In 2015, a small gathering in San Francisco made a decision that barely registered in the global media cycle. There were no headlines, no speeches and no sense of historical consequence. Yet, in retrospect, that moment marked the beginning of a structural shift in the global economic order. The founders of OpenAI didn’t build another tech company. They tried to industrialize the intelligence services.
Elon Musk saw artificial intelligence as both a civilizational risk and a transformative force. Sam Altman approached it as an execution challenge: scale models, secure computation, and deploy intelligence as an economic tool. Different styles, same strategic conclusion – scalable intelligence would shape productivity, capital flows and geopolitical leverage in the 21st century.
At the same time, in Santa Clara, Jensen Huang made a quieter but far more consequential bet. Nvidia was still seen as a gaming chip company. Huang had already turned to parallel computing, CUDA architecture and GPU acceleration. The markets did not immediately understand the change.
Deep learning remained a niche field. Venture capital chased consumer apps. Politicians debated social media while the computational foundations of the AI age were built with little public attention.
When Nvidia introduced purpose-built AI systems, commercial demand was limited.
The machines were expensive, complex and ahead of their time. Yet they would soon become the core infrastructure behind almost every major breakthrough in artificial intelligence. The real ‘picks and shovels’ of the AI revolution were not apps, but computer clusters measured in megawatts, with high capital intensity and significant energy consumption.
What followed was exponential, not linear. The partnership between OpenAI and Microsoft transformed frontier research into industrial-scale implementation. GPT-3 demonstrated new capabilities that surprised even its developers.
Then, in November 2022, ChatGPT entered the public domain, triggering one of the fastest adoption curves in technology history. Within months, hundreds of millions of users were interacting with machine intelligence capable of writing, coding, analyzing and synthesizing information at scale.
This wasn’t just a software milestone. It marked the beginning of an AI-driven capex supercycle. Hyperscalers committed tens of billions of dollars to AI infrastructure.
Data center investments increased. Energy demand projections were revised upwards. Training frontier models began to require computational resources comparable to national research facilities of previous decades. Nvidia’s rise to multi-trillion dollar valuation levels reflected a deeper shift: compute had become a strategic commodity.
Across Asia, policy makers and businesses are reading these signals with clarity. India accelerated investments in digital infrastructure, AI ecosystems and scaling of IT services. Vietnam deepened production integration, export competitiveness and technology-enabled supply chains.
China stepped up investment in semiconductors, artificial intelligence models and industrial automation despite external constraints. The region is increasingly treating artificial intelligence as a productivity multiplier embedded in the national economic strategy.
Now consider, in parallel, the intellectual and political trajectory of Pakistan in the same decade. While the world industrialized intelligence, Pakistan’s national discourse remained dominated by political scandals, institutional confrontations and ideological flashpoints.
Media revolves around recycled accusations. Social media amplified the outrage. Strategic debate on computing infrastructure, AI adoption, industrial automation and technological competitiveness remained peripheral.
Pakistan’s R&D expenditure remains around 0.16% of GDP, among the lowest globally. Innovation rankings lag behind regional peers. Network readiness remains weak. AI readiness indicators place the country behind not only India and China, but also increasingly Bangladesh and Vietnam. These are structural indicators of technological underinvestment, not perception gaps.
The oft-cited claim of a large STEM pipeline is also weakened when a quality filter is applied. The effective pool of high skills relevant to an AI economy is concentrated within a narrow set of institutions – NED, GIKI, LUMS, FAST and a handful of credible engineering departments.
Even within this group, exposure to advanced computing, research ecosystems and frontier AI tools is limited compared to regional competitors. Heading graduation figures suggest scale; the underlying talent depth remains uneven and thin in critical areas such as advanced computing, applied artificial intelligence and advanced engineering.
This structural weakness now intersects with Pakistan’s core export sectors, particularly textiles and IT services. Textiles, the backbone of export earnings, are entering an era defined by automation, AI-driven optimization and digitally integrated supply chains.
