Over the course of this series, I have traced the AI Polyconflict through four interlocking threads. Part I examined the compression of the kill chain and the cheap drone paradox. Part II built the theoretical foundation: Augmented Intelligence as the merger of distributed cognition and political technology, militaries as artificial intelligences, and the new cyborg as fearsome and structurally stupid. Part III mapped the Stack War between the American Prediction Stack and the Chinese Production Stack, with the Gulf states hedging between them. Part IV revealed the metabolic dimension of both AI Stacks - the energy, water, and capital that the compute infrastructure demands.

In this last and most speculative essay of this series, I want to weave these four threads into one cloth.

The Highest Stage of the Algorithm

In 1902, the British economist J.A. Hobson published a study of imperialism that identified its root cause as domestic inequality. The working classes could not afford to consume what they produced. Capital accumulated at the top with no profitable domestic outlet. This excess capital had to go somewhere, so it went abroad - seeking new markets, raw materials, and investment opportunities in territories that could be politically controlled. Territorial imperialism was the military and political expression of an economic problem that could have been solved at home through redistribution, but wasn’t.

Lenin sharpened Hobson’s analysis in 1916 with Imperialism, the Highest Stage of Capitalism. For Lenin, the key transition was from the export of manufactured goods to the export of finance capital. Banks, cartels, and monopolies no longer needed to sell things to foreign markets. They needed to invest in them - to export capital itself, which would then generate returns through the control of foreign productive capacity. This was imperialism’s mature form: not armies conquering territory for its own sake, but finance capital dividing and redividing the world among monopolies, with armies following to enforce the arrangement.

I want to suggest that the global economy is now entering the highest stage of the algorithm: the transition from the export of finance capital to the export of inference capital (which is tied to a longer analysis of how inference might replace money one day, but that would take us too far afield). The pattern is similar to what Hobson and Lenin described, but the commodity being exported is different. It is not cotton, or steel, or even money. It is cognition, or if you prefer, intelligence.

Artificial Intelligence functions as the ultimate absorber of excess capital. At a time of staggering global inequality, hyperscalers and tech monopolies are pouring hundreds of billions into AI infrastructure such as chips, energy, real estate, cooling systems while material human needs go unmet across most of the globe. Unlike physical railroads or oil wells, the raw material of AI - data and compute - is infinitely scalable in principle, which also means AI is a bottomless sink for stagnant wealth, which makes it the most attractive investment frontier since colonial real estate.

But capital expenditure of this magnitude must yield a return. The technology must find new markets to monetize, and in the process uncover more data that feeds into the next stage of expansion. The two competing stacks are establishing a system in which every nation on earth, from Nigeria to India to the Gulf states, is encouraged (required?) to rent its intelligence through cloud platforms owned by a handful of corporations in California and, on the other side, to embed its physical infrastructure with AI systems manufactured in Shenzhen.

Macaulay’s Mirror

This is where the framework I built in Part II becomes essential. Foucault’s political technology was originally a theory of how institutions produce docile bodies - individuals whose physical capacities are maximized while their political autonomy is minimized. The drill sergeant formats soldiers. The prison formats inmates. The school formats students. In each case, the disciplinary apparatus works by structuring the environment so that compliance becomes the path of least resistance.

The Prediction Stack - the American AI architecture I described in Part III - is a political technology of this kind, but operating at civilizational scale. It produces docile bodies within docile polities. BTW, we have already seen that with social media - disciplining at population scale is political reality across the world. When a nation’s hospital logistics run on a proprietary model owned by a Silicon Valley corporation, when its port management relies on cloud infrastructure controlled from Virginia, when its military targeting depends on algorithms licensed from American defense contractors, and when its civic administration operates through platforms whose optimization logics were designed for a different culture in a different language - that nation’s institutional cognition has been formatted by an external disciplinary apparatus. Its cognitive capacities have been extended and it can do things it could not do before, so the leash is long. But it’s there.

Classical imperialism extracted raw materials like rubber, cotton, minerals. Cognitive imperialism extracts data: the accumulated behavioral, cultural, and institutional output of entire societies, compressed into training sets that feed the next generation of foundation models. One fear is that the patterns congealed in those models encode the priorities of the extractor - English-language epistemic hierarchies, Western legal frameworks, Silicon Valley optimization logics, American cultural assumptions about what counts as intelligent, efficient, or normal. When a nation in the Global South operates its government through these models, it runs its institutional cognition through a foreign cognitive artifact. There’s legitimate fear that its own knowledge - its local institutional wisdom, its cultural context, its situated ways of understanding - is systematically underweighted in the statistical distribution.

But the greater fear is that the prediction provider is even more firmly in charge when the localize their prediction engine for your geography; you will see yourself in a mirror made in California, and be happy that you are seeing yourself in a sari, not a suit, but so what? The mirror is not yours, and the more you get used to seeing yourself in it, the less you can walk away from it. You do not need to occupy a nation’s territory and extract its minerals. You occupy its cognitive infrastructure - the tools, the platforms, the models, the optimization logics through which its institutions think - and the territory follows.

In 1835, when British power in India was ascendant and they were confident about the superiority of their race and their civilization, Thomas Babington Macaulay wrote his Minute on Indian Education, where he says:

I have never found one among them who could deny that a single shelf of a good European library was worth the whole native literature of India and Arabia. The intrinsic superiority of the Western literature is, indeed, fully admitted by those members of the Committee who support the Oriental plan of education....We have to educate a people who cannot at present be educated by means of their mother-tongue. We must teach them some foreign language. The claims of our own language it is hardly necessary to recapitulate. It stands pre-eminent even among the languages of the west....We must at present do our best to form a class who may be interpreters between us and the millions whom we govern; a class of persons, Indian in blood and colour, but English in taste, in opinions, in morals, and in intellect.

Macaulay was more successful than he thought, but his successes will be nothing compared to what AI will usher. He who controls the Stack controls the (mental) State.

Compute Stations and the Comprador Class

The British Empire required physical coaling stations across the globe to project naval power. Ships needed to refuel, so Britain secured harbors, built infrastructure, and stationed garrisons at strategic points along its sea lanes. The host territories provided the coal, the labor, and the land. They did not own the ships.

The AI empires of the 2020s require compute stations. Data centers need energy, water, and land. The host nations provide these metabolic inputs - and, in the Gulf’s case, the capital to build the facilities — to sustain an intelligence they do not fully own, control, or understand. The snapback clauses and governance safeguards are the twenty-first-century version of the coaling station agreement: access to advanced technology in exchange for structural dependency.

The comprador class follows from this arrangement. In the history of European imperialism, the comprador class consisted of local agents who facilitated resource extraction on behalf of foreign empires. They were intermediaries - materially rewarded, culturally shaped by the imperial center, structurally necessary for the system to function. What Macaulay called ‘a class of persons, Indian in blood and colour, but English in taste, in opinions, in morals, and in intellect.’ The British Raj in India ran not on British administrators alone but on an English-educated Indian middle class that staffed the civil service, practiced British law, and managed the extraction of revenue from the countryside. They were not coerced at gunpoint. They were incentivized by the architecture of the system.

Cognitive imperialism will produce its own comprador class: the local elites, technologists, and policymakers who facilitate the integration of foreign cognitive infrastructure into their societies. They are not villains; they are rational actors responding to incentives.

India makes this visible at scale. India’s entire technology services economy - the Infosys-TCS-Wipro model - was built on cognitive labor extraction: the body shop. Indian engineers processed information for Western corporations at low cost, providing execution capacity without intellectual ownership. This was already cognitive semi-colonialism. The next stage is worse. AI now replaces the Indian IT worker while the AI itself remains owned by the same Western corporations. The software engineer in California who is replaced by AI has some political recourse, but when Oracle fires 12000 engineers in Hyderabad in one day, to whom can they appeal?

The Indian comprador is doing very well for themselves, thank you. Indian-origin CEOs of American AI companies - Sundar Pichai at Google, Satya Nadella at Microsoft - are smart individuals making rational decisions for themselves. I might do the same too. Indian startup founders are building thin application layers on top of AWS and Azure. The NASSCOM policy establishment has consistently advocated for India’s integration into the American tech stack. Like the pharmaceutical company CEO choosing to outsource APIs to China, we could be heading into a future where we are mere assemblers of AI systems, unprepared for a foundation model supply shock.

I don’t want to oversell the doom. India has mounted a more serious resistance than most. Aadhaar, UPI, ONDC - India has built genuinely impressive public digital infrastructure that is interoperable, open, and domestically owned. The IndiaAI Mission, Sarvam AI, etc - there are real attempts to build Indian-language models grounded in local linguistic and cultural reality. However, India does not have the semiconductor fabrication capacity, the training infrastructure, or the energy leverage to compete at the top of the stack. It is attempting managed interdependence, the same strategy as the Gulf states, but without the Gulf’s metabolic bargaining power.

The Great Enclosure

The AI Polyconflict potentially represents a Great Enclosure of the human mind. Between the fifteenth and nineteenth centuries, the enclosure of the English commons forced subsistence farmers off shared land and into wage labor, creating the proletariat that powered the Industrial Revolution. Land was privatized - transferred from a commons that sustained a community into a productive asset that generated returns for its owners.

Something similar is happening now. The collective intelligence of humanity - our aggregated data, our art, our social interactions, our institutional knowledge, our agricultural wisdom, our legal traditions, our languages - has been enclosed, extracted, and refined into a rent-seeking asset (if they can make any money!) controlled by a small number of corporations. Large language models are, as Ted Chiang observed, lossy JPEGs of the internet.

The enclosure is not incidental to the technology. It is the business model. Foundation model providers spend billions training on the cognitive commons - the open internet, digitized libraries, public datasets, the accumulated record of human thought and expression - and then charge access fees to the very societies whose collective output made the models possible.

Public Intelligence

If the AI Polyconflict is a Great Enclosure, then the defense against it must be a form of Public Intelligence, and it needs to have teeth, not just good intentions. Public Intelligence means cognitive architecture that is genuinely situated rather than abstracted into cloud services owned elsewhere. It means intelligence that is embodied in local physical and cultural reality, embedded in local institutional context, enacted through local practice and knowledge production, and extended through tools and artifacts that communities actually own and control.

A realistic public intelligence for now - no nation can build frontier foundation models without access to the global semiconductor supply chain, and pretending otherwise is fantasy. But the application layer - the layer where AI actually touches governance, agriculture, healthcare, education, legal reasoning, cultural production - is defensible. India’s UPI is proof of concept: a public digital infrastructure that is interoperable, open, and domestically governed, even though it runs on hardware manufactured elsewhere. The question is whether this model can be extended to the AI layer - whether nations can own the cognitive interface between their societies and the frontier models, rather than ceding that interface to the model providers.

Across this entire series, from the twenty-second kill-chain confirmation in Part I to the algorithmic velocity of the Stack War in Part III to the metabolic acceleration of the compute buildout in Part IV, the consistent pattern has been the compression of human deliberative time. Cognitive imperialism colonizes space and time at once. The human durée - the lived time of ethical deliberation, cultural reflection, democratic consensus - is treated as inefficiency by the algorithm. Your local knowledge is too slow. Optimize it away. Compression of time goes hand in hand with its fragmentation; we have seen that already with the attention and gig economies, where the feed distracts us toward one shiny object after another, and the Uber or Swiggy driver whose workday is revealed only one trip at a time.

The colonization of time is central to cognitive imperialism.

The defense of durée is not nostalgia for a slower world. It is the precondition for cognitive sovereignty. A society that cannot think at its own pace cannot think for itself.

Closing

IMHO, the AI Polyconflict is a contest over the cognitive architecture through which humanity will understand itself in the twenty-first century.

The five threads of this series - the industrialization of lethality, the planetary cyborg, the Stack War, the metabolic loop, and the Great Enclosure - are facets of a single transformation. Human societies are being reorganized around augmented cognitive systems of unprecedented capability combined with stupidity, systems that carry the congealed patterns of their creators and impose those patterns on everyone they touch. The empires that control these systems - the prediction and production empires - with their different architectures but identical ambitions - are engaged in the most consequential division of the world since the Cold War. That division will pass though - sometimes fractally so - the Gulf, South Asia, Africa, and Latin America, whose capacity for self-governance is at stake, and that is what Public Intelligence must defend.

Preface

Part III ended with a promise that the AI Polyconflict has a metabolic dimension most analyses of the AI race completely ignore. I want to make good on that promise. So far, I have been talking about Augmented Intelligence, i.e., the merger of distributed cognition and political technology, as though it exists in the realm of strategy, software, and institutional design. It does. But it also exists in the material realm. Information has energetic and entropic implications. Every inference token, every trained neural weight, every algorithmic decision I described in Part III has a material cost measured in watts, in liters, and in dollars (the last one isn’t physics, but it’s still hooked into materiality). The cloud is made of silicon and copper, cooled by water, powered by hydrocarbons, and financed by sovereign wealth. At least in the Gulf.

The Metabolic Dimension

In the 4E Cognition tradition, one of the things that distinguishes extended cognition from ordinary tool use is that the cognitive system becomes dependent on the artifact for its basic functioning. The navigator who uses a chart is cognitively different from the navigator who has memorized the stars; take the chart away and the first navigator is lost. The system’s intelligence depends on maintaining its material extensions.

Scale that to the stack. The Prediction and Production Stacks I described in Part III are cognitive systems that span continents. The American Prediction Stack extends institutional cognition through centralized cloud platforms, proprietary models, and precision hardware. The Chinese Production Stack distributes cognition through AI-enabled industrial infrastructure such as ports, factories, power grids. Both stacks, like the Navy bridge crew in Hutchins’ original study, can accomplish things no individual component could do alone. But both stacks also have metabolic requirements that most commentary on the AI race fails to register.

