The Lift Line
In the column’s crash test, the machine spared the four homeless pedestrians. It was the human who wanted it taught that they had no worth.
Why This Editorial Matters for Your Exam
The AI industrial policy debate, the ethics of automation and the classical individual-vs-nation-state distinction in Indian political thought all appear in this one piece. It is a rare editorial that pulls GS 2 (governance and welfare), GS 3 (technology, growth and employment) and GS 4 (ethics and public philosophy) simultaneously, which is exactly the pattern the current Mains is testing.
GS Paper 3: Awareness in the fields of IT, Space, Computers, Robotics, Nano-technology, Bio-technology; Effects of liberalization on the economy. GS Paper 4: Ethics and human interface; contributions of moral thinkers and philosophers from India and world.
| Concept | Meaning | Why it is testable |
|---|---|---|
| Zero-sum efficiency | Automation gains that reallocate rather than expand welfare, so one group loses what another gains | The piece’s diagnosis of the AI economy in its current form |
| Society above the nation-state | Tagore’s proposition that the individual and the community precede the machinery of the state | GS 4 anchor for the Tagore reference |
| Technological unemployment | Job displacement caused by capability advance rather than by demand shortfall | The mechanism at work in the Tenderloin backdrop |
| AI value alignment | Ensuring an AI system optimises for outcomes consistent with human values including equal worth | The frame for the crash-test example |
Background and Context
This is a signed opinion column by Ateesh Tankha, founder-CEO of ALSOWISE Content Solutions and MD of eTrans Solutions, and the argument below is his rather than the newspaper’s. It uses two street scenes and one historical reference to make its case.
The first setting is San Francisco’s Tenderloin district, adjacent to the AI-driven tech boom. The second is a hotel-bar exchange over a father’s account, to his adult son, of an alleged plot, linked to Ghadar activists, against Rabindranath Tagore on his second US visit in 1916, when he stayed at the Palace Hotel in San Francisco. The Tagore reference matters because his lectures on Nationalism (delivered in Japan and the United States in 1916-17 and published in 1917) placed society above the nation-state, and his position on India’s freedom struggle was to refuse to condone violence, not to support British rule as his critics claimed at the time. His long correspondence with Mahatma Gandhi, from 1915 to 1941, debated nationalism, non-cooperation and the charkha.
The third scene is a crash-test simulation for a full self-driving vehicle in which the program swerves into the divider rather than hit four homeless pedestrians. A drunk tech worker complains that it must be taught “the relative worth of each individual. Those four had none.” The vignette is fictional; the value-alignment concern it dramatises is not.
The Analysis
1. Efficiency as a zero-sum game. The piece’s diagnostic is that when labour is optimised by “insatiable efforts of enlightened capitalism”, the outcome is either conformity and profit or redundancy and exclusion. In a productivity revolution whose gains flow disproportionately to capital, the aggregate can rise while the median stagnates. This is the standard reading of the US labour-market data from the last decade.
2. The homelessness backdrop is data, not scenery. The Tenderloin district is one of the densest concentrations of unhoused people in the United States, at the doorstep of one of the richest capital pools in world history. That coexistence is not accident, and it is not eliminated by growth; it is produced by the specific distribution of the growth. India, at an earlier stage of the AI capability curve, has the choice to design around that outcome rather than to arrive at it.
3. Tagore’s positioning is the piece’s ethical anchor. In Nationalism (1917), Tagore argued that the nation-state forces the marriage of politics and commerce and streamlines the efforts of individuals, at the cost of the individual’s inner life. His refusal to condone violence during the freedom movement, which in the column’s telling made him the target of an alleged plot linked to Ghadar activists, is not a footnote; it is the operational form of the same principle. “Society above the nation-state” is the phrase; the practice was ahimsa.
4. “Slaves are better than beggars” is a false choice. The piece stages the interlocutor’s claim that “nation stands above society” and “slaves are better than beggars” as a self-refuting argument. A doctrine that accepts either slavery or beggary as the terms of trade has surrendered the design question, which is precisely the question the AI transition puts on the table.
5. The value-alignment example cuts the other way. In the column’s vignette the model protects the vulnerable, and it is the human who wants that behaviour trained out. The general principle is that AI systems carry the values their builders and evaluators choose to reward. India’s AI ecosystem faces the same choice when it sets its training-data and evaluation regimes.
6. India’s AI industrial policy is at the design stage. The IndiaAI Mission, approved in March 2024 with a budget of Rs 10,371.92 crore over five years, funds compute, foundation models, datasets, applications, safety and skilling. The design opportunity is to require workforce-transition and social-protection provisions alongside capability funding, rather than in a later, separate legislative track. The Digital Personal Data Protection Act, 2023, gives the regulatory frame for personal data; the workforce piece is still being written.
