AI, Work,
and India's
Next Bargain
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India is vulnerable, but has optionality.
AI is best understood as a general purpose technology: it will not simply "take jobs" or "create jobs." It will force a search for new patterns of production, new contracts, and new ladders into skilled work.
The first shock lands where India has proudly built capability.
Code, back-office work, customer support, analytics, content, testing, and entry-level professional services are unusually exposed to software that can read, write, summarize, classify, and act.
The next gains come from faster discovery.
English-language talent, an IT services base, digital public infrastructure, frugal firms, and a large domestic market give India many places to try new combinations.
Sources: Bresnahan and Trajtenberg on general purpose technologies; Kling on PSST; Anthropic Economic Index; ILO, India Employment Report 2024.
A 90-minute map in five moves.
Start with history
What makes a technology "general purpose," and why social change usually arrives through complements.
Use the task lens
Separate tasks, jobs, firms, contracts, and measured productivity.
Read today's evidence
Usage is broad, labor-market effects are concentrated, and capabilities are moving fast.
Bring it to India
Map the shock to cities, services, youth, migration, manufacturing, and state capacity.
End with choices
Policy, safety nets, taxation, and the capabilities young Indians should build.
Clear-eyed optimism
We should expect stress and still build for more experiments, not fewer.
A GPT is not one invention. It is a long reorganization.
It can be used across a large part of the economy, not just in one narrow industry.
The technology itself keeps improving, often through learning curves and engineering feedback.
Its full value depends on new processes, skills, institutions, infrastructure, and business models.
That third feature is the important one for society. Steam, electricity, computers, and the internet mattered because factories, cities, offices, schools, firms, and laws changed around them.
Sources: Bresnahan and Trajtenberg, General Purpose Technologies; David, The Dynamo and the Computer; Helpman, General Purpose Technologies and Economic Growth.
Earlier GPTs changed society by changing the production map.
Factories and transport
Power became less tied to muscle, animal energy, and local water sites. Industry and railways changed where work could happen.
The factory got redesigned
Motors did not instantly raise productivity. Factories had to be rebuilt around flow, layout, and flexible power.
Information became cheaper
Office work, logistics, finance, and design changed once calculation, storage, and communication became programmable.
Markets became searchable
Search, e-commerce, cloud software, and platforms reduced matching costs and reorganized distribution.
The lesson is not "all change is good." The lesson is that the first-order technology is only the beginning; the complements decide the social result.
Hayek explains why the relevant knowledge cannot be centralized in advance; Amara explains why timing fools us while local actors discover complements.
Sources: David on electricity and productivity delay; Bresnahan and Trajtenberg; Helpman, GPT volume; Hayek, The Use of Knowledge in Society; Greenstein on Amara's Law.
Output can rise while the social bargain becomes unstable.
When technology changes the relative scarcity of skills, capital, and coordination, society has to renegotiate who gets paid, who bears risk, and who gets a path into the new system.
Sources: Acemoglu and Restrepo, Automation and New Tasks; Acemoglu, The Simple Macroeconomics of AI; Trammell on capital substitution.
People rarely fight the machine. They fight the bargain around the machine.
Work standards
The Luddite protests of 1811-1816 were not a generic hatred of technology. They were a fight over wages, skill, and control in textile work.
Codified skill
The Jacquard loom used punched cards to automate patterned weaving, making a beautiful craft more programmable.
A useful myth
The shoe-thrown-into-the-machine origin story is probably folk etymology. But the myth survives because it captures a real social fear.
A better global frame: every society has its own version of "what happens when a productive pattern breaks before a new one is ready?"
Sources: Britannica on Luddites; Britannica on the Jacquard loom; Etymonline on sabotage; Acemoglu and Restrepo on displacement and reinstatement.
Automation is not fate. It is a tug of war between three effects.
Capital or software performs tasks that workers previously did.
Cheaper production expands demand and can raise demand for remaining tasks.
New human work appears: design, maintenance, judgment, sales, coordination, and care.
