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If someone had asked this even six months back, my answer would have been that the disruption from and then gradual adoption of General Artificial Intelligence (GenAI) would likely play out over multiple decades. And the threat to jobs would be offset greatly by the fact that global population has peaked, most regions barring Africa have reached a point where fertility rate is at least equal to the replacement level if not already lower – which means that at some point in the not so distant future, the net addition of people to the workforce would come down and thereafter, the size of the working age population itself would start to decline.
But, safe to say, the answer doesn’t hold relevance anymore. So what could happen? Here is a take, which I think is fairly optimistic with a dollop of cynicism (hence, the term Reasonable Optimist, coined by Morgan Housel).
The most prevalent argument out there is that humans will find a way. There have been a number of technological innovations in human history, from the Industrial Revolution to computers, robotics, and the internet. A plethora of jobs became redundant after each break-through however we have not seen massive civil unrest around the world yet. Because new business opportunities were created (think SaaS jobs to tele-medicine to space tech to podcasting) and existing businesses were able to scale up faster (adding new jobs in design, marketing, HR, etc.). Net-net, there has always been an increase in jobs in human history. The global population has increased but unemployment rate has remained relatively stable.
But we should be very careful about extrapolating the conclusion for Gen-AI. No other technological innovation in the past has competed with human intelligence. The adoption of the technology would also be rapid with the democratization of access and affordability. While there would be many businesses created around AI infrastructure and application, it would have a far greater impact on jobs compared to automation and software, affecting not just ‘data collection and processing’ roles (entry-level roles) but also ‘insight generation and recommendation’ roles (some senior roles at least).
Another argument is that AI would free up bandwidth for humans to spend more time ‘thinking’, which means more time spent on R&D and creative pursuits – AI’s ability to disrupt these professions is unproven. However, AI is already aiding professionals in these fields greatly, which means projects can be done with much leaner teams (so with fewer junior staff, for example).
Some argue that impact on high-skilled services such as education and healthcare would be limited. Let’s take an illustrative example in the field of education. A university physics teacher can spend more time researching on string theory, with AI helping her to deliver classroom sessions efficiently. First, all her classes would need to be recorded for a year, they would then be fed into an AI tool which learns her teaching style to the minutest detail and then the following year onwards, delivery of lecture happens through an avatar of the teacher projected in the classroom as a hologram. The teacher still works full time on the university’s payroll, working on research projects and constantly provides feedback to the AI model. She also takes some extra classes for the weaker students, who might have inadvertently been left behind by the less empathetic ‘AI-programmed’ teacher. Because of the efficiencies created however, the institution lets go off another physics teacher on their payroll. A few years after such AI integration, the institution is well regarded in delivering good quality education with a lean cost structure, with only one teacher for each subject and a few people in administration. A large part of the extra income generated is used to expand the infrastructure and increase student intake, upgrade labs and sports facilities, and develop more holistic learning programs for students. The example shows that while jobs could be affected even in the high-skilled services space, AI adoption could accelerate economic growth through non-linear growth in labour productivity. Consequently as the GDP pool increases faster, it could well be that some people become wealthy quicker; that leads them to retire early or at least reduce their working hours after amassing a certain amount of wealth; they consume more, travel more – thereby supporting job creation in the economy as well as freeing up space in the job market.
No one can quantify the impact of AI on jobs but there is a significant risk to the downside. It is likely that governments and regulators will step in if they see a threat of social disruption or even before, triggered by fiscal pressures of providing social safety nets to a larger population. We could see protectionism even in the services sector in the future. Are you listening, Mr. Trump?
These pieces are being published as they have been received – they have not been edited/fact-checked by ThePrint.