Policy Brief No. 230 — March 2026
The Fourth Path: Middle-Income Countries and Prosocial AI
Cornelia C. Walther
Key Points
→ Middle-income countries, home to 75 percent of the global population, have an opportunity to pioneer a prosocial approach to regulating artificial intelligence (AI).
→ Prosocial AI — systems tailored, trained, tested and targeted to advance human well-being and planetary health — offers a framework for these countries to take a fourth path to AI policy that avoids the pitfalls of the three current models and aligns technological development with sustainable and equitable outcomes, embedding these values from the design phase onward.
→ This fourth path demands regional cooperation among middle-income countries to share information on AI policy experiments and their outcomes; build collective capacity; and demonstrate that prosocial innovation can drive both economic competitiveness and human development.
The Challenge
The global conversation about humanity’s technological future oscillates between familiar poles: Silicon Valley’s market-driven techno-optimism, Brussels’ comprehensive regulatory frameworks and Beijing’s state-directed technological governance. Yet none of these three paths adequately serves the 6.5 billion people living outside these paradigms. The future will be forged in New Delhi, Brasília, Jakarta and Nairobi — across the Global South where three-quarters of the world’s population lives.
Middle-income countries occupy a unique position. They are neither trapped in extreme poverty nor locked into the legacy systems of wealthy nations. This positioning makes them particularly capable of pioneering a fourth path that is genuinely prosocial — pro-people, pro-planet and focused on developing human potential. These 108 nations, as defined by the World Bank’s middle-income country classification (including both lower- and upper-middle-income groups), are home to roughly 75 percent of the global population and produce
About the Author
Cornelia C. Walther is a senior fellow at CIGI, the Sunway Centre for Planetary Health, the Wharton Neuroscience Initiative/Wharton AI & Analytics Initiative and the Harvard Learning and Innovation Lab, as well as an adjunct associate professor at the School of Dental Medicine at the University of Pennsylvania. Cornelia is an external expert to the United Nations Population Fund on hybrid intelligence and collaborates with universities across the Americas and Europe as a lecturer, executive coach and researcher.
Previously, she worked as a humanitarian practitioner with the United Nations for two decades in large-scale emergencies in West Africa, Asia and Latin America, focusing on hybrid advocacy and social and behavioural change.
She is also affiliated with MindCORE and the Center for Social Norms and Behavioral Dynamics at the University of Pennsylvania. Cornelia has a Ph.D. in law and is a trained yoga and meditation teacher. Her books are available in English and German. She pioneers research on hybrid intelligence and prosocial AI to build “agency amid AI for all.”
40 percent of global economic output (World Bank 2024, 33). They represent both the demographic centre of gravity and a diversity of development trajectories.
What sets many of these countries apart is their demographic advantage. Populations are young, with Sub-Saharan Africa’s median age under 25 years. Demographic dividends — that crucial window when the working-age population expands beyond the numbers of the very young and the very old — offer significant potential. Millions of young people entering labour markets in the coming decade represent not just a population bulge, but also a reservoir of human potential that could reshape global development if channelled toward productive and fulfilling pursuits.
Yet most middle-income countries face what the World Bank (ibid.) has termed the middle-income trap: a developmental stasis where nations lose competitive edge in manufactured goods exports due to rising wages but cannot compete in high-valueadded markets. Since the 1970s, income per capita in median middle-income countries has remained consistently below onetenth of US levels (ibid., 2). The traditional escape routes — cheap labour, resource extraction and technology adoption — are closing as geopolitical tensions rise, populations age and climate imperatives demand new economic models.
Why Current Paths Fall Short
A market-driven model, typical of major US platforms, optimizes for scale, speed and shareholder value. When revenue depends on time-on-platform and ad targeting, platforms are structurally pushed toward whatever captures attention fastest, even when the downstream costs are borne by society. In practice, this can reward disruption without responsibility: the system performs well on growth metrics while externalizing social and environmental harms. Two representative research streams illustrate the mechanism with concrete examples:
→ Engagement advantages misinformation: In a large-scale study of Twitter diffusion over more than a decade, false news consistently spread farther, faster and more broadly than true news, especially in politics, because people were more likely to share novel and emotionally resonant claims (Vosoughi, Roy and Aral 2018). This helps explain why attention-ranked feeds can accelerate misinformation cascades even without explicit intent to promote falsehoods.
