital edition that might hopefully inspire you to discover Karl Marx, Yesterday and Today – The nineteenth-century philosopher’s ideas may help us to understand the economic and political inequality of our time.

Back to the point that motivates this post. Barack Obama has just published in The Economist a short text in which he describes the challenges ahead. This is a brilliant article. It also creates a certain mystery for me around the American president. Is he very well surrounded by knowledgeable advisors and / or has he become interested so deeply in topics to the point of finding the time to write (I should say to describe) himself the world’s complexity. An absolute must-read: The Way Ahead.

20161008_fbp666

In comparison, Adding a Zero in the same Oct. 10 New Yorker – entitled in the electronic version Sam Altman’s Manifest Destiny with however an identical subtitle Is the head of Y Combinator fixing the world, or try trying to take over Silicon Valley? This very long article describes perfectly the reasons why we can equally love and hate Silicon Valley. It is a Pharmakon (both a remedy and a poison according Stiegler’s words). I encourage you to read it too, but your priority should go to reading Barack Obama.

I’ll try to explain myself. Obama has tried a lot and has not been so successful, but there has a consistency in his acts, I think. In The Economist, he wrote: “Fully restoring faith in an economy where hardworking Americans can get ahead requires addressing four major structural challenges: boosting productivity growth, combating rising inequality, ensuring that everyone who wants a job can get one and building a resilient economy that’s primed for future growth.” Obama is an optimist and a moderate. All but a revolutionary. There is a beautiful sentence in the middle of the article: “The presidency is a relay race, requiring each of us to do our part to bring the country closer to its highest aspirations.” The highest aspirations. I sincerely believe that is why Obama deserved the Nobel Peace Prize despite all the difficulties of his task.

Silicon Valley has the same optimism and the same belief in technological progress and well-being that it brings (or may bring). Growth is a mantra. Sam Altman is no exception to the rule. Here are some examples: “We had limited our projected revenue to thirty million dollars,” Chesky [the founder and CEO of Airbnb] said. “Sam said, ‘Take all the “M”s and make them “B”s.’ ” Altman recalls telling them, “Either you don’t believe everything you said in the rest of the deck, or you’re ashamed, or I can’t do math.” [Page 71] then a little further “It is one of the rarer mistakes to make, trying to be too lean,” Altman said, “Don’t worry about a competitor until they’re beating you in the market,” … “Competitors are one of the last monsters that haunt your dreams.”… “Always think about adding one more zero to whatever you’re doing, but never think beyond that.” [Page 75]

161010_r28829-863x1200-1475089022 Illustration by R. Kikuo Johnson

Clearly risk taking steps accordingly: In a class that Altman taught at Stanford in 2014, he remarked that the formula for estimating a startup’s chance of success is “something like Idea times Product times Execution times Team times Luck, where Luck is a random number between zero and ten thousand.” [Page 70] The strategy of accelerators such as Y Combinator looks pretty simple: “What we ask of startups is very simple but very hard to do. One, make something people want”—a phrase of Graham’s, which is emblazoned on gray T-shirts for the founders—“and, two, all you should be doing is talking to your customers and building stuff.” [Page 73] The result of this strategy lies in the performance of these acceleration mechanisms: A 2012 study of North American accelerators found that almost half of them had failed to produce a single startup that went on to raise venture funding. While a few accelerators, such as Tech Stars and 500 Startups, have a handful of alumni worth hundreds of millions of dollars, Y Combinator has graduates worth at least a billion—and it has eleven of them. [Page 71] but Altman is dissatisfied: Venture capitalists believe that their returns follow a “power law,” by which ninety per cent of their profits come from one or two companies. This means that they secretly hope the other startups in their portfolio fail fast, rather than staggering onward as resource-consuming “zombies.” Altman pointed out that only a fifth of YC companies have failed, and said, “We should be taking crazier risks, so that our failure rate would be as high as ninety per cent. [Page 83]

“Under Sam, the level of YC’s ambition has gone up 10x.” Paul Graham, who was leaving soon after the dinner for a sabbatical year in England, told me that Altman, by precipitating progress in “curing cancer, fusion, supersonic airliners, A.I.,” was trying to comprehensively revise the way we live: “I think his goal is to make the whole future.” [Page 70] Recently, YC began planning a pilot project to test the feasibility of building its own experimental city. It would lie somewhere in America, or perhaps abroad, and would be optimized for technological solutions: it might, for instance, permit only self-driving cars. “It could be a college town built out of YC, the university of the future,” Altman said. “A hundred thousand acres, fifty to a hundred thousand residents. We crowdfund the infrastructure and establish a new and affordable way of living around concepts like ‘No one can ever make money off of real estate.’ ” He emphasized that it was just an idea—but he was already looking at potential sites. You could imagine this metropolis as an exemplary post-human city-state, run on A.I. — a twenty-first-century Athens — or as a gated community for the élite, a fortress against the coming chaos. [Page 83] YC’s optimism goes very far: “We’re good at screening out assholes,” Graham told me. “In fact, we’re better at screening out assholes than losers. […] Graham wrote an essay, “Mean People Fail,” in which—ignoring such possible counterexamples as Jeff Bezos and Larry Ellison—he declared that “being mean makes you stupid” and discourages good people from working for you. Thus, in startups, “people with a desire to improve the world have a natural advantage.” Win-win. [Page 73]

Altman is not devoid of social conscience, well not quite. “If you believe that all human lives are equally valuable, and you also believe that 99.5 per cent of lives will take place in the future, we should spend all our time thinking about the future.” [He looks at] the consequences of innovation as a systems question. The immediate challenge is that computers could put most of us out of work. Altman’s fix is YC Research’s Basic Income project, a five-year study, scheduled to begin in 2017, of an old idea that’s suddenly in vogue: giving everyone enough money to live on. … YC will give as many as a thousand people in Oakland an annual sum, probably between twelve thousand and twenty-four thousand dollars. [Page 81] But the conclusion of the article is perhaps the most important sentence of the whole article, which brings us back to Obama’s moderation. Comparing himself to another wildly ambitious project creator, Altman says, “At the end of his life, he did also say that it should all be sunk to the bottom of the ocean. There’s something worth thinking about in there.”

Ultimately, Obama, Altman, Marx, Piketty and Stiegler all have the same faith in the future and progress and the same concern about the growing inequalities. Altman seems to be the only one (together with many people in Silicon Valley) to believe that disruptions and revolutions will solve everything, while the others see their destructive features and prefer a moderate and progressive evolution. Over the years, I tend to prefer moderation too…

PS: if you would not have enough reading, then continue with the series of interviews President Obama gave to Wired: Now Is the Greatest Time to Be Alive.

