India in a World of Open Markets and Closed Chokepoints
Why access to technology and platforms is not the same as control over them
Two recent technology debates appear unrelated.
The first began when NVIDIA and other technology companies asked the US government to avoid premature restrictions on open-weight AI models. Critics pointed out that CUDA, NVIDIA’s most valuable software ecosystem, remains proprietary. The second concerns the Cockroach Janta Party, or CJP, which built an enormous following on Instagram before becoming part of a wider protest movement in India.
One debate concerns artificial intelligence and the other political attention. Both raise the same question: who controls the infrastructure through which a society communicates, builds technology, and participates in the modern economy?
Edward Fishman’s Chokepoints offers a useful framework. Fishman applies it to economic warfare, but the logic extends to digital networks. Modern power often comes from controlling a few indispensable nodes that others must use and cannot easily replace. These chokepoints usually begin as successful commercial products. Dependence builds quietly. Eventually, access can be priced, prioritised, restricted, or withdrawn.
A system can look open on the surface while remaining concentrated underneath.
The Two Gateways into the Digital Economy
Attention and artificial intelligence are two gateways into the same digital economy.
The attention layer determines what people see, which businesses find customers, and which political movements gain visibility. The intelligence layer determines what developers can build, what enterprises can automate, and who captures the gains from AI. Beneath both lie the harder layers: semiconductor equipment, fabs, accelerators, cloud data centres, electricity, software ecosystems, advertising systems, and global distribution.
India is deeply involved at the upper layers. It supplies users, creators, developers, advertisers, and demand. Control of much of the underlying machinery sits elsewhere. That is the connection between Instagram, open weights, CUDA, TSMC, ASML, and the large cloud providers.
Attention Is Infrastructure
The CJP example should be handled carefully. Its follower count does not reveal where its audience came from, and it does not prove foreign funding or coordination. What we do know is that a movement founded by a Boston University student grew rapidly on Instagram and later acquired a street presence after its founder returned to India.
The grievances may be domestic, the supporters genuine, and the movement organic. The structural point remains. An organisation can build legitimacy and mobilise people in India through distribution infrastructure controlled by a foreign company.
Instagram determines how content is recommended. Meta controls moderation, appeals, verification, advertising rules, and most of the data available to researchers. A recommendation change, an automated moderation mistake, or a policy revision can materially damage an organisation that depends on the platform. Malicious intent is not necessary. Dependency itself creates power.
This extends far beyond politics. Indian businesses find customers through Google and Meta. Professionals build reputations through LinkedIn. Creators depend on YouTube and Instagram. App companies rely on Android and iOS. India bears the social and political consequences while foreign companies operate much of the distribution machinery.
Attention should therefore be treated as strategic infrastructure. This does not justify banning foreign platforms or placing political speech under direct government control. Both responses could cause more harm than the dependence they seek to address.
India instead needs stronger institutions for the digital public sphere. Meta already maintains a searchable archive for political advertising in India, but formal political ads are only part of the influence system. Transparency should also cover issue-based campaigns, proxy advertisers, paid creators, and cross-border sponsorship. Users need meaningful appeals against automated moderation, while independent researchers need better access to platform data.
Regulation is the defensive response. The offensive response is to build important Indian networks of our own.
UPI shows how public digital infrastructure can widen access and competition. Yet open rails do not guarantee that Indian companies will own the customer relationship, behavioural data, intelligence layer, or distribution built above them. An open protocol at the bottom can coexist with concentration at the application layer.
The Same Structure Appears in AI
Open models are genuinely valuable. They reduce dependence on a few foreign APIs, lower experimentation costs, allow local deployment, and give enterprises more control over data and inference. NVIDIA’s commercial interest does not make those benefits any less real.
But the open-weights debate is also a negotiation over which part of the AI stack becomes cheaper and which parts retain pricing power.
The commercial logic is simple. Companies favour openness in layers that create demand for their bottlenecks and protect the layers that make them difficult to replace. NVIDIA can contribute heavily to open-source AI while keeping CUDA under its control. Open models encourage more training and inference, which increases demand for accelerators, networking, servers, and NVIDIA’s software ecosystem. Opening model weights makes models easier to use. Making CUDA hardware-neutral would make NVIDIA hardware easier to replace.
The pattern appears elsewhere. Meta can release model weights because its deepest advantages lie in attention, advertising, data, and distribution. Microsoft can support open models while protecting cloud contracts, enterprise identity, and customer relationships. Companies want the inputs they buy to become cheaper while the products they sell remain differentiated. That is ordinary platform economics.