Baumol's cost disease is the slow-motion version: when some sectors get more productive, wages rise economy-wide, but hard-to-automate services become relatively expensive. AI matters because it may raise service productivity directly - or push cost pressure into the remaining human tasks.
The key question is not whether AI automates. It is whether the whole human-AI system—prediction, verification, error costs, and fallback—creates enough productivity and new tasks to build better ladders.
Sources: Acemoglu and Restrepo, Automation and New Tasks; Acemoglu, The Simple Macroeconomics of AI; Baumol, Macroeconomics of Unbalanced Growth; Bryan & Gans, Training AI For When Humans Will Use It.
Replacing income does not replace what work does.
A paycheque buys material independence. Work also provides impersonal routes to competence, standing, routine, and belonging.
Independence
Resources without relying on family, patron, or clan.
Mastery
A place to become useful and have skill recognized.
Respect
An impersonal way to earn dignity beyond inherited identity.
Common purpose
Coworkers, routine, shared effort, and a role in public life.
Income support may be necessary in a high-automation economy, but it is not sufficient. The institutional problem is to preserve contribution and earned respect without making people dependent on private relationships.
Source: After Work, We'll Have Each Other. This is a historical and sociological argument, not a causal forecast of AI unemployment.
The task is not the job.
Labor markets hire bundles: routine work, exceptions, judgment, accountability, and relationships.
What AI is good at first
Codified, pattern-rich work: drafting, coding, classifying, testing, and planning.
What remains bundled
Authority, trust, tacit know-how, local context, and messy exceptions.
U.S. travel-agent employment is more than 60% below its dot-com peak. Yet the survivors' weekly pay rose from 87% of the private-sector average in 2000 to 99% by 2025. Software removed the separable tasks and raised the value of what remained.
If AI takes 40% of your tasks, which part of the remaining job becomes scarce?
Sources: Luis Garicano, The task is not the job; Alex Imas on scarcity and the human sector; Anthropic Economic Index task data; New Yorker on Cowen's Average Is Over.
Optimism should come from search capacity, not inevitability.
Kling's PSST frame sees a technology shock breaking old patterns of specialization and opening a search for new ones.
Workers learn new tools.
Firms redesign workflows.
Customers discover new wants.
Schools change credentials.
Cities absorb migration.
Finance funds experiments.
Regulators allow sandbox learning.
Public goods reduce friction.
Bad bets die quickly.
Good patterns scale.
AI expands the pattern set; India's advantage depends on how quickly it can test, kill, scale, and teach. The institutional test: do schools, regulators, public goods, and exit rules lower the cost of entry, feedback, and failure - or protect incumbents?
Sources: Arnold Kling, Macroeconomics: Some Defects; Greenstein on Amara's Law.
AI touches the white-collar ladder itself.
Natural language becomes a way to command software and produce drafts.
Code generation turns expert workflows into semi-automated loops.
Models increasingly act across tools, not just answer inside a chat window.
Digital work can diffuse much faster than physical machinery.
The risky part is not that every job disappears. The risky part is that the old apprenticeship model for professional skill may be partially automated before the new one is built.
Sources: Anthropic Economic Index; Anthropic geography and enterprise adoption report; METR long-task benchmark; Stanford Digital Economy Lab labor-market review.
AI is already broad, but not yet evenly economic.
Approximate weekly users by July 2025 in OpenAI's usage study.
Share of Anthropic Economic Index conversations classified as augmentation rather than automation.
Share of Claude.ai conversations that are "directive" - full task delegation rather than collaboration - in the Anthropic Economic Index.
Usage data says "diffusion is real." It does not yet say "productivity transformation is complete." That distinction matters for policy and careers.
Sources: OpenAI, How People Use ChatGPT; Anthropic Economic Index; Anthropic geography and enterprise adoption report.
Capability, adoption, and adjustment produce different economies.
The same technical frontier can yield very different labor markets. Anthropic's tool separates three internally consistent paths; it does not pretend to forecast which one arrives.
