A Scenario — 2026 to 2035
The US and China are racing toward artificial general intelligence. India is watching from the stands. This is a scenario — two possible futures — for what happens next.
For a thousand years, a pattern has repeated on the Indian subcontinent. A civilizational power — the wealthiest, most intellectually fertile society on Earth — failed to match the technological and organizational capacity of smaller, hungrier forces. The result was not defeat in a single battle. It was centuries of systematic extraction, first by Islamic sultanates, then by a British trading company that arrived as a partner and stayed as a sovereign.
Artificial intelligence is the defining technology of the next century. The US and China are building it. India is not even in the running. This scenario traces how that gap unfolds — and what it would take to close it.
This is not prediction. It is a scenario — one of many possible futures, written to be specific enough to be falsifiable, provocative enough to force a response. At the end, the path splits. You choose how it ends.
This scenario is directly inspired by AI 2027 — Daniel Kokotajlo, Eli Lifland, Thomas Larsen, and Scott Alexander's research-backed forecast of how AGI unfolds over the next few years. Read theirs first. This is India's chapter of the same story.
I used fictional company names to avoid singling out any one organization. "OpenMind" refers to the leading US frontier AI company. "DragonMind" refers to China's state-coordinated AI effort. "BharatMind" is India's would-be sovereign AI — existing, in 2026, mostly as aspiration. "Relion" refers to India's largest digital and telecom conglomerate. "Arkani Group" refers to India's largest infrastructure conglomerate.
Act I
The race is underway. Two civilizations are building the intelligence infrastructure of the next century. India has barely gotten started.
In the spring of 2026, India's government announces a new AI mission with considerable fanfare and a five-year budget of $1.25 billion. In the same week, a single American company announces a single data centre investment larger than India's entire plan.
The United States alone spends an estimated $660–690 billion on AI infrastructure in calendar year 2026 — twelve times India's entire defence budget. China, through state-directed capital and private megafunds, spends another $70 billion.
The silicon gap is physical. The US has roughly 14 million AI-grade GPUs. China has 9.5 million. India — the world's most populous nation, the "IT superpower" — has approximately 58,000. Every Indian AI startup, every government analytics initiative, every ambitious engineer in Bengaluru runs their models on American cloud infrastructure or, increasingly, on platforms quietly routed through Chinese-backed servers.
"The British didn't arrive with tanks. They arrived with a trading company. By the time India understood what was happening, the extraction was already structural."
Historical parallel, 1757Meanwhile, China's Digital Silk Road moves quietly but relentlessly. In 2026, Beijing signs AI cooperation frameworks with Pakistan, Bangladesh, Myanmar, Nepal, and Sri Lanka — embedding Chinese technical standards, Chinese surveillance systems, and Chinese data pipelines into India's immediate neighbourhood. India notices. India does not respond at the required scale.
The talent picture is equally sobering. India's universities produce extraordinary AI researchers — and then exports the best of them to OpenAI, DeepMind, Anthropic, and Meta. 45% of India's top AI talent works abroad. China retains 95% of its equivalent cohort. India is subsidizing the R&D budgets of its competitors with its own education spending.
Less discussed but equally consequential is Physical AI: the race to embed intelligence into the machines that build things. China has 187 industrial robots per 10,000 factory workers. India has 4. China deploys a quarter-million new units this year alone. India’s factory floors are among the least automated of any major economy — not because the machines are unavailable, but because no one has built the industrial policy to absorb them.
The economics accelerate the problem. A Unitree humanoid robot costs $13,500 in 2026 — below the annual salary of a junior factory hand in India’s export clusters. The price is falling 35% a year. By 2028, a Chinese humanoid costs less than two months of Indian minimum wage. The “demographic dividend” — the young, abundant workforce India’s planners have counted on for two decades — is not a strategy. It is a resource. Resources without investment depreciate faster than anyone is willing to say out loud.
AI Power Index — 2026
Act II
OpenMind releases Agent-4. The world's largest economy begins to automate knowledge work at scale. India's IT sector — built on human cognitive labour — stares into an existential void.
By early 2027, OpenMind's Agent-4 achieves what researchers had called "software engineer parity" — the ability to complete any coding task a senior engineer could, at 30x the speed, for a fraction of the cost. (Benchmark models had already crossed 80% on standard coding tests in late 2025; Agent-4 is the first system to close the remaining gap on novel, unseen codebases.) DragonMind's comparable system follows within seven months, having benefited from a model weight acquisition that US intelligence describes — in classified memos — as "the most consequential technology transfer in history."