Vietnam integrates smart manufacturing, predictable logistics and automation into production networks. India is leveraging scale, policy support and technology adoption to move up the textile value chain.
In contrast, Pakistan’s textile sector remains energy constrained, technologically underinvested and vulnerable to efficiency shocks. As automation reduces the importance of labor cost advantages alone, countries that fail to modernize risk losing competitiveness even in traditional labor-intensive sectors.
The threat is sharper in IT exports. India’s IT services sector is rapidly integrating AI tools across software development, business services and customer operations, significantly improving productivity per employee. Vietnam is positioning itself as a credible technology outsourcing hub with coordinated policy support and skill alignment.
Pakistan’s IT sector, despite pockets of excellence, risks stagnating if AI adoption remains shallow and infrastructure deficiencies persist. In a world where AI increasingly automates coding, testing and documentation, low-end outsourcing models face structural compression.
Yet the political discourse continues to revolve around IMF programs as if they constitute an economic strategy. In practice, repeated IMF arrangements function primarily as creditor stabilization mechanisms designed to ensure external repayment capacity and short-term macroeconomic stability.
They are not development frameworks and do little to address the structural transformation needs of a rapidly growing, young population. Stabilization without productivity growth merely postpones crises and at the same time protects creditor confidence more than long-term industrial competitiveness.
The deeper issue is that external funding has repeatedly replaced internal reforms. Instead of sustained investment in technological capacity, industrial upgrading and research infrastructure, policy responses have defaulted to short-term stabilization cycles. Public discourse often frames economic distress through political or conspiratorial narratives rather than confronting low productivity, weak export diversification and technological backwardness.
In the modern economy, focus is a strategic resource. Over the past decade, leading economies have focused on artificial intelligence, automation and digital infrastructure. Pakistan allocated disproportionate attention to political theaters and institutional disputes. The opportunity cost of this misallocation now increases.
The global economy is entering a phase where intelligence is embedded across production systems – manufacturing, logistics, finance, healthcare and services. Companies that implement artificial intelligence will reduce costs, improve efficiency and capture market share at scale. Countries that fail to integrate AI into their industrial base will see a gradual erosion of competitiveness, even in sectors where they historically had advantages.
This is the new risk for Pakistan: not sudden collapse, but constant strategic erosion. A slow weakening of textile competitiveness as automated factories in Vietnam and technologically scaled manufacturers in India outperform in terms of efficiency, compliance and delivery times.
A plateau in IT exports as AI-enabled competitors offer higher productivity and greater value added. A growing technology gap that translates into slower export growth, currency pressures and deeper dependence on external financing.
Globally, the decade from 2015 to 2025 will likely be remembered as the period when intelligence became industrial infrastructure—as fundamental as electricity or the Internet in earlier eras.
Countries that recognized the shift early generated productivity gains, attracted capital and strengthened strategic leverage. Countries that remained intellectually introverted widened their structural vulnerabilities.
The technology unites silently and asymmetrically. While others invested in computing, research and industrial automation, Pakistan remained preoccupied with cyclical political narratives. While competitors built AI ecosystems and upgraded industrial capabilities, Pakistan overestimated readiness without matching infrastructure or political urgency.
The real danger is not exclusion from the AI revolution. It is far more insidious: participation at the lowest value-creating levels, while regional competitors capture higher value segments in textiles, manufacturing and IT services. In an AI-driven global economy, competitiveness will be determined less by labor costs and more by computing access, technological depth and institutional focus.
On each of these fronts, the gap with countries like India and Vietnam is not static. It’s getting bigger—and in a complex technological era, widening gaps tend to turn into long-term structural disadvantages that become progressively harder to reverse.
The author is a former head of Citigroup’s investments in growth markets and author of ‘he Gathering Storm’.
Disclaimer: The views expressed in this piece are the author’s own and do not necessarily reflect Pakinomist.tv’s editorial policy.
Originally published in The News
