In 2015, a small meeting in San Francisco made a decision that barely registered in the global media cycle. There were no headlines, no speeches, no sense of historical consequences. However, in retrospect, that moment marked the beginning of a structural change in the global economic order. The founders of OpenAI weren’t building another technology company. They were trying to industrialize intelligence.
Elon Musk saw artificial intelligence as a risk to civilization and a transformative force. Sam Altman approached it as an execution challenge: models at scale, secure computing, and deploying intelligence as an economic utility. 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 was making a quieter but much more consequential bet. Nvidia was still seen as a gaming chip company. Huang had already pivoted toward parallel computing, CUDA architecture, and GPU acceleration. Markets did not immediately understand the change.
Deep learning remained a niche field. Venture capital chased consumer apps. Policymakers debated social media as the computational foundations of the AI era were being 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. However, they would soon become the central infrastructure behind almost all major advances in artificial intelligence. The real “picks and shovels” of the AI revolution were not applications, but computing 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 cutting-edge research into industrial-scale implementation. GPT-3 demonstrated emerging capabilities that surprised even its developers.
Then, in November 2022, ChatGPT entered the public domain and triggered one of the fastest adoption curves in technological history. Within months, hundreds of millions of users were interacting with artificial intelligence capable of writing, encoding, analyzing and synthesizing information at scale.
This was not just a software milestone. It marked the beginning of an AI-driven capital spending supercycle. Hyperscalers committed tens of billions of dollars to AI infrastructure.
Investments in data centers increased. Energy demand projections were revised upwards. Training frontier models began to require computational resources comparable to those of national research facilities of previous decades. Nvidia’s rise to multibillion-dollar valuation levels reflected a deeper shift: computing had become a strategic asset.
Across Asia, policymakers and corporations interpret these signals clearly. India accelerated investments in digital infrastructure, artificial intelligence ecosystems and expansion of IT services. Vietnam deepened manufacturing integration, export competitiveness and technology-based supply chains.
China stepped up investments in semiconductors, artificial intelligence models and industrial automation despite external constraints. The region is increasingly treating AI as a productivity multiplier integrated into national economic strategy.
Let us now consider, in parallel, the intellectual and political trajectory of Pakistan during the same decade. While the world industrialized intelligence, Pakistan’s national discourse remained dominated by political scandals, institutional confrontations and ideological foci.
The media recycles accusations. Social media amplified the outrage. The strategic debate on IT infrastructure, AI adoption, industrial automation and technological competitiveness remained peripheral.
Pakistan’s R&D spending remains around 0.16% of GDP, one of the lowest in the world. Innovation rankings lag behind regional peers. Network readiness remains weak. AI readiness indicators place the country not only behind 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 great STEM pipeline also weakens when a quality filter is applied. The effective pool of high skills relevant to an AI economy is concentrated in 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 cutting-edge AI tools is limited compared to regional competitors. The headline graduation figures suggest a scale; The depth of underlying talent remains uneven and scarce in critical areas such as advanced computing, applied artificial intelligence, and high-level engineering.
This structural weakness now intersects with Pakistan’s main 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 is incorporating smart manufacturing, predictive logistics and automation into production networks. India is leveraging scale, policy support and technology adoption to move up the textile value chain.
Pakistan’s textile sector, on the other hand, remains energy constrained, lacks technological investment and is vulnerable to efficiency crises. 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 most acute in IT exports. India’s IT services sector is rapidly integrating AI tools into software development, business services and customer operations, significantly improving productivity per worker. Vietnam is positioning itself as a credible technology outsourcing hub with coordinated policy support and skills alignment.
Pakistan’s IT sector, despite pockets of excellence, risks stagnating if AI adoption remains shallow and infrastructure gaps persist. In a world where AI increasingly automates coding, testing and documentation, low-level outsourcing models face structural compression.
However, political discourse continues to revolve around IMF programs as if they constituted an economic strategy. In practice, repeated IMF arrangements function primarily as creditor stabilization mechanisms designed to ensure short-term external payment capacity and 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 simply postpones crises and at the same time protects creditor confidence rather than long-term industrial competitiveness.
The deeper problem is that external financing has repeatedly replaced internal reform. Instead of sustained investment in technological capacity, industrial modernization and research infrastructure, policy responses have opted for short-term stabilization cycles. Public discourse often frames economic difficulties through political or conspiratorial narratives rather than addressing low productivity, weak export diversification, and technological lag.
In the modern economy, focus is a strategic resource. Over the past decade, major economies have focused on artificial intelligence, automation and digital infrastructure. Pakistan paid disproportionate attention to political theater and institutional disputes. The opportunity cost of this misallocation is now compounding.
The global economy is entering a phase in which intelligence is integrated into all production systems: manufacturing, logistics, finance, healthcare and services. Companies that implement AI will reduce costs, improve efficiency, and capture market share at scale. Countries that fail to integrate AI into their industrial base will experience a gradual erosion of competitiveness, even in sectors where they historically had advantages.
This is the emerging risk for Pakistan: not a sudden collapse, but a steady strategic erosion. A slow weakening of textile competitiveness as automated factories in Vietnam and technologically scaled producers in India outperform in efficiency, compliance and delivery times.
A stagnation in IT exports as AI-enabled competitors offer greater productivity and greater value added. A widening technological gap that translates into slower export growth, exchange rate pressure and greater dependence on external financing.
Globally, the decade between 2015 and 2025 will likely be remembered as the period in which intelligence became industrial infrastructure, as fundamental as electricity or the Internet in earlier times.
Countries that recognized the change early generated productivity gains, attracted capital and strengthened strategic leverage. Countries that remained intellectually introverted expanded their structural vulnerabilities.
The technology is composed silently and asymmetrically. While others invested in computing, research and industrial automation, Pakistan remained engrossed in cyclical political narratives. While competitors built AI ecosystems and improved industrial capabilities, Pakistan overstated its readiness without matching infrastructure or political urgency.
The real danger is not exclusion from the AI revolution. It is much more insidious: participation at the lowest value-added 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 access to computing, technological depth, and institutional focus.
On each of these fronts, the gap with countries like India and Vietnam is not static. It is widening, and in an increasingly complex technological era, widening gaps tend to harden into long-term structural disadvantages that become increasingly difficult to reverse.
The writer is former director of emerging markets investments at Citigroup and author of “He Gathering Storm.”
Disclaimer: The views expressed in this article are those of the writer and do not necessarily reflect the editorial policy of PakGazette.tv.
Originally published in The News