The global impact is immediate and brutal. The market for outsourced software development — a market India has dominated for two decades, generating $250 billion annually and employing 5.4 million people — begins contracting at 20% annually. Not because Indian engineers are bad. Because AI engineers don't need salaries, visas, or sleep.
Infosys, TCS, and Wipro collectively announce workforce restructuring affecting 340,000 employees over 18 months. Their framing: "AI-first transformation." The financial press calls it a pivot. Former employees call it something else.
The same week, OpenMind announces 200,000 "AI software agents" available for enterprise licensing at $8 per agent per hour. Tata Consultancy Services' average billing rate: $28 per human hour.
What India does not fully reckon with in 2027 is the second-order effect. India's IT sector was not just a source of foreign exchange. It was the country's primary pipeline for producing a technically sophisticated middle class — engineers, managers, and founders who would eventually build the next generation of Indian industry. That pipeline is rupturing.
India's response, in 2027, is to announce an "AI Services" industry — essentially positioning India as a premier destination for deploying and customizing foreign AI systems. Relion — India's largest conglomerate, controlling telecom, retail, and green energy under its digital arm — launches "MioSphere AI." It runs on Meta's open-source LLaMA architecture. The press release calls it a breakthrough. What it is, precisely, is a foreign model with an Indian brand painted on top.
Meanwhile, Arkani Group — the infrastructure behemoth that controls India's largest ports, airports, and power transmission networks — announces a $14 billion AI data centre investment across seven campuses. This is celebrated as India's entry into serious AI infrastructure. The details are less celebrated: the servers run Nvidia chips on foreign supply chains, the cooling systems are engineered by American firms, and the primary clients are foreign hyperscalers looking for cheap land and power.
The pattern is identical to the diwan class of the Mughal and British eras — Indian intermediaries who prospered enormously by facilitating foreign power, accumulating personal wealth while the structural sovereignty of the civilization eroded around them. India is, in this moment, building hotels in a city whose roads, electricity, and water are all owned by someone else.
And then a second disruption arrives that India was entirely unprepared for — not software intelligence but physical intelligence. Chinese humanoid robots begin appearing on Indian factory floors in 2027. Unitree’s G1 at under $12,000. AgiBot’s Expedition A2 in mass production. Not through any grand policy. Through ordinary procurement decisions by plant managers seeking efficiency gains. They are 40% cheaper than German equivalents and integrate natively with the Chinese industrial AI stack already inside them.
India has no competitive equivalent at scale. A handful of domestic startups are building humanoid prototypes. None are close to commercial deployment. Its factories — textile mills in Surat, automotive assembly in Pune, electronics plants in Tamil Nadu — begin a quiet transition: the “Made in India” label applied to goods built by Chinese-brained machines. The physical dependency takes root alongside the digital one, unnoticed by the same policymakers still debating the digital version.
China now operates over 30,000 “dark factories” — facilities running on autonomous AI-managed production with 80–91% reduction in direct human labour. Their physical AI flywheel compounds at the same rate as their digital one: more deployed robots collect more real-world operational data, which trains smarter models, which enables cheaper, more capable machines. India deploys fewer than 10,000 industrial robots across its entire economy. China deploys that many every eleven days.
India's IT Sector — Crisis Indicators 2027
Every Indian user interacting with OpenMind's AI enriches OpenMind's model with Indian behavioral data, Hindi linguistic patterns, and Indian cultural context. India is exporting raw data so American companies can weave it into intelligence — then selling that intelligence back to India as a service.
In the 19th century, India exported raw cotton to Lancashire. Lancashire sold manufactured cloth back to India. The mechanism is identical. Only the commodity has changed.
Act III
The architecture of a new colonialism takes shape — not through conquest, but through contracts, APIs, and switching costs that compound quietly until they become inescapable.
Five distinct dependency chains now define India's digital economy, each reinforcing the others. Together, they constitute what historians will later describe as the period when India's digital sovereignty was quietly transferred — with India's enthusiastic participation.