7. The comparative case is not the United States alone. The Tenderloin outcome is one point on a distribution. The European response to automation has been closer to the “flexicurity” of Denmark and the industrial adjustment programmes of Germany, in which the transition is a first-class fiscal commitment. India can borrow from both the failure and the corrective.
8. What the piece asks the reader to hold onto. The closing line, “we have to stop treating people like they are numbers”, lands ironically: the Tagore figure walks off with the very tech workers who treat people as numbers. The productive question is whether industrial policy takes the line seriously before the exclusion is built in, or only after.
Data and Institutions Vault
Prelims-grade facts:
The Tagore reference:
- Rabindranath Tagore’s second US visit: 1916, stayed at the Palace Hotel in San Francisco.
- An alleged assassination plot linked to Ghadar activists was reported during that visit.
- Tagore’s “Nationalism” lectures: delivered 1916-17 in Japan and the US; published 1917.
- Tagore’s correspondence with Mahatma Gandhi: 1915 to 1941.
- Tagore won the Nobel Prize in Literature in 1913 for Gitanjali.
The San Francisco backdrop:
- The Tenderloin is a district in central San Francisco with one of the highest concentrations of unhoused people in the United States.
- The AI-driven tech boom in the San Francisco Bay Area is centred on the same city and the wider Silicon Valley.
- Emma Lazarus wrote “The New Colossus” in 1883; it is inscribed at the base of the Statue of Liberty.
India’s AI policy scaffolding:
- IndiaAI Mission approved by the Union Cabinet in March 2024 with an outlay of Rs 10,371.92 crore over five years.
- IndiaAI Mission pillars: compute infrastructure, foundation models, datasets, application development, future skills, startup financing, safety and trust, and IndiaAI Innovation Centre.
- Digital Personal Data Protection Act, 2023: the current statute for personal data.
- Ministry of Electronics and Information Technology (MeitY) is the nodal ministry for AI policy.
- National Strategy for Artificial Intelligence (#AIforAll) released by NITI Aayog in June 2018.
The philosophical grounding:
- Tagore’s “society above the nation-state” is the frame in Nationalism (1917).
- “The New Colossus” (Emma Lazarus, 1883) is the poem whose lines “Give me your tired, your poor, your huddled masses...” are inscribed on the Statue of Liberty.
Watch the trap: Tagore was not opposed to Indian independence; he refused to condone violence and placed society above the nation-state. Describing him as “unwilling to support the freedom struggle” is a Prelims trap that the piece explicitly corrects.
The Debate
FOR the piece’s frame: The direction of the AI wave will be set at the design stage. If capability funding proceeds without a workforce-transition track and a social-protection floor, the exclusion outcome is not a possibility but a consequence. Tagore’s placement of society above the nation-state, and of the individual above the aggregate, is the public philosophy that keeps the productivity gain from being read out only in the aggregate. The Tenderloin is the counterfactual India can still avoid.
AGAINST reading the framing as urgent: The productivity gain from AI is real, the fiscal capacity it generates is real, and past technological revolutions have expanded rather than contracted employment on any long enough time horizon. A “growth first, distribution later” doctrine has domestic political appeal, and treating the Tagore ethic as an operational principle risks slowing the capability build without preventing the distribution outcome.
Balanced verdict: The productivity gain and the distribution outcome are independent variables and both are decided by policy design. Capability build cannot wait; workforce transition and social protection cannot lag it. The IndiaAI Mission budget structure, the Digital Personal Data Protection Act implementation and any AI liability framework the coming session of Parliament brings are the near-term instruments through which the piece’s ethic becomes policy or does not.
How to Think About This
For any technology-and-society question, ask three sequential questions. First, is the productivity gain a real capability increment or a reallocation? Real gains create fiscal space; reallocations only shift it. Second, is the distribution of the gain designed or residual? A residual distribution is a political outcome, not a design outcome, and it tends to concentrate. Third, is the individual being read as an end or as a number? The design that reads the individual as a number will produce, at scale, the outcome the column’s drunk tech worker wants. An answer that runs the three is the one that treats the AI transition as a policy problem rather than a moral panic.
Diagram-in-Words
PYQ Linkage
- UPSC CSE Mains 2020, GS4: “‘Education is not an injunction, it is an effective and pervasive tool for all-round development of an individual and social transformation’. Examine the New Education Policy, 2020 (NEP, 2020) in light of the above statement.” Uses the same individual-and-society register.
- UPSC CSE Mains 2021, GS4: “Analyse the following passage. ‘Every work has got to pass through hundreds of difficulties before succeeding. Those that persevere will see the light, sooner or later’. Vivekananda.” A cognate case-study format on Indian philosophical writings applied to public choice.
