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By:

Apurva Rakesh Pandey

27 August 2024 at 9:53:07 am

From Gold to Chips: The Quiet Return of Mercantilism

Globalisation has not been abandoned but is being rewired around resilience, strategic trust and national power. Foreign Trade and Domestic Compared, by William Herbert, 1754 Economic history rarely buries its most influential ideas. It merely waits for circumstances to make them relevant again. Mercantilism is one such idea. For much of the post-Cold War era, it seemed safely confined to textbooks - a relic of a time of colonial monopolies and favourable trade balances. The triumph of...

From Gold to Chips: The Quiet Return of Mercantilism

Globalisation has not been abandoned but is being rewired around resilience, strategic trust and national power. Foreign Trade and Domestic Compared, by William Herbert, 1754 Economic history rarely buries its most influential ideas. It merely waits for circumstances to make them relevant again. Mercantilism is one such idea. For much of the post-Cold War era, it seemed safely confined to textbooks - a relic of a time of colonial monopolies and favourable trade balances. The triumph of globalization appeared to have settled the debate. Economic interdependence promised not only prosperity but also peace. Three decades later, that confidence has begun to dissolve. While the world has not abandoned globalization, it has become less certain about the assumptions on which it rested. Across the major economies, governments are reclaiming a role they once seemed willing to surrender. Washington has embraced tariffs, industrial subsidies and technology export controls. Beijing has tightened restrictions on critical minerals while accelerating technological self-reliance. The European Union increasingly frames trade through the language of economic security. Philosophical Shift The implications represent a change in the philosophy of globalization itself. If the first era of globalization was organised around efficiency, the emerging one is being organised around security. As Dani Rodrik has argued, industrial policy has returned to the centre of economic governance. Comparative advantage is steadily yielding to a new vocabulary of technological sovereignty, resilient supply chains and economic security. The defining question is no longer where production is cheapest, but where it is safest. The resemblance to mercantilism is impossible to ignore. Not because the world is returning to the seventeenth century, but because governments are once again judging commerce by the strategic capabilities it creates rather than the wealth it generates. Classical mercantilists controlled trade routes and protected monopolies. Today’s strategic states compete for semiconductors, artificial intelligence, critical minerals and technological standards. Gold has given way to chips and spices to rare earths. The pandemic exposed the fragility of supply chains built almost exclusively for efficiency. Russia’s invasion of Ukraine demonstrated how energy, food and finance could become instruments of coercion, while the freezing of Russian foreign exchange reserves revealed that even global financial networks were no longer politically neutral. At the same time, strategic rivalry between the United States and China expanded beyond tariffs into semiconductors, artificial intelligence, quantum computing and critical minerals. The geography of competition had shifted from factories to the technologies that would shape future economic and military power. Henry Farrell and Abraham Newman describe this landscape as one of “weaponized interdependence”, where states exploit their central positions within global networks to pursue geopolitical objectives. Interdependence, once celebrated as a guarantee of stability, has become a source of leverage. The lesson is that efficiency without resilience is no longer an economic virtue but a strategic vulnerability. Shaping Markets Once that vulnerability became visible, policy could hardly remain unchanged. Governments are no longer content merely to regulate markets; they are shaping them. From the CHIPS and Science Act in the United States to China’s drive for technological self-reliance and the European Union’s Economic Security Strategy, industrial policy has returned to the centre of economic governance. Japan, South Korea and Singapore have adopted similar approaches, strengthening economic-security frameworks while investing in advanced manufacturing and digital infrastructure. This is an acknowledgement that markets alone cannot guarantee national resilience. In many ways, it is mercantilism without colonies. Export controls, industrial subsidies, technology alliances and strategic stockpiles have become essential instruments of economic statecraft. Record central-bank gold purchases, reserve diversification and growing interest in local-currency settlement mechanisms reflect a broader search for financial resilience in an uncertain world. As Robert Blackwill and Jennifer Harris argue in War by Other Means, economic instruments have become as consequential to statecraft as military power itself. Yet to interpret these developments as the end of globalization would be to misunderstand the moment. The world is not ‘de-globalising;’ it is ‘re-globalising’ on strategic terms. International trade continues to expand and multinational firms remain deeply embedded in global value chains. What has changed is the organising principle. The first wave of globalization rewarded efficiency and scale; the next is likely to reward resilience, trusted partnerships and strategic diversification. Friend-shoring, near-shoring, China+1 and the shift from just-in-time to just-in-case production are not signs of globalization’s retreat but evidence that globalization is being fundamentally rewritten. Supply chains are not disappearing; they are being rewired around trust, resilience and strategic alignment. Economic openness is giving way neither to protectionism nor autarky, but to a more selective form of integration. Every transformation of the global economy reshapes the hierarchy of opportunity. The present one may prove unusually favourable to India. As multinational firms diversify production networks and governments increasingly favour trusted partners, India is well placed to emerge not merely as an alternative manufacturing destination but as a pivotal node in resilient global supply chains. Advantage India Its advantages extend beyond labour costs to a large domestic market, democratic institutions, strategic autonomy, a rapidly expanding digital economy and a central position in the Indo-Pacific. In an age where geopolitical trust has become an economic asset, these strengths constitute a strategic advantage. New Delhi’s response reflects an awareness of this shift. Rather than relying on market liberalisation alone, it has increasingly focused on building industrial capability. The Production Linked Incentive (PLI) programme across fourteen strategic sectors, together with the India Semiconductor Mission, PM Gati Shakti and the National Logistics Policy, aims to strengthen manufacturing competitiveness, logistics and technological capacity. At the same time, leadership in semiconductors, artificial intelligence, quantum technologies, biotechnology, space and clean energy is becoming central not only to economic growth but also to strategic influence. Modern mercantilism is no longer about protecting industries but about building capabilities. Industrial capability, however, cannot flourish behind closed borders. That explains New Delhi’s renewed emphasis on trade diplomacy. The Comprehensive Economic Partnership Agreement with the United Arab Emirates, the Economic Cooperation and Trade Agreement with Australia, and the recently concluded free trade agreements with the United Kingdom, the European Union and New Zealand, alongside negotiations with the United States and the Gulf Cooperation Council, reflect an approach best described as strategic openness. India is seeking deeper integration with global markets while preserving policy space in sectors vital to long-term resilience. The objective is to become a trusted manufacturing, technology and supply-chain partner in an increasingly fragmented global economy. That said, global manufacturers continue to point to land acquisition, regulatory complexity, logistics costs and judicial delays as obstacles to large-scale investment. Competing with established manufacturing ecosystems will require sustained reforms in infrastructure, education, research, innovation and ease of doing business. India’s emergence as a pivotal player in the next phase of globalization will ultimately depend on the consistency of these reforms. History nevertheless suggests that periods of global economic reordering often favour countries that adapt before others do. The redistribution of manufacturing after the Second World War transformed Japan, later South Korea and Taiwan, and eventually China. Today’s reconfiguration of supply chains may prove equally consequential. Countries that combine openness with resilience, innovation with industrial capability, and competitiveness with institutional credibility will shape the next chapter of globalization. India has the opportunity to be among them only if strategic ambition is matched by sustained execution. Adam Smith taught that wealth is created through markets. Kautilya understood that markets ultimately serve the interests of the state. The twenty-first century suggests that both captured part of the truth. Prosperity still depends on openness, competition and innovation, but enduring power increasingly belongs to nations capable of converting economic capability into strategic advantage. (The author is an alumnus of the University of Allahabad and a writer and analyst on international relations and strategic affairs. Views personal.)