Challenges and Opportunities of Industry 4.0

I must say that last week I did not understand very well the “Industry 4.0” concept. And after a brief stay in Munich this week – where I had an explanation by E&Y – see below – but especially after reading the text of a speech entitled “Smart Industry 4.0 in Switzerland” (see pdf) given by Matthias Kaiserswerth, the “Business and Innovation Forum Slovakia – Switzerland” in Bratislava on June 20, I fully understood the importance of the subject. I also found out this morning two excellent reports: “Industry 4.0 – The role of Switzerland Within a European manufacturing revolution” (see pdf) by the firm Roland Berger and the “Digital Vortex – How Digital Disruption Is Redefining Industries’ (see pdf) published by Cisco and IMD. I got permission from Matthias Kaiserswerth to publish his speech here (I thank him for this) and this speech is an excellent introduction to the subject with many interesting ideas to solve the challenges ahead…

Smart Industry 4.0 in Switzerland

Matthias Kaiserswerth, Business and Innovation Forum Slovakia – Switzerland, 20.06.2016, Bratislava

In this brief input speech, I want to talk about some of the challenges and opportunities that the on-going digitalization has for the Swiss economy, our labor force and the education system.

Current State and Challenges

Unfortunately, Switzerland is not yet a leader in digitalization. When we compare ourselves with other OECD countries, we play at best in the middle field. From a policy point of view, we are behind the European Union. This month, June 7, our Ständerat, the smaller parliamentary chamber representing the cantons, has asked our government to analyze what economic effect the emerging EU single digital market will have on our country. Our current president, the minister for economy, education, and research in his response admitted that until the beginning of this year Switzerland had underestimated the 4th industrial revolution and now is trying to catch up with various measures[1].

ICTSwitzerland, the association of the Swiss ICT industry, earlier this year launched a scorecard [2] digital.swiss in which they rate Switzerland’s state of digitalization in 15 dimensions. While we have excellent basic infrastructure and rank highly on a generic international competitive index, we don’t yet sufficiently leverage digital technologies in the various sectors of our economy.

Scorecard
SI4.0-SwissScorecard

The scorecard reflects a classic Swiss paradox. Because of our very direct democratic system, built on subsidiarity, we provide good infrastructure and general economic framework. When it comes to leveraging these foundations, we leave it to private initiative as we don’t pursue an active industrial policy – certainly not at the federal level. So far our companies – mostly SMBs, 99.96% of our companies have less than 250 employees – have excelled at incremental innovation. Incremental innovation can be good for a long time, but it impedes dealing with major technology shifts that can disrupt an entire industry.

This happened in the 70s and early 80s when the “Quartz Revolution” almost extinguished Swiss watchmaking. Now again history may repeat itself as the watch industry missed or were too slow to embrace the trend towards smartwatches. Apple within the course of only one year managed to surpass with their watch-related revenues all Swiss watch companies even Rolex [3].

With the digital revolution, driven out of Silicon Valley, we compete with an entirely different innovation model, namely disruptive innovation.

Just look at examples from the sharing economy such as Airbnb and Uber.

But it doesn’t stop there. Consider computer companies now building the future self-driving electric car – Google being a prime example. While European OEMs had experimented for a long time with self-driving cars putting all the intelligence into the car, Google took an entirely different approach. Because of their maps, their work with Streetview, they already have very precise information about where the car is going and thus can leverage the power of connectivity and the cloud as well.

While we would strive to build the perfect battery for an electric car, Tesla took what we would consider inferior laptop batteries and leveraged IT to make them useful in their cars.

Opportunities

With the long Swiss tradition of bringing foreign talents into the country and allowing them to succeed, we have an outstanding opportunity not to miss out on the current industrial revolution. Many of our successful international companies got started by foreigners – just think of Nestle, ABB, or Swatch.

Businesses have now realized that meeting the pressures of the strong Swiss Franc with only cutting costs is insufficient. They are looking for different forms of innovation leveraging IT. About a year ago, various Swiss industry associations launched an initiative “Industry 2025” to change the mindset in our machine industry and alert them to the new opportunities [4].

Some companies though have seen these chances already long before our national bank stopped pegging the Swiss Franc to the Euro.

For example in 2012, Belimo a company producing actuator solutions to control heating, ventilation and air conditioning systems launched their “Energy Valve”. It consists of a 2-way characterized control valve, volumetric flow meter, temperature sensors and an actuator with integrated logic, that combines the five functions of measuring, controlling, balancing, shutting and monitoring energy into a single unit with its own web server as IT interface. The intelligent valve can be used to optimize water flow in heating and cooling systems and yields significant energy savings for its customers [5].

Other companies in the Swiss machine industry have started to think about how they can leverage Internet of Things (IoT) to create new businesses based on the data that their machines generate. A good example is LCA Automation, a company in the business of building factory automation solutions. They want to offer predictive maintenance based on dynamic condition monitoring of their installed factories. Leveraging existing information like current and position from frequency converters in their drives help understand how the machines are used. In select cases they install additional sensors to measure vibration, acoustic noise to allow their clients to schedule maintenance instead of running their installations to failure [6].

In my opinion, the challenges in addressing more of these opportunities are (1) cultural, (2) an IT skills gap, (3) finding and realizing new business models that best exploit the digital opportunity and finally (4) creating an environment where collaboration with external partners can let you innovate with speed.

Contrary to software, industrial products cannot be easily updated in the field, they are engineered to last 10 to 20 years. The mindset of the computer scientist: “we can fix it remotely with an update,” requires the mechanical and electrical engineers to rethink how they construct their systems. When Tesla had issues in 2013 with one of their cars catching fire because its suspension at high speeds lowered the car too close to the road, they did not issue a massive recall but instead remotely overnight changed the software in the cars to guarantee a higher distance between car and road.

Getting these diverse cultures to collaborate requires respect among the different professional disciplines and would call for the occasional computer scientist to serve on the board of industrial companies to challenge their established way of thinking.

The skills gap, finding enough software engineers interested to work in industrial companies is significant. Current predictions are that by 2022 Switzerland will lack 30’000 IT experts. Considering that industrial companies compete with the better paying finance industry for the same talents, means that industrial companies need to become very creative to address this shortage.

Implementing new business models that exploit the digital opportunities is a significant challenge for established industrial companies. If a company whose core business is selling industrial machines, wants to start offering value added subscription based services to optimize the industrial process realized by their products, they get into an entirely new business. They will need to decide whether these services are only available for a process realized by only their machines or whether they want to offer it also on competitors’ installations. They need to devise a new sales incentive scheme based on a recurring revenue stream. They need to build a support infrastructure that matches the optimized process and no longer consists of experts that only know about their own machine. In short, they need to build an entirely new business. Doing so inside an established large company is extremely hard maybe even more so than doing it in an external startup.

Finally, creating a collaborative environment with external partners to innovate with speed is not something unique to the age of digitalization, however it will be key for industrial companies to capture the opportunity. In spite of the good examples from large industrial companies like Procter and Gamble around Open Innovation, a concept coined 13 years ago, many firms still have a strong sentiment of doing everything themselves or with their established supply chain partners. In the case of digitalization, however, new partners from outside the traditional industry need to be involved and made part of the solution. “Rather than using their own R&D budget, enterprises can leverage venture capitalist investments and integrate a technology solution in an accelerated timeframe” [7].

