The fear of losing control and the essential exponential

Imagine the following situation. Two people walk 35 steps each. One person, which can be also seen as a traditional corporation, makes normal, but long steps of 1 m and is 10 km ahead. The other person, let us call it an ambitious start up, which organized itself around accelerating returns of technology starts very slow with a first step of 10cm only, but doubles the step lengths by any of its additional steps. For the first five steps the traditional company even gets further ahead, beyond the 10km advantage it already has. Hard to see any competition. Where are the two, after 35 steps. The traditional company stands proud at 10km and 35m. The initially slow and irrelevant competitor might have wet feet, it crossed oceans and orbited the globe more than 30 times.

There is no reasonable doubt about the fact that technology is a decisive factor in the long-term development of economies and societies. Of course, technology is not the only single and determining factor. The institutional framework of a society e.g. of the 19^th century China e.g. has a major impact too. In the 19^th century the governing Qing emperors and their administration had been opposed against the modernization of the infrastructures of the country. The first railroad built by a British trading firm 1876 from Shanghai to Woosung was bought by the Chinese government only to stop its operations and to remove the rails in the following year. The fear of a loss of control by rapid technological and social change and ‘Westernization’ might have been behind such policies which seem to be highly irrational from a contemporary perspective. (c.f. Mark, R. B., 2002, The Origins of the Modern World, London)

The fear of losing control obviously is not an attitude specific for the Chinese government of the 19^th century it is also a central factor and major impediment for political and economic institutions, societal groups, trade unions, workers and even consumers to adopt new technologies and to benefit from them.

The institutional and social context are determining decisions and the pace of change

Most of us think we make choices individually because of our specific personality. We feel it is us when we make decisions. This seems sensible, but what “feels” right is not necessarily the truth. Studies based on neuroscience, cognitive and behavioral psychology getting more influential in economics and are to read as a fundamental criticism of the economic theory of rational individual behavior. (cf. Glimcher, P.W./ Fehr, E. edit., 2014, Neuroeconomics, 2^nd edit., Neuroeconomics, Decision Making and the Brain, Amsterdam e.a..)

We are social creatures, and the context (rather than the individual calculus) dominates our decisions. If decisions can be forecasted (a) and regulated (b) by tweaking the situational design, then really, where did the choice originate? Who’s in charge? The theoretical foundations of a system based on the assumption of rational actors are based on folk psychology of the19th century and related theories of efficient markets are obviously undermined by their stark contrast to empirical evidence, experimental methods and the continuous progress in research about the neurological and socio-biological context of individual behavior and decision making.

• Three dimensions of societal change in the context of digital transformation
• Therefore, digital transformation based on modern empirical sciences inclusive of economics and management and organization research are bound to view all processes, physical, chemical, biological, psychological and social essentially represented by data and algorithms. Biologists have already defined life as based on code, DNA and DNA replication, and therefore neuroscience, computer science and social sciences are converging in their empirical bases.
• As a consequence of accelerating technological progress in computing and digital communication (analytical) intelligence decouples from the limitations of individual human consciousness. (AI as a system intelligence based on machine learning exceeding human capabilities by far).
• Algorithms (or machines serving and monitoring us in daily and work life) will soon know us much better than we know ourselves enhancing the capabilities of predicting and influencing behavior.
• The impact of digital transformation on economy and society

Digital transformation has a real impact on the global economy and society. Accelerating technological progress, access to data and enhanced technologies to process and analyze data based on machine learning through new methods of parallel computing, ‘neural networks’ simulating the synaptic plasticity of the human brain is known as ‘machine learning’ and provides a form of artificial intelligence which is already applied on a large scale. The business models of the US and Asian transnational corporations with the highest evaluations by the financial markets are based on machine learning technologies. Cloud computing and social networks as well as the marketing practices of the most valuable corporations like Microsoft, Apple, Amazon, Google, Facebook, Alibaba, Tencent, Baidu, Softbank etc. are focused on these new forms of data analysis and artificial intelligence.

Digital transformation means that firms have been moving to an increasingly digital core based on software, data, and digital networks for more than a decade now and are requiring a fundamentally new operating architecture and strategic framework

Recent changes in strategies and operations of extraordinary successful transnational corporations

When it comes to transnational corporations we can see a dramatic change of their capitalization in terms of evaluation through the international financial markets. Figure 5.1 below displays what hurts managers of large established corporations most: The bureaucratic dinosaurs of the industrial age, laid out hierarchically as efficient social machines, lose the favor of the capital markets on a permanent basis. A new-type of companies with a new type of strategy are threatening to take over large portions of future profits based on human creativity and technological change. Most of the companies on the list of the world most valuable corporations made it to the top within less than two decades after they had been founded (Amazon, Alphabet, Facebook, Alibaba and Tencent). The older Software and technology giants Microsoft and Apple founded 1975 and 1976 managed to adopt major features of the technology-based platform strategies in the last two decades and multiplied their market capitalization in the last decade nearly as fast as the newcomers.