A typical hyperscale data center requires around 100 megawatts of continuous power - the output of a small gas turbine running without pause. Gigawatt (1000 megawatt) scale data centers are under construction. As an aside, it’s instructive that the most widely used measure of datacenter size is its energy footprint. A single such facility consumes approximately two million liters of water every day just to keep its processors from overheating. In the United States, data centers already account for roughly 4.4% of annual energy consumption, and that figure is expected to triple in the coming years. The resulting friction with local communities over water rights and grid stability has produced severe domestic permitting bottlenecks.

This metabolic demand is the third thread of the AI Polyconflict. Not who has the best algorithms. Not who controls the cognitive heights. But who controls the physical substrate - the energy, the water, the capital - that makes the compute possible in the first place. If your cognitive extensions depend on material infrastructure, then whoever controls that infrastructure has leverage over your intelligence. This is true whether the extensions are centralized in a cloud or distributed across a continent of factories. The brain needs a body, and the body has to eat. Energy, water, and capital are to the AI stacks what grain and iron were to premodern empires.

“Amateurs talk strategy, professionals talk logistics” - saying attributed to Omar Bradley.

Two Architectures, Two Metabolisms

In Part III, I described the American and Chinese AI stacks as two competing visions of augmented institutional cognition - one centralizing intelligence in the cloud, the other embedding it in physical infrastructure. Now I want to look at the same rivalry from below: through its metabolism.

The American strategy amounts to Intelligence as a Service. The United States controls the foundational hardware (Nvidia chips), the hyperscale data centers (AWS, Azure, Google Cloud), and the proprietary foundation models (OpenAI, Anthropic). It offers the world the ability to rent American cognition. Foreign nations and global enterprises do not own their cognitive capabilities; they subscribe to them. This is the European Navigator’s approach to global intelligence - abstract, centralized, portable, and powerful. But it concentrates massive metabolic demands in a small number of nodes. Centralized cloud infrastructure means centralized metabolic dependencies: specific power grids, specific water supplies, specific cooling systems. A blockade on critical cooling gases like helium, a kinetic strike on a server farm, a cyberattack on a utility grid - any of these can degrade or extinguish the cognitive system.

China’s approach is Infrastructure as a Service. Rather than renting cognition from the cloud, China integrates AI into the physical body of production: robotics, port logistics, manufacturing, energy management. China constructs the roads, the power grids, the factories, and the logistics networks with the cognitive algorithms baked in. This is the Trukese Navigator at national scale. Its metabolic demands are spread across thousands of smaller installations embedded in industrial infrastructure. Harder to knock out with one strike, but also harder to upgrade, harder to audit, and far harder to decouple once installed.

Both create deep dependencies. Not least upon one another.

The United States requires Chinese manufacturing capacity and supply chain resilience to physically build the hardware for its exquisite cognitive architecture - the rare earth processing, the precision machining, the sheer volume of fabrication that American industry cannot currently replicate at scale. China requires Western chip architectures, particularly advanced GPU designs from Nvidia and AMD. Each stack’s metabolism depends on inputs that the other controls.

From Gas Station to Compute Hub

This is where the Gulf enters: as the metabolic center of the AI Polyconflict.

The Gulf states sit on two of the inputs that both competing stacks need in vast quantities: energy and capital. They have a third advantage that receives less attention: geopolitical flexibility, which means they can engage both, as I described in Part III - purchasing American targeting systems and cloud platforms while running their ports and smart cities on Chinese infrastructure. OK, maybe that’s a bit too much - they have been American vassals for much of the post war era, but this war against Iran might precipitate more hedging than in the previous 75 years.

But hedging is not sovereignty. To rent your intelligence from two competing empires is to be cognitively dependent on both of them. So the wealthy Gulf states are attempting something more ambitious: a phase transition from planetary gas station to planetary compute hub. Data centers in the United States face severe domestic permitting constraints, localized noise resistance, and acute power grid bottlenecks. The Gulf has abundant, low-cost energy - hydrocarbons today, solar tomorrow - vast tracts of buildable land, and governments that can approve infrastructure at speed. Western hyperscalers need somewhere to build. The Gulf is offering itself as the site. The GCC can congeal their abundant sunlight and fossil fuels into neural networks.

In parallel, the Gulf states are building their own cognitive substrate. The UAE’s Technology Innovation Institute developed the Falcon family of large language models under permissive open-source licenses. The UAE’s Jais model and Saudi Arabia’s ALLaM are Arabic-first models that natively grasp the linguistic and cultural nuances of the region rather than filtering everything through English-language training data. These sovereign models are deployed alongside commercial Western tooling - Saudi Arabia hosts ALLaM on the DEEM Cloud alongside IBM watsonx - maintaining local control over critical inference and data while retaining access to frontier capabilities where useful.

This strategy of managed interdependence is itself a political technology, that uses financial entanglement and energy leverage to discipline the larger powers rather than being disciplined by them. The 1973 oil embargo is a more extreme example. The Gulf states will not passively accept whatever congealed patterns come bundled with American or Chinese infrastructure.

The Market Makers

The capital required to build this infrastructure is unprecedented. Hyperscale data center construction, frontier model training, talent acquisition, and the supporting energy systems demand investment at a scale that exceeds traditional venture capital by orders of magnitude.

The Gulf Sovereign Wealth Funds are among the few entities on the planet that can write checks at this scale. Saudi Arabia’s Public Investment Fund, managing over $900 billion in assets, has established a dedicated $40 billion AI investment fund operating under the newly launched national champion HumAIn - designed to advance domestic AI infrastructure, support local startups, and establish joint ventures with global firms like Andreessen Horowitz. The UAE has established MGX, a sovereign investment group exploring plans to raise up to $50 billion in third-party capital to expand its AI portfolio, building on existing stakes in OpenAI and xAI. OpenAI itself, pushing toward a reported target of $125 billion in revenue by 2029 and seeking to fund its massive Stargate data center project, has actively courted both PIF and MGX.

This financial leverage comes with disciplining power. When a Gulf sovereign wealth fund provides the capital to build a hyperscale AI facility, it shapes the conditions under which that facility operates: where the data is stored, which models are deployed, whose assumptions are embedded in the system. The oil-for-security relationship that defined the US-Saudi alliance for decades is giving way to an AI-for-partnership arrangement in which the Gulf’s capital buys not just a financial return but a seat at the cognitive table. Maybe.... Capital flow is becoming a disciplinary apparatus of its own - not through snapback clauses, but in the softer mode of financial entanglement.

Why threaten the hand that funds you?

The Data Center in the War Zone

All of this compute, whether American, Chinese, or sovereign, has to physically exist somewhere. And wherever it exists, it needs to be cooled. Over the next five years, data center capacity in the Gulf is expected to triple, rising from roughly one gigawatt today to 3.3 gigawatts by 2030. These facilities will operate in a climate where summer temperatures regularly exceed 45°C. They cannot be cooled by the ambient air. They require massive quantities of water. And in the hyper-arid Middle East, that water must come from the sea.

Desalinating the ocean in order to feed data centers feels like a dystopian movie come to life doesn’t it? Data centers in Saudi Arabia alone are projected to consume 15 billion liters of water per year. If the current growth trajectory holds, that figure could scale to 87.52 billion liters, roughly 4% of the country’s total water output. Desalination is itself an energy-intensive and environmentally costly process. Consider the three dominant technologies. Seawater reverse osmosis, the most efficient option currently available, consumes 3.7 to 4.5 kilowatt-hours per cubic meter of freshwater produced and emits over 3 kilograms of CO₂ per cubic meter. Multi-stage flash distillation, still widely used across the Gulf, requires roughly 4 kilowatt-hours per cubic meter of electrical energy plus vast thermal energy inputs, and emits over 23 kilograms of CO₂ per cubic meter. Multi-effect distillation sits between the two in efficiency but produces comparable emissions and pollutant loads. All three technologies discharge concentrated brine back into the Gulf’s shallow, enclosed waters - an ecological cost borne by the non-human inhabitants of the Gulf.

This creates a dependency loop that tightens with every new gigawatt of installed capacity. The Gulf states burn hydrocarbons to generate electricity. That electricity powers desalination plants that produce freshwater. That freshwater cools the data centers that run the AI. The AI, in turn, manages the smart grids and logistics systems that distribute the power and water.

The Gulf’s AI infrastructure is not being built on blank ground. It is being built on top of the existing political economy of the petrostate - an economy historically organized around hydrocarbon extraction, desalination, and sovereign wealth accumulation. The new cognitive architecture inherits these dependencies and cements them deeper. What was a contingent historical arrangement - we have oil, but we don’t have water, so we built desalination - is becoming a central dependency for the global AI system.

BTW, Iran holds all these developments hostage, because data centers are targets for their missiles as they have demonstrated.

Every cable cut in the Red Sea, every drone strike on energy infrastructure, every cyberattack on utility controls is an attack on the extended cognitive system that powers global AI. This fragility is unavoidable, the product of building the planet’s cognitive infrastructure on top of the same geography that supplies its energy, in a region where the kinetic and digital battlefields overlap. A thousand individually rational decisions by hyperscalers seeking cheap energy, by Gulf states seeking economic diversification, by sovereign wealth funds seeking returns, by militaries seeking cognitive advantage, have collectively produced a system of great vulnerability.

Next Up

The metabolism of intelligence creates new forms of economic dependency, new asymmetries between those who control the metabolic inputs and those who must rent them. In Part V, the last essay in this series, I want to turn to this macroeconomic dimension: how the cognitive imperialism emerging from both the American and Chinese stacks is reshaping the global economy, what it means for the nations caught between them, and why the Gulf’s phase transition from gas station to compute hub is an example of this imperialism at work.

In Part II, I argued that Augmented Intelligence in Society - the thing we should actually be paying attention to when we talk about AI, unless you’re a machine learning researcher or engineer - should be understood as a merger of two ideas: distributed cognition (from the 4E Cognition tradition) and political technology (from Michel Foucault and his successors). Militaries have always been artificial intelligences, vast distributed cognitive systems that format human bodies into standardized components and extend their capacities through material artifacts. The cybernetic military-industrial complex of the Cold War was the most consequential iteration of this political technology before our own. Contemporary AI systems are its children, carrying the congealed patterns of that complex in their statistical weights.

Part II ended with a question: what new forms of discipline and institutional design does AI require? The answer, I’m afraid, is already taking shape - and it looks less like international humanitarian law and more like a global protection racket. I want to retreat from the battlefield and look at the geopolitical architecture of the AI Polyconflict. Because the kinetic conflicts in the Gulf, in Ukraine, and along the India-Pakistan border are not strictly local disputes. They function as surrogate battlegrounds for the defining superpower rivalry of our century: the contest between the United States and China over who will control the cognitive infrastructure of the planet (read that in a deep ominous voice introducing a PBS documentary on the Second Cold War).

The Surrogate Sandbox

At the end of Part I, I wrote that militaries are stress-testing AI warfare architecture in the Gulf, and that nothing beats a war for finding out if your weapons work. That was the narrow version of the claim. The broader version is this: the Gulf has become a surrogate sandbox not just for weapons systems, but for entire civilizational models of Augmented Intelligence.

The physical geography of the Middle East and elsewhere is being inscribed by the digital geography of competing AI stacks. The United States and China are each projecting a different architecture for how human institutions should be cognitively augmented - a different vision of what the human-machine ensemble should look like at the scale of nations and economies. The Gulf, with its vast capital reserves, its cheap energy, and its active war zone, is one of those places where these competing visions collide.

To understand what’s happening here, I want to use the same framework from Part II. If Augmented Intelligence is the merger of distributed cognition and political technology, then the Stack War between the US and China is a contest between two different political technologies, each producing its own kind of augmented institutional cognition, each disciplining its client states in different ways, and each carrying the congealed patterns of the civilization that produced it.

The Prediction Stack

The United States projects power through I have called the Prediction Stack: a technological ecosystem built on cutting-edge semiconductor fabrication (Nvidia GPUs above all), massive centralized cloud computing platforms (AWS, Azure, Google Cloud), proprietary foundation models (OpenAI, Anthropic, Google), and precision military hardware. This is a stack designed to dominate the cognitive heights (should remind you of commanding heights) to control the most advanced forms of computation and rent them out to the world. Washington treats data centers, cloud access, and compute licensing as instruments of power projection in the same way it once treated military bases and aircraft carrier groups. The goal is to bind allied and client states into the American technology ecosystem while systematically excluding Chinese influence.

India is very much in their sights.

The Microsoft-G42 deal is a clear example. Microsoft invested $1.5 billion in the UAE’s sovereign AI holding company, G42, as the cornerstone of a broader $15.2 billion commitment to UAE digital infrastructure. This was not an ordinary business transaction. It was orchestrated with explicit U.S. government oversight, and in exchange for access to advanced American chips and deep integration onto the Azure platform, G42 was required to execute a hard decoupling from Beijing: divesting from Chinese technology firms, selling its stake in ByteDance, and stripping Huawei equipment from its data centers.

The deal includes restrictive chip licensing, American board representation, governance safeguards, and snapback clauses - provisions that automatically revoke compute access and degrade system capabilities if restricted vendors or personnel touch the technology stack. USG has veto power over the UAE’s digital future. Rows of servers and racks of GPUs now carry the strategic weight that forward-operating military bases once did; not that it’s one or the other - the UAE has both.

Looking at this through the lens of political technology, these deals are about disciplining nations. The snapback clause is a mechanism of control that works structurally the same way Foucault’s disciplinary apparatus works on individual bodies. You don’t need to invade the UAE or threaten it with military force. You build dependency into the cognitive infrastructure, and then you establish automatic penalties for deviation.