Education

Before I close, let me get back to education, a topic of particular importance in this new era. Switzerland has an excellent education system. However, we have a significant shortage of students that pursue a career in the Science Technology Engineering and Mathematics field (in short STEM) in addition to a skills gap in STEM for all the other students.

In 2014, the German speaking cantons launched a new common competence oriented curriculum “Lehrplan 21” (LP21) to address the skills gap by putting more focus on STEM subjects. For example, by introducing a new subject called Media and Informatics, the cantonal education ministers have accepted the notion that all students need basic skills in computer science to succeed in the professional or academic education system. As we speak, this LP21 is being implemented in the German speaking part of Switzerland, albeit not fast enough for my taste.

To succeed with LP21 we also need to qualify the teachers to competently teach these subjects in a way that keeps all students motivated. Specifically female students have a significantly lower self-perception in how they master technology and what they can use technology for [8]. The consequence is that we lose the female talent also in our workforce. So for example, in IT there are only 13% women in the Swiss workforce.

Promoting women in technology as role models and broadening specific programs to get girls interested in technology at a primary school age will hopefully help to bridge the gender gap in the long run.

Summary

When we look at the system of the Federal Polytechnic Schools (ETH Zurich and EPF Lausanne), the universities and specifically also the universities of applied science, government funding for research then we have an outstanding foundation upon which we can build to effectively compete in this 4th Industrial Revolution. It now requires a new mind set for our industrial companies to embrace the emerging IoT, Big Data, and artificial intelligence trends and the courage to experiment with the new business models that they enable.

You don’t get disrupted because you don’t see the technological shift and opportunity, you get disrupted because you chose to ignore it.


1: http://www.inside-it.ch/articles/44100
2: http://digital.ictswitzerland.ch/en/
3: http://www.wsj.com/articles/apple-watch-with-sizable-sales-cant-shake-its-critics-1461524901
4: http://www.industrie2025.ch/industrie-2025/charta.html
5: http://energyvalve.com
6: http://www.industrie2025.ch/fileadmin/user_upload/casestudies/industrie2025_fallbeispiel_lca_automation_2.pdf
7: https://www.accenture.com/ch-en/insight-enterprise-disruption-open-innovation
8: http://www.satw.ch/mint-nachwuchsbarometer/MINT-Nachwuchsbarometer_Schweiz_DE.pdf

Postscript: I mentioned above the presentation by E&Y, here is the slide that struck me…

The Rise and Fall of BlackBerry

Very interesting article in the very good ParisTech Review: The Rise and Fall of BlackBerry. The article shows how disruption is more and more threatening not only for established companies but also fast growing start-ups.

Blackberry was founded in 1984 as Research in Motion by two young engineering students from the University of Waterloo – Mike Lazaridis – and the University of Windsor – Douglas Fregin. They were about 23 years-old. Eight years later, an experienced business man, James Balsillie, would join, invest some of his money ($250k) and become Co-CEO with Lazaridis. RIM funded a lot of its initial activity with partners (Ontario New Ventures – $15k; General Motors – $600k, Ericsson, – $300k, University of Waterloo – $100k, Ontario local development – $300k) so that it raised investor money in 1995 only, including Intel in 1997. The company went public on the Toronto Stock Exchange in October 1997 and then on Nasdaq in 1999.

ParisTech-Blackberry-en

As the authors notice, “though BlackBerry has less than 1% of the smartphone market share today, it once had more than 50%. […] In this era of disruption, the mother of disruption stories is the BlackBerry story. A company that introduced the BlackBerry in 1998 became a $20 billion company from nothing in less than a decade. Then four or five years later, it was back down to a $3 billion company, gasping for breath. It’s not only a disruption story; it is a story of the speed of the technology race today.”

They explain how Lazaridis was a visionary when mobile phones had to be simple devices and how he failed a few years later: “The pivotal moment is January 2007 when Steve Jobs walks onto the stage in San Francisco and holds up that shiny glass object that we all [now] know and love so much, and says, “This is an iPhone.” […] The really compelling part of the BlackBerry story is how they reacted that day. Over in Mountain View, California, you had the folks at Google under a secret project. One was for a new keyboard phone and the other was for a touch screen phone that was going to be run on Android. The minute they watched that live, streaming on the internet, they realized that their project keyboard was dead, and they immediately shifted everything to the touch screen phone…. Mike Lazaridis looked at this announcement, looked at what Steve Jobs was offering, and said, “This is an impossibility.” Again, the conservative engineer brought up on conservation said, “The networks won’t be able to carry this. It’s an impossibility. It’s illogical that anyone would even propose this.” He was right for the first two years. Remember all the dropped calls, all the frustrations, all the lawsuits against Apple and the carriers. It didn’t work…. But then it did, and RIM got it wrong. Two years is a lifetime at a technology rate, and by the time they realized what a serious threat it was, they were at that point followers.”

Blackberry was (still is) the success story of the University of Waterloo and Wikipedia mentions how much Lazaridis has given back to his alma mater: in 2000, Lazaridis founded the Perimeter Institute for Theoretical Physics. He has donated more than $170 million to the institute. In 2002, Lazaridis founded the Institute for Quantum Computing (IQC) at the University of Waterloo. He, with wife Ophelia, has donated more than $100 million to IQC since 2002. This looks very similar to what Logitech and Daniel Borel are to EPFL (where I work). You should read the full article and I conclude here with my usual cap. table…

Blackberry CapTable

The Complexity and Beauty of Innovation according to Walter Isaacson

The Innovators by Walter Isaacson is a great book because of its balanced description of the role of geniuses or disruptive innovators as much as of teamwork in incremental innovation. “The tale of their teamwork is important because we don’t often focus on how central their skill is to innovation. […] But we have far fewer tales of collaborative creativity, which is actually more important in understanding how today’s technology evolution was fashioned.” [Page 1] He also goes deeper: “I also explore the social and cultural forces that provide the atmosphere for innovation. For the birth of the digital age, this included a research ecosystem that was nurtured by the government spending and managed by a military-industrial collaboration. Intersecting with that was a loose alliance of community organizers, communal-minded hippies, do-it yourself hobbyists, and homebrew hackers, most of whom were suspicious of centralized authority.” [Page 2] ”Finally, I was struck by how the truest creativity of the digital age came from those who were able to connect the arts and sciences.” [Page 5]

the-innovators-9781476708690_lg

The computer

I was a little more cautious with chapter 2 as I have the feeling that the story of Ada Lovelace and Charles Babbage is well known. I may be wrong. But chapter 3 about the early days of the computer was mostly unknown to me. Who invented the computer? Probably many different people in different locations in the US, the UK and Germany, around WWII. “How did they develop this idea at the same time when war kept their two teams isolated? The answer is partly that advances in technology and theory made the moment ripe. Along with many innovators, Zuse and Stibitz were familiar with the use of relays in phone circuits, and it made sense to tie that to binary operations of math and logic. Likewise, Shannon, who was also very familiar with phone circuits, would be able to perform the logical tasks of Boolean algebra. The idea that digital circuits would be the key to computing was quickly becoming clear to researchers almost everywhere, even in isolated places like central Iowa.” [Page 54]