→ Outrage outcompetes nuance: Research on political messaging shows that posts containing moral-emotional language (a common marker of outrage) are more likely to be shared and can intensify within-group diffusion, fuelling polarization dynamics when algorithms learn to prioritize what spreads (Brady et al. 2017). The effect was demonstrated across contentious topics such as gun control, same-sex marriage and climate change, where moralized framing increases virality.
Taken together, these findings help clarify why the “maximize engagement” playbook can generate digital environments that profit from polarization and rapid diffusion — producing a small number of extraordinarily valuable firms while contributing to inequality, mental health strain and pressure on democratic institutions.
The algorithmic architecture of social media platforms has been shown to maximize engagement over well-being, creating digital environments that can profit from polarization. This model has produced a handful of trillion-dollar companies alongside growing inequality, mental health concerns and challenges to democratic institutions.
European regulatory governance offers important safeguards through frameworks such as the General Data Protection Regulation and the AI Act, prioritizing consumer protection, ethical standards and risk-based regulatory approaches. The EU framework has served as a reference point for countries such as Brazil in developing their own AI governance systems. Yet this approach can sometimes struggle to move beyond constraint into enabling innovation. Regulations designed to protect citizens in wealthy nations can inadvertently create barriers to entry that favour established stakeholders over emerging innovators. For populations still building basic infrastructure and seeking to lift millions from poverty, compliance costs alone can be prohibitive for
start-ups and small enterprises in middle-income countries. Brazil’s approach may offer a middle way, adapting EU principles while maintaining flexibility for innovation (Lippoldt 2025).
State-directed technological governance, exemplified by China’s approach, delivers efficiency through centralized coordination but can sacrifice individual autonomy in favour of collective advancement. While such systems may deliver impressive infrastructure and economic growth metrics, research on digital governance models shows they can limit individual freedom, creative expression and the unpredictable innovations that emerge from open societies.
None of these paths fully align incentives with prosocial outcomes. Current global reward systems tend to celebrate GDP growth over wellbeing indicators, quarterly profits over longterm sustainability and individual accumulation over collective flourishing — defined here as the expansion of capabilities and opportunities for all members of society. Financial markets often reward companies that externalize environmental costs and extract value rather than create it, though emerging frameworks such as ESG (environmental, social and governance) policies and sustainability scoring by services such as Morningstar represent positive developments. However, these remain insufficient to fundamentally reorient incentive structures. Tax systems in many jurisdictions privilege capital over labour, inheritance over entrepreneurship, and speculation over productive investment. Educational systems frequently sort and rank rather than develop human potential. Nations in the Global South inheriting these misaligned incentives risk reproducing their dysfunctions at scale, missing the opportunity to design something better from the start.
The Opportunity: Prosocial AI and the Fourth Path
Middle-income countries in the Global South possess unique assets to pioneer a different approach. Unlike wealthy nations sometimes constrained by legacy systems and political gridlock, they have greater freedom to leapfrog
outdated infrastructure. Unlike the poorest nations, they have sufficient institutional capacity and human capital to experiment. This combination creates what systems theorists call an “adjacent possible” — the next-step set of innovations that are realistically reachable from today’s starting conditions, where existing capabilities can be recombined into new configurations and each successful trial expands what becomes feasible next. Which means, in this case, an opportunity to devise new configurations that richer or poorer nations cannot easily access.
It is important to acknowledge both the diversity within this grouping and the AI accomplishments already emerging across middle-income countries. Local entrepreneurs are already delivering AI software applications and products developed using AI input. Research has identified approximately 2,500 locally appropriate, naturallanguage, AI-focused businesses across 10 middleincome countries as of early 2025 (ibid.). In some cases, middle-income economies are not starting from scratch. Policy frameworks are in place and local entrepreneurs are innovating in countries such as Brazil, Colombia, India and Indonesia. This existing foundation provides a platform for more deliberate prosocial development.
Consider the innovation imperative. Insufficient development of domestic innovation capabilities lies at the heart of the middle-income trap (Lee, Baek and Yeon 2021). But what if this necessity became an opportunity to innovate differently — technologically and systemically? What if these countries designed innovation ecosystems that embedded prosocial values from inception instead of adding them as afterthoughts?
At the heart of the fourth path — the collective effort by middle-income countries to pioneer prosocial AI governance — lies a fundamental shift in how we conceive of AI. The prevailing approach tends to treat AI as a commercial product, optimizing for market efficiency and shareholder returns. The fourth path requires reframing AI as a social determinant of well-being, designing systems to advance human capabilities and planetary health.
