The artificial intelligence revolution isn’t coming — it’s here. But unlike previous technological waves, AI’s transformative power is being concentrated in the hands of remarkably few players, creating global imbalances that threaten to entrench existing inequalities for generations. As AI systems increasingly shape our economies, societies, and daily lives, we face a critical choice: Will we allow narrow market forces and geopolitical power dynamics to dictate AI’s development, or will we proactively steer this technology toward benefiting humanity as a whole? It is late to set the stage for global prosocial AI governance, but it is not too late – yet.
Hardware Chokehold: Where Power Really Lives
Before examining governance frameworks, we must confront an uncomfortable truth: the AI revolution is built on a foundation of extreme market concentration that makes Big Tech’s dominance look almost quaint by comparison. Nvidia controls approximately 80 percent of revenues and shipments for datacenter GPU computing, the essential infrastructure powering modern AI systems. This isn’t just market leadership — it’s approaching technological hegemony.
The implications extend far beyond corporate balance sheets. Collectively, the global south is home to just over 1 percent of the world’s top computers, and Africa just 0.04 percent. Meanwhile, the U.S. government further restricts AI chip and technology exports, dividing up the world to keep advanced computing power in the United States and among its allies. This creates what development economists call a digital colonialism scenario — entire regions become structurally dependent on technology controlled by a handful of corporations and governments.
The concentration isn’t limited to hardware. Three cloud providers — Amazon, Microsoft, and Google — control over 65% of global cloud infrastructure, creating additional bottlenecks for AI access. When you need specialized chips from one company, hosted on infrastructure controlled by three others, and governed by regulations written primarily in wealthy nations, the barriers to entry become virtually insurmountable for most of the world’s population.
The Global Divide: More Than Just Digital
This hardware concentration translates into stark global inequalities that dwarf previous technological divides. The economic and social benefits of AI remain geographically concentrated, primarily in the Global North. But unlike the gradual rollout of previous technologies like the internet or mobile phones, AI’s infrastructure requirements create immediate exclusion rather than delayed adoption.
Consider the practical reality: training a state-of-the-art AI model requires computational resources that cost millions of dollars and consume as much electricity as entire cities. The rise of AI could exacerbate both within-country and between-country inequality, placing upward pressure on global inequality as high-income individuals and regions benefit disproportionately while resource-poor regions risk being left behind.
This creates a vicious cycle. Countries and regions without access to AI infrastructure become less competitive economically, reducing their ability to invest in the very infrastructure they need to participate in the AI economy. Meanwhile, AI-enabled automation threatens to disrupt traditional export industries that many developing economies rely on, from manufacturing to service outsourcing.
The result is what economists call premature deindustrialization — developing countries losing industrial competitiveness before achieving full industrialization. But now it’s happening at digital speed, compressed from decades into years.
Intent Behind Code Governance
Yet maybe the fundamental challenge with AI isn’t the technology itself — it’s the intention behind its development and deployment, now amplified by a sharpened concentration of control. Today’s AI systems are predominantly designed to maximize engagement, extract value, or optimize narrow business metrics determined by a small number of actors. Social media algorithms amplify divisive content because controversy drives clicks. Hiring algorithms perpetuate bias because they’re trained on historical data that reflects past discrimination. Financial AI systems may optimize for short-term profits while creating systemic risks.
This is where prosocial AI governance becomes essential. Unlike traditional regulatory approaches that focus on constraining harmful outcomes, prosocial AI governance aims to actively incentivize beneficial behaviors from the outset. ProSocial AI can enhance access to essential services, improve efficiency in resource use, and promote sustainable practices across all levels of society — but only if we design governance systems that prioritize broad-based benefits over narrow optimization.
A Fragmented Landscape
The global AI regulation landscape is fragmented and rapidly evolving. Earlier optimism that global policymakers would enhance cooperation and interoperability within the regulatory landscape now seems distant. The European Union has pioneered comprehensive AI regulation through its AI Act, while other jurisdictions take vastly different approaches — from the United States’ innovation-first philosophy to China’s state-directed development model.
This fragmentation creates several problems. First, it allows AI developers to engage in regulatory arbitrage, developing systems in jurisdictions with the most permissive rules. Second, it prevents the emergence of global standards that could ensure AI systems operate prosocially across borders. Third, it creates competitive disadvantages for companies that voluntarily adopt higher ethical standards.