Infrastructure: 75% of India's cloud workloads run on AWS, Azure, and Google Cloud. Migrating would cost Indian enterprises an estimated $18 billion and 3–5 years of disruption. Nobody migrates.
Data: India's agricultural yield data, patient health records, financial transaction patterns — the most intimate data about 1.4 billion people — flows daily into models trained in California. India has a data protection law. It has no mechanism to retrieve what has already left.
Platform: 90% of Indian government AI initiatives now run on foundations built by OpenMind, Google, or Meta's open models. The systems that assess tax compliance, flag welfare fraud, screen visa applications, and monitor state borders increasingly run on foreign intelligence with foreign supply chains.
Talent: 45% of India's top AI talent is abroad. Those who return increasingly join Indian subsidiaries of American and Chinese firms — technically in India, strategically elsewhere.
Physical AI: When Indian manufacturers automate, they buy Chinese hardware — actuators, servo motors, lidar sensors, AI control systems. China controls over 70% of global lidar production and has integrated the full physical AI stack. With each installation comes a Chinese data pipeline feeding operational intelligence back to Shanghai. The robot on the floor is the sensor. The factory is the dataset.
In October 2028, India's Ministry of Defence tables a classified assessment. Its finding: in the event of a major geopolitical crisis, India's military decision-support systems — logistics, target assessment, satellite analysis — carry a meaningful dependency on foreign AI infrastructure components. The assessment recommends immediate action. It is marked "restricted." It does not generate immediate action.
The year ends with a number that should be burned into the consciousness of every Indian policymaker: ₹4.8 lakh crore ($58 billion) in annual digital economy value is effectively accruing to foreign platform owners. Not through unfair trade. Through agreements India signed freely, because the alternative — building it yourself — seemed too expensive and too slow. It always does, until it doesn't.
India's physical dependency compounds the digital one. More than 90% of robotic systems deployed in Indian factories — actuators, sensors, control hardware — are Chinese in origin. The "Made in India" motorcycle assembled by Chinese servo motors and directed by a Chinese AI control stack is a useful metaphor for the broader condition. Manufacturing sovereignty is being forfeited through the same mechanism as digital sovereignty: convenience, incrementally chosen, compounding quietly until the exit costs are prohibitive.
Dependency Depth — 2028
| Domain | Foreign Dependency | Trend |
|---|---|---|
| Cloud Infrastructure | 75% | ↑ Rising |
| Foundational AI Models | 90% | ↑ Rising |
| GPU / Compute Hardware | >95% | ↑ Rising |
| Semiconductor Chips | 95% | → Flat |
| Defense AI Systems | ~40% | ↑ Rising |
| Government AI Platforms | 90% | ↑ Rising |
| Industrial Robotics | >90% | ↑ Rising |
The Encirclement Problem — 2028
India is surrounded — not by armies, but by dependency architectures that point toward Beijing. Every data centre built by Huawei in Dhaka is a node in a network that treats India as a geopolitical problem to manage, not a partner to serve.
2029 — The Fork in the Road
The dependency chains are visible. The encirclement is documented.
The GDP cost is calculable. The historical parallel is unmistakable
to anyone willing to look.
The question is whether India — its government, its industry, its citizens —
responds at the scale the moment demands.
Choose the path India takes.
Bear Case
The New
Dependency
India continues on its current trajectory. Good intentions, incremental funding, and the wrong bets.
Bull Case
The Third
Pole
India declares AI sovereignty a national emergency and mobilises at a scale matching the threat.
Bear Case — 2029 to 2035
India chose incrementalism. History, as it has before, chose India.
Facing an economy with 1.2 million IT workers displaced and an AI sector generating revenue almost entirely for foreign shareholders, India's government negotiates a landmark deal: the US–India AI Framework Agreement. The terms, dressed in the language of partnership and co-investment, read differently on close examination.
India receives preferential API access to OpenMind models. Priority compute allocation during non-peak hours. A joint research center in Hyderabad — staffed largely by Indian researchers building American intellectual property on American infrastructure. In exchange: India agrees to data localisation carve-outs for American firms, alignment with US AI governance standards, and a pledge not to subsidise "competing" Indian foundational model efforts classified as creating "market distortions."
