Today, we are confronted by technological revolutions - from self-driving vehicles and semi-autonomous robots to intelligent algorithms and predictive analytical tools. Machines are increasingly becoming capable of performing a wide range of jobs that have long been human domains. Meanwhile, there are dire predictions of job losses, alongside palpable positivity that the nature and type of jobs currently undertaken by humans will not completely vanish before 2050. Optimism not withstanding, the very changes one is seeing today to jobs, particularly at the bottom-end of complexity does suggest that fundamental shifts are in the offing. I am inclined to take a peek not just into the near term future where automation, algorithms and machine learning technologies are being viewed as emergent threats to traditionalist views with jobs, but also into the longer term future where the very definition of jobs is seen as obsolete.

Non-Biological Interventions
In the near-term, algorithms are the rage, ruling our daily lives - social media, mobile apps, GPS systems, trackers, wearables, computers, financial transactions, travel, and many others. Interestingly, the most underrated component with algorithms is the misunderstanding that algorithms earlier had functional specificities with which they undertook tasks efficiently. Today, algorithms are on a self-learning mode, thereby giving us a window into a more interesting future where machine to machine interactions may soon supersede human to machine transfer of knowledge. For instance, in January 2017, Google Translate developed its own intermediate language so as to enhance its ability to provide translation services. The most amazing thing about this development is that the algorithms governing it not only learned from millions of users the ability to translate meanings, but have now amassed enough information/experience to build a 'context based translation language’ to provide accurate contextual translation. The possibilities such self-learning tools offer is phenomenal, yet scary at times. I hear of many arguments from leaders around the world that the recent trends with automation, robotics and attendant technologies are just that – technological interventions that will not fundamentally alter needs or drivers to businesses, but remain subsidiary in the context of value delivery. At the moment, with the discrete nature of such interventions (both from a standpoint of development, and their adoption by organizations) it may be so. However, a thorough consideration of the implications necessitates rigorous discussions from a standpoint of applicability and ability to deal with the shake-ups that will necessarily follow.

Service automation technologies (incorrectly labeled as RPA) have already begun to take over transactional jobs around the world. Jobs in office & administration, construction & extraction, arts, design, entertainment, media, legal, installation and maintenance, amongst several others are slated to vanish, while ‘cognitive’ jobs in management, engineering, design, computing, architecture, sales, marketing, and education, along with others are expected to grow. Median ages in the various emerging regions are skewed toward youth, with consistently positive birth rates. Hence, envisioning a scenario into the next two decades is quite scary. Will adoption of such technologies therefore slow-down, or be rejected altogether? The economics of push-back on such developments can be quite disastrous - exports would become low-value, imports would become expensive, lifestyles may stagnate, incomes surely would dwindle, purchasing power drastically reduced, and consequent political fallouts may be severe, if not disastrous. While I am not advocating aggressive adoption of such technologies just to be 'in', a complete rejection has irreversible implications in the context of growth and development.I do see that policy makers will have to remain cognizant of the complexities surrounding modernization and job creation (that at the moment seem to be in dichotomy of each other).

The scenario within corporations of course is another interesting story. Bereft of the need to think about socio-economics, their strategies are governed more by competitiveness, growth and shareholder value. Adoption of transactional automation technologies is already being seen as an unavoidable imperative. Most corporations have or are rigorously developing strategies where with in the medium-term, their organizations would reflect a workforce constituting both biological and non-biological employees. This transition requires not just an innate understanding of the organization’s levers where technologies could be effectively leveraged, but also a clear view to the future that is driven by goals other than technological modernity. Meanwhile, the inevitable conflict will come to be, pitting organizations against governments. I believe that one cannot ignore this reality. A good friend recently wondered whether it is time for governments to start taxing a non-biological workforce as well. But that is a different conversation for another day.