AI adoption adds further pressure. Chip companies want more workloads, cloud companies want higher utilisation, and application companies want intelligence to become an inexpensive input. Open weights, distillation, routing, smaller models, and self-hosting all push model prices down.
The harder bottlenecks remain below: advanced lithography, foundry capacity, high-bandwidth memory, packaging, accelerator design, interconnects, CUDA, electricity, and data centres. NVIDIA does not need one model company to win. It needs total AI computation to grow. Cloud providers do not need model companies to preserve large margins. They need AI workloads consuming cloud capacity.
The economic pressure therefore runs in one direction. Model intelligence becomes cheaper and more abundant while the owners of scarce infrastructure continue to collect the toll.
Open Weights Are Useful. They Are Not Sovereignty.
A company can release final model weights while retaining its training data, data-selection methods, training code, post-training systems, evaluations, inference optimisations, and production infrastructure. Indian developers can still run the model locally, adapt it, and reduce their dependence on a single API. But receiving the weights is not the same as receiving the productive system that created them.
Open weights are like receiving an industrial machine rather than the factory that built it. You can operate and modify the machine without possessing the knowledge, tooling, capital, supply chain, or organisation required to reproduce it or build its successor.
India’s progression has five stages: API access, weight access, adaptation capability, reproduction capability, and original technological leadership. Most discussions stop at stage two and call it democratisation. A country running foreign-designed models on foreign accelerators through foreign software and cloud infrastructure has gained useful access. It has not gained sovereignty. It has diversified its dependence.
Open Markets and Physical Chokepoints
The attention and AI stories converge at the infrastructure layer.
ASML holds a critical position in advanced lithography. TSMC combines process knowledge, yield learning, packaging, and customer trust. NVIDIA combines accelerators with CUDA, networking, libraries, and developer familiarity. The large cloud providers combine capital, electricity, data centres, software, and enterprise distribution.
These positions were built over decades. Open markets do not distribute power evenly because participants enter them with different levels of capital, knowledge, scale, and control over standards. Free trade and open source still benefit latecomers by lowering costs and accelerating learning. But access is not productive capacity. Model access is not model-building capability. GPU rental is not control of the compute stack. Access to a foreign fab is not accumulated process knowledge.
Chokepoints are not permanent. Once they are used for coercion, customers and countries have stronger incentives to find substitutes. Their power comes from the fact that replacement is slow, expensive, and uncertain. That delay is where leverage lives.
From Access to Capability
Indian technology policy often focuses on access to GPUs, foreign fabs, open models, cloud capacity, capital, and export markets. All of it is useful. But access still leaves someone else in control of the terms. The sharper question is what the rest of the world will eventually need from India.
Fishman’s framework gives India two jobs.
The first is resilience. India must reduce the leverage others hold over it through supplier diversification, interoperability, domestic maintenance, alternative vendors, and credible substitutes for critical foreign systems.
The second is indispensability. India must build capabilities that others cannot easily replace through patient capital, domestic procurement, technical learning, supplier coordination, standards, exports, and developer ecosystems.
Resilience reduces the leverage others hold over India. Indispensability creates leverage for India. A serious strategy needs both.
India cannot build everything, so it must choose. The strongest candidates will combine large domestic demand, an existing base of skills and suppliers, learning that spreads into adjacent industries, export potential, and a realistic path to global relevance. Semiconductor equipment subsystems, advanced packaging, power electronics, grid technology, multilingual computing, and software for the physical economy deserve consideration by those standards.
In AI, the capability ladder runs from consuming APIs to adapting models, building training systems, evaluations, compilers, and inference platforms, and eventually creating original model factories. In attention and distribution, the equivalent task is to create accountable rules for foreign platforms while building Indian networks to meaningful scale.
The Real Question
This was never mainly about whether NVIDIA is hypocritical or whether one Instagram account is suspicious. It is about the architecture of power in an economy where communication, commerce, and innovation move through a small number of privately controlled networks and industrial systems.
The CJP episode shows how a political movement can grow through infrastructure controlled outside India. The open-weights debate shows how one layer of a technology stack can open while the layers beneath remain concentrated. The semiconductor industry shows how open trade can coexist with extraordinary control over a handful of machines and production systems.
India should continue using global platforms, foreign capital, open-source software, open models, and international supply chains. The goal is not autarky. It is selective indispensability and meaningful control over the dependencies that matter.
Twenty years from now, India should not simply supply users, labour, data, and demand for systems someone else controls. There should be parts of the global economy where other countries need India’s technology, manufacturing systems, networks, and knowledge, with no easy substitute.
That is what a moat looks like at the level of a nation.