This is the essence of prosocial AI, a framework built on four foundational principles that can be remembered as the 4T Framework, that is, AI systems should be tailored, trained, tested and targeted (Walther 2024):
→ Tailored: AI solutions must address specific societal challenges rather than pursuing general-purpose optimization. Health-care AI designed for urban hospitals in wealthy nations, for example, will fail rural clinics with different infrastructure, resources and patient needs. Prosocial AI is explicitly adapted to local contexts, languages, cultural norms and constraints. For middle-income countries, this means developing AI systems that solve their particular development challenges — from agricultural optimization accounting for variable climates and smallholder farming patterns, to educational platforms that function effectively with limited connectivity and diverse linguistic landscapes. Indigenous wisdom is woven in from the start so that technological solutions respect and build on local knowledge systems instead of displacing them.
→ Trained: AI systems reflect the data they learn from. When trained exclusively on data sets from wealthy countries, AI perpetuates existing inequalities and fails to serve diverse populations. Prosocial AI demands intentional inclusion of voices, languages and contexts from the Global South in training data. This is both an ethical requirement and a practical necessity for AI to function effectively and equitably across different contexts. The principle recognizes that representation in data is representation in outcomes.
→ Tested: Beyond technical performance metrics, rigorous testing — including ethical audits and stress tests — ensures AI systems function equitably and align with societal values. This means examining potential biases, stress-testing edge cases affecting vulnerable populations and assessing whether systems truly serve intended beneficiaries. Middle-income countries can establish testing frameworks that prioritize local needs and values rather than import standards designed for different contexts, creating accountability mechanisms that reflect their own priorities. Testing must be ongoing and adaptive, recognizing that AI systems continue to learn and evolve after deployment.
→ Targeted: Prosocial AI focuses on measurable outcomes such as reducing carbon emissions, improving education access or expanding health-care coverage that will make meaningful contributions to society. Rather than chasing abstract capabilities or technological
sophistication for its own sake, it asks: What specific problems can this technology solve? For middle-income countries, this means AI deployment guided by development priorities — whether improving agricultural yields, expanding health-care access or building climate resilience — rather than by following technological trends from elsewhere.
Prosocial AI operates at multiple levels: at the individual level, through personalized interventions; at the community level, by extending services to underserved populations; at the societal level, by informing inclusive policies; and at the planetary level, by supporting environmental sustainability. This multi-level approach aligns with the fourth path’s commitment to systemic rather than isolated interventions.
Importantly, prosocial AI recognizes that technology reflects human values. We cannot expect AI to embody values that the people who design, deliver and use it do not manifest. This principle — values in, values out — underscores that the fourth path requires simultaneous transformation of technological systems and human institutions. Building prosocial AI demands that societies cultivate the values they wish to see reflected in their technologies, creating a reciprocal relationship between human development and technological advancement.
Why Middle-Income Countries Can Lead
The historical parallel with mobile telephony is instructive. Many middle-income countries skipped landline infrastructure entirely, leapfrogging to mobile networks that now enable financial inclusion, health-care delivery and educational access in ways that fixed-line systems never could. Kenya’s M-Pesa transformed financial inclusion by enabling people to transfer money without waiting for traditional banking infrastructure (Suri and Jack 2016). Bangladesh’s community health worker models show how hybrid systems — local human delivery capacity supported by simple tools, logistics and supervision — can extend primary care reach efficiently (World Health Organization 2021). Costa Rica generates the vast majority of its electricity from renewables
while maintaining steady economic growth, illustrating how policy plus infrastructure choices can shift a national trajectory (International Renewable Energy Agency [IRENA] 2025). India is developing an “AI autonomy” approach that emphasizes open innovation and public-sector leadership to shape an ecosystem aligned with national development goals (Mandal, Neema and Jain 2025). These examples demonstrate that alternative development pathways are achievable.
Concretely, this is not only about phones. Mobile leapfrogging matters because it demonstrates a broader infrastructure logic: when legacy systems are thin or costly to expand, countries can move straight to modular, interoperable “rails” that lower transaction costs and accelerate inclusion. That can mean:
→ Digital public infrastructure (DPI) beyond mobile apps — for example, shared building blocks such as digital ID, instant payments and secure data sharing that let governments and markets deliver services faster and more transparently (Organisation for Economic Co-operation and Development [OECD] 2024).