There would be a patent fight I did not know about. Read pages 82-84. You can also read the following on Wikipedia: “On June 26, 1947, J. Presper Eckert and John Mauchly were the first to file for patent on a digital computing device (ENIAC), much to the surprise of Atanasoff. The ABC [Atanasoff–Berry Computer] had been examined by John Mauchly in June 1941, and Isaac Auerbach, a former student of Mauchly’s, alleged that it influenced his later work on ENIAC, although Mauchly denied this. The ENIAC patent did not issue until 1964, and by 1967 Honeywell sued Sperry Rand in an attempt to break the ENIAC patents, arguing the ABC constituted prior art. The United States District Court for the District of Minnesota released its judgement on October 19, 1973, finding in Honeywell v. Sperry Rand that the ENIAC patent was a derivative of John Atanasoff’s invention.” [The trial had begun in June 1971 and the ENIAC patent was therefore made invalid]

I also liked his short comment about complementary skills. “Eckert and Mauchly served as counterbalances for each other, which made them typical of so many digital-age leadership duos. Eckert drove people with a passion for precision; Mauchly tended to calm them and make them feel loved.” [Pages 74-75]

Women in Technology and Science

It is in chapter 4 about Programming that Isaacson addresses the role of women. “[Grace Hopper] education wasn’t as unusual as you might think. She was the eleventh woman to get a math doctorate from Yale, the first being in 1895. It was not at all uncommon for a woman, especially from a successful family, to get a doctorate in math in the 1930s. In fact, it was more common than it would be a generation later. The number of American women who got doctorates in math during the 1930s was 133, which was 15 percent of the total number of American math doctorates. During the decade of the 1950s, only 106 American women got math doctorates, which was a mere 4 percent of the total. (By the first decade of the 2000 things had more than rebounded and there were 1,600 women who got math doctorates, 30 percent of the total.)” [Page 88]

Not surprisingly, in the early days of computer development, men worked more in hardware whereas women would be in software. “All the engineers who built ENIAC’s hardware were men. Less heralded by history was a group of women, six in particular, who turned out to be almost as important in the development of modern computing.” [Page 95] “Shortly before she died in 2011, Jean Jennings Bartik reflected proudly on the fact that all the programmers who created the first general-purpose computer were women. « Despite our coming of age in an era when women’s career opportunities were generally quite confined, we helped initiate the era of the computer. » It happened because a lot of women back then had studied math and their skills were in demand. There was also an irony involved: the boys with their toys thought that assembling the hardware was the most important task, and thus a man’s job. « American science and engineering was even more sexist than it is today, » Jennings said. « If the ENIAC’s administration had known how crucial programming would be to the functioning of the electronic computer and how complex it would prove to be, they might have been more hesitant to give such an important role to women.” [Pages 99-100]

The sources of innovation

“Hopper’s historical sections focused on personalities. In doing so, her book emphasized the role of individuals. In contrast, shortly after Hopper’s book was completed, the executives at IBM commissioned their own history of the Mark I that gave primary credit to the IBM teams in Endicott, New York, who had constructed the machine. “IBM interests were best served by replacing individual history with organizational history,” the historian Kurt Beyer wrote in a study of Hopper. “The locus of technological innovation, according to IBM was the corporation. The myth of the lone radical inventor working in the laboratory or basement was replaced by the reality of teams of faceless organizational engineers contributing incremental advancements.” In the IBM version of history, the Mark I contained a long list of small innovations, such as the ratchet-type counter and the double-checked card feed, that IBM’s book attributed to a bevy of little-known engineers who worked collaboratively in Endicott.
The difference between Hopper’s version of history and IBM’s ran deeper than a dispute over who should get the most credit. It showed fundamentally contrasting outlooks on the history of innovations. Some studies of technology and science emphasize, as Hopper did, the role of creative inventors who make innovative leaps. Other studies emphasize the role of teams and institutions, such as the collaborative work done at Bell Labs and IBM’s Endicott facility. This latter approach tries to show that what may seem like creative leaps – the Eureka moment – are actually the result of an evolutionary process that occurs when ideas, concepts, technologies, and engineering methods ripen together. Neither way of looking at technological advancement is, on its oqn, completely satisfying. Most of the great innovations of the digital age sprang from an interplay of creative individuals (Mauchly, Turing, von Neumann, Aiken) with teams that knew how to implement their ideas.”
[Pages 91-92]

Google about Disruptive and Incremental Innovation

This is very similar to what I read about Google and posted recently in The Importance and Difficulty of Culture in Start-ups: Google again…: “To us, innovation entails both the production and implementation of novel and useful ideas. Since “novel” is often just a fancy synonym for “new”, we should also clarify that for something to be innovative, it needs to offer new functionality, but it also has to be surprising. If your customers are asking for it, you aren’t being innovative when you give them what they want; you are just being responsive. That’s a good thing, but it’s not innovative. Finally “useful” is a rather underwhelming adjective to describe that innovation hottie, so let’s add an adverb and make it radically useful, Voilà: For something to be innovative, it needs to be new, surprising, and radically useful.” […] “But Google also releases over five hundred improvements to its search every year. Is that innovative? Or incremental? They are new and surprising, for sure, but while each one of them, by itself is useful, it may be a stretch to call it radically useful. Put them all together, though, and they are. […] This more inclusive definition – innovation isn’t just about the really new, really big things – matters because it affords everyone the opportunity to innovate, rather than keeping it to the exclusive realm of these few people in that off-campus building [Google[x]] whose job is to innovate.” [How Google Works – Page 206]

Maybe more about The Innovators soon…

Was Christensen wrong and is Disruptive Innovation a shaky theory?

Clayton Christensen has been one of my heroes. Will I have to kill this father figure? The often excellent New Yorker magazine published recently The Disruption Machine with subtitle What the gospel of innovation gets wrong. Author Jill Lepore knows a lot about the Innovation gurus from Schumpeter to Porter and Christensen and what she has to say is at least very disturbing.

NY-Disrupt
“Disruption is a theory of change
founded on panic, anxiety,
and shaky evidence.”

You have to read the article: Lepore seems to have strong arguments about the weaknesses of Christensen’s. In the Disk Drive industry, she claims, Seagate Technology was not felled by disruption. Same with Bucyrus and Caterpillar for the mechanical-excavator industry or “Today, the largest U.S. producer of steel is — U.S. Steel”. Difficult for me to assess the claims. I have to admit I had read more recent books of Christensen which were really disappointing but I thought his first breakthrough remained strong.

