Technological Unemployment: automation and the purpose of life
By: Ziad Al-Ziadi
Tags: Theory, Artificial Intelligence, Economics

© Copyright Rune Fisker 2016

The relationship between technological advancement and unemployment has been at the centre of scrutiny and debate for well over a century. That is, the advancements in technological innovation have been a driving force for unemployment. Today, the debate has come back in many academic and policy discussions, with automation fueled by the rise of advancements in artificial intelligence, robotics, the Internet of Things (IoT), and 3D printing.

Nevertheless, automation is nothing new. Machines have been replacing workers at a gradual rate since the First and Second Industrial Revolution. In his seminal 1930 essay, Economic Possibilities for our Grandchildren, John Maynard Keynes puts forward the notion of what society would look like given the prolific increase of technological efficiency. Keynes addresses the idea of technological unemployment by way of rapid improvements in the standards of life, namely, innovative machinery and the unforgiving accumulation of capital. 90 years on, we are undoubtedly witnessing the axioms highlighted by Keynes. As these events unfold the question of human relevancy has been put into the mix with many arguing that without employment, we may lose the meaning and value in our lives.

A brief history of technological unemployment

There have been times in history in which the warning signs of automation and new technology have caused many to feel a threat towards their employment and livelihood. In the 1800s, the Luddite movement gained heavy traction in which large groups of English textile workers rioted and even destroyed machines and factories that automated their jobs. Towards the beginning of the 20th Century, the argument of technological unemployed was once again in the spotlight. This time mass unemployment re-emerged as rural workers were being replaced by new and improved technology such as tractors and other heavy equipment.

In 1900, roughly 40% of the US workforce was employed in agriculture. One hundred years later in 2000, that figure was around 2% and from 1997 to 2016 agriculture value-added per work had gone from ~$30,000 to ~$80,000. This was mostly due to a wide range of different technologies and machinery being introduced to the sector. These mass-produced technologies meant that there was less demand for equestrian occupations. As technology continued to improve and became cheaper at the unit level, less manual and low-skill labour was needed.

As a result, mass migration into urban regions occurred in many (Western) countries as workers sought employment. The transition from farm work to an automobile factory production line meant those once unemployed were absorbed back into the labour markets. It didn’t take much for an unemployed farmer or textile worker to acquire the skills needed to work in the automobile factory. Interestingly enough, the increase in production lines increased the demand for labour. In short, while assembly lines had required vast quantities of human operatives, the use of electricity allowed many stages of the production process to be automated, which in turn increased the demand for relatively skilled blue-collar production workers to operate the machinery.

Throughout the 20th Century, the arguments around technological unemployment fluctuated as computerised technology become more widespread. The argument became more centred around the upskilling of workers given the rate of technology improvements. As typewriters, calculators and telephones became common in offices, the demand for educated labour increased, whilst demand for manual labour flattened.

The Computer Revolution began with the first commercial uses of computers in the 1960s and continued through the development of the Internet in the 1990s. As the cost per computation declined at an annual average of 37% between 1945 and 1980 (Nordhaus, 2007), telephone operators were made redundant, the first industrial robot was introduced by General Motors in the 1960s, and in the 1970s airline reservations systems led the way in self-service technology. During the 1980s and 1990s, computing costs declined even more rapidly, on average by 64 % per year, accompanied by a surge in computational power. At the same time, bar-code scanners and cash machines were spreading across the retail and financial industries, and the first personal computers were introduced in the early 1980s, with their word processing and spreadsheet functions eliminating copy typist occupations and allowing repetitive calculations to be automated (Gordon, 2012). Although this might not have replaced workers in the masses, it played a part in wage gaps between skilled and non-skilled.

A new age of technological unemployment?

Traditionally, technological innovations take place in two forms; process and product. The former consists of innovation that moves labour accordingly or replaces it altogether, whilst the latter being capable of creating new industries, new assemblies and subsequently, new jobs. The recent number of technological advancements, predominantly in software, have allowed for the automation of repetitive manual jobs. Moreover, the innovation in hardware in the form of robotics is quickly catching up and is already deployed in many industries today.

Today, near workless factories run by computer programs and intelligent robotics is quickly becoming the norm. Both Ocado and Amazon are two of many companies achieving large efficiency returns by way of automation. The robot workforce is increasing around the world as global sales of service robots continue to grow; in 2019 sales value increased by 32% alone. The rapid advances in the ability of computing and processing power are not showing any signs of slowing down. Let me put this into perspective. In 1996 the US government invested somewhere in region of $55m in ASCII RED, a supercomputer that was able to reach one teraflop of processing speeds. Nine years later, the Playstation gaming console achieved this level of processing power at a cost of $100/ unit. See Moore’s Law.