Such a change in market capitalization in only one decade is historical unprecedented in regard of pace but also can be seen in line with the rise of the first transnational corporations, the international trading and shipping companies which based their strategies and structures on the discoveries of the 15^th century and the emerging colonial world order of the 16^th, 17^th, 18^th and 19^th century. They had been sidelined by conglomerates based on technological infrastructures like railways, coal and steel, chemistry and electricity in the 19^th century during the so called ‘gilded age’ in the second half of the 19^th and the beginning of the 20^th century. Transnational companies like Royal Dutch Shell, Mitsubishi, General Electric, Siemens and Daimler as well as many other automotive maker and industrial conglomerates can be rooted back to the extraordinary innovation dynamics of the ‘gilded age’.

It seems to not too farfetched to assume that we again live in a kind of new ‘gilded age’ with an even more rapid change of institutions, political and economic structures in the context of accelerated technological change, to summarize as ‘digital transformation’ of the economy and society on a global scale. Of course, it is ruled out that we conduct comprehensive analysis of all aspects of digital transformation in these lecture materials, we need to limit our investigation towards the most significant impacts on transnational corporations by exemplary analysis. The next subchapter 5.2 is linking the appreciation of the financial markets with the phenomenon of accelerated technological progress and institutional change as a consequence of digital transformation, comparable with the gilded age of industrialization.

The link between digital transformation and corporate strategy

Two of the Massachusetts Institute of Technology brilliant minds, Erik Brynjolfsson and Andrew MacAfee promised us 2017, that we ain’t have seen nothing yet of the further acceleration of returns on technology and point out:

„Machine learning systems get better as they get bigger as they run on faster and more specialized hardware, get access to more data, and have improved algorithms – all these improvements are happening now, so machine learning is developing rapidly.“ Brynjolfson, E., MacAfee, 2017, pos 982

In 1998 Google’s servers had been able to perform 100 million floating point operations (FLOPS) per second. The Graphic Processing Units (GPU) Google was using 2017 already performed 20 trillion of such computations a second; nowadays Google’s revolutionary Tensor Processing Units (TPU) for accelerated machine learning, artificial intelligence (AI) and cloud computing are capable of 180 trillion FLOPS a second. While being successful with its prototype of a quantum computer project the two founders of Google/ Alphabet gave an outlook towards the development of a potentially 10 to the power of 105 times faster processing technology (Brin, S. 2018). But how this is to be converted in a sustainable competitive advantage in nearly every industry you can imagine?

The Figure below shows an impressive (please note the logarithmic scale) display of progressing efficiency in major digital technologies principally in line with ‘Moore’s law’ which is not a law of nature but an observation which Gordon Moore (1965) made in regard of technological progress in field of microelectronics already in a paper published 1965. He observed that the number transistors which can be placed on a microprocessor or a chip, the Central Processing Unit (CPU), doubles in an average rhythm of 18 to 24 month. From there he extrapolated that the capacity or performance of a computer will double roundabout every two years or 18 months at constant cost or that the same capacity will be available in similar time spans at only half of the previous cost. Moore’s law had been quite prescient for five decades and seems to be applicable on a wide range of progress in digital technology. Of course, there are physical boundaries for chipsets, the number of transistors to be put on limited space can’t be endless, but there are also new ways of computing in sight, which might accelerate computing power not only further but even faster.

Ray Kurzweil (2013: 177) calls this broader version of Moore’s law not only in respect of artificial intelligence the law of accelerating returns (LOAR), applicable to both biological and technological evolution. His visionary claims are based on the emergence of a new paradigm guiding contemporary research. The new and comprehensive scientific paradigm, encompassing social sciences, psychology and biology is based on the pictured rapid development of information and communication technologies.  It defines or depicts the basic phenomena of life (DNA replication), cognition and thinking, behavior, and communication through algorithms (mathematical formulas describing processes of system replication, emergence of processes and change of processes).

Already perceptible in everyday and professional life, cognition and data processing as bases of intelligent behavior are decoupled from uncontrollable human consciousness (perceptions, sensations and the flow of thought). Applications of artificial intelligence are impacting, supporting and directing private activities as well as professional work in organizations. One is more inclined to ask Google or his smart phone than a knowledgeable person. Consequently, many tasks, even classic management tasks of planning, organization and control are either better or more reliably taken over by machines or supported by digital assistants.

