"What explains the slowing growth of abstract task-intensive employment? One interpretation is that automation, information technology, and technological progress, in general, are encroaching upward in the task domain and is beginning to substitute strongly for the work done by professional, technical, and managerial occupations. While one should not dismiss this possibility out of hand, it does not fit well with the pattern of computer and software investment. If information technology is increasingly replacing workers high in the skill distribution, one would expect a surge of corporate investment in computer hardware and software. Instead, data from early 2014 shows that information processing equipment and software investment was only 3.5 per cent of GDP, a level last seen in 1995 during the outset of the “dot-com” era.
I. Humanistic approach - non-routine tasks: most people look on to work as monotonous, impersonal, regular works. But before the onset of these works, there were works which were atypical to democratized routine works. For example, a medical nurse who needs to give doses of medicine should use her immediate discretion power of whether to use it or not in an already sedated patient. Even though he/she might think about her work as something routine, it is not always a pre-planned setting, where automated machines can apply their coded response. “Computers largely substitute for routine tasks, how do we characterize the no routine tasks for which they do not substitute?"
In Autor, Levy, and Murnane (2003), they distinguish two broad sets of tasks that have proven to be stubbornly challenging computerization. One category includes tasks that require problem-solving capabilities, intuition, creativity, and persuasion. These tasks, which we term “abstract,” are characteristic of professional, technical, and managerial occupations. They employ workers with high levels of education and analytical capability, and they place a premium on inductive reasoning, communications ability, and expert mastery.
The second broad category includes tasks requiring situational adaptability, visual and language recognition, and in-person interactions—which we refer to as “manual” tasks." Both of these extreme ends of the labour spectrum shows the diverse characteristics in it, and how they respond to automation and other accused technologies supremacy. The author also opines that this polarisation will not exist for a longer period. The prosperous period for abstract, high skill jobs in the one end of the spectrum are getting complimented by the technology and the low- skilled, manual works are overflowed from the supply of labour force, who were belonged previously to the middle range, mediated jobs such as office staffs and clerks. It is also important to note how much per cent of the labour force was previously working in this post-world war middle range service sector jobs or before the advent of the so-called technological interventions and automation. The increased accessibility and lowered skills make the lower stratum of work called manual works became a "daunting challenge for automation" in the US labour markets.
II. The over-emphasis upon literary tools in social sciences such as postmodernism and post-industrial society, had hidden the empirical truth upon production? Does production really cease to exist? As David Harvey noted in his book 'Spaces of capitalism’ (2001), the biggest advantage of capitalism is its flexibility and that the opposition to this is highly fragmented and not united. Clearly depicting a Marxist interpretation. But when we look deeper into the strategy of capitalism, behind the boogeymen of globalization and technological digital world, the real production became an invisible project. Theoreticians are contesting upon whether our society is passing through modernity or postmodernity, but the basic empirical scene is yet hidden under the name of the automation. The author argues that most of the semi-skilled and skilled jobs are offshored to the 'third world ' where the labour and resources are cheap. This shows that there are many factors apart from the influence of technology and automation which causes the reduced number of jobs in North America and the European Union. As noted earlier," technological change is far from the only factor affecting US labour markets in the last 15 years. For example, the deceleration of wage growth and changes in occupational patterns in the US labour market after 2000, and further after 2007, is for sure associated to some extent with two types of macroeconomic events. First, there are the business cycle effects—the bursting of the “dot-com” bubble in 2000, and the collapse of the housing market and the ensuing financial crisis in 2007–2008—both of which curtailed investment and innovative activity. Second, there are the employment dislocations in the US labour market brought about by rapid globalization, particularly the sharp rise of import penetration from China following its accession to the World Trade Organization in 2001 (Autor, Dorn, and Hanson 2013; Pierce and Schott 2012; Acemoglu, Autor, Dorn, Hanson, and Price forthcoming,)
According to the author this 'multidimensional complementarity among causal factors' leads to a faraway conception of single and pure reason for job losses and shift in production paradigm.
The Polanyi's paradox and automation: The social philosopher Polanyi’s idea of our inherent inability to express completely on the matters we know is used by the author to explain how certain ideas upon discretion and aesthetic knowledge cannot be translated and being unable to be expressed into the language of technology. "Polanyi's paradox—“we know more than we can tell”—presents a challenge for computerization because, if people understand how to perform a task only tacitly and cannot “tell” a computer how to perform the task, then programmers cannot automate the task—or so the thinking has gone to one." As an impartial researcher, the author gives accounts of how technology “through a process of exposure, training, and reinforcement, machine learning algorithms may potentially infer how to accomplish tasks that have proved dauntingly challenging to codify with explicit procedures(). But, “an irony of machine learning algorithms is that they also cannot “tell” programmers why they do what they do. “He gives examples of google car and automation programs started by Amazon and how they paradoxically highlighted the limitation of technology to accomplish certain non-routine tasks where the capacity of 'human ingenuity' is integral in removing such obstacles and ‘reengineering the environment' while the particular tasks are accomplished. He also argues that " the issue is not that the middle-class workers are doomed by automation and technology, but instead that human capital investment must be at the heart of any long-term strategy for producing skills that are complemented by rather than substituted for"