→ Public-service leapfrogging, where front-line workers are strengthened (not replaced) by lightweight decision support, remote training and better supply-chain visibility — often improving coverage without building hospitalheavy systems first (Shrestha et al. 2024).
→ Energy leapfrogging, where distributed renewables and smarter grids reduce dependence on imported fossil fuels and avoid locking in high-carbon infrastructure (IRENA 2025).
In short, the (adjacent) opportunity is to leapfrog systems: beyond new devices to innovative forms of payments, identity, service delivery and energy.
The demographic advantage is substantial. Millions of young people entering labour markets in the coming decade represent a reservoir of human potential that could reshape global development. Yet without strategic innovation, demographic dividends become demographic burdens when young people cannot find dignified work. Middle-income status without high-income transition means perpetual second-tier status in a globalizing economy.
The timing matters because many middle-income countries are in a once-per-generation “build
phase.” Over the next decade or two, they will lock in choices about how cities grow, how power is generated and priced, how people move, how data and identity are governed, and how skills are cultivated — choices that typically persist for decades because infrastructure is long-lived, capital-intensive and politically hard to unwind once vested interests form.
That creates a specific kind of historical momentum:
→ Path dependence sets in early. When a country commits to a grid architecture, urban form, transport corridors, or digital ID and payment rails, it shapes what becomes affordable and normal later — often for 20–50 years.
→ First systems define the rules. Early procurement standards, interoperability requirements and regulatory defaults determine whether markets become competitive and open — or extractive and locked into proprietary vendors.
→ The compounding effect is fastest now. As platforms, training pipelines and publicservice delivery systems scale, every additional user, institution and data set increases returns to the initial design — making mid-course correction harder.
Unlike wealthy nations that are trying to retool carbon-intensive grids, car-dependent urban design and slow-moving administrative systems, many middle-income countries can set smarter baselines now: distributed renewables paired with storage rather than fossil-heavy baseload; compact, transit-led growth rather than endless road expansion; digital public infrastructure built on open standards rather than proprietary lock-in; and education oriented toward hybrid literacy rather than industrial-era rote learning. Unlike the poorest countries still focused on reaching minimum service levels, many middle-income countries also have enough institutional capacity and fiscal space to pilot, learn quickly and scale what proves effective.
This is why timing matters. Across the Global South, major investments in energy, connectivity, mobility and skills are being made in real time. Once built, these systems shape incentives, markets and everyday behaviour for decades — creating path dependence that is expensive and politically difficult to undo. In practical terms, middle-income
countries can embed sustainability, equity and resilience before infrastructure and vested interests harden around less adaptive choices.
The same chance to pivot toward prosocial innovation exists across domains — and it extends beyond technology. It includes building renewable microgrids instead of centralized fossil plants; designing digital identity and data sharing with privacy and consent as defaults rather than surveillance; and creating learning ecosystems that develop capabilities and agency rather than reproducing classroom hierarchies. Some of the most consequential breakthroughs may be institutional: governance models that combine speed with participation, economic rules that price long-term planetary costs into today’s decisions, and education systems that widen opportunity instead of merely sorting talent for narrow labour-market signals.
Policy Recommendations
Establish Prosocial Innovation Funds
Governments should create dedicated funding mechanisms, potentially administered through regional development banks (such as the Asian Development Bank, the African Development Bank or the Inter-American Development Bank) or the World Bank (2024) to ensure transparency, governance and accountability. These funds should support enterprises embedding prosocial values from inception, prioritizing projects demonstrating measurable outcomes in human well-being, environmental sustainability and capability expansion — not just financial returns.
Unlike traditional venture capital that optimizes for rapid scaling and exit strategies, these funds should support patient capital approaches that allow innovations to develop in ways that serve community needs. Criteria should include evidence of co-design with intended beneficiaries, plans for equitable distribution of benefits and accountability mechanisms that go beyond profit metrics. External administration through regional institutions can promote collaboration and division of labour among nations for project development, particularly where individual countries may not have sufficient AI-sector scale to achieve economies of scale independently.
Cultivate Regional Learning Networks
Middle-income countries should establish formal mechanisms — potentially coordinated through UN agencies, the OECD (2025), regional development banks, or trade groupings such as the Association of Southeast Asian Nations (ASEAN), the Regional Comprehensive Economic Partnership (RCEP) or the African Continental Free Trade Area — to share information on policy experiments, technological innovations and institutional designs. Regional cooperation can build collective capacity to forge a different path, creating models that inspire rather than coerce.