Every country bordering India except Bhutan now runs Chinese-built AI infrastructure at the state level. DragonMind's governance AI — processing judicial recommendations, social credit inputs, and predictive policing models — operates in Dhaka, Islamabad, Kathmandu, and Naypyidaw. The Colombo Port City, financed by Chinese capital and governed by Chinese-standard smart city systems, has become a data node with direct pipelines to Beijing.
India's intelligence services flag the architectural implication: any future regional crisis will involve adversaries with AI systems that have been trained on decades of South Asian data, in languages India's own AI systems still struggle to parse fluently. India is now fighting a potential war with tools its adversaries helped design.
An internal Ministry of External Affairs document, leaked to Indian media, contains a single paragraph that becomes India's most-shared political text of the decade:
"In the event of armed conflict with a major neighbouring power, India's decision-support systems, logistics AI, and satellite analysis platforms carry estimated foreign dependency components of 35–60%. The structural nature of this dependency means it cannot be resolved within a conflict timeline. We are, in the relevant sense, not self-sufficient in the tools of modern warfare."
The physical dimension of the reckoning arrives on schedule. Chinese humanoid robots are now available at $7,000 — below the annual minimum wage for an Indian factory worker. The demographic dividend that India's planners spent two decades anticipating quietly inverts: a young workforce with no pathway to manufacturing employment because the capital-intensive entry point — robotic automation — is owned, supplied, and trained by a strategic competitor. The robots work the night shift. The data goes to Beijing.
The structural extraction has become self-sustaining. India's digital economy generates ₹38 lakh crore ($460 billion) annually. Of this, an estimated 12–15% flows to foreign platform owners as API fees, cloud margin, licensing, and data rents — a permanent tax on Indian economic activity paid to foreign sovereigns.
Indian languages have degraded in frontier AI systems. The models trained on English, Mandarin, and Spanish have no commercial incentive to achieve true fluency in Tamil, Telugu, or Odia. India's cultural and linguistic diversity — the raw material of its civilizational identity — is underrepresented in the systems that increasingly mediate how Indians access information, services, and opportunity.
A parliamentary committee report, tabled in late 2035, draws a comparison nobody in the government wants to hear but none can honestly refute: India's digital economy in 2035 bears structural resemblance to its agrarian economy in 1850 — highly productive for the primary extractor, generating local employment but not local wealth, dependent on external capital for every meaningful upgrade, and offering no plausible exit without a disruption equivalent to Independence.
Bull Case — 2029 to 2035
India chose urgency. What followed was the most consequential industrial mobilisation in the country's modern history.
In March 2029, India's Prime Minister addresses a joint session of Parliament. The speech is 47 minutes long. It contains no euphemism. It acknowledges, with an explicitness that stuns the diplomatic community, that India is on the verge of a structural dependency that will take a generation to reverse if it is not arrested now. The speech is called the "AI Swaraj address" — invoking Gandhi's demand for self-rule not as nostalgia but as a living imperative.
1. AI Sovereign Fund: ₹5 lakh crore ($60B) over 10 years, ring-fenced from the Union Budget, for compute, talent, and foundational model development.
2. National AI Service Act: India's top 2,000 AI researchers — wherever in the world they work — are offered a "National AI Service" package: ₹5 crore tax-free return grant, world-class facilities, and equity in national AI projects.
3. BharatCompute Authority: Target of 2 million sovereign GPUs (~20 GW of dedicated AI compute capacity) within 36 months. No foreign cloud provider permitted to handle classified or critical-infrastructure data.
4. BharatMind Initiative: Ten competing foundational model projects, each funded at ₹2,000 crore. First competitive frontier model target: 36 months.
5. IndiaAI Global Alliance: Offer India's AI capabilities, infrastructure, and governance framework to 50 partner nations — positioning India as the democratic, non-predatory alternative to US and Chinese AI dominance.
6. BharatRobotics Mission: ₹80,000 crore ($10B) physical AI initiative — targeting India's first sovereign humanoid robot architecture by 2032. India will not win the hardware race outright. It will win the intelligence race: BharatMind as the operating system for physical AI deployed across the developing world.
The global reaction is immediate and divided. OpenMind's stock drops 3% on fears of a lost market. China's Foreign Ministry issues a statement calling India's data sovereignty provisions "protectionist." These reactions are, in their own way, confirmation that India has finally done something that matters.
The first 18 months are brutal. Building a frontier AI lab from near-scratch, in a country whose best AI talent has spent the last decade working in California and London, is not elegant. The early BharatMind prototypes are embarrassing by Silicon Valley standards. The engineers know it. They build anyway.