The idea is to turn the UAE, in a specific and non-trivial sense, into a docile body within the American technological order - its digital capabilities maximized, its geopolitical autonomy constrained. If the UAE violates the terms of the deal, its AI systems degrade. Its military targeting platforms lose access to frontier models. Its smart city infrastructure loses cloud support. The punishment is cognitive diminishment, not boots on the ground. And like all effective discipline, it works best when it never has to be applied - when the threat alone is sufficient to ensure compliance.

This is the nature of the fast developing cognitive imperialism that I will write about in the fifth essay in this series.

The Prediction Stack is an augmented intelligence at civilizational scale. It extends American institutional cognition - the logic of Silicon Valley, the priorities of the Pentagon, the cultural assumptions embedded in English-language foundation models - across the globe. The nations that plug into this stack get American patterns of thought, American optimization logics, American definitions of what counts as intelligent behavior for free, like it or not. California is a State of Mind right - well, now you’re going to get it as a service.

The Production Stack

China’s approach looks very different. Rather than competing for dominance at the cognitive heights - the most advanced LLMs, the most powerful chips - China projects power through the Production Stack, an Industrial Nervous System. The Chinese strategy focuses on ubiquitous, society-wide integration: embedding AI into the physical infrastructure of logistics, manufacturing, energy, and urban management. Through the Digital Silk Road, the technological arm of the Belt and Road Initiative, China provides developing nations and Gulf states with affordable 5G networks, cloud computing platforms, surveillance technology, and smart city infrastructure.

In the Gulf, this manifests as deep integration into the region’s material base. Chinese technology firms are building the actual nervous system of Gulf economies. COSCO Shipping Ports and Chinese AI firms jointly developed the automation systems for Khalifa Port in Abu Dhabi, integrating AI-driven logistics and digital twin technologies. Huawei built the Smart City 3.0 platform for Yanbu Industrial City in Saudi Arabia, connecting broadband, security, public services, and industrial clusters across a critical petrochemical hub. SenseTime established a $776 million joint venture with Saudi Arabia’s sovereign wealth apparatus to localize computer vision and deep learning for smart city applications. Aramco signed cooperation agreements with Chinese tech firms for predictive maintenance and operational optimization across the energy sector.

Notice the difference in where the cognition sits. The American stack concentrates intelligence in the cloud - in centralized data centers and proprietary models that client states must access remotely. The Chinese stack distributes intelligence into physical infrastructure - into ports, factories, power grids, and urban management systems. The American model is, in Suchman’s terms, the European Navigator’s approach to geopolitics: a centralized plan, computed in advance, projected onto the world. The Chinese model is closer to the Trukese Navigator: embedded, situated, responsive to local material conditions.

The American stack is powerful but brittle in some ways, because so much of the intelligence is concentrated in centralized cloud platforms, a disruption to connectivity from kinetic strikes on data centers, sabotage of undersea cables, or cyberattacks on cloud infrastructure can cognitively decapitate client states. The Chinese stack, by distributing intelligence into physical infrastructure at the edge, is harder to knock offline in one blow, but its components are also harder to update, harder to audit, and harder to extract once installed.

I am partial to the distributed cognition approach in general, but I don’t want to romanticize China’s approach. The Industrial Nervous System comes with the surveillance priorities of the Chinese security state, the optimization logics of state-directed capitalism, the disciplinary mechanisms of social credit and algorithmic governance. When a Gulf state integrates Huawei’s smart city platform into its urban infrastructure, it is importing a particular vision of how human behavior should be monitored, predicted, and managed. It is still political technology.

And the disciplining works differently in the two cases. Where the American model disciplines through a gun to the head - threatening to revoke compute if you deviate - the Chinese model disciplines through dependency. Once your ports, your power grid, and your industrial logistics run on Chinese AI, switching costs become prohibitive. The dependency is physical and material, embedded in concrete and fiber optic cable.

GCC Intelligence

This section is even more speculative than the others since I have very little understanding of the situation on the ground - caveat emptor!

The Gulf states are not passive recipients of these competing stacks. They are sophisticated actors playing a two sided market. A Gulf state might purchase an American AI targeting system for its border security, run its enterprise software on Microsoft Azure, and rely on Palantir for intelligence analysis - while simultaneously operating its massive industrial ports on Chinese-built automation, managing its smart cities through Huawei platforms, and training its petroleum engineers on SenseTime’s computer vision tools. This is metabolic hedging as statecraft, and it works because the two stacks, despite being strategically opposed, are functionally complementary. The prediction stack handles the cognitive heights; the production stack handles the material base. As long as the Gulf states can keep both plugged in without triggering the snapback clauses, they get the benefits of both augmented intelligences.

This is, if you like, distributed cognition at the geopolitical level, a patchwork of cognitive artifacts drawn from competing prediction and production models, stitched together by local administrative practice and sovereign capital. The question is whether a distributed cognitive system assembled from structurally antagonistic components can function coherently under stress (that’s polyconflict as technology!)- or whether it fractures precisely when coherence matters most.

Both superpowers tolerate this arrangement because the Gulf functions as a live lab for their respective stacks. The US tests how far it can push decision compression, cloud-based surveillance, and hardware export controls in a hot conflict environment. China observes, refines, and demonstrates how its industrial AI can maintain infrastructural resilience and supply chain efficiency under extreme operational pressure. The Gulf War is also a product demonstration, and here, one must include the Israelis and their tacit marketing of war crimes as military progress.

But the Gulf states also recognize the danger of this position. To rent your intelligence from two competing empires is to be cognitively dependent on both of them. When your hospital logistics run on an American cloud platform and your port logistics run on Chinese automation, you don’t fully control either system. Your institutional cognition is partly owned by Washington and partly owned by Beijing. He who controls the spice controls the world.

Sovereign AI: Building Your Own Mind

This is why the wealthiest Gulf states have begun pursuing what they call Sovereign AI - the attempt to develop indigenous cognitive capabilities that reduce dependency on either superpower stack.

The UAE’s Technology Innovation Institute developed the Falcon family of large language models and released them under permissive open-source licenses, explicitly asserting technological independence from traditional power centers. Saudi Arabia developed ALLaM, and the UAE developed Jais - both designed as Arabic-first models that natively understand the linguistic nuances, cultural values, and historical contexts of the Middle East, rather than importing the epistemic biases and safety guardrails engineered in Silicon Valley.

Viewed through the lens of 4E Cognition, the Sovereign AI movement is an attempt to build augmented institutional cognition that is genuinely situated - grounded in local cultural and linguistic reality rather than running on borrowed American or Chinese patterns. A foundation model trained primarily on English-language internet data carries the congealed patterns of English-speaking institutions. When you use that model to run Arabic-language civic administration, you’re filtering your institutional cognition through a foreign cognitive artifact. The output will be shaped by assumptions, priorities, and blind spots that originated in a different cultural and institutional context.

Sovereign AI is, in this sense, an attempt to produce locally grounded congealed patterns - to train models on data that reflects the actual situated reality of Gulf societies, rather than importing a lossy compression of someone else’s reality. IMHO, sovereign AI won’t work for foundation models or cutting edge GPU’s - the talent and the infrastructure for those are only available in the US and China; the Gulf states don’t have the semiconductor fabrication capacity or the training infrastructure to compete with the US or China at the frontier. Neither does India or France, for that matter. What they’re pursuing is something more like managed interdependence using sovereign models for culturally sensitive and strategically critical applications such as civic administration, Arabic-language services and military command while continuing to access American and Chinese frontier capabilities for less sensitive tasks.

Sovereign AI in the application layer seems doable for many middle-powers.

Whether sovereign or dependent, the problem of Augmented Intelligence is not just a problem of who owns the models, but also a problem of how the models interact with the political technologies of the institutions that deploy them. A sovereign foundation model integrated into a military bureaucracy that has twenty-second human oversight is just as capable (perhaps more so?) of catastrophic stupidity as an American or Chinese one. The Minab school bombing wasn’t caused by the foreign ownership of the AI system. We have never paid attention to either of them, but the Saudi funded war in Yemen and the UAE funded war in Sudan are as horrendous as they get, and will only get worse with AI.

The Stack War as Polyconflict

What I’ve been describing is more than a technology race; it is a polyconflict in the sense I defined at the start of this series: a structural knot where material reality, in the form of semiconductor supply chains, energy grids, submarine cables, and water resources, tangles with institutional systems, military strategy, and the cognitive architectures that organize human societies.

The prediction and production stacks are two competing models of planetary augmented intelligence, each seeking to extend its institutional cognition across the globe, each disciplining client states through different mechanisms, each carrying the congealed patterns of its parent nation (civilization?). Everyone else is caught at the intersection, trying to build sovereign cognitive capabilities while hedging between two empires that each want to be the sole operating system of the twenty-first century.

And all of this is happening inside an active war zone where the physical infrastructure - data centers, submarine cables, desalination plants, power grids - has been attacked and will continue to do so in the future, with all the impact that will have on local cognitive architectures.

That material vulnerability is where the next essay in this series will go. The AI Polyconflict has a metabolic dimension that most analyses of the AI race completely ignore: the energy, water, and capital requirements of the compute infrastructure that all of these augmented intelligences depend on. To understand why the Gulf is the geographic center of the AI Polyconflict, you need to understand energy-compute arbitrage. The Gulf states are attempting a phase transition from planetary gas station to planetary compute hub.

Will they succeed? That’s Part IV.

In Part I of this series, I traced how the Gulf War is exposing the AI Polyconflict through two concrete developments: the compression of the kill chain (where AI systems like Lavender and Project Maven are replacing human ethical deliberation with algorithmic speed) and the Cheap Drone Paradox (where $500 quadcopters are credibly threatening billion-dollar weapons platforms). I ended with a question that I want to pick up here:

how is it that cheap drones with embedded spatial intelligence are defeating legacy military systems that cost thousands of times more?

To answer that question, and to understand the AI Polyconflict at a deeper level, I need to step back from the Gulf and take a theoretical detour. Bear with me: this detour will pay off, because it changed how I see this conflict, and therefore, it changes how you see everything that follows in this series.

My claim is this: to understand what AI is doing in the world right now, you need two ideas that most people working on AI or people who are solving the world’s problems don’t care about. The first comes from cognitive science: distributed cognition. The second comes from a certain strand of philosophy: political technology. When you put them together, you get a framework for understanding Augmented Intelligence - the actual thing we should be concerned about, as opposed to the science-fiction version of AI that dominates most public debate on AI doomerism.

Militaries Have Always Been Artificial Intelligences

Let me start with distributed cognition. In the early 1990s, the cognitive scientist Edwin Hutchins published a book called Cognition in the Wild. Hutchins had spent years studying how the crew of a U.S. Navy ship navigates into port, and what he found upended a basic assumption of cognitive science: that thinking happens inside individual heads.

It doesn’t, or at least, not only there. When a Navy crew brings a warship into San Diego harbor, the thinking required to do so is spread across the entire bridge team and their instruments. One sailor takes a bearing through an alidade, another plots it on a chart, a third relays headings to the helm. The navigational tools themselves such as the charts, the compasses, the plotting instruments, carry centuries of accumulated mathematical knowledge built directly into their physical design. A sailor using a scale doesn’t need to know trigonometry; the tool does the trigonometry for him. The intelligence of the operation doesn’t live in any one person’s brain. It lives in the system: the people, their training, their tools, and the social organization that connects them all.

Hutchins’ work was part of a broader movement in cognitive science now called 4E Cognition - the idea that human thinking is Embodied (grounded in the body), Embedded (situated in an environment), Enacted (emerging through action), and Extended (relying on tools and artifacts outside the skull). For Hutchins, the right unit of analysis for understanding cognition isn’t the individual mind. It’s the whole sociotechnical system.

Now the second idea. In Discipline and Punish, the philosopher Michel Foucault traced how modern institutions - prisons, hospitals, schools, and above all, militaries - developed specific techniques for reshaping human bodies and minds into standardized, controllable components. He called this a political technology of the body. The key innovation of modern military discipline, starting in the seventeenth century with tacticians like Maurice of Nassau, was to take the chaotic mass of infantry and reorganize it through relentless drill, precise time-keeping, and rigid spatial ordering. Every movement was standardized. Every soldier became interchangeable. The military camp became a machine for producing what Foucault called docile bodies - people whose physical capacities were simultaneously maximized and whose political autonomy was minimized.

Foucault’s point was that the military didn’t just use technology. The military was a technology - an artificial apparatus for organizing human beings into a productive, lethal machine. Long before anyone built a computer, the army was already a kind of computational system, processing information about terrain, logistics, and enemy positions through hierarchies of disciplined human processors.

Augmented Intelligence

Here’s where these two ideas converge in a way that I think changes how we understand AI.

If Foucault argues that militaries are artificial systems - political technologies that format human beings into standardized components - and Hutchins shows us that cognition in complex institutions is distributed across people, tools, and organizational structures, then juxtaposing their insights leads to the inevitable conclusion: militaries have always been artificial intelligences (also see “AI is a cultural technology”). They are vast, distributed cognitive systems composed of disciplined human elements, material artifacts, and institutional procedures, all organized to process information and produce lethal outputs.

The Roman legion was an artificial intelligence. The Napoleonic staff system was an artificial intelligence. The thing we now call AI - machine learning using neural networks - is only the latest cognitive artifact to be plugged into this ancient apparatus. Like I said last week, what we’re witnessing today is better described as Augmented Intelligence: the merger of distributed human-institutional cognition with new machine capabilities that extend, accelerate, and sometimes replace elements of that distributed system.

Engineers building deep learning systems think of AI as a property of the machine - its parameters, its training data, its benchmark scores. But when that machine gets deployed inside a military bureaucracy or a corporate supply chain, it becomes something else entirely. The intelligence that matters is no longer the machine’s. It’s the intelligence of the ensemble: the humans, the algorithms, the organizational incentives, and the material infrastructure, all entangled. And that ensemble can only be understood through the lens of distributed cognition and political technology.

