Background

At the beginning of the 20th century, Joseph Schumpeter developed the concept of creative destruction (2008 [1934]). It was meant to characterize waves of development based on clusters of technological and institutional innovations. These waves have included developments in petrochemical products, automobiles, information technologies and biotechnologies, and genetic modification, to name a few things.

In developed Western societies of the postwar era, scientific research and technological development included research councils, scientific advisory boards, expert commissions, and specialized government agencies in different areas, such as health, agriculture, and atomic energy. The target was to transform scientific and technological development into a profitable endeavour. As a result, new kinds of chemical products have been introduced, for example fertilizers, insecticides, and additives used in food production.

According to Eric Hobsbawn, already in the 1960s there were serious concerns regarding social implications of these scientific and technological trends. Signs of social discontent involved the critique of science and technology applied to military objectives, and of the deleterious effects of automation technologies on labour and working conditions. The social concerns were also related to health and environmental costs of widely used chemicals in agriculture.

More recently, the globalization processes resulted in a more direct impact of financial and business interests on scientific and technological development, leading to gradual commodification of science and technology. In line with the argument of Pierre Dardot and Christian Laval (2013), there is a widespread set of values in Western societies that supports progress in terms of “efficiency” disguised as a “truth”, something that is an objective fact about economic reality. This scenario calls for a serious reflection about the social implications of current scientific and technological trends and related ethical issues that are manifested in social disembeddedness of science and technology.

At the beginning of the 21st century, sustainable or green investments have gone through three stages, called respectively envirotech, cleantech, and sustaintech (Tsing Capital Strategy & Research Center, 2017). The envirotech stage was driven by the advent of environmental technology, in addition to government policy and regulations. Envirotech investments were aimed at addressing traditional environmental issues, such as solid waste treatment, quality water treatment, and renewable energy. Envirotech business models were characterized by capital intensive investments reliant on scaling up for competitive advantage.

Afterwards, the so-called cleantech stage emerged. It stands for green investments driven by technological innovations and cost-reductions. Examples include solar photovoltaics, electric vehicles, LEDs, batteries, semiconductors, and energy efficiency-related investments. The requirement of long research and development periods inherent to the cleantech business models created high technological barriers for competitors.

The latest evolution of green investments is defined as the sustaintech stage, where disruptive digital and cloud-based technologies are applied to accelerate sustainable investments through the removal of environmental, energy, and resource constraints. Venture Capital investments have successfully funded sustaintech companies through the past several years, such as Opower, Nest, Solarcity, and Tesla. While Google acquired Nest for $3.2 billion in 2014, Oracle acquired Opower for $532 million in 2016.  

New business models followed: sustaintech firms have shifted towards less capital intensive investments and the proliferation of disruptive technologies. Disruptive technologies (e.g., the Internet of Things, Artificial Intelligence, Augmented Reality/Virtual Reality, Big Data, 3D Printing and Advanced Material), play now a key role in sustainable development:

• Internet of Things sensor technology enhances sustainability with regard to energy efficiency, water resources, and transportation.
• Artificial investment technology, satellite imagery, and computational methods are used to improve predictions in order to boost sustainable agriculture.
• Virtual reality and augmented reality (AR/VR) technologies have the potential to transform business processes in a wide range of industries.
• Big Data has been oriented to optimize energy efficiency and to reduce the cost of clean technologies related to solar panels and electric vehicles.
• 3D Printing technology can improve resource efficiency in manufacturing, and increase the use of green materials.
• Advanced materials technology can substitute non-renewable resources by recyclables, and enable efficiency in power devices.

On the labour landscape, a significant growth of deregulated finance has been associated with a new financial regime and transformations in the pattern of economic growth. Looking back, there has been a close relationship between the crisis of the post-war accumulation pattern, evolution of the international monetary system, and the process of financial deregulation. In recent decades, different growth models affected this global scenario: while some countries had a consumption-driven growth model fuelled by credits, usually followed by current account deficits, other countries shown an export-driven growth model, characterized mainly by modest consumption growth and large current accounts surpluses. The growth of financial assets, generated by a new debt cycle, involved development of sophisticated risk management practices and the subordination of the pace of investment to short-run profits. The overall changes strengthened the private and public debt, increasing social inequalities.