Funnier: “The theory of disruption is meant to be predictive. On March 10, 2000, Christensen launched a $3.8-million Disruptive Growth Fund. Less than a year later, the fund was quietly liquidated. In 2007, Christensen told Business Week that “the prediction of the theory would be that Apple won’t succeed with the iPhone,” adding, “History speaks pretty loudly on that.” In its first five years, the iPhone generated a hundred and fifty billion dollars of revenue.”

There has been a debate following Lepore’s claims which I will let you discover:

– Business Week: Clayton Christensen Responds to New Yorker Takedown of ‘Disruptive Innovation’: here.

– Forbes: What Jill Lepore Gets Wrong About Clayton Christensen and Disruptive Innovation: here.

– Slate: Even the Father of Disruption Thinks “Disruption” Has Become a Cliche: here.

PS: thanks to Martin for pointing that amazing article to me!

A few lessons from disruptive innovators

My friend Jean-Jacques (thanks :-)) sent me a link about the CNBC Disruptor 50, a list of 50 “private companies in 27 industries — from aerospace to enterprise software to retail — whose innovations are revolutionizing the business landscape”. One could criticize the method, the fields, what is disruptive and what is not, but the list is by itself interesting. And I have done a few quick and dirty analyses. (I mean by Q&D a very fast analysis on the age of founders based on available data – their age or the year of their bachelor – my full analysis is available at the end of the post)

cnbc-disruptors

I found the following:
– Disruptive innovators are young (33 years-old)
– They raise a lot of money: more than $200M!!!
– and yes, they are mostly based in Silicon Valley.

Disruptor50-stats

Disruptive innovators are young

The average age of founder is 33 (whereas the age of founders of start-ups is closer to 39 – see my recent post Age and Experience of High-tech Entrepreneurs). As it was the case with that general analysis, founders in biotech and energy are much older than in software or internet. This was something I had already addressed in that paper: disruption might be the field of young creators.

They raise a lot of money

A really striking point is the amount of money raised by these disruptive companies. With an average age of 6 years, these companies have raised on average $200M… In energy, it is more than $400M and even more than $250M for the internet.

Silicon Valley leads

Not surprisingly though, Silicon Valley seems to be the place where to be. 27 companies are based there (a little more than 50%). It is also where they have access to the most capital ($280M on average). Then comes the East Coast (25%). Surprisingly they are based in NYC, not in Boston anymore when East Coast is concerned. Only 3 are Europeans… (Spotify, Transferwise and Fon) even if a few Europeans have also moved to SV…

Here is my full analysis which as I said before might contain mistakes (particularly on the founders’ age…). You might also disagree with my field classification…

Disruptor50
click on picture to enlarge

When Peter Thiel talks about Start-ups – Final Thoughts: Human After All

As you noticed if you read my previous posts, I’ve been quite impressed by Peter Thiel’s notes about start-ups. I’ve written 7 long parts. I had been similarly impressed by Mariana Mazzucato’s The Entrepreneurial State even if with only 5 posts!

Thiel-Mazzucato

I said it already, I would have loved to attend their debate in a few days at the conference Human After All, Toronto 2014. But apparently they do not participate to the same roundtable anymore… (After reading what follows, I see that Taleb would have been a great addition).

– He will discuss “The Economics of Radical Uncertainty.”
How do human beings truly react when confronted with conditions of genuine “unknown unknowns”? According to Frank Knight, “Uncertainty must be taken in a sense radically distinct from the familiar notion of risk, from which it has never been properly separated…The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far – reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating… It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all.” The economics literature from Knight onward is very good at laying out the propensity of markets to greatly overshoot and undershoot the fundamentals. However, economics does not adequately address the implications of “Knightean” uncertainty, because the discipline finds it hard to model this phenomenon. To get a full measure of this, one has to enter into the realm of psychology and neuroscience. That’s where the definition lies. Radical uncertainty, like so much else, is too important to be left to the realm of economics alone.

– She will be part of “Innovation: Do Private Returns Produce the Social Returns We Need?”
The machines of the first age replaced and multiplied the physical labour of humans and animals. The machines of the second age will replace and multiply our intelligence. The driving force behind this revolution will, argue the “techno-positivists,” exponentially increase the power (or exponentially reduce the cost) of computing. The celebrated example is Moore’s Law, named after Gordon Moore, a founder of Intel. For half a century, the number of transistors on a semiconductor chip has doubled at least every two years. But the information age has coincided with – and must, to some extent, have caused – adverse economic trends: stagnation of median real incomes; rising inequality of labour income and of the distribution of income between labour and capital; and growing long – term unemployment. Are the great gains in
wealth and material prosperity created by our entrepreneurs in and of themselves sufficient to produce desired social returns demanded in today’s world?

Start-ups are a great area to study the tension between individuals and society. A kind of chicken and egg situation… Indeed they might explain the growing gap between the USA and Europe in many dimensions. Mazzucato would be on the social side, Thiel closer to the individual. But do not see any provocative statement here. The thoughts of Thiel and Mazzucato are profound. I agree with most of what they say, disagree with smaller pieces, though most people could think their thinking can not be reconciled. I really think that combining their point of views is an interesting approach to what innovtaion really is…

PS (May 8, 2014): I just found that video of Thiel at SXSW.

When Peter Thiel talks about Start-ups – part 7: luck & uncertainty

This is my last post about Thiel’s class notes at Stanford and it is about Class 13 – Luck. Now I need to wait for his book to be published…

zerotoone

I love accidents. I mentioned it in a post which has nothing to do with start-ups (related to Street Art). The accident here is funny: I totally forgot to copy-paste Thiel’s class 13 and it is only when I began to read class 14 that I noticed my mistake. Now let me quote Thiel: “Note that this is class 13. We are not going to be like the people who build buildings without a 13th floor and superstitiously jump from class 12 to 14. Luck isn’t something to circumvent or be afraid of. So we have class 13. We’ll dominate luck.” Strange, right? I had to call this final part, part 7…

So what does Thiel say about luck? Well it is a debated topic, as I experienced in my activity at EPFL. Thiel feels the same. He begins with: “The biggest philosophical question underlying startups is how much luck is involved when they succeed. As important as the luck vs. skill question is, however, it’s very hard to get a good handle on. Statistical tools are meaningless if you have a sample size of one. It would be great if you could run experiments. Start Facebook 1,000 times under identical conditions. If it works 1,000 out of 1,000 times, you’d conclude it was skill. If it worked just 1 time, you’d conclude it was just luck. But obviously these experiments are impossible.” adding the famous Thomas Jefferson’s line: “I’m a great believer in luck, and I find the harder I work the more I have of it.”