Beyond the AI Arms Race: The Apple Way to AI

As the economics and ecology of hyperscale AI begin to crack, Apple’s delayed strategy appears remarkably prescient.

Apple’s WWDC 2026 delivered one unmistakable message: it has rebuilt its entire software and device ecosystem around Apple Intelligence. Siri AI has been re‑engineered from the ground up with on‑device reasoning, Private Cloud Compute now anchors Apple’s hybrid AI architecture, macOS and iOS have become AI‑native operating systems, and developers received new agentic frameworks that make Apple silicon the center of future AI workflows. The company’s long‑delayed AI strategy has arrived, privacy‑preserving(?), and infrastructurally distinct from the hyperscale LLM race. Apple has officially entered the full‑AI mode.

 

To understand the significance of Apple’s 2026 pivot, we must revisit the strange, almost theatrical silence of 2025. Last year, the global AI industry was in a frenzy. OpenAI, Google, Meta, and Microsoft were locked in a war of model sizes, parameter counts, and compute budgets. Every quarter brought a new LLM, a new benchmark, a new promise of “general intelligence.” The world was intoxicated by chatbots; investors, governments were anxious; and the public was swept into a wave of AI immersion that was reckless. And yet, Apple stood apart.

 

At WWDC 2025, instead of unveiling a Siri‑GPT or an Apple‑branded conversational agent, the company introduced Liquid Glass, a shimmering, polarizing UI experiment that seemed almost indifferent to the global AI race. Critics mocked the absence of an Apple chatbot. Analysts speculated that Apple had fallen behind. A few commentators even suggested that Apple’s “end days” were approaching, as if the company’s refusal to join the hype cycle were a sign of weakness rather than discipline.

 

But inside Apple, something was cooking. Siri’s 2024 prototype, though functional, did not meet Apple’s quality bar. Craig Federighi and Greg Joswiak said openly that Apple would not release “just another chatbot.” They wanted something “Apple‑like”—a personal, intelligent companion that understood context, respected privacy, and integrated seamlessly with the device ecosystem. Apple’s R&D culture, unchanged since the days of Steve Jobs, demanded long‑term coherence over short‑term spectacle. In 2025, this stance looked eccentric. In 2026? Pragmatic.

 

The Cracks in Hyperscale AI

 2025 was also the year the global AI infrastructure began to shudder. The LLM boom depended on hyperscale data centers—vast campuses consuming huge quantities of electricity and water. These centers were the unseen engines behind ChatGPT, Gemini, LLaMA, and Copilot. But the engines were overheating, physically, politically and ecologically.