Fearsome and Stupid

A paradox runs through the entire history of institutional cognition: these systems can be simultaneously fearsome and stupid.

Hutchins saw this clearly on the Navy bridge. The distributed cognitive system of ship navigation could perform calculations far beyond any individual crew member. But it was also fragile in unexpected ways. A misheard bearing, a misplotted line, a moment of miscommunication between stations - and the whole system could produce catastrophically wrong outputs. The intelligence of the system was real, but it was brittle, dependent on every component performing its role correctly within tight tolerances.

Foucault saw the same thing from the other direction. The political technology of discipline could produce armies of extraordinary effectiveness, but the very process of doing so created docile bodies stripped of individual judgment, and as a result, also created institutions capable of extraordinary stupidity. When the situation didn’t match the drill, when reality deviated from the plan, the disciplined apparatus could grind forward into disaster with mechanical persistence.

This dual character - of fearsome capability married to structural stupidity - is the permanent condition of complex institutional cognition. And it runs straight through the history of the military-industrial complex that gave birth to our current AI systems.

The Bumbling Cyborg

The most consequential iteration of Augmented Intelligence before our own was the cybernetic military-industrial complex that arose after World War II. The Cold War and the nuclear arms race demanded a new scale of institutional cognition. Operations research, game theory, systems analysis, cybernetic feedback loops - these became the intellectual infrastructure of a vast human-machine apparatus spanning the Pentagon, RAND Corporation, defense contractors, and research universities.

This apparatus was, in every meaningful sense, a new kind of cyborg - a term coined in the 1960s for a fusion of biological and technological systems functioning as a single organism. The post-war military-industrial complex fused human judgment with computational machinery on an unprecedented scale. It could model nuclear exchange scenarios, coordinate global logistics networks, and project force across continents. It possessed the apocalyptic capacity to end life on earth in an afternoon.

And it was also, frequently, bumbling, inefficient, and stupid.

The same apparatus that developed precision-guided munitions also produced the catastrophic miscalculations of Vietnam, where Pentagon planners mistook body counts for strategic progress and tonnage dropped for political influence - and I am not even talking about the moral cretinism involved in doing so; the bombings and body counts were failures according to the standards set by the military planners themselves. The systems were brilliant at processing quantifiable data and terrible at grasping the situated, cultural, political realities that actually determined outcomes on the ground.

Moral hazards are particularly important when it comes to military systems.

This is the European Navigator problem, to use a distinction the anthropologist Lucy Suchman writes about in Plans and Situated Actions. The European Navigator creates a rigid, detailed plan in advance and then tries to force reality to conform to it. The Pentagon’s systems analysts were the ultimate European Navigators: they built elaborate models, ran simulations, and then expected the world to behave accordingly. When it didn’t - which was often - the apparatus doubled down, which was both stupid and evil.

The horrendous mistakes this apparatus produced were not always the result of overt malice. Many of them arose from apathy and structural stupidity - the inability of a vast, distributed cognitive system to account for what it couldn’t measure. The cybernetic complex was a political technology of extraordinary power and extraordinary blindness, a cyborg that could destroy a continent but couldn’t understand a village.

The cybernetic military-industrial complex wasn’t just a military phenomenon, either. Its logic seeped into civilian life. The interstate highway system, corporate management science, standardized testing, urban planning - all carry the DNA of the first era of systems theory and cybernetic control: centralized modeling, quantified metrics, top-down optimization. The same political technology that organized Cold War lethality organized the American economy and, eventually, the consumer internet.

Congealed Patterns

Contemporary artificial intelligence is the child of this complex. The foundational research on neural networks, pattern recognition, and machine learning was incubated inside Cold War defense institutions and funded by military research agencies. The internet itself - the source of much of the data used to train modern AI - was originally a DARPA project designed to ensure military communications could survive nuclear war.

When today’s large language models and deep learning systems are trained, they consume the digital output of the entire internet. They absorb the accumulated, compressed record of decades of human activity - much of it shaped by the institutional logics, biases, and priorities of the cybernetic complex and its commercial descendants. The writer Ted Chiang called large language models ‘lossy JPEGs of the internet’. Just as a compressed image loses detail while preserving dominant patterns, AI models capture the dominant patterns of their training data while losing the situated context, the ambiguity, and the ethical nuance that characterized the original human activity.

When an AI system makes a prediction, it is drawing on the accumulated logic of the bureaucracies, markets, and military apparatuses that shaped the digital world. The system doesn’t think in any meaningful sense. It reproduces, at tremendous speed, the patterns laid down by the human-machine complexes that preceded it. These are congealed patterns, institutional behavior compressed into statistical weights.

The New Cyborg Goes to War

Which brings us back to the Gulf and to the wars currently reshaping how organized violence works.

The Augmented Intelligence systems now being deployed by the United States, Israel, Ukraine, Russia, and others represent the latest political technology - the newest iteration of the cyborg. They fuse the distributed cognition of military bureaucracies with the pattern-matching speed of deep learning. And they display the same dual character we’ve seen throughout the history of institutional cognition: fearsome capability and structural stupidity, now operating at unprecedented velocity.

In Gaza, the Israeli Defense Forces industrialized the kill chain using a triad of AI systems. Lavender analyzed surveillance data to flag tens of thousands of individuals as potential targets. Habsora generated over a hundred structural targets per day - compared to the roughly fifty per year that human analysts had previously produced. Where’s Daddy tracked the phones of flagged individuals and alerted operators when they entered their family homes. The human role in this system was reduced to roughly twenty seconds of confirmation per target. The intelligence of the system was distributed across the algorithms, the surveillance infrastructure, and the military hierarchy. The individual human operator was reduced to being a switch in a circuit.

When this architecture expanded into the direct conflict between the US-Israeli coalition and Iran in 2026, Project Maven, augmented by large language models, nominated over three thousand targets in a single week during Operation Epic Fury. The system could sift through satellite imagery, classify targets, recommend weapons systems, and draft legal justifications for strikes in seconds. It was, by any measure, a formidable cognitive apparatus.

And it was also capable of catastrophic stupidity. The bombing of the Shajareh Tayyebeh primary school in Minab, Iran - which killed over 170 civilians, mostly children - occurred because the AI system relied on a Defense Intelligence Agency database that hadn’t been updated to reflect the building’s conversion from an IRGC compound into a school. The system processed congealed historical data rather than situated current reality, and no human in the chain caught the error (they don’t have any incentives for doing so, just FYI).

In Ukraine, the same dynamics play out at the edge of the network. The grinding war of attrition between Russia and Ukraine has compressed the evolutionary cycle for autonomous weapons from years to weeks. Ukrainian FPV drones and Russian Lancet munitions increasingly operate with autonomous terminal homing and machine vision. When electronic warfare jams their radio links, these drones don’t fail - they switch to onboard AI, recognizing targets by thermal signature and executing strikes based on immediate environmental feedback.

These are Trukese Navigators in the sky. They don’t follow rigid, centralized plans beamed from a command center. They sense, adapt, and act based on local conditions. The intelligence is embedded in the device, enacted through its interaction with the environment, distributed across the swarm. Shoot one down and the network barely registers the loss.

But this situated, embodied capability comes with its own form of stupidity. Edge AI operates on narrow pattern recognition. It can identify a thermal signature that matches its training data, but it cannot assess whether the heat source is a tank, a tractor, or a school bus. The speed and autonomy that make these systems tactically effective also make them ethically uncontrollable. The drone is situated in the physical environment but is alienated from the moral context of its actions.

The Monster We Are Building

Here’s what I want you to take from this essay:

The thing we’re watching emerge in the Gulf, in Ukraine, and along every contested border on the planet is not just “how is AI being used in war?” It is a new kind of cyborg, a new political technology that fuses centuries of institutional discipline with the latest cognitive artifacts, producing an Augmented Intelligence of frightening capability and familiar stupidity.

This cyborg can coordinate force with mathematical precision. It can process information at scales beyond individual human comprehension. And like its predecessors, it can produce catastrophic errors - not despite its sophistication, but because of it. The very mechanisms that allow these systems to operate at superhuman speed also strip away the human capacities that might catch their mistakes: deliberation, contextual judgment, moral friction etc.

The frameworks of distributed cognition and political technology help us see that the emergence of this new cyborg was, in retrospect, predictable. Every major military innovation has been an exercise in extending the distributed intelligence of the military apparatus through new material technologies while simultaneously demanding new forms of human discipline to interface with those technologies. The musket required the drill sergeant. The telegraph required the signal corps. The nuclear weapon required the systems analyst.

And AI requires... what?

That’s the question the AI Polyconflict forces us to confront. We are building new forms of Augmented Intelligence at a pace that far outstrips our ability to develop the institutional wisdom needed to govern them. The political technologies of discipline that once constrained military cognition - chains of command, rules of engagement, international humanitarian law - were designed for a world where human beings had time to think, not for a world where the kill-chain can go from detection to assassination in thirty seconds.

The future of war, and of organized violence more broadly, depends on whether we can develop new political technologies capable of governing the augmented intelligences we are building. That is a problem of institutional design, not of engineering. And it requires us to pay attention to the people who study how cognition actually works in the wild.

That’s, of course, assuming that we want to arrive at some constraints on planet-scale violence. Maybe we won’t, and our rulers are fine with waging a war of all against all.

In Part III of this series, I’ll turn from kinetic warfare to the geopolitical architecture of the AI Polyconflict: how the Gulf has become a surrogate sandbox for superpower rivalry. This is what I promised at the end of Part I, but I hope this detour has been instructive, if not enjoyable.

Over the last few weeks, I have been exploring how the ongoing war in the Gulf is exposing a series of polyconflicts, starting with Helium, Metformin and then Firewood.

But there’s a polyconflict that underlies them all, the mother of all polyconflicts, so to speak: Artificial Intelligence. Today’s newsletter is the first of five or six essays on the AI polyconflict, which looks at the changing face of kinetic warfare (i.e., shooting wars) through the lens of the current Gulf War and AI.

1. Except that I am redefining AI as Augmented Intelligence for reasons I will explain below.

2. Caveat Emptor: I am no expert and this is just my attempt to satisfy my curiosity...

From what I can tell, the usual suspects - US, Israel and China - are key players in this race to the bottom, but so are Russia, Ukraine, India, Pakistan, Turkey.

On the targeting front, or what’s called the kill-chain, systems like Lavender and Project Maven fuse satellite data and digital exhaust to generate thousands of targets in seconds, replacing human ethical deliberation with algorithmic speed.

On the “Cheap Drone Paradox” front, we have the use of drones by Iran, AI-assisted loitering munitions in Ukraine and India’s transformed Line of Control.

Brave New World for sure.

Framing the “AI Polyconflict” Series

AI is implicated in this Gulf War in interlocking ways. It shows up in targeting decisions and the systems that help identify military objectives. It shows up in attacks on data centers and digital infrastructure, because compute and data are strategic assets. And it shows up in the expanding Cold War between the US and China over who will control the future, because whoever leads in AI will shape economics, security, and influence for decades.

The Gulf War is a lens. It’s a way to see the world we’re building, or more likely, the world we’re drifting into without knowing why. AI - and I use that term very broadly, so don’t confuse it for Generative AI alone - changes incentives, changes what counts as a target, and it changes how nations gauge power. In fact, for me, AI is Augmented Intelligence, not artificial intelligence. A military bureaucracy combining satellite data and ground intelligence to inform targeting decisions is already ‘AI as augmented intelligence;’ and in that sense, AI was already alive and kicking in the 1990s - Ed Hutchins wrote a book about it then, as part of the first wave of writing on what’s now called 4E Cognition.

My Hypothesis: ideas from 4E Cognition aren’t seen as particularly useful to the engineers building Deep Learning systems, but a reworked version of those ideas is exactly what we need to understand Augmented Intelligence, and in particular, how AI as in artificial intelligence is used by human institutions to augment their capacities, sometimes with tragic consequences.

In short: if you want to understand where the tech came from, read Geoff Hinton, but if you want to understand where the world is going, read Ed Hutchins first.

In this series, I’m going to use the Gulf War to grasp the Augmented Intelligence polyconflict: how AI becomes a weapon, how it becomes infrastructure, and how it becomes a bargaining chip in global politics. Today’s essay introduces the AI Polyconflict, and then moves on to its use in military decision making, where the Gulf War is building upon patterns we have already seen in the Ukraine-Russia conflict and in Israel’s destruction of Gaza.

As always, I am no expert on AI’s military uses (in either its augmented or artificial form), and I have absolutely no insight into the “kill-chain” except for what’s available publicly. Fools rush in where angels fear to tread; let me be your fool for the day.

Introduction

To treat our interconnected world as polyconflicts is to take a sober, perhaps pessimistic systems perspective on the world. A polyconflict is not merely a complicated problem or geopolitical friction; it is a nexus where material reality - the extraction of finite minerals, the generation of megawatts, the synthesis of industrial chemicals, and the flow of global capital - tangles with human systems, military strategy, and below all that, biology and geology. A polyconflict presents concurrent, mutually reinforcing challenges that overwhelm traditional modes of governance and strategic foresight.

IMHO, polyconflicts are an essential aspect of the interregnum between the Globe and the Planet.

We have covered three polyconflicts so far. The Metformin Shock demonstrated how deep petrochemical dependencies and fragile Active Pharmaceutical Ingredient (API) supply chains collided with the global public health crisis of type 2 diabetes. Because China had emerged as the dominant supplier of global APIs, geopolitical tensions and subsequent U.S. tariff measures exposed the acute vulnerability of Indian and Western medical systems, forcing efforts to optimize continuous manufacturing processes like wet granulation and relocate production.