Useful gains diffuse gradually. Workers and firms have time to reorganize.
AI can do half of knowledge work by 2030, but adoption lags capability; growth rises while knowledge wages flatten.
Nearly all knowledge work becomes autonomous; annual GDP growth surges while unemployment rises above recession levels.
The policy variable is not capability alone. Watch adoption speed, whether new human tasks appear, and whether institutions can redistribute bargaining power and status during adjustment.
Source: Anthropic, Scenarios for our Economic Future, based on Korinek et al. (2026). The extreme case is a thought experiment; the tool excludes some data-center demand and may understate innovation feedback.
And the leading edge is already moving from chat to delegation.
Growth in Codex weekly users in the first half of 2026.
Rise in users delegating at least one 8+ hour expert task.
More than 10% of users run three or more agents in parallel.
OpenAI workers' work-related output tokens flowing through agents, not chat.
Read it carefully
This is frontier behavior, not the average firm. But it is delegated production, not merely consultation.
Sources: Johnston et al., The Shift to Agentic AI: Evidence from Codex (OpenAI, June 2026); OpenAI, How agents are transforming work.
The capability frontier is moving from answers toward work products.
The chart is conceptual. METR estimates a fast-rising time horizon for software tasks; GDPVal evaluates economically valuable work products.
Why this matters
Benchmarks are moving away from puzzles and toward deliverables: memos, spreadsheets, code changes, professional analysis, and multi-step tasks. That is closer to how workers are evaluated.
The policy caveat: benchmarks are not the economy. They tell us what may become automatable, not whether firms will reorganize well.
Sources: METR, Measuring AI Ability to Complete Long Tasks; METR arXiv paper; OpenAI GDPVal companion site; GDPVal paper.
The aggregate job apocalypse has not shown up. The margins are split.
No broad collapse
Little sign yet that AI is shrinking US payrolls. In a 2026 Census survey, only about 1% of firms said AI changed their headcount - split between up and down.
Entry-level risk
Some early-career workers in highly exposed occupations show clear relative employment declines.
Intensity matters
Ramp/Revelio finds high-intensity adopters grew headcount 10.2%; low-intensity adopters did not.
This is exactly the pattern a PSST lens would expect early in a shock: not one national average, but many firm-level and local adjustments moving at different speeds.
Sources: Stanford Digital Economy Lab, AI and Labor Markets; Brynjolfsson, Chandar, and Chen, Canaries in the Coal Mine; Anthropic Economic Index; Kharazian, Simon and Stevens, A New Look at AI’s Impact on Jobs (Ramp/Revelio, June 2026); Bonney et al., The Microstructure of AI Diffusion (Census CES 26-25, April 2026); Guy Berger, An Update on AI Adoption in the United States (April 2026).
The young professional ladder is the part to watch.
The group where Brynjolfsson, Chandar, and Chen find the sharpest relative declines in exposed occupations.
Employment gap for 22-25s in the most AI-exposed jobs (top two fifths) versus less-exposed peers, Nov 2022 to June 2026. Raw gap, no controls.
The initial adjustment appears more in employment than in compensation.
The hypothesis is intuitive: early-career workers often sell codified knowledge before they have tacit judgment. The authors now hedge: with firm controls the drop is about 7% for the most exposed fifth and 11% for the next; an education control roughly halves the raw estimates; survey data to 2024 shows a much smaller gap, except in professional and finance jobs. The dissent: Guy Berger thinks AI is probably playing "a role, but not a dominant one". The US hiring rate was 3.2% in July 2026, comparable to 2010-11, and weak hiring hits newcomers first. New graduates also have less work history than same-age non-graduates, which overstates the graduate penalty - though he thinks the Canaries result survives it. Watch the canary; do not call it settled.
Sources: Brynjolfsson, Chandar, and Chen, Canaries in the Coal Mine (Aug 2026); Stanford Digital Economy Lab labor-market review; Guy Berger, No Country for Young People; Berger, Measuring Grad Unemployment Properly; Berger, JOLTS Recap (July); BLS JOLTS hiring rate.