Sources: Economic Times, Ministry of Electronics and Information Technology, NITI Aayog
The tech boom and its wealth creation are equal parts curse and blessing when efficiency becomes a zero-sum game, and the argument that “nation stands above society” so that “slaves are better than beggars” is not a development doctrine but the abdication of one; Tagore’s placement of society above the nation-state and of the individual above the aggregate is the frame that keeps automation from becoming exclusion by design.
- San Francisco’s Tenderloin district, at the doorstep of the AI-driven tech boom, shows growing homelessness lightly confined and prodded by authorities, an outcome that “enlightened capitalism” produces when labour is optimised without redistribution.
- The mural over a fallen Statue of Liberty inscribes Emma Lazarus’ 1883 “The New Colossus”, which invited “your tired, your poor, your huddled masses yearning to be free”; the counter-image, an occupant of a driverless Waymo taxi cocking a snook at street-dwellers sharing a sandwich, captures the reversal.
- Tagore, on his second visit to the United States in 1916, staying at the Palace Hotel in San Francisco, was reportedly the target of an alleged plot linked to Ghadar activists; the column reads the episode as the price of refusing to condone violence and of placing society above the nation-state.
- In the column’s crash-test vignette, the self-driving program spares four homeless pedestrians and swerves into the divider, and it is a drunk tech worker who complains that it must learn “the relative worth of each individual. Those four had none”; the danger lies in the human instruction to strip that protection out.
A pro-growth counter-view holds that periods of concentrated wealth creation have historically preceded distributional gains, that tech-sector productivity spillovers lift wages across the economy, and that a “growth first, welfare later” doctrine has worked for the United States and can work for India; Tagore’s framing is aesthetic, not economic.
Design AI industrial policy in India (through the IndiaAI Mission and adjacent instruments) with three parallel tracks, capability build, workforce transition and social protection, so that no significant capability push proceeds without an accompanying transition programme; treat displacement as an anticipated cost line, not a residual; and preserve, in law and in curriculum, the distinction between the individual and the aggregate that Tagore’s work insists on.
Rabindranath Tagore placed society above the nation-state. Examine this proposition against the backdrop of the current AI-led wealth creation, using the San Francisco example, and evaluate how India can design an AI industrial policy that does not reproduce the exclusion the piece describes. (250 words)
The direction of any technological revolution is set by the framing under which it is deployed. When automation is optimised for efficiency alone, exclusion is not a side effect; it is the design choice made by omission.
The current AI wave is the fastest capability increment of the century so far, and the choice of framing under which India adopts it will decide whether the productivity gain shows up in aggregate output alone or in the lives of the workers it displaces.
Three ethical registers frame the choice. The first is individualist: does the model treat the human as an end or as a number? In a crash-test thought experiment where the program protects four homeless pedestrians, it is the human who wants it retrained to assign them no worth; the risk lies less in the model than in the values its builders choose to impose.
The second is communitarian: does the growth doctrine place society above the nation-state, or the nation-state above society? Rabindranath Tagore’s consistent position, from Nationalism (1917) to his correspondence with Mahatma Gandhi, was that the nation-state, when it forces the marriage of politics and commerce, makes slaves of individuals, and that a society-first ethic is the corrective. The third is distributional: does the growth create the fiscal capacity to reach the displaced, or does it treat the displaced as an externality? The Tenderloin, at the doorstep of San Francisco’s tech boom, answers the question by omission.
India’s AI industrial policy, anchored in the IndiaAI Mission with support for compute, foundation models, datasets and applications, is at the design stage where those three registers can still be built in as a first principle rather than retro-fitted. That means a workforce-transition track alongside every capability push, a social protection floor that captures gig-worker-like AI-adjacent roles, and an anticipatory reskilling budget indexed to sector automation intensity.
It also means the curriculum, at school and undergraduate levels, has to hold the distinction between the individual and the aggregate that Tagore’s work insists on, because a workforce that has read only the aggregate cannot resist being read only as one.
The productivity of the AI economy is not in dispute. The distribution is.
A doctrine that says “slaves are better than beggars” is not an answer to the exclusion problem; it is a trade that Tagore’s framing of society above the nation-state rejects. An AI industrial policy that builds capability, transition and protection together, and a public philosophy that keeps society above the nation-state, is the design that reads people as people and delivers the productivity gain to more of them.
Interview Angle
"You are asked whether India should adopt the "growth first, distribution later" development doctrine that has produced the Tenderloin at the doorstep of San Francisco's tech boom. What would you answer, and what would you build into the AI industrial policy to keep that outcome off the table?"
Cite this page: Tagore, the AI Economy and the People Left Behind: Why "Slaves Are Better Than Beggars" Is Not a Development Doctrine — Ujiyari.com | Free UPSC & State PCS Editorial Analysis