The enormous scope, speed and significance of digital transformation obviously fueled by the broader version of Moore’s law, Kurzweil’s LOAR, has of course also an enormous impact on the global economy and its organizations. Just within a couple of years new corporations and businesses with global impact and unprecedented growth emerged while other players failed to develop business models and forms of social organizations suitable to exploit and further drive the dynamics of the LOAR within the global economy. Our hypothesis is that new forms and changes of social organization are playing a major role in development, adoption and use of technologies and that it is a misleading way of thinking to construct a cause and effect relationship between technological artifacts and social structures. Instead the platform strategies of the leading transnational tech companies are based on a novel concept of innovation based on technological and institutional change at the same time and comprehending the follow three elements:

• Scenarios to shape the future: What happens or should happen at the level of society, politics and economics, and in the working and everyday lives of individuals, groups as well as in regard of institutions, organizations, markets, global and regional cultures and the public, if – as foreseeable in the near future – the cognitive capacity (intelligence) of machines and networks are developed far beyond of those of individuals with perceptions, feelings and largely uncontrollable thoughts?

• Leading and managing change: How are social structures based on the acceptance of interpretations of reality („narratives“), by individuals, social groups, institutions, markets, and organizations changing? (Think, for example, of the moon-shot projects of Alphabet/Google venturing into mobility services by self-driving cars, medicine by diagnosis and development of treatments driven by data analysis through algorithms or through payments systems e.g. like Alibaba Ant or other apps.

• Analyzing and evaluating alternatives, implementing viable solutions: What are the concrete consequences of data-driven, largely automated process and behavioral control in business and society? For example, in relation to the organization of mobility and traffic, or in regard to financial services and banking system. (E.g.: blockchain technology applications are calling traditional institutions such as financial markets and national sovereignty over currencies into question).

From incremental improvement to disruptive innovation

The underlying concept of strategy development is based on linking technological progress with the possibility of futures fundamentally different from the past. Unlike normal or incremental innovation strategies aiming at improving

1. existing products and services,
2. organizational structures and processes
3. or expanding markets and customer bases
4. or sources of supply gradually

And as a fifth exceptional mode of innovation

(5) strategies based on breakthrough technologies like electric power in the late 19^th century or digital transformation and platform strategies of the 21^st century aiming at fundamental or radical changes of industries on a global scale or the establishment of new industries

These five types type of innovation already had been distinguished and exemplified by Joseph A. Schumpeter (1911; 1948). Schumpeter considered type 5 of innovations as unique and typical for the ‘gilded age’ because the application of a large number of novel technologies in transport, energy, physics and chemistry, led to the formation of new monopoles and the destruction of traditional structures.

Nowadays we are not talking about a fifth, fundamental type of innovation like Schumpeter, but about radical or disruptive innovation vs. incremental innovation or like Peter Thiel, the eccentric libertarian PayPal founder and Silicon Valley Venture Capitalist, expressed it at in his lecture series on start-ups at Stanford University: The vision or idea to do new things differently vs. scaling up and improving things that already works are the fundamental difference between promising start up and longtime established ventures in various industries. (Thiel, P., Masters B., 2015).

Schumpeter argued that the dynamics of the early days of large corporations the late 19th and early 20th century won’t occur again because the created large bureaucratic structures are interested in gradual improvement only. He didn’t believe that new revolutionary scientific discoveries would come up; corporations would compete on efficiency improvements only based on the existing set of basic technologies and their improvement. In his landmark volume on Capitalism, Socialism and Democracy (1942) he also foresaw the differences between capitalism and socialism dwindling as the innovation dynamics of capitalist market economies inevitably would slow down by time: A creative destruction of existing economic structures and ways of social life was no longer to be expected. Nowadays we know that Schumpeter had good arguments but he was wrong! The current generations of humans are experiencing life changing radical, disruptive innovation on a global scale not only in the economy but also in other institutional context of the society and in their daily lives.

Defining digital transformation and linking it precisely to the historical development in digital computation and communication is nearly impossible. Terms like the ‘fourth industrial revolution’ (coined by Klaus Schwab and the World Economic Forum, WEF) are as complex as the ‘second machine age’ announced by the MIT Economists Brynjolfson and MacAfee. However, the idea that ‘Artificial Intelligence’ or ‘Machine Learning’ is at the core of the current technology driven innovation strategies of extraordinary successful transnational or multinational corporations has arrived in the mainstream. The consultancy giant McKinsey and its MGI Global institute are dishing out guides to AI applications on regularly base (McKinsey, 2019) Business Faculties and Business Schools across the globe launching programs to cope with the current innovation dynamics in fields like digital leadership, strategy, organization, marketing, finance etc..

When it comes to a comprehensive account of the science behind the technological development the reader is referred to the seminal book of Terry Sejnowski (2018) ‘The Deep Learning Revolution’ or the compilation of interviews with leading scientists in the field by Martin Ford (2018), ‘Architects of Intelligence’. But for now, it is enough to point out that all of the 7 MNC’s with the highest market evaluation are organized around the idea of digital transformation as explained above.