In a world brimming with technological noise, it is artificial intelligence that stands out — not just as a powerful engine of innovation but also as one that quietly reconfigures the very architecture of economic interdependence. In so many ways, AI revives today and extends the foundational insights of Nobel laureate Wassily Leontief, who first showed how industries are linked through flows of input and output.

Consequently, what Leontief could see through matrices and production tables, AI can operationalize today in real time, across geographies and cultures. But the real promise does not lie in computation alone; it lies in embedding AI within systems of inclusive growth, decentralized participation and cultural adaptation.

Furthermore, Leontief’s input-output model was an elegant representation of how an economy works: the output of one industry is the input of another, forming a complex network of dependencies. It was a deep step forward in economic planning, enabling governments to visualize what investment, policy changes or sectoral interlinkages could result in. However, Leontief’s model assumed data to be a static input. 

Beyond borders: AI’s silent transmission of intelligence

With AI today, data is dynamic, in real time, and deeply contextual. Machine learning systems draw upon information from millions of sources, user behavior, satellite images, medical scans, voice recordings, language patterns — forming intelligent networks that can inform decision-making across sectors like never before. This transformation is much more than digital acceleration; it is a structural shift toward interconnected intelligence.

What makes this development particularly apposite today is the emerging fault lines in global cooperation. From trade wars to technological bifurcation, the promise of seamless globalization has frayed. Supply chains are becoming more insular, intellectual property regimes more protectionist and technological ecosystems more fragmented. Yet amidst this fragmentation, AI emerges as a unifying force. It does not respect borders in the classical sense. A model trained on agricultural data from Vietnam may be adapted for use in Ethiopia; voice-to-text tools developed in Hyderabad are improving accessibility for visually impaired users in Argentina; and logistics optimization systems from Singapore are being repurposed for rural markets in Ghana. This is not the flow of capital, nor the movement of goods; it is the silent transmission of intelligence. And this, in essence, is the extension of Leontief’s vision beyond production into the digital realm of insight.

What AI adds to Leontief’s formulation is the ability to integrate not just industrial output but human context. Data collected in a coastal village in Kerala about crop disease patterns can be merged with satellite data on rainfall, and machine learning models can forecast agricultural risks that guide both local farmers and insurance policy designers. In this expanded input-output ecosystem, education feeds into innovation, which in turn enhances health systems and manufacturing. 

Cultural intelligence and inclusive AI: bridging the global divide

The circularity of development becomes tangible. In today’s AI-enabled world, these loops are not linear; they are dynamic, adaptive and capable of learning. The promise is immense: inclusive, responsive and culturally rooted economic policy reflecting the lived realities of people rather than abstract aggregates

Subsequently, one of the most striking things in the rise of AI is how it carries cultural memory and nuance with it into technical systems. Conventional economic models struggle to account for nonmarket activities, social hierarchies, or local knowledge. But AI can embed, if it is trained in ethical and inclusive ways, multiple languages, dialects and region-specific practices within the very design of its systems

In the Indian context, platforms like Sarvam AI are building multilingual large language models in Indian languages and contexts, ensuring that voice-based interfaces can speak as fluently to the rural woman in Chhattisgarh as to a city-based engineer. Moreover, in Africa, local developers are feeding Swahili, Yoruba and Zulu into models that interpret public service needs so much more accurately than any Western imports ever could. This isn’t cultural homogenization; this is cognitive expansion. AI acts as glue not only across sectors but also between ways of knowing.

However, like all transformative technologies, AI’s impact rests on its architecture of access. As of 2024, most of the computing power, foundational models and talent pipelines are controlled by a few countries. The serious emerging concern is data colonialism — where data extracted from the Global South powers profits in the North. Here lies the real test of inclusive development — whether countries such as India can shape the terms of engagement. One viable way could be through open-source models, public digital infrastructure and participatory governance mechanisms. The Digital Public Infrastructure framework, inclusive of the Universal Payments Interface,  Aadhaar and Open Network for Digital Commerce (ONDC) — of India has already shown the strength of creating interoperable systems that serve citizens first. If extended into AI, this can democratize access to datasets, hold algorithms accountable and anchor innovation in public purpose.

Therefore, it is not some theoretical vision, but it is unfolding. AI-based remote sensing helps Indian states forecast floods better. In this regard, credit-scoring models using alternative data help first-time borrowers access loans. AI-enabled virtual labs allow students from resource-starved regions to conduct complex science experiments. These are modern-day input–output loops — not between coal and steel, but between voice data and policy, between satellite imagery and disaster relief, and between language processing and job creation. AI is making the logic of Leontief come alive in a radically new form, with very real consequences for human development.

AI’s cultural bridge: democratizing intelligence, expanding possibilities

Going forward, the task is very clear: to avoid a branching whereby AI continues to be built in a handful of countries, while the rest of the world remains limited to passive consumers of smart solutions. The only way to do that is by building actively AI-integrated economic planning rooted in local contexts but open to global collaboration. Leontief’s tables now have to be re-imagined as neural maps tracking how education policy affects research output, how healthcare diagnostics impact labour productivity and how cultural inclusion drives technological adoption.

Policy needs to zoom from the macro down to the micro, where AI is the connective tissue. International institutions, in this context, have to assume a more facilitative role — not to prescribe models but to enable code commons, transnational datasets and cooperative regulatory frameworks. A global AI ethics council — possibly under the G20 or a re-energized United Nations Educational Scientific and Cultural Organisation (UNESCO) AI Ethics Recommendation — could lay down protocols for equitable use, data dignity and algorithmic transparency. India, with its techno-democratic ethos, is uniquely placed to lead this conversation across the North and South, tech and tradition, code and community.

At the end of the day, this is not about AI for automation but about AI for augmentation: augmenting human capacity, institutional resilience and cultural depth. Leontief could not have foreseen neural networks, but he most certainly foresaw systems whereby parts would work harmoniously for the whole. 

Concludingly, AI can deliver just that — if shaped wisely — not a fragmented digital privilege, but systemic, inclusive growth. It is now time to reclaim the lost promise of globalization through an intelligence that learns from the world and returns value to it. The future may not belong to those with the biggest server farms but to those who can make sure intelligence, much as development, is shared, ethical and deeply human.

[Cheyenne Torres edited this piece.]

[Ainesh Dey edited this piece.]

The views expressed in this article are the author’s own and do not necessarily reflect Fair Observer’s editorial policy.