Once the infrastructure is ready, Ace also acts as an Active Service Provider (ASP) in installing the required equipment as per the active command received from its client

A Hyper-Intelligent Tomorrow
At the other end of the spectrum, we are witness to some great strides being made in various fields, some exciting and some conflicting: gene editing technologies to reduce instances of heart attacks, leveraging skin from fish to treat severe burn injuries, stem cell regeneration from all types of body cells, gene splicing to increase crop resistance to diseases and enhanced yield, GMO crops to eliminate hunger altogether, credit rating based on needs and not incomes, 3D and 4D printing, gesture-based computing, womb-based therapy, virtual health trackers and real-time treatments, water labeling of products, industrial repatriation and many more. Considerations for such technologies can be driven either by an inherent ‘visionary’ need, for e.g. moon mining, or beaming internet from space, or by envisioning a larger future where ‘homo-sapiens superiority’ is pushing the gauntlet on innovative technologies and their applications. There are a range of futurists in the world predicting the future of work in many ways, with most of them today focused on identifying ‘jobs that would be created in future’ and comparing them to ‘jobs that will be lost’. Such predictions may seem overwhelming for governments and organizations. Some of them have turned the question upside down. Instead of asking what jobs are needed in future, their question is - to what end do we ready ourselves. Emphasis is on the imperative to put human context into all our endeavors and predicaments. I firmly believe it is crucial that policy makers and industry, along with civil society begin to engage in these inherently complex questions to understand implications, and then determine steps that are complimentary of their collective needs, and opportunities that could consequently be created. In the interim, I believe that almost all our efforts at transforming our workforce, and reinvigorating economic pursuits with new skill development initiatives will remain necessary but severely insufficient. Trends like localism, contextual deficit, urbanization, increasing rage, instant gratification, increased longevity, cultural intimacy, digital narcissism, sense of entitlement, and many more are critical to appreciate than just admiring technologies and rushing to adopt them.

Do We Need Jobs?
Humans over the past two centuries have built a successful societal model that rested on the back of two broad entities: corporations and governments. With globalization, some rationalization of rules permitted cross-border trade and exchange. Yet, a significant proportion of the global population is getting increasingly disconnected with this economic reality. Perhaps the time has come to ask some fundamental questions. I have always wondered this: Why do we need jobs? Can we do without them? Perhaps in such categorization of jobs, we have built an inequitable system that can never change. Hence all initiatives aimed at inclusion, equality et al will always remain pipe dreams. What if we were to upturn this basic tryst? Recently, citizens of Finland and Switzerland rejected a proposal for Universal Basic Income (UBI). Perhaps it presents a unique opportunity for humanity to liberate itself from the clutches of its own pursuits. California is now experimenting with it. One cannot ignore UBI being compared with existing Social Security coverage in many nations, and the attendant disappointment many have with such social systems. Jobs in the current definition may become passé. There may be significant opportunities to restructure and redirect existing subsidies into the UBI model, thereby freeing up resources to invest in growth and new opportunities. It is easier said than done. I think the discussions are complex, yet in context of incomes and sustenance, UBI presents an opportunity that may essentially eliminate categorization of individuals - through jobs - into various roles, and transform them into a conglomerate, where each individual is a producer, consumer, supplier, aggregator and enabler rolled into one.

Of course this presents another interesting question. Should we be able to eliminate jobs altogether, and have machines do them for us, who then remains in control? We as a species lost the battle to control nuclear technology in 1945. Lessons from it influenced a 1975 conference amongst scientists who had discovered DNA, resulting in an agreement not to recombine DNA from different species and possibly lose control. That agreement holds even today. Earlier in January 2017,a similar agreement was reached by technologists and global leaders, wherein 23 principles for AI have been established. Again, the goals around non-circumvention, retention of human control and other aspects govern this agreement. Would we as a species adhere to such an agreement as we did with DNA, in a time and age where animosity amongst and between nations is at its highest? Nevertheless, what remains true is that machines have begun to learn from each other, and are slowly making humans redundant. Google Brain’s recent tryst with neural networks is a scary example of what possibly remains in store for us. We may have to make some radical choices in the next decade around policy and technology.