Similarly, the Helium Shock revealed how the Gulf War has cascaded through advanced high-tech sectors. Helium is a non-renewable control point for global production capacity. Driven by the demands of advanced semiconductor fabrication and the cooling of superconducting magnets in MRI machines, global helium demand is projected to grow more than fivefold by 2035. When disruptions tied to halted gas processing in Qatar removed 5.2 million cubic meters of helium per month from the market, it triggered force majeure declarations and doubled spot prices, exposing the fragility of the materials base required to sustain healthcare as well as the digital economy.

The LPG/Firewood Squeeze provided another example, highlighting how global energy scarcity forces regressive shifts back to traditional biomass fuels, placing a disproportionate burden on women’s labor and respiratory health in the Global South. As Liquid Petroleum Gas (LPG) becomes scarce and expensive, vulnerable households will be forced to abandon clean cooking fuels, leading to increased exposure to black carbon and household air pollution, which significantly elevate the risk of poor fetal growth and maternal health complications.

Yet, these crises - pharmaceutical, chemical, and energetic - are sectoral in comparison to the polyconflict about which I am starting to write today: Artificial Intelligence. AI isn’t simply a novel tool utilized by belligerents. It is the ultimate prize, strategic vulnerability, and the primary accelerant of violence, for the compute stack is at the heart of our age of cognitive capitalism and its imperial tendencies. To untangle the AI Polyconflict, it is necessary to examine five distinct threads that are currently rewriting the metabolism of the planet:

1. The changing face of kinetic warfare,

2. The use of the Gulf as a surrogate sandbox for superpower rivalry,

3. The structural divergence of global AI architectures,

4. The immense material demands of the energy-compute arbitrage,

5. The macroeconomic emergence of cognitive imperialism.

Every one of these five deserves standalone analysis, so I am going to devote an essay to each of the five.

The Changing Face of Kinetic Warfare

Kinetic warfare refers to the use of lethal force and physical weaponry to achieve military objectives. The term is primarily used to distinguish traditional shooting wars from non-kinetic forms of conflict, such as cyber warfare, economic sanctions, or psychological operations.

The kill chain is a military concept that describes the end-to-end stages of a kinetic attack, from the initial identification of a target to its eventual destruction and the assessment of the results.

The first thread of the AI Polyconflict reveals a fundamental shift in the ontology of war. Throughout the history of conflict, technology - from the horse to the longbow to the telegraph to precision-guided munitions - provided a tactical edge in executing human decisions. Today, however, technology is actively replacing the human durée - the lived time of ethical deliberation, moral friction, and strategic pause with automated decision making and automated coordination of lethality.

Decision Compression

The claim - perhaps not yet realized - is that modern military AI systems, integrating large language models (LLMs), vast arrays of sensor data, and continuous signals intelligence, are capable of fusing satellite imagery and digital exhaust to generate thousands of targets in a fraction of the time it would take human analysts. This speed outpaces the capacity of human cognition, handing the metabolism of conflict over to algorithms.

Imagine high frequency trading (HFT) with bullets and drones - not a pleasant thought is it?

Here’s what I have learned: the Israel Defense Forces (IDF) have effectively industrialized the kill chain in Gaza and the broader conflict against Iranian-aligned assets. Rather than relying on human intelligence gathering, the IDF utilizes a suite of AI systems to automate lethality. A system known as Lavender analyzes massive surveillance datasets to flag suspected militants based on behavioral patterns, identifying tens of thousands of potential targets with an estimated 10% error rate. Another system, Habsora, generates up to 100 structural targets a day by analyzing surveillance data, a massive acceleration compared to human analysts who previously identified roughly 50 targets a year. Finally, Where’s Daddy tracks the mobile phones of flagged individuals, alerting military operators when targets enter private family residences. From this interview of Yuval Abraham on 972 Mag (which has done some amazing work):

One of the sources that I spoke with, he said something that really stuck with me, he said that “the logic was quantity over quality.” They wanted to create as many targets as possible. This was called the “Dahiya Doctrine,” which is a doctrine that was developed in Lebanon to create a shock effect. They got it from the Americans, from “Operation Shock and Awe” in Iraq in 2003. This is a doctrine, a military doctrine, that emphasizes quantity. It emphasizes creating terror or fear among civilians by creating this massive shocking bombing campaign. What that source told me was that when you place the emphasis on quantity, this leads you to the private houses — say you want to get the rocket launchers, the ammunition warehouses, the military bases, if they exist, the tunnel infrastructure where Hamas is controlling the war — okay, there are a few hundred targets that you can create like that. But if you want to go to tens of thousands of targets, to place this emphasis on quantity, to bomb Gaza a lot, then you move on to civilian houses because you have so much of that. Because what that source said, he said, “if we suspect that there are 40,000 militants in Hamas, each one of those militants has a private house.” That immediately creates an aspiration to get 40,000 houses. If there are 40,000 militants, there are going to be 40,000 civilian houses.

“That emphasis quantity” is the key phrase above, showing Augmented Intelligence at work. International Humanitarian Law (IHL) requires human judgment to weigh the principles of proportionality, distinction, and military necessity before lethal force is applied. When a human operator spends roughly 20 seconds rubber-stamping an AI-generated kill order, how likely is it that they will be adequately weighing the principles of proportionality, distinction and military necessity? How likely is it that they are outsourcing the moral and analytical labor of killing to a machine?

This industrialization of targeting is rapidly becoming the global standard.

The United States military relies heavily on Project Maven, an AI platform built with private tech partners like Palantir and Anthropic. Maven claims to sift through drone and satellite footage to classify targets, recommend specific weapons systems based on stockpile data, and even draft automated legal justifications for each strike, compressing thousands of hours of human labor into seconds. I am not sure how much is in the ‘fake it till you make it’ territory right now, but its definitely in the future. In Ukraine, the integration of Palantir’s intelligence software with domestic battle-management networks like Delta and GIS Arta has reduced the time between detecting a target and striking it from hours to mere moments. Not to be left behind, Beijing is developing the Atlas system - a fully autonomous operational chain that integrates reconnaissance, target selection, and the simultaneous launch of drone swarms into one networked action.

When one military actor takes thirty minutes to deliberate the legal and ethical implications of a strike, and their adversary takes thirty seconds to execute an algorithmic recommendation, the deliberative side loses any kinetic advantage it might possess otherwise. Consequently, AI guarantees an augmented/automated race to the bottom.

The Cheap Drone Paradox

Simultaneously, the physical manifestation of this conflict has shifted away from exquisite, high-cost platforms toward attritable, autonomous quantity. The Gulf and the Red Sea have become global showcases for what military theorists term the Cheap Drone Paradox. Inexpensive, sub-$500 autonomous drones equipped with edge AI can now navigate, identify targets, and swarm without requiring a continuous cloud connection, credibly threatening multi-billion-dollar naval assets and national infrastructure.

Iranian-aligned forces, particularly the Houthis in Yemen, have leveraged this paradigm to disrupt maritime trade corridors in the Strait of Hormuz. Because these drones feature growing edge autonomy, they can execute missions even when advanced electronic warfare jamming degrades their communication signals. The cumulative effect is a saturation environment where defensive success is no longer guaranteed, but probabilistic - a certain percentage of attacking drones will inevitably penetrate the defense. This capability was starkly demonstrated when a Houthi drone navigated over 2,600 kilometers to strike Tel Aviv in July 2024, altering the regional balance of power. In response to this asymmetric threat, advanced militaries are mirroring the tactic. Israel, for example, has deployed an AI-driven mother launcher system over Iranian skies - swarms of micro-drones equipped with facial recognition to execute network-centric, data-driven strikes based on behavioral patterns.

The gulf war is not the only theater in which the cheap drone paradox is visible - it is equally pronounced in the grinding war of attrition between Russia and Ukraine. Russia has leveraged a pipeline of Chinese dual-use components and Iranian designs to manufacture massive fleets of AI-assisted loitering munitions, such as the Lancet, to hunt Ukrainian artillery and armor. As the skies over Eastern Europe become choked with drones, both sides are locked in a relentless cycle of electronic warfare (kinetic warfare and cyberwarfare are merging in the sky...). Because traditional radio links are constantly jammed, developers are pushing greater autonomy to the edge (i.e., the standalone drone in the sky), enabling drones to use machine vision and AI to find and strike targets without a human pilot ever giving the final command.

The cheap drone paradox had its day during Operation Sindhoor when India and Pakistan came closest to a full-fledged war since 1971. From what is known publicly, the Line of Control (LOC) has been completely transformed. India has deployed a vast network of AI-based surveillance systems and integrated advanced swarm drones and stealthy unmanned combat aerial vehicles (UCAVs) into its command networks. Pakistan, in response, has accelerated its own drone ecosystem with Chinese assistance, prioritizing low-cost countermeasures and dispersed basing.

It’s prohibitively expensive to man the LOC with human beings all along the border, but swarms of drones feels doable. Drones offer cheap precision and deniable force without the political risks of a manned incursion, but they also severely compress decision-making timelines in a nuclearized theater. I hope we are not going in the direction of automated nuclear war.

That specter of automated nuclear escalation is not science fiction; it is the logical endpoint of the polyconflicts we have been tracking.

Augmenting the Drone

Of all the questions about the future of warfare, here’s the one I find most interesting:

How is it that $500 off-the-shelf quadcopters, retrofitted with explosives, are successfully paralyzing multi-million-dollar air defense systems and neutralizing exquisite, legacy military platforms?

These are the new guerilla tactics embraced by weaker state actors from Iran to Ukraine (and not just state actors - the Houthis are masters of this art). How might one explain their success?

Traditional military doctrine is built on the logic of what the anthropologist Lucy Suchman famously called the “European Navigator.” The European Navigator creates a highly detailed, centralized plan in advance, calculating the exact trajectory, and then tries to force reality to conform to that rigid plan. This is the logic of the aircraft carrier and the billion-dollar stealth jet: heavy, centralized, top-down, and enormously expensive (or that’s what I think - someone who knows how these billion dollar pieces of equipment are used might rebut my claims).

The cheap drone swarm, powered by edge computing, operates on a completely different paradigm. Suchman contrasted the European Navigator with the Trukese Navigator of Micronesia, who doesn’t follow a rigid plan but instead continuously adjusts to the waves, the stars, and the wind. The Trukese navigator relies on situated action - intelligence that emerges dynamically in response to the immediate environment.

A swarm of cheap drones with embedded spatial intelligence operates exactly like this. Because compute is now pushed to the edge (directly onto the device rather than relying on a vulnerable connection to a central server), the drone doesn’t need to phone home to a centralized command structure for a plan. It senses the terrain, spots a thermal signature, coordinates locally with its peers, and enacts a strike.

The cheap drone paradox is ultimately a triumph of 4E Augmented Intelligence. By distributing cognition across a swarm of cheap, expendable, embedded nodes, the system becomes resilient. If you shoot down one drone, the network’s cognitive process barely registers the loss.

You cannot destroy a distributed mind with a single bullet.

Conclusion

The Metformin shock revealed our deep dependency on monopolized, just-in-time chemical supply chains that can snap under tariff pressures. The Helium squeeze proved that the pinnacle of the digital economy and advanced healthcare remain tethered to easily disrupted subterranean gas reserves. And the LPG-to-firewood squeeze showed us that when modern energy markets fracture, the fallout forces the poorest populations back into respiratory nightmare.

These earlier crises were sectoral warnings about the fragility of the Globe. Now, we are superimposing automated lethality directly onto this precarious edifice. The cheap drone paradox and the compression of the kill chain do not exist in a vacuum. Edge computing is very much in evidence in your pockets - every time you ask for turn by turn directions, for example. The algorithms executing drone strikes run on the same semiconductors and require the same rare earth materials as your smartphone.

This is what makes AI the mother of all polyconflicts.

As militaries deploy cognition in the wild by handing over their decision-making to human-silicon meshes (with pure silicon decision-making down the road), they are stress-testing AI warfare architecture - nothing like a war to find out if your weapons work or not, and to show them off when they do - and that is where we will turn in Part II: how the Gulf has become the indispensable sandbox for AI rivalry - military and otherwise.

I wasn’t planning on writing another essay in the Polyconflict series this quickly, but a twitter thread sparked a reverie on the relationship between the Gulf War, gendered harms and the need for metabolic sovereignty.

When we hear the term “World War,” we think of nations the world over fighting pitched battles across their landscape, that they strike everyone, everywhere all at once. But that’s not the reality at all is it? Polyconflicts (of which WWs are one example), have a core - periphery grading to them. The ongoing gulf war has an epicenter, a blast radius, and a fallout zone (to use a nuclear metaphor).

At the core of this conflict are the belligerents - the US, Israel, and Iran - trapped in a zone of air and drone strikes, ballistic missiles, and perhaps soon: land battles. Then, there is the immediate line of fire: the Gulf Cooperation Council (GCC) countries. Nations like Qatar, Saudi Arabia, and the UAE aren’t the primary combatants, but their airspace is restricted, their shipping lanes in the Strait of Hormuz are choked, and their petrochemical hubs are threatened by collateral damage.

Further out, in the fallout zone, you have countries like India. We are not launching missiles, nor are our refineries under physical bombardment. Yet, we are caught entirely in the polyconflict trap. We are the outer ring, lacking the geopolitical agency to stop the core conflict, but requiring urgent responses to its cascading consequences.

I have argued that we cannot navigate this time of monsters using the bird’s-eye managerial view from the sidelines. Polyconflicts require an embedded perspective, grounded in the situation as it is being affected by the conflict, tracing the shocks commodity by commodity. I have done this for two commodities already. We traced how a drone strike on a Qatari gas field instantly chokes the global supply of Helium, endangering MRI machines across India. We mapped how that same strike squeezes the petrochemical precursors for Metformin, creating a metabolic trap for the Indian diabetic.