What changes the trajectory is not a single breakthrough but two forces arriving simultaneously: return and recognition.
The National AI Service Act brings back 8,400 researchers in its first year — more than expected, but the composition surprises everyone. The first wave is mostly mid-career engineers: people who left India in their twenties, built careers at Google Brain, DeepMind, Meta AI, and OpenAI, and never fully resolved their ambivalence about that choice. They return because the grant is generous and the problem is real.
The second wave matters more. Senior researchers — including several who worked on frontier model architecture at leading US labs — choose India for a reason that has nothing to do with compensation. The problems are harder. Training a model to reason fluently across 22 languages, to work with incomplete agricultural data from a smallholder farm in Vidarbha, to provide medical guidance in a district hospital with no lab infrastructure: these are not toy benchmarks. The US labs don't solve them because they don't need to. India does. The researchers who find that more interesting than building a slightly better English coding assistant come home. Some researchers from the Indian diaspora — many of whom built the core systems at the labs they are now leaving — choose India specifically because they want to work on something they could not have built anywhere else.
The IIT-AI campuses in Bengaluru, Hyderabad, and Chennai begin operating at a standard that can recruit internationally. Not because the campuses are better than MIT — they aren't yet — but because the work is.
BharatMind's competitive edge is not compute scale. It is training data that no American or Chinese lab can access — because it belongs to 1.4 billion people living Indian lives, transacting through Indian systems.
In 2029, India passes the AI Data Sovereignty Act, converting its DPI rails into consented, privacy-preserving training infrastructure for national AI development:
UPI / FinStack: 18 billion monthly transactions, 400 million active users, a decade of financial behaviour spanning street vendors in Varanasi and fund managers in Mumbai — the world's most complete picture of economic life across an income distribution.
ABDM: 800 million+ health records carrying India's specific disease burden — TB, diabetes, cardiovascular, malaria — with clinical notes language-tagged across 22 scripts. India's disease profile is not a subset of the US dataset. It is a different dataset entirely.
AgriStack: 70 million+ farmer records — crop selection, soil profiles, rainfall correlations, yield outcomes across 25 states. The world's largest precision agriculture dataset, invisible to any global model until now.
ONDC: India's open commerce network — purchase behaviour, logistics patterns, supplier chains across a $1.2 trillion consumer market that operates in 15 languages and six distinct cultural contexts.
The result is a model that understands something GPT cannot: what it means to live in India. When BharatMind advises a crop rotation in Tamil for a farmer in Thanjavur, or flags a drug interaction in a district hospital in UP, it draws on ground truth no Silicon Valley lab has seen. That is not a gap BharatMind closes with the frontier. It is a gap that compounds in India's favour every year.
The BharatMind team debates release strategy for three months. The closed-model camp argues for API revenue, enterprise licensing, and controlled deployment. They lose the argument — not on ideology but on strategy.
India has no interest in winning the API revenue race. That game is already won by OpenAI and Anthropic, with a years-long head start and a trillion-dollar ecosystem built around their platforms. India's strategic interest is different: to become the AI infrastructure of the non-aligned world. And the fastest path to that outcome is not a subscription tier. It is open weights.
The open release also solves India's talent problem in a way no recruitment programme could. When BharatMind's weights are public, the world's engineers contribute. Researchers in Lagos, Jakarta, São Paulo, and Cairo build on the model — fine-tuning it for local languages, local diseases, local agricultural conditions. India provides the foundation; the Global South builds the application layer. The model improves faster than any closed system because the incentives are aligned: every improvement made by a developer in Nairobi compounds value for every other developer building on the same base. India's open AI ecosystem becomes a self-reinforcing network that no closed competitor can replicate by writing cheques.
BharatMind-1 has demonstrated something unexpected: the highest-leverage position in the physical AI stack is not the robot body but the intelligence running it. Chinese humanoid hardware is cheap, widely available, and improving rapidly. The intelligence layer — the model that perceives environments, reasons about tasks, and coordinates physical action — is where the compounding value accumulates.
India's BharatRobotics Mission does not attempt to out-manufacture China's Shenzhen supply chains. Instead, it targets the operating system layer: a sovereign physical AI model, built on BharatMind's architecture, capable of running on commercially available robotic hardware from any manufacturer. The strategy is deliberate. India trains the brain. The world supplies the body. The model — not the machine — is the asset.
