Economists and Modern-Day Court-Poets

There is an old Tamil story from the literary memory of ancient Madurai and its Pandyan court, preserved in the later Tiruvilaiyadal Puranam, about a poet who refused to be intimidated by power. A Pandyan king had been presented with a poem claiming that a woman’s hair possessed natural fragrance. Nakkeerar, the celebrated poet presiding over the literary assembly, found a flaw in it. The dispute became extraordinary when the poet who had composed the verse revealed himself to be Shiva. Nakkeerar persisted. Even divine identity could not settle the argument. The opening of Shiva’s third eye did not alter his judgement. The argument had to stand on its own merit.

The story has survived for centuries because its subject is considerably larger than poetry or divine intervention. It concerns the relationship between power and truth, and the obligation of an intellectual to preserve that distinction. Indian literary traditions are filled with memories of court poets who enjoyed proximity to kings and the rewards that accompanied it. The more consequential figures, however, were those prepared to question the court when the court was wrong. That instinct of dissent, deeply embedded in our intellectual history, has acquired an unexpectedly contemporary relevance.

Consider the reaction to India’s latest GDP numbers. The economy has been reported to have grown by 7.8% in the first quarter of 2026-27, with real GVA expanding 8.2%, substantially higher than expectations. Yet questions have emerged around the revised national accounts, the 2022-23 base year, the GDP deflator, revisions to earlier estimates and the relationship between the headline number and household experience. These questions touch a larger issue: the credibility of how we know what we think we know about the economy.

The debate over GDP can, and probably will, continue indefinitely. One has rarely seen two economists agree completely on an interpretation. But in a political economy, intellectual disagreement cannot become an end in itself. When economic narratives shape public policy, the cost of prolonged abstraction is ultimately borne by citizens. The urgency, therefore, is to keep debating the numbers while ensuring that the debate sharpens our understanding of the economy and leads to better public policy.

We write this as observers of the economy rather than economists or experts in GDP construction. Our point comes only from what we see and encounter in our lives and professional worlds, rather than from the charts and slides through which economic aggregates are often presented.

The Scarcity Behind Economic Expertise 

For much of the modern era, economics benefited from specialised knowledge and scarcity. Economists worked with data, models and statistical tools that governments, central banks and institutions controlled, leaving citizens dependent on expert interpretation. Artificial intelligence is dismantling that hierarchy. A curious citizen can now ask an AI system to explain a statistic, identify its assumptions, compare measures and examine inconsistencies. The cost of the second question has collapsed.

Data analysis, forecasts, statistical techniques and economic models will become easier to generate and interrogate. Economics has always been built on assumptions: inflation is measured through indices, GDP is estimated, informal activity inferred through proxies and nominal output translated into real output through deflators. Productivity depends upon definitions and measurement choices. Every statistical system creates a representation of reality because the economy is too vast to capture directly.

When Numbers Meet Lived Reality

For millions of citizens, however, GDP is an abstraction they neither understand nor experience. They experience food prices, work, income security, education and healthcare costs, debt repayments and their children’s prospects. The data may be entirely correct for the period and methodology over which they have been calculated, yet that correctness does not answer the elemental question: what does this number mean for my life? An economy can look robust in aggregate while gains remain unevenly distributed and pressures concentrated elsewhere. If we sit in air-conditioned rooms, sipping fancy cuppas, debating aggregate numbers and the fate of millions whose lives and livelihoods depend on public policy decisions made from them, we should have the humility to ask whether the reality inside our spreadsheets resembles the reality outside our windows.

The difficulty begins when an analytical framework becomes so institutionally established that questioning its assumptions is perceived as questioning the institution. Methodologies acquire histories, forecasts acquire reputations and researchers acquire professional identities around particular frameworks. Defending an established conclusion can become more instinctive than examining its premises. The resulting condition is epistemic closure: a system better at explaining its conclusions than absorbing contrary evidence.

The 7.8% estimate may prove to be an accurate reflection of strong underlying activity. Equally, questions about the statistical architecture deserve examination. A low implied GDP deflator does not automatically invalidate the estimate because GDP deflation is conceptually different from WPI or CPI. Methodological legitimacy cannot mean immunity from scrutiny.

AI Changes The Test Of Relevance

AI could fundamentally alter economic debate. A technical question once confined to specialists can now be translated, tested and circulated rapidly. If an assumption appears inconsistent with another indicator and the discrepancy resonates with observed experience, it can become a broader challenge to an established interpretation. It can generate a preference cascade or mass confusion. The democratisation of analysis does not democratise truth. It democratises the capacity to challenge.

Economists will increasingly have to explain how the data came to say what they say, which assumptions matter most, where uncertainty enters and what evidence could change the conclusion. Credibility will depend upon the reasoning surrounding the number.

What Remains Scarce In Economics

AI could make economists less necessary for many tasks that once defined their expertise while making genuinely thoughtful economists more valuable. Tables, forecasts, correlations and first-order interpretations will increasingly become machine-assisted commodities. Judgement remains harder to automate because it begins where evidence becomes ambiguous: recognising when apparently contradictory observations illuminate different parts of the economy, identifying what the model has excluded and knowing when an elegant explanation has become too comfortable.

There is a deeper cultural lesson here for India. Our intellectual inheritance has never been entirely deferential to authority. Nakkeerar story survives because he was willing to subject even divine assertion to scrutiny. His lesson was that authority carried no exemption from argument. Economists will still be needed for monetary policy, fiscal choices, productivity, employment, trade, capital allocation and technological change. Their authority, however, will have to be earned through curiosity about anomalies, transparency about assumptions, willingness to regard disagreement as information and proximity to lived reality.

The needless GDP controversy, and the traction it takes away from recognising a resilient economy, risks obscuring a deeper faultline: India still has miles to go before growth becomes genuinely inclusive. Growth must ultimately mean something in the lives of every citizen, translating into better livelihoods, greater security and a demonstrably better quality of life.

The question is whether economists can preserve intellectual authority when the tools of economic interrogation are no longer scarce. The audience now possesses tools to interrogate performance and challenge premises. Perhaps that is what the age of AI will demand of economics: fewer court poets, and many more Nakkeerars.