AI and Human Development: Toward a Future of Co-Evolution, Not Displacement

 

AI and Human Development: Toward a Future of Co-Evolution, Not Displacement

Rahul Ramya

30.04.2025

Patna India

Why Prioritizing AI Over Human Development Is a Strategic Error

In the ongoing discourse surrounding artificial intelligence, a troubling pattern has emerged—one that prioritizes AI development while neglecting the foundational imperative of human development. This strategy is not only shortsighted but potentially self-defeating. No matter how advanced an AI system becomes, it cannot thrive in societies marked by extreme inequality, poor education, and eroded ethical capacities. AI does not operate in a vacuum; it is embedded in social, political, and moral contexts shaped by human development.

This is not a “Red Queen” moment—where both AI and Human Resources must run together competitively but also cooperatively just to stay in the same place—because AI and human progress are not inherently in contradiction . Any discourse that misleads us into thinking that technological acceleration alone ensures survival or success is self defeating. Instead, the relationship between AI and human development must be reimagined as co-evolutionary: AI systems must not outpace, but evolve in tandem with, the enhancement of human capacities.

Here is a brief discussion on  Red Queen dynamic between AI and human development, using the same analytical lens as Acemoglu and Robinson’s The Narrow Corridor, and now enriched with examples from India and the Global South:

AI and Human Development: The Red Queen Race Outside the Corridor

In The Narrow Corridor, Acemoglu and Robinson explain that liberty is sustained when both the state and society remain in dynamic tension—each checking, challenging, and evolving in response to the other. While the Red Queen effect highlights the pace of adaptation, The Narrow Corridor emphasizes the balance of power necessary for liberty. Together, they warn us that neither speed nor technology alone guarantees freedom—only structured co-evolution does.  They borrow the Red Queen effect from evolutionary theory to capture this idea: one must keep running just to stay in place. But liberty only survives when both state and society run together, not when one outruns the other.

When we transpose this metaphor to the AI–human relationship, the analogy becomes even more urgent. In this case, AI is advancing rapidly—pushed by corporate innovation, state investment, and geopolitical competition—while human development in many parts of the world, particularly the Global South, struggles to keep pace. But unlike the state, AI has no moral agency, no interest in human flourishing. It cannot constrain us. The race, therefore, becomes dangerously asymmetrical—with one side accelerating and the other limping.

India: Uneven Terrain in the Red Queen Race

Take India—a country simultaneously celebrated for its AI talent and criticized for vast socio-economic disparities. On one hand, India is a global hub for AI development, with initiatives in healthcare, education, and agriculture. On the other hand, literacy, digital access, and basic healthcare remain out of reach for large sections of the rural and urban poor.

For example:

   •   AI in healthcare: Tools like AI-based tuberculosis diagnosis or radiology analysis are technically sophisticated but fail to deliver impact in areas lacking trained health workers, electricity, or internet connectivity. Without investment in frontline human infrastructure, the AI race runs on a treadmill—impressive motion, no real progress.

   •   AI in education: Platforms like DIKSHA or adaptive learning apps promise personalized learning. Yet, in tribal and low-income districts, students lack digital devices, stable internet, or trained facilitators—a clear instance where AI speeds ahead while human development lags behind.

Here, the AI-human relationship is not co-evolutionary, but extractive: AI is piloted for data collection or scaling up services, but with little investment in the human scaffolding that gives it meaning.

Brazil and South Africa: Algorithmic Gaps in Inequality-Ridden Societies

In Brazil, the government introduced AI-based student learning analytics to address poor academic performance. Yet, high dropout rates in favelas persisted because of hunger, domestic violence, and lack of support. AI provided metrics, not motivation or meals. Once again, the Red Queen race is run by the algorithm while the child sits hungry and still.

South Africa has adopted AI in policing and smart cities. But in post-apartheid urban spaces, where economic segregation remains extreme, AI-enhanced surveillance systems have often reinforced racial biases, profiling poor Black communities more intensely—just as predictive policing has done in the U.S. Here, AI does not “run with” people’s empowerment but codifies old inequalities in faster code.