Thiel is not so much interested in luck as in determinism vs. indeterminism. “If you believe that the future is fundamentally indeterminate, you would stress diversification. […]. If the future is determinate, it makes much more sense to have firm convictions. […] Overlay this diversification/conviction dynamic over the optimism/pessimism question and you get further refinement. Whether you look forward to the future or are afraid of it ends up making a big difference. And here is his vision of the world:

Thiel-World1

With an even more surprising and quite convincing:

Thiel-World2

“But the indeterminate future is somehow one in which probability and statistics are the dominant modality for making sense of the world. Bell curves and random walks define what the future is going to look like. The standard pedagogical argument is that high schools should get rid of calculus and replace it with statistics, which is really important and actually useful. There has been a powerful shift toward the idea that statistical ways of thinking are going to drive the future.”

But here I’d love to ask Peter Thiel what he makes of Black Swans if he believes in 0 to 1 more than in 1 to n. 1 to n belongs to statistics, not 0 to 1… (read again my part 1 if this is cryptic)

Thiel-World-SFBA

Thiel likes crazy ideas, like Reber’s project for the Bay Area in the 1940s above. He also still believes in finance despite its excesses: “In a future of definite optimism, you get underwater cities and cities in space. In a world of indefinite optimism, you get finance. The contrast couldn’t be starker. The big idea in finance is that the stock market is fundamentally random. It’s all Brownian motion. All you can know is that you can’t know anything. It’s all a matter of diversification. There are clever ways to combine various investments to get higher returns and lower risk, but you can only push out the efficient frontier a bit. You can’t know anything substantive about any specific business. But it’s still optimistic; finance doesn’t work if you’re pessimistic. You have to assume you’re going to make money. […] Indeterminacy has reoriented people’s ideas about investing. Whereas before investors actually had ideas, today they focus on managing risk. Venture capital has fallen victim to this too. Instead of being about well-formed ideas about future, the big question today is how can you get access to good deals. In theory at least, VC should have very little in common with such a statistical approach to the future.“

And he might agree with Mariana Mazzucato during his debate to come with her (at Human After All, Toronto 2014 – program in pdf) : “The size of government hasn’t changed all that much in the last 40-50 years. But what the government actually does has changed radically. In the past, the government would get behind specific ideas and execute them. Think the space program. Today, the government doesn’t do as many specific things. Mainly it just shifts money around from some people to other people. What do you do about poverty? Well, we don’t know. So let’s just give people money, hope it helps, and let them figure it out.”

Darwinism and design.

And all of a sudden, while reading this class 13, Thiel again surprises me! Obviously, the indeterminate optimism can be quite easily linked to Darwin’s theory of evolution. Accidents happen, but there is general positive evolution. And “Applied to start-ups, obsession with indeterminacy leads to the following phenomena:
• Darwinistic A/B testing
• Iterative processes
• Machine learning
• No thinking about the future
• Short time horizons”

Typical Blank’s messages! But Thiel envisages another possibility: “Apple is absolute antithesis of finance. It does deliberate design on every level. There is the obvious product design piece. The corporate strategy is well defined. There are definite, multi-year plans. Things are methodically rolled out.” (I do not think Thiel talks here about intelligent design which is opposed to Darwinian theory, but the coincidence is a little puzzling!

“On the heels of Apple has come the theme of well-designed products being really important. Airbnb, Pinterest, Dropbox, and Path all have a very anti-statistical feel. […] That link—great design—seems to work better and faster than Darwinistic A/B testing or iteratively searching through an incredibly large search space. The return of design is a large part of the countercurrent going against the dominating ethos of indeterminacy. Related to this is the observation that companies with really good plans typically do not sell. If your startup gets traction, people make offers to buy it. In an indefinite world, you will take the money and sell, because money is what you want. […] But when companies have definite plans, those plans tend to anchor decisions not to sell.[..] In an indefinite world, investors will value secret plans at zero. But in a determinate world, robustness of the secret plan is one of the most important metrics […] It’s important to note that you can always form a definite plan even in the most indefinite of worlds. […] We’re falling downwards towards pessimism. Can we shift instead to definite optimism?”

This is the end of my notes on Thiel’s great vision about start-ups.

When Peter Thiel talks about Start-ups – part 6: founder uniqueness, technology singularity

Thiel’s concludes his Class Notes Essays (CS183 —Stanford, Spring 2012) with philosophical considerations about the uniqueness of founders (class 18) and the singularity of technology (class 19). Founders are a topic I regularly covered, for example with European Founders at Work or Founders at Work.

Again Thiel presents unusual ideas about founders. He sees them as a combination of extreme outsiders and extreme insiders.
Thiel-extreme-in-out
which he reinforces with this virtuous/vicious circle:
Thiel-extreme-in-out-circle

If this is not clear, two examples may help:
– “All [these] questions apply to Gates. Was it nature or nurture? He was a Harvard insider but a dropout outsider. He wore big glasses. Did he become a nerd unwillingly? Did he prosper by accentuating his nerdiness? It’s hard to tell.”
– “And then there’s the Steve Jobs version. […] He had all the classic extreme outsider and extreme insider traits. He dropped out of college. He was eccentric and had all these crazy diets. He started out phreaking phones with Steve Wozniak. He took LSD.”

Thiel is convincing when he explains that a start-up is not a democracy. Founders are Kings, and Thiel may have followed René Girard at Stanford since he then develops a theory of scapegoats. The god may become a victim.
Thiel-monarchy

Thiel is a little short about the dual-founder situation: “The dual founder thing is worth mentioning. Co-founders seem to get in a lot less trouble than more unbalanced single founders. Think Hewlett and Packard, Moore and Noyce, and Page and Brin. There are all sorts of theoretical benefits to having multiple founders such as more brainstorming power, collaboration, etc. But the really decisive difference between one founder and more is that with multiple founders, it’s much harder to isolate a scapegoat. Is it Larry Page? Or is it Sergey Brin? It is very hard for a mob-like board to unite against multiple people—and remember, the scapegoat must be singular. The more singular and isolated the founder, the more dangerous the scapegoating phenomenon. For the skeptic who is inclined to spot fiction masquerading as truth, this raises some interesting questions. Are Page and Brin, for instance, really as equal as advertised? Or was it a strategy for safety? We’ll leave those questions unanswered and hardly asked.”

Thiel-dual-founders

Thiel’s vision (as well as the visions of his guests – I mixed them here) of technology was mentioned in my previous post. Again quite fascinating. “People do tend have some view of the future. They usually project relative stagnation. People tend to believe that, not only will most things not change, but what will change won’t change very quickly.” But “there’s a compelling case that we’ll very likely see extraordinary or accelerated progress in the decades ahead.”

One guest: “My take is that innovation comes from two places: top-down and bottom-up. There’s a huge DIY community. These hobbyists are working in labs they set up in their kitchens and basements. On the other end of the spectrum you have DARPA spending tons of money. Scientists are talking to each other from different countries, collaborating. All this interconnectedness matters. All these interactions in the aggregate will bring the change.