Given the borderless nature of this issue, an internationally coordinated response is necessary. AI systems don’t respect national boundaries — a biased hiring algorithm developed in one country can perpetuate discrimination globally, while misinformation generated by AI can destabilize societies worldwide.
Beyond Regulation: The Prosocial Governance Imperative
Traditional regulatory approaches tend to prove inadequate for rapidly evolving technologies. By the time regulators identify and respond to harms, the damage has already been done. Prosocial AI governance offers a different approach: building beneficial outcomes into the DNA of AI systems from the beginning.
This means designing AI systems that actively promote human flourishing rather than merely avoiding harm. Instead of social media algorithms that maximize engagement at all costs, we need systems that promote constructive dialogue and community building. Rather than AI systems that automate away human jobs without consideration for displaced workers, we need technologies that augment human capabilities and create new opportunities for meaningful work.
Companies with strong environmental, social, and governance frameworks, enhanced by AI, outperform competitors financially and foster greater brand loyalty. This suggests that prosocial AI isn’t just morally imperative — it’s also economically advantageous for businesses that adopt it early.
The Business Case For Prosocial AI Governance
Forward-thinking business leaders are beginning to recognize that prosocial AI governance isn’t a constraint on innovation—it’s a competitive advantage. Organizations that proactively embed prosocial values into their AI systems build stronger relationships with customers, employees, and communities. They reduce regulatory risk, attract top talent who want to work on meaningful problems, and position themselves as leaders in an increasingly values-driven marketplace.
Moreover, prosocial AI often leads to better technical outcomes. Systems designed with diverse stakeholders in mind tend to be more robust, adaptable, and effective across different contexts. AI systems built with fairness and transparency as core requirements often discover innovative solutions that benefit everyone.
The economic argument becomes even stronger when considering systemic risks. AI systems that prioritize narrow optimization over broader social welfare can create negative externalities that ultimately harm the very markets they operate in. Financial AI that ignores systemic risk can contribute to market crashes. Recommendation systems that polarize societies can undermine the social cohesion that stable markets depend on.
The Path Forward: Practical Steps
Establishing global prosocial AI governance requires coordinated action across multiple levels. International bodies need to develop frameworks that incentivize prosocial AI development while allowing for innovation and adaptation to local contexts. These frameworks should focus on outcomes rather than specific technologies, creating space for diverse approaches while ensuring consistent prosocial objectives.
At the organizational level, companies need to move beyond compliance-based approaches to AI ethics. This means embedding prosocial considerations into product development processes, establishing clear accountability mechanisms, and investing in the technical infrastructure needed to build genuinely beneficial AI systems.
Technical standards organizations should develop metrics and evaluation frameworks that measure prosocial outcomes, not just traditional performance metrics. We need ways to assess whether AI systems actually promote human flourishing, environmental sustainability, and social cohesion.
The Window Is Closing For ProSocial AI Governance
The urgency cannot be overstated. As AI systems become more powerful and pervasive, the window for establishing prosocial governance frameworks is rapidly closing. Once entrenched systems and business models become established, changing them becomes exponentially more difficult and expensive.
We’re at a pivotal moment where the next generation of AI systems will be designed and deployed. The decisions we make now about how to govern these systems will shape society for decades to come. We can either allow narrow economic interests to drive AI development, or we can proactively steer this technology toward broadly beneficial outcomes.
A Call to Action
The challenge of prosocial AI governance isn’t someone else’s problem — it’s a defining challenge of our time that requires leadership from every sector of society. Business leaders, policymakers, technologists, civil society organizations and ultimately each of us have roles in the AI-infused play that society has become.
Two suggestion for for business leaders in particular
- Start by auditing your organization’s AI systems for prosocial outcomes. Are they genuinely benefiting all stakeholders, or are they optimizing for narrow metrics that might create negative externalities?
- Invest in technical capabilities that enable prosocial AI development, and engage with industry initiatives that promote beneficial AI governance.
One simple takeaway
Prosocial AI governance isn’t a constraint on innovation — it’s the foundation for sustainable technological progress that benefits everyone. The time to act is now, before today’s AI solutions become tomorrow’s entrenched problems.