Beside the breaking rung, a new one: the age of the solopreneur.
Americans earning $100k+ primarily from solo work in 2023.
Rise since 2023 in solo firms crossing $5m and $10m revenue.
Greater chance a 2025 Stripe cohort hits $1m within a year.
Rise in AI-influenced journeys among new Stripe sign-ups.
The flip side of the canary
AI can fill capability gaps that once required hiring. The U.S. signal is suggestive, not settled: applications reached 5.7 million in 2025, while roughly 500,000 firms actually formed in 2023. Federal data so far read as side gigs, not people leaving jobs: applications from businesses not planning to hire are up sharply since 2024, but solo work as a main job is flat. For India, this is optionality - not yet evidence.
Sources: Tedeschi, Rama and Cruickshank, The Age of the Solopreneur (Stripe Economics, June 2026); Sydney Ember, America’s Enterprising Spirit Is Booming After Decades-Long Slump (New York Times, July 2026); Nathan Goldschlag, AI and Entrepreneurship: What is Really Happening? (Economic Innovation Group, September 2026).
Productivity may appear first as a different firm shape.
There are now two useful firm-level clues: AI-native startups look smaller and flatter; high-intensity adopters look like firms learning enough to expand.
Smaller average employment in one 2026 study of AI-native startups.
Higher engineer share, with lower entry-level and manager shares.
Total headcount growth among high-intensity AI adopters over two years.
Entry-level headcount growth among high-intensity adopters; low-intensity adopters show no significant change.
This is not macro proof. It is a mechanism: AI changes employment only when it changes the firm, and the sign can differ between new AI-native firms and incumbents reorganizing around AI.
Sources: Kim and Koning, AI-Native Firms; Marginal Revolution, AI-Native Firms; Kharazian, Simon and Stevens, A New Look at AI’s Impact on Jobs (Ramp/Revelio, June 2026).
Coase and Cheung explain why productivity statistics move late.
The tool arrives first. Then firms have to discover which contracts, boundaries, vocabularies, and accountability systems make the new division of labor legible.
Why firms?
Firms exist when internal coordination is cheaper than repeated market contracting.
Which contract?
The real choice is among contracts when output, effort, quality, and risk are hard to measure.
What changes?
Monitoring, drafting, matching, and delegation get cheaper. But responsibility does not disappear.
That is why "firms organize fast enough" is not a small detail. It is the channel through which task-level capability becomes social productivity, and why the important gains may be invisible until the practice changes.
Sources: Coase, The Nature of the Firm; Cheung, The Contractual Nature of the Firm; Kim and Koning, AI-Native Firms; Carlo Cordasco, What we can’t measure about AI – yet.
The same GPT lands on a very different economy.
The U.S. story is not India's story. India has a services-heavy GDP, agriculture-heavy employment, a thin formal job ladder, strong IT capability, weak manufacturing absorption, and enormous internal variation.
The question is not "Will AI help India?" The question is: which Indians, which cities, which firms, and which ladders?
Sources: ILO, India Employment Report 2024; DataForIndia, PLFS explainer; CSEP, India at Work.
India is a large Claude market—and a thin, concentrated one.
India generated 7.1% of Claude use in May 2026, up from 5.8% in November 2025.
Of 121 countries with enough data, per working-age person (tied with two others). November: #101 of 116.
Computer and maths work, against 23.8% globally: about 31st. November's brief ranked India first at 45.2%, on a measure we cannot reproduce.
Share from Maharashtra, Karnataka, Delhi, and Tamil Nadu. November: 51.9%.
India's average task was estimated at about 36 minutes with Claude against 5.7 hours without it: roughly 10×, against 7× globally. November's figures came from one week of Claude.ai chats; these cover a full month and include Cowork, so read changes since November as indicative. This is concentrated frontier use, not a measure of all Indian AI adoption.