Today, let’s examine a third commodity implicated in the Gulf War Polyconflict. It is not a high-tech supercoolant, nor is it a complex synthetic molecule. It is one the most primal commodities in human history: Firewood.

The Illusion of Neutral Energy

From the bird’s-eye view, energy is a neutral resource. When policymakers look at energy models, they tend to see numbers on a spreadsheet: barrels of oil, cubic meters of gas, gigawatts of solar, BTUs of heat. But on the ground, energy is never neutral and gender is often an axis of uneven distribution. For millennia, the primary source of household energy for cooking and heating across the Indian subcontinent has been biomass - firewood, crop residue, and dried cattle dung, burned in unventilated mud stoves known as chulhas.

Whatever its historical value, the chulha is an ongoing catastrophe now. Environmentally, the relentless need for cooking fuel drives the slow, steady stripping of local scrublands and forests, destroying the ecological commons that rural communities rely on for resilience. The human cost is equally, if not more, severe, and it is borne almost exclusively by women.

Burning solid biomass in an enclosed space produces horrifying levels of Indoor Air Pollution (IAP). Cooking three meals a day over a chulha is the physiological equivalent of smoking hundreds of cigarettes. It is a primary driver of chronic obstructive pulmonary disease (COPD), acute respiratory infections, and debilitating cataracts among rural Indian women. And that’s not its only harm - firewood extracts a brutal ‘time tax’ for it does not pipe itself into a kitchen. It must be foraged, cut, and carried. This labor falls squarely on the shoulders of women and girls, who often spend hours every single day walking miles to gather fuel. Every hour spent foraging for firewood is an hour stolen from education, from income-generating labor, and from rest.

Once again, whatever its past usefulness, the chulha is a mechanism of gender inequality. It chains women to the hearth, destroys their lungs, and consumes their time.

The LPG Bridge and Its Collapse

Recognizing this profound injustice, the Indian state launched one of the most ambitious public health and gender-equity interventions in modern history: the Pradhan Mantri Ujjwala Yojana (PMUY). By heavily subsidizing Liquefied Petroleum Gas (LPG) connections for below-poverty-line households, the state aimed to move hundreds of millions of women away from toxic chulhas and toward clean, efficient cooking fuel. The blue flame of the LPG stove became a symbol of modernization, freeing up millions of hours for women and drastically clearing the air in rural kitchens.

Note: we too easily forget how much fossil fuels are woven into our idea of modernity.

But the Ujjwala Yojana contained a hidden, structural vulnerability. LPG is a byproduct of natural gas processing and crude oil refining. India simply does not produce enough of it. To fuel the Ujjwala revolution, India became the world’s second-largest consumer of LPG, importing roughly 55% of its supply - the vast majority of it shipped directly from Qatar, Saudi Arabia, and the UAE. Over the last two decades, India’s LPG imports have surged dramatically, increasing nearly 12-fold from 1,722 TMT in 1998–99 to over 20,667 TMT by 2024–25.

Those LPG tankers are all stranded in the strait of Hormuz. Supply has constricted, and the global price of gas has surged. For the Indian state, the subsidy burden to maintain the Ujjwala scheme at these new war-time prices will become fiscally impossible. That’s catastrophe enough, but I worry that a geoeconomic shock will wipe out a decade of hard-won progress on gender equality.

Will girls be pulled out of school to help forage?

Escaping the Trap

TLDR; Metabolic Sovereignty is more important than ever.

We cannot simply accept this regression. We cannot allow the promise of the Ujjwala Yojana to die on the altar of Middle Eastern geopolitics; we cannot afford to tether the dignity of Indian women to the Persian Gulf. We should maintain the spirit of our household energy policies, but we must shift their physical architecture towards DRE (decentralized renewable energy). We need to aggressively institutionalize the distribution and adoption of solar-thermal cooking stoves, build village-level renewable micro-grids to power electric induction cooktops, and (not so renewable) invest in community-level biogas consortiums that turn agricultural waste into piped cooking gas.

If a household’s cooking energy is generated locally - derived from the sun beating down on the roof, or derived from the agricultural waste of the adjacent fields - it won’t be at the mercy of a naval blockade in the Strait of Hormuz.

But to understand what this transition truly entails, we have to look deeper into the nature of the new cold war. It is increasingly clear that the geopolitical struggles defining our era are not strictly ideological; they are metabolic. When we transition a rural Indian kitchen from Qatari LPG to an electric induction stove powered by solar panels, we are shifting from the Petrostate nexus to the Electrostate nexus. Which is, on the whole, a good thing, but it does bring some strategic worries along with it: in our attempt to escape the volatility of the Persian Gulf, we will deepen our dependency on China.

To break this cycle of merely swapping one set of masters for another, India must strive for true Metabolic Sovereignty. A metabolic sovereign is a political unit that possesses monopoly control - or what systems theorists Humberto Maturana and Francisco Varela would call autopoiesis (self-creation and self-maintenance) - over the vital energy and information that flows across its borders. Achieving this does not mean walling off the country, but it absolutely means executing the renewable transition with clarity. It requires the Indian state to fiercely protect, subsidize, and incubate the indigenous manufacturing of solar panels, battery storage systems, and high-efficiency induction stoves.

We must stop viewing the manufacturing of a solar stove as a mere consumer goods market best left to the cheapest foreign bidder. We must treat it as the foundational infrastructure of a new metabolic regime, necessary not just for the physical liberation of Indian women, but for the self-governance of the nation.

Bad Intersectionality and the Necessity of Solidarity

In academic and progressive circles, we often talk about intersectionality - the idea that various forms of oppression exacerbate each other. Polyconflicts are intersectional in the most destructive sense of the word. They act like water under pressure, seeking out the deepest, most historically vulnerable fault lines in a society and cracking them wide open.

We have known that about war zones for a long time: the gendered realities are brutal - men die on the front lines, while women are haunted by sexual violence. But modern geoeconomic warfare - supply chain decoupling and commodity shocks also have a gendered fallout.

When global energy prices spike due to a war in the Gulf, it is rural Indian women who will pay the physical price of the LPG squeeze. Their lungs will fill with toxic smoke from chulhas. Their time will be stolen by the daily march for firewood and their daughters will be kept home from school to aid in housework.

If we are to achieve metabolic sovereignty, we must track metabolic risk as seriously as we track macroeconomics. We must measure localized heat-mortality exposure, village-level grid fragility, and the respiratory toll of indoor air pollution with the same urgency we reserve for the stock market.

In this interregnum as new monsters roam the streets, citizens owe their allegiance to the tangible institutions that keep life possible and do so within planetary boundaries. The transition to clean, localized energy must not be rolled out as a technocratic imposition from above. It must be championed as an expansion of entitlements - a right to breathe, a right to time, and a right to health.

One more essay on the Polyconflict before I shift back to the Planetarity Syllabus. I have several essays queued up, but am holding them back while I get these depressing thoughts out of my head.

A couple of days ago, I wrote about the impending Helium Shock due to the Gulf war. But Helium isn’t the only commodity under threat. It’s not even the only medically relevant commodity under threat. Surprise, surprise: the pharmaceutical industry is deeply dependent on the fossil fuel industry, and not just as an energy source: petrochemicals such as benzene and ethylene are the building blocks of modern drugs. Pharmaceuticals are petrochemicals!

In the two previous essays, I argued that polyconflicts cannot be managed from a control room. They are structural, open-ended scrambles for survival that must be traced node by node, commodity by commodity. We looked at how a drone strike in the Persian Gulf dictates the cost of a neurological MRI in Bihar by choking the global supply of helium. We saw how escaping one geopolitical trap frequently means wading into another.

The pharma industry is no different - the path to understanding the polyconflict is non-linear. I am going to illustrate that nonlinearity with a disease and a drug of great importance to Indians: diabetes and metformin respectively, where the metabology of the conflict is literally about human metabolism.

1. Diabetes

If you’re Indian, you almost certainly have a close family member who has diabetes, probably many such. According to the 2023 ICMR-INDIAB study, over 101 million Indians are currently living with diabetes, with an additional 136 million lingering in a state of pre-diabetes. We currently account for roughly one-quarter of the global diabetic burden.

Why are we so uniquely prone to diabetes?

It comes down to a metabolic profile known in the medical literature as the ‘Thin-Fat’ Indian phenotype. For epigenetic reasons due to nutritional stress and agrarian scarcity due to colonial era famines, Indians possess a lower metabolic capacity; we have smaller internal organs, particularly the pancreas and liver, and significantly less skeletal muscle mass. We are built to survive starvation, not abundance.

But we now live in an environment of unprecedented metabolic load. An Indian with a seemingly normal Body Mass Index (BMI) of 22 often carries the same hidden metabolic risk as a Caucasian with a BMI of 30. We are genetically predisposed to store our fat viscerally, packed around our internal organs and deep within our abdomens, rather than safely under the skin. This visceral fat is highly inflammatory. It triggers systemic insulin resistance and leads to premature beta-cell failure in the pancreas.

Unfortunately, nature and nurture have conspired to make the situation worse. Our biological vulnerability is exacerbated by the carb-heavy nature of our diet. Rice and wheat are the anchors of the Indian plate and palate. In the 1960’s, the threat of starvation - though there are serious doubts as to its accuracy - led to the Green Revolution and the massive expansion of monocultured grains in our diet.

The cheap rice and wheat that sustains Indians are only made possible by the application of urea, a highly concentrated nitrogen fertilizer. India is dependent on the Persian Gulf for its urea. Over 45% of our urea imports come from Oman alone, with Qatar and Saudi Arabia providing much of the rest. With the Strait of Hormuz acting as a highly militarized choke point in the current Gulf War, the supply of urea is threatened. A urea shortage means drops in crop yields for the upcoming Kharif season.

Lower crop yields ➡️ inflation in agricultural staples.

When food inflation hits, you would be surprised to know that the pauper does not pivot to eating expensive lean proteins, imported quinoa, and fresh, organic vegetables. Instead, they default to the cheapest, most heavily processed, high-glycemic carbohydrates available on the market. Strange isn’t it? They eat more refined flour, more sugar, and more cheap starch because those are more affordable. IMHO, the war in the Gulf will lead to a more diabetogenic diet for a lot of people.

Just when our access to insulin regulating drugs like Metformin is about to decrease.

2. Metformin

If you are one of the hundred million Indians with diabetes, Metformin is likely keeping you alive. But what actually is Metformin? While its ancient origins trace back to the medicinal use of the French Lilac plant (Galega officinalis) in medieval Europe, modern Metformin is effectively solidified natural gas.

The industrial synthesis of Metformin relies on two chemical building blocks: Dimethylamine and Dicyandiamide. To manufacture Dimethylamine, a chemical plant requires enormous quantities of methanol and ammonia. To get methanol and ammonia, you need massive, continuous flows of methane (natural gas). To synthesize Dicyandiamide, you need nitrogen-rich feedstocks processed in energy-intensive, high-heat environments.

Therefore, the Metformin manufacturing cycle starts with the Persian Gulf. The industrial cathedrals of Qatar’s Ras Laffan complex and Saudi Arabia’s Jubail industrial city are the world’s sources for the methane and ammonia that go into Metformin. The former was lit on fire by Iranian missiles last week and the second has been threatened many times.

The threat to the Metformin supply chain is terrifying because of the drug’s indispensability. Globally, it is the undisputed first-line oral therapy for Type 2 diabetes. It works by activating the AMPK enzyme, essentially tricking the liver into halting its overproduction of glucose while making the body’s muscle tissues more sensitive to whatever insulin remains. It is cheap to produce, highly effective at lowering blood sugar, and unlike older drugs, carries a very low risk of inducing fatal hypoglycemia.

In India, Metformin is even more important. The ICMR guidelines position Metformin as the foundational treatment for almost all newly diagnosed patients. Because Indians experience beta-cell failure and crippling insulin resistance so much earlier in life - often diagnosed in their late 20s or 30s rather than their 60s - Metformin is prescribed aggressively and preemptively, even to lean, young patients who do not fit the Western profile of a diabetic.

Furthermore, the Indian pharmaceutical market is driven by Fixed-Dose Combinations (FDCs). In this model, Metformin is chemically combined with other drugs, like Glimepiride or Teneligliptin, into a single, high-density tablet to reduce the pill burden (the more pills you are asked to swallow, the less likely you’re going to take them all) for patients and ensure compliance.

In short: Metformin is the bedrock of India’s public health response to a crisis affecting 101 million citizens with another 136 million waiting in the wings. If the supply of Metformin is compromised, the edifice of Indian diabetes management is on shaky grounds, for there is no viable, scalable, equally affordable substitute. We cannot simply switch to $1000-a-month GLP-1 receptor agonists (like Ozempic) or afford complex, refrigerated insulin regimens. It is Metformin or nothing.

PS: Even more irony - the patent for Semaglutide (Ozempic and Wegovy) expired recently, and tons of Indian firms want to manufacture generic versions of these drugs, but for that to happen, Indian pharma needs petrochemicals from Qatar and KSA.

4. Upstream of Metformin - the China factor

If the Gulf provides the raw energy and base molecules, how does the pill actually get made? The answer isn’t flattering to India during a polyconflict.

TLDR; we saw in the first essay on this polyconflict that India has to walk the tightrope between the Gulf and China for its energy needs. Turns out, we have to play the same game for Metformin too.

The production of Metformin is split between China and India, with China acting as the upstream brick-maker, while India acts as the downstream house-builder. China currently dominates the global production of Active Pharmaceutical Ingredients (APIs) - the actual, synthesized medicinal chemicals that do the work in the body. As of 2024, China accounted for roughly 45% of all new global API filings. Massive, heavily subsidized Chinese chemical mega-factories in provinces like Shandong synthesize the Dimethylamine and Dicyandiamide into pure, bulk Metformin API.

