In medieval London, we needed two craftsmen to make an arrow. Bowyer made bows while Fletcher fitted the shafts. In pre-British India, caste-based occupations such as a blacksmith, a carpenter, or a shopkeeper created specialised job roles.
Both are examples of specialisation. The Industrial Revolution shifted occupations to task-based specialisation. Adam Smith, widely regarded as the Father of Economics, saw eighteen workers for different tasks in a pin factory and suggested that it is the most efficient and profitable way to organise people.
By the 21st century, falling transport and communication costs split those tasks into sub-tasks and made geographical presence irrelevant. That's how we welcomed hyperspecialisation, where medical transcriptions for patients in Ohio were typed in Coimbatore while bolts for American cars came from factories on the Chinese coast.
Today, AI is trying to make another shift. There is a threat of mass-scale job losses and a massive disruption in how we work, study and earn a livelihood. But before that, we may see a wave where AI reduces the need for specialisation.
Rise of DIY
In a recent essay, Dr Frey, an economics professor at Oxford University, cited how washing machines took the jobs of launderers but added another chore for the housewife. Similarly, he says that AI chatbots are becoming DIY enablers. People are learning to invest, fixing TVs, getting customised diet plans, and filing tax returns. We would dial experts in such cases. But the access to the cheaper expert has become so easy that we end up spending a couple of hours and getting the job done for free.
In organisations, this has translated into AI helping managers review drafts, send emails, manage calendars, and even take minutes of meetings - chores that seemed boring for a manager. This means some jobs may become redundant, and teams may get leaner. It matters for countries like India, which benefited enormously from global hyperspecialisation. India became the world's back office because companies could split work into defined tasks and distribute them globally. But if AI can automate or compress many of those modular tasks, the labour advantage behind outsourcing itself may weaken.
But Specialisation Will Not Disappear Completely
But AI may not threaten all specialists.
Jobs involving physical work, human judgment, trust, creativity, or real-world unpredictability will remain relatively safer. One of the most protected jobs are that of the frontier creators like scientists discovering new drugs, breakthrough engineers, or original filmmakers. AI can be a research assistant to them rather than a replacement.
Then, there are the likes of surgeons, chefs, plumbers, bakers, therapists, and structural engineers who deal with physical complexity and need tacit skills, who will also be relatively safer. At the same time, professionals like lawyers, chartered accountants, architects, editors, and doctors may use AI a lot but might see leaner teams going ahead. However, the personal and professional qualities such as judgment, ethics and know-how will carry more value than today.
The greatest pressure falls on process specialists - junior stock market analysts, software testers, HR screeners, payroll processors, customer-support agents, routine coders, and template-based copywriters. These are skilled professionals, but their work is repetitive, codifiable, measurable, and largely screen-based. They are perhaps the laundresses of the AI era: tasks that were once specialised enough to create entire professions but are now becoming increasingly legible to machines.
A Move from Specialist to Generalist
As AI reduces specialist work, some workers may expand into adjacent functions. A software engineer may move toward product and operations, while a chartered accountant may shift from compliance into advisory. That's a movement towards becoming a "generalist." It is about widening the expertise from the existing base to other areas.
This could also flatten organisations. Top-level management would still be needed for strategy, judgment, and accountability, while many low-cost operational roles may survive because human labour can remain cheaper and more flexible than full automation. But the sharpest pressure may fall on the middle layer, including managers, coordinators, analysts, and process-heavy knowledge workers, who may now be expected to handle broader functions with AI assistance. For many workers in their 40s and 50s, this transition could be difficult because they have spent decades building specialised skills and careers around the older organisational structure.
Narratives or Evidence - What Matters?
The changes around us, for example, companies encouraging the use of AI, news of layoffs, tech tycoons speaking of no need for employment in the future, and billions of dollars of investment in the sector, make us cautious about the future. That sets the narrative around us, changing our psyche about the future of education, work, income and broader purpose of life.
Robert Shiller, in his book Narrative Economics, talks about how these narratives, themselves, precede and sometimes even cause the changes that we are afraid of. Here's how this logic pans out.
A manager who believes AI will make her role redundant within three years may stop developing specialist skills, lobby for a different role, and move to a generalist role. Such a move of acting on the narrative itself could bring the reality much earlier than otherwise. Take a fresh graduate. If he believes in the "degrees are dead" argument, he will not study anything in depth and may focus on building breadth. At a macro level, this kind of behaviour will result in a de-specialised workforce, which the narrative predicted.
Final Take
Just a few years ago, coding seemed like the safest path for the future. Everyone around us sent even eight-year-old kids to Python classes. But software development faced the first large-scale white-collar job threat from AI. That itself sent a warning signal for India's broader service economy.
Economic history tells us that the future may not be an employment doomsday. But AI does appear to be changing the logic of work itself. The first sign could be reduced hyperspecialisation for repetitive, screen-based knowledge work. This will compel us to learn across functions and adapt continuously in any job as much as possible. But that transition will not be easy. Not everyone has the time, access, financial cushion, or institutional support to constantly rebuild skills alongside a full-time job.
For over two centuries, economic progress moved toward narrower forms of specialisation. Ironically, just as machines are becoming more specialised, humans themselves may now need to become broader again.
Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.
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