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By:

Dr. Kishore Paknikar

29 January 2025 at 2:43:00 pm

The Thrifty Scientist

The question is no longer whether scientists can produce more but whether science can rediscover what is enough. There was a time when scientists struggled not for recognition, but for survival. Research grants were scarce, journals were limited, laboratories were modest, instruments were primitive, and communication moved at the speed of postal mail. A scientist often spent years conducting experiments before publishing a single paper. Citations were not counted daily. Rankings did not...

The Thrifty Scientist

The question is no longer whether scientists can produce more but whether science can rediscover what is enough. There was a time when scientists struggled not for recognition, but for survival. Research grants were scarce, journals were limited, laboratories were modest, instruments were primitive, and communication moved at the speed of postal mail. A scientist often spent years conducting experiments before publishing a single paper. Citations were not counted daily. Rankings did not dominate academic conversations. Research was difficult, uncertain and deeply personal. Over time, the culture of science shifted. Now, many scientists appear caught in an unending cycle of accumulating achievements by publishing more papers, securing citations, projects and patents; attending conferences, garnering awards and visibility. The desire for success seems insatiable. Even top researchers relentlessly pursue more metrics with remarkable dedication, as if there is never enough. Perhaps this behaviour is not entirely cultural. Perhaps it is also evolutionary. Famed Thesis In 1962, geneticist James V. Neel proposed the famous ‘Thrifty Gene Hypothesis’ which posited that human beings evolved in environments marked by uncertainty, scarcity, and repeated famine. Under such conditions, individuals who could efficiently store energy had a survival advantage. The body learned to save calories because the next meal was never guaranteed. But when modern society created an environment of continuous abundance, the same biological tendency became problematic. Mechanisms that once protected survival started contributing to obesity, diabetes, and metabolic disorders. What if academia has developed its own version of the thrifty gene? For generations, scientists worked within ecosystems where opportunities were scarce and unpredictable. A research grant could determine a laboratory’s future. One rejection from a journal might delay recognition for years. Permanent academic positions were scarce. Access to advanced equipment was limited to a few elite institutions. Scientists learned to compete intensely because the next opportunity was uncertain. In such an environment, professional “storage behaviour” may have become deeply ingrained. A scientist who accumulates publications gains visibility. One who accumulates grants gains security. One who accumulates students builds a workforce. One who accumulates citations gains influence. One who accumulates committee memberships gains institutional power. Each additional achievement functions almost like stored academic fat for future survival. Altered Ecosystem The problem is that the ecosystem has changed dramatically, but the instinct remains. Today, digital publishing has exploded. Thousands of journals operate globally. Preprint servers allow immediate dissemination. Citation databases update in real time. Universities increasingly evaluate scientists through measurable indicators. Funding agencies ask for metrics. Ranking systems reward volume. Academic dashboards display h-indices and citation counts like stock market tickers. The modern scientist is no longer merely doing science; he is continuously managing “academic metabolism.” Beyond a certain point, achievements stop serving scientific curiosity and begin serving professional anxiety. The system silently trains researchers to feel that whatever they have is insufficient. A young scientist anxiously refreshes citation counts before a promotion interview. A senior professor with hundreds of papers still fears becoming professionally irrelevant. Laboratories expand continuously because shrinking is seen as a decline. Universities celebrate the number of papers produced far more visibly than the quality of questions asked. Scientific ambition slowly shifts from discovery to accumulation. Like metabolic disorders in the human body, excessive academic accumulation also produces side effects. One consequence is scientific overproduction. The world now produces millions of research papers annually. Many are incremental, repetitive, poorly reproducible or rarely read after publication. The pressure to publish continuously creates what some scholars call “paper inflation.” Intellectual Obesity Another consequence is intellectual obesity. Laboratories grow larger, administrative responsibilities multiply, and scientists become managers of projects, finances, students, collaborations, rankings, and institutional branding. Ironically, many senior researchers spend less time thinking deeply about science. The scientific ecosystem thus becomes quantitatively richer but cognitively poorer. Modern evaluation systems unintentionally intensify this behaviour. Universities and funding agencies often reward measurable accumulation more easily than originality. A researcher with numerous papers and grants appears more productive than someone who spends years solving one difficult problem. Quantity becomes easier to count than intellectual courage. This may explain why disruptive ideas often struggle initially. Radical thinking is slow, risky, uncertain, and sometimes lonely. Accumulation-driven systems naturally favour predictable output. The irony is profound. Science, which is supposed to challenge assumptions, may itself be trapped inside an evolutionary-style survival mechanism. The comparison with the thrifty gene hypothesis becomes even more interesting psychologically. Human beings are naturally poor at sensing “what is enough.” Evolution optimized survival, not satisfaction. A person who stopped storing food too early risked starvation. Similarly, a scientist who stopped accumulating credentials too early may have risked becoming irrelevant. The modern academic ecosystem continuously activates this insecurity. Even retirement no longer guarantees disengagement. Many scientists continue to publish aggressively late into their careers because their professional identity gradually becomes inseparable from their output. Ambition itself is not the problem. Civilization advances because some individuals relentlessly push boundaries. Science requires drive, persistence and intellectual hunger. The problem arises when accumulation becomes detached from purpose. A healthy scientific ecosystem should ask not only “How much is being produced?” but also “What is genuinely being understood?” History repeatedly shows that transformative scientific advances often emerge from deep thinking, patience, and intellectual courage rather than sheer numerical output. Yet modern systems increasingly reward speed, visibility, and measurable productivity. This tension lies at the heart of the present scientific culture. Many modern professions operate within similar cultures of endless accumulation, where people continually chase more visibility, success, influence and wealth. Science today needs metabolic balance. Just as medicine recognizes that excessive calorie accumulation can harm health, academia may need to recognize that endless metric accumulation can harm scientific creativity. Institutions may need to reward originality, courage, mentorship, replication, long-term thinking, and difficult problem-solving more strongly than sheer output volume. The thrifty gene hypothesis reminds us that systems optimized for survival in one era can become dysfunctional in another. The same may be true of modern science. (The writer is an ANRF Prime Minister Professor at COEP Technological University, Pune, and former Director of the Agharkar Research Institute, Pune. Views personal.)