India then imports these Chinese APIs by the megaton. In vast formulation plants in Hyderabad, Ahmedabad, and Mumbai, Indian companies press this Chinese powder into tablets, package them into aluminum blister packs, and ship them around the world. As of recent data, India relies on China for 70% to 80% of its total API and bulk drug imports.

Not to belabor the analogy too much, but this is the same model through which fentanyl enters the US: the Chinese make the precursor chemicals, ship them to Mexico where the cartels assemble fent and ship it to their markets in the US and and Europe.

If the Gulf War creates a severe energy shock, it hits China’s chemical factories first. China, prioritizing its own domestic stability and the health of its own massive population, can easily and instantly weaponize export controls. In a time of molecular scarcity, Beijing can restrict the export of Metformin API or the critical, energy-intensive solvents required to synthesize it. Indian generic manufacturers will be starved of their core ingredients, and will find their assembly lines paralyzed.

Why aren’t we making our own APIs? It turns out we used to, but....

5. How India Lost Its Pharmaceutical Sovereignty

It was not always this way. To understand how India fell into this disastrous double-bind, we must look at the history of our pharmaceutical industry, which tracks perfectly with the rise of neoliberal managerialism.

Following the Patents Act of 1970, which recognized process patents but not product patents, India built an independent, vertically integrated pharmaceutical sector. In the 1970s and 80s, Indian scientists reverse-engineered global blockbuster drugs and successfully manufactured the APIs domestically. We owned the chemistry. In 1991, Chinese ingredients made up less than 1% of India’s bulk drug imports.

But in the 1990s and 2000s, after liberalization, globalized capitalism took hold. Indian pharma CEOs realized that high profit margins lay in marketing the finished formulations to Western markets, not in the dirty, highly regulated, energy-intensive, low-margin business of chemical synthesis and fermentation. Simultaneously, China made a strategic state decision to capture the base of the global supply chain, building massive chemical mega-parks powered by cheap coal and heavily subsidized grids.

Makes sense from an individual pharma company’s POV: outsourcing API production to China was the rational choice. And so, India voluntarily hollowed out its own chemical foundation. We dismantled our fermentation plants and allowed our chemical synthesis capacity to wither because it was cheaper to buy the molecules from Shandong.

Today, as the Indian government scrambles to launch Production Linked Incentive (PLI) schemes to bring API manufacturing back to Indian soil, we face a brutal situation. To rebuild these energy-intensive API factories, India needs cheap, reliable power, stable grids, and raw petrochemical feedstocks. Where does that energy come from? The Persian Gulf or Chinese Solar manufacturers. The very attempt to escape our pharmaceutical dependence on China drives us deeper into our energy dependence on the Middle East and China.

A true polyconflict if there was one. There is no escape hatch, only a shifting of vulnerabilities from one sector to another.

6. Not Just India: The Generic Squeeze

India supplies roughly 40% to 50% of all the generic drugs consumed in the United States and the United Kingdom. If Chinese export curbs and Gulf energy shocks squeeze the Indian manufacturer in Hyderabad, the downstream effect hits the pharmacy shelf in London, Detroit, and Boston within weeks. The rationing hierarchy we saw with Helium - where hospitals compete for scarce resources - will replicate itself in pharmaceuticals.

The American and European working classes - already burdened by decaying, hyper-financialized healthcare systems - could be plunged into further medical debt just to afford basic metabolic maintenance. The pauper in Darbhanga and the pauper in Detroit are tethered to the same fragile supply chain.

7. The Perfect Storm: Inflation, Diet, and Disease

Let us return to India, where various macro-level shocks caused by the Gulf War converge into a perfect storm.

On one front, the cost of Metformin and other essential diabetes treatments could rise as API supply chains fracture, energy costs spike, and transportation insurance premiums surge. On another front, food inflation driven by fertilizer (urea) shortages will make healthy, protein-rich diets economically impossible for the majority of the population.

Imagine the daily reality of the tenant farmer in Bihar or the gig worker in Bengaluru: their diet is forced to become more carb-heavy, more ultra-processed, and more diabetogenic precisely at the historical moment when the medication required to manage the physiological fallout of that diet becomes scarce and expensive. We are looking at a negative spiral of human health and economic agency.

There are no quick answers to this polyconflict, but we can take steps to address the situation in the short as well as long term.

8. Interconnected Pathways

Because the threats to our biology and our economy are deeply interconnected, our solutions must be equally holistic. We simply address the pharmaceutical crisis without simultaneously addressing the agricultural crisis. I am well aware that I am out of my depth in most of these topics and my suggestions could well be laughable - but it’s better to put my views out there and invite rebuttal than to stay silent.

• Food: We must aggressively pivot our agricultural systems away from fossil-fuel dependency. This is no longer just an environmental goal; it is a metabolic imperative. This means a structural shift toward agroecology and the large-scale cultivation of indigenous, climate-resilient grains like millets and sorghum, which require a mere fraction of the imported urea that paddy demands. A shift in agriculture forces a shift in diet - moving from refined, cheap carbs to local, protein-rich, complex carbohydrates that respect and nourish the Indian phenotype rather than poisoning it.

• Pharma: The pharmaceutical industry must be insulated from foreign API supply shocks. This cannot simply mean building dirty, coal-fired plants in Maharashtra to mimic Shangdong. It requires pioneering localized API synthesis powered by decentralized green energy grids. We must treat the production of essential, life-saving medicines as critical, non-negotiable sovereign infrastructure, not just a high-margin export business.

• Healthcare: We must fundamentally redefine healthcare from a reactive system of disease management via global drug regimes to a proactive, holistic framework of wellness, which has roots in local consumption patterns, community health literacy, preventive nutrition, and equitable access to localized medical facilities.

9. Commodity by Commodity: The Necessity of Solidarity

There is no master switch to turn off a polyconflict. Machiavelli’s grim insight remains entirely true for our era: systemic conflict is structural, and the prince who waits for stability before acting will wait forever.

These challenges must be resolved commodity by commodity, molecule by molecule.

We desperately need state intervention to heavily subsidize local food systems, fund indigenous pharmaceutical technology, and protect local manufacturing from global dumping. But the state alone is too slow, too cumbersome, and too easily captured by capital.

Therefore, we also need a massive, unprecedented mobilization of civic networks too. Solidarity is the only rational and moral response to a world where the conflicts are simply too large, too complex, and too planetary for any individual to survive alone. We must urgently weave localized, cooperative safety nets such as farmer-producer cooperatives sharing agroecological knowledge. We also need pharmaceutical consortiums pooling API resources and intellectual property, and community health workers prioritizing wellness over corporate margins.

What else?

PS: I hope you are getting a sense of my approach to system analysis: the earthworm’s way, so to speak.

Instead of talking about economic or political crises from a purely macro lens, it’s better to narrow down to one issue or commodity and trace its interconnections - they are far more likely to throw surprises than the view from up above, as I learned by tracing those connections for Helium as well as Metformin.

I am confident you can do this type of trace-analysis in every sector.

A quick follow up to my previous essay on Polyconflicts - I wanted to dig deeper into one commodity and trace its web of connections. Helium it is.

A couple of days ago, I wrote an essay arguing that polycrisis-talk is a coping mechanism for the establishment technocrat, while the world’s emergencies - war, supply chain collapse, inflation - are not just tangled together, they lead us towards intertwined conflicts, or what I call “polyconflicts.” Polyconflicts can’t be solved - it’s not a matter of data, better models, and putting the right managers in control.

We might be looking for a new equilibrium, but we are not going to find it.

As I discussed in the Gulf-India-China context, escaping one geoeconomic shock means stepping into another. And because these conflicts are overlapping scrambles for leverage (sometimes survival), they defy the bird’s-eye view of macroeconomic forecasting - not that I want to throw away those models - this is not about a new imperial vibe. You cannot understand the shape of this new world by looking at GDP charts or summit communiqués.

I don’t think there’s an overarching frame that explains all polyconflicts. At least not yet. Every unhappy dispute is unhappy in its own way. Instead, the conflict must be traced node by node, commodity by commodity. Each essential material that makes modern life possible carries its own distinct geography of risk, dragging entirely different populations into its specific battles. To see how this non-linear, inescapable web actually functions, we need only look at the second lightest element in the universe: helium.

Why helium? Because it came up during a conversation with a colleague and intrigued me enough that I decided to trace its connections to the ongoing war in the Gulf.

Tracing Helium

When missiles strike the energy infrastructure of the Persian Gulf, it’s obvious that the conflagration will impact the price of Brent Crude and will cause shipping delays for Liquefied Natural Gas (LNG). Oil and gas are first-order casualties of Middle Eastern wars - the disruptions everyone expects and the dependencies every state actively manages. Then again, maybe not - India had plenty of opportunity to build a natural gas reserve but it didn’t.

But it should have!

Polyconflicts have second-order dependencies that are less visible. Helium is a good example. It’s not a hydrocarbon, it’s not a rare earth material and it’s mostly known for its use in balloons and blimps.But as I found out, helium is embedded in a fragile, critical supply chain that sits at the intersection of Gulf volatility, advanced tech rivalry, and health systems.

First, some chemistry.

Helium is not manufactured on Earth; there’s a lot of it in the Sun, but over here it is a scarce resource. It is generated over hundreds of millions of years by the radioactive decay of elements like uranium and thorium deep within the Earth’s crust (uranium decay produces alpha particles which are nothing but helium minus its electrons - the alpha particles pick up two electrons from the surrounding rock and become helium). Helium is very light (the second lightest element) so left to itself, it will escape out of the Earth’s atmosphere and dissipate into space, but as it so turns out, sometimes it gets trapped in the exact same geological formations as natural gas. Consequently, it is extracted almost exclusively as a by-product of natural gas processing. It requires massive infrastructure - cryogenic fractional distillation towers that cool the raw gas to extreme temperatures to separate the helium molecules from the methane and nitrogen. Since helium has the lowest liquefaction temperature, if you cool natural gas to a low enough temperature, the other gases in it will liquify, leaving only helium in gas form.

This geological reality chains helium directly to the world’s major hydrocarbon nodes. Not only is Qatar’s Ras Laffan Industrial City complex the world’s biggest gas terminal, it also accounts for roughly 30% to 38% of the world’s entire helium supply. So the attack on Ras Laffan immediately created a helium shock in the world’s supply chains. In a matter of days, a third of this critical industrial gas vanished from the market. And in any case, with the Strait of Hormuz closed, there’s no shipping that helium anywhere else even if you could extract it.

Who cares? What is helium good for?

Turns out, it’s good for some important things, and in particular, it’s crucial for MRI machines and chip manufacturing.

Why Helium is Non-Substitutable

As I mentioned earlier, helium is the second-lightest element in the universe, and because it is so light, any helium released into the atmosphere escapes Earth’s gravity and bleeds off into space. That doesn’t make it valuable though. What makes it essential is that it is chemically inert, and it stays liquid at colder temperatures than any other substance in existence. These two properties make it the invisible architect of modern medicine and advanced computing.

Liquid Helium and the MRI

The first critical use of liquid helium - used to be its biggest use, still second biggest - is in Magnetic Resonance Imaging (MRI). MRIs are large superconducting magnets. To generate the incredibly powerful magnetic fields required to see inside the human body, the internal coils of the machine, typically made of a Niobium-Titanium alloy, must achieve superconductivity. This metallurgical magic only happens at roughly 4 Kelvin, or -269°C - just a few degrees above absolute zero. Liquid helium is literally the only element capable of reaching and maintaining these temperatures. Liquid nitrogen is too warm. There is no synthetic chemical substitute. If you do not have liquid helium, you do not have an MRI. Older MRI machines needed about 1500 liters of helium, and since the gas keeps escaping, it has to be topped-up all the time. Not so for the newer models, some of whom have factory sealed helium (and need only 7 liters) but those models are too expensive to be bought by hospitals in India or other parts of the Global South.

Helium in Chip Fabs

The semiconductor industry relies on gaseous helium for an entirely different set of extreme conditions. As chip architectures shrink to 5-nanometers and below, the margin for error is vanishingly small. During processes like extreme ultraviolet (EUV) lithography and plasma etching, silicon wafers are bombarded with highly energetic lasers and plasma inside a vacuum chamber. They get incredibly hot. The vacuum doesn’t have any air that can transport the heat away from the chip (it’s vacuum!), so to prevent the delicate, atomic-level structures from melting or warping, gaseous helium is pumped directly into the microscopic gap between the cooling chuck and the back of the silicon wafer.

Because helium’s thermal conductivity is unmatched, it pulls heat away from the silicon wafer. Furthermore, because it is a noble gas, it is chemically inert and so does not react with any of the highly volatile chemicals used in chip fabrication, leaving the delicate wafers uncontaminated. Even other noble gases won’t work. If a semiconductor fab runs out of helium, they cannot substitute another noble gas such as argon which is both bigger and heavier, and has far less thermal conductivity.

OK, once again, so what? Where’s the squeeze when there’s a supply shock?

India’s MRI Infrastructure

To understand the depth of India’s vulnerability, you first have to understand the three generations of MRI technology. First, there are the traditional scanners, the legacy machines of the late 20th century. These require a massive bath of 1,500 to 2,000 liters of liquid helium that constantly boils off into the atmosphere, demanding frequent, expensive refills just to survive.

Second, there are zero boil-off (ZBO) scanners. Introduced in the mid-2000s, these still rely on a massive helium bath but are equipped with a complex refrigeration unit called a cold head that captures the boiling gas and re-condenses it back into liquid (how does it work exactly? I need to learn more about the mechanisms involved). Under perfect conditions, they rarely need a refill for the lifetime of the machine. Finally, there is the newest frontier of sealed micro-cooling scanners. Pioneered around 2018, these marvels use only 7 liters of helium permanently sealed at the factory. No hospital-level refills needed.