Rwanda and Kerala: Entering the Corridor Together

However, some examples show how the Red Queen race can become co-evolutionary—more like the mutual tension described in The Narrow Corridor.

   •   In Rwanda, Zipline’s AI-assisted drone delivery of blood and medical supplies succeeds because it’s embedded within a network of trained healthcare workers, community health initiatives, and public trust. AI is not a substitute, but an amplifier of human service delivery.

   •   In Kerala, the state’s use of AI in public health surveillance during COVID-19 was effective because it was paired with strong public health systems, decentralised governance, and high literacy levels. AI didn’t race ahead—it was carried forward by a capable society.

Toward a Just Red Queen Race: Rebalancing the Relationship

If we are to learn from Acemoglu and Robinson’s Narrow Corridor, then the lesson is this: no amount of algorithmic speed can compensate for human stagnation. Just as the state must be empowered but constrained by society, AI must be guided and shaped by empowered, educated, and ethically aware human communities.

The true Red Queen race in the AI-human context is not about competition but co-movement. And that requires:

   •   Democratising AI literacy (as Finland has done with its national AI curriculum)

   •   Embedding AI in inclusive welfare systems, not just markets

   •   Measuring progress not just in terms of patents or productivity, but in terms of equity, dignity, and human development

If we fail to maintain this co-evolutionary balance, AI will continue to advance—but in hollow corridors, uninhabited by real human empowerment. And unlike the Red Queen’s race where one runs to stay in place, in this case we risk running fast towards social collapse, unless we slow down to lift human development alongside.

Before we hasten to advocate for AI at all costs, we must pause and ask: Who benefits from this development? Who is left behind? A sustainable AI future is not one where machines outperform people, but one where technology is designed to empower people—especially those historically marginalized or excluded. If human potential remains stunted, AI, no matter how powerful, will eventually hit a wall of diminishing returns.

1. The Illusion of Autonomous AI Progress

Despite overwhelming investment and media focus on AI as a self-sustaining force of progress, real-world deployments consistently reveal that AI systems remain deeply dependent on human infrastructures—educational, ethical, and social. When AI tools are introduced in environments lacking digital literacy or basic infrastructure, their efficacy collapses. For instance, AI-based agricultural prediction tools in sub-Saharan Africa have underperformed not because of flawed algorithms but due to farmers’ lack of access to digital platforms, local language support, and financial inclusion.

2. Technology Cannot Outrun Unequal Capability Landscapes

Countries like India and Brazil present stark illustrations of how technological leapfrogging without corresponding human capability enhancement results in deepening inequality. In India, AI-driven healthcare solutions such as diagnostic imaging tools are concentrated in urban centers, while rural populations remain underserved due to the absence of trained personnel and digital infrastructure. Similarly, Brazil’s public education system has introduced adaptive learning platforms, but in underserved areas, dropout rates remain high due to poverty, food insecurity, and lack of basic digital devices. These cases show that technology cannot compensate for neglected social investments.

3. The Myth of the “AI-First” Model in the West

Even in the Global North, an AI-first development model has shown its limitations. In the United States, predictive policing algorithms have disproportionately targeted marginalized communities, not due to technological error but because of biased historical data and lack of ethical oversight. Meanwhile, in the United Kingdom, the 2020 A-level grading algorithm scandal—where students from disadvantaged backgrounds were unfairly downgraded—demonstrated that AI systems applied without inclusive policy frameworks can exacerbate injustice, not correct it.

4. Successful Models of AI-Human Co-Development

A few countries provide instructive counterpoints. Estonia, for instance, has pursued a “human-first digital society” model where AI tools in governance and education are developed alongside universal digital literacy and trust-building measures. Its e-residency and e-health platforms thrive because citizens are not just users, but informed stakeholders. In Rwanda, drone delivery of medical supplies by Zipline works because it complements strong local healthcare training and community trust—underscoring that AI needs a well-developed human network to succeed.

5. AI as an Amplifier—Not a Substitute—of Human Potential

At its best, AI amplifies human abilities. The AI model used in India’s tuberculosis (TB) screening, integrated with the Nikshay Poshan Yojana (a cash transfer scheme for TB patients), succeeded because it combined technological diagnosis with human follow-up, nutrition support, and public health outreach. Here, AI wasn’t a “solution,” but a component of a comprehensive development strategy—showing what co-evolution looks like in practice.