In addition, macroeconomic policies that currently privilege fiscal austerity and labour and market flexibility can be socially costly. In this setting, employability seems to be conditioned to private strategies that aim at cost reduction, labour flexibility, and efficiency targets. Longer working hours, job destruction, turnover, outsourcing, workforce displacement and loss of rights are also a part of the spectrum of management alternatives aimed at cost reduction. The current dynamics of labour markets take advantage of the vulnerability of workers, mainly young people, and favour precarious jobs.

A comprehensive understanding of this political and social reality is an important element of reflection on current labour challenges. Facing these challenges includes taking into account the changing employment relationship. The technological change, and the diffusion of innovative practices at the micro-level, have significantly transformed the labour scenario. The technological impact on the future of work was thoroughly analysed by Jeremy Rifkin (2011). He posits that we are facing a new phase of history – the Third Industrial Revolution – characterized by a steady and inevitable decline of jobs in production and marketing of goods and services. Alongside this new phase, new and challenging scenarios emerge in relation to business and labour.

Today, the Third Industrial Revolution involves a convergence of internet and renewable energy in order to build a new infrastructure. This infrastructure will alter the distribution of economic power in the 21st century. Specifically, changes in power may provoke a fundamental reordering of human relationships – from hierarchical to lateral power – that will in turn impact the way we conduct economic and social activities.

The intelligent infrastructure called the Internet of Things may virtually connect every aspect of economic and social life. The connections will feed the Big Data in every node—businesses, homes, vehicles, etc. — in real time. In turn, the Big Data will be analysed with advanced analytics, transformed into predictive algorithms, and programmed into automated systems. According to Rifkin, this process will improve efficiency, increase productivity, and reduce the marginal cost of producing and delivering a full range of goods and services across the entire economy.

Many leading global IT companies are already working on the build-out of the Internet of Things infrastructure for the Third Industrial Revolution. Among these projects, we can highlight GE’s “Industrial Internet”, Cisco’s “Internet of Things”, IBM’s “Smarter Planet” and Siemen’s “Sustainable Cities”. These initiatives aim to bring online an intelligent infrastructure that can connect the world economy in a global “neural network” designed to be open, distributive, and collaborative. As a result, this network will allow anyone, anywhere, and at any time, to access the Big Data and to create new apps for managing daily lives. It is worth noting that although the Big Data belongs to the Third Industrial revolution, artificial intelligence is considered to be the main driver of the so-called Fourth Industrial Revolution, a term introduced by Klaus Schwab in 2015. The reason for distinguishing the Fourth Industrial Revolution is linked with the prediction that the spread of AI will significantly transform business and jobs over the next 10 years (World Economic Forum, 2019). This will happen due to the evolution of machine learning systems into the developers of knowledge. In this scenario, the identification and understanding of the patterns and trends in data — with smart analytics techniques — is vital to AI as a software engine that may create competitive advantages to the marketplace and business management. 

The differences in classification of the ongoing technological changes aside, it remains that uncertainty exists with regard to the consequences (i.e. benefits and risks) of the ongoing digitalisation changes. In the wake of the high-tech revolution, the number of people underemployed and unemployed will sharply rise. Computers, robotics, telecommunications, and other cutting-edge technologies gradually replace human beings in manufacturing, retail, financial services, transportation, agriculture, and the government sector. In an increasingly automated world, workers are polarized into two forces: on the one hand, there is an elite that controls and manages the high-tech global economy; on the other hand, there is a growing number of displaced workers who have few prospects for job opportunities that could fulfil human needs.

Considering this background, the Conference calls for a focused reflection on the benefits and risks of the high-tech revolution as an important element of shaping sustainable business and just labor. 

REFERENCES

Dardot, P. and Laval, C. (2013) The New Way of the World: On Neoliberal Society. New York: Gregory Elliott London.

Rifkin, Jeremy (2011). The Third Industrial Revolution; How Lateral Power is Transforming Energy, the Economy, and the World. UK:: Palgrave Macmillan

Schumpeter, J.A. (2008 [1934]). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle, translated from the German by Redvers Opie, New Brunswick (U.S.A) and London (U.K.): Transaction Publishers.

Tsing Capital Strategy & Research Center (2017). Tsing Capital Sustaintech Whitepaper 2017. 17th Anniversary Special Edition. May 2017. Authors. Qi Lu.

World Economic Forum (2019). Artificial Intelligence and Machine Learning. Retrieved from  https://www.weforum.org/communities/artificial-intelligence-and-machine-learning.