India has a low density of MRI scanners: roughly 2,500 to 4,800 machines for a population of 1.4 billion people. By comparison, the US has over 12000 for a population a fourth the size of India. But the true structural trap lies in which machines make up that number.

India possesses very few zero-boil-off machines, and a negligible number of the newer sealed micro-cooling systems. Both the ZBO and the sealed machines are too expensive for most Indian hospitals, remaining financially out of reach for all but the most elite, well-capitalized corporate hospitals in metropolitan hubs like Mumbai or Delhi.

Instead, a massive portion of India’s MRI fleet - particularly in smaller private diagnostic centers and Tier-2 or Tier-3 cities - consists of refurbished systems imported from the West to save on steep capital costs. These 10-to-15-year-old models are the traditional, high-bleed legacy machines. They are constantly venting helium, tethering Indian hospitals to the volatility of Gulf supply chains.

Even when a well-funded Indian hospital manages to purchase a modern zero boil-off machine, they run into localized, structural friction. ZBO technology was engineered for the predictable infrastructure of the Global North. The cold head requires a highly stable power supply and massive, uninterrupted industrial air conditioning to function. In the Indian context, frequent power grid fluctuations and extreme ambient heat often cause these cold heads to trip or fail. When the cold head stops working, the helium immediately begins boiling off, instantly transforming a state-of-the-art ZBO machine back into a legacy bleeder, forcing the hospital into purchasing emergency refills.

So, when the Ras Laffan complex goes offline and global prices triple, it’s terrible news for hospitals across India. If the hospital cannot secure a helium delivery or pay for it, the MRI machine’s magnet will warm up, lose superconductivity, and potentially destroy itself - a catastrophic event known as a “quench” that results in crores in damages.

As an aside - the semiconductor side of this helium story also has implications for India. Delhi is pouring billions into establishing domestic semiconductor manufacturing. But those nascent fabs require the exact same Ultra-High Purity helium that the hospitals need, and they will be competing with TSMC for the gas.

Not looking good IMHO.

The Allocation War

Helium cannot be stored or hoarded easily - since the helium atom is the second smallest in the universe, it eventually leaks through the microscopic pores of almost any container. It cannot be stockpiled for years; it must be a continuous, cryogenic flow. And when that flow is interrupted, India does not find itself competing against the usual nation-state rivals; it finds itself in a bizarre, asymmetrical war against a cross-section of the 21st-century elite.

An Indian hospital network or a newly minted semiconductor fab isn’t bidding against the Chinese state. They are competing against TSMC and Samsung - tech titans that alone consume over 20% of the world’s helium to keep their silicon wafers cool. They are competing against Kaiser Permanente and other massive, well funded Western healthcare networks. They are competing against SpaceX and the ESA, which require helium to purge rocket fuel lines and manage satellite systems.

Industrial gas distribution is a highly consolidated oligopoly dominated by giants like Linde and Air Liquide. Like QatarEnergy these corporations can invoke force majeure (force majeure is a contractual clause that frees both parties from liability when an uncontrollable event prevents one or both from fulfilling their obligations), and place all their customers on “allocation,” a rationing system where buyers receive only a percentage of their usual order. The claims on their stocks of cryogenic ISO containers will be fiercely contested. In this rationing hierarchy, Tier-1 priority technically goes to life-saving medical use and national defense. Tier-2 belongs to critical economic infrastructure, like the semiconductor fabs.

How will life-saving in Darbhanga compete with life-saving in Dublin?

The saddest part of this story is that there used to be a safety net. For decades, the U.S. Federal Helium Reserve acted as a massive, sovereign shock absorber, holding billions of cubic feet of helium to stabilize the market during crises. But in a familiar neoliberal move, the U.S. government deregulated and fully privatized the reserve, auctioning off its final assets to private industry in 2021.

I guess no one thought helium deserves a reserve in the public interest, which, if I may say so, is the exact same structural delusion that birthed our modern polyconflicts: the belief that private markets always allocate resources more efficiently than the state, and that optimizing for short-term financial returns is more important than maintaining buffers for long-term survival.

By dismantling this strategic stockpile, the U.S. removed the only buffer capable of mitigating a sudden global shortage. Private distributors are forced to make impossible, opaque choices. They must decide between fulfilling an allocation for a politically connected mega-hospital in Boston or a refurbished diagnostic center in Bihar. For a tenant farmer in Darbhanga, a decommissioned or significantly more expensive MRI machine could mean an undiagnosed tumor and far greater healthcare costs in the near future. It means pooling the family’s savings - such as they are - to pay for much more expensive care, and slipping into generational medical debt just to secure a scan. In the agrarian economy, healthcare debt is among the primary engines of rural dispossession.

More debt, more migration, more dispossession could be the outcome of the helium shock.

BTW, notice how the helium shock intersects with the larger story of the third Gulf war (similar to LPG scarcity and oil prices) but also has its own specificities - the helium reserve, direct impact on healthcare availability etc.

Surviving the Shock: Best and Worst Case Scenarios

The Worst-Case Scenario

The conflict in the Middle East stops Qatar’s LNG output for an extended period. The Strait of Hormuz remains contested, adding massive risk premiums to shipping. Global distributors slash India’s allocation by 50%. Prices quadruple. Smaller hospitals in India cannot absorb the operational cost of top-offs. They are forced to power down their MRI machines permanently. Simultaneously, the lack of UHP helium delays India’s semiconductor fabrication plants by years.

The Best-Case Scenario: The immediate supply chokehold eases within a few months, but the trauma of the near-miss serves as a wake-up call for Indian self-reliance. Startups in Bengaluru, like VoxelGrids are pioneering the first fully indigenous MRI scanners designed specifically for Indian reality. These machines do not require liquid helium. They operate on conductive cooling, require significantly less power, and survive the heat.

The best-case scenario is that the Indian state recognizes this as a matter of localized sovereignty. Delhi must radically subsidize the deployment of indigenous, helium-free machines, alongside other tech (diagnostics etc) that can be deployed in health centers at scale: local MRI machines won’t work unless they come with a localized healthcare system committed to the public interest. Concurrently, the state must secure diversified helium partnerships with emerging producers in structurally safer regions like Canada or Australia for the nascent semiconductor sector.

Concluding thoughts

The helium shock is a perfect microcosm of the polyconflict. It demonstrates how today’s conflicts are non-linear, with second-order effects that shatter our traditional economic models. A drone strike on a gas field in the Gulf dictates the cost of a neurological scan in Muzaffarpur, which in turn dictates whether a family falls into medical debt, which drives rural-to-urban migration, which stresses urban infrastructure in Delhi.

We can bring some general heuristics to understand the polyconflict - tracing the networks of energy and information (i.e., metabology) will always be helpful, but those traces will not be enough. Every commodity has to be understood on its own terms.

All the more reason to build robust mechanisms of civic solidarity.

I can only speculate as to what this might look like - could be regional medical consortiums that pool diagnostic resources instead of hoarding them, open-source hardware collectives that share the intellectual property for conductive-cooling technologies, and community-funded health trusts that prevent medical debt before it forces a farmer into dispossession. I don’t know how we are going to make it happen, but we need to willy-nilly. As the Benjamin Franklin quote goes:

We must, indeed, all hang together or, most assuredly, we shall all hang separately.

I am no doomer but when Trump retook power in 2020, the old world died. New monsters were waiting in the wings.

That was a premonition, but I am now seeing the monster take shape, and it’s driving me a little insane.

I posted some scattered thoughts earlier today on social media. Having worked on the underlying themes some more, I am sharing the fleshed out version below.

As the current escalating conflict between the US/Israel and Iran demonstrates, geopolitics no longer stops at the border. We are inextricably connected to developments elsewhere, and rarely in ways we can control.

When the global system works, it operates invisibly. We can sleep assured that natural gas will be delivered on time, and the systemic externalities of that comfort will be kicked down the road for our children to deal with (hello, climate change!). But when the system breaks down, geopolitical and geoeconomic volatility suddenly becomes your immediate, localized problem (hello, climate change!).

Yet, our existing media apparatus is woefully unequipped to explain how this planetary turbulence lands on our doorsteps. We get headlines like, “Brent Crude hitting $120 a barrel will lead to an extra 5% inflation in India.” Legacy newspapers and television channels, built for mass-audience broadcasting, are forced to communicate in these abstract, macroeconomic generalizations.

We must do a lot better.

The Local Impact of Global Shocks

Consider the micro-impacts of macro-shocks. Benchmark prices for urea jumped 30–40% in just the first two weeks of March. To an urban dweller, this is invisible data, at least until food prices spike a few months later. But to a farmer planning for the Zaid or Kharif crop seasons, it is an immediate existential threat. To financial planners in Delhi, it means a sudden need to offset mounting fertilizer subsidies with budgetary cuts elsewhere.

What gets cut, and who bleeds for it?

Every day brings new variables that shatter our existing models. Yesterday, the world’s largest gas fields were attacked, first by Israel and then by Iran. Gas supplies could be constrained for months, if not longer. The Kharif season is in trouble.

Sensemaking…

Suppose I am a paddy farmer in Punjab, or a sugarcane farmer in Mandya. What is going to happen to the cost of my inputs? What are my realistic alternatives? Is agroecology a viable shift for me right now, or a luxury I can’t afford? How do I balance these surging agricultural costs with my household realities: after all, I rely on LPG, and I need medical supplies whose supply chains are also fracturing. My needs aren’t the same as the farmer in the next district, let alone the software engineer in Bengaluru.

Yet, there is no personalized sensemaking infrastructure that scours the globe from the village next door to Beijing, translating planetary events into localized, actionable intelligence. This is exactly where I see the true promise of AI: delivering fact-based, hyper-personalized summaries of how events at all scales impact a specific human life today.

From Polycrisis to Polyconflict

While my engineers are hard at work building that newspaper of the future, we have a new reality to navigate today. We have a Polyconflict on our hands. Notice I didn’t say Polycrisis, which is the buzzword of the Davos class. Polycrisis acknowledges multiple, intersecting emergencies, but it carries the hidden assumption that the crisis is temporary, that it will resolve. Things will eventually settle into a new equilibrium, perhaps with new winners and losers, but a familiar, manageable landscape nonetheless.

What if they don’t?

What if the correct word isn’t crisis, but conflict - ongoing, structural, open-ended, and immune to being fixed by the right policy, the right summit, or the right experts in the right room? That shift in framing from polycrisis to polyconflict is staring us in the face with the US-Israel-India-Gulf-China situation.

Escaping One Conflict, Wading Into Another

Look again at the paddy farmer in Punjab. His input costs just jumped because urea prices spiked because of an attack on the South Pars gas field during a war with no end date, in a region that has been volatile for decades and is actively unraveling. No global institution is coming to fix this mess. The Rules-Based International Order is dead. What has replaced it is a world where the strong do what they can, and the weak suffer what they must. That is a conflict, not a crisis.

And the farmer in Mandya or Jalandhar is living inside the conflict, whether they know it or not.

India’s rational, strategic response to Gulf energy volatility is electrification. Except the green transition depends on physical supply chains, and those supply chains run almost entirely through China: solar panels, lithium batteries, EV components, rare earths.

No alternative currently exists at India scale.

So, every rooftop solar panel installed in Pune, every electric two-wheeler sold in Coimbatore, deepens a dependency on a rival with whom we share a heavily militarized, contested border, and one with demonstrated willingness to weaponize economic leverage. We should mend the relationship, but the potential for conflict will never disappear.

The faster India tries to escape the first conflict, the deeper it wades into the second.

The sugarcane farmer in Mandya is hammered by a Gulf conflict he has no voice in. If Delhi covers the fertilizer subsidy by cutting rural health allocations, the farmer’s children might decide to migrate to Bengaluru. Where will they live? What will they do?

Meanwhile, the EV buyer in Bengaluru thinks she is making a responsible, localized ecological choice. It is a better choice, but it is one deeply embedded in a macroeconomic strategic rivalry. Tomorrow, she may not get spare parts because rare earth minerals needed for magnets are no longer available. There is no escaping the polyconflict for her either. These are struggles landing on ordinary people who don’t have a framework to understand what is hitting them, or why.

The Interregnum Revisited

Antonio Gramsci wrote from a fascist prison cell that “the old world is dying, and the new world struggles to be born: now is the time of monsters.” We are living in that interregnum. The Rules-Based International Order wasn’t perfect, it wasn’t equal, and it was rarely rules-based. But it was our monster. As it dies, new, planetary-scale monsters are arriving.

This 👇🏾 is where I was at the beginning of last year - note the date

Machiavelli wrote The Prince for Lorenzo de Medici as a manual for holding power in exactly this kind of moment - when the old order had collapsed and the new one hadn’t settled. For the last year, I have been trying to write the opposite book. Not a manual for the Prince, but a survival guide for the Pauper. For the paddy farmer, the sugarcane grower, and the EV buyer who are all navigating a world nobody prepared them for.

The polyconflict frame is what lends the Pauper urgency. The old frame of polycrisis was deeply embedded in neoliberal managerialism. It relied on the bird’s eye view of complexity scientists who believed that if you just modeled the system well enough, you could steer it back to stability from a control room. But we are now in the Third Systems Age. We don’t need the bird’s eye view - we need the earthworm’s view; embedded, grounded, sensing the vibrations of the soil, and navigating from inside the conflict rather than trying to administer it from above.

This 👇🏾 is when I started writing about the Pauper in earnest

Machiavelli’s insight was that systemic conflict is structural, not exceptional, and the prince who waits for stability before acting will wait forever. The pauper’s equivalent insight must be this: when the conflicts are too large for any individual to survive alone, solidarity is the only rational response. The olive garden owner in Palermo and the software engineer in Bengaluru are not living in different worlds.

In the age of polyconflict, they can’t separate their fates even if they wanted to.