6. Rethinking Innovation Incentives and Metrics

Global innovation metrics still reward algorithmic novelty and private profit over social utility and equitable deployment. This must change. The OECD, UNDP, and World Bank should begin measuring AI success not merely by patents filed or GDP growth but by indicators such as human development index (HDI) gains, inequality reduction, and sustainability impacts. This would align AI innovation with global justice and long-term human flourishing.

7. A Call for Ethical Regulation Rooted in Social Contexts

Rather than universal AI regulation frameworks detached from ground realities, we need contextual ethics that vary by region. The EU’s AI Act, while ambitious, risks exporting regulatory models inappropriate for developing nations unless paired with capacity-building programs. For instance, an African or South Asian AI policy must account for caste, ethnicity, and access disparities in ways a Brussels-centered model cannot foresee.

8. The Way Forward: From Displacement to Dialogue

Finally, we must move from a paradigm where AI and human workers are seen in competition, to one of dialogue. The goal must be to create education systems that prepare people not to “outrun” AI but to work with it—an approach exemplified by Finland’s nationwide AI literacy program, where citizens of all ages are taught the basics of AI to foster a sense of participation, not alienation.

9. Power Dynamics

The concentration of AI development power in a handful of tech giants shapes who benefits from these technologies. Just five companies—Google, Microsoft, Meta, Amazon, and Apple—control much of the AI infrastructure, from training data to computing resources to deployment platforms. These corporations invest billions in AI not primarily to advance human development but to capture markets, increase shareholder value, and consolidate their dominance. In India, international companies extract data from millions of users while the benefits flow primarily outward. Even China's apparent technological sovereignty comes with its own concentration of power among state-affiliated companies like Alibaba and Baidu. In Kenya, M-Pesa's mobile banking revolution ultimately benefited Vodafone shareholders more than local communities. Even well-intentioned AI projects for global challenges like climate change or healthcare often reinforce this power imbalance when they rely on proprietary systems controlled by Western corporations. In Southeast Asia, agricultural AI tools developed by multinational companies collect valuable farm data while farmers remain price-takers in global markets. Meanwhile, Mexico's adoption of proprietary healthcare algorithms has created dependencies on external systems rather than building local capacity. Data extracted from vulnerable populations becomes a resource that flows upward to these companies, while benefits trickle down unevenly. Any meaningful co-evolution of AI and human development must address this corporate concentration through stronger democratic governance, public alternatives, and genuine community ownership of technological resources.

10. Indigenous Perspectives

Indigenous knowledge systems offer crucial alternatives to the efficiency-maximizing logic of current AI. Many indigenous communities prioritize relationships over resources, long-term sustainability over short-term gains, and collective wellbeing over individual advancement—values often missing from algorithmic systems. For example, the Maori concept of kaitiakitanga (guardianship of the environment) could inform AI environmental applications by embedding intergenerational responsibility. In India, traditional water harvesting systems like Tamil Nadu's eri (tank) systems incorporate centuries of climate adaptation knowledge that modern predictive algorithms often miss. The Dongba knowledge systems of China's Naxi people contain sophisticated biodiversity classifications that could enhance AI conservation tools. Similarly, many indigenous communities practice decision-making that considers impacts seven generations forward—a stark contrast to AI systems optimizing for immediate metrics. The Andean concept of "Buen Vivir" (good living) offers frameworks for AI that measure success beyond efficiency and profit. In healthcare, combining indigenous knowledge of local plants and healing practices with AI diagnostic tools has proven more effective than either approach alone in parts of Latin America and Africa. South Africa's integration of traditional healers with modern medical systems offers lessons for complementary knowledge systems. By incorporating these worldviews that emphasize interconnection, balance, and reciprocity, we can develop AI systems that enhance rather than exploit human and ecological communities.​​​​​​​​​​​​​​​​

