Why We Need Worldwide Prosocial AI Governance– Now
Prosocial AI governance isn’t a constraint– however a foundation for progress that benefits everyone. It is time to act before today’s AI options are tomorrow’s issues.
The expert system 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 gamers, developing worldwide imbalances that threaten to entrench existing inequalities for generations. As AI systems progressively shape our economies, societies, and every day lives, we deal with a critical choice: Will we permit narrow market forces and geopolitical power dynamics to determine AI’s advancement, or will we proactively steer this technology toward benefiting humankind as a whole? It is late to set the stage for international prosocial AI governance, but it is not too late–. Before taking a look at governance structures, we should face an unpleasant fact: the AI revolution is developed on a foundation of extreme market concentration that makes Big Tech’s dominance appearance practically charming by contrast. Nvidia manages approximately 80 percent of earnings and deliveries for datacenter GPU computing, the important facilities powering modern-day AI systems. This isn’t simply market leadership– it’s approaching technological hegemony. The ramifications extend far beyond corporate balance sheets. Collectively, the worldwide south is home to just over 1 percent of the world’s top computers, and Africa simply 0.04 percent. 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 produces what advancement economists call a digital colonialism situation– entire areas become structurally based on innovation controlled by a handful of corporations and governments. The concentration isn’t restricted to hardware. Three cloud suppliers– Amazon, Microsoft, and Google– control over 65% of global cloud facilities, creating extra traffic jams for AI access. When you require specialized chips from one company, hosted on infrastructure controlled by three others, and governed by guidelines composed mostly in rich nations, the barriers to entry end up being essentially overwhelming for the majority of the world’s population. This hardware concentration translates into stark international inequalities that overshadow previous technological divides. The social and financial benefits of AI stay geographically focused, mostly in the Worldwide North. Unlike the progressive rollout of previous technologies like the internet or mobile phones, AI’s infrastructure requirements produce immediate exemption rather than delayed adoption. Consider the practical reality: training a modern AI model needs computational resources that cost countless dollars and take in as much electricity as entire cities. The rise of AI could intensify both within-country and between-country inequality, putting upward pressure on international inequality as high-income people and areas benefit disproportionately while resource-poor regions run the risk of being left behind. This develops a vicious circle. Countries and regions without access to AI infrastructure end up being less competitive economically, decreasing 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 markets that many developing economies rely on, from manufacturing to service outsourcing. The result is what economic experts call premature deindustrialization– developing nations losing industrial competitiveness before achieving full industrialization. And now it’s happening at digital speed, condensed from decades into years. Yet perhaps 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 primarily 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 necessary. Unlike traditional regulatory approaches that focus on constraining harmful outcomes, prosocial AI governance aims to actively incentivize beneficial behavior from the outset. ProSocial AI can enhance access to essential services, improve efficiency in resource utilization, and promote sustainable practices across all levels of society– but only if we design governance systems that prioritize broad-based benefits over narrow optimization. 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 approach to China’s state-directed development model. This fragmentation creates several challenges. It allows AI developers to engage in regulatory arbitrage, developing systems in jurisdictions with the most permissive regulations. Second, it hinders 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, a globally coordinated response is necessary. AI systems do not respect national boundaries– a biased hiring algorithm developed in one country can perpetuate discrimination globally, while misinformation generated by AI can destabilize societies worldwide. Traditional regulatory approaches tend to prove inadequate for rapidly evolving technologies. By the time regulators respond and identify 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 outset. This means developing AI systems that actively promote human flourishing rather than simply preventing harm. Instead of social media algorithms that maximize engagement at all costs, we need systems that promote constructive dialogue and community building. Instead of AI systems that automate away human jobs without consideration for displaced workers, we need technologies that enhance human capabilities and create new opportunities for meaningful work. Companies with strong environmental, social, and governance frameworks, enhanced by AI, outperform competitors financially and promote greater brand loyalty. This suggests that prosocial AI isn’t just morally important– it’s also economically advantageous for organizations that adopt it early. Forward-thinking business leaders are starting to realize 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 communities, customers, and employees. They reduce regulatory risk, attract top talent who want to work on meaningful problems, and position themselves as leaders in an increasingly values-driven market. 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 principles often discover innovative solutions that benefit everyone. When considering systemic risks, the economic argument becomes even more compelling. AI systems that prioritize narrow optimization over broader social well-being 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 rely on. Developing 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 truly beneficial AI systems. Technical standards organizations should develop metrics and evaluation frameworks that assess prosocial outcomes, not just traditional performance metrics. We need ways to measure whether AI systems actually promote human flourishing, environmental sustainability, and social cohesion. The urgency cannot be overstated. As AI systems become more powerful and pervasive, the window for establishing prosocial governance structures is rapidly closing. Once entrenched systems and business models become established, changing them becomes significantly more costly and difficult. 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 years to come. We can either allow narrow economic interests to drive AI development, or we can proactively guide this technology towards broadly beneficial outcomes. 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 drama that society has become. Start by auditing your company’s AI systems for prosocial outcomes. Are they truly benefiting all stakeholders, or are they optimizing for narrow metrics that may produce negative externalities? Invest in technical capabilities that enable prosocial AI development, and engage with industry initiatives that promote beneficial AI governance.