Another guest: “I disagree. There are a very few visionary people who can make a real difference at the formative early stage. This is why mainstream opinion formers are absolutely pivotal. Perhaps no other subset of people could do more to further radical technology. By overpowering public reluctance and influencing the discourse, these people can enable everyone else to build the technology. If we change public thinking, the big benefactors can drive the gears.”

The third guest: “I do not think that progress will come from the top-down or from the bottom-up, really. Individual benefactors who focus on one thing, like Paul Allen, are certainly doing good. But they’re not really pushing on future; they’re more pushing on individual thread in homes that it will make the future come faster. The sense is that these people are not really coordinating with each other. Historically, the big top-down approaches haven’t worked. And the bottom-up approach doesn’t usually work either. It’s the middle that makes change—tribes like the Quakers, the Founding Fathers, or the Royal Society. These effective groups were dozens or small hundreds in size. It’s almost never lone geniuses working solo. And it’s almost never defense departments or big institutions. You need dependency and trust. Those traits cannot exist in one person or amongst thousands.”

Peter Thiel: “That’s three different opinions on who makes the future: a top-down bottom-up combo, social opinion molders, and tribes.”

To be honest, I was more convinced with his analysis of founders than of technologies. His conclusion is worth reading as inspiration: “This course has largely been about going from 0 to 1. We’ve talked a lot about how to create new technology, and how radically better technology may build toward singularity. But we can apply the 0 to 1 framework more broadly than that. There is something importantly singular about each new thing in the world. There is a mini singularity whenever you start a company or make a key life decision. In a very real sense, the life of every person is a singularity. The obvious question is what you should do with your singularity. The obvious answer, unfortunately, has been to follow the well-trodden path. You are constantly encouraged to play it safe and be conventional. The future, we are told, is just probabilities and statistics. You are a statistic. But the obvious answer is wrong. That is selling yourself short. Statistical processes, the law of large numbers, and globalization—these things are timeless, probabilistic, and maybe random. But, like technology, your life is a story of one-time events. By their nature, singular events are hard to teach or generalize about. But the big secret is that there are many secrets left to uncover. There are still many large white spaces on the map of human knowledge. You can go discover them. So do it. Get out there and fill in the blank spaces. Every single moment is a possibility to go to these new places and explore them. There is perhaps no specific time that is necessarily right to start your company or start your life. But some times and some moments seem more auspicious than others. Now is such a moment. If we don’t take charge and usher in the future—if you don’t take charge of your life—there is the sense that no one else will. So go find a frontier and go for it. Choose to do something important and different. Don’t be deterred by notions of luck, impossibility, or futility. Use your power to shape your own life and go and do new things.”

Reading these last lines, I remembered the conclusion of my book: “And I suddenly remembered an essay by Wilhelm Reich, the great psychoanalyst, which he wrote in 1945: “Listen, Little Man”. A small essay by the number of pages, a big one in the impact it creates. “I want to tell you something, Little Man; you lost the meaning of what is best inside yourself. You strangled it. You kill it wherever you find it inside others, inside your children, inside your wife, inside your husband, inside your father and inside your mother. You are little and you want to remain little.” The Little Man, it’s you, it’s me. The Little Man is afraid, he only dreams of normality; it is inside all of us. We hide under the umbrella of authority and do not see our freedom anymore. Nothing comes without effort, without risk, without failure sometimes. “You look for happiness, but you prefer security, even at the cost of your spinal cord, even at the cost of your life”.

When Peter Thiel talks about Start-ups – part 5: a vision of the future of technology

I am still not sure how Thiel’s class notes on start-ups will finish, but they are more and more fascinating, class after class. At least his vision of this world is.

Class 14 is about cleantech and energy. “Alternative energy and cleantech have attracted an enormous amount of investment capital and attention over the last decade. Almost nothing has worked as well as people expected. The cleantech experience can thus be quite instructive. […] To think about the future of energy, we can use the [another] matrix. The quadrants shake out like this:
Determinate, optimistic: one specific type of energy is best, and needs to be developed
Determinate, pessimistic: no technology or energy source is considerably better. You have what you have. So ration and conserve it.
Indeterminate, optimistic: there are better and cheaper energy sources. We just don’t know what they are. So do a whole portfolio of things.
Indeterminate, pessimistic: we don’t know what the right energy sources are, but they’re likely going to be worse and expensive. Take a portfolio approach.”

Both for energy and transportation, Thiel’s fills his quadrants with interesting examples:
Thiel-World3

and he adds: “Petroleum has dominated transportation. Coal has dominated in power generation. […] Typically a single source dominates at any given time. There is a logical reason for this. It doesn’t make sense that the universe would be ordered such that many different kinds of energy sources are almost exactly equal. Solar is very different from wind, which is very different from nuclear. It would be extremely odd if pricing and effectiveness across all these varied sources turned out to be virtually identical. So there’s a decent ex ante reason why we should expect to see one dominant source. This can be framed as a power law function. Energy sources are probably not normally distributed in cost or effectiveness. There is probably one that is dramatically better than all others.”

But the analysis explaining the cleantech bubble were far from clear. “One problem was that people were ambiguous on what was scarce or problematic. Was there resource scarcity? Or were the main problems environmental?” […] “To have a successful startup, you must have good answers—or at least a good plan for getting those answers.” Answers to many issues such as
– the market
– the secrets
– the team and its culture
– the funding
and unfortunately many mistakes were made.

Regarding the market, there was the issue of both explaining how to become a leader of one segment (PV, wind,…) and why a segment was better. Regarding the secret: “If you want to start a company, you should have some important secret. But in practice, most wind, solar, and cleantech ventures relied on incremental improvements.” Even worse, “most cleantech companies in the last decade have had shockingly non-technical teams and cultures. Culture defaulted toward zero-sum competition. Savvy observers would have seen the trouble coming when cleantech people started wearing suits and ties. Tech people and computer people wear t-shirts and jeans. Cleantech people, by contrast, looked like salesmen. And indeed they were. This is not a trivial point. If you’re dealing in something that’s incremental and of questionable durability, you actually have to be a really good salesman to convince people that it’s dramatically better.” Finally “a good, broad rule of thumb is to never invest in companies who are looking for less than $1 million or more than $1 billion. If companies can do everything they want for less than a million dollars, things may be a little too easy. There may be nothing that is very hard to build, and it’s just a timing game. On the other extreme, if a company needs more than a billion dollars to be successful, it has to become so big that the story starts to become implausible.”

If Thiel were to bet on soemthing, it would apparently be Thorium as a nuclear fuel.

Class 15 is about other future bets.