11. Economic Analysis: Labor Transformation and Wealth Distribution

The economic implications of AI deployment in developing economies require urgent attention beyond simplistic narratives of job displacement or creation. In India, where approximately 400 million workers remain in the informal sector, AI-driven automation threatens to disrupt traditional pathways to industrialization before adequate social protection systems are established. Unlike previous technological transitions in Western economies, Global South countries face "premature deindustrialization" where manufacturing jobs disappear before their economies reach middle-income status—a phenomenon already visible in Indonesia's manufacturing sector and Brazil's service industry. Meanwhile, the economic value generated by AI disproportionately accumulates in the hands of those controlling intellectual property rather than those providing the data or labor that makes these systems possible. African countries contributing valuable facial recognition data see minimal returns while companies headquartered elsewhere capture billions in market value. Addressing these imbalances requires new economic frameworks: universal basic income pilots in Kenya have shown promise in creating safety nets, while Thailand's progressive taxation of data-extractive business models offers another approach. Most crucially, countries like Malaysia and Vietnam are exploring "pre-distribution" policies that ensure communities maintain ownership stakes in AI systems trained on their data from the outset, rather than relying solely on redistributive measures after wealth concentration has occurred.

12. Agency and Resistance: Community Innovation and Digital Sovereignty

Across the Global South, communities and governments are not passive recipients of AI technologies but active shapers and resisters of exploitative models. In Bolivia, indigenous communities have established data cooperatives that protect traditional knowledge while selectively engaging with AI developers on their own terms, ensuring benefits flow back to knowledge holders. Senegal's government has mandated that international AI deployments include technology transfer components and local capacity building, refusing access to national data otherwise. In the Philippines, grassroots tech collectives have developed alternative, community-owned AI applications for disaster response that operate independently of corporate platforms, demonstrating practical digital sovereignty. Meanwhile, India's "Digital Public Infrastructure" approach offers a distinctive model where foundational digital systems remain publicly owned while encouraging innovation atop these platforms. The Latin American Network for AI Sovereignty unites researchers across ten countries to develop region-specific foundation models trained on local languages and cultural contexts. These diverse strategies—from data cooperatives to legislative requirements to homegrown alternatives—demonstrate that the Global South is not simply adapting to Northern AI paradigms but actively constructing alternative development paths that center community ownership, local knowledge, and equitable benefit-sharing. These resistance movements don't reject technological advancement but reshape it to serve broader human development goals, offering crucial lessons even for technologically "advanced" economies facing similar questions of algorithmic governance and digital rights.​​​​​​​​​​​​​​​​

13. Historical Context

Today's race toward AI dominance echoes previous technological revolutions that promised development but often reinforced existing power structures. During colonial times, new technologies like railroads and telegraphs were presented as tools for "civilizing" nations while extracting wealth from them. In India, the British-built railway system primarily served colonial extraction rather than local development, creating patterns of uneven infrastructure that persist today. Similarly, China's "century of humiliation" included technological inequalities that shaped its current determination to achieve technological sovereignty. In the postcolonial era, technology transfer programs often created dependency rather than empowerment, as seen in Tanzania's failed industrialization efforts under Structural Adjustment Programs. The 1990s neoliberal wave brought digital technologies to developing nations through market-based approaches that prioritized profit over public access, leading to Brazil's highly unequal internet penetration that still affects its AI readiness. AI development follows this troubling pattern—advanced economies design systems that developing nations must adopt on unequal terms, creating what scholars call "digital colonialism." Without acknowledging this historical context, we risk repeating cycles where technological "progress" becomes another vehicle for extraction and inequality.

Conclusion

Human development is not an auxiliary to AI advancement—it is its very foundation. Without capable, educated, healthy, and ethically empowered human beings, no AI system can reach its potential or fulfill its promise. The future lies not in a race between humans and machines, but in a carefully crafted partnership where each strengthens the other. To prioritize one at the cost of the other is to ensure the failure of both. The Andean concept of "Buen Vivir" (good living), rooted in harmony with nature and community, could help reframe AI development goals around holistic wellbeing rather than extractive metrics. Integrating such epistemologies into AI governance could democratize not only who builds AI but also why and how it is built.