Careers in the Age of Artificial Intelligence: What Is Safe and What Is Not?

Understanding how different professions may respond to AI can help students, parents, educators, and policymakers make wiser choices for the future.

AI generated image
AI generated image

A quiet anxiety is spreading across classrooms, workplaces, and households across the world. As artificial intelligence becomes increasingly capable in writing reports, analysing data, generating images, and even producing computer code, many people are beginning to ask a simple but unsettling question: Which jobs will survive the age of AI?


For students planning their careers, parents advising their children, and educators designing the next generation of curricula, this question is no longer theoretical. The choices made today may determine how well individuals adapt to a rapidly changing technological landscape.


To better understand this emerging reality, a broad classification of occupations was attempted by grouping jobs into three categories: highly immune to AI, moderately immune to AI, and vulnerable to AI. The purpose of this exercise is not to predict the future with certainty, but to identify patterns in how technology interacts with different kinds of work.


Several international studies have attempted to understand how automation and artificial intelligence may reshape employment. Research from institutions such as Oxford University, the OECD, and the World Economic Forum suggests that while many work activities may be automated, only a relatively small proportion of occupations are likely to disappear entirely.


Most professions consist of multiple tasks. Some of these tasks can be automated, while others continue to require human judgment, creativity, or interaction. In many cases, the future of work will therefore involve humans and machines working together, rather than machines simply replacing humans.


Understanding which human capabilities remain difficult to automate is therefore key to thinking about future careers.


Immune to AI

The first category includes professions that are highly resistant to AI replacement. These occupations typically require human interaction, emotional intelligence, physical dexterity, or complex judgment in unpredictable environments.


Healthcare professions provide clear examples. Doctors, nurses, physiotherapists, and mental health counsellors rely not only on knowledge but also on empathy and trust. Caregivers for children, the elderly, and persons with disabilities similarly perform roles that machines cannot easily replicate.