Curated as safe spaces for thinkers and practitioners across disciplines, sectors and generations, these networks could facilitate regular exchanges among policy makers, researchers and practitioners to share both successes and failures with an open, non-dogmatic approach. Joint research programs can pool resources to develop AI systems and other technologies tailored to regional needs. Shared testing frameworks can create economies of scale while ensuring technologies meet prosocial standards. Regional development banks and multilateral institutions should support these networks, recognizing that SouthSouth cooperation offers pathways unavailable through traditional North-South relationships.
Given the considerable diversity of capacities, areas of comparative advantage and cultural affinities across middle-income countries, some cross-cutting global standards may be developed and applied broadly, while countries may also find it advantageous to collaborate with subsets of peer countries on specific initiatives where there is already alignment due to existing trade and investment accords.
Redesign Incentive Structures
National governments and regional bodies should move beyond incremental reforms to fundamental restructuring of reward systems. Progressive taxation structures can fund universal basic services without undermining entrepreneurship. Carbon-pricing mechanisms can make clean energy economically rational. Procurement policies can channel government purchasing power toward businesses meeting social and environmental standards. Digital infrastructure can be designed for privacy and autonomy rather than surveillance and manipulation.
These changes require political will from legislatures and executives to overcome entrenched interests that profit from current arrangements. The middle-income trap is fundamentally a political economy challenge where economic solutions exist but implementation faces resistance from incumbent elites. Breaking this requires new political coalitions and strategic use of crises — whether climate, economic or technological — to build consensus for different reward structures.
Invest in Institutional Capacity Through Double Literacy
“Double literacy” refers to competence in both technical domains and the humanistic understanding necessary to deploy technology for social good. Education, health-care and governance systems must be explicitly designed to maximize human flourishing — defined as the expansion of what people are able to do and be — rather than simply replicating models from elsewhere. Universities should redesign curricula around systems thinking and sustainability, preparing graduates to lead prosocial innovation. Health-care systems should emphasize prevention and primary care accessible to all, rather than concentrating resources in urban specialist facilities.
Governance institutions require reliable rule of law, fair competition and strategic public investment in research and infrastructure — but reimagined for contexts that differ fundamentally from wealthy nation histories. Innovation ecosystems do not emerge spontaneously; they require deliberate design and sustained investment. Middle-income countries have both the urgency and capacity to implement these at scale.
Establish Prosocial AI Assessment Frameworks
National or regional bodies, potentially linked under a harmonized umbrella that is localized at a neutral site, that is, an academic institution that serves as a facilitating hub, should establish frameworks to evaluate AI systems against prosocial criteria. Evaluation could take the form of a certification system similar to International Organization for Standardization standards or a screening system for deployment authorization, depending on national preferences and capacities.
A widely adopted “Prosocial AI Index” could serve dual purposes: assessing whether technologies truly serve development priorities and vulnerable populations, while also functioning as a sensitization tool to reframe how businesses approach AI technologies. Testing protocols could include ethical audits examining potential biases, stress tests for edge cases affecting marginalized communities, and impact assessments measuring effects on human well-being and environmental health.
Certification programs can create incentives for developers to prioritize prosocial outcomes. Transparency requirements should mandate disclosure of training data sources, algorithmic decision-making processes and performance metrics disaggregated by demographic groups. Such frameworks position middleincome countries as leaders in responsible AI governance while accommodating variation in national capacities and priorities.
Foster Pro-People Economics
National governments should design economic structures where prosperity is measured by more than consumption, implementing comprehensive metrics to track capability expansion and wellbeing alongside GDP. This means developing reward systems that incentivize businesses to create quality jobs, invest in worker development and contribute to community resilience.
Several nations across the Global South are already experimenting with well-being budgets, stakeholder capitalism models and social enterprise frameworks that could be scaled and refined. Governments should pilot universal basic services in key sectors, demonstrating that meeting fundamental needs for all citizens enhances rather than constrains economic dynamism. Economic policy should explicitly aim to expand what people are able to do and be, measured against frameworks such as the UN Sustainable Development Goals, rather than simply maximizing aggregate output.
Embed Regenerative Intent
Rather than viewing environmental sustainability as a constraint on growth, national and regional policy makers should embed it as a design principle with regenerative intent from inception. Countries building new energy infrastructure today can skip the carbon-intensive phase entirely, creating
competitive advantages in clean technology manufacturing while preserving natural capital, and building planetary health at scale.
