Information technology and robotic automation are destructive to the labour market for routine and repetitive jobs, and such disruptive force is likely going to transform our work altogether. Many of those who resist change will ultimately become permanently unemployed while those who are bold enough to embrace or propose change will become the new elitists.
This is the general tone of The Future of Profession by Richard Susskind and Daniel Susskind. Although many have contended that professional work is complicated and non-routine and thus is not subject to the threat of being replaced by the computer program and robots, Susskinds find that empirical evidence suggest that many tasks professionals perform will be automated, albeit the process is incremental. Susskinds then propose a model that captures the process of commoditizing tasks executed by professionals.
This is where my belief departs from Susskinds’ argument. The model introduced by Susskinds features a streamlined approach that fails to represent the outcome of externalization. Most systematized techniques have a spillover effect after externalization, enabling professionals to find new approaches to the existing problem. Professionals will be able to draw insights from the data generated by the automated tasks and make fundamental changes to their business model. We observe that after accounting firms digitized their work in tax preparation, the Big Four accounting firms have shifted their focus to tax planning, a previously unconceivable task. Thus, we conclude that the nature of commoditization should not be represented as a straight line. Instead, it should take the form of a circle. This being said, I redrew the model below:
Then, it becomes clear that digitization and automation of the professional tasks don’t necessarily lead to the destruction of the professions. However, if we fail to innovate and race ahead of computers, we would then be forced to live in a jobless society. Assuming that there are almost unlimited opportunities to innovate within each profession, I anticipate that the nature of the professions will transform from today’s service-based model to a research-based model.
However, this assumption will be met with concerns and objections suggesting that the assumption is overoptimistic—we might eventually reach the bottleneck of innovation, and any further improvement will be unfeasible. I would respond to this concern by contending that we are very far from that bottleneck. Although we currently see signs that suggest the end of Moore’s law, the microprocessors we currently use are very far from reaching computronium, or optimal configuration for computation. This observation can be applied to many other professions. Professionals might be wrong on suggesting that their profession has reached or will reach optimal efficiency where no further improvement can be achieved any time soon.
Another concern our assumption faces is that this model only works if the speed of innovation matches the speed of automation. Since we are unable to stay ahead of the machine, our job will eventually be replaced by machines. However, we can find empirical evidence suggesting that this should not be of our concern. After years of rapid development of artificial intelligence technology in 1980s, AI researchers find that the expert system they developed is too expensive to maintain and doesn’t have the capacity of thinking, which then result in decreased funding and reduced interest in the field. Instead of abandoning the field altogether, researchers shift their focus onto developing machine learning capability with deep learning algorithm. This new approach, accompanied by increasingly capable processing power and readily available big data, has revolutionized the AI research field. Although it takes many decades for researchers to overcome AI winter, researchers still find myriad opportunities in implementing the existing technology during the period. This can be applied to other professions. After digitizing the task, the marginal cost of providing such service will be driven to zero, and professionals will then be able to address latent demand while they discover new ways in innovating the work.
The last concern we face is that some might suggest that we are wrong to think that machines are incapable to innovate. Machines do indeed have limited capability in finding new and interesting relationships between seemingly unrelated data sets. However, these findings are not considered as innovative and that these findings are unable to transform the modern professions. We are building this model based on currently available technologies and their reasonable future developments, and thus, we should not consider the possibility of competing with machines to innovate. This is the best strategy for us at the current stage and it is best suited for our need to transform the professions in the decades to come.
Next, I will present some examples of how professions will transform to become research-based.
Education. We start by looking at K-12 schools. Kindergarten and elementary schools will employ new methods to inspire youngsters. The output will then be monitored by machine and compiled into data sets, which can be used to find the most efficient teaching methods. Teachers will start to rely on computer games and websites to personalize teaching experience. For high schools, the focus of teacher will shift from traditional teaching to activity based teaching. Since the online teaching tools (such as Khan Academy and Duolingo) will be better at teaching curriculum bonded materials, teachers should then emphasize on teaching student how to research and apply their knowledge to real-world scenarios. For Universities and colleges, the utility of lecturers will be reduced since students can always find better teaching materials online –massive open online courses (MOOC) will be widely available. Students will use the university facilities to apply their knowledge and conduct experiments. Eventually, the university will once again become a place of research.
Health Care. As we continue to digitize tasks in health care industry, we are going to become less reliant on doctors. Health gadgets and online communities will help us to monitor health conditions, diagnose symptoms and adjust to better lifestyle. This will, in turn, free up doctor’s time, and allow doctors to specialize and emphasize on finding new treatment to diseases. Systemization and externalization of surgical tasks will also enable para-professional to conduct minor surgeries. The replacement of doctor in the operating room will allow doctors to further concentrate their effort on research and development.
There are drawbacks to the research-based model. Since the professions are research oriented, professionals will be forced to join bigger organizations and corporations in order to receive research grant and stay up to date in their own practices. Those who resist changes will be demoted to the status of para-professionals and are forced to compete with a greater number of competitors including machines and lower skilled workers. The benefit of adopting research-based model is that it will enable more people with lower educations to obtain a para-professional job through vocational training, and thus would be helpful in alleviating the threat of robotic automation. We would then see the labour market shift from industrial jobs to service sector jobs.
The book The Future of the Professions provides us with a fascinating outlook on how the increasingly capable technology will replace human professionals. This marks the beginning of a revolutionary period in which we transform the professions from service-based to research-based. The book is superb at explaining the progress and morale behind this unconventional transformation. I would recommend this book to anyone who is interested in the future of society and the future of the professions.