Skilled trades such as electricians, plumbers, carpenters, masons, and appliance technicians also fall into this category. Their work requires manual skill, situational awareness, and real-world problem solving in constantly changing environments.


Many occupations rooted in community life are equally resilient. Farmers, gardeners, chefs, artisans, musicians, sports coaches, and hospitality workers rely heavily on creativity and human connection. Even traditional roles such as priests, funeral service providers, and cultural performers remain difficult to automate because they are deeply embedded in social and cultural relationships.


Moderately Immune to AI

The second category includes professions that are moderately immune to AI. In these fields, artificial intelligence can serve as a powerful tool, but it cannot replace human expertise entirely.


Scientists, engineers, lawyers, chartered accountants, civil servants, and university professors belong to this group. Software developers and AI engineers themselves also fall into this category. Artificial intelligence can assist them by analysing data, generating code, or identifying patterns, but human reasoning, accountability, and creativity remain essential.


Similarly, many analytical and planning professions rely on interpretation and decision-making. Environmental auditors, energy auditors, policy analysts, and logistics managers must evaluate complex situations and make judgments that carry social and economic consequences. AI can assist their analysis, but the final responsibility for decisions still rests with humans.


Vulnerable to AI

The third category consists of jobs that are more vulnerable to automation. These occupations often involve routine, repetitive tasks or structured information processing.


Data entry operators, clerical staff, telemarketing executives, and certain types of call centre work are typical examples. Activities such as processing forms, maintaining records, or handling standardised transactions can increasingly be performed by algorithms and automated systems.


As digital technologies advance, many such tasks may gradually be absorbed by software systems that operate faster and more efficiently than manual processes.


This transition is unlikely to be abrupt, but rather a steady reallocation of routine work from humans to machines, often unnoticed until its cumulative effects become visible. In many sectors, automation will not eliminate jobs entirely but will redefine them, reducing the need for repetitive functions while increasing the value of oversight and decision-making.


When these categories are examined together, a striking pattern emerges. Many hands-on and community-oriented professions appear more secure than several desk-based clerical jobs.


Skilled trades, caregiving roles, and hospitality services require flexibility, judgment, and human understanding - qualities that machines struggle to replicate.


Another important observation is that resilient careers tend to combine several uniquely human abilities: problem solving, creativity, emotional intelligence, communication, and adaptability. Occupations that depend mainly on routine information processing are the most vulnerable to technological disruption.


It is important to recognize that this classification is not fixed. Technological progress is dynamic, and the relationship between humans and machines continues to evolve.


Some professions that appear secure today may change in the future, while entirely new careers will emerge. A century ago, professions such as software engineering, cybersecurity, and data science did not even exist. The coming decades will undoubtedly create new roles that we cannot yet fully imagine.


The real lesson from this exercise is not simply identifying which job is “safe.” Rather, it highlights the importance of developing capabilities that complement technology instead of competing with it.


This requires a shift in mindset - from viewing machines as rivals to understanding them as tools that can extend human potential.


For students choosing careers, the message is clear: cultivate skills that machines struggle to replicate like curiosity, creativity, empathy, communication, and the ability to solve complex problems in real-world settings. For parents and educators, the challenge is to encourage learning that goes beyond rote knowledge and prepares young people for a world where humans and intelligent machines work together.


Artificial intelligence will undoubtedly reshape the world of work, but it will not eliminate the need for human imagination, judgment, and compassion. As machines become more capable, these distinctly human qualities may become even more valuable.


To make this discussion more concrete, a detailed classification of occupations has been compiled and organized into the three categories described above. Readers who wish to explore the full list of professions and the reasoning behind the classification can download the dataset here:


Such classifications should be viewed as evolving guides rather than final answers. As artificial intelligence advances, the boundaries between categories will continue to shift. What will remain constant, however, is the enduring value of human creativity and judgment in shaping the future of work.


(The author is an ANRF Prime Minister Professor at COEP Technological University, Pune; former Director of the Agharkar Research Institute, Pune; and former Visiting Professor at IIT Bombay. Views personal.)

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