The transition costs are real, but climate change and resource constraints provide opportunities to forge consensus for transformative policy reforms that deliberately break historical patterns. Land use policies should prioritize ecosystem preservation and restoration. Industrial policies should favour circular economy approaches that eliminate waste. Infrastructure investments should enhance rather than degrade natural systems. By positioning environmental sustainability as a source of competitive advantage rather than regulatory burden, middle-income countries can attract investment while building resilience.
The Path Forward: Operationalizing the Framework
Making the fourth path real requires concrete institutional architecture. Several governance models merit consideration:
→ Institutional coordination: Regional development banks (Asian Development Bank, African Development Bank, Inter-American Development Bank) or the World Bank could coordinate funding and provide technical assistance, ensuring neutral administration external to any single national government. This promotes transparency and accountability in investment management, procurement and project evaluation. UN agencies such as the UN Development Programme or specialized bodies such as the International Telecommunication Union could provide guidance on policy effectiveness and facilitate knowledge sharing.
→ Flexible governance: To accommodate variation in national capacities and priorities, governance frameworks should operate at multiple levels. Core global standards on safety, privacy and ethical baselines could apply broadly, while regional groupings pursue deeper integration where aligned interests exist (through organizations such as ASEAN and the African Union, or trade blocs such as
RCEP and the Comprehensive and Progressive Agreement for Trans-Pacific Partnership). This multi-layered approach respects sovereignty while enabling cooperation.
→ Streamlined implementation: To avoid encumbering development, institutional frameworks must remain lean and agile. Rather than creating entirely new bureaucracies, the approach should build on existing institutions: adding prosocial assessment criteria to development bank funding processes, incorporating AI governance into existing trade and investment frameworks, and leveraging existing research networks for knowledge sharing.
→ Evaluation mechanisms: Success metrics should extend beyond GDP to include indicators such as access to education and health care, environmental quality, income distribution and subjective wellbeing measures. Regular assessment and adaptive management will allow policies to evolve based on evidence of what works.
Already existing, albeit scattered, innovations demonstrate that pragmatic alternatives to the preceding paradigms are possible. From digital payment systems in East Africa to renewable energy transitions in Latin America, from community-based health-care models in South Asia to social enterprise ecosystems in Southeast Asia, experiments are proliferating. The question is whether these will coalesce into a coherent path forward or remain scattered innovations overwhelmed by inherited dysfunctions and misaligned incentives. The difference between isolated experiments and transformative change lies in whether middleincome countries can build the institutional architecture, political coalitions and cultural narratives to support prosocial innovation at scale.
This work requires courage, compassion, creativity and clarity. Courage, to challenge entrenched interests that profit from current arrangements. Compassion, to acknowledge genuine human needs and expectations that a prosocial future must accommodate. Creativity, to experiment with new models, knowing that some will fail. Clarity, to prioritize long-term well-being over short-term growth metrics, and about the fact that the fourth path demands more than just new technologies; it requires new institutions. An inclusive future where
everyone has a fair chance to fulfill their potential requires not only different policies but also different values. Beyond economic transformation, this is a quest for social transformation.
Middle-income countries face genuine constraints: limited resources, geopolitical pressures and institutional weaknesses. Yet they retain genuine advantages: flexibility, urgency, demographic potential and freedom to learn from others’ experiences. The prosocial future begins with concrete experiments in places willing to try something new. It grows through networks of innovators sharing what works and what does not. It scales when political leaders, business executives, educators and citizens align around shared values and measurable outcomes. It succeeds when a critical mass of middle-income countries demonstrate that another path is not only possible but preferable — creating models that inspire rather than coerce, empower rather than extract and flourish rather than merely grow.
Six billion people deserve better than being constrained to choose between a corporateled approach, regulatory frameworks designed for different contexts or state-directed technological governance — the current three paths of AI governance. They deserve a fourth path built on their own terms, reflecting their own values, serving their own aspirations. Middle-income countries can pioneer this path. If they seize it, they will not only determine their own futures but also offer a new model for how humanity navigates the digital age.
The future is not predetermined. It will be shaped by choices made today, particularly in the dynamic middle of the global income distribution. Recognizing both the genuine constraints middleincome countries face and the genuine advantages they possess, the fourth path offers a framework for making those choices differently, with eyes open to what has not worked, and decisions grounded in what exists and committed to what could be.
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