Source: our calculation from Anthropic Economic Index data released with the June 2026 report: Claude chat and Cowork, Free, Pro, and Max plans, May 2026 (April is similar). November baseline: India Country Brief, 13–20 November 2025.
India's output structure and work structure do not line up neatly.
Approximate visual: services dominate output, while agriculture still absorbs roughly half of employment.
The policy trap
AI first hits many formal, urban, service-sector tasks. But India's larger employment challenge remains moving people from low-productivity work into better, more stable work.
That means India's AI question is also a development question.
Sources: ILO, India Employment Report 2024; DataForIndia, PLFS explainer; CSEP, India at Work; DataForIndia on manufacturing jobs.
Services are not one thing.
India's services strength is real. The bridge to exportable, well-paid work is much narrower than "services share of GDP" suggests.
Across TCS, Infosys, and Wipro, FY2026 revenue averaged about $52,500 per employee across 1.16 million workers. AI compresses this headcount-linked model. If better ladders do not grow, workers spill into lower-productivity urban services.
The counter-case, told by a buyer
Databricks cut connector lead time by redesigning work: it outsourced test environments, pooled seven engineers, and shipped seven connectors in one quarter. AI compressed one service bundle and created demand for another. India's question is whether its firms become the redesigners - or the suppliers they now buy from.
Sources: FY2026 revenue/headcount: TCS ($30,017m; 584,519), Infosys ($20,158m; 328,594), Wipro IT Services ($10,478m; 242,156). Ghodsi, Stanford MS&E 435 (20:39-25:28); ILO; CSEP; Anthropic; OpenAI; Bakshi.
China's lesson is not "copy China." It is "learning needs a machine."
The China contrast
China built dense manufacturing ecosystems, infrastructure, process-learning, and state capacity at unusual speed. That created ladders for rural migrants and suppliers.
India's missing middle
India's manufacturing problem is not just too little factory employment. It is too few large, labor-absorbing, productivity-raising firms in the middle of the distribution.
One root is human capital. China built mass literacy before scaling engineering and vocational training; India expanded higher education before universal basic schooling. Education inequality explains roughly a quarter of India's wage inequality, versus under an eighth in China.
AI makes this sharper. If tradable services absorb fewer entry-level graduates, manufacturing and city-building cannot remain permanently "next decade's reform."
Sources: ADB, firm size distribution in Indian manufacturing; Bharti and Yang, Human capital in China and India, 1900-2020; DataForIndia on manufacturing jobs; Joe Studwell, How Asia Works; Dan Wang, Breakneck; Peter Hessler, Other Rivers.
The first Indian AI shock will be local, urban, and occupational.
Bengaluru
Software products, IT services, startups, cloud tooling, analytics.
Hyderabad
IT services, global capability centers, pharma services, back-office operations.
Gurugram and Noida
Consulting, BPO, analytics, finance operations, customer support.
Pune and Chennai
Engineering services, automotive software, IT services, support operations.
This is not a prediction that these cities collapse. It is a claim about exposure: their growth stories are tied to tasks that AI can increasingly perform or radically reshape.
Sources: Anthropic Economic Index occupational task exposure; ILO, India Employment Report 2024; CSEP, India at Work.
What China did to some U.S. towns, AI may do to some Indian task clusters.
The China Shock papers connect a national shock to local labor markets. For India, map AI exposure to city-industry-occupation clusters.
Which tasks?
Code, QA, support, documentation, moderation, analytics, finance operations.
Which cities?
Measure city employment shares in exposed service clusters.
Which buffers?
Firm upgrading, new entry, migration options, education quality, local services.
The model is exposure × local specialization × adjustment capacity. Borrow the method, not the analogy: India's shock is to digital service tasks, not factory trade - and may move faster.
Sources: Autor, Dorn, and Hanson; The China Shock; Anthropic Economic Index; DataForIndia on PLFS.
A city shock is also a family, education, and migration shock.
Push-pull changes
If services ladders weaken, the pull of major cities changes for graduates and families.