 

Across the world, communities began resisting data‑center construction. In the United States alone, more than a hundred projects faced delays or cancellations. A Gallup poll showed that 71 percent of Americans opposed data centers in their neighborhoods. States considered moratoriums. Local governments demanded environmental impact audits. Litigation was routine. Water scarcity became the flashpoint. Evaporative cooling—the method used to keep AI servers from melting—requires enormous volumes of potable water. Not groundwater. Not recycled wastewater. Potable, drinkable water to avoid panel contamination from bacteria, dirt, minerals etc. Google’s proposed Santiago data center requiring 7 billion liters of water annually, and the Stargate project near Marfa, Texas planning to use 260 million gallons of clean water per year, exceeding the town’s own potable water needs were not isolated cases. They were early warnings. By late 2025, drought‑zone communities from Arizona to Virginia were blocking hyperscale projects. Environmental groups mobilized. Farmers protested. Legislators intervened. The infrastructure that powered the AI revolution was becoming politically reactive.

 

As a result, Big Tech began to retreat. Microsoft paused gigawatts of planned capacity. Google reconsidered multiple sites. Meta faced legal challenges. The Stargate megaproject—once envisioned as a $500‑billion monument to AI—was scaled back, with UK and Norway sites halted and pushed timelines to 2028. The AI bubble, inflated by compute optimism, met head-on with the hard physics of water and electricity.

 

Divergent Path

This is where Apple’s 2025 silence becomes meaningful. While other companies were racing to build larger models and larger data centers, Apple was quietly building an alternative architecture—one that did not depend on hyperscale campuses, evaporative cooling towers, or gigawatt substations. Apple’s bet was simple:  AI should run on the device people already own. This was not a marketing slogan. It was an infrastructural philosophy. By running AI directly on the devices people already own, Apple sidestepped many of the constraints that are beginning to define the industry’s future. On-device intelligence eliminates the enormous water requirements of hyperscale data centres, reduces dependence on already strained electricity grids, and avoids the political backlash that accompanies large-scale AI infrastructure projects. At the same time, because data remains on the user's device rather than being continuously transmitted to the cloud, it offers stronger privacy protections while also easing the regulatory burdens associated with data sovereignty and compliance.


Apple’s early articulation of personal cloud computing—a hybrid model where sensitive tasks run locally and only anonymized requests touch the cloud—was not merely a privacy innovation. It was an infrastructural innovation. It was a way to build AI without building megacenters. In 2025, this looked like caution. But today, it looks like foresight.

 

WWDC 2026 was Apple’s declaration that its long game had matured. Siri is no longer a voice assistant. It is a contextual reasoning agent capable of understanding on‑screen content, executing multi‑step tasks across apps, and maintaining conversational memory—all without siphoning user data into distant servers. Apple Intelligence is now woven into Safari, Messages, Mail, Calendar, Photos, Home, and third‑party apps. It organizes tabs, summarizes content, interprets images, and automates workflows. When cloud compute is necessary, Apple will use micro‑compute clusters designed for privacy and efficiency—not hyperscale farms. These clusters are geographically distributed, energy‑efficient, and architecturally distinct from the water‑hungry campuses of other Big Tech firms. iOS 27 and macOS 27 “Golden Gate” are built around contextual assistance. Siri integrates directly into Spotlight. The OS understands user intent, not just user input. Last but not the least, the company introduced Foundation Models Framework, Core AI, and expanded App Intents—tools that allow developers to build AI agents optimized for Apple silicon rather than cloud GPUs. Even for industry insider-outsiders or as I call the Adult Technocrats like us, who are inherently skeptic of privacy concerns associated with LLMs, this is not Apple catching up but Apple redefining the playing field.

 

Meanwhile, the rest of Big Tech is being forced by social politics, civil rights movements, ecology, and economics to reconsider its dependence on hyperscale AI. The industry that once worshipped scale is now learning the limits of scale. The companies that once believed “bigger models solve everything” are now confronting the reality that bigger models require bigger data centers, and bigger data centers require bigger quantities of water and electricity—resources that communities are no longer willing to sacrifice. The AI revolution is being forced to shrink, decentralize, and become intimate. Exactly the direction Apple chose in 2025.

 

Strategic Implication

Apple’s 2026 pivot is not just a technological milestone. It is a philosophical stance about the future of AI. Apple has implied that intelligence should be: personal, private (highly questionable when Siri accesses your data to reply emails), local, efficient, sustainable, integrated with hardware, and free from infrastructural fragility – a post hyperscale vision of AI; a stance miles away from the LLM maximalism of 2023–2024.  The global retreat from hyperscale data centers, the political backlash against water consumption, the energy‑grid constraints, the regulatory pressure, and the infrastructural fragility of LLM‑centric AI—all of these forces are pushing Big Tech toward the very path Apple chose a year earlier- which leaves us with one final question: What Apple thinks today, other Big Tech thinks tomorrow. Or do they?


(The writer is a Lead Process Engineer with GE HealthCare in France and a columnist with four books to his credit. Views personal.)

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