Artificial Intelligence has the potential to lead the new frontier of automation in different areas of the economy and even society. AI allows software-driven processes to learn how to execute complicated tasks by analysing large amounts of data by essentially learning from trial and error. The increased processing speed has allowed computers to learn these tasks at near real-time speed and efficiently achieving close forms of human intelligence. The increasing availability of data is creating never before seen paradigms for AI. The combining of large datasets with the processing power now possible has meant previous tasks that were too complex for computers are now a reality. This includes driving where some autonomous vehicles can collect and analyse 1.3m points of data per second allowing it to create a 3D model of its surroundings that the car’s algorithm can react and therefore drive accordingly.

“Digital technologies are doing for human brainpower what the steam engine and related technologies did for human muscle power during the Industrial Revolution” — (McAfee, Brynjolfsson, 2015)

There is a slight difference in technological unemployment for the 21st Century that affects workers looking to re-enter the labour market. It is this: the barriers to (re) entry into the labour market will be substantially high given the wide skills gap created. In other words, it was much easier for peasant farmers to migrate into the city and begin working at an auto factory; said farmer could acquire the factory skills needed in a relatively short amount of time to begin working. In the modern job market, however, the time and resources needed to bridge the skills gap are larger; picture an unemployed lorry driver or factory worker needing to learn software development or financial modelling.

The prevailing economic theory goes something like this: supply creates demand and that newer technologies reduce costs which then increase consumption which then bring about more production which ultimately creates new jobs in the labour market. Nonetheless, since the turn of the 21st Century, there is evidence to suggest this isn’t entirely true in some cases. As another example, the U.S. manufacturing industry between 2000 and 2008 increased output by around 60% whilst also eliminating around 3.9m jobs for the same period (Rifkin, 2013). This dichotomy can be attributed to an increase in productivity levels given the higher output achieved with fewer workers. These productivity gains were achieved with the application of new technologies such as robotics and software on the factory floor.

Employment and our worthiness

As the world continues to propel towards intelligent automation, the inherent worthiness of life is thrown into question. Let me explain. What separates the human being from other subspecies is arguably our intelligence, amongst other things. Humans possess the intelligent capacity that has allowed us to do things most other living forms cannot even comprehend. Our brains have allowed us to create fire, building rocket ships and turn gibberish into frameworks called languages. Over the centuries, we have become more intelligent predominantly due to trial and error which has allowed us to progress exponentially as a species.

The way we have progressed over time has almost entirely been driven by our intelligent capacity as opposed to our physical capacity. Of course, for centuries humans have relied on physical forms of working for both progression and livelihood. However, it has been our intelligent capacity that has moved the needle for our species: think of the wheel, the employment of animals and the construction of roads (yes, they’re still physically intensive but they have drastically increased our productive output).

Since the last five hundred years, the professions humans have chosen has not only shaped society but also our self-worth. We can argue that life is meaningful to the extent that the person living it brings about certain valuable states to the world. These typically involve a person setting themselves goals, whether professionally or academically, in which they set about achieving them and deriving satisfaction. Historically, most people have found meaning by joining religious groups or military groups. Yet as society progressed, many people found purpose in the work they chose; an accountant doesn’t have to belong to a church to find meaning. The point I’m making here is that we can easily find meaning in the work we do which was much harder centuries ago.

Technology is increasingly coming after more intelligent and intellectual based labour. The possibility of lawyers becoming automated is not hard to comprehend: computers can process vasts amount of (contextual) data and reach fairer, and arguably, less biased decisions than humans at a much faster and cheaper rate. In fact, they’re already able to read contracts to assist lawyers. Perhaps at some point in our lifetime, algorithms will be able to distribute resources better than most lawmakers whilst equally being better at finding tax cheats, identifying terrorists and other societal irregularities (Danaher, 2017). These systems often still involve human actors, given the nature of democratic processes involved, but the roles of the humans will become more passive, less creative and interactive. The machines start doing much of the work themselves; the logic of their decision-making becomes more and more opaque to those who interact with them. Therefore, once again, the rise of automation reduces the space in which humans can engage in meaningful and fulfilling activities (Danaher, 2017). As mentioned above, not everyone will be sufficiently equipped to be trained if they do not possess the skills to be part of the process. Even for those that are, relatively few of them would be in demand given the nature of how advanced automated technologies have become, needing less maintenance and upgrading.

Ultimately, humans will be left to enjoy the fruits of life, as Keynes put it in 1930. Of course, as humans, there are other ways we derive meaningfulness and purpose which do not directly involve our professions. The only domain we’d discover new meaning is within recreational aesthetics; the pursuit of art, sports, games and spirituality. In these domains, I find it difficult to see where automating technologies would find any relevance. Yes, computers currently produce music and art however not entirely as good as its human counterpart: we connect more to natural creativity as opposed to artificial ones. If one thing is clear perhaps, is that as humans we ought to invest more heavily into our emotional capacity and in activities that allow us to be creative. Greater emphasis should be placed on the joys of art, music and sport and exploring the spiritual capacity of being human.

Ziad.