The IndiaAI Global Alliance, launched in 2029 as aspiration, has by 2032 become a genuine multilateral institution. 62 nations — primarily across Africa, Southeast Asia, South America, and the Middle East — have signed partnership agreements. The terms are notably different from China's model: no equity in critical infrastructure, no governance system dependencies, no embedded surveillance architecture. India offers capability and asks for cooperation.
BharatMind-2, released in early 2033, achieves what independent benchmarks describe as "genuine competitive performance" with the leading American models — particularly in multilingual tasks, medical diagnosis in low-resource settings, and agricultural yield prediction for tropical climates. It is the first Indian technology product that the world's most sophisticated users choose not because it is Indian, but because it is better for their specific needs.
India's Semicon 2.0 program, running since 2029, achieves its first 7nm fabrication run at the Dholera campus — four years ahead of the schedule that critics called "hopelessly optimistic." The breakthrough is attributed to a technology transfer partnership with a Taiwanese foundry, negotiated in exchange for India's strategic neutrality guarantees — and the credible alternative India could now offer China's Belt and Road digital partners.
India is not yet at the frontier of semiconductor fabrication. But it has broken the most dangerous dependency: the assumption that it never could be.
The phrase "Third Pole" enters the lexicon of international relations in 2034, when a Foreign Affairs essay notes that the AI world order — assumed since 2025 to be a bipolar US-China contest — has a third centre of gravity. India does not control the frontier. It does not need to. What India controls is something more durable: the trust of the non-aligned world in a digital era where alignment means data dependency, surveillance infrastructure, and platform lock-in.
India's AI sector reaches $420 billion in annual revenue by 2035. BharatMind-class models are used operationally in 74 countries. India sets the AI governance agenda at the UN's AI Safety Council — not because it has the most powerful models, but because it represents the largest coalition of nations that have chosen neither Silicon Valley nor Beijing as their digital sovereign.
None of this was inevitable. It required a political decision, made in 2029, to treat the threat with the seriousness it deserved. It required a generation of engineers who chose to stay and build rather than leave and build for others. It required India to remember what it once was, understand clearly what it risked becoming, and decide — without ambiguity — that those were not the same thing.
This scenario was written in 2026. The data in it is real. The investments cited are real. The dependency chains described are forming right now. The historical parallels are not metaphors — they are structural descriptions of the same mechanism operating in a new medium.
The choice described at the fork point is not fictional. It is the choice India's government, industry, and citizens are making today — through budgets, through talent policy, through procurement decisions, through what they choose to build and what they choose to buy.
Sovereignty is not taken. It is traded away — convenience by convenience — until the option to reclaim it quietly expires.
Hi, I'm Rahul Rai.
IIT Bombay dropout. Wharton undergrad. I previously co-founded a $100M+ quant hedge fund and a Google-backed edge AI startup.
Outside of markets and technology, I love reading about history, Indian mythology and Buddhist philosophy. I believe that the longest lenses give the clearest views.
Follow me on X / Twitter or reach out at rahulplus@gmail.com.
Co-authored with Perplexity Computer.
Sources · Stanford HAI AI Index 2025 (AI investment, talent, vibrancy rankings) · Futurum Research AI Infrastructure Capex Report 2026 (US compute spend) · IFR World Robotics Report 2024 (robot density, installation figures) · NASSCOM Technology Sector Report 2025 (IT sector size, employment, revenue) · SemiAnalysis GPU Supply Report 2025 (GPU fleet estimates by country) · Goldman Sachs AI Infrastructure Outlook 2025 (compute capacity, GW estimates) · IndiaAI Mission: Government of India (compute targets, sovereign model budget) · Carnegie Endowment for International Peace: China’s Digital Silk Road (neighbourhood AI frameworks) · Unitree Robotics product disclosures 2025–2026 (humanoid pricing) · AgiBot / Zhiyuan Robotics press releases 2025–2026 (humanoid production scale) · McKinsey Global Institute: The Economic Potential of Generative AI (digital value extraction) · Maddison Project Database (historical GDP share data) · AI 2027 — Kokotajlo, Lifland, Larsen & Alexander (scenario methodology)