Come to think of it,
There is a huge degree of similarities between training an AI model and preparing for civil service, at least at the fundamental level. Let’s see how?
An AI model does not begin by “thinking.” It begins by consuming data—huge volumes of it. The quality of this data determines the quality of the output. If it fed with biased, noisy, or irrelevant information, and the model fails spectacularly.
Civil services preparation works the same way. Aspirants start with sources—NCERTs, standard textbooks, current affairs, previous year questions. Too many sources create a lot of confusion; poor sources create distortion. The early months of preparation are not about speed or brilliance, but about choosing the right inputs. This is where many journeys derail silently.
There is a popular myth that learning is sudden—that one-day clarity strikes. AI training exposes this myth. AI Models learn through repeated exposure, running over the same data again and again, adjusting minutely each time. Progress is invisible, incremental, and often boring.
So is serious civil service preparation. One reading rarely teaches anything. It is the second, third, and fourth revisions that build understanding. Confidence does not come from brilliance; it comes from familiarity. Like an AI model, an aspirant does not “get smarter” overnight—they get less wrong every day.
In AI training, errors are not embarrassing; they are essential. The system calculates how wrong it was and adjusts itself accordingly. This process—learning through error—is the very engine of improvement.
Aspirants, however, often fear mistakes. A bad score in mock feels personal. A poor answer feels like a verdict. But preparation does not punish mistakes; it rewards those who study them. Every wrong answer is data. Every weak topic is a signal. Those who treat failure as feedback eventually outperform those who chase perfection.
AI models can memorize training data so well that they fail when faced with new situations. They look intelligent but collapse in the real world.
This is a learning for aspirants as well. Rote learning, model answers, and recycled phrases may work in practice, but the exam demands application, synthesis, and judgment. The exam does not test memory; it tests adaptability. Like a well-trained model, a successful candidate understands patterns, not just facts.
No matter how well an AI is trained, its true worth is revealed only when it encounters new data. Similarly, no matter how many tests an aspirant takes, the real exam day is unfamiliar, unpredictable, and unforgiving.
There are no notes in the exam hall. No shortcuts. No retries. What remains is what has been internalized.
Even after deployment, AI systems continue to learn and adapt. Likewise, the civil services are not a destination but a beginning. Governance demands continuous learning and adaptation. So, the training never ends.
Intelligence—natural or artificial—is not magic. It is patience, feedback, discipline, and humility, applied consistently over time.
In the end, the civil services exam, like AI model training, rewards:
- Not those who rush, but those who refine.
- Not those who avoid mistakes, but those who learn from them and
- Not those who appear intelligent, but those who are quietly, steadily becoming so.
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Steve Ovett, the famous British middle-distance athlete, won the 800-metres gold medal at the Moscow Olympics of 1980. Just a few days later, he was about to win a 5,000-metres race at London’s Crystal Palace. Known for his burst of acceleration on the home stretch, he had supreme confidence in his ability to out-sprint rivals. With the final 100 metres remaining,
[wptelegram-join-channel link=”https://t.me/s/upsctree” text=”Join @upsctree on Telegram”]Ovett waved to the crowd and raised a hand in triumph. But he had celebrated a bit too early. At the finishing line, Ireland’s John Treacy edged past Ovett. For those few moments, Ovett had lost his sense of reality and ignored the possibility of a negative event.
This analogy works well for the India story and our policy failures , including during the ongoing covid pandemic. While we have never been as well prepared or had significant successes in terms of growth stability as Ovett did in his illustrious running career, we tend to celebrate too early. Indeed, we have done so many times before.
It is as if we’re convinced that India is destined for greater heights, come what may, and so we never run through the finish line. Do we and our policymakers suffer from a collective optimism bias, which, as the Nobel Prize winner Daniel Kahneman once wrote, “may well be the most significant of the cognitive biases”? The optimism bias arises from mistaken beliefs which form expectations that are better than the reality. It makes us underestimate chances of a negative outcome and ignore warnings repeatedly.
The Indian economy had a dream run for five years from 2003-04 to 2007-08, with an average annual growth rate of around 9%. Many believed that India was on its way to clocking consistent double-digit growth and comparisons with China were rife. It was conveniently overlooked that this output expansion had come mainly came from a few sectors: automobiles, telecom and business services.
Indians were made to believe that we could sprint without high-quality education, healthcare, infrastructure or banking sectors, which form the backbone of any stable economy. The plan was to build them as we went along, but then in the euphoria of short-term success, it got lost.
India’s exports of goods grew from $20 billion in 1990-91 to over $310 billion in 2019-20. Looking at these absolute figures it would seem as if India has arrived on the world stage. However, India’s share of global trade has moved up only marginally. Even now, the country accounts for less than 2% of the world’s goods exports.
More importantly, hidden behind this performance was the role played by one sector that should have never made it to India’s list of exports—refined petroleum. The share of refined petroleum exports in India’s goods exports increased from 1.4% in 1996-97 to over 18% in 2011-12.
An import-intensive sector with low labour intensity, exports of refined petroleum zoomed because of the then policy regime of a retail price ceiling on petroleum products in the domestic market. While we have done well in the export of services, our share is still less than 4% of world exports.
India seemed to emerge from the 2008 global financial crisis relatively unscathed. But, a temporary demand push had played a role in the revival—the incomes of many households, both rural and urban, had shot up. Fiscal stimulus to the rural economy and implementation of the Sixth Pay Commission scales had led to the salaries of around 20% of organized-sector employees jumping up. We celebrated, but once again, neither did we resolve the crisis brewing elsewhere in India’s banking sector, nor did we improve our capacity for healthcare or quality education.
Employment saw little economy-wide growth in our boom years. Manufacturing jobs, if anything, shrank. But we continued to celebrate. Youth flocked to low-productivity service-sector jobs, such as those in hotels and restaurants, security and other services. The dependence on such jobs on one hand and high-skilled services on the other was bound to make Indian society more unequal.
And then, there is agriculture, an elephant in the room. If and when farm-sector reforms get implemented, celebrations would once again be premature. The vast majority of India’s farmers have small plots of land, and though these farms are at least as productive as larger ones, net absolute incomes from small plots can only be meagre.
A further rise in farm productivity and consequent increase in supply, if not matched by a demand rise, especially with access to export markets, would result in downward pressure on market prices for farm produce and a further decline in the net incomes of small farmers.
We should learn from what John Treacy did right. He didn’t give up, and pushed for the finish line like it was his only chance at winning. Treacy had years of long-distance practice. The same goes for our economy. A long grind is required to build up its base before we can win and celebrate. And Ovett did not blame anyone for his loss. We play the blame game. Everyone else, right from China and the US to ‘greedy corporates’, seems to be responsible for our failures.
We have lowered absolute poverty levels and had technology-based successes like Aadhaar and digital access to public services. But there are no short cuts to good quality and adequate healthcare and education services. We must remain optimistic but stay firmly away from the optimism bias.
In the end, it is not about how we start, but how we finish. The disastrous second wave of covid and our inability to manage it is a ghastly reminder of this fact.