Thiel-World4a
Thiel-World4b

Thiel is a strong believer in contrarian (and sometimes huge) bets. He is interested in or at least puzzled by transportation, robotics, weather and energy storage. And his way of choosing is to look at what did not work (yet) in the past: “Various VC firms in Silicon Valley warned expressed concern about [investing in unique technologies]. They warned us that investing in SpaceX was risky and maybe even crazy. And this wasn’t even at the very early stage. […] (Danielle Fong:) People like to act like they like being disruptive and taking risks. But usually it’s just an act. They don’t mean it. Or if they do, they don’t necessarily have the clout within the partnership to make it happen. (Peter Thiel:) It is very hard hard for investors to invest in things that are unique. The psychological struggle is hard to overstate. People gravitate to the modern portfolio approach. The narrative that people tell is that their portfolio will be a portfolio of different things. But that seems odd. Things that are truly different are hard to evaluate. […] The upside to doing something that you’re unfamiliar with, like rockets, is that it’s likely that no one else is familiar with it, either. The competitive bar is lowered. You can focus on learning and substantive things over process, which is perhaps better than competing against experts.”

Class 16 is about maybe the highest of all bets: life and death.

I have not so far mentioned the sentence which comes at the top of each series of class notes: “Your mind is software. Program it. Your body is a shell. Change it. Death is a disease. Cure it. Extinction is approaching. Fight it.”

The problem.

“Like death itself, modern drug discovery is probably too much a matter of luck. Scientists start with something like 10,000 different compounds. After an extensive screening process, those 10,000 are reduced to maybe 5 that might make it to Phase 3 testing. Maybe 1 makes it through testing and is approved by the FDA. It is an extremely long and fairly random process. This is why starting a biotech company is usually a brutal undertaking. Most last 10 to 15 years. There’s little to no control along the way. What looks promising may not work. There’s no iteration or sense of progress. There is just a binary outcome at end of a largely stochastic process. You can work hard for 10 years and still not know if you’ve just wasted your time.

To be fair, we must acknowledge that all the luck-driven, stats-driven processes that have dominated people’s thinking have worked pretty well over the last few decades. But that doesn’t necessarily mean that indeterminacy is sound practice. Its costs may be rising quickly. Perhaps we’ve found everything that is easy to find. If so, it will be hard to improve armed with nothing but further random processes. This is reflected in escalating development costs. It cost $100 million to develop a new drug in 1975. Today it costs $1.3 billion. Probably all life sciences investment funds have lost money. Biotech investment has been roughly as bad a cleantech.”

The perspectives.

“Drug discovery is fundamentally a search problem. The search space is extremely big. There are lots of possible compounds. An important question is thus whether we can use computer technology to reduce scope of luck. Can Computer Science make biotech more determinative?”

“These are big secrets that play out over long time horizons, not web apps that have a 6-week window to take over the world.”

“The sequencing of the genome is like the first packets being sent over ARPANET. It’s a proof of concept. This technology is happening, but it isn’t yet compelling. So there is a huge market if one can make something compelling enough for people to actually go and get a genome sequenced. It’s like e-mail or word processing. Initially these things were uncomfortable. But when they become demonstrably useful, people leave their comfort zones and adopt them.”

“Biotech got quite a burst in late 70s early 80s, with new recombinant DNA and molecular biology techniques. Genentech led the way from the late 70s to the early 80s. Nine of the 10 biggest American biotech companies were founded during this really short time. Their technology came out some 7-8 years later. And that was the window; not very many integrated biotech companies have emerged since then. There was a certain amount of stuff to find. People found it. And before Genentech, the paradigm was pharma, not biotech. That window (becoming an integrated pharmaceutical company) had been closed for about 30 years before Genentech. So the bet is that while the traditional biotech window may be closed, the comp bio window is just opening.”

“There’s really no rush to spill the secret plans. This space is very much unlike fast-moving consumer Internet startups. Here, if you have something unique, you should nurse it.”

“Slow iteration is not law of nature. Pharma and biotech usually move very slowly, but both have moved pretty fast at times. From 1920-1923 Insulin moved at the speed of software. Today, platforms like Heroku have greatly reduced iteration times. The question is whether we can do that for biotech. Nowhere is it written in stone that you can’t go from conception to market in 18 months. That depends very much on what you’re doing. Genentech was founded the same year as Apple was, in 1976. Building a platform and building infrastructure take time. There can be lots of overhead. Ancillary things can take longer than a single product lifecycle to accumulate. [… the] VC is broken with respect biotech. Biotech VCs have all lost money. They usually have time horizons that are far too short. VCs that say they want biotech tend to really want products brought to market extremely quickly. “Integrated drug platform” is an ominous phrase for VCs. More biotech VCs are focused on globalization than on real technical innovation. VCs typically found a company around a single compound and then pour a bunch of money into it to push it through the capital-intensive trial process. Most VCs not interested in multi-compound companies doing serious pre-clinical research.”

And as a conclusion of class 16, “Startups are always hard at the start. There are futons and ironing boards in the office. You have to rush to clean up for meetings. But maybe the hardest thing is just to get your foundation right and make sure you plan to build something valuable. You don’t have to do a science fair project at the start. You just have to do your analytical homework and make sure what you’re doing is valid. You have to give yourself the best chance of success as things unfold in the future.”

Class 17 is about the brain, artificial intelligence, maybe the last frontier in technology, certainly going further than the previous topics addressed here.

thiel-world5

Not much more to add except maybe the short description of the approach by 3 start-ups:
Vicarious is trying to build AI by develop algorithms that use the underlying principles of the human brain. They believe that higher-level concepts are derived from grounded experiences in the world, and thus creating AI requires first solving a human sensory modality.
Prior Knowledge (acquired by Salesforce since Thiel’s class) is taking a different approach to building AI. Their goal is less to emulate brain function and more to try to come up with different ways to process large amounts of data. They apply a variety of Bayesian probabilistic techniques to identifying patterns and ascertaining causation in large data sets. In a sense, it’s the opposite of simulating human brains.
– The big insight at Palantir (…) isn’t regression analysis, where you look at what was done in the past to try to predict what’s going to be next. A better approach is more game theoretic. Palantir’s framework is not fundamentally about AI, but rather about intelligence augmentation.

And one more comment: “For the most part, academics aren’t (working on strong AI or crazy things) because their incentive structure is so weird. They have perverse incentive to make only marginally better things. And most private companies aren’t working on it because they’re trying to make money now.(…) Bold claims also require extraordinary proof. If you’re pitching a time machine, you’d need to be able to show incremental progress before anyone would believe you. Maybe your investor demo is sending a shoe back in time. That’d be great. You can show that prototype, and explain to investors what will be required to make the machine work on more valuable problems. It’s worth noting that, if you’re pitching a revolutionary technology as opposed to an incremental one, it is much better to find VCs who can think through the tech themselves. When Trilogy was trying to raise their first round, the VCs had professors evaluate their approach to the configurator problem. Trilogy’s strategy was too different from the status quo, and the professors told the VCs that it would never work. That was an expensive mistake for those VCs. When there’s contrarian knowledge involved, you want investors who have the ability to think through these things on their own.”

End of part 5!