Urban pressure
Job-market stress interacts with rents, commutes, infrastructure, and city governance.
Credential bets
Families invest in degrees and coaching based on yesterday's ladder into formal work.
Wider effects
Urban professional earnings support consumption, siblings, parents, and local aspirations elsewhere.
The India story cannot end with a productivity statistic. It has to include the social geography of adjustment.
Sources: ILO, India Employment Report 2024; CSEP, India at Work; Peter Hessler, Other Rivers; Dan Wang, Breakneck.
AI exposure may become a new layer on old inequalities.
The distributional question is not only whether a task is exposed. It is who gets access to exposed, well-paid tasks, and who gets displaced from them.
A 2026 working paper links AI-exposed graduate jobs in India to existing caste inequality.
Mobility, safety, care work, and social norms shape who can move into new AI-complementary roles.
AI opportunity is likely to cluster around cities, English fluency, firms, and institutions.
A fair optimism has to ask not just how much AI raises output, but how widely Indians can enter the new patterns.
Sources: Mishra, The Privilege of Exposure; ILO, India Employment Report 2024; DataForIndia on PLFS.
India has more shots on goal than the pessimistic story admits.
A large English-using professional class can adopt frontier tools early - though English is double-edged: it is also what makes India's entry-level tasks the most automatable.
India already has service firms that know global clients, software delivery, and process discipline.
Digital public infrastructure can lower transaction and verification costs.
A large domestic market allows adaptation for local language, cost, and institutional realities.
The strategic question is whether these advantages become learning machines, not whether they look impressive in a slide about "India's demographic dividend."
Sources: India Stack; ILO, India Employment Report 2024; CSEP, India at Work; Joe Studwell, How Asia Works.
The fragile part is the ladder into good work.
Codified tasks
Many first jobs train people through tasks AI can now draft. In Autor's trial, AI lifted juniors' drafts, but only seniors kept the skill without it.
Uneven quality
Degrees do not reliably certify the judgment needed beyond basic output.
Low-end absorption
Delivery and platform work absorb labor when better ladders fail.
Underbuilt
Housing and infrastructure limits make adjustment expensive.
The adoption choice is the hinge: cost-cutting automation can snap the entry rung; augmentation can bend it without breaking it. Early Indian signals: only 23% of employees are engaged at work, and IT fresher-hiring intent fell from 81% to 76% in 2026. Watch the automation/augmentation split by sector and gig-work absorption by city.
Sources: ILO, India Employment Report 2024; DataForIndia on PLFS; CSEP, India at Work; Brynjolfsson et al., Canaries; Ishan Bakshi, Economic troubles have eased, but foundations for long-term growth are still missing; Autor et al., AI assistance and expertise (NBER w35720); Gallup, Quiet Quitting Is on the Rise in India; TeamLease EdTech, Career Outlook H1 2026 and H2 2026 coverage; Mukherjee, Quiet Quitting Comes to India.
India should build an AI shock dashboard before the shock is obvious.
Task exposure
Map Indian occupations and tasks to AI exposure, separating automation from augmentation.
Local concentration
Track city-industry clusters, hiring, entry-level wages, layoffs, migration signals, and gig-work absorption.
Adoption quality
Measure whether firms are using AI for cost cutting alone or for new products, new markets, and worker leverage.
The PLFS redesign is helpful, but AI diffusion needs higher-frequency, task-level, city-level data. Gig-platform counts belong on the dashboard because they may show stress first. For the habit, copy Guy Berger's weekly US read of claims, job postings, payrolls and business surveys.
Sources: DataForIndia, PLFS explainer; ILO, India Employment Report 2024; Anthropic Economic Index methodology; Mishra, India graduate AI exposure; Ishan Bakshi, Economic troubles have eased, but foundations for long-term growth are still missing; Guy Berger, High Frequency Labor Market Indicators (weekly series, Macro Mostly).
The PSST policy goal: make good experiments cheaper.
Workflow labs
Help small firms test AI for sales, compliance, inventory, customer service, and accounting.
Procurement as learning
Buy measurable AI improvements in courts, health, education, inspections, and citizen services.
Interoperability
Support open data standards, audit trails, procurement templates, and liability clarity.
Do not subsidize "AI adoption" as a slogan. Subsidize discovery of repeatable, measurable patterns that raise output and widen access.
Sources: Kling on PSST; Anthropic Economic Index project; India Stack; ILO, India Employment Report 2024.
Frontier access decides who discovers what AI is for.
Technology is already steered: labs, investors, and policy choose trajectories. When frontier capability cannot diffuse, both the social search and the set of people who can contest that direction narrow.
If only approved institutions can touch frontier capability, who gets to decide what AI is for in Indian conditions?
Sources: Farrell, The Acemoglu Wars Are All About AI; METR incident investigation; Thompson; Ball; Kling. Farrell is political framing, not neutral causal evidence.
AI policy is also infrastructure, energy, cities, and education policy.
Sources: IEA, Energy and AI; ILO, India Employment Report 2024; CSEP, India at Work; Dan Wang, Breakneck.
The UBI question for India is really a state-capacity question.
A universal income floor is attractive when adjustment is broad and hard to target. But India's practical question is what kind of floor the state can finance, deliver, and update without weakening the search for new work.
Basic security
Cash support can reduce desperation and make retraining or migration less fragile.
Active help
Placement, apprenticeships, wage insurance, and training matter when the shock is occupational.
Hard tradeoffs
India must compare UBI to health, schooling, infrastructure, nutrition, and city investment.
The best Indian frame may be a layered system: a reliable cash floor, portable benefits, and aggressive support for moving into new patterns of work.
Sources: Economic Survey 2016-17, UBI chapter; ILO, India Employment Report 2024.
A global token tax sounds elegant until you ask what it taxes.
The temptation
If AI automates labor, tax compute or tokens and use the revenue to fund the social transition.
The problem
Compute is a mobile, hard-to-define intermediate input - often exactly what society wants cheaper. Korinek and Lockwood compare taxing it to taxing steel during the industrial revolution.
Sources: Korinek and Lockwood, Brookings; underlying NBER paper; Albrecht on compute taxes; Anthropic Economic Index; Coasean singularity chapter.
For India's youth, the right question is: what stays scarce?
Use the machine well
Prompting is too small a word. Learn delegation, verification, tool choice, context design, and error detection.
Know something real
Healthcare, law, finance, logistics, education, energy, agriculture, design, operations, public policy.
Own responsibility
Taste, ethics, accountability, client trust, ambiguity, and the courage to decide under uncertainty.
Design education around projects where students must use AI, explain what they trusted, show what they rejected, and defend the final judgment.
Sources: Arnold Kling on project learning and AI; Alex Imas on scarcity and human value; Brynjolfsson et al., Canaries; ILO, India Employment Report 2024.
The future is not a forecast. It is a coordination problem.
AI increases the space of possible production patterns. Some old ladders will weaken. Some new ladders will be built. India should not wait to find out which is which.
Build task, city, firm, and youth dashboards before aggregate statistics move.
Let firms, schools, states, and cities test new productive patterns quickly.
Protect people through the transition without taxing away the tools that help them adapt.
India is vulnerable, but optionality is real if we build the institutions that turn experiments into ladders.
Sources: Kling on PSST; Acemoglu and Restrepo; ILO, India Employment Report 2024.
A reading list for turning the talk into an essay.
The sources to keep closest when turning the talk into an essay.
Source note: Selected from the user's reading notes and the public sources linked throughout the deck.
How the deck changed since June.
Sources: TCS; Infosys; Wipro; Ghodsi; Korinek & Lockwood; Thompson; Ember; Cordasco; Bakshi; Ramp/Revelio; Autor et al.; Gallup; Mukherjee.
What changed on 19 September?
Sources: Bryan & Gans; Anthropic scenarios; After Work